Climate, Fire, and Native Americans: Identifying Forces of Paleoenvironmental Change in the Southern Sierra Nevada, California

University of Nevada, Reno

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Geography

Anna Klimaszewski‐Patterson

Dr. Scott Mensing/Dissertation Advisor

August 2016

THE GRADUATE SCHOOL

We recommend that the dissertation prepared under our supervision by

ANNA KLIMASZEWSKI-PATTERSON

Entitled

Climate, Fire, And Native Americans: Identifying Forces Of Paleoenvironmental Change In The Southern Sierra Nevada, California

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

DOCTOR OF PHILOSOPHY

Scott Mensing, Ph. D., Advisor

Jill Heaton, Ph. D., Committee Member

Kenneth Nussear, Ph. D., Committee Member

Christopher Morgan, Ph. D., Committee Member

Peter Weisberg, Ph. D., Graduate School Representative

David W. Zeh, Ph. D., Dean, Graduate School

August, 2016

Abstract

Humans have altered landscapes across North America for millennia, changing vegetation composition and fire frequency through clearing land, grazing livestock, and fire suppression (Anderson and Moratto, 1996; Beaton, 1991; Bowman et al., 2011; Cronon, 1983;

Gayton, 1948; Kroeber, 1959, 1925; Lewis, 1973; Lightfoot and Lopez, 2013; Lightfoot and

Parrish, 2009a; Martin and Sapsis, 1992; Voegelin, 1938). Though there is general agreement that they influenced the composition of the western United States’ landscape through use of fire (Denevan, 1992; Boyd, 1999; Vale, 2002; Whitlock & Knox, 2002; M.K. Anderson, 2005;

Bowerman et al., 2011), the extent of that modification is unclear. Some researchers argue for minimal impact, with disturbance being limited to the areas immediately surrounding permanent settlements (Vale, 2002a; Whitlock and Knox, 2002a). Others argue that Native

American influence on the environment was at the landscape scale, especially through the use of fire (Denevan, 1992; Boyd, 1999; M.K. Anderson, 2005; Bowerman et al., 2011). Native

Americans have inhabited California since the terminal Pleistocene. California’s Native

Americans were proto‐agriculturalists, relying not on crops such as maize, but on managed native taxa such as oak and pine (Anderson and Moratto, 1996; Anderson, 1999; Gayton, 1948;

Kroeber, 1959, 1925; Lewis, 1973; Voegelin, 1938).

Disentangling paleoclimatic versus anthropogenic vegetation change in relation to fire history is difficult, especially in the absence of clearly anthropogenic plant manipulation (e.g. maize cultivation). I use two mid‐elevation sedge meadows in Sequoia National Forest (Holey

Meadow and Trout Meadow), within the southern Sierra Nevada of California as study sites to identify climatic versus Native American‐influenced changes in forest structure and composition.

This dissertation uses data from multiple lines of evidence (paleoecology, ethnographies, ii archaeology, plant ecology, and landscape modeling) to explicitly examine whether Native

American use of fire altered forest structure to the point that it can be observed in the paleoecological record, or whether climate and natural processes overwhelm any impact Native

Americans had on the environment.

This dissertation addresses the following questions:

1. Can we identify the impact of Native American‐set fires on the paleolandscape?

2. Are these impacts local or at a landscape‐scale?

3. Could climate alone have produced the forest composition observed in the

paleorecord, or was the addition of cultural burning necessary?

To answer these questions, the paleolandscape at these two sites was reconstructed using a combination of pollen analysis (vegetation), charcoal analysis (fire history), and process‐ based landscape modeling. Charcoal analysis demonstrated consistency with regional fire reconstructions, which is expected given that charcoal is typically produced by severe, fast‐ moving crown fires. Anomalous periods of vegetation change were identified at both sites by comparing changes in climatic and fire‐sensitive taxa (Abies and Quercus) with annually reconstructed paleoclimate data. Anomalies between vegetation and climate were most evident at Holey Meadow. Paleolandscape modeling at Holey Meadow further supported a Native

American‐influenced fire regime at the site.

This research provides support for the hypothesis that Native American‐set fire can be identified through the paleoenvironmental record, and that these high‐frequency, low intensity fires were necessary to produce the forest composition observed in the pollen record. Both study sites demonstrate periods of local anthropogenic influence not explained by climate. iii

These results, combined with three previously studied sites in mountainous areas of California begin to hint at spatially dispersed Native American influences on Sierra Nevadan forests. While this research increases our knowledge of potential periods of climatic and anthropogenic‐ influenced fire regimes in the southern Sierra Nevada, replication of this cross‐disciplinary methodology throughout the Sierra Nevada is necessary to help determine the geographic extent of this land‐use pattern.

Dedication

To my family, for their unfailing belief in me and my ability to accomplish anything I set my mind to, and to friends who have become family.

Dla Mami, Janina Klimaszewski (March 22, 1935 – January 27, 2015)

Acknowledgements

I would like to thank my family, especially my husband Josh and daughter Julia, for encouraging me in pursuing my degree and never losing confidence. To Julia, who has effectively been a graduate student and TA since the age of 2.5, thank you for keeping Mama sane, for being an amazing teacher, student, and researcher in your own right, and for practically raising yourself so well (there’s no way I’m taking credit for the incredible person you are!).

Second, the Geography students at the University of Nevada, Reno (UNR), without whose friendship I would be lost. Chelsea Canon, thank you for being a sounding board, fellow geek, partner in brainstorming crime, and all around amazing person. Scotty Strachan for helping keep my wings level, fortune cookie fun, coffee breaks, and making sure the programmer in me didn’t override the scientist. Mackenzie Kilpatrick for acceptance into the

Dendro Lair and discourse on Star Trek and Star Wars’ finer points. Sarah, Cassie, Theo, Alison, and Chelsea for cheap lunch Tuesdays (taco pizza!) as a break from the rest of the week. And to all of you for enduring, and participating in, Geography Awareness Week fun!

Third, my committee members Scott Mensing, Chris Morgan, Peter Weisberg, Jill

Heaton, and Ken Nussear. To Scott especially, thank you for helping form the scientist I aspire to become. I value your patient guidance and the latitude in allowing my inner programmer freedom to try something “new”. To all my committee for the confidence you have shown in

me, for all your encouragement, and support in expanding my multi‐disciplinary knowledge and horizons.

The tremendous Geography faculty and staff, notably Scott Mensing, Shari Baughman,

Jill Heaton, and Paul Starrs. vi

The Geography faculty at New Mexico State University from whence I attained my

Masters in Applied Geography –your continued support and check‐ins on my progress, notably

Michael N. DeMers, Michaela Buenemann, and Carol Campbell. You set me on the path to continuing my education and providing me a background in geotechnical skills. Especially Dr. D for his continued support, confidence, friendship, and constant pestering of “when do you graduate?” I couldn’t have asked for a better Masters advisor who fostered and encouraged my non‐conventional approaches and questions. Thank you for setting me on the scientific track.

This research would not have been possible without collaboration with Sequoia National

Forest, especially Zone Archaeologist Linn Gassaway, and support of the Tule River Indian

Reservation Tribal Council. Further thanks to field support from the Mackay School of Earth

Science and Engineering, College of Science, Linn Gassaway, Jehren Boehmn, Jason Mann, Susan

Zimmerman, Anna Higgins, Theodore Dingemans, Trason Hirsch, and U.S. Forest Service

Passport in Time volunteers. Significant laboratory assistance provided by Jehren Boehm,

Alexandra Crowe, Taylor Gipe, James Crane, Christopher Gay, and Brandon Hill.

Funding for data collection, processing, and conference presentations came from the

National Science Foundation (NSF) Geography and Spatial Sciences (GSS) grant 0964261, the

Association of Pacific Coast Geographers Women’s Network Margaret Trussell Scholarship, UNR

Graduate Student Association, Department of Geography, and Scott Mensing. Academic funding assistance provided by the Mackay Scholar award and the UNR Department of Geography.

Finally, because it’s best to end at the beginning, my mother Janina, father Wesley, and sister Margaret. I wouldn’t be the person I am today without you as my first teachers. vii

Table of Contents

Introduction ...... 1 Purpose and Primary Questions ...... 2 Study Area ...... 4 Physiographic and climatic setting ...... 4 Vegetation ...... 5 Cultural setting ...... 6 Regional Fire ...... 8 Dissertation Chapters Overview ...... 10 Chapter 1 Multi‐disciplinary approach to identifying Native American impacts on Late Holocene forest dynamics in the southern Sierra Nevada range, California, USA ...... 13 Introduction ...... 14 Study Area ...... 17 Methods ...... 20 Sediment coring ...... 20 Sediment Analysis ...... 21 Independent Climate Analysis ...... 24 Results ...... 25 Chronology ...... 25 Sediment Analysis ...... 26 Pollen and Charcoal Analysis ...... 26 Discussion ...... 32 Conclusions ...... 41 Chapter 2 Paleoecological Evidence for Native American‐set Fires of Prehistoric Landscapes in the Sierra Nevada, California ...... 43 Introduction ...... 43 Study Area ...... 46 Methods ...... 49 Sediment Coring ...... 49 Sediment Analysis ...... 50 Pollen and Charcoal Analysis ...... 50 Vegetation Response Index ...... 51 viii

Independent Climate Analysis ...... 52 Regional Comparison ...... 52 Results ...... 53 Chronology ...... 53 Sediment Analysis ...... 55 Pollen Analysis ...... 55 Regional Comparison ...... 60 Discussion ...... 61 Conclusions ...... 68 Chapter 3 Modeling Native American Burning and the Paleolandscape ...... 71 Introduction ...... 71 Study Area ...... 75 Methods ...... 77 Creating monthly paleoclimatic records ...... 79 Process‐based vegetation modeling ...... 81 Landscape‐scale modeling ...... 81 Paleoclimate evaluation of modeling outputs ...... 86

Modeling Scenarios (H1: Climatic fire) ...... 87

Modeling Scenarios (H2: Climatic + Native American‐set fires) ...... 90 Results ...... 93 Baseline (Lightning‐caused crown fires) + Native American‐set fires ...... 93 Climatic lightning‐caused fire ...... 97 Discussion ...... 102 Conclusions ...... 106 Summary and Conclusions ...... 108 Scientific Merit ...... 111 Broader Impacts ...... 112 References ...... 114 Appendix A Raw Pollen Counts for Holey Meadow ...... 128 Appendix B Raw Pollen Counts for Trout Meadow ...... 131 Appendix C Charcoal and Loss‐on‐Ignition for Holey Meadow ...... 134 Appendix D Charcoal and Loss‐on‐Ignition for Trout Meadow ...... 136 ix

Appendix E Alternative Native American‐Set Fires Modeling Scenario ...... 138 Appendix F Additional Figures for Chapter 3 Modeling Scenario ...... 143

H1 Scenarios (Climatic lightning‐caused fires) ...... 143

H2 Scenarios (Climatic baseline + Native American‐set fires) ...... 146

Table of Tables

Table 1‐1. Holey Meadow radiocarbon dates...... 25

Table 1‐2. Comparison of r values between standardized sedimentary proxies and independent climate reconstructions interpolated at 50‐year intervals using a smooth spline ...... 28

Table 2‐1. Trout Meadow (TRT) radiocarbon dates...... 54

Table 2‐2. Comparison of Spearman’s ƿ values between 50‐year smooth splined and standardized VRI, percent charcoal, and PDSI values...... 57

Table 3‐1. Data and source for modeling inputs used in this study ...... 80

Table 3‐2. Overall patch area to apply 70% prescription burn annually by fire region, by temporal period and scenario...... 86

Table 3‐3. Climatic fire regime parameters annually (and by 5‐year timestep), by temporal period and scenario...... 88

Table 3‐4. Percent area to prescription burn annually (and by 5‐year timestep), by temporal period and scenario...... 91

Table 3‐5. Correlation values between mVRI and pVRI for various periods through the simulation...... 94

Table 3‐6. Number of climatic lightning‐caused crown fires and percent study area burned by crown fires per cumulative 50 years (average)...... 96

Table 3‐7. Correlation values between mVRI (H1 scenarios) and pVRI for the period (1050 ‐100 cal yr BP; A.D. 900 ‐ 1850)...... 98

Table 3‐8. Number of crown fires and percent study area burned by crown fires per cumulative 50 years, by scenario (averaged) ...... 99

Table 3‐9. Increase in area burned from 750‐100 cal yr BP from lightning‐caused crown fires . 101

Table E‐1. Correlation values between mVRI and pVRI for various periods through the simulation ...... 138

Table E‐2. Number of climatic lightning‐caused crown fires and percent study area burned by crown fires per cumulative 50 years (average)...... 139

Table of Figures

Figure 1‐1. Climate‐driven model of forest succession (A) versus a human‐influenced (B) model ...... 16

Figure 1‐2. Study area showing paleoenvironmental reconstruction site (HLY) and nearby archaeological site CA‐TUL‐819. YNP: Yosemite National Park...... 20

Figure 1‐3. Bayesian age vs depth model for Holey Meadow...... 26

Figure 1‐4. Pollen percentage diagram of selected taxa, pollen accumulation rate (Acc. Rate), total organic matter (%TOM), and charcoal accumulation rate (CHAR)...... 27

Figure 1‐5. Vegetation Response Index (green), charcoal accumulation (orange), and PDSI (grey) standardized values interpolated at 50‐year intervals using a smooth spline...... 28

Figure 1‐6. Environmental proxies compared against regional‐to‐local Native American habitation periods...... 37

Figure 2‐1. Trout Meadow in the Golden Trout Wilderness Area, Sequoia National Forest, California, United States...... 48

Figure 2‐2. Age model for Trout Meadow (TRT) ...... 54

Figure 2‐3. Pollen diagram of taxa with ≥ 2% maxima, VRI, pollen accumulation rate (Acc. Rate), %TOC and percent charcoal (%CHAR)...... 56

Figure 2‐4. Vegetation Response Index (VRI; thick green line), charcoal (dashed orange line), and PDSI (thin grey line) values, smooth splined at 50‐year intervals and standardized ...... 57

Figure 2‐5. Cyperaceae and wet meadow indicator non‐pollen palynomorphs (NPPs) shown with pollen zones/CONISS...... 58

Figure 2‐6. Gaeumannomyces (A), and unknown spores Fungal 1 (B), Fungal 2 (C) and Fungal 3 (D) NPP ...... 58

Figure 2‐7. Paleovegetation reconstruction sites used in comparison to Trout Meadow (TRT). .. 61

Figure 2‐8. Five kilometer foraging radius (yellow area) around identified bedrock mortar (BRM) and pre‐historic milling stations in the northern district of Sequoia National Forest (SNF)...... 68

Figure 3‐1. Standardized PDSI (grey line) and pVRI values (green line) from Holey Meadow (HLY)...... 75 xii

Figure 3‐2. Extent of study area modeled within Sequoia National Forest (SNF) in relation to paleoecologic site Holey Meadow (HLY; Klimaszewski‐Patterson and Mensing, 2015) and nearby fire scar sites (Taylor, unpub.) ...... 77

Figure 3‐3. Overview flowchart demonstrating modeling inputs and flow ...... 79

Figure 3‐4. Overview flowchart of LANDIS‐II inputs and extensions ...... 84

Figure 3‐5. Standardized modeled VRI (mVRI) vs paleo VRI (pVRI) values ...... 95

Figure 3‐6. Amount of hectares burned by background “natural” climatic crown fires per year. 97

Figure 3‐7. Average number of hectares burned per year by crown fires (does not include surface fires) ...... 100

Figure 3‐8. Standardized modeled VRI (mVRI) vs paleo VRI (pVRI) values ...... 101

Figure E‐1. Standardized modeled VRI (mVRI) vs paleo VRI (pVRI) values ...... 140

Figure E‐2. Amount of hectares burned by background “natural” climatic crown fires per year140

Figure E‐3. Alternative H2 scenario of climatic crown fires per year ...... 141

Figure E‐4. Alternative H2 scenario boxplots of actual modeled vs paleo VRI values for all iterations...... 141

Figure E‐5. Alternative H2 scenario scatterplots of standardized values for modeled VRI (averaged) vs paleo VRI, symbolized by time periods ...... 142

Figure F‐1. H1 scenario boxplots of actual mVRI values vs pVRI (green dots) for climatic crown‐ fire only scenarios (H1‐C1 to C5)...... 143

Figure F‐2. H1 scenario boxplots of actual mVRI values vs pVRI (green dots) for climatic crown‐ fire plus 6% surface fire scenarios (H1‐C6 and C7)...... 143

Figure F‐3. H1 climatic crown fires per year (crown fire‐only scenarios)...... 144

Figure F‐4. H1 climatic crown fires per year (crown fire‐plus 6% surface fire scenarios) ...... 144

Figure F‐5. H2 scenario scatterplots of standardized values for modeled VRI (averaged) vs paleo VRI, symbolized by time periods for crown fire‐only scenarios ...... 145

Figure F‐6. H2 scenario scatterplots of standardized values for modeled VRI (averaged) vs paleo VRI, symbolized by time periods for crown fire‐only plus 6% surface fire scenarios ...... 145

Figure F‐7. H2 scenario boxplots of actual mVRI values vs pVRI (green dots)...... 146 xiii

Figure F‐8. H2 climatic crown fires per year ...... 146

Figure F‐9. H2 scenario scatterplots of standardized values for modeled VRI (averaged) vs paleo VRI, symbolized by time periods...... 147

Introduction Native Americans have inhabited California since the terminal Pleistocene. Though there is general agreement that they influenced the composition of the western United States’ landscape (Denevan, 1992; Boyd, 1999; Vale, 2002; Whitlock & Knox, 2002; M.K. Anderson,

2005; Bowerman et al., 2011), the extent of that modification is unclear. Some researchers argue for minimal impact, with disturbance being limited to the areas immediately surrounding permanent settlements (Vale, 2002a; Whitlock and Knox, 2002a). Others argue that Native

American influence on the environment was at the landscape scale, especially through the use of fire (Denevan, 1992; Boyd, 1999; M.K. Anderson, 2005; Bowerman et al., 2011). To some extent, academic discipline determines which side of the debate a person supports. Earth scientists, climatologists, and paleoecologists tend to favor the low‐impact interpretation because they often focus on and view landscape changes in the context of broad‐scale climate drivers (Millar & Woolfenden, 1999; Whitlock & Knox, 2002; Briles et al., 2008). Anthropogenic causes, while discussed, are not typically integrated into these studies through site selection or sampling design (Swetnam et al., 2009). Anthropologists, archaeologists, and geographers who rely on ethnographies, oral histories, exploration journals, and archaeological site data tend to favor the high‐impact interpretation (Denevan, 1992; M.K. Anderson & Moratto, 1996). There is a general lack of collaborative, multi‐disciplinary efforts reconstructing paleoecology and fire near archaeological sites with well‐documented histories of Native American use (Stewart, 1963;

Gassaway, 2005; Gassaway, 2007; Lloyd & Graumlich, 2011).

A comprehensive framework for studying the problem needs to include multiple lines of evidence, including paleoecology, ethnographies, archaeology, plant ecology, and dendrochronology (Lewis & Anderson, 2002). Establishing a credible link between fire regimes 2 and land‐use requires knowledge about Native American group’s adaptations, technologies, population densities, and interaction with the environment (Pullen, 1996; Whitlock & Knox,

2002). Fire regimes and vegetation signals not predicted by climate patterns may suggest a human cause. The literature is still quite limited for peer‐reviewed studies in California that implement a multiproxy approach, using pollen to reconstruct vegetation (Anderson and

Carpenter, 1991; Cowart and Byrne, 2013; Crawford et al., 2015), sedimentary charcoal and/or fire scars to develop fire histories (Anderson and Carpenter, 1991; Cowart and Byrne, 2013;

Crawford et al., 2015; Gassaway, 2009, 2005), and archaeological evidence for statistical variation in fire not accounted for by climate (Anderson and Carpenter, 1991; Cowart and Byrne,

2013; Crawford et al., 2015; Gassaway, 2009, 2005; Lightfoot et al., 2013). In Yosemite National

Park (YNP), R.S. Anderson & Carpenter (1991) found that fire increased at about the same time as the entrance of the Miwok. Through frequent burning, the tribal group maintained oak woodlands through cooler, wetter conditions more favorable to closed‐canopy conifer forest

(Moratto et al., 1978; Biswell, 1999). Gassaway (2009, 2005) found that fire patterns in the dendrochronologic record in YNP could not be attributed to climate or lightning ignitions. Few would argue that Native Americans did not significantly alter Yosemite Valley, yet there is less agreement about Native American influence throughout the rest of the Sierra Nevada. The argument remains that spatial complexity, disturbance regimes, climate change, and natural processes overwhelm any impact that humans may have had on the environment (Parker,

2002).

Purpose and Primary Questions This study tests the hypothesis that Native American’s use of fire to modify the environment was significant enough to alter forest structure at a landscape scale in California, 3 specifically the southern Sierra Nevada. Further, I contend that this anthropogenic impact can be identified by changes in relative abundance of endemic, “natural” vegetation. Fire frequency and severity can alter plant composition on the landscape. When Native Americans set fires in the western United States, they used low intensity surface fires to clear brush, drive game, and increase natural resource yields (Dincauze, 2000; Fowler, 2008). More intensive use of the land can result in exploiting new resources (acorns, cattails, insects, etc.), changing work/processing strategies, or increasing plant yields through pruning, coppicing, whipping, and most importantly, burning (Fowler, 2008). This dissertation addresses the following questions:

1. Can we identify the impact of Native American burning on the paleolandscape?

2. Are these impacts local or at a landscape‐scale?

3. Could climate alone have produced the forest composition observed in the

paleorecord, or was the addition of cultural burning necessary?

The question whether Native Americans significantly altered forest structure in the

West has not been sufficiently tested because studies have rarely been designed specifically to address this question. This dissertation uses multiple lines of evidence (paleoecology, ethnographies, archaeology, plant ecology, and landscape modeling) to explicitly examine whether Native American land use practices altered forest structure to the point that it can be seen in the paleoecological record, or whether climate and natural processes overwhelm any impact Native Americans had on the environment.

Understanding to what extent Native Americans impacted the environment is critical to modern land management and policies. If the landscape was altered by anthropogenic fire at the time of European exploration and immigration, policies regarding land management and fire 4 suppression need to be reexamined with Native American practices in mind (M.K. Anderson,

1999; 2005; DellaSulla et al., 2004; Fry & Stephens, 2006). Knowing what landscape characteristics are human caused will allow for a better understanding of the dynamics that created forest baseline conditions and potentially improve climate models that use pollen as a calibration input.

Study Area

Physiographic and climatic setting The Sierra Nevada is a primarily north‐south trending mountain range (granitic batholith) that separates California’s San Joaquin Valley to the west from the Great Basin ecoregion to the east. Elevation increases southward to 4421 m at Mt Whitney, the highest point in the contiguous United States. Sites for this study (Holey Meadow and Trout Meadow)

are at the southern tip of the range, which remained unglaciated throughout the Pleistocene

(Bowerman and Clark, 2011). Holey Meadow is located within Sequoia National Monument and is the southernmost of the two sites. Trout Meadow is within the Sequoia National Forest‐ administered portion of the Golden Trout Wilderness Area, which includes a portion of the 2023 km2 (500,000 acre) Kern Plateau. The Kern River, which originates near Mount Whitney and is fed by snowmelt, is the only river in the Sierra Nevada that drains southward. Prior to modern diversion of water for irrigation and consumption, the river emptied into Buena Vista Lake in San

Joaquin Valley. Both sites are south of where the crest of the Sierra Nevada splits in two, bisected by the Kern River. HLY is located near the crest on the western slope of the Western

Divide, which parallels the Sierran Crest to the east. TRT is within the Kern River Basin, between the Western Divide and Sierran Crest. 5

Most precipitation at the study sites comes between October and April in association with Pacific frontal systems (Millar et al., 2006), though summer afternoon thunderstorms are common. Lightning in the southern Sierra Nevada is common in the summer and fall, increasing in frequency with elevation (SNEP, 1996). Lightning‐caused fires peak between June and

September, with June (31%) and September (19%) having the highest percentage (van

Wagtendonk and Cayan, 2008; Vankat, 1985). The modern climate is Mediterranean at lower elevations and Boreal between 2100 and 3600 m (Soulard, 2012). Vegetation life zones on the western slope are: oak woodlands/chaparral (up to ~1300 m elevation), montane/mixed conifer forests (1300 – 2200 m), and upper montane forests (2200+ m) (Anderson and Davis, 1988).

The most detailed climate reconstructions in the Sierra Nevada for the last 2000 years come from the North American Drought Atlas, a gridded network of dendroclimatic reconstructions (Cook et al., 2008, 2004, 1999; Graumlich, 1993), tree‐line elevation studies

(LaMarche, 1974; Scuderi, 1993), and fire scar studies (Swetnam, 1993; Swetnam et al., 2009,

1990). Dendroclimatic records indicate the Medieval Climate Anomaly (MCA) occurred between

1000 to 750 cal yr BP (A.D. 950 ‐1200) (Stine, 1994). The Little Ice Age (LIA) is recorded regionally between ~750 and 500 cal yr BP (A.D. 1200 and 1450) and lasted until ~100 cal yr BP (A.D. 1850), with temperatures averaging 0.5OC cooler than current temperatures (Graumlich, 1993; Stine,

1994).

Vegetation Tree species commonly occurring in the montane/mixed conifer zone include: Jeffrey pine (Pinus jeffreyii), ponderosa pine (Pinus ponderosa), white fir (Abies concolor), California black oak (Quercus kelloggii), incense‐cedar (Calocedrus decurrens), and willow (Salix spp.). 6

Though not overly common on the modern landscape, twoneedle piñon pine (Pinus edulus), oneneedle piñon pine (Pinus monophyllia), and giant sequoia (Sequoiadendron giganteum) are worth noting. Common shrub species include: bush chinquapin (Chrysolepis sempervirens); members of the rose family (Rosaceae) including Woods’ rose (Rosa woodsii), western chokecherry (Prunus virginiana), and Holodiscus; Fremont’s gooseberry (Chenopodium fremontii), and Ceanothus spp., including Kern ceanothus (C. pinetorum), which occurs only in the Kern Plateau.

The ecoregion includes numerous edible plants (mentioned above) such as C. sempervirens, P. virginiana, C. fremontii, P. monophyllia, P. edulus, and Q. kelloggii, the latter three species highly prized by Native Americans for their nuts and acorns (Voegelin, 1938;

McCarthy, 1993; Gorden, unpub). Beyond foodstuffs, plant resources were and continued to be used medicinally and for construction. Pinus, Abies, Cupressaceae, Salix, and sedge (Cyperaceae) species are traditionally used for making basketry, while Pinus and Abies species are used in building hunting blinds. Native Americans, including the Tübatulabal and Foothill Yokuts people, continue to use plant resources from the region as they have for millennia (Voegelin, 1938; M.

K. Anderson, 1999; Gorden,unpub.). Sites in the far southern Sierra Nevada are suitable for this study given the presence of archaeological data, availability of paleoecologic sites, and independent paleoclimate records.

Cultural setting The main Native American groups in the Kern Plateau are the Tübatulabal and Foothill

Yokuts (Yokuts). Tübatulabal is a branch of the Uto‐Aztecan linguistic family. Speakers of

Tübatulabal consider the Kern River Plateau their ancestral home (Voegelin, 1938). Linguists 7 estimate the Tübatulabal’s arrival in the Sierra Nevada, and subsequent linguistic divergence from other Uto‐Aztecan languages, at ~ 4000 ‐ 5000 years ago (Moratto, 1984). The Yokuts are more recently arrived to the Sierra Nevada foothills, approximately 2000 years ago (Moratto,

1984). In the last 2000 years there are three main culture phases: Canebrake (1200 B.C. – A.D.

600; 3200 – 1400 yr B.P.), Sawtooth (A.D. 600 – 1300; 1400 – 700 yr B.P.), and Chimney (A.D.

1300 (700 yr B.P.) – historic period) (Moratto, 1984; Garfinkel, 2007; Ramirez et al., 2010).

Cultural phases are archaeological units of time with characteristic, distinguishable traits spatially and chronologically limited to a relatively small locality or brief time interval (Willey &

Phillips, 1958). Though culture history predates the use of radiocarbon dates, and I use every opportunity possible to temporally compare landscape changes with dated archaeological data, referencing culture phases may prove necessary for understanding the timing of ecologic shifts within the paleoenvironmental record. The Canebrake phase (1200 B.C. – A.D. 600; 3,200 –

1,400 yr B.P.) is associated with a greater use of plant resources from previous culture phases through an increasing focus on acorn and nut harvesting. Seasonally‐occupied base camps associated with food processing became more common than in prior cultural phase (McGuire,

1981; Moratto, 1984; Ramirez et al., 2010). The Sawtooth phase (A.D. 600 – 1300; 1,400 – 700 yr

B.P.) sees a shift from the atlatl to bows and arrows. Characteristic artifacts of the Sawtooth phase include arrow‐sized Eastgate and Rose Spring projectile points. Fishing, hunting, and gathering intensify in the San Joaquin Valley, with an emphasis on bulk harvesting and processing (Rosenthal et al., 2007). Increasing exploitation of natural resources may indicate greater population pressures in the region (Moratto, 1984). The Sawtooth period roughly coincides with the Medieval Climate Anomaly, which may have made exploitation of higher mountain elevations preferable due to greater water availability and resource productivity 8

(Goldberg & Skinner, 1990; Morgan, 2006). The Chimney phase (A.D. 1300 [700 yr B.P.] – historic period) is characterized by even more high intensity use and occupation. Chimney‐phase archaeological sites include house pits, villages, soapstone objects, Olivella and clam shell beads,

Owens Valley brownware, and Desert side‐notched type projectile points (Moratto, 1984;

Garfinkel, 2007; Ramirez et al., 2010).

Regional Fire Fire is a natural disturbance of California’s Sierra Nevadan forests. As climate changes, so does the frequency of natural fires (Dale et al., 2001, 2000; Flannigan et al., 2000; Keeley et al., 2011; Swetnam, 1993), which results in changes of forest composition though successional processes. Climate drives wildfires by affecting vegetation type, density, and fuel moisture

(Swetnam, 1993; Brunelle & R.S. Anderson, 20003). Cooler/wetter climates are associated with less frequent fires, more available soil moisture, and a successional process leading towards more closed forest canopies with more shade‐tolerant/fire‐sensitive taxa such as Abies (fir) and

Calocedrus decurrens (incense‐cedar) (Dale et al., 2001; Lenihan et al., 2003). Warmer/drier climates have less available soil moisture and thus greater opportunity for fire disturbance events, leading to the opening of forest canopies and a greater prevalence of shade‐ intolerant/fire‐adapted taxa (i.e. increasing Pinus, Quercus, Poaceae, and Rosaceae) (Engber et al., 2011; Fites‐Kaufman et al., 2007; Henne et al., 2012; Innes et al., 2006; Peterson and

Hammer, 2001). When attempting to identify Native American use of fire on the paleolandscape, the difficulty comes in disentangling shifts in forest composition due to climate and climatically‐driven fires from those caused by cultural burning. 9

Fires typically synchronize with regional climate patterns (Swetnam, 1993; Marlon et al.,

2009). According to the global record for the last 2000 years, burning of biomass declined from

2000 – 200 yr BP (A.D. 1 – 1750), peaked around 80 yr BP (A.D. 1870), and sharply declined again. Minima for global record biomass burning occured at 1550 – 1050 yr BP and 250 yr BP

(A.D. 400 – 900 and 1700) (Marlon et al., 2009). Multi‐millennial fire histories reconstructed from Sequoiadendron giganteum (giant sequoia) tree rings over seven geographically separated groves in the Sierra Nevada, from Yosemite National Park to Sequoia National Park (Swetnam,

1993; Swetnam et al., 2009), identified fire events that were persistently synchronous for centuries. According to these reconstructions, fire frequency decreased during the cool period preceding the MCA between 1450‐1150 cal yr BP (A.D. 500 – 800), began to increase, then peaked from 1150 to 750 cal yr BP (A.D. 800 to 1350). Except for a brief increase between 400‐

350 cal yr BP (A.D. 1650s), fires generally decline after 650 cal yr BP (A.D. 1300). All groves sampled exhibited a sharp decline in fire occurrences after 90 cal yr BP (A.D. 1860). This sharp

decline in fire after 90 cal yr BP demonstrates modern anthropogenic influence through intensive sheep grazing, reduced Native American influence, and fire suppression policies.

Regionally synchronous fires demonstrate centennial‐scale climate patterns driving fire regime

(Swetnam, 1993). Reconstructions revealed decadal to centennial associations with summer temperatures, where cooler temperatures were associated with reduced fire frequency and warmer, drier conditions with higher fire frequency (Swetnam, 1993; Swetnam et al., 2009).

Fire intensity and size are inversely proportional to fire frequency. High fire frequency tends to correspond with low‐intensity surface fires, while high intensity crown fires occur in low fire frequency areas (Martin and Sapsis, 1992; Swetnam, 1993). Combining precipitation, temperature, and dendroclimatic reconstructions allows for multi‐scale assessments on climate‐ 10 fuels‐fire dynamics (Kilgore and Taylor, 1979; Martin and Sapsis, 1992; Swetnam, 1993;

Swetnam et al., 2009), under natural and anthropogenic regimes.

Dissertation Chapters Overview This dissertation is formatted in three chapters. Chapter one has been previously published (Klimaszewski‐Patterson and Mensing, 2015, The Anthropocene) and is reprinted here. References for all chapters appear at the end of the document. Chapter one presents a

2000 year paleoenvironmental reconstruction from a mid‐elevation wet meadow (Holey

Meadow) in Sequoia National Forest. Holey Meadow represents the most southerly paleoecologic reconstruction in the Sierra Nevada, with the highest temporal resolution to date

(sub‐centennial). This chapter examines whether landscape‐level shifts in native vegetation and/or charcoal can be identified that are contrary to what is expected from successional processes during periods of climatic change. I compare the timing of vegetation change to both annual tree ring‐derived climate reconstruction from the North American Drought Atlas and records of Native American occupation in the Sierra Nevada. This chapter’s goal is to explore whether Native American land use can be identified in the paleoenvironmental record in the absence of agricultural taxa such as Zea mays. The results of this chapter are significant because it provides a method to identify Native American signals of land use on “natural”, non‐ agricultural landscapes.

The second chapter provides an empirical test of the methods and findings from

Chapter 1 using a newly analyzed geographically and culturally‐paired paleoecologic site in

Sequoia National Forest (Trout Meadow). I comparatively analyze results from Trout Meadow,

Holey Meadow, and three other existing paleoenvironmental, sub‐centennially reconstructed mountainous sites in California regarding anomalous shifts in vegetation with local climate 11 reconstructions (Anderson and Carpenter, 1991; Crawford et al., 2015). I apply the three lines of evidence set forth by Bowman et al. (2011) to identify Native American burning: (1) temporal changes in proxy reconstructions of fire and vegetation that (2) differ from (locally) climatic expectations and (3) occur temporally with identified changes (cultural phase shifts) in Native

American activity. I compare the results from all five sites to explore whether Native American burning appears locally intensive or regionally extensive. The results of this chapter are significant because if the extent of Native American use of fire on the landscape changed forest structure regionally, then the open forest structure that existed at the time of contact with

European settlers in the A.D. 1800s was the result of both climatic and human impacts. This perspective may help contribute to future fire management policies.

The third and final chapter uses forest landscape models to explore the hypothesis that the addition of Native American‐set fires was necessary to create the forest composition observed in the paleoenvironmental record. I use a landscape simulation model, LANDIS‐II, to reconstruct 1100 years of vegetation change under climatic and inferred anthropogenic fire regimes. LANDIS‐II is a spatially‐explicit, raster‐based, stochastic model designed to simulate spatial interactions among succession, natural disturbances, and forest management (Scheller and Mladenoff, 2004a; Scheller et al., 2007). The model simulates individual tree and shrub species based on their life history traits, including longevity, fire tolerance, shade tolerance, resprouting ability, age of maturity, seed dispersal distance, and reproduction following fire.

Trees are not modeled individually, but rather as species and age cohorts, allowing multiple types of cohorts at a single site (grid cell). LANDIS‐II is appropriate for this study because of the interaction focus between fire and vegetation at a fine temporal scale over long periods of time.

My goal is to explore the timing, frequency, type, and extent of fire initiation necessary to 12 recreate the forest composition observed at Holey Meadow. I test whether climate and climatic‐ induced fires alone can create a landscape that approximates observed paleovegetation dynamics, or whether the addition of Native American burning is required, indicating an anthropogenic landscape. Further, I explore whether Native American burning was only necessary during the Little Ice Age (LIA), and period of recognized intensification, or if anthropogenic burning was an integral part of the landscape prior to the Medieval Climate

Anomaly (MCA). The results of the chapter are important because this is the first use of a landscape‐scale model at a high temporal resolution for paleoenvironmental reconstruction. It is also the first attempt in the literature to experimentally test the hypotheses that the addition of

Native American‐set fires was necessary to produce the paleolandscape using a landscape‐scale model, and that climate alone could not have created the landscape observed in the A.D. 1850s.

Chapter 1 Multi‐disciplinary approach to identifying Native American impacts on Late Holocene forest dynamics in the southern Sierra Nevada range, California, USA

Klimaszewski‐Patterson, Anna and Mensing, Scott, Anthropocene 2015 doi: 10.1016/j.ancene.2016.04.002

Abstract

Fire is a natural disturbance component and driver of forest composition in the western

United States. Cooler/wetter climates are typically associated with less frequent fires and succession of montane forests to more shade‐tolerant, fire‐sensitive taxa. Native Americans have lived in California since the terminal Pleistocene and used fire to alter the landscape and maximize natural resources; however, determining the extent and impact of anthropogenic burning on California’s landscape is difficult because the archaeological record is mostly silent on the subject, and the region’s ethnographies mention the practice only in passing. Here we show that comparing the prevalence of fire‐sensitive to fire‐adapted taxa in the pollen record can help distinguish periods when vegetation does not respond as expected to climate change.

We argue that the prevalence of shade‐intolerant/fire‐adapted taxa during climatically cool, wet periods such as the Little Ice Age provide evidence for anthropogenic burning. At Holey

Meadow, in Sequoia National Forest, we find two periods in the last 2000 years when this type of situation prevails: 1550‐1000 cal yr BP, and 750‐100 cal yr BP. We also see a strong anthropogenic effect on modern vegetation following European settlement in A.D. 1854, a period marked by a precipitous decline in traditional tribal use of the area and the inception of modern fire exclusion policies. These results indicate that anthropogenic impacts on forest composition can be distinguished from climatic drivers through the use of paleoenvironmental 14 proxies, and further indicate that anthropogenic burning helped structure Late Holocene southern Sierra Nevada biomes.

Introduction Fire is a natural disturbance component in many forests, especially in the western U.S.

As climate changes, the frequency at which natural fires occur on the landscape also changes

(Dale et al., 2001, 2000; Flannigan et al., 2000; Keeley et al., 2011; Swetnam, 1993), and succession theory predicts associated changes in forest composition. Cooler/wetter climates are associated with less frequent fires and more available soil moisture, with succession leading to closed forest canopies with more shade‐tolerant/fire‐sensitive taxa (i.e. increasing Abies spp. and Calocedrus decurrens) (Dale et al., 2001; Lenihan et al., 2003). Warmer/drier climates have less available soil moisture and greater fire occurrences, leading to the opening of forest canopies and more shade‐intolerant/fire‐adapted taxa (i.e. increasing Pinus, Quercus, Poaceae, and Rosaceae (Engber et al., 2011; Fites‐Kaufman et al., 2007; Henne et al., 2012; Innes et al.,

2006; Peterson and Hammer, 2001). The climate‐driven model of forest succession (Figure 1‐1a) assumes climate is the main driving force of forest structure and composition, and predicts that any variation in fire and vegetation as reconstructed from charcoal and pollen to be positively correlated to changes in independently derived climate reconstructions (i.e. drier climate = more charcoal and more shade‐intolerant taxa). This model also predicts a positive correlation between vegetation and fire with regards to fire‐sensitive/fire‐adapted taxa (i.e. more fire‐ adapted taxa during periods of increased charcoal accumulation).

In the southern Sierra Nevada, Abies concolor (white fir) is a classic example of a very shade‐tolerant/fire‐sensitive species in mid‐elevation, mixed‐conifer forests. Saplings show high mortality when exposed to low‐intensity fires (Kilgore, 1973). Further, fire suppression policies 15 in the last 150 years have allowed documented expanded succession of Abies spp. (Franklin and

Fralish, 2002; Kilgore, 1973). Conversely, Quercus kelloggii (California black oak) is fire‐tolerant and drought‐resistant. While Quercus kelloggii can tolerate some shade, it prefers open canopy and direct sunlight for best development (Franklin and Fralish, 2002). In cooler, wetter climates, or with less fire, Abies can outcompete Quercus given its rapid growth habit; therefore, in a climate‐driven regime (Figure 1‐1a), we expect an inverse relationship between shade‐ tolerant/fire‐sensitive Abies and shade‐intolerant/fire‐adapted Quercus spp. The role of fire in maintaining oak woodlands is widely recognized, where fire exclusion results in forest structure change to more fire‐sensitive, shade‐tolerant taxa (Bond and Keeley, 2005; Crawford et al.,

2015; Engber et al., 2011; Higgins et al., 2000; Peterson and Hammer, 2001; Peterson and Reich,

2001), but climate is not the only source of fires. Native Americans have lived in California since the terminal Pleistocene, and their use of fire is extensively documented in the ethnographic record (Anderson and Moratto, 1996; Lewis, 1973; Voegelin, 1938). This situation elicits questions as to whether, when, and to what degree burning by Native Americans throughout the Anthropocene structured the region’s biomes (Bowman et al., 2011). A human‐influenced model (Figure 1‐1b) may explain deviations from the expected climate‐driven model (Figure

1‐1a)

Fires were set by hunter‐gatherers of prehistoric California to clear brush, drive game, facilitate hunting, promote seed germination, improve viewsheds, open travel corridors, and

increase natural resource yields (Anderson and Moratto, 1996; Anderson, 1999; Codding and

Bird, 2013; Dincauze, 2000a; Fowler, 2008; Gassaway, 2009; Lightfoot and Lopez, 2013;

Lightfoot and Parrish, 2009a; Lightfoot et al., 2013). Shade‐intolerant/fire‐adapted open canopy taxa provided important food supplies (e.g., acorns, a wide variety of small‐seeded grasses and 16 roots, and young, fresh browse for deer) for Native Americans in California, while shade‐ intolerant/fire‐sensitive close canopy taxa typically did not (Jordan, 2003; Kroeber, 1925; Lewis,

1973; Lightfoot and Parrish, 2009a). While is it clear from ethnographic records that such practices occurred regularly and were widespread (Gassaway, 2009; Jordan, 2003; Lewis, 1973;

Stewart, 2014; Voegelin, 1938), the challenge is distinguishing human‐induced changes in natural vegetation from climatically‐induced changes in the absence of agricultural taxa such as

Zea mays (Bowman et al., 2011).

We argue that anthropogenic burning was responsible for the persistence or pre‐ dominance of shade‐intolerant/fire‐adapted taxa, and that the pre‐A.D. 1850 forest of

California’s Sierra Nevada range represented a human‐influenced model of forest composition

(Figure 11b). This influence will be most apparent during periods of cool and wet climate, such as the Little Ice Age (LIA; ~750‐500 to 100 cal yr BP; Bowerman and Clark, 2011; Graumlich,

1993; Stine, 1994). Warm, dry periods such as the Medieval Climate Anomaly (MCA; 1000 to

750‐500 cal yr BP; Bowerman and Clark, 2011; Graumlich, 1993; Stine, 1994) will obfuscate anthropogenic impacts of fire because any influence by Native Americans would be in‐line with climatic changes (increased fire and open‐canopy taxa).

Climate‐Driven Model Human‐Influenced Model

A B

Figure 1‐1. Climate‐driven model of forest succession (A) versus a human‐influenced (B) model

Three lines of evidence are required to identify anthropogenic burning: (1) paleoenvironmental proxies demonstrating temporal change in fire and vegetation that (2) differ from expected climate‐fire‐vegetation relationships and (3) correlate temporally with changes in Native American activity (Bowman et al., 2011; Codding and Bird, 2013).

Archeological data that reconstruct changes in Native American activity in the southern Sierra

Nevada range are still scarce, but research done throughout California shows relatively similar timing of cultural change with regard to Native American habitation, land‐use intensification, tool use, and social structure (Beaton, 1991; Bowman et al., 2011; Gassaway, 2009; Hull, 2009,

2007; Jones and Schwitalla, 2008; Moratto, 1984; Morgan, 2009; Ramirez et al., 2010). We hypothesize that forest structure can be inferred from the relative prevalence of shade‐ tolerant/fire‐sensitive taxa to shade‐intolerant/fire‐adapted taxa. We also hypothesize that

Native Americans had an identifiable impact on the forest structure of the southern Sierra

Nevada, and that this impact is most evident during climatically cool, wet periods when Native

American burning would have preserved shade‐intolerant/fire‐adapted taxa even while climate promoted succession of shade‐tolerant species. The results of this research are significant because they examine the impacts Native Americans may have had on forest composition, and contribute to the debate regarding the scale and extent of Native American burning in California during the Anthropocene.

Study Area Holey Meadow (HLY; 35° 57.319' N, 118° 36.925' W; elev. 1925 m) is a sedge meadow located within Sequoia National Monument at the southern end of the Sierra Nevada range

(Figure 1‐2). The Sierra Nevada range is a north‐south trending mountain range that separates 18

California’s Central Valley to the west from the Great Basin to the east. ). The Kern Plateau is an unglaciated high‐elevation plateau in the southern Sierra Nevada range located in both Sequoia and Inyo National Forest‐administered lands. From Yosemite National Park (YNP) to the southern tip of the range, exhibit similar hydroclimatic responses in relation to El Nino‐Southern

Oscillation variability (Wise, 2010). Elevation is highest in the south at Mt. Whitney (4420 m), decreasing northward. The climate is Mediterranean, with most precipitation from Pacific frontal storms in winter months. Topographic relief affects temperature and precipitation distribution throughout the range, with summer and fall lightning common in the southern

Sierra Nevada and increasing with elevation (SNEP, 1996). Ecozones on the western slope are: oak woodlands/chaparral (up to ~1300 m elevation), montane/mixed conifer forests (1300–

2200 m), and upper montane forests (>2200 m) (R.S. Anderson & Davis, 1988).

The main Native American tribes of the Kern Plateau and surrounding area are the

Tübatulabal and Foothill Yokuts (Yokuts). The Tübatulabal are a branch of the Uto‐Aztecan linguistic family and consider the Kern River Plateau their ancestral home (Voegelin, 1938).

Linguists estimate the Tübatulabal’s arrival in the Sierra Nevada, and subsequent linguistic

divergence from other groups in their language family, ~4000–5000 years ago (Moratto, 1984).

The Yokuts, constrained mainly to the lower‐elevation oak woodland/chaparral ecozone, are more recently arrived to the Sierra Nevada foothills and thought to have migrated ~1000‐2000 years ago (Gayton, 1948; Latta, 1977; Moratto, 1984).

The far southern Sierra Nevada is well‐suited to this study given the presence of archaeological data, availability of paleoecologic sites, and independent paleoclimate records.

Site selection for our paleoecologic reconstruction required three criteria: (1) deep sediments for palynological recovery, (2) proximity to a palynologically‐sensitive ecotone, and (3) proximity 19 to archaeological sites. HLY is located at the upper ecotone of Quercus kelloggii, a species highly prized by Native Americans for its acorns (Lightfoot and Parrish, 2009a), and is near known archaeological sites, allowing us to better capture potential human influence on vegetation in the area.

While few archaeological excavations have been conducted in Sequoia National Forest‐ administered lands, there are considerable records of archaeological sites via surface artifacts, bedrock mortars, and bedrock basins. Unfortunately, while these data convey presence of

Native Americans throughout the Sierran landscape, they do not provide a temporal context of land use/occupation.

Archaeological site CA‐TUL‐819 (Figure 1‐2) is located 7 km (4.5 miles) east‐northeast of

HLY (Ramirez et al., 2010). CA‐TUL‐819 is interpreted as an autumn high‐elevation Tübatulabal camp occupied intermittently from ~4500 BP through European contact (Ramirez et al., 2010).

The site has evidence for acorn (Quercus spp.), Hesperoyucca whipplei (yucca), and Distichlis spicata (saltgrass) processing.

The ecoregion includes numerous edible plants such as Chrysolepis sempervirens (bush chinquapin), Prunus virginiana (western chokecherry), Chenopodium fremontii (Fremont’s goosefoot), and Q. kelloggii. Surrounding modern tree species include Pinus jeffreyi (Jeffrey pine), P. ponderosa (ponderosa pine), Abies concolor (white fir), Q. kelloggii, Calocedrus decurrens (California incense‐cedar), and Sequoiadendron giganteum (giant sequoia). Common shrub species include: Chrysolepis sempervirens; Rosaceae (rose family), including Rosa woodsii

(Wood’s rose), Prunus virginiana (chokecherry), and Holodiscus discolor (oceanspray);

Chenopodium fremontii (Fremont’s goosefoot), and Ceanothus spp. (California lilac), including

Kern ceanothus (C. pinetorum), an endemic to the Kern plateau. 20

Figure 1‐2. Study area showing paleoenvironmental reconstruction site (HLY) and nearby archaeological site CA‐TUL‐819. YNP: Yosemite National Park.

Methods

Sediment coring The meadow was probed at several locations to determine the greatest depth of soft sediment with the least sand. Duplicate adjacent 4.95 m sediment cores (HLY‐11‐1 and HLY‐11‐2) were recovered in August 2011 using a 5‐cm diameter modified Livingstone square rod piston corer. A separate 50 cm surface core (HLY‐12‐4) was extracted using a serrated blade due to the near 21 impenetrable dense mat‐like root structure of the peat. Cores were extruded in the field into pre‐split plastic tubes and wrapped in plastic wrap before transport.

Sediment Analysis For this paper we use 0‐50cm of HLY‐12‐4 and 50‐64cm from HLY‐11‐1 for sediment analysis, spanning the last 2000 years (additional depths were used to constrain the age model). Cores were photographed, described, and placed in cold storage at 4oC. Sediment type was described qualitatively by feel (sand, silt, clay). Eight samples (seed, charcoal, and plant material) were selected for AMS radiocarbon dating from 0‐87 cm. Total organic matter (%TOM) was processed at continuous 1‐cm samples using the loss‐on‐ignition method at 550oC (Dean, 1974). Magnetic susceptibility was measured using a ring magnetometer.

Pollen Analysis Forty (40) samples were removed for pollen analysis (0.625 cm3). Samples were taken at a 55‐year average interval (every 1‐3cm; 21–74 years between sample) based on the age model

(see Results). Pollen samples were prepared using standard chemical digestion methods (Faegri and Iverson, 1964). Lycopodium spores, an exotic tracer, were added to samples during processing to calculate absolute pollen concentration and accumulation rate (Stockmarr, 1971).

When necessary, samples were sieved through 180µm mesh to remove large fragments

(generally peat, roots, or small gravel) following potassium hydroxide (KOH) digestion.

A minimum of 400 terrestrial pollen grains were counted at 400x magnification and identified to the lowest taxonomic level using reference guides (Kapp, 1969) and laboratory reference collections. We calculated pollen percentages based on the sum of terrestrial pollen, 22 excluding wet meadow taxa such as Cyperaceae. Cluster analysis was created with CONISS, a stratigraphically‐constrained incremental sum of squares cluster analysis, using the ‘rioja’ package in R (Grimm, 1987; Juggins, 2015).

Changes in percent pollen taxa though time are commonly interpreted as evidence for changes in the surrounding vegetation (Faegri and Iverson, 1964). Our understanding of the ecology of these taxa allows us to then infer ecologic change. When taxa respond inversely to climatic variability (e.g. moist vs. dry tolerant) palynologists have used this relationship to calculate the ratio (vegetation response index) of difference between the taxa to create a single variable that illustrates change more clearly than the plots of multiple individual taxa (Anderson et al., 2008b; Crawford et al., 2015; Mensing, 2001; Mensing et al., 2013, 2008, 2004). For example, this method has been used to create a moisture index to highlight potential hydrological changes (Anderson et al., 2008b). We use the relationship of Abies (shade‐tolerant, fire‐sensitive) to Quercus (shade‐intolerant, fire‐adapted) as a vegetation response index (VRI) based on the life history traits of these non‐ambiguous pollen types.

In the western United States, we have observed that the exclusion of fire for the last

150 years has resulted in the encroachment of fire‐sensitive taxa such as Abies into shade‐ intolerant Quercus woodlands (Engber et al., 2011; Peterson and Hammer, 2001). As stand density increases, understory composition further favors shade‐tolerant taxa and alters fuelbed structure by decreasing fine fuels and reducing the likelihood of low‐intensity fires (Engber et al., 2011; Peterson and Hammer, 2001); therefore, during cooler, wetter periods when fire disturbance is less likely, we expect Abies to outcompete Quercus. Conversely, during warmer, drier climatic periods when fire disturbances are more likely, we expect an increased presence of Quercus to fire‐sensitive Abies, resulting in a more open forest canopy structure (Bond and 23

Keeley, 2005; Higgins et al., 2000; Peterson and Reich, 2001). We exclude Pinus spp. from the

VRI because Pinus pollen tends to be overrepresented in the record due to prolific production and long‐distance wind dispersal (Bradshaw and Webb, 1985). Thus VRI is calculated as (Abies ‐

Quercus) / (Abies + Quercus). A positive VRI indicates a greater proportion of Abies to Quercus pollen, which we infer as a more closed forest canopy. A negative VRI indicates a greater proportion of Quercus to Abies pollen, which we infer as a more open forest canopy. We use the

VRI in comparison to both generally accepted climatic periods (MCA, LIA) and local independently‐derived climate reconstructions for interpretation of forest structure change in relation to climate change.

Charcoal Analysis Charcoal samples (1.25 cm3) were processed at continuous 1 cm intervals using sodium hexametaphosphate (5%) and stacked sieves of 500, 250, and 125µm mesh (Long et al., 1998).

Charcoal larger than 125µm is thought to come from local fires rather than extra‐local sources

(Whitlock and Larsen, 2001). Because of the large quantity of charcoal recovered at these size fractions, only macroscopic charcoal fragments ≥250 µm were counted. Each charcoal sample was counted at 40x magnification by at least two trained technicians, and/or the authors, to within 10% or five piece tolerance. We did this to ensure inter‐operator reliability and consistency. Charcoal was visually identified and tracked categorically as wood, grass, or other.

Charred, but not fully combusted material, was tracked separately.

Charcoal accumulation rates (CHAR) and in/significant peaks were calculated using

CharAnalysis 1.1 (Higuera et al., 2009) with 50‐year interpolation and natural log transform. We used a Gaussian mixture model to determine charcoal peaks. Fire frequency and fire return 24

intervals were smoothed over 1000 years. Cbackground was transformed using a natural log and smoothed over 250 years.

Independent Climate Analysis The only independent, annually‐resolved climate reconstructions in the Sierra Nevada range spanning the last 2000 years come from tree‐ring datasets analyzed and compiled into the North

American Drought Atlas (NADA; Cook et al., 2009, 2008, 2004). The NADA is a gridded network

(2.5o cells) using tree‐ring reconstructions to calculate annual paleodrought conditions via the

Palmer Drought Severity Index (PDSI). We use annually reconstructed PDSI values from grid cell

047 of the NADA as an independent measure of climate change at HLY.

We interpolated PDSI, VRI, and charcoal reconstructions every 50 years using a smooth spline to calculate correlations and explore relationships between all three proxies. Each splined reconstruction was standardized using (x‐x/̄ sd), where x represents the value, x ̄ the mean of the reconstruction, and sd the standard deviation of the reconstruction, to allow for comparability between records.

Pearson’s correlations were calculated at the 95% confidence interval. We further calculated a

50‐year weighted moving average of PDSI (50yr WMA) to compare relative trends between non‐ standardized sedimentary proxy reconstructions, climate, and Native American activity.

Results

Chronology Sediment spans the full Holocene, with a basal date of 13,100 cal yr BP. In total, 20 AMS radiocarbon (14C) dates were obtained on plant macrofossils and charcoal from HLY‐11‐1 and

HLY‐12‐4. Eight 14C dates from the top 87cm span the last 2300 years (Table 1‐1). Seven dates were used and calibrated using IntCal13 (Reimer et al., 2013) within the Bayesian age‐model program Bacon v2.2 (Blaauw and Christen, 2011) to create the age model (Figure 1‐3). Sample

168589 was determined to be an intrusive root and discarded from the age model.

Table 1‐1. Holey Meadow radiocarbon dates. Shaded row discarded from the final age model. Weighted mean (wmean) calculated by Bacon (Blaauw and Christen, 2011). Core Depth CAMS 14C Age cal yr B.P. Material (cm) Lab # 2Σ min wmean 2Σ max HLY‐12‐4 11 168588 0 ± 35 50 230 280 Seed HLY‐11‐1 13 157905 255 ± 40 110 290 400 Charcoal HLY‐12‐4* 23 168589 95 ± 35 130 270 840 Intrusive root HLY‐11‐1 26 156535 1070 ± 120 560 840 1150 Wood HLY‐12‐4 46 164056 1710 ± 50 1400 1600 1710 Wood HLY‐11‐1 49 168590 1705 ± 45 1540 1660 1770 Charcoal HLY‐11‐1 68 168591 2120 ± 45 1950 2080 2250 Charred needles HLY‐11‐1 87 164055 2315 ± 45 2250 2410 2710 Charcoal * Not included in the age model because review of microscopy imagery taken before radiometric dating indicates the sample was root fiber and not leaf material

Figure 1‐3. Bayesian age vs depth model for Holey Meadow. Grey cloud represents 95% confidence interval, blue bars represent calibrated radiocarbon ages at two sigma error calculated by Bacon 2.2 (Blaauw and Christen, 2011).

Sediment Analysis Sediment is humified to well‐humified peat throughout the core. No changes in

magnetic susceptibility are observed. Changes in %TOM are discussed in relation to derived

pollen zonation below.

Pollen and Charcoal Analysis Thirty‐one terrestrial pollen types, plus unknown and indeterminate grains, were

identified. Pollen taxa with at least 1% sum are presented in Figure 1‐4. We used CONISS (a

stratigraphically‐constrained cluster analysis) to identify five main zones of change. We 27 recognize that in a full Holocene record we would not identify five pollen zones within the last

2000 years; however, because we are interested in identifying periods of centennial change, and not millennial, we use a finer level of zonation identified by breaks in CONISS. These breaks independently conform to known climatic periods such as the MCA and LIA. Cyperaceae is not included in the statistical analyses or pollen accumulation rate, but is shown to demonstrate the site as a wet meadow throughout the time period. Statistical correlations between interpolated and standardized VRI, CHAR, and PDSI are based on pollen zones (Table 1‐2 and Figure 1‐5).

Figure 1‐4. Pollen percentage diagram of selected taxa, pollen accumulation rate (Acc. Rate), total organic matter (%TOM), and charcoal accumulation rate (CHAR). Dots represent fire peaks (red significant, grey insignificant) calculated by CharAnalysis. 28

Table 1‐2. Comparison of r values between standardized sedimentary proxies and independent climate reconstructions interpolated at 50‐year intervals using a smooth spline. Red indicates negative correlation values Pollen zones Zone 1 Zone 2a Zone 2b Zone 3 Zone 4 Zone 5 Zones 2‐4 Cal yr BP 2000‐1550 1550‐1000 1000‐750 750‐500 500‐100 100‐A.D. 2003 1550‐100 VRI v Charcoal ‐0.47 X ‐0.08 *0.94 **‐0.98 X ‐0.11 X 0.57 ‐0.30 VRI v PDSI *0.69 X ‐0.21 **0.94 *‐0.89 X ‐0.16 X 0.57 **‐0.47 PDSI v Charcoal **‐0.85 **0.93 *0.83 **0.95 **0.99 X‐0.34 **0.84 * p‐value < 0.05 X p‐value > 0.4 ** p‐value < 0.01

Figure 1‐5. Vegetation Response Index (green), charcoal accumulation (orange), and PDSI (grey) standardized values interpolated at 50‐year intervals using a smooth spline. Pollen zones are denoted by grey vertical lines.

Zone 1 (2000–1550 cal yr BP; 64–46 cm)

Zone 1 (Figure 1‐4) has maxima for Poaceae, C. sempervirens, Salix spp., Apiaceae,

Asteraceae, and Cyperaceae. This zone also has the highest pollen accumulation rate in this record. Pinus remains high (~50%) for the rest of the record until the historic period (100 cal yr

BP; 1850 A.D.). The VRI is dominantly negative (interpreted as a more open canopy) during this period, indicating a higher proportion of shade‐intolerant, fire‐adapted Quercus (Figure 1‐5).

There is a weak negative statistical relationship between VRI and charcoal during this period (r =

‐0.47; p‐value = 0.20; Table 1‐2), possibly relating to environmental lag from two large fire 29 events immediately preceding this study (data not shown; peaks at ~2200 and 2050 cal yr BP, but consistently high charcoal counts through this time). The highest levels of charcoal are also found in this period, with one significant fire peak ~1690 cal yr BP (Figure 1‐4).

PDSI reconstructions indicate a variable climate oscillating between slightly wet (0.5) and slightly dry (‐0.5) conditions based on the 50yr WMA (Figure 1‐6). Climate has a strong negative correlation with charcoal (r = ‐0.85; p‐value = 0.004) and a moderate correlation with

VRI (r = 0.69; p‐value=0.04; Table 1‐2; Figure 1‐5).

Zone 2 (1550 – 750 cal yr BP; 46‐28 cm)

Zone 2 has high levels of Poaceae, and Apiaceae. Indeterminate pollen types (~5%) remain high from this point forward. Zone 2a (1550–1000 cal yr BP; 46‐35 cm) has a greater

%TOM than Zone 1, and the lowest prolonged pollen accumulation rates in the record (1400–

1100 cal yr BP). C. sempervirens and Salix spp. reach maxima and maintain their highest levels throughout this subzone. Poaceae and Cyperaceae decrease from Zone 1 levels but remain relatively constant through the period. One insignificant fire peak occurs at ~1350 cal yr BP, and one significant fire peak at ~1050 cal yr BP. VRI and charcoal have no statistical relationship (r = ‐

0.10; p‐value = 0.72) within this zone.

Zone 2a has both the wettest (0.7; wet) and driest (‐1.8; drought) 50yr WMA PDSI in the entire record. There is no correlation between VRI and climate (r = ‐0.21; p‐value = 0.52) or VRI and fire (r = ‐0.08; p‐value = 0.81) during this period; however, there is a very strong positive relationship between fire and climate reconstructions (r = 0.93; p‐value < 0.001). 30

Zone 2b (1000–750 cal yr BP; 35‐28 cm), which corresponds with the MCA, has the lowest %TOM in the last 2000 years. Pollen accumulation rate remains low. Pinus spp. decreases with a reciprocal increase in Cupressaceae. There is a general shift to positive VRI, indicating a more shade‐tolerant, closed forest canopy (increased Abies). Charcoal levels also have a decreasing trend. There is a very strong positive relationship between VRI and charcoal in this subzone (r = 0.94; p‐value = 0.02). The %TOM is lowest in this zone with minima of 15%, while

%TIC reaches a maxima of 4.4%.

There are also a strong positive correlations between VRI and climate (r = 0.94, the strongest in the record; p‐value = 0.02), and climate with fire (r = 0.83; p‐value = 0.08). This period is generally dry, experiencing pronounced drought except for a pluvial recorded by the

50yr WMA between 877–866 cal yr BP.

Zone 3 (750–500 cal yr BP; 28–21 cm)

Zone 3, transitioning between MCA and LIA, is marked as a whole by consistently low pollen accumulation and relatively stable pollen percentages and %TOM. Pinus recovers from lower levels during Zone 2b. Apiaceae is at maxima. Quercus percentages are variable while

Abies remains relatively constant. Fraxinus reappears and remains present through the rest of the record. VRI is mostly positive during this time indicating a more closed, shade‐tolerant forest cover, but demonstrates an consistent linear trend towards a more open canopy (‐VRI). One significant charcoal peak is recorded ~750 cal yr BP, and one insignificant peak at ~600 cal yr BP.

Unlike Zone 2b, Zone 3 has a very strong negative relationship between VRI and charcoal (r = ‐

0.97; p‐value = 0.004). 31

This period has an overall trend towards slightly drier than “normal” climatic conditions, but generally wetter than Zone 2b. There is a very strong negative correlation between climate and VRI (r = ‐0.89; p‐value = 0.004), the strongest negative relationship between vegetation and climate in the record. Conversely, there is a very strong positive relationship between climate and fire (r = 0.95; p‐value = 0.01).

Zone 4 (500–100 cal yr BP; 21–7 cm)

Zone 4, corresponding to the LIA, is marked by an increase in Rosaceae from its minima transitioning between Zones 3 and 4. Quercus and Poaceae demonstrate a generally increasing trend. Fraxinus reaches maxima, as does Cupressaceae. Pinus generally declines during this period, as do Abies and Asteraceae. VRI is almost entirely negative through this zone, indicating a prevalence of shade‐intolerant Quercus. Charcoal levels decrease considerably, with only one insignificant fire peaks recorded at ~250 cal yr BP. There is no correlation between fire and VRI in this zone (r = ‐0.11; p‐value = 0.79).

The 50yr WMA for PDSI is effectively centered on “normal” conditions. Relatively wetter

PDSI possibly has a very strong positive relationship with decreasing charcoal (r = 0.99; p‐value <

0.001). There is no relationship between climate and VRI (r = ‐0.17; p‐value = 0.69).

Zone 5 (100 cal yr BP–A.D. 2003; 7–0 cm)

Zone 5, the modern period, is marked by the greatest change in the last 2000 years.

Pinus percentages drop markedly to almost half their percentage from maxima 2000 years ago. 32

Both Abies and Quercus increase in unison, reaching their maxima by almost 10% more than previously in the record. VRI shows an abrupt increase in shade‐tolerant taxa ~A.D. 1880, then another abrupt shift to shade‐intolerant taxa around A.D. 1980. Charcoal levels are at their lowest in the entire record. There is no statistically significant relationship between fire and VRI

(r = 0.57; p‐value = 0.61).

This period exhibits mild fluctuations in PDSI centered on “normal” conditions. There is no correlation between VRI and climate (r = 0.57; p‐value = 0.61) or climate and fire (r = ‐0.34; p‐

value = 0.77).

Discussion Human‐influenced model supported for historic period

During the historic period (beginning ~ A.D. 1850) human impacts on the forest intensified through European immigration (A.D 1854), establishment of the Tule River Indian

Reservation (A.D. 1873), establishment of the Sierra Forest Reserve (A.D. 1893) and Sequoia

National Forest (A.D. 1908), fire suppression policies (A.D. 1900), disease epidemics (A.D. 1900 and 1910), introduction of livestock grazing, and lumbering in the study area. We indeed observe no relationship between vegetation, fire, and climate in our sedimentary records, demonstrating that known anthropogenic influences are reflected in our proxy data and support a human‐influenced model beginning at least 100 cal yr BP. We see a dramatic decline in Pinus

(from 40–60% of the pollen signal to less than 30%) and synchronous increases in Abies (due to fire suppression) and Quercus (due to timber harvests creating open spaces in canopy). These results agree with a similar study in the Klamath Mountains of northern California (Crawford et 33 al., 2015). Given this observation, we explore potential Native American sources of forest composition influence over the last 2000 years.

Radiocarbon dating from the closest archaeological site, CA‐TUL‐819, (Ramirez et al.,

2010) suggests continuous site use over the last 2000 years. The number of artifacts recovered suggests possibly reduced site use between 1900–1650 cal yr BP, intensified site use around

1150 cal yr BP, and continued site use until the modern period.

Charcoal abundance during the last 2000 years is highly correlated with tree‐ring PDSI reconstructions (r = 0.99, p‐value < 0.001; Table 1‐2; Figure 1‐6), suggesting climate (i.e. drought) appears to be the primary driving force of the regional fire regime at HLY. This result is supported by research that demonstrates regional fire regimes are climatically driven (Beaty and

Taylor, 2009; Brunelle and Anderson, 2003; Hessl, 2011; Sherriff and Veblen, 2008; Swetnam,

1993; Swetnam et al., 1998), and that charcoal is likely produced under high and mixed‐severity fire types. Low severity fires are often not captured in the charcoal record (Mohr et al., 2000;

Whitlock and Anderson, 2003). Fire frequency in giant sequoia groves between ~1450–1150 cal yr BP (A.D. 500‐800) is low, with a regional increase in fire frequency after 1150 cal yr BP (A.D.

800), and highest fire frequency between ~950–650 cal yr BP (A.D. 1000–1300) (Swetnam, 1993;

Swetnam et al., 2009). Our charcoal accumulation rates are in agreement with Swetnam’s regional long‐term fire history patterns over the last 1500 years. Few independent data points exist for fire and/or climate from 0–500 cal yr BP.

Vegetation change at HLY

2000–1550 cal yr BP and 1150–750 cal yr BP – Climate‐driven model predominant 34

Charcoal accumulation between 2000 and 1550 cal yr BP exceeds what we expect based on climate reconstructions. Though VRI indicates more shade‐intolerant taxa, changes in VRI are not quite synchronous with charcoal accumulation. This could be due to forest recovery from intense local and regional fires preceding this period (data not shown). Climate reconstructions by Moratto et al. (1978) indicate warm, dry conditions in the southern Sierra Nevada foothills from 2900‐1700 cal yr BP, at odds with Cook’s PDSI calculations, but consistent with observations in charcoal and VRI. Current PDSI calculations for this period are based on very few tree‐ring chronologies at higher elevations, making the point‐by‐point regression model of PDSI climate reconstructions weakest through this period. While the possibility exists that the charcoal and VRI signal observed from 2000‐1550 cal yr BP have an anthropogenic component, independent climate reconstructions to date are not robust enough for us to make a reasonable inference.

As expected, vegetation, fire, and climate are strongly, positively correlated during the

Medieval Climate Anomaly (MCA; 1000–750 cal yr BP) – a period of increased fire and drought, also identified in the archaeological record as potentially resulting in a decline in the Native

American population throughout California (Figure 1‐6) (Hull, 2007; Jones and Schwitalla, 2008;

Moratto, 1984). Trade routes appear to deteriorate among coastal and inland peoples between

950 and 650 cal yr BP (A.D. 1000–1300) (Jones et al., 1999). If population was not decreasing, there was likely a greater dispersal of people across the landscape such that large habitation centers were no longer the focus of occupation; population may have remained stable, but evidence for dispersal is difficult to identify. This period is when we hypothesize that if there were any anthropogenic alterations on the landscape, they would have produced the same ecological effects as a warmer climate and be indistinguishable from the climate‐driven model. 35

1550–1150 cal yr BP – Ambiguous model dominance

Weak evidence for a human‐influenced landscape exists from 1550–1150 cal yr BP.

Native populations in California are thought to have dispersed, with only sporadic occupation of larger sites as drought conditions intensified over the period. The timing of Native American use of Sierran resources from 1550–1150 cal yr BP is variable, as the number of artifacts found in

Yosemite declines through this time period while sites along marshy areas in the foothills and

Central Valley indicate increased occupation (Dillon et al., 1991; Moratto et al., 1978). Because permanent habitation sites are more likely to be excavated and present in the archaeological record than smaller, more ephemeral sites, the timing and spatial extent of Native American land use is presently ambiguous. Our VRI and charcoal reconstructions exhibits the same kind of variableness, but the causal relationship is ambiguous due to a lack of local archaeological data.

750–100 cal yr BP ‐ Human influenced model predominant

Strong evidence for a Native American‐influenced landscape occurs from 750–100 cal yr

BP. Between 750–500 cal yr BP, there is a statistically significant negative relationship between vegetation and fire/climate (Table 2), possibly indicating that high‐frequency/low‐intensity fires, which are not captured in the charcoal record, are altering forest composition significantly from climatic expectations. During this period, Native Californians were intensifying resource procurement (Basgall, 1987; Beaton, 1991; Bettinger, 2015; Hull, 2009; Wohlgemuth, 1996). In

California, this time also corresponds to increasing Native American population density (Hull,

2007; Moratto, 1984) during the onset of the Little Ice Age (LIA; 700–100 cal yr BP), and 36 reoccupation of earlier village sites (Arnold et al., 2004). CA‐TUL‐819 was also occupied during this period (Ramirez et al., 2010). In Yosemite Valley, Anderson and Stillick (2013) find evidence for frequent surface fires between 650–400 cal yr BP, supporting previous work associated with

Native American population expansion and fire (Anderson and Carpenter, 1991). Nearby, the

Western Mono migrated to the Sierra Nevada just north of the Kern Plateau (in the San Joaquin,

Kings, and Kaweah river watersheds) during the end of the Numic expansion, between 650 and

350 cal yr BP. This immigration could have conceivably increased population pressure due to the displacement of existing Sierra Nevadan peoples such as the proto‐Yokuts and Miwok groups

(Kroeber, 1925; Morgan, 2010; Powers, 1976). Changes in procurement strategies are evident, as bow technology became the dominant hunting weapon from ~750–550 BP (A.D. 1200–1400), with arrowheads dominating lithic assemblages throughout California from that point forward

(Jones, 1995).

Figure 1‐6. Environmental proxies compared against regional‐to‐local Native American habitation periods.

From 500–100 cal yr BP, as native populations and trade at village sites increased (Hull,

2009, 2005; Moratto, 1984), our proxy records show no statistical relationship between vegetation and either fire or climate (Table 1‐2; Figure 1‐6), supporting the human‐influenced model. Our findings are supported by work in Yosemite Valley, where little decline in fire was observed during the LIA, as would be expected climatically (Anderson and Stillick, 2013). During the LIA climatic period, when we expect more shade‐tolerant, fire‐sensitive taxa, we instead observe an increase in the pollen of shade‐intolerant/fire‐adapted taxa. A similar phenomenon is observed in Yosemite Valley, where there is an abrupt shift to a higher ratio of Quercus to

Abies pollen from 650–150 cal yr BP (Anderson and Carpenter, 1991). One possible explanation for both observations is that maintenance of a high‐frequency/low‐severity anthropogenic fire regime partially decoupled vegetation response to climate and produced an anthropogenic landscape as expected by our human‐influenced model (Figure 1‐1b). We can clearly observe disconnects between vegetation, fire, and climate response beginning ~100 cal yr BP with

European settlement of the area (A.D. 1854), removal of Native Americans from the landscape, establishment of the Tule River Indian Reservation, fire suppression policy, and influenza epidemics.

We suggest that frequent low‐intensity human‐set fires created a forest with higher than expected shade‐intolerant fire adapted plants during the cool and wet LIA. Fire scar studies demonstrate that frequent, low‐intensity fires were indeed common in the southern Sierra

Nevada range, especially in the last 500 years (Gassaway, 2009; Swetnam, 1993; Swetnam et al.,

2009; Taylor, n.d.). At two sites ~40 miles NNW of HLY, Swetnam et al. (2009) found mean fire return intervals (FRI) from 1750‐250 cal yr BP (A.D. 200–1700) of 2.2 years (1–13 years) and 3.0 years (1–19 years) based on fire scars. The last two fire scar dates found were A.D. 1863 and 39

1915, coinciding with European migration and establishment of fire‐suppression policies

(Swetnam et al., 2009). A preliminary fire scar study in 2005 by Taylor (unpub) ~14 km NE from

HLY at Freeman Creek Grove, Long Meadow, and near Route 190, has a chronology dating from

433 to ‐55 cal yr BP (A.D. 1517 to 2005). The composite record shows frequent scarring between

A.D. 1660 and ~1865, with the last fire scar in A.D. 1910. The median fire return interval between the 3 locations is 3–6 years, with modal intervals between fires of 1.0, 1.8, and 0.68, respectively (Taylor, n.d.). The fire scars often occurred on an annual or biannual basis near HLY

(Taylor, n.d.), as would be expected based on ethnographic records. These frequent fires likely explain the deviation in vegetation from climatic expectations; further strengthening the argument that Native Americans use of fire did indeed alter the forest structure surrounding

HLY.

One explanation for the general disconnect between vegetation response and fire is that our charcoal reconstruction, while based on large charcoal fragments thought to be indicative of local fires, is still based on charcoal likely produced under high and mixed‐severity fires. Low severity fires are often not captured in the charcoal record (Mohr et al., 2000;

Whitlock and Anderson, 2003), but are, instead, captured through fire scars on witness tress.

Comparison with previous studies

A study using fine temporal resolution to explore Native American use of fire at two sites in the western Klamath Mountains of California shows a similar, albeit weaker, pattern

(Crawford et al., 2015). That study found that vegetation, fire, and climate generally changed in a manner consistent with climate, except for two anomalous periods: 1560–1110 cal yr BP, and 40 during the LIA. They observed an increase of shade‐intolerant taxa, such as Quercus, even though these were periods of regional cooling in northern California. We observe the same phenomenon at HLY, of a more open than expected forest canopy during both these periods

(corresponding to our Zone 2b and 4). This pattern has not specifically been noted in other pollen and charcoal studies in the central and southern Sierra Nevada (Anderson and Carpenter,

1991; Anderson and Smith, 1994; Anderson and Stillick, 2013; Anderson, 1994; Anderson et al.,

2008a, 2008b; Davis, 1992; Dull, 1999; Koehler and Anderson, 1994), however, we would note that the temporal resolution of earlier studies is typically not sufficiently high to confidently identify century‐scale changes, and that the location of these studies may not have facilitated observation of this signal. Although Native Americans used fire for many purposes, natural fire is also common throughout mountain regions of California, and distinguishing a human impact is challenging. Previous work has found that it is critical to study sites located at ecotonal boundaries in a region of potential environmental stress to be able to observe the impacts of

Native American use of fire on natural ecosystems (Crawford et al., 2015).

Beyond fire, possible confounding factors affecting forest composition include disturbances such as wind throw, pathogens, and insect outbreaks, all of which are part of forest ecosystem processes. The methods we use in reconstructing paleovegetation are incapable of detecting or reconstructing the precise nature of competitive dynamics and their mechanisms.

Studies on southern Sierra Nevada forests have demonstrated that these types of disturbances affect already drought‐stressed individuals (Ferrell, 1996) and have no significant difference in effect between shade‐intolerant and shade‐tolerant taxa (Fettig et al., 2007; Hilimire et al.,

2013; Smith et al., 2005). These findings are similar to work done in the Klamath Mountains

(Halofsky et al., 2011; Knapp and Hadley, 2011; Zobel et al., 1985). Accordingly, we assume 41 these disturbances to be effectively synchronous with external climate forces and thus indistinguishable in our sedimentary proxy records.

Conclusions Fires are a frequent, natural disturbance in the western United States, and disentangling natural ignition sources from anthropogenic ones is a challenge. Using the knowledge that the frequency at which fires naturally occur on the landscape changes with climate, we hypothesized that Native Americans had an identifiable impact on the forest structure of the southern Sierra Nevada, and that this impact is most readily identified through the preservation of shade‐intolerant/fire‐adapted taxa during climatically cool, wet periods through the use by

Native Americans of frequent low‐intensity fires

While HLY’s sedimentary charcoal record has a strong correlation with regional fire reconstructions and climate, the pollen record demonstrates two pre‐A.D. 1850 anomalies to forest structure change unexplained by either regional fires or independent climate reconstructions (1550–1150 cal yr BP, and 750‐100 cal yr BP). We find strong support for a

Native American‐influenced landscape from 750–100 cal yr BP. We clearly see evidence of post‐

Contact settlement (100 cal yr BP–present), with forest dynamics showing the elimination of fire on the landscape and an increase in both Quercus and Abies taxa ‐‐ phenomenon unseen in the paleoenvironmental record, and one demonstrative of modern forest policies and management.

We temporally cross‐referenced anomalous periods of VRI at HLY with available fire scar data, and found that frequent, low severity fires do not appear to be captured in the sedimentary charcoal record. Low frequency fires are abundant in at least the last 500 years. 42

The archaeological record also supports more intensive land use and increasing/coalescing

Native American populations during the LIA climatic period.

We find support for the human‐influenced model (Figure 1‐1b) of forest succession via changes in vegetation reconstruction unexplained by fire or climate and the timing of Native

American population increase and intensification of resources near HLY and throughout

California. This impact is most noticeable in the proxy record during the LIA.

Our finding supports previous paleoenvironmental work (Anderson and Carpenter,

1991; Anderson and Stillick, 2013; Crawford et al., 2015) and begin to suggest that the pattern of Native American influence on forest structure is more widespread than just a single site.

Additional paleoenvironmental sites throughout the Sierra Nevada range are necessary to test how widespread Native American impacts were on Sierran forests. Site selection is critical in assessing potential Native American impacts, and reconstructions must be performed at a fine, stratified temporal scale. Increased collaboration among archaeologists, paleoecologists, and tribal members, and the addition of co‐situated archaeological excavations at sensitive, marginal sites would greatly improve our understanding of the scale, proximity, and spatial extent of past

Native American landscape influences.

Chapter 2 Paleoecological Evidence for Native American‐set Fires of Prehistoric Landscapes in the Sierra Nevada, California

Introduction Today’s forests of the Sierra Nevada range in California look very different than when

European explorers first entered the Range of Light. Historic accounts and photographs show what was often referred to as “park‐like settings” with mixed‐aged, patchy cohorts of trees

(Lewis, 1973; Muir, 1894; National Park Service, 2011; A. J. Parker, 2002; Stephenson et al.,

1991). This contrasts with the thick underbrush that has developed since the 1850s through fire exclusion, grazing, selective logging, road construction (both paved and dirt roads can act as firebreaks), and forest suppression policies (Bowman et al., 2011; Martin and Sapsis, 1992;

Silcox, 1910). While we can certainly observe the impact modern land management policies have had on Sierran forests (Roy and Vankat, 1999; Vankat and Major, 1978), the question remains whether climate, or Native Americans kept forests relatively open and park‐like pre‐

European contact.

Was the pre‐A.D. 1850 park‐like forest composition produced primarily by climate and climatically‐induced natural fires, or Native Americans? While it is recognized that Native

Americans, who have inhabited California since the terminal Pleistocene, used fire and prescribed burns to increase land resources, the impact their burning had on the broader landscape remains contested. The role of Native American set fires on forest change can be considered two ways: (1) locally increased burning near areas of habitation (i.e. limited in geographic scope) to increase food yields but with climate remaining the driving force of broader landscape change (Vale, 2002b; Whitlock and Knox, 2002b), and (2) regional or geographically wide‐spread use of surface fires on the landscape sufficient to change forest structure (Anderson, 2006; Denevan, 1992; Lewis, 1973). 44

Fire is a natural disturbance in the Sierra Nevada, and as climate changes, so too does the frequency of natural fires (Dale et al., 2001, 2000; Flannigan et al., 2000; Keeley et al., 2011;

Swetnam, 1993; Swetnam et al., 2009). Cooler, wetter climates are typically associated with less frequent fire disturbance, though large, high severity fires can occur during dry years. Greater available soil moisture and less disturbance allows for the succession of fire‐sensitive/shade‐ tolerant taxa such as fir (Abies) and incense‐cedar (Calocedrus decurrens) (Dale et al., 2001;

Lenihan et al., 2003). Warmer, drier climates are associated with more frequent fire, providing conditions for a relative increase in fire‐tolerant/shade‐intolerant taxa such as oaks (Quercus) and grasses (Poaceae) (Engber et al., 2011; Fites‐Kaufman et al., 2007; Henne et al., 2012; Innes et al., 2006; Peterson and Hammer, 2001). Fire disturbance is a driving force of change on the

Sierran landscape, and the role of fire in preserving and maintaining oak woodlands is widely recognized (Bond and Keeley, 2005; Crawford et al., 2015; Engber et al., 2011; Higgins et al.,

2000; Peterson and Hammer, 2001; Peterson and Reich, 2001). Further, fire stimulates plant growth, decreases plant competition, and provides the soil with nutrients (Jordan, 2003).

Native Americans used fire to increase natural resource yields (Anderson and Moratto,

1996; Anderson, 1999; Codding and Bird, 2013; Dincauze, 2000a; Fowler, 2008; Gassaway, 2009;

Jordan, 2003; Lightfoot and Lopez, 2013; Lightfoot and Parrish, 2009a; Lightfoot et al., 2013). A productive landscape was important given their hunter‐gatherer lifeways, and California’s

Native Americans were effective in their use of fire to increase natural resource production in order to support large populations pre European contact (Baumhoff, 1963; Kroeber, 1925).

While their use of fire is mentioned in the ethnographic record (Anderson and Moratto, 1996;

Lewis, 1973; Voegelin, 1938), the magnitude and extent to which fire was employed over several millennia is not known. 45

Modern forest fire severity is increasing in the Sierra Nevada (Miller et al., 2009), and costly and extensive fires such as the 2013 Rim Fire in Yosemite (1040 km2, largest Sierran wildfire on record) and the 2015 Rough Fire in Sequoia National Forest (610 km2) are becoming

more prevalent. As temperatures continue to increase and projections for future megadroughts become more prevalent (Diffenbaugh et al., 2008), recognizing the extent of Native American use of fire on the landscape becomes vital in understanding prehistoric fire regimes, the development of pre‐A.D. 1850 forests, deviation of modern forest composition from that observed in the paleorecord, and the impacts of modern forest conditions on future fire management policies.

Previous work by Klimaszewski‐Patterson and Mensing (2015) at Holey Meadow in

Sequoia National Forest (Chapter 1) indicates that identifying impacts of prehistoric Native

American burning is possible in a landscape of natural vegetation. They identify what they argue is clear indication of an anthropogenically‐altered landscape during the Little Ice Age (750‐100 cal yr BP) through the relative prevalence of shade‐intolerant/fire‐sensitive taxa and shade‐ intolerant/fire‐adapted taxa in comparison with local, annually‐derived climate reconstructions from tree rings.

In this chapter I test the methods and findings of Klimaszewski‐Patterson and Mensing

(2015) by using sediments from a geographically and culturally paired meadow in Sequoia

National Forest for paleoenvironmental reconstruction. I apply the three lines of evidence set forth by Bowman et al. (2011) to identify Native American burning: (1) temporal changes in proxy reconstructions of fire and vegetation that (2) differ from (locally) climatic expectations and (3) occur temporally with identified changes (cultural phase shifts) in Native American activity. I further compare my results and findings with other regional sub‐centennial 46 paleoenvironmental reconstructions to explore whether signals of an anthropogenic landscape are locally intensive or regionally extensive.

Study Area The north‐south trending, granitic Sierra Nevada range separates California’s Central

Valley from the Great Basin. Similar hydroclimatic responses in relation to El Niño‐Southern

Oscillation variability occur from Yosemite National Park (just north of Fresno, California) to the southern extent of the range (Wise, 2010). Temperature and precipitation distribution are

affected by topographic relief, with lightning common in summer and fall months and increasing with elevation (SNEP, 1996). The vast majority of precipitation comes from Pacific frontal storms in the winter months. Oak woodlands and chaparral occur up to ~1300 m elevation on the western slopes. Montane/mixed conifer forests are found from 1300–2200 m elevation, and upper montane forests occur above 2200 m (R.S. Anderson & Davis, 1988).

Trout Meadow (TRT; 36.2° N, 118.4° W; elev. 1890 m) is a spring‐fed sedge meadow located at the southern end of the Sierra Nevada, in the Golden Trout Wilderness Area, Sequoia

National Forest, California (Figure 2‐1). The meadow is located on a western slope within the

Kern River Basin, oriented north to south, approximately 1250 m long, and bifurcated towards its northern extent. TRT is widest at the point of bifurcation, approximately 155 m. The meadow is situated in a cold‐air drainage in the montane/mixed conifer zone. Adjacent tree species include Calocedrus decurrens (California incense‐cedar), Pinus jeffreyi (Jeffrey pine), P. ponderosa (ponderosa pine), Abies concolor (white fir), Quercus kelloggii (California black oak).

Sequoiadendron giganteum (giant sequoia) occurs nearby, but is not local to the site. P. monophylla (single‐leaf piñon pine) can be found within 3 km. Common shrubs occurring in the pollen record include: Chrysolepis sempervirens (bush chinquapin), various Rosaceae spp. (rose 47 family, including Holodiscus discolor (oceanspray), Prunus virginiana (chokecherry), and Rosa woodsii (Wood’s rose), Chenopodium fremontii (Fremont’s goosefoot), and Ceanothus spp.

(California lilac). Nearby edible plants include: C. fremontii, C. sempervirens, P. virginiana, Pinus spp. (seeds), and Quercus spp. (acorns). On the modern landscape, A. concolor occurs primarily on north and east facing slopes, while P. jeffreyi, P. ponderosa, and Q. kelloggii occur on south and west facing slopes immediately surrounding the meadow. The meadow is approximately

1700 m east of the Little Kern River when following an easily navigable drainage (140 m elevational difference), providing ready access to river resources including the endemic Little

Kern golden trout (Oncorhynchus mykiss whitei).

The main Native American peoples who occupied what is the present‐day Sequoia

National Forest are the Tübatulabal and Foothill Yokuts (Yokuts). The Tübatulabal consider the

Kern River Plateau their ancestral home (Voegelin, 1938), and are estimated to have arrived in the Sierra Nevada ~4000–5000 years ago (Moratto, 1984). The Yokuts migrated to the Sierra

Nevada foothills ~1000‐2000 years ago and inhabited primarily lower‐elevation oak woodland/chaparral zones (Gayton, 1948; Latta, 1977; Moratto, 1984). Most archaeological data in Sequoia National Forest‐administered lands are limited to cultural resources management reports recording surface artifacts, bedrock mortars/milling stations, slicks, and bedrock basins (Gassaway, pers. comm.). Unfortunately, while these data convey the presence of Native Americans throughout the Sierran landscape, they do not provide a good temporal context of land use/occupation. Surface artifacts are recorded during archaeological surveys, 48

Figure 2‐1. Trout Meadow in the Golden Trout Wilderness Area, Sequoia National Forest, California, United States.

but are rarely put into context of land use. Bedrock features (mortars, slicks, basins) are pecked into the granitic bedrock and permanently demonstrate the presence of seed, nut, and acorn processing (Arnold et al., 2004), but do not provide a temporal context due to the inability of 49 current methods to date the features. Archaeological excavations are necessary to provide temporal context in relation to artifacts; however, the few archaeological sites that have been excavated in the mountains of the southern Sierra Nevada range occurred as single‐site cultural resource reports (Lloyd et al., 2011; Ramirez et al., 2010) with minimal reliable dating.

Recent work done by Kraus (2016) on archaeological excavations (CA‐TUL‐2027/2077) conducted adjacent to TRT in 2012 and 2013 through the Forest Service’s Passport in Time outreach program provides a coarse temporal context for use and habitation directly at site.

Based on obsidian hydration (OH) dating, TRT was utilized, at least periodically, from 6000 cal yr

BP until the A.D. 1800s. Given the depth of time interpreted for site use based on OH dating, I use the Tübatulabal culture periods associated with the Sawtooth Phase (1350‐650 cal yr BP;

A.D. 600‐1300) and Chimney Phase (650‐100 cal yr BP; A.D. 1300‐historic) (Moratto, 1984) for broad comparisons between culture phases and landscape change (per Bowman et al. 2011).

Methods Sediment Coring Sediment from the wet meadow was extracted using a 5‐cm diameter modified

Livingstone square rod piston corer. Duplicate sediment cores (354 and 370 cm) were recovered in October 2011, but cumulatively half of the top meter of sediment was fine to coarse sand, indicating the meadow’s spring‐fed stream channel meandered through the sampling location.

The bottom 25 cm of each core was very coarse gravel. I resampled the site in July 2013 for more consistently peaty sediments in duplicate cores (TRT‐13‐1, 349 cm and TRT‐13‐2, 323 cm).

Basal dates of cores recovered in 2011 and 2013 indicate that each core spans the full Holocene epoch. Sediment cores were extruded into pre‐split ABS plastic tubes in the field and wrapped in plastic wrap before transport back to the University of Nevada Paleoecology lab. 50

Sediment Analysis I use 0‐90 cm from TRT‐13‐2 for sediment analysis, spanning the last 1300 years

(additional depths were used to constrain the age model). I limit the analysis to the last 1300 years to have statistically reliable climate reconstructions against which to compare the results while still capturing conditions preceding the MCA. Cores were described, photographed, and placed in 4oC cold storage. Sediment was qualitatively described by feel (sand, silt, clay). Seven samples (wood, charcoal) were selected for AMS radiocarbon dating from 0‐97 cm. Continuous

1‐cm samples were processed for total organic matter (%TOM) and total inorganic matter (%TIC) using the loss‐on‐ignition method at 550oC (Dean, 1974). Given the lack of signal in magnetic susceptibility from the 2011 cores, no magnetic susceptibility was conducted for TRT‐13‐2 and therefore not included in this analysis.

Pollen and Charcoal Analysis Twenty‐nine (29) 1 cm thick samples were removed for pollen analysis (0.625 cm3) at a

47‐year average interval (every 2‐4 cm; 25‐73 years between samples) based on the age model

(see Results). Samples were prepared using standard chemical digestion (Faegri and Iverson,

1964), with the exotic Lycopodium tracer spores added during processing to calculate absolute pollen accumulation rate and concentration (Stockmarr, 1971). I sieved samples with large fragments of roots or small gravel through a 180 µm mesh following potassium hydroxide (KOH) digestion.

Four hundred terrestrial pollen grains were counted in high pollen concentration samples, 200 grains if pollen concentration was low. Grains were counted at 400x magnification and identified to the lowest taxonomic level using laboratory reference collections and guides 51

(Kapp, 1969). Pollen percentages were calculated based on the sum of terrestrial pollen, but excluding the wet meadow taxa Cyperaceae. I used CONISS (a stratigraphically‐constrained incremental sum of squares cluster analysis) via the ‘rioja’ package in R (Grimm, 1987; Juggins,

2015) to identify pollen zones.

Due to the observable high quantity of charcoal greater than 250 µm, I used the chemical assay method (Winkler, 1985) for analysis. Sediment (2.5 cc) was sampled at continuous 1‐cm intervals. 30‐40ml concentrated nitric acid was added to dried samples in 50ml test tubes and placed in a hot water bath for one hour to dissolve non‐charcoal organic material.

Samples were rinsed with deionized water at least 3 times, dried, weighed, and combusted at

450oC for three hours before percent charcoal (%CHAR) was calculated. Separate analysis using data from Holey Meadow (Chapter 1) showed that changes in %CHAR trended similarly with macroscopic counted charcoal (≥250µm).

Vegetation Response Index I use a vegetation response index (VRI) to identify shifts between shade‐tolerant/fire‐ sensitive and shade‐intolerant/fire‐adapted taxa on the landscape (Crawford et al., 2015;

Klimaszewski‐Patterson and Mensing, 2015). Following methods established by Klimaszewski‐

Patterson and Mensing (2015) at Holey Meadow (HLY), ~33 km (20 miles) to the southwest, I use the relationship between Abies and Quercus to calculate the VRI for comparison between forest structure change in relation to independently‐derived local climate reconstructions. A positive

VRI (+VRI) indicates a greater proportion of Abies to Quercus, which I infer as a more closed canopy (climatically expected during the LIA). A negative VRI (‐VRI) indicates a greater proportion of Quercus to Abies, which I infer as a more open canopy (climatically expected during the MCA). 52

Independent Climate Analysis The only available independent, annually‐resolved climate reconstructions in the Sierra

Nevada range for the last 2000 years come from tree rings. The North American Drought Atlas

(NADA; Cook et al., 2009, 2008, 2004) is a gridded network (2.5o cells) of compiled tree‐ring reconstructions used to calculate annual Palmer Drought Severity Index (PDSI) values

(paleodrought conditions). I use grid cell 047 as an independent annually reconstructed measure of climate change at TRT.

Following the same methodology as Klimaszewski‐Patterson and Mensing (2015), I used standardized 50‐year smooth splines to investigate relationships between VRI, charcoal, and

PDSI. I use Spearman’s rank‐order two‐tail correlations at the 95% confidence interval to calculate non‐parametric correlation trends between the proxy datasets.

Regional Comparison I identified for comparison four other paleovegetation sites that met the following conditions to determine whether Native American impacts at TRT are locally contained or regional in scope: (1) at least centennial‐scale vegetation reconstructions for the last 1300 years

(2) in mountainous California settings (3) at elevations with Quercus kelloggii populations that

(4) considered Native American impacts on the landscape. These sites were Lake Ogaromtoc

(OGA) in Klamath National Forest, Fish Lake (FSH) in Six River National Forest (Crawford et al.,

2015), Woski Pond (WOS) in Yosemite National Park (Anderson and Carpenter, 1991), and Holey

Meadow (HLY) in Sequoia National Forest (Klimaszewski‐Patterson and Mensing, 2015; Chapter

1). While other Sierran paleoenvironmental studies exist (Anderson and Smith, 1994; Anderson,

1994, 1990; Anderson et al., 2008a, 2008b, 2002; Brunelle and Anderson, 2003; Davis et al., 53

1985; Dull, 1999; Koehler et al., 2005; Smith and Anderson, 1992; Street et al., 2012), they unfortunately are not at sufficiently high temporal resolution over the last 1300 years for sub‐ centennial reconstructions.

Pollen data and author‐calibrated dates for OGA, FSH, and HLY were acquired directly from the authors, while WOS data was acquired from the North American Pollen Database

(NAPD). I used NAPD calibrated dates (using IntCal13) for WOS. VRI values for OGA, FSH, and

WOS were calculated using Abies and Pseudotsuga as closed‐forest taxa, and Quercus,

Pteridium, and Poaceae as open‐forest taxa (Crawford et al., 2015). VRI for TRT and HLY were calculated using Abies as closed‐forest taxa and Quercus as open‐forest taxa. Pseudotsuga and

Pteridium are not present at TRT or HLY. Poaceae may be over‐represented at TRT and HLY as they are wet meadows coring sites not lake coring sites (OGA, FSH, and WOS).

Results Chronology Sixteen (16) AMS radiocarbon (14C) dates were obtained from TRT‐13‐2 on wood and charcoal spanning the Holocene (basal date 10,900 cal yr BP). Nine 14C dates from the top 115 cm were used to generate the age model for this study (Table 2‐1 and Figure 2‐2) using IntCal13

(Reimer et al., 2013) and Bacon v2.2, a Bayesian age‐model program (Blaauw and Christen,

2011). Bacon uses Markov Chain Monte Carlo (MCMC) iterations to produce estimates of accumulation histories. Dates are modeled using a students‐t distribution, making the age‐depth model more robust against outlying dates. At TRT, the influence of outlier sample 168595 was

statistically minimized by Bacon, and thus does not alter the trajectory of the age model significantly. I hypothesize that sample 168595 may have been bioturbated to its lower core depth by thick Cyperaceae roots which are present in the core.

Table 2‐1. Trout Meadow (TRT) radiocarbon dates. Weighted mean (wmean) and 2Σ range calculated by Bacon (Blaauw and Christen, 2011). Core Depth (cm) CAMS 14C Age cal yr B.P. Material Lab # 2Σ min wmean 2Σ max TRT‐13‐2 12 168592 185 ± 30 20 150 230 Charcoal TRT‐13‐2 17 168593 195 ± 30 150 210 290 Charred wood TRT‐13‐2 17 (rep) 168601 190 ± 30 150 210 290 Charred wood TRT‐13‐2 17 (rep) 168602 205 ± 30 150 210 290 Charred wood TRT‐13‐2 30 164057 375 ± 30 320 400 500 Charcoal TRT‐13‐2 54 168594 680 ± 110 530 660 820 Charcoal TRT‐13‐2 + 72 +168595 380 ± 90 *660 *890 *1120 Charcoal TRT‐13‐2 97 168596 1520 ± 90 1190 1360 1510 Charcoal TRT‐13‐2 115 168596 1685 ± 35 1520 1620 1750 Charcoal + Outlier sample that was effectively excluded by Bacon * assigned date based on MCMC estimate of accumulation at depth and not the sample itself

Figure 2‐2. Age model for Trout Meadow (TRT). Red dotted line indicates the “best” single model for each depth based on the weighted mean average (wmean). Blue bars represent calibrated radiocarbon ages at two sigma error calculated by Bacon 2.2 (Blaauw and Christen, 2011) using IntCal13 (Reimer et al., 2013). Dotted line bounding the grey cloud of points represents the 95% confidence interval.

Sediment Analysis Sediment was primarily humified peat, with the top 25 cm containing a thick mass of nearly impenetrable roots. Some fine to coarse sand co‐occurs within the peat from 55‐68 cm depth. %TOM remained between 40‐60% throughout the core except for two intervals with low values (<25%) from 55‐68 cm (875‐730 cal yr BP) and 37‐17cm (560‐200 cal yr BP) (Figure 2‐3).

%TIC was consistent and less than 4%.

Pollen Analysis I identified 28 terrestrial pollen types plus indeterminate and unknown grains. Pollen taxa with at least 2% maxima are presented in Figure 2‐3. CONISS, calculated using taxa with at least 1% maxima, indicated three main zones of change with two minor zones (1A and 1B). In a full Holocene record I would not identify this minor zone; however, I included it given my interest in identifying centennial periods of change. Pollen zones conformed closely to known climatic periods such as the MCA (1050‐750 cal yr BP) and LIA (750‐100 cal yr BP). Statistical correlations between the interpolated and standardized VRI and PDSI values were based on pollen zones (Table 2‐2 and Figure 2‐4).

Cyperaceae, Gaeumannomyces spp., and unknown non‐pollen palynomorph (NPP) spore types Fungal 1‐3 were identified and counted (Figure 2‐5) but not included in the terrestrial pollen types, statistical analysis, or pollen accumulation rate. They are shown to demonstrate the site as a wet meadow. Gaeumannomyces (Figure 2‐6A) is an NPP fungal pathogen known to attach to the roots of Carex spp (Mazurkiewicz‐Zapałowicz and Okuniewska‐

Nowaczyk, 2015)and was included given the low occurrence of identifiable Cyperaceae pollen grains. Unknown NPP Fungal 1, 2, and 3 (Figure 2‐6B‐D) appear to co‐occur with both

Cyperaceae and Gaeumannomyces. 56

percent

and

%TOC

Rate),

(Acc.

rate

accumulation

pollen

VRI,

CONISS.

maxima,

2% ≥ with

identified

taxa

zones

Pollen

diagram

Pollen

(%CHAR). 3.

‐ 2

Figure charcoal

Table 2‐2. Comparison of Spearman’s ƿ values between 50‐year smooth splined and standardized VRI, percent charcoal, and PDSI values. Pollen zones Zone 1 Zone 1A Zone 1B Zone 2 Zone 3 Cal yr BP 1250‐800 1250‐1100 1100‐800 800‐200 200‐A.D.2003 VRI v Charcoal X0.28 1.0 X‐0.03 0.28 X‐0.70 VRI v PDSI X0.03 ‐1.0 *0.94 X0.25 *1.0 PDSI v Charcoal 0.42 ‐1.0 X0.26 0.01 ‐0.70 * p‐value < 0.05 ** p‐value < 0.01 o p‐value < 0.1 X p‐value > 0.4

MCA LIA

Figure 2‐4. Vegetation Response Index (VRI; thick green line), charcoal (dashed orange line), and PDSI (thin grey line) values, smooth splined at 50‐year intervals and standardized. Pollen zones are denoted by grey vertical dashed/dotted lines.

Figure 2‐5. Cyperaceae and wet meadow indicator non‐pollen palynomorphs (NPPs) shown with pollen zones/CONISS.

A B C D

Figure 2‐6. Gaeumannomyces (A), and unknown spores Fungal 1 (B), Fungal 2 (C) and Fungal 3 (D) NPP

Zone 1 (1250‐800 cal yr BP; 90‐65 cm) was a period of markedly low pollen accumulation (~3000 grains/cm/year) with the highest percentages of TOC and charcoal. Pinus steadily increased (33‐65%) while Poaceae (19.6%), Brassicaceae (12.2%), and Astearceae (9.3%) decreased from maxima throughout the period. Cupressaceae (7.3%) and Alnus (2.2%) were at maxima while Abies (0.4%) was at a minimum. VRI varied but remained predominantly negative 59

(shade‐intolerant/fire‐adapted taxa. Zone 1A (1250‐1100 cal yr BP; 90‐83 cm) and 1B (1100‐800 cal yr BP; 83‐65 cm) were differentiated by relative high percentages of herbaceous taxa

Poaceae, Brassicaceae, and Astearceae, with their maxima in Zone 1A. Zone 1B has a maximum of %CHAR (6.9%), and independently corresponded closely with the MCA. There was no statistical relationship between charcoal and either PDSI or VRI. VRI and PDSI demonstrated a strong correlation in zone 1B (ƿ = 0.94, p‐value 0.002), but not through Zone1 as a whole (ƿ =

0.30, p‐value 0.44).

Zone 2 (800‐200 cal yr BP; 65‐16 cm) encompassed the end of the MCA and the full LIA.

%TOC dropped precipitously from earlier levels in Zone 1 (from ~60% to 15%) and remained under 15% except for a period between 700‐450 cal yr BP, when it recovered to 50‐60% levels.

%CHAR was relatively constant at 2%, except for a slight increase to 3% from 700‐475 cal yr BP coinciding with increased %TOC. Pollen accumulation rate was highest in this zone, with a maximum ~450 cal yr BP (coinciding with +VRI). Quercus, Rosaceae, Asteraceae, and Poaceae percentages were high, with Quercus reaching a maximum (11%) ~400 cal yr BP (concurrent with a spike in Poaceae (14.9%)). Pinus reached a maximum (77.7%), but oscillated in relative abundance (54.7‐77.7%) through this section. Cupressaceae percentages declined while Abies increased from previous levels. Brassicaceae pollen abruptly disappeared from the record at 800 cal yr BP and minimally reoccurred 400 years later. There was no identified statistical correlation between any combination of PDSI, VRI, or charcoal.

Zone 3 (200 cal yr BP – A.D. 2013; 16‐0 cm) included the transition from the LIA to the modern period (100 cal yr BP; A.D. 1850). %TOC remained constant through this zone at ~55% and pollen accumulation rates slowly but consistently increased from 250 to 0 cal yr BP. VRI remained negative until 100 cal yr BP when it shifted to a positive state and remained so 60 through the end of the zone. This was the only instance in the last 1300 years that VRI remained positive for more than a 50‐year period. Abies reached a maximum (7.5%) in the modern period.

Quercus increased (from 2.9 to 6.2%) concurrently with Abies (from 4.7 to 7.5%) in the last 50 years. VRI and %CHAR had a negative relationship in this period (ƿ = ‐0.70, p‐value 0.23), while

VRI and PDSI had a very strong positive correlation (ƿ = 1.0, p‐value 0.02).

Regional Comparison I calculated VRI for OGA, FSH, WOS, TRT (this study), and HLY to compare each site against its local PDSI reconstruction and against each other (Figure 2‐7). Pollen zones are reported as published by the original authors. Data are truncated to correspond with the time period of this analysis. Comparing each site’s VRI with its associated PDSI reconstruction provides regional climatic and temporal controls. By comparing these five sites, there are apparent climatic vs non‐climatic driving forces of forest composition, especially during the LIA. 61

Figure 2‐7. Paleovegetation reconstruction sites used in comparison to Trout Meadow (TRT). 50‐year smooth spline and standardized local PDSI (thin line) and VRI (thicker line) values for OGA and FSH (Crawford et al., 2015), WOS (Anderson and Carpenter, 1991), TRT (this study) and HLY (Klimaszewski‐Patterson and Mensing, 2015; right) for the last 1300 years. Light bands along the bottom identify site‐specific periods identified by the original authors as having possible anthropogenic impacts/more open forest canopies than expected. Vertical lines denote site‐specific pollen zones. Crosshatched bands indicate periods of regional (from Mariposa Grove to Giant Forest) fire frequency increase, hatched areas periods of low frequency (Kilgore and Taylor, 1979; Swetnam, 1993; Swetnam et al., 2009).

Discussion Local patterns, Native Americans, and anomalous change in forest composition

If climate and climatically‐induced factors (such as fire disturbance) were the only driving force of forest composition change, I would expect changes in vegetation (analyzed via 62 the VRI) and charcoal production to be consistent with changes in local climate reconstructions

(PDSI). The proxy reconstructions show periods of relative agreement during the MCA (1050‐750 cal yr BP), consistent both with climatic and anthropogenic expectations; however, periods before and after the MCA are inconsistent with climatic expectations.

The MCA is identified as a period markedly warmer and drier than preceding conditions, which is reflected in the PDSI reconstruction. Yet from 1250‐1050 cal yr BP, during a relatively wetter period, forest composition is skewed more than expected from the climate towards shade‐intolerant/fire‐adapted (‐VRI) and herbaceous taxa. VRI and %CHAR indicate forest conditions almost as open and disturbed as at the height of drought during the MCA. As PDSI steadily indicated progressively wetter conditions during LIA, I expected a steadily increasing positive VRI resulting from the succession of shade‐tolerant, fire‐sensitive taxa. Instead, I observed fluctuations towards negative VRI, indicating greater disturbance that promoted more shade‐intolerant/fire‐adapted taxa than expected from the climate. VRI remained negative except at 450 cal yr BP and from 100 cal yr BP to present. Interestingly, the canopy remained in a more open state as the wettest 50‐year weighted mean average PDSI conditions (not shown) in the last 1000 years occurred at ~350 cal yr BP and from 300‐200 cal yr BP. This is contrary to my expectations of a more positive climate‐driven VRI, and indicative of more disturbance than predicted, possibly through Native American‐set fires.

Adjacent to TRT is archaeological site CA‐TUL‐2027/2077, which includes numerous bedrock mortars. In 2012 and 2013, excavations were conducted at CA‐TUL‐2027/2077 by U.S.

Forest Service archaeologists. One‐cubic meter units were excavated, and additional augers were conducted from the bottom of the units to bedrock (120‐140 cm total depth). Artifacts were found throughout all depths of the excavations. While I cannot say with certainty exactly 63 when Native Americans actively managed resources at TRT, I can observe that the site was indeed inhabited and used.

The two main interpretations for anthropogenic influences to VRI variability at TRT are that (1) the site required less active management by Native Americans for gathered food resources because of its situation which is more conducive to Quercus establishment and persistance, and/or (2) the site was less consistently used because it is located at the fringes of

(recognized) Tübatulabal territory (non‐Kern Plateau).

Alternative hypotheses for climatic causes to the VRI variability rely on inherent properties of the site’s location. TRT’s topographic position within the Kern River Basin results in some rain shadow effect, as reflected by the modern presence of Pinus monophylla within 3 km and the differentiation of Pinus, Quercus, and Abies by topographic aspect. Strong slope aspect differentiation and topographic relief (Figure 2‐1) also provides potential micro‐refugia, which may have allowed for persistence of long‐lived tree species through otherwise less favorable climatic conditions. Climatic fires and other disturbances near TRT may have been decoupled from strong climatic forcings due to these microrefugia and the rugged terrain.

Given that the meadow is surrounded by bedrock mortars and Quercus, is locally fed by a perennial spring, a short distance from riverine resources, and inhabited at least intermittently within the last 2000 years, I find it reasonable that Native Americans had an impact on the local landscape. I interpret VRI variability to most likely result from intermittent active management

by Native Americans who took advantage of the site’s physiographic characteristics to maximize resources as needed.

Regional patterns and anomalous change

Culture phases are often used when discussing archaeologically‐inferred changes throughout California. Cultural phases are archaeological units with characteristic, distinguishable traits spatially and chronologically limited to a relatively small locality or brief time interval (Willey & Phillips, 1958). The difficulty in comparing regional shifts is that culture phases are named and defined locally, not regionally; however, shifts between culture phases roughly occur at the same time throughout California, especially in the Sierra Nevada (Hull,

2007). To simplify characterization of regional changes between sites, I use the following modified cultural periods identified for Central California (Jones et al., 2007):

Late (700 – 100 cal yr BP; A.D. 1250‐1850)

Middle (2550 – 700 cal yr BP; 600 B.C. – A.D. 1250; includes the Middle/Late Transition

(950 – 700 cal yr BP; A.D. 1000 – 1250))

The Middle Period is characterized by marked increase in populations and hunting from the

Early Period (6400 ‐ 2550 cal yr BP; 3500 ‐ 600 B.C.).The Middle/Late Transition (which I have included within the Middle Period to better align with Sierran culture phases) denotes a shift from the atalatl to bow and arrow technology. The Late Period is characterized by even more high intensity use and occupation. Arrowheads, bedrock mortars, small bifacial drills, and

Olivella beads are common and widespread. These cultural periods are defined and identified primarily by distinctive bead types which were traded/shared throughout much of California

(Jones et al., 2007), and not by environmental changes.

OGA and FSH in the Klamath Mountains demonstrate predominantly climatic signals, with two potential periods of anthropogenic impacts at FSH (Figure 2‐7). OGA and FSH are more mesic, coastal influenced sites located in northern California. Average variation in PDSI 65 reconstruction at the sites is relatively flat, with the MCA not noticeably observable in the record. This lack of amplitude in the PDSI signal indicates that trees in the area are relatively complacent to change given minimal changes in drought stress, and alteration of composition is prone to inertia. Crawford et al. (2015) found no significant evidence of anthropogenic influence at OGA. At FSH, the authors identified two periods of anomalous change in vegetation:

1560‐1110 cal yr BP (pre‐MCA) and around 400 cal yr BP (during the LIA). A more open canopy from 1560‐1110 cal yr BP occurs during a period of regional cooling and decreased regional charcoal and is inconsistent with climatic expectations. The authors indicate that the timing of this anomaly coincides with the shift during the Middle Period to a new culture phase (Gunther;

1500 – 100 cal yr BP), which is locally characterized by increasing populations (both in situ and through immigration) and a greater reliance on more sedentary subsistence strategies such as tribal burning for acorn harvesting and other terrestrial resources. The more open canopy at FSH is temporally consistent with negative VRI observed at TRT and HLY further to the south. During the anomalous period around 400 cal yr BP, Crawford et al. argue that an absolute increase in

Quercus pollen over the preceding 2500 years, and a slightly more negative VRI during the LIA are indicative of an anthropogenic influence. Low amplitude changes in VRI are to be expected as coastal populations of northwest California did not rely on terrestrial resources as heavily as populations in the Sierra Nevada, and therefore had less need to burn consistently for resource extraction (Baumhoff, 1963; Lewis, 1973). Though weak, the negative VRI during the LIA is notable, especially given the absolute increase in Quercus pollen. Comparison of PDSI and VRI at

FSH supports Crawford et al.’s conclusions for the two reported anomalous periods of potential anthropogenic influence. 66

WOS and HLY (in the central and southern Sierra Nevada range) indicate clear and striking non‐climatic signals during the LIA that I interpret as Native American impacts on forest composition (Figure 2‐7). WOS shows a dramatic shift to the prevalence of shade‐intolerant/fire‐ adapted taxa from 700 to 600 cal yr BP, with VRI remaining negative through the remainder of the record. This shift occurs at the same time as the transition to the Late Period (from the

Tamarack (1300‐600 cal yr BP; Middle Period) to Mariposa (600‐150 cal yr BP; Late Period) culture phases in Yosemite (Moratto, 1999)). Change at HLY also occurs at the transition to the

Late Period (Sawtooth (1350‐650 cal yr BP; Middle Period) to Chimney Phase (650‐100 cal yr BP;

Late Period)). Both WOS and HLY indicate more shade‐intolerant/fire‐adapted taxa through the

LIA than at any point during the MCA. The timing of environmental change at the onset of the

Late Period, characterized by intensified site and resource use, strongly argues for an increased influence of Native American land‐tending practices.

Potential scale of Native American impacts on forest composition

Researchers can only identify where Native Americans existed on the landscape based on non‐ephemeral cultural remains (bedrock mortars, milling stations, middens, lithic scatters, etc). Lack of archaeological presence does not necessarily mean lack of pre‐historic land use, as many organic cultural tools (e.g. wooden arrow shafts, basketry) and effects (e.g. coppicing, pruning, selective harvesting, sowing) have been lost to taphonomic processes (e.g. decay, regrowth). To what spatial extent then might Native Americans have actively applied fire for resource management? If cultural fire was only used to improve resource yields, then one could argue that Native American influence on landscape structure should have been locally limited, and that the paleoenvironmental sites used here for comparison are not reflective of the larger 67 landscape because of their proximity to local archaeological sites. If fire was only used to locally to improve foraging, then I would expect sites near food processing locations such as bedrock mortars (BRMs) to exhibit a Native American influence on the landscape. Daily foraging within 5 km is a reasonable distance from food processing locations (Bettinger et al., 1997; Morgan,

2008). Using this foraging radius as a spatial buffer from known BRMs in the northern district of

Sequoia National Forest (SNF) shows that the vast majority of the district could have been actively managed by cultural fire (Figure 2‐8). This suggests that considerable portions of the district, most likely low to mid elevations which support fuels conducive for surface fires, could have been actively managed by Native Americans.

Figure 2‐8. Total area within a five kilometer foraging radius (yellow area) around identified bedrock mortar (BRM) and pre‐historic milling stations in the northern district of Sequoia National Forest (SNF).

Conclusions According to Bowman et al. (2011), three sets of information from the paleoecological record are needed to reliably distinguish anthropogenic burning: (1) spatial or temporal changes in vegetation and fire activity, (2) proof that these changes are not predicted by climate or climate‐fuels‐fire relationships, and (3) proof that changes in the fire regime coincide in space 69 and time with changes in cultural phases. At TRT, WOS, and HLY, the results reflect (1) temporal changes that (2) are not predicted or supported by local climate reconstructions. At TRT, variation in vegetation may occur on an as‐needed management basis, tentatively supported through (3) preliminary OH‐dating (not shown) (Kraus, 2016; Skinner and Thatcher, 2013) of archaeological artifacts at the site.

This work supports the findings of earlier paleoenvironmental studies by Anderson and

Carpenter (1991), Crawford et al. (2015) and Klimaszewski‐Patterson and Mensing (2015) for anthropogenically‐modified landscapes in California, while adding a new paleoecological site to the literature. Of the five sites used for regional comparison, signals of paleoanthropogenic landscapes are strongest in the Sierra Nevada range (WOS, HLY, and TRT), where vegetation is more sensitive and responsive to disturbance than the more coastal sites (FSH and OGA). This observation is especially evident in the last 650 years. The timing of regional fires, which are interpreted as climatically driven (Kilgore and Taylor, 1979; Swetnam, 1993; Swetnam et al.,

2009), do not explain changes in vegetation composition observed at the Sierran sites. This study supports previous observations that climatic fires and disturbances may not have been the only force to drive forest composition change, and that additional ignitions set by Native

Americans were necessary (Reynolds, 1959; Vankat, 1970). Fire use and purpose, as documented in the ethnographic record to create patchy, mixed‐age, resource‐diverse, and productive landscapes for subsistence (Anderson and Moratto, 1996; Anderson, 1999; Codding and Bird, 2013; Dincauze, 2000b; Fowler, 2008; Gassaway, 2009; Lewis, 1973; Lightfoot and

Lopez, 2013; Lightfoot and Parrish, 2009c; Lightfoot et al., 2013; Voegelin, 1938) was necessary to generate the park‐like settings observed in the A.D. 1850s (Vankat, 1977). 70

To further refine when Native Americans used specific areas of the Sierra Nevada would require dating bedrock mortars and other milling features – technologies which do not exist to date. Additional studies towards the edges of hypothesized foraging radii and near ecotonal

(ecologically‐sensitive) boundaries would further test the extent to which anthropogenic impacts are chronicled in the paleoenvironmental record. Three of the five sites (FSH, TRT, and

HLY) indicate Native American influences prior to the MCA that warrant exploring. Four of the five sites indicate anthropogenic landscapes through the LIA. All five sites demonstrate forest composition changes in the last 100 years incongruous with 1300 years of record (ex: preponderance of Pseudotsuga, simultaneous increases in Abies and Quercus), demonstrating strong effects of modern land management practices (especially fire suppression and logging) on the landscape that may have severe impacts on future fire‐fuels conditions.

While large, severe fires occurred in the past, the combined paleoenvironmental record demonstrates a decrease in intensity (less sedimentary charcoal production) spatially over the last few hundred years. As we move towards more active fire management of federal lands, we can note the practices, implementation, and timing of Native American use of fire. By taking into

account Native fire‐use practices, and actively engaging tribal communities, modern land management policies may move towards a more comprehensive and holistic approach to natural resources management that improves forest health, increases resource yields, and decreases the likelihood of catastrophic fires. 71

Chapter 3 Modeling Native American Burning and the Paleolandscape

Introduction Humans have altered landscapes across North America for millennia (Anderson and

Moratto, 1996; Beaton, 1991; Bowman et al., 2011; Cronon, 1983; Gayton, 1948; Kroeber, 1959,

1925; Lewis, 1973; Lightfoot and Lopez, 2013; Lightfoot and Parrish, 2009a; Voegelin, 1938). We alter vegetation composition and change fire frequency through clearing land, grazing livestock,

and fire suppression (Bowman et al., 2011; Martin and Sapsis, 1992). California’s Native

Americans were proto‐agriculturalists, relying not on crops such as maize, but on managed native taxa such as oaks and pines (Anderson and Moratto, 1996; Anderson, 1999; Gayton,

1948; Kroeber, 1959, 1925; Lewis, 1973; Voegelin, 1938). The use of fire created a patchy mosaic between edges of burned parcels, increasing both biodiversity of plants and animals, as well as resource yields (Jordan, 2003; Lewis, 1973; Lightfoot and Parrish, 2009b; Martin and Sapsis,

1992).

Native American use of fire also altered fuel structures and abundance. Surface fires burn through an area quickly, consuming fine fuels and reducing understory vegetation, thus decreasing resource competition for the surviving trees. Because surface fires burn at a relatively low to moderate intensity, trees are scarred but not killed, recording the presence of fire. Once a fire scar is recorded on a tree, it scars more easily from subsequent fires (Speer,

2010). Ethnographic accounts record regular Native use of fire (Gayton, 1948; Kroeber, 1959;

Lewis, 1973; Voegelin, 1938), but not the exact quantity, frequency, or range to which fire use and management were employed (Lightfoot and Parrish, 2009a; Martin and Sapsis, 1992).

Because the overall impact and extent of native fire practices is uncertain (Allen, 2002; Bowman et al., 2011; Lightfoot and Lopez, 2013; Lightfoot and Parrish, 2009a; K. C. Parker, 2002; Vale, 72

2002b; Whitlock and Knox, 2002a), the fundamental questions become (1) can we identify past changes in forest vegetation composition that can be attributed to human impacts, and (2) how do we distinguish that change from the concurrent influence of a changing climate?

To investigate these questions in the southern Sierra Nevada range of California,

Klimaszewski‐Patterson and Mensing (2015) developed a vegetation response index (VRI), in which the pollen prevalence of shade‐tolerant/fire sensitive taxa (Abies) was compared to the pollen prevalence of shade‐intolerant/fire adapted taxa (Quercus). Based on life history traits of these two taxa, they expected a positive VRI (more Abies than Quercus) during cooler, wetter climates with less fire disturbance, such as the Little Ice Age (LIA; 750‐100 cal yr BP; A.D. 1250‐

1850), and a negative VRI (more Quercus than Abies) during warmer, drier climates with an increased likelihood of natural fire disturbance such as the Medieval Climate Anomaly (MCA;

1050‐750 cal yr BP; A.D. 1000‐1250). Thus a negative VRI during climatically cool, wet periods

(e.g. LIA) with less fire disturbance does not reflect expected successional processes based on these taxa’s life history traits. Instead, this would be suggestive of other frequent disturbance forces, such as low intensity burning by Native Americans.

The Palmer Drought Severity Index (PDSI) from the North American Drought Atlas (Cook et al., 2008, 2004, 1999; Herweijer et al., 2007) was used as an independent climate model of wetter and drier periods against which to compare the VRI. Klimaszewski‐Patterson and

Mensing (2015) found that the changes in VRI matched climatic PDSI trends through the MCA, but began to diverge from PDSI trends starting in 750 cal yr BP (1250 A.D.), and strongly differed from climatic expectations between 500 and 100 cal yr BP (1450‐1850 A.D.). They noted that the divergence of VRI from climatic expectations (negative VRI during cool, wet periods) coincided with archaeological findings of increasing population density throughout California. They 73 suggested that this change in forest structure was due to Native American‐set fires. Changes from 100 cal yr BP (A.D. 1850) onward coincided with the arrival of European settlers to the southern Sierra Nevada, the removal of Native Americans from the landscape, the establishment of Sequoia National Forest, fire suppression policies, and logging.

Typically in paleoecology, this is where the story ends – a paleolandscape reconstruction is interpreted and supporting or dissenting temporal evidence is discussed in a broad context.

How do we ascertain that these changes observed in the paleoenvironmental record are not a coincidence of timing or artifact of stochastic events? Paleoecology is a historical, empirical science that by its very nature does not and cannot allow for experimentation, only replication, and requires an extensive and intensive sampled spatial scale to support conclusions. Landscape change modeling (Aber and Federer, 1992; He, 2008; He et al., 2008, 1999; Miller and Urban,

1999a; Mladenoff, 2004; Pastor and Post, 1985; Shugart, 2002; Solomon et al., 1980) is an alternative approach that dynamically represents individual processes (e.g., succession, fire) interacting over space and time and can allow for experimentation with different climatic and ecological conditions, and can incorporate different paleoecological scenarios at various spatial and temporal scales.

In this paper, I use forest landscape models to explore the hypothesis that the addition of Native American‐set fires caused the forest composition observed in the paleoenvironmental record. Many forest landscape modeling studies in the western United States focus on current forest structure and fire regime, using models as a tool for forest treatments or future forecasts of forest response to climate change (Miller and Urban, 2000, 1999a; Scheller et al., 2011;

Sturtevant et al., 2009; Syphard et al., 2011; Yang et al., 2015b). The models are calibrated or evaluated based on current and historic conditions. 74

My goal is to explore the frequency, type, and extent of fire initiation necessary to recreate the forest composition observed in Klimaszewski‐Patterson and Mensing’s (2015) paleoenvironmental reconstruction at Holey Meadow, Sequoia National Forest, California, USA.

Using landscape parameters from Syphard et. al (2011) and independent climate (PDSI) and fire scar reconstructions (Swetnam, 1993; Swetnam et al., 1990; Taylor, n.d.), I seek to reconstruct a portion of the southern Sierra Nevada landscape over the last 1100 years and compare the modeled taxa results to sedimentary pollen reconstructions of climatically‐sensitive forest composition (VRI; Figure 3‐1). I pose two hypotheses:

H1: Modeled predictors of forest composition from climate and climatically‐induced fires

approximate observed paleovegetation dynamics, suggesting that Native American set

fires are not necessary to explain past vegetation.

H2: The addition of Native American set fires is needed to approximate observed

paleovegetation dynamics, and changes in the VRI indicate an anthropogenically

modified landscape.

If the second hypothesis is not rejected, I will explore the temporal occurrence of Native

American‐set fires on the landscape. Were Native American‐set fires necessary during prolonged wetter periods (1550‐1050 cal yr BP and 750‐100 cal yr BP) to approximate paleovegetation, or were cultural fires only necessary during the LIA (750 – 100 cal yr BP), when the archaeological literature indicates a higher population density and more intense resource exploitation than previous periods? The findings from each case could have important implications in archaeological interpretations of Native American intensification and land‐use. 75 More open/dry open/dry More

MCA LIA Modern More closed/wet closed/wet More

Figure 3‐1. Standardized PDSI (grey line) and pVRI values (green line) from Holey Meadow (HLY).

Study Area Holey Meadow (HLY; 35o 57.3' N, 118o 36.9' W; elev. 1945m; Figure 3‐2) is a sedge

meadow in the southern Sierra Nevada, located at the upper ecotone of California black oak

(Quercus kelloggii) distribution, and near archaeological food‐processing sites (Klimaszewski‐

Patterson and Mensing, 2015). For this paper, I limit the extent of spatial modeling efforts to

69.92 km2 (6992 hectares) centered on HLY, developed from a 4 km radius around HLY

converted to a square raster. I limit the spatial extent in this manner to (1) mimic the

approximate extent of distance traveled by pollen to the site from which the paleo VRI (pVRI) is

derived and (2) to focus on events directly surrounding the observed paleoenvironmental site

rather than infer from a broader landscape. I am not interested in broad landscape trends or

natural landscape‐level fire regimes for the purposes of this paper, but on potential, direct 76

Native American use of fire on the landscape and the frequency of fire necessary to create the

VRI (Figure 3‐1) inferred from the HLY paleoenvironmental record. A 4 km distance is also consistent with the foraging distance of Native Americans and thus the area most likely to have been manipulated.

Three fire scar sites from a preliminary report (Taylor, n.d.) indicate frequent fires in the vicinity of HLY over the last 425 years (Figure 3‐2). The spatial extent of each grove is not reported in the study, but given the small number of samples (35 total) at each fire scar site

(Long Meadow Grove=12 samples; Freeman Creek Grove=9 samples; 190=14 samples), each extent is likely small. The longest chronology (Long Meadow Grove) dates to 433 cal yr BP (A.D.

1517), with the first fire scar recorded at 370 cal yr BP (A.D. 1580). Reconstructed fire history at the three sites is from 250 cal yr BP (A.D. 1700) to A.D. 2005. Both Long Meadow and Freeman

Creek Grove individually showed a median fire interval (MFI) of 6 years. The 190 fire scar site had a MFI of three years. The modal interval ranged from 0.68‐1.08 years, indicating frequent intervals of annual fire. The last recorded fire scar at each site occurred at: A.D. 1855 (Long

Meadow), A.D. 1903 (190), and A.D. 1910 (Freeman Creek Grove), coinciding with establishment of the reservation system and implementation of fire suppression policy (Silcox, 1910).

The median and modal frequencies of these local fire scar sites are consistent with findings by Swetnam et al. (2009) in the Giant Forest of Sequoia National Park (~68 km N of

HLY). There fire intervals statistics were calculated at three different spatial scales: tree group

(1 ha=6 trees), grove (70 ha=37 trees), and forest (350 ha=52 trees). The tree group was

calculated between 1450‐250 cal yr BP (A.D. 500‐1700), while grove and forest scales were calculated from 1750‐250 cal yr BP (A.D. 200‐1700). Reported MRIs based on all fire indicators of 77

6, 2, and 1 respectively. The modal interval was 1 at all three scales, again indicating frequent annual fires.

Methods I use a landscape simulation model to reconstruct 1100 years (1050 to ‐50 cal yr BP; A.D.

900 – 2000) of possible landscape change as inferred from a paleolandscape reconstruction 78

(Figure 3‐1) using natural and anthropogenic fire regimes. LANDIS‐II is a spatially‐explicit, raster‐ based, stochastic model designed to simulate spatial interactions among succession, natural disturbances, and forest management (Scheller and Mladenoff, 2004a; Scheller et al., 2007). The model simulates individual tree and shrub species based on their life history traits, including longevity, fire tolerance, shade‐tolerance, resprouting ability, age of maturity, seed dispersal distance, and reproduction following fire. Trees are not modeled individually, but rather as species and age cohorts, allowing multiple types of cohorts at a single site (grid cell). LANDIS‐II is appropriate for this study because of the interaction focus between fire and vegetation at a fine temporal scale over long periods of time. LANDIS‐II does not incorporate climate directly.

Climate is approximated through the proxies of species probability of establishment (SEP) and annual net primary productivity (ANPP). SEP and ANPP values are derived from a process‐based vegetation model, such as PnET‐II for LANDIS‐II (Xi and Xu, 2010). PnET‐II requires monthly environmental variables, which were assigned through an association with independently‐ derived Palmer Drought Severity Index (PDSI) values from dendroclimatic data via the North

American Drought Atlas (Cook et al., 2008, 2004, 1999; Herweijer et al., 2007). 79

Modeling Tools

Paleoclimate proxy

Figure 3‐3. Overview flowchart demonstrating modeling inputs and flow

Creating monthly paleoclimatic records Including paleoclimate in a process‐based model such as PnET requires reconstructing monthly climate data from paleo proxy records that are, at best, annually resolved (Table 3‐1).

Because PnET‐II requires monthly temperature (T) and precipitation (P), I used a cross‐walk approach between modern and paleoclimatic proxies and analogs.

The North America Drought Atlas (NADA; Cook et al., 2008, 2004, 1999; Herweijer et al.,

2007), a gridded network of drought reconstructions based on annual tree‐ring chronologies, provides reconstructed PDSI values for the last 2000 years (through A.D. 2003) at a 2.5o x 2.5o

spatial resolution. PDSI is a climatic index of relative dryness that reasonably captures the effect and potential magnitude of drought (NCAR, 2013). Monthly PDSI, T, and P values from A.D.

1895‐2015 are available for this study area from the Western Regional Climate Center (WRCC). I 80 created a PDSI cross‐walk program in R (R Development Core Team, 2008) using modern PDSI

(mPDSI) from WRCC (A.D. 1895‐2015) and paleo PDSI (pPDSI) from NADA (1550 – ‐63 cal yr BP;

A.D. 400 – 2003), by identifying the closest annual mPDSI analog per pPDSI year and assigning

the mPDSI year’s monthly T and P data to the pPDSI year. In the case of multiple modern analogs, the program randomly selected a modern analog year to assign to the paleo year. The data are formatted for use by the process‐based vegetation model PnET‐II for LANDIS‐II.

Table 3‐1. Data and source for modeling inputs used in this study Data Type Source Climate Parameters Paleoclimate (PDSI) NADA Modern climate analogs (PDSI, T, P) WRCC PnET‐II Parameters Climate parameters This study; Etheridge et al., 1998, 1996; Ferretti et al., 2005; MacFarling Meure, 2004; MacFarling Meure et al., 2006 Vegetation parameters Thorne et al. 2015; Wythers et al. 2013, 2005; Radtke et al 2001 Ecoregion Syphard et al., 2011 LANDIS‐II Parameters Landscape (slope and upslope) Syphard et al., 2011 Ecoregions Syphard et al., 2011 Initial communities Syphard et al., 2011 Species life history Syphard et al., 2011 Biomass succession (ANPP, SEP, maximum This study (PnET output) biomass) Fire regions (baseline) modified from Syphard et al., 2011 to exclude wildland‐urban interface (WUI) Fire regions (anthropogenic) This study Fire weather Syphard et al., 2011 Fire regime Syphard et al., 2011 (natural) and this study Fuel type Syphard et al., 2011 Harvest regions This study (based on anthropogenic fire regions) Harvest parameters Syphard et al., 2011 and this study

Process‐based vegetation modeling PnET‐II for LANDIS‐II is a process‐based model that simulates changing climate conditions on forest ecosystem processes, ecophysiology, and establishment through T, P, carbon dioxide (CO2), and photosynthetic active radiation (PAR) values (Xi and Xu, 2010). For T and P data, I use values derived from the monthly paleoclimatic records (above). Atmospheric carbon dioxide (CO2) inputs came from reconstructions of the Antarctic Law Dome ice core

(Etheridge, D.M. et al., 1998; Etheridge et al., 1996; Ferretti et al., 2005; MacFarling Meure,

2004; MacFarling Meure et al., 2006). Because PAR is highly variable and dependent on daily conditions that cannot be reasonably reconstructed (i.e. cloud cover), I used generic monthly values determined latitudinally that do not vary throughout the modeling period. Species parameters were set as defined in the literature (Table 3‐1). I used the seven LANDIS‐II ecoregions defined by Syphard et al. (2011) for my study area as ecoregion inputs. The final output from PnET‐II for LANDIS‐II provided paleoclimate‐derived SEP, ANPP, and maximum biomass annually for each species by ecoregion, suitable for use in LANDIS‐II.

Landscape‐scale modeling LANDIS‐II is a dynamic, spatially‐explicit model of forest succession, disturbance, and management (Scheller and Mladenoff, 2004a; Scheller et al., 2007). LANDIS‐II is appropriate for this modeling effort because it allows me to explore the effects of various fire regimes on the landscape, and how species respond to climate and the levels of disturbance. Eleven scenarios

(50‐year aggregate timesteps averaged over 20 replicates) were explored, each with a one hectare cell resolution (6992 hectare study area). Each simulation represents a different application of climatic and anthropogenic fire regimes. 82

The following inputs were used to develop the fundamental structure for modeling efforts (Table 3‐1; Figure 3‐4): paleoclimate‐derived annual SEP, ANPP, and maximum biomass

(described above) and initial communities, species parameters, ecoregion parameters, biomass succession parameters, fuels parameters, fire weather, ecoregions, and fire regions defined by

Syphard et al. (2011; Table 3‐1). I altered the fire regions to exclude the wildland‐urban interface

(WUI) because the WUI is defined on modern infrastructure (homes, roads, etc) and is not representative of fire regimes over the last 1100 years. I calculated the number of ignitions per hectare per fire region identified by Syphard et al. and applied the result as baseline data to my smaller spatial extent. The actual number of ignitions varies over time based on modeled scenarios (below).

Species biomass is important to this study because I compare modeled outputs with the paleorecord (see next section). To collect information on how species biomass changed over each modelled scenario I used the Biomass Succession and Biomass Output extensions (Scheller and Mladenoff, 2004b) to create and output biomass. Biomass was calculated annually by

LANDIS‐II and output at 50‐year timesteps to match the 50‐year timestep of the pVRI.

Fire regimes and fuels are a critical component of this study. Because analysis relied on changes in species biomass, I used the Dynamic Fire System (DFS; Sturtevant et al., 2009) and

Dynamic Biomass Fuels System (DBFS; Syphard et al., 2011) extensions to create fire/fuel outputs. I used 5‐year timesteps to correspond with the median fire return intervals of three and six years based on local fire‐scar records (Taylor, n.d.). DBFS calculates and uses species age, conifer mortality, time‐since‐last‐fire, cohort biomass, and species values to classify cells into season‐independent fuel types used by DFS. DBFS considers biomass available to fuel fire, which affects fire frequency, size, and severity (Syphard et al., 2007). DFS uses DBFS values to simulate 83 fire ignition, initiation, severity, and spread dependent on weather, topography, and calculated fuel types. DFS allows for the development of dynamic fuels and the inclusion of topography, such that fire ignitions on a ridge or hilltop will burn slowly downhill, whereas fire ignitions at the base of a slope will spread more quickly.

DFS requires a number of expected ignitions/year within each fire region. For the purposes of this study, consider an ignition comparable to a lightning strike. A lightning strike

(ignition) does not necessarily mean that a fire will occur, simply that the opportunity for one exists. Depending on the fuel type in the cell, DFS determines the likelihood of the entire cell burning (fire initiation). Fuel class, topography, wind speed, and wind direction determine the spread of fire. Fire duration, severity, and rate of spread are randomly selected at the time of modeled ignition and dependent on fire weather and availability of adjacent cells to burn

(Sturtevant et al., 2009). Fire damage is modeled by severity (class 1‐5) and simulate crown fires, killing mature trees; surface fires are not produced by DFS. Cohorts are killed based on the differential between their fire tolerance and the severity of the fire. For example, based on life history traits, a fire severity of 1 at a site (grid cell) would kill all Abies ≤ 80 years old, all Q. kelloggii ≤ 150 years old, and 100% of all other oak trees regardless of age cohort. DFS effectively models climatic, lightning‐strike fires through the number of ignitions. It does not approximate fires set in a prescribed manner the way land managers do today with prescribed burns.

Figure 3‐4. Overview flowchart of LANDIS‐II inputs and extensions. Items outlined in red indicate variable inputs

Because DFS simulates crown fires and does not model low‐mortality surface fires, I used the Base Harvest module to simulate surface fires. Base Harvest selectively removes species age‐cohorts (Syphard et al., 2011) by a given area per timestep. Though Base Harvest does not simulate fire dynamics, it has been used by other studies to simulate prescribed burn treatments (Scheller et al., 2011; Sturtevant et al., 2004; Syphard et al., 2011). As such, modeled surface fires are contained and cannot spread uncontrollably or convert into crown fires.

For scenarios that involve surface fires, I apply climatic ignitions (Scenario H1‐C1 below)

in the model first, then harvest (burn) remaining stands in 30 hectare patches. Minimum stand 85 age for harvesting is 30 years. If a patch did not meet the minimum stand age requirement it was bypassed, allowing for less than the assigned amount of study area to burn.

How patches were burned varied by the hypothesis being tested. For climatic, lightning‐ caused surface fires scenarios (H1‐C6 and C7), 100% of each patch within a stand was burned to emulate indiscriminate firing of the landscape. Up to 6% of the study area was burned annually

(30% per 5‐year timestep) for the entire duration of the model.

For Native American‐set fires (prescribed burns), 70% of each 30 hectare patch was burned, with variation between fire regions (using WHI) to emulate a more conscious application of anthropogenic fire (Table 3‐2). California’s Native Americans used fire selectively, burning vegetation under ideal environmental conditions (weather, seasonality) to achieve select cultural results (reduction of understory, preservation of taxa, improve productivity)

(Anderson and Moratto, 1996; Bean and Lawton, 1973; Lewis, 1973). In a vein similar to the wildland‐urban interface, I introduced the concept of a wildland‐habitation interface (WHI), an area of increased likelihood of fires set by Native Americans. I generated 500 m buffers around all archaeological sites under the assumption that Native Americans may have set fires more consistently near areas of recorded use. The maximum possible overall annual percent area burned by Native American‐set fires was determined by the scenarios below. An alternative sequence of scenarios using no WHI and 100% patch cutting is provided in Appendix E, but is excluded from this analysis due to weak statistical correlation with pVRI.

Table 3‐2. Overall patch area to apply 70% prescription burn annually by fire region, by temporal period and scenario. Fire region Mid Mid Mid‐ Mid‐high Mid Mid Mid‐ Mid‐high +WHI high +WHI +WHI high +WHI cal yr BP 1550‐1000 750‐100

(a) ‐ ‐ ‐ ‐ 10% 20% 6% 12% A1 (b) ‐ ‐ ‐ ‐ 20% 40% 12% 24% (a) 10% 20% 6% 12% 10% 20% 6% 12% Scenarios

2 A2 H (b) 10% 20% 6% 12% 10% 20% 6% 12%

Because spatial data and biomass values for initial vegetation communities do not exist for the year 1050 cal yr BP (onset of the MCA), I initiated all LANDIS‐II modeling runs at 1550 cal yr BP using PnET‐II derived values. I excluded the first 500 years from analysis as a modeling

“burn in” period. Preceding the model by 500 years allowed for at least one full successional cycle to develop an initial condition of forest community distribution that reflected climate relationships of dominant tree species. While I recognize and argue that modern conditions should not be used for model calibration given known anthropogenic impacts on Sierra

Nevada’s forest structure in the last 150 years, I can generalize from existing landscape model parameters of climate response, vegetation, ecology, and topography to reconstruct the forcings that caused landscape change over the last 1100 years. I used varying fire event inputs depending on the modeling scenario as described below.

Paleoclimate evaluation of modeling outputs I used the pVRI developed by Klimaszewski‐Patterson and Mensing (2015) from Holey

Meadow in Sequoia National Forest as my indicator of past forest response to fire.

Klimaszewski‐Patterson and Mensing identified the Medieval Climate Anomaly (MCA; 1050‐750 cal yr BP) as a period of record when PDSI, sedimentary charcoal, and VRI correlated positively, indicating that anthropogenic fire was not required to explain observed shifts in vegetation 87 composition. They identified the period from 750‐100 cal yr BP (especially 500‐100 cal yr BP) as a period where pVRI deviated from that expected by climate (Figure 3‐1), suggesting influence by Native American set fires.

I used the relationship in simulated biomass between Abies concolor and Quercus

kelloggii to create a modeled VRI analog (mVRI) for comparison to the pVRI. In the same manner that paleoecologists use changes in pollen percentages to infer changes in relative abundance of taxa on the landscape (Faegri and Iverson, 1964; Fagerlind, 1952; Webb III et al., 1981), I use the relative change in abundance of specific taxa biomass to interpret change on the landscape.

This allows for comparison between observed empirical data from the paleoenvironmental record and the simulated data over time. Overall changes in vegetation communities and landscape structure are not investigated in this chapter, but are worthy of further exploration.

This is due to a systematic lack of pollen transport and calibration data throughout the western

United States.

I used both Pearson’s r and Spearman’s rho (ρ) 2‐tail tests to statistically evaluate changes in pVRI and mVRI from 1050‐100 cal yr BP. I excluded 100 cal yr BP to present because forest structure has been highly manipulated in the Sierra Nevada since 96 cal yr BP (A.D. 1854) through fire suppression, lumber practices, and exclusion of Native American influences.

Modeling Scenarios (H1: Climatic fire)

The following seven scenarios (Table 3‐3) test hypothesis 1 (H1), that modeled predictors of forest composition derived from climate and climatically‐induced fires can approximate observed paleovegetation dynamics. Each scenario number is prefaced by a “C” for “climatic fire”. 88

Table 3‐3. Climatic fire regime parameters annually (and by 5‐year timestep), by temporal period and scenario. Baseline represents reconstructed baseline of climatic conditions. cal yr BP 1550 ‐ 750 750 ‐ 100 100 ‐ A.D. 2011 Fire type Crown Surface Crown Surface Crown Surface C1 baseline ‐ baseline ‐ baseline ‐ C2 baseline ‐ baseline*10 ‐ baseline ‐

C3 baseline ‐ baseline*20 ‐ baseline ‐ C4 baseline ‐ baseline*40 ‐ baseline ‐ Scenarios

1 C5 baseline ‐ baseline*80 ‐ baseline ‐ H C6 baseline 6% (30%) baseline 6% (30%) baseline 6% (30%) C7 baseline*10 6% (30%) baseline*10 6% (30%) baseline*10 6% (30%)

Scenario H1‐C1: Reconstructed baseline of climatic conditions (Lightning‐caused crown fires)

A recent study in the Lake Tahoe Basin of the Sierra Nevada demonstrated that the number of lightning‐caused fires from 1986 to 2009 had a positive linear relationship with both climatic water deficit (CWD) and lightning density (Yang et al., 2015a). Because both PDSI and

CWD are estimates of drought stress, I assumed a similar linear relationship between lightning ignitions and PDSI. An ignition does not necessarily mean that a fire will catch and spread

(initiate), but rather increases the chances of a fire occurring over the course of the year. To allow climatic variability in determining the potential number of lightning ignitions on the landscape I used the 50‐year smooth splined PDSI values as a multiplier for each modeled timestep (‐(PDSI) * baseline number of fires for each fire region). A PDSI value of ‐4 would have four times more fire ignitions than present, and a PDSI of 0.5 would have half the number of ignitions as present. This adjustment allows for climatic variation in the number of potential ignitions over the temporal duration of the model. I used fire weather and the number of observed modern ignitions by fire region from Syphard et al. (2011) as baseline data.

Scenarios H1‐C2, C3, C4, and C5: Increased lightning ignitions from 750‐100 cal yr BP

I increased the number of potential lightning strikes (ignitions) from baseline conditions in each scenario to evaluate just how many potential ignitions beyond my inferred climatic expectations (H1‐C1) were necessary to approximate pVRI values. I emulated the scenario of a

“natural” fire regime through the MCA using Scenario H1‐C1 data, then increased the calculated number of lightning strikes (ignitions) during the LIA from 750 cal yr BP to 100 cal yr BP. At 100 cal yr BP I returned the model to climatically‐calculated ignition values to emulate fire suppression.

The number of ignitions over climatically‐calculated values (Scenario H1‐C1) increased

10‐fold in Scenario H1‐C2, 20‐fold in Scenario H1‐C3, 40‐fold in Scenario H1‐C4, and 80‐fold in

Scenario H1‐C5.

Scenarios H1‐C6 and C7: Lightning‐caused crown fires plus surface fires

Because ignitions in the DFS module only result in crown fires and do not replicate smoldering surface fires which can also be lightning caused, I use the Base Harvest module as outlined above to add in a lightning‐caused surface fire component. It is estimated that ~2 to 5 million hectares (6‐16%) of pre‐historic California burned annually, excluding the southeastern deserts, possibly a result of both Native American‐set fires (Martin and Sapsis, 1992; Stephens et al., 2007) and lightning set fires. These two scenarios test whether lightning alone (producing both crown and surface fires) can reproduce observed paleovegetation dynamics. I use the same baseline parameters as Scenario H1‐C1 with the addition of surface fires via the Base Harvest module. Surface fires are allowed to burn up to 6% of the study area annually (30% overall/time step) over the entire duration of the model. Scenario H1‐C6 scenario tests whether the addition 90 of a consistent surface fire regime over the entire modeling duration is reasonable and a result of climate and not Native American influences.

Scenario H1‐C7 is identical to H1‐C6, but with the number of potential lightning strikes

(ignitions) increased 10‐fold for the entire duration of the model. This increase is to capture potential lightning strikes that may not have been recorded in the observed modern baseline data, thus increasing the potential for climatic fires to explain observed changes in paleovegetation.

Modeling Scenarios (H2: Climatic + Native American‐set fires)

The following four scenarios test hypothesis 2 (H2), that the addition of Native American set fires is needed to approximate observed paleovegetation dynamics. Further, these scenarios explore the temporal occurrence of Native American‐set fires on the landscape: that they were only necessary during the LIA (750 – 100 cal yr BP), or that they were necessary during

prolonged wetter periods (1550‐1050 cal yr BP and 750‐100 cal yr BP). Each scenario number is prefaced by an “A” for “anthropogenic”.

Similar to Scenarios H1‐C6 and C7, the H2 scenarios use the Base Harvest module, but assume that most Native Americans set fire result in surface fires. The amount, timing, and spatial extent of surface fire events varies over the modeling period to emulate Native

American‐set fires with respect to ethnographic descriptions of their use. Going forward I will

refer to this prescription as a “prescribed burn”. Baseline climatic fires as established in Scenario

H1‐C1 occurred in these scenarios prior to each prescribed burn.

Previous studies estimate 6‐16% of pre‐historic California burned annually from Native

American‐set fires, with a statewide average of 13% (Martin and Sapsis, 1992; Stephens et al., 91

2007). I used the lower to mid‐range of estimated annual burning (6% and 12%) because pre‐ historic population density in the southern Sierra Nevada was considerably lower than the rest of the state (Baumhoff, 1963; Kroeber, 1925). Table 3‐4 identifies the timing of and percent of the total maximum study area burned by prescription for each H2 scenario.

Table 3‐4. Percent area to prescription burn annually (and by 5‐year timestep), by temporal period and scenario.

cal yr BP 1550 ‐ 1000 750 ‐ 100

(a) ‐ 6% (30%) A1 (b) ‐ 12% (60%) (a) 6% (30%) 6% (30%) Scenarios

2 A2 H (b) 6% (30%) 12% (60%)

I applied prescribed burning to two different temporal cases based on the findings of

Klimaszewski‐Patterson and Mensing (2015) and Chapter 1. The H2‐A1(a) scenarios tested the case of a strong anthropogenic signal during the LIA observed in their paleoenvironmental record, applying prescribed burns from 750‐100 cal yr BP. The H2‐A1(b) scenarios tested the case of the “ambiguous period” of anthropogenic influence by adding prescribed burns from 1550‐

1050 cal yr BP. These case variations tested when Native American‐set fires were necessary to approximate paleovegetation.

Scenario H2‐A1(a): Baseline (lightning‐caused crown fires) + Native American‐set fires from

750‐100 cal yr BP (6%)

This scenario explores the application of prescribed burns to a total of ~6% of the study area annually only during the LIA. The 6% prescription is at the low end of estimated annual area burned (Fites‐Kaufman et al., 2007; Martin and Sapsis, 1992). I applied the prescribed burn 92 prescription to 10% of the mid‐elevation fire region and 6% to the mid‐high elevation from 750 to 100 cal yr BP because populations were thought to be greater at lower elevations. I doubled the percent of area “burned” within the wildland‐habitation interface (WHI; 20% and 12% respectively; Table 3‐4) under the assumption that Native Americans would have burned more extensively/intensively in areas near archaeological sites.

Scenario H2‐A1(b): Baseline + Native American‐set fires from 750‐100 cal yr BP (12%)

The same as Scenario H2‐A1(a) except all percentages of the prescription were doubled

(20%, 12%, 40%, and 24%, respectively). This scenario approximated that up to 12% of the study area burned annually from prescribed surface fires. This prescription is at the mid‐range of estimated annual area burned (Fites‐Kaufman et al., 2007; Martin and Sapsis, 1992).

Scenario H2‐A2(a): Baseline + Native American‐set fires from 1550‐1000 and 750‐100 cal yr BP

(6%)

Identical to Scenario H2‐A1(a), but prescribed burning was applied during both periods of prolonged wetter climate (1550‐1000 and 750‐100 cal yr BP; Table 3‐4). Burning during the

MCA was excluded because Native American populations are thought to have either declined or dispersed widely across the landscape, and their need for intensified land‐use diminished (Hull,

2005; Jones et al., 1999; Lewis, 1973). Periods with Native American‐set fires approximate 6% of the study area burned annually (as per H2‐A1(a)).

Scenario H2‐A2(b): Baseline + Native American‐set fires from 1550‐1000 (6%) and 750‐100 cal yr BP (12%)

Identical to Scenario H2‐A1(a) from 1550‐1000 cal yr BP (6% area burned), but using 12% area burned from 750‐100 cal yr BP (as per H2‐A1(b)). Prescribed fire was excluded during the MCA as in H2‐A2(a). The higher percentage of area of area burned from 750‐100 cal yr BP was to approximate inferences in the archaeological literature of increased population density and resulting intensification during the LIA prior to all other periods of time (Beaton, 1991; Hull,

2009, 2005). The removal of prescribed burns at 100 cal yr BP coincides with settlement of the area by European colonists and fire suppression policies.

Results I modeled two main hypotheses: (1) that climatic‐induced fires were the driving of paleovegetation dynamics (Scenarios H1), and (2) that the addition of Native American‐set fires

(prescribed burns) on the landscape were necessary to approximate the paleoenvironmental record (Scenarios H2).

Baseline (Lightning‐caused crown fires) + Native American‐set fires The addition of Native American‐set fires best approximated pVRI, especially when prescribed burns were applied during all prolonged climatically wetter periods (1550‐1050 cal yr

BP and 750‐100 cal yr BP; Scenarios H2‐A2; Table 3‐5; Figure 3‐5). These two scenarios (H2‐A2(a) and H2‐A2(b)) show strong statistical correlations with pVRI (r/ρ = 0.61; p‐values = 0.005) over the entire analyzed record (Table 3‐5). Further, these scenarios appeared to stabilize the general trend in amount of area burned, with total hectares burned annually consistently decreasing over time (Table 3‐6). This trend is consistent with modern land‐management practices and 94 expectations, where application of prescribed fire is done with the purpose of reducing the extent of wildfires through removal of fine fuels and reduced fuel loads.

The “natural” background amount of hectares burned as a result of climatic lightning‐ caused crown fires was highest in the MCA and decreased throughout the LIA as climatically expected. This result is supported by the sedimentary charcoal record at HLY, which

Klimaszewski‐Patterson and Mensing (2015) found to correspond with variation in PDSI and representative of a regional fire regime. The number of climatic crown fires and hectares burned

(Table 3‐6; Figure 3‐6) were comparable to the climate scenario (H1‐C1; below). Whether or not there was a reduction in severity of crown fires is beyond the scope of this analysis.

Table 3‐5. Correlation values between mVRI and pVRI for various periods through the simulation. Bolded values show statistically significant correlations. Scenario H2‐A1(a) H2‐A1(b) H2‐A2(a) H2‐A2(b) 750‐100 cal yr BP 1550‐1000 cal yr BP; 750‐500 cal yr BP 6% area 12% area 6% area 6%; 12% area MCA r/p‐value ‐0.86/0.03 ‐0.87/0.03 0.98.<0.001 0.98/<0.001 ρ /p‐value ‐0.89/0.03 ‐0.89/0.03 0.99/<0.001 0.99/0..001 LIA r/p‐value 0.69/0.009 0.69/0.008 0.59/0.03 0.59/.03 ρ/p‐value 0.53/0.07 0.53/0.07 0.51/0.07 0.51/.08 1050‐100 cal yr BP r/p‐value 0.38/0.11 0.38/0.11 0.61/0.005 0.61/0.005 ρ /p‐value 0.48/0.04 0.48/0.04 0.61/0.005 0.61/0.005

Figure 3‐5. Standardized modeled VRI (mVRI) vs paleo VRI (pVRI) values. Dark grey line represents the standardized mVRI, grey dots mVRI per run, and grey band a 95% confidence interval. Green line represents 50‐year splined pVRI values to which mVRI is compared.

While the two H2‐A1 scenarios (LIA prescribed burns only) were moderately effective at approximating LIA conditions (r/ρ = 0.69/0.45; p‐values = 0.01/0.07; Table 3‐5), they did not explain paleovegetation during the MCA well and indicate the need for prescribed burning preceding the MCA (the H2‐A2 scenarios). Negative correlations between mVRI and pVRI were found during the MCA in both H2‐A1(a) (r/ ρ = ‐0.86/‐0.89; p‐values = 0.03) and H2‐A1(b)

(r/ρ = ‐0.87/‐0.89; p‐values = 0.03).

Table 3‐6. Number of climatic lightning‐caused crown fires and percent study area burned by crown fires per cumulative 50 years (average). Scenarios describe the timing and percent area of applied prescribed burns (Native American‐set fires). Darker cells represent years with prescribed burns. Scenario H1‐C1 H2‐A1 H2‐A2 Climatic (a) (b) (a) (b) 750‐100 cal yr BP 1550‐1000 cal yr BP; . 750‐500 cal yr BP 6% area 12% area 6% area 6%; 12% area cal yr BP # % # % # % # % # % 1050 10 24 10 29 10 29 10 28 11 37 1000 11 37 11 37 11 37 11 37 8 33 950 10 34 9 41 9 41 9 33 9 33 900 7 22 6 28 6 28 8 33 10 31 850 6 24 5 19 5 19 6 15 10 28 800 9 19 9 18 9 18 10 31 8 24 750 11 28 12 41 12 41 10 24 10 24 700 9 17 10 23 10 23 8 24 7 20 650 6 19 6 17 6 17 8 15 6 20 600 5 18 6 9 6 9 6 17 7 17 550 6 18 7 22 7 22 7 16 6 17 500 6 14 5 20 5 20 7 20 3 17 450 6 14 7 28 7 28 7 17 7 16 400 5 21 5 13 5 13 5 11 8 15 350 5 8 5 19 5 19 6 20 6 15 300 2 10 2 7 2 7 2 2 2 14 250 2 12 2 4 2 4 2 7 5 11 200 2 5 2 4 2 4 1 4 2 7 150 1 4 2 12 2 12 2 14 3 6 100 3 5 3 4 3 4 3 6 3 5 50 2 3 3 9 3 9 3 5 1 4 0 2 4 3 14 3 14 3 17 2 2

The amount of area subjected to prescribed burning (6% vs 12%) did not significantly alter the overall response of mVRI (Table 3‐5). The paired H2‐A1 (LIA burning only) and H2‐A2

(1550‐1050 cal yr BP and LIA burning) scenarios had nearly identical mVRI responses within each pair (Table 3‐5), indicating that the amount of area affected by modeled anthropogenic burning is reasonable under both ethnographically reconstructed values.

Figure 3‐6. Amount of hectares burned by background “natural” climatic crown fires per year. Does not include prescribed burns. Dark grey line represents the average number of annual fires, grey dots modeled averages per run, and grey band a 95% confidence interval. Red line is a 50‐year splined average of fires, dark red line a 50‐year mean average.

Climatic lightning‐caused fire A significant increase in modeled lightning ignitions (at least 20‐fold) over climatic expectations, with no surface fire component, was necessary for these scenarios to approximate trends in the paleoenvironmental record (Table 3‐7).

Using PDSI‐adjusted values (Scenario H1‐C1) of climatically‐expected lightning strikes

(ignitions) as baseline climatic conditions proved necessary as there was little variation in the number of fires or area burned (between 1‐7% of the study area) when no adjustment was applied throughout the time period (scenario not shown). This result was expected as LANDIS‐II does not use climate as a direct input. Modeled output for Scenario H1‐C1 (climatic) matched

climatic trends between PDSI and percent area burned (Table 3‐7; Figure 3‐7), but did not approximate pVRI (Figure 3‐8), having the most negative statistical correlation over the entire period (r/ρ = ‐0.55/‐0.64; p‐values = 0.01/0.004). The number of hectares burned was highest in 98 the MCA (27% average) and decreased throughout the LIA (from 28 to 4%, 14% average) as climatically expected. Increasing the number of lightning strikes (ignitions) in Scenarios H1‐C2

(10‐fold) through H1‐C5 (80‐fold) caused a non‐linear increase in the number of modeled fire events and hectares burned. A greater number of fire events did not result in a linear increase of hectares burned (Table 3‐8). Changes in the amount of area burned over climatic expectations became apparent once the number of lightning strikes increased 20‐fold (Scenario H1‐C3).

Table 3‐7. Correlation values between mVRI (H1 scenarios) and pVRI for the period (1050 ‐100 cal yr BP; A.D. 900 ‐ 1850). Bolded values indicate statistically significant correlations. Red values indicate negative correlations Scenario H1‐C1 H1‐C2 H1‐C3 H1‐C4 H1‐C5 H1‐C6 H1‐C7

Climatic 10‐fold 20‐fold 40‐fold 80‐fold Climatic + 10‐fold + 6% 6% surface surface MCA r/p‐value ‐0.87/0.03 ‐0.88/0.02 ‐0.82/0.04 ‐0.87/0.03 ‐0.74/0.09 ‐0.81/0.05 ‐0.73/0.19 ρ/p‐value ‐0.88/0.03 ‐0.94/0.02 ‐0.77/0.10 ‐0.86/0.03 ‐0.66/0.18 ‐0.89/0.03 ‐0.76/0.08 LIA r/p‐value ‐0.45/0.12 ‐0.38/0.20 0.89/<0.001 0.89/<0.001 0.88/<0.001 ‐0.35/0.25 NA* ρ/p‐value ‐0.58/0.04 ‐0.61/0.03 0.91/0.0 0.93/0.0 0.84/<0.001 ‐0.58/0.04 NA* 1050‐100 cal yr BP r/p‐value ‐0.55/0.01 ‐0.36/0.13 0.43/0.06 0.54/0.02 0.62/0.004 ‐0.25/0.30 0.06/0.81 ρ/p‐value ‐0.64/0.004 ‐0.62/0.005 0.67/0.002 0.67/0.002 0.66/0.002 ‐0.43/0.07 0.16/0.51 *NA = Not applicable. During this period, Abies becomes locally extinct and there is no variance in modeled VRI (mVRI)

Crown fire‐only scenarios (H1‐C1 to H1‐C5) with a higher proportion of area burned better approximated pVRI values (Table 3‐8 and Figure 3‐8). Negative correlations between mVRI and pVRI were found in climate‐only Scenario H1‐C1 and H1‐C2 (10‐fold ignitions; r/ρ = ‐

0.36/‐0.62; p‐values = 0.13/0.005). Scenarios H1‐C3 (r/ρ = 0.43/0.66; p‐values = 0.06/0.002), H1‐

C4 (r/ρ = 0.54/0.67; p‐values = 0.02/0.002), and H1‐C5 (r/ρ = 0.62/0.66; p‐values = 0.004/0.002) showed strong positive statistical correlations with pVRI over the entire record, especially during the LIA (Table 3‐7; Figure 3‐8). These latter scenarios require a minimum increase of 215% area 99 burned over PDSI‐adjusted values, equating to a minimum of 870% more area burned annually by crown fires over modern conditions (Table 3‐9).

Table 3‐8. Number of crown fires and percent study area burned by crown fires per cumulative 50 years, by scenario (averaged). Reburned areas may cause area totals over 100%. Darker cells represent years with lightning strike (ignition) increase over calculated baseline conditions. Blue columns represent statistically significant correlations between mVRI and pVRI. Surface fires (s.f.) are excluded from area burned (H1‐C6 and C7). Scenario No H1‐C1 H1‐C2 H1‐C3 H1‐C4 H1‐C5 H1‐C6 H1‐C7 PDSI Adjust Climatic 10‐fold 20‐fold 40‐fold 80‐fold Climatic 10‐fold +6% s.f. +6% s.f. cal yr BP # % # % # % # % # % # % # % # % 1050 2 1 10 24 9 18 9 23 9 35 9 28 11 39 78 85 1000 1 2 11 37 11 38 10 36 11 41 12 44 13 29 92 74 950 2 4 10 34 8 31 8 17 8 24 9 26 10 27 66 54 900 2 3 7 22 7 22 7 21 6 34 7 31 7 29 58 46 850 2 6 6 24 5 28 5 16 6 16 7 20 7 17 47 38 800 2 2 9 19 10 35 8 24 10 47 9 29 10 30 68 60 750 2 4 11 28 76 28 154 48 277 81 477 112 10 40 88 67 700 1 1 9 17 71 35 138 40 243 59 438 89 8 21 65 57 650 1 5 6 19 52 18 102 40 193 58 348 72 6 25 63 51 600 1 1 5 18 55 22 103 33 190 46 330 63 7 23 46 43 550 2 3 6 18 52 23 96 38 193 44 338 66 7 16 47 49 500 1 4 6 14 50 18 98 34 190 46 329 58 7 15 47 50 450 1 2 6 14 54 20 103 38 191 44 335 56 7 16 46 46 400 2 2 5 21 33 19 71 27 139 38 261 46 6 19 46 40 350 2 7 5 8 38 15 75 29 135 35 254 48 5 14 50 52 300 2 5 2 10 27 9 56 19 113 33 218 38 1 6 32 35 250 2 4 2 12 30 10 56 22 112 32 210 37 2 5 34 39 200 2 2 2 5 29 8 57 18 114 38 213 40 2 4 36 53 150 2 7 1 4 30 9 56 22 111 31 215 51 2 8 34 40 100 2 5 3 5 3 8 2 5 2 6 2 2 3 11 26 33 50 1 6 2 3 2 7 2 4 4 8 3 10 3 8 27 47 0 1 3 2 4 3 10 3 6 3 7 3 6 3 9 28 37

100

. Figure 3‐7. Average number of hectares burned per year by crown fires (does not include surface fires). Dark grey line represents the average area burned, grey dots number of hectares per run, and grey band a 95% confidence interval. The red line is a 50‐year splined average of annual hectares burned, the dark red line is a 50‐year mean average.

The addition of surface fires burning 6% of the study area annually resulted in strongly negative correlations between mVRI and pVRI values (Table 3‐7; Figure 3‐8). Scenario H1‐C6

(baseline climatic crown fires + 6% surface fires) had a negative correlation throughout the record (r/ρ = ‐0.25/‐0.43; p‐value s = 0.30/0.07), with a strongly significant negative correlation during the MCA (r/ρ = ‐0.81/‐0.89; p‐values = 0.05/0.03) and LIA (r/ρ = ‐0.35/‐0.58; p‐values =

0.25/0.04). Increasing lightning strikes 10‐fold (H1‐C7) while maintaining 6% surface fires caused 101

Abies to become locally extinct in all modeled runs by 850 cal yr BP (Figure 3‐8). As a result, there was no statistical relationship for H1‐C7 (r/ρ = 0.06/0.16; p‐values = 0.81/0.51).

(sd)

Index

Standardized

pVRI

average mVRI/run

individual mVRI

. Figure 3‐8. Standardized modeled VRI (mVRI) vs paleo VRI (pVRI) values. Dark grey line represents the standardized mVRI, grey dots mVRI per run, and grey band a 95% confidence interval. Thick green line represents 50‐year splined pVRI values to which mVRI is compared.

Table 3‐9. Increase in area burned from 750‐100 cal yr BP from lightning‐caused crown fires H1‐C1 H1‐C2 H1‐C3 H1‐C4 H1‐C5 Scenario Climatic 10‐fold 20‐fold 40‐fold 80‐fold Over modern conditions (no PDSI adjustment) 400% 500% 870% 1245% 1650% Over climatically adjusted (Scenario H1‐C1) ‐ 125% 215% 310% 410%

102

Discussion Fires at HLY were simulated through (1) “natural”, lightning‐caused crown fires, which tend to kill off all but the oldest pines, fir, and giant sequoia (scenarios H1‐C1 to C5) and (2) the addition of patchy, frequent surface fires that kill off primarily the youngest cohort (1‐10 years old; H1‐C6 and C7, all H2 scenarios). The modeled results of anthropogenic surface fires (H2 scenarios) combined with climatically induced lightning fires were more consistent with paleoecologic reconstructions than the climatic lightning‐caused only models (H1 scenarios).

Scenarios of Native American‐set fires most successfully approximate the paleoecologic data from HLY (Figure 3‐5; Table 3‐5; Klimaszewski‐Patterson and Mensing 2015). The best estimates deduced from fire history studies, vegetation studies, and ethnographic data suggest that Native Americans annually burned 6‐16% of California (Fites‐Kaufman et al., 2007; Martin and Sapsis, 1992). The modeled results suggest that burning 6% area annually with prescribed surface fires during the LIA successfully approximated pVRI during that period. Increasing the prescription to 12% of the study area further reduced the variability of “natural” crown fire intervals and amount of area burned, consistent with expectations by Martin and Sapsis (1992).

The strongest correlations over the entire record occurred when prescribed burns were applied from 1550‐1050 and 750‐100 cal yr BP (Scenarios H2‐A2). This strongly suggests that not only were Native American‐set fires preceding the MCA necessary, but that a considerable reduction in surface fires during the MCA was needed to develop the pVRI observed over the entire record at HLY.

In the climatic scenarios, lightning‐caused crown fires (H1‐C1 to C5) needed to annually burn at least 215% more of the study area over climatic expectations (870% over modern conditions; Table 3‐9) to achieve a forest composition similar to that observed in the pVRI

(Figure 3‐8). While obtainable, these scenarios are unrealistic and require a dramatic increase in 103 lightning strike density during the LIA over modern Sierra Nevada observations. Lightning strikes are more common when warmer temperatures increase active transport of moisture and heat

(Price and Rind, 1994; van Wagtendonk and Cayan, 2008). For every 1oC of global cooling, it is suggested that lightning frequency decreases 5‐6% (Price and Rind, 1994). Climatically, we would expect greater lightning density, and thus fires, during the MCA, not LIA. Yet local and regional fire scar data suggest frequent fires during the LIA. The paleovegetation signal at HLY further suggests more open canopy dynamics during the LIA rivaling the MCA. It is possible that storm frequency and fuels were greater during the LIA; however, the frequency at which modeled crown fires occurred is inconsistent with regional fire scar data (Swetnam, 1993). Using multiple giant sequoia groves further north in Yosemite National Park (YNP), Swetnam (1994,

2009) observed highest fire frequencies across the region from ~950‐650 cal yr BP. This period is consistent with peaks in natural background crown fires during the MCA (H1‐C1 and all H2 scenarios), but not with Scenarios H1‐C2 to H1‐C5. While scenarios H1‐C2 to H1‐C5 are able to statistically approximate pVRI, they are inconsistent with reconstructed fire histories and are rejected as unreasonable approximations of the amount of fire on the landscape.

To improve consistency with regional fire scar data, climatic scenarios with the addition of lightning‐caused surface fires (H1‐C6 and C7) were modeled; however, they were also unsuccessful in approximating paleovegetation response. Statistically, the addition of consistent surface fire was less effective at approximating pVRI than modeling increases only in the frequency of crown‐fires. This is possibly due to the indiscriminant nature of where surface fires occurred on the modeled landscape, as opposed to a greater percentage of prescribed burning occurring in wildland‐habitation interface (WHI) area (H2). Further, a reduction in surface fires is needed during the MCA to prevent the local extinction of Abies. The inconsistencies between 104 the expected pattern and that empirically observed through vegetation and fire records does not support an increase in lightning caused fires as a likely explanation for observed paleovegetation dynamics.

The southern Sierra Nevada is a landscape defined by disturbance, primarily in the form of fire (Flannigan et al., 2000; Miller and Urban, 1999b; Miller et al., 2009). Numerous plant taxa have adapted to the Sierra Nevadan regime of frequent fires through traits such as resprouting

(e.g. Quercus kelloggii, Q. chrysolepis), thick bark (e.g. Pinus ponderosa, P. jeffreyi), and serotiny

(e.g. Sequoiadendron giganteum, P. contorta). However, the changes in VRI seen in our record could possibly be associated with other forms of disturbance, such as insect/pathogen outbreaks.

Insect outbreaks such as bark beetle are common in coniferous forests of the western

United States, and there is a significant link between drought and bark beetle outbreaks

(Dobbertin et al., 2007; Fettig et al., 2007). Warmer conditions and resource competition create more suitable environments for infestations, further driving coniferous tree mortality under already stressful conditions. Mortality then produces more fuel for crown fires. However, the

LIA is expected to be a period of less stress for vegetation than the MCA. Therefore, insect outbreaks should be decreased during the LIA and do not provide a reasonable explanation for a more open canopy during the LIA than MCA.

The possibility exists that the relationship between relative modeled biomass and observed pollen percentages is inappropriate for comparison; however, a subsequent literature review revealed previous paleolandscape modeling studies in Europe that also used comparison between modeled biomass and pollen percentages (Colombaroli et al., 2010; Heiri et al., 2006;

Keller et al., 2002). These studies differed in their analysis by either using a pollen/biomass 105 dissimilarity index per iteration (Keller et al., 2002), treeline elevation (Heiri et al., 2006), or generalized additive models for species response curves (Colombaroli et al., 2010). The pollen/biomass dissimilarity index (Keller et al., 2002) is similar in principle to my mVRI/pVRI comparisons in that it also shows differences between the scenario. My comparisons differ in that I am not quantifying the importance of the differences between modeled and observed taxa, but rather that differences exist.

Modeling simulations suggest that Native American‐set fires during climatically cool, wet periods better approximate observed palevegetation dynamics than climatic lightning‐ caused fires alone. While simulated climatic, lightning‐caused fires (Scenarios H1‐C3 to H1‐C5) can approximate the observed pollen signal at HLY, the number of increased lightning ignitions required during the LIA is highly unlikely and far beyond that observed in the southern Sierra

Nevada. There is no plausible climatic driver that could increase the number of lightning ignitions necessary from 750‐100 cal yr BP. It is far more plausible that Native American‐set prescribed burns (scenarios H2) targeting the youngest cohorts preserved an open forest environment as represented by the pVRI. Further, forest resilience is enhanced through the use of prescribed burns by reducing competition, limiting insect and pathogen outbreaks, and lowering the risk of high‐intensity crown fires (Dobbertin et al., 2007; Fettig et al., 2007).

Native American‐set fire scenarios are also consistent with fire scar and sedimentary charcoal records. Swetnam (1994, 2009) regionally observed short periods of increased fire frequency around 350 and 250 cal yr BP. This increase in fire frequency coincides with a slight increase in modeled area burned around 400 and 250 cal yr BP in H1‐C1 and all H2 scenarios.

Climatic fire peaks still occur in H2 scenarios, roughly corresponding temporally with 106 sedimentary charcoal peaks observed at HLY (significant peaks at 1040 and 740 cal yr BP; insignificant peaks at 590 and 240 cal yr BP).

Conclusions Forest landscape models can be used to test hypotheses about which forces cause change in the paleoenvironmental record (Berland et al., 2011; Colombaroli et al., 2010; Heiri et al., 2006). I used LANDIS‐II to test two main hypotheses: (H1) that climate and climatic fire‐only drove observed paleovegetation dynamics in the southern Sierra Nevada, or (H2) the addition of

Native American burning was needed to approximate the forest composition observed in the paleoenvironmental record. I further tested H2 as to whether Native American burning would only have affected the landscape during the LIA (H2A), or whether burning was also required during a cooler climatic period prior to the MCA to create the observed landscape (H2B).

Based on my modeled results, Native American‐set surface fires are necessary to most reasonably simulate observed changes in the paleoenvironmental record. Escalating the number of lightning strikes (scenarios H1‐C2 to H1‐C5) demonstrates that a minimum increase of 870% in the average annual amount of area burned over the contemporary fire regime is necessary to approximate paleoforest composition. In contrast, only 6% of the study area would need to be annually burned by anthropogenic “prescribed” surface fires (Scenarios H2) to approximate the same paleovegetation dynamics. Further, modeled results indicate that Native American‐set fires were necessary prior to the MCA to best approximate the observed paleolandscape

(Scenarios H2‐A2), indicating that Sierra Nevada’s forests have been an anthropogenically‐ influenced landscape for most of the last 1500 years.

The Native American‐set fire scenarios both support the sedimentary charcoal record with high‐intensity fires (background lightning‐caused fires) and corresponding changes in pVRI 107 explained by surface fires. Further, the frequency of prescribed‐burn surface fires is in keeping with fire scar data observations of 3 to 6 years (Swetnam et al., 2009), which the lightning‐ caused fire (H1‐C1 to C7) scenarios do not.

These modeled results of HLY confirm observations by Klimaszewski‐Patterson and

Mensing (2015; Chapter 1) that HLY represents an anthropogenically‐modified landscape.

Frequent fire return intervals observed in local fire scars (Taylor, n.d.), and regional fire reconstructions (Kilgore and Taylor, 1979; Swetnam, 1993; Swetnam et al., 2009) further support the Native American‐set fire scenarios . As Kilgore and Taylor (1979: 139) state, “further evidence that the Indians used fire comes from our fire‐scar records. Fires burned through a given site every 8‐18 yr. Such frequencies are greater than can be attributed to lightning fire ignitions alone”. Swetnam et al. (2009: 144) reinforce this idea when they “acknowledge that it is quite possible that [Native Americans] had important effects, as other authors have concluded… This is a subject worthy of more study…” The modeling results presented here advance the idea of an anthropogenically‐modified landscape in the Sierra Nevada for at least the most recent millennium.

108

Summary and Conclusions This dissertation directly tested the hypotheses that Native American‐set fires altered forest structure at a landscape scale in the southern Sierra Nevada. I used data from multiple lines of evidence (paleoecology, ethnographies, archaeology, plant ecology, and landscape modeling) to explicitly examine whether climate alone could have caused the changes observed

in the paleoenvironmental record, or if Native American‐set fires were necessary to generate the inferred landscapes.

My goal in Chapter 1 was to explore whether Native American land use could be identified in the paleoenvironmental record in the absence of agricultural taxa such as Zea mays, using relative abundance of naturally occurring taxa and locally‐resolved annual paleoclimate reconstructions. I used a vegetation response index (VRI), where Abies represented shade‐tolerant/fire‐sensitive taxa (closed canopy) and Quercus represented shade‐ intolerant/fire‐adapted taxa (more open canopy). I compared changes in VRI to locally‐inferred paleoclimate reconstructions and found periods of time when climate appeared to be a driving force of forest composition (climate‐driven model), and periods when climate alone could not explain changes in forest composition (human‐influenced model). I found climate to be the driving force of forest composition from 2000 – 1550 cal yr BP, and again during the MCA, from

1150‐750 cal yr BP. I identified a distinct period from 750 – 100 cal yr BP (most strongly from

500 – 100 cal yr BP) when VRI changed in a manner inconsistent with what would be expected from climate alone (increase in Quercus during cooler, wetter periods with less regional fire), which I interpreted as an anthropogenic signal of low intensity surface fires. I also identified a climatically and anthropogenically ambiguous signal from 1550‐1150 cal yr BP. Both the 1550‐

1150 cal yr BP and 750‐100 cal yr BP periods correspond with increasing native population density in California. I concluded with support for the human‐influenced model (Figure 1‐1B) of 109 forest succession via changes in VRI that were unexplained by climate or climatic fire. The findings support previous paleoenvironmental work (Anderson and Carpenter, 1991; Anderson and Stillick, 2013; Crawford et al., 2015) and begin to suggest that the pattern of Native

American influence on forest structure is more widespread than just a single site. The results of this research are significant because they identify a means to explore the impacts Native

Americans may have had on forest composition, and contribute to the debate regarding the scale and extent of Native American burning in California during the Anthropocene.

In the second chapter I tested whether Native American impacts on the landscape were locally intensive or regionally extensive. I compared results from five sites: Trout Meadow and

Holey Meadow (this dissertation), and three other paleoenvironmental sites in California: Lake

Ogaromtoc and Fish Lake (Crawford et al., 2015) in the Klamath Mountains region, and Woski

Pond (Anderson and Carpenter, 1991) in Yosemite National Park using methods established in

Chapter 1. I applied the three lines of evidence set forth by Bowman et al. (2011) to identify

Native American‐set fires: (1) temporal changes in proxy reconstructions of fire and vegetation that (2) differ from (locally) climatic expectations and (3) occur temporally with identified changes (cultural phase shifts) in Native American activity. I compared the results from all five sites to locally‐derived paleoclimate reconstructions and archaeological timing. At four of the five sites I found evidence of anthropogenic burning through anomalous changes in VRI, locally‐

inferred paleoclimate reconstructions, and regional fire scar studies. The timing of regional fires, which are interpreted as climatically driven (Kilgore and Taylor, 1979; Swetnam, 1993; Swetnam et al., 2009), did not explain changes in vegetation composition observed at the Sierran sites (3 of the 5 analyzed). I suggest that fire use and purpose, as documented in the ethnographic record was necessary to generate the forest composition in the paleorecord, and the park‐like 110 settings observed in the A.D. 1850s. The results of this research are significant because they clearly identify periods of Native Americans influence on forest composition at multiple sites in the mountains of California, and suggest a broad extent of human‐influenced structure of forest and woodlands due to Native American‐set fires in California.

The third and final chapter used forest landscape models to directly explore the hypothesis that the addition of Native American burning was necessary to create the forest composition observed in the paleoenvironmental record. I used LANDIS‐II, a spatially‐explicit, raster‐based, stochastic model designed to simulate spatial interactions among succession, natural disturbances, and forest management, to reconstruct 1100 years of vegetation change under climatic and inferred anthropogenic fire regimes. My goal was to explore the timing, frequency, type, and extent of fire initiation necessary to recreate the paleoforest structure observed at Holey Meadow. I tested whether climate and climatic‐induced fires alone could approximate paleolandscape composition, or whether the addition of Native American‐set fires was required. I also tested whether Native American‐set fires were only necessary during the

LIA, a period of recognized human intensification, or if anthropogenic burning was an integral part of the landscape prior to the MCA. I found that the addition of Native American‐set fires was necessary to reconstruct landscapes that approximated the paleoenvironmental record over the last 1100 years. I further found that the addition of anthropogenic burning was required from 1550‐1050 cal yr BP in order to approximate the landscape observed at the beginning of the MCA. The period from 1550‐1050 cal yr BP corresponds with the ambiguous climate/anthropogenic signal observed at Holey Meadow in Chapter 1, and strengthens the observation that even weak (e.g.: Fish Lake) or ambiguous changes in VRI should be temporally closely examined as possible indicators of anthropogenic influence on the landscape. This 111 chapter is important for multiple reasons. First, this chapter includes the first use of a landscape‐ scale model to reconstruct the paleolandscape at a high temporal and climatically variable resolution. Second, it is also the first attempt in the literature to experimentally test the hypotheses that the addition of frequent Native American‐set surface fires was necessary to produce the paleolandscape using a landscape disturbance model. Finally, the results of this research suggest that climate alone probably did not create the landscape observed in the paleorecord, and that the addition of Native American‐set fires was necessary, thereby creating an anthropogenically influenced landscape.

Scientific Merit The hypotheses, objectives, and study sites used in this research were specifically selected to test whether Native Americans played a significant role in shaping the landscape through forest structure and composition. Though the results can only be applied at the specific sites reconstructed, strong inferences can be made regionally given the extent of human habitation throughout the Sierra Nevada. Identifying these broad spatial and temporal anthropogenic influences on vegetation adds evidence to the scientific debate of pristine‐ versus‐humanized landscape. I demonstrate that, at least for the last 2000 years, Native

Americans have influenced the landscape, and thus the paleoenvironmental record. Future studies must explicitly consider and test for Native American land use of the paleolandscape.

In conducting this research, I developed two new techniques of analysis. First, direct comparison of changes in VRI against annually‐resolved PDSI paleoclimate reconstructions. This technique allowed me to not only identify anomalous changes at my own study sites, but allowed me to reanalyze three other paleoenvironmental sites for direct comparison. Second, the application of a the LANDIS‐II model to reconstruct a multi‐millennial paleolandscape at a 112 subcentennial scale for comparison against observed paleovegetation data. Previous modeling efforts have assumed stationary climate (Berland et al., 2011), interpolated or centennially resolved climate inputs (Colombaroli et al., 2010; Heiri et al., 2006; Keller et al., 2002), focused on the last few hundred years (Berland et al., 2011), or were outside the United States

(Colombaroli et al., 2010; Heiri et al., 2006; Keller et al., 2002). I was able to model the landscape at a high temporal resolution for the last 1100 years using LANDIS‐II and test different hypotheses regarding climatic and anthropogenic fire regimes. This chapter adds to the

beginnings of a new body of literature in paleolandscape modeling and reconstruction.

Finally, this research fills a gap in environmental proxy data in the southern Sierra

Nevada, and does so in a way that acknowledges potential Native American impacts to biota.

The combination of (1) location, (2) paleoecologic reconstruction, (3) multiple lines of evidence,

(4) landscape modeling, and (5) focus on identifying whether Native Americans significantly altered forest structure makes this research unique.

Broader Impacts Knowing the role Native Americans played in shaping the landscape is critical to understanding landscape evolution through today. First, we must recognize that the Sierra

Nevada forests encountered by European settlers were a result of an anthropogenic influence. It would be useful to re‐examine policies regarding land management and fire suppression with

Native American practices in mind (M.K. Anderson, 1999; 2005; DellaSulla et al., 2004; Fry &

Stephens, 2006). Second, historic pollen data used in climate prediction models need to re‐ evaluated, as these pollen data may be the result of an anthropogenic landscape rather than a climatically‐driven one. Knowing what landscape characteristics are human caused will allow for better forest baseline conditions and potentially improve climate models that use pollen as a 113 calibration input. As we move towards more active fire management of federal lands, we can learn from the practices, implementation, and timing of Native American use of fire. It would be constructive to take into account Native fire‐use practices, and actively engage tribal communities towards a more comprehensive and holistic approach to natural landscape management. 114

References

Aber, J.D., Federer, C.A., 1992. A generalized, lumped‐parameter model of photosynthesis, evaporation and net primary production in temperate and boreal forest ecosystems. Oecologia 92, 463–474. Allen, C.D., 2002. Lots of lightning and plenty of people: an ecological history of fire in the upland Southwest, in: Vale, T.R. (Ed.), Fire, Native Peoples, and the Natural Landscape. Island Press, Washington, D.C., pp. 143–193. Anderson, M.K., 2006. Tending the wild: Native American knowledge and the management of California’s natural resources. University of California Press, Berkeley. Anderson, M.K., 1999. The fire, pruning, and coppice management of temperate ecosystems for basketry material by California Indian tribes. Hum. Ecol. 27, 79–113. Anderson, M.K., Moratto, M.J., 1996. Native American land‐use practices and ecological impacts, in: Sierra Nevada Ecosystem Project: Final Report to Congress. University of California, Centers for Water and Wildland Resources, Davis, pp. 187–206. Anderson, R.S., 1994. Paleohistory of a Giant Sequoia Grove: The record from Log Meadow, Sequoia National Park. USDA For. Serv. Gen. Tech. Rep. PSW‐151 49–55. Anderson, R.S., 1990. Holocene forest development and paleoclimates within the central Sierra Nevada, California. J. Ecol. 78, 470–489. Anderson, R.S., Allen, C.D., Toney, J.L., Jass, R.B., Bair, A.N., 2008a. Holocene vegetation and fire regimes in subalpine and mixed conifer forests, southern Rocky Mountains, USA. Int. J. Wildl. Fire 17, 96. doi:10.1071/WF07028 Anderson, R.S., Carpenter, S.L., 1991. Vegetation change in Yosemite Valley, Yosemite National Park, California, during the Protohistoric period. Madrono 38, 1–13. Anderson, R.S., Davis, O.K., 1988. Contemporary pollen rain across the central Sierra Nevada, California, USA: relationship to modern vegetation types. Arct. Alp. Res. 448–460. Anderson, R.S., Power, M.J., Smith, S.J., Springer, K., Scott, E., 2002. Paleoecology of a Middle Wisconsin deposit from southern California. Quat. Res. 58, 310–317. doi:10.1006/qres.2002.2388 Anderson, R.S., Smith, S.J., 1994. Paleoclimatic interpretations of meadow sediment and pollen stratigraphies from California. Geology 22, 723–726. Anderson, R.S., Smith, S.J., Jass, R.B., Geoffrey, W., 2008b. A Late Holocene Record of Vegetation and Climate from a Small Wetland in Shasta County, California. Madroño 55, 15–25. doi:10.3120/0024‐9637(2008)55 Anderson, R.S., Stillick, R.D., 2013. 800 years of vegetation change, fire and human settlement in the Sierra Nevada of California, USA. The Holocene 23, 823–832. doi:10.1177/0959683612471985 Arnold, J.E., Walsh, M.R., Hollimon, S.E., 2004. The Archaeology of California. J. Archaeol. Res. 115

12, 1–73. doi:10.1023/B:JARE.0000016694.82226.62 Basgall, M.E., 1987. Resource intensification among hunter‐gatherers: acorn economies in prehistoric California. Res. Econ. Anthropol. 9, 21–52. Baumhoff, M.A., 1963. Ecological Determinants of Aboriginal California Populations. Univ. Calif. Publ. Am. Archaeol. Ethnol. 49, 155–236. doi:10.1525/aa.1964.66.3.02a00360 Bean, L.J., Lawton, H.W., 1973. Some explanations for the rise of cultural complexity in Native California with comments on proto‐agriculture and agriculture, in: Bean, L.J. (Ed.), Patterns of Indian Burning in California: Ecology and Ethnohistory. Ballena Press Anthropological Papers No. 1, Ramona, CA, pp. v–xlvii. Beaton, J.M., 1991. Extensification and intensification in central California prehistory. Antiquity 65, 946–952. Beaty, R.M., Taylor, A.H., 2009. A 14 000 year sedimentary charcoal record of fire from the northern Sierra Nevada, Lake Tahoe Basin, California, USA. The Holocene 19, 347–358. doi:10.1177/0959683608101386 Berland, A., Shuman, B., Manson, S.M., 2011. Simulated Importance of Dispersal, Disturbance, and Landscape History in Long‐Term Ecosystem Change in the Big Woods of Minnesota. Ecosystems 14, 398–414. doi:10.1007/s10021‐011‐9418‐x Bettinger, R.L., 2015. Orderly Anarchy: Sociopolitical Evolution in Aboriginal California. University of California Press, Berkeley. Bettinger, R.L., Malhi, R., McCarthy, H., 1997. Central Place Models of Acorn and Mussel Processing. J. Archaeol. Sci. 24, 887–899. doi:10.1006/jasc.1996.0168 Blaauw, M., Christen, J.A., 2011. Flexible paleoclimate age‐depth models using an autoregressive gamma process. Bayesian Anal. 6, 457–474. doi:10.1214/11‐BA618 Bond, W.J., Keeley, J.E., 2005. Fire as a global “herbivore”: the ecology and evolution of flammable ecosystems. Trends Ecol. Evol. 20, 387–394. doi:10.1016/j. tree.2005.04.025 Bowerman, N.D., Clark, D.H., 2011. Holocene glaciation of the central Sierra Nevada, California. Quat. Sci. Rev. 30, 1067–1085. doi:10.1016/j.quascirev.2010.10.014 Bowman, D.M.J.S., Balch, J., Artaxo, P., Bond, W.J., Cochrane, M.A., D’Antonio, C.M., DeFries, R., Johnston, F.H., Keeley, J.E., Krawchuk, M.A., Kull, C.A., Mack, M., Moritz, M.A., Pyne, S., Roos, C.I., Scott, A.C., Sodhi, N.S., Swetnam, T.W., 2011. The human dimension of fire regimes on Earth. J. Biogeogr. no–no. doi:10.1111/j.1365‐2699.2011.02595.x Bradshaw, R.H.W., Webb, T., 1985. Relationships between contemporary pollen and vegetation from Wisconsin and Michigan, USA. Ecology 66, 721–737. Brunelle, A., Anderson, R.S., 2003. Sedimentary charcoal as an indicator of late‐Holocene drought in the Sierra Nevada, California, and its relevance to the future. The Holocene 13, 21–28. doi:10.1191/0959683603hl591rp Codding, B.F., Bird, D.W., 2013. Forward: A Global Perspective on Traditional Burning in 116

California. Calif. Archaeol. 5, 199–208. doi:10.1179/1947461X13Z.00000000010 Colombaroli, D., Henne, P.D., Kaltenrieder, P., Gobet, E., Tinner, W., 2010. Species responses to fire, climate and human impact at tree line in the Alps as evidenced by palaeo‐ environmental records and a dynamic simulation model. J. Ecol. 98, 1346–1357. doi:10.1111/j.1365‐2745.2010.01723.x Cook, E.R., Meko, D.M., Stahle, D.W., Cleaveland, M.K., 1999. Drought reconstructions for the continental United States. J. Clim. 12, 1145–1163. doi:10.1175/1520‐ 0442(1999)012<1145:DRFTCU>2.0.CO;2 Cook, E.R., Seager, R., Heim Jr., R.R., Vose, R.S., Herweijer, C., Woodhouse, C.A., 2009. Megadroughts in North America: Placing IPCC projections of hydroclimatic change in a long‐term palaeoclimate context. J. Quat. Sci. doi:10.1002/jqs Cook, E.R., Woodhouse, C.A., Eakin, C.M., Meko, D.M., Stahle, D.W., 2008. North American Summer PDSI Reconstructions, Version 2a. IGBP PAGES/World Data Center for Paleoclimatology. Data Contribution Series #2008‐046. NOAA/NGDC Paleoclimatology Program, Boulder, CO. Cook, E.R., Woodhouse, C.A., Eakin, C.M., Meko, D.M., Stahle, D.W., 2004. Long‐term aridity changes in the western United States. Science 306, 1015–8. doi:10.1126/science.1102586 Cowart, A., Byrne, R., 2013. A paleolimnological record of Late Holocene vegetation change from the central California coast. Calif. Archaeol. 5, 337–352. doi:10.1179/1947461X13Z.00000000018 Crawford, J.N., Mensing, S.A., Lake, F.K., Zimmerman, S.R., 2015. Late Holocene fire and vegetation reconstruction from the western Klamath Mountains, California, USA: A multi‐ disciplinary approach for examining potential human land‐use impacts. The Holocene 25, 1341–1357. doi:10.1177/0959683615584205 Cronon, W., 1983. Changes in the Land: Indians, Colonists, and the Ecology of New England, 2003rd ed. Hill and Wang, New York. Dale, V.H., Joyce, L.A., McNulty, S., Neilson, R.P., 2000. The interplay between climate change, forests, and disturbances. Sci. Total Environ. 262, 201–204. doi:10.1016/S0048‐ 9697(00)00522‐2 Dale, V.H., Joyce, L.A., McNulty, S., Neilson, R.P., Ayres, M.P., Flannigan, M.D., Hanson, P.J., Irland, L.C., Lugo, A.E., Peterson, C.J., Simberloff, D., Swanson, F.J., Stocks, B.J., Wotton, B.M., 2001. Climate change and forest disturbances. Bioscience 51, 723–734. doi:10.1641/0006‐3568(2001)051[0723:CCAFD]2.0.CO;2 Davis, O.K., 1992. Rapid climatic change in coastal southern California inferred from pollen analysis of San Joaquin Marsh. Quat. Res. 37, 89–100. doi:10.1016/0033‐5894(92)90008‐7 Davis, O.K., Anderson, R.S., Fall, P.L., O’Rourke, M.K., Thompson, R.S., 1985. Palynological evidence for early Holocene aridity in the Southern Sierra Nevada, California. Quat. Res. 24, 322–332. Dean, W.E., 1974. Determination of carbonate and organic matter in calcereous sediments by 117

loss on ignition comparison to other methods. J. Sediment. Petrol. 44, 242–248. Denevan, W.M., 1992. The pristine myth: the landscape of the Americas in 1492. Ann. Assoc. Am. Geogr. 82, 269–285. Diffenbaugh, N.S., Giorgi, F., Pal, J.S., 2008. Climate change hotspots in the United States. Geophys. Res. Lett. 35, 1–5. doi:10.1029/2008GL035075 Dillon, B.D., Porcasi, J., Gutman, T., Porcasi, P., Wood, F., 1991. Preliminary report on excavations at Creighton Ranch Site (CA‐TUL‐1613), Tulare County, California, in: Wallace, W.J., Riddell, F.A. (Eds.), Contributions to Tulare Lake Archaeology 1: Background to the Study of Tulare Lake’s Archaeological Past. Tulare Lake Archaeological Research Group, Redono Beach, pp. 61–70. Dincauze, D.F., 2000a. Environmental Archaeology: Principles and Practice. Cambridge University Press, Cambridge. Dincauze, D.F., 2000b. Concepts for paleoenvironmental reconstruction, in: Environmental Archaeology: Principles and Practice. Cambridge University Press, Cambridge, pp. 20–35. Dobbertin, M., Wermelinger, B., Bigler, C., Bürgi, M., Carron, M., Forster, B., Gimmi, U., Rigling, A., 2007. Linking increasing drought stress to Scots Pine mortality and bark beetle infestations. Sci. World J. 7, 231–239. doi:10.1100/tsw.2007.58 Dull, R.A., 1999. Palynological evidence for 19th century grazing‐induced vegetation change in the southern Sierra Nevada, California, U.S.A. J. Biogeogr. 26, 899–912. Engber, E.A., Morgan Varner, J., Arguello, L.A., Sugihara, N.G., 2011. The effects of conifer encroachment and overstory structure on fuels and fire in an oak woodland landscape. Fire Ecol. 7, 32–50. doi:10.4996/fireecology.0702032 Etheridge, D.M., Steele, L.P., Francey, R.J., Langenfelds, R.L., 1998. Atmospheric methane between 1000 A.D. and present: evidence of anthropogenic emissions and climatic variability. J. Geophys. Res. 103, 15979–15996. Etheridge, D.M., Steele, L.P., Langenfelds, R.L., Francey, R.J., Barnola, J.‐M., Morgan, V.I., 1996. Natural and anthropogenic changes in atmospheric CO2 over the last 1000 years from air in Antarctic ice and firn. J. Geophys. Res. 101, 4115–4128. Faegri, K., Iverson, J., 1964. Textbook of Pollen Analysis. Scandinavian University Books, Copenhagen. Fagerlind, F., 1952. The real significance of pollen diagrams. Bot. Not. 105, 185–224. Ferrell, G.T., 1996. The influence of insect pests and pathogens on Sierra Forests. Sierra Nevada Ecosyst. Proj. Final Rep. to Congr. Assessments Sci. Basis Manag. Options II, 1177–1192. Ferretti, D.F., Miller, J.B., White, J.W.C., Etheridge, D.M., Lassey, K.R., Lowe, D.C., MacFarling Meure, C.M., Dreier, M.F., Trudinger, C.M., van Ommen, T.D., 2005. Unexpected changes to the global methane budget over the last 2,000 Years. Science (80‐. ). 309, 1714–1717. Fettig, C.J., Kleipzig, K.D., Billings, R.F., Munson, A.S., Nebeker, T.E., Negron, J.F., Nowak, J.T., 118

2007. The effectiveness of vegetation management practices for the prevention and control of bark beetle infestation in coniferous forests of the western and southern United States. For. Ecol. Manage. 238. Fites‐Kaufman, J.A., Rundel, P., Stephenson, N.L., Weixelman, D.A., 2007. Montane and subalpine vegetation of the Sierra Nevada and Cascade Ranges, in: Barbour, M.G., Keeler‐ Wolf, T., Schoenherr, A.A. (Eds.), Terrestrial Vegetation of California. University of California Press, Berkeley, California, pp. 456–501. Flannigan, M.D., Stocks, B.J., Wotton, B.M., 2000. Climate change and forest fires. Sci. Total Environ. 262, 221–229. doi:10.1016/S0048‐9697(00)00524‐6 Fowler, C.S., 2008. Historical perspectives on Timbisha Shoshone land management practices, Death Valley, California, in: Reitz, E.J., Scarry, C.M., Scudder, S.J. (Eds.), Case Studies in Environmental Archaeology. Springer Science+Business Media, LLC, New York, pp. 43–57. Franklin, J.S., Fralish, S.B., 2002. Taxonomy and Ecology of Woody Plants in North American Forests (Excluding Mexico and Subtropical Florida). John Wiley & Sons, Inc., New York. Gassaway, L., 2009. Native American fire patterns In Yosemite Valley: archaeology, dendrochronology, subsistence, and culture change in the Sierra Nevada. SCA Proc. 22, 1– 19. Gassaway, L., 2005. HUJPU‐ST: Spatial and temporal patterns of anthropogenic fire in Yosemite Valley. San Francisco State University. Gayton, A.H., 1948. Yokuts and Western Mono ethnography I: Tulare Lake, Southern Valley, and Central Foothill Yokuts. Anthropol. Rec. 10, 1–142. Gorden, M.A., n.d. Yokut plants: their names and uses. Graumlich, L.J., 1993. A 1000‐year record of temperature and precipitation in the Sierra Nevada. Quat. Res. 39, 249–255. Grimm, E.C., 1987. CONISS: A FORTRAN 77 program for stratigraphically constrained cluster analysis by the method of incremental sum of squares. Comput. Geosci. 13, 13–35. Halofsky, J.E., Donato, D.C., Hibbs, D.E., Campbell, J.L., Cannon, M.D., Fontaine, J.B., Thompson, J.R., Anthony, R.G., Bormann, B.T., Kayes, L.J., Law, B.E., Peterson, D.L., T. A. Spie, 2011. Mixed‐severity fire regimes: Lessons and hypotheses from the Klamath‐Siskiyou Ecoregion. Ecosphere 2, 1–19. He, H.S., 2008. Forest landscape models: Definitions, characterization, and classification. For. Ecol. Manage. 254, 484–498. doi:10.1016/j.foreco.2007.08.022 He, H.S., Keane, R.E., Iverson, L.R., 2008. Forest landscape models, a tool for understanding the effect of the large‐scale and long‐term landscape processes. For. Ecol. Manage. 254, 371– 374. doi:10.1016/j.foreco.2007.09.069 He, H.S., Mladenoff, D.J., Crow, T.R., 1999. Linking an ecosystem model and a landscape model to study forest species response to climate warming. Ecol. Modell. 114, 213–233. doi:10.1016/S0304‐3800(98)00147‐1 119

Heiri, C., Bugmann, H., Tinner, W., Heiri, O., Lischke, H., 2006. A model‐based reconstruction of Holocene treeline dynamics in the Central Swiss Alps. J. Ecol. 94, 206–216. doi:10.1111/j.1365‐2745.2005.01072.x Henne, P.D., Elkin, C., Colombaroli, D., Samartin, S., Bugmann, H., Heiri, O., Tinner, W., 2012. Impacts of changing climate and land use on vegetation dynamics in a Mediterranean ecosystem: insights from paleoecology and dynamic modeling. Landsc. Ecol. 28, 819–833. doi:10.1007/s10980‐012‐9782‐8 Herweijer, C., Seager, R., Cook, E.R., Emile‐Geay, J., 2007. North American droughts of the last millennium from a gridded network of tree‐ring data. J. Clim. 20, 1353–1376. doi:10.1175/JCLI4042.1 Hessl, A.E., 2011. Pathways for climate change effects on fire: Models, data, and uncertainties. Prog. Phys. Geogr. 35, 393–407. doi:10.1177/0309133311407654 Higgins, S.I., Bond, W.J., Trollope, W.S.W., 2000. Fire, resprouting and variability: a recipe for grass‐tree coexistence in savanna. J. Ecol. 88, 213–229. doi:10.1046/ j.1365‐ 2745.2000.00435.x Higuera, P.E., Brubaker, L.B., Anderson, P.M., Hu, F.S., Brown, T.A., 2009. Vegetation mediated the impacts of postglacial climatic change on fire regimes in the south‐central Brooks Range, Alaska. Ecol. Monogr. 79, 201–219. Hilimire, K., Nesmith, J.C.B., Caprio, A.C., Milne, R., 2013. Attributes of windthrown trees in a Sierra Nevada mixed‐coniferforest. West. J. Appl. For. 28, 85–88. doi:10.5849/wjaf.12‐027 Hull, K.L., 2009. Pestilence and Persistence: Yosemite Indian Demography and Culture in Colonial California. University of California Press, Berkeley. Hull, K.L., 2007. The Sierra Nevada: archaeology in the range of light, in: Jones, Terry L., Klar, K.A. (Ed.), California Prehistory: Colonization, Culture and Complexity. Altamira Press, New York, pp. 177–190. Hull, K.L., 2005. Process, perception, and practice: Time perspectivism in Yosemite native demography. J. Anthropol. Archaeol. 24, 354–377. doi:10.1016/j.jaa.2005.06.003 Innes, J.C., North, M.P., Williamson, N., 2006. Effect of thinning and prescribed fire restoration treatments on woody debris and snag dynamics in a Sierran old‐growth , mixed‐conifer forest. doi:10.1139/X06‐184 Jones, T.L., 1995. Transitions in prehistoric diet, mobility, exchange, and social organization along California’s Big Sur coast. University of California, Davis. Jones, T.L., Brown, G.M., Raab, L.M., McVickar, J.L., Spaulding, W.G., Kennett, D.J., York, A., Walker, P.L., Basgall, M.E., Bettinger, R.L., Biró, K.T., Haas, J., Creamer, W., Lanata, J.L., Lilley, I., Wake, T.A., 1999. Environmental imperatives reconsidered: demographic crises in western North America during the Medieval Climatic Anomaly. Curr. Anthropol. 40, 137– 170. Jones, T.L., Schwitalla, A., 2008. Archaeological perspectives on the effects of medieval drought in prehistoric California. Quat. Int. 188, 41–58. doi:10.1016/j.quaint.2007.07.007 120

Jones, T.L., Stevens, N.E., Jones, D.A., Fitzgerald, R.T., Hylkema, M.G., 2007. The Central Coast: a midlatitude milieu, in: Jones, T.L., Klar, K.A. (Eds.), California Prehistory: Colonization, Culture and Complexity. AltaMira Press, New York, pp. 125–146. Jordan, T.A., 2003. Ecological and cultural contributions of controlled fire use by Native Californians : A survey of literature. Am. Indian Cult. Res. J. 27, 77–90. Juggins, S., 2015. rioja: Analysis of Quaternary science data, R package. Kapp, R.O., 1969. How to Know Pollen and Spores. William C. Brown Co. Publishers, Dubuque, IA. Keeley, J.E., Franklin, J., D’Antonio, C., 2011. Fire and invasive plants on California landscapes, in: McKenzie, D., Miller, C., Falk, D.A. (Eds.), The Landscape Ecology of Fire, Ecological Studies. Springer Science+Business Media B.V., Dordrecht, pp. 193–221. doi:10.1007/978‐94‐007‐ 0301‐8 Keller, F., Lischke, H., Mathis, T., Möhl, A., Wick, L., Ammann, B., Kienast, F., 2002. Effects of climate, fire, and humans on forest dynamics: Forest simulations compared to the palaeological record. Ecol. Modell. 152, 109–127. doi:10.1016/S0304‐3800(02)00011‐X Kilgore, B.M., 1973. The ecological role of fire in Sierran conifer forests: its application to National Park management. Quat. Res. 3, 496–513. doi:10.1016/0033‐5894(73)90010‐0 Kilgore, B.M., Taylor, D., 1979. Fire history of a sequoia mixed‐conifer forest. Ecology 60, 129– 142. Klimaszewski‐Patterson, A., Mensing, S.A., 2015. Multi‐disciplinary approach to identifying Native American impacts on Late Holocene forest dynamics in the southern Sierra Nevada range, California, USA. Anthropocene. doi:10.1016/j.ancene.2016.04.002 Knapp, R.O., Hadley, K.S., 2011. Lewis and Clark’s tempest: “The perfect storm” of November 1805, Oregon, USA. The Holocene 21, 693–697. Koehler, P. a., Anderson, R.S., Spaulding, W.G., 2005. Development of vegetation in the Central Mojave Desert of California during the late Quaternary. Palaeogeogr. Palaeoclimatol. Palaeoecol. 215, 297–311. doi:10.1016/j.palaeo.2004.09.010 Koehler, P.A., Anderson, R.S., 1994. The paleoecology and stratigraphy of Nichols Meadow, Sierra National Forest, California, USA. Palaeogeogr. Palaeoclimatol. Palaeoecol. 112, 1–17. Kraus, G., 2016. Ever upward?: lithics and intensification on the Western Divide. University of Nevada Reno. Kroeber, A.L., 1959. Ethnographic interpretations 7–11, in: University of California Publications in American Archaeology and Ethnology. University of California, Berkeley, pp. 235–307. Kroeber, A.L., 1925. Handbook of the Indians of California. Bureau of American Ethnology Bulletin No. 78, Washington, D.C., D.C. Latta, F.F., 1977. Handbook of Yokuts Indians. Bear State Books, Santa Cruz. Lenihan, J.M., Drapek, R., Bachelet, D., Neilson, R.P., 2003. Climate change effects on vegetation 121

distribution, carbon, and fire in California. Ecol. Appl. 13, 1667–1681. doi:10.1890/025295 Lewis, H.T., 1973. Patterns of Indian burning in California: ecology and ethnohistory. Ballena Press Anthropological Papers No. 1, Ramona, CA. Lightfoot, K.G., Cuthrell, R.Q., Boone, C.M., Byrne, R., Chavez, A.S., Collins, L., Cowart, A., Evett, R.R., Fine, P.V.A., Gifford‐Gonzalez, D., Hylkema, M.G., Lopez, V., Misiewicz, T.M., Reid, R.E.B., 2013. Anthropogenic burning on the Central California coast in Late Holocene and Early Historical times: findings, implications, and future directions. Calif. Archaeol. 5, 371– 390. Lightfoot, K.G., Lopez, V., 2013. The study of indigenous management practices in California: an introduction. Calif. Archaeol. 5, 209–219. doi:10.1179/1947461X13Z.00000000011 Lightfoot, K.G., Parrish, O., 2009a. California Indians and their environment: an introduction, California. ed. University of California Press, Berkeley. Lightfoot, K.G., Parrish, O., 2009b. The central role of fire, in: California Indians and Their Environment: An Introduction. California Natural History Guides. University of California Press, Berkeley, California, pp. 14–36. Lightfoot, K.G., Parrish, O., 2009c. The first fire managers, 95th ed, California Indians and their Environment: an Introduction. California Natural History Guides. University of California Press, Berkeley, California. Lloyd, J.B., Baloian, M.C., Harro, D.R., McKim, R.L., 2011. Archaeological Testing and National Register of Historic Places Eligibility Evaluation of CA‐TUL‐ 1400/2025/2066 (FS 05‐13‐ 52‐ 0013/33/184) Upper Coffee Camp, Western Divide Ranger District, Sequoia National Forest, Tulare County, California, Recreation S. Long, C.J., Whitlock, C., Bartlein, P.J., Millspaugh, S.H., 1998. A 9000‐year fire history from the Oregon Coast Range, based on a high‐resolution charcoal study. Can. J. For. Res. 28, 774– 787. doi:10.1139/cjfr‐28‐5‐774 MacFarling Meure, C., 2004. The natural and anthropogenic variations of carbon dioxide, methane and nitrous oxide during the Holocene from ice core analysis. University of Melbourne. MacFarling Meure, C., Etheridge, D., Trudinger, C., Steele, P., Langenfelds, R., van Ommen, T., Smith, A., Elkins, J., 2006. The Law Dome CO2, CH4 and N2O ice core records extended to 2000 years BP. Geophys. Res. Lett. 33, L14810. doi:10.1029/2006GL026152 Martin, R.E., Sapsis, D.B., 1992. Fires as agents of biodiversity: pyrodiversity promotes biodiversity. Proc. Symp. Biodivers. Northwest. Calif. Mazurkiewicz‐Zapałowicz, K., Okuniewska‐Nowaczyk, I., 2015. Mycological and palynological studies of early medieval cultural layers from strongholds in Pszczew and Santok (western Poland). Acta Mycol. 50. doi:10.5586/am.1059 McCarthy, H., 1993. A political economy of Western Mono acron production. University of California, Davis. 122

Mensing, S.A., 2001. Late‐glacial and early Holocene vegetation and climate change near Owens Lake, eastern California. Quat. Res. 55, 57–65. doi:10.1006/qres.2000.2196 Mensing, S.A., Benson, L. V., Kashgarian, M., Lund, S., 2004. A Holocene pollen record of persistent droughts from Pyramid Lake, Nevada, USA. Quat. Res. 62, 29–38. doi:10.1016/j.yqres.2004.04.002 Mensing, S.A., Sharpe, S.E., Tunno, I., Sada, D.W., Thomas, J.M., Starratt, S., Smith, J., 2013. The Late Holocene Dry Period: multiproxy evidence for an extended drought between 2800 and 1850 cal yr BP across the central Great Basin, USA. Quat. Sci. Rev. 78, 266–282. doi:10.1016/j.quascirev.2013.08.010 Mensing, S.A., Smith, J., Burkle Norman, K., Allan, M., 2008. Extended drought in the Great Basin of western North America in the last two millennia reconstructed from pollen records. Quat. Int. 188, 79–89. doi:10.1016/j.quaint.2007.06.009 Millar, C.I., King, J.C., Westfall, R.D., Alden, H.A., Delany, D.L., 2006. Late Holocene forest dynamics, volcanism, and climate change at Whitewing Mountain and San Joaquin Ridge, Mono County, Sierra Nevada, CA, USA. Quat. Res. 66, 273–287. doi:10.1016/j.yqres.2006.05.001 Miller, C., Urban, D.L., 2000. Modeling the effects of fire management alternatives on Sierra Nevada mixed‐conifer forests. Ecol. Appl. 10, 85–94. Miller, C., Urban, D.L., 1999a. Interactions between forest heterogeneity and surface fire regimes in the southern Sierra 212, 202–212. Miller, C., Urban, D.L., 1999b. A model of surface fire, climate and forest pattern in the Sierra Nevada, California. Ecol. Modell. 114, 113–135. doi:10.1016/S0304‐3800(98)00119‐7 Miller, J.D., Safford, H.D., Crimmins, M., Thode, A.E., 2009. Quantitative evidence for increasing forest fire severity in the Sierra Nevada and southern Cascade Mountains, California and Nevada, USA. Ecosystems 12, 16–32. doi:10.1007/s10021‐008‐9201‐9 Mladenoff, D.J., 2004. LANDIS and forest landscape models. Ecol. Modell. 180, 7–19. doi:10.1016/j.ecolmodel.2004.03.016 Mohr, J.A., Whitlock, C., Skinner, C.N., 2000. Postglacial vegetation and fire history, eastern Klamath Mountains, California, USA. The Holocene 10, 587–601. doi:10.1191/095968300675837671 Moratto, M.J., 1999. Cultural chronology 2: the Yosemite data. Yosemite Res. Cent. Publ. Anthropol. 21, 17–26. Moratto, M.J., 1984. California Archaeology. Academic Press, New York. Moratto, M.J., King, T.F., Woolfenden, W.B., 1978. Archaeology and California’s climate. J. Calif. Anthropol. 5, 147–161. Morgan, C., 2010. Numic Expansion in the Southern Sierra Nevada. J. Calif. Gt. Basin Anthropol. 30, 157–174. 123

Morgan, C., 2009. Climate change, uncertainty and prehistoric hunter–gatherer mobility. J. Anthropol. Archaeol. 28, 382–396. doi:10.1016/j.jaa.2009.07.004 Morgan, C., 2008. Reconstructing prehistoric hunter‐gatherer foraging radii: a case study from California’s southern Sierra Nevada. J. Archaeol. Sci. 35, 247–258. doi:10.1016/j.jas.2007.02.025 Muir, J., 1894. The mountains of California. Ten Speed Press, Berkeley. National Park Service, 2011. A sense of place: design Guidelines for Yosemite National Park. Yosemite Nationa Park. Parker, A.J., 2002. Fire in Sierra Nevada forests: evaluating the ecological impact of burning by Native Americans, in: Vale, T.R. (Ed.), Fire, Native Peoples, and the Natural Landscape. Island Press, Washington, D.C., pp. 233–267. Parker, K.C., 2002. Fire in the pre‐European lowlands of the American Southwest, in: Vale, T.R. (Ed.), Fire, Native Peoples and the Natural Landscape. Island Press, Washington. Island Press, Washington D.C., pp. 101–141. Pastor, J., Post, W.M., 1985. Development of a linked forest productivity‐soil process model, Environmental Sciences Division Publication No. 2455. Peterson, D.L., Hammer, R.D., 2001. From open to closed canopy: a century of change in a Douglas‐fir forest, Orcas Island, Washington. Northwest Sci. 75, 262–269. Peterson, D.W., Reich, P.B., 2001. Prescribed fire in oak savanna: Fire frequency effects on stand structure and dynamics. Ecol. Appl. 11, 914–927. doi:10.1890/1051‐ 0761(2001)011[0914:PFIOSF]2.0.CO;2 Powers, S., 1976. Tribes of California (Reprinted from Contributions to North American Ethnology, Volume III [1877]). University of Chicago Press, Berkeley. Price, C.G., Rind, D., 1994. Possible implications of global climate change on global lightning distributions and frequencies. J. Geophys. Res. Ramirez, R.S., Murray, S., Dietler, J., Steely, J.W., 2010. Cultural Resources Inventory and Eligibility Assessment of the Johnsondale Work Center Site, CA‐TUL‐819 (FS 05‐13‐53‐08), SWCA Cultural Resources Report Database No. 2009‐320. South Pasadena, CA. Reimer, P.J., Bard, E., Bayliss, A., Beck, J.W., Blackwell, P.G., Bronk, M., Grootes, P.M., Guilderson, T.P., Haflidason, H., Hajdas, Hatte, C., Heaton, T.J., Hoffman, D.L., Hogg, A.G., Hughhen, K.A., Kaiser, J.F., Kromer, B., Manning, S.W., Niu, M., Reimer, R.W., Richards, D.A., Scott, E.M., Southon, J.R., Staff, R.A., Turney, C.S.M., van der Plicht, J., 2013. IntCal 13 and Marine 13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55, 1869–1887. Reynolds, R.D., 1959. Effects of natural fires and aboriginal burning upon the forests of the central Sierra Nevada. University of California, Berkeley. Roy, D.G., Vankat, J.L., 1999. Reversal of human‐induced vegetation changes in Sequoia National Park, California. Can. J. For. Res. 29, 399–412. doi:10.1139/x99‐007 124

Scheller, R.M., Domingo, J.B., Sturtevant, B.R., Williams, J.S., Rudy, A., Gustafson, E.J., Mladenoff, D.J., 2007. Design, development, and application of LANDIS‐II, a spatial landscape simulation model with flexible temporal and spatial resolution. Ecol. Modell. 201, 409–419. Scheller, R.M., Mladenoff, D.J., 2004a. A forest growth and biomass module for a landscape simulation model, LANDIS: design, validation, and application. Ecol. Modell. 211–229. Scheller, R.M., Mladenoff, D.J., 2004b. A forest growth and biomass module for a landscape simulation model, LANDIS: Design, validation, and application. Ecol. Modell. 180, 211–229. Scheller, R.M., Spencer, W.D., Rustigian‐Romsos, H., Syphard, A.D., Ward, B.C., Strittholt, J.R., 2011. Using stochastic simulation to evaluate competing risks of wildfires and fuels management on an isolated forest carnivore. Landsc. Ecol. 26, 1491–1504. doi:10.1007/s10980‐011‐9663‐6 Sherriff, R.L., Veblen, T.T., 2008. Variability in fire–climate relationships in ponderosa pine forests in the Colorado Front Range. Int. J. Wildl. Fire 17, 50. doi:10.1071/WF07029 Shugart, H.H., 2002. Forest Gap Models, in: Mooney, H.A., Canadell, J.G. (Eds.), The Earth System: Biological and Ecological Dimensions of Global Environmental Change. John Wiley & Sons, Chichester, pp. 316–323. Sierra Nevada ecosystems, 1996. , in: Sierra Nevada Ecosystem Project: Final Report to Congress, Vol. I, Assessments Summaries and Management Strategies. University of California, pp. 1–11. Silcox, F.A., 1910. Fire prevention and control on national forests, in: Yearbook of Department of Agriculture. Government Printing Office, Washington. Skinner, C.N., Thatcher, J.J., 2013. X‐ray fluorescence analysis and obsidian hydration measurement of artifact obsidian from the Trout Meadow site (CA‐TUL‐2027/2077/H), Sequoia National Forest, Tulare County, California, in: Report 2013‐30 Prepared for Sequoia National Forest, Dunlap, California. Corvallis, OR. Smith, S.J., Anderson, R.S., 1992. Late Wisconsin paleoecologic record from Swamp Lake, Yosemite National Park, California. Quat. Res. 38, 91–102. Smith, T., Rizzo, D., North, M., 2005. Patterns of mortality in an old growth mixed‐conifer forest of the southern Sierra Nevada, California. For. Sci. 51, 266–275. Solomon, A.M., Delcourt, H.R., West, D., Blasing, T.J., 1980. Testing a simulation model for reconstruction of prehistoric forest‐stand dynamics. Quat. Res. 14, 275–293. Soulard, C.E., 2012. Sierra Nevada ecoregion summary [WWW Document]. USGS L. Cover Trends Proj. Speer, J.H., 2010. Fundamentals of Tree Ring Research. The University of Arizona Press, Tucson. Stephens, S.L., Martin, R.E., Clinton, N.E., 2007. Prehistoric fire area and emissions from California’s forests, woodlands, shrublands, and grasslands. For. Ecol. Manage. 251, 205– 216. doi:10.1016/j.foreco.2007.06.005 125

Stephenson, N.L., Parsons, D.L., Swetnam, T.W., 1991. Restoring natural fire to the Sequoia‐ mixed conifer forest: should intense fire play a role?, in: Proceedings of the 17th Tall Timbers Fire Ecology Conference. pp. 321–337. Stewart, O.C., 2014. Barriers to understanding the influence of use of fire by aborigines on vegetation. Fire Ecol. 10, 4–9. doi:10.4996/fireecology.1002001 Stine, S., 1994. Extreme and persistent drought in California and Patagonia during mediaeval time. Nature 369, 546–549. Stockmarr, 1971. Tablets with spores used in absolute pollen analysis. Pollen et spores 13, 615– 621. Street, J.H., Anderson, R.S., Paytan, A., 2012. An organic geochemical record of Sierra Nevada climate since the LGM from Swamp Lake, Yosemite. Quat. Sci. Rev. 40, 89–106. doi:10.1016/j.quascirev.2012.02.017 Sturtevant, B.R., Gustafson, E.J., He, H.S., 2004. Modeling disturbance and succession in forest landscapes using LANDIS: introduction. Ecol. Modell. 180, 1–5. doi:10.1016/j.ecolmodel.2004.07.001 Sturtevant, B.R., Scheller, R.M., Miranda, B.R., Shinneman, D., Syphard, A.D., 2009. Simulating dynamic and mixed‐severity fire regimes: A process‐based fire extension for LANDIS‐II. Ecol. Modell. 220, 3380–3393. doi:10.1016/j.ecolmodel.2009.07.030 Swetnam, T.W., 1993. Fire history and climate change in giant sequoia groves. Science (80‐. ). 262, 885–889. doi:10.1126/science.262.5135.885 Swetnam, T.W., Baisan, C.H., Caprio, A.C., Brown, P.M., Touchan, R., Anderson, R.S., Hallett, D.J., 2009. Multi‐millennial fire history of the Giant Forest, Sequoia National Park, California, USA. Fire Ecol. 5, 120–150. doi:10.4996/fireecology.0503120 Swetnam, T.W., Baisan, C.H., Morino, K., Caprio, A.C., 1998. Fire history along elevational transects in the Sierra Nevada, California, Sierra Nevada Global Change Research Program. Tuscon. Swetnam, T.W., Touchan, R., Baisan, C.H., Caprio, A.C., Brown, P.M., 1990. Giant Sequoia fire history in Mariposa Grove, Yosemite National Park, in: Yosemite Centennial Symposium Proceeding‐Natural Areas and Yosemite: Prospects for the Future. Syphard, A.D., Radeloff, V.C., Keeley, J.E., Hawbaker, T.J., Clayton, M.K., Stewart, S.I., Hammer, R.B., 2007. Human Influence on California Fire Regimes. Ecol. Appl. 17, 1388–1402. doi:10.1890/06‐1128.1 Syphard, A.D., Scheller, R.M., Ward, B.C., Spencer, W.D., Strittholt, J.R., 2011. Simulating landscape‐scale effects of fuels treatments in the Sierra Nevada, California, USA. Int. J. Wildl. Fire 20, 364. doi:10.1071/WF09125 Taylor, A.H., n.d. Sequoia fire history sites ‐ Preliminary information. Team, R.D.C., 2008. R: A language and environment for statistical computing. 126

Vale, T.R., 2002a. Reflections, in: Vale, T.R. (Ed.), Fire, Native Peoples, and the Natural Landscape. Island Press, Washington, D.C., pp. 295–301. Vale, T.R., 2002b. The Pre‐European landscape of the United States, in: Vale, T.R. (Ed.), Fire, Native Peoples, and the Natural Landscape. Island Press, Washington, D.C., pp. 1–39. van Wagtendonk, J.W., Cayan, D.R., 2008. Temporal and spatial distribution of lightning strikes in California in relation to large‐scale weather patterns. Fire Ecol. 4, 34–56. Vankat, J.L., 1985. Patterns of lightning ignitions in Sequoia National Park, California, in: Lotan, J.E., Kilgore, B.M., Fischer, W.C., Mutch, R.W. (Eds.), Proceedings of the Symposium and Workshop on Wilderness Fire. USDA General Technical Report INT‐182. Ogden, Utah, pp. 408–411. Vankat, J.L., 1977. Fire and man in Sequoia National Park. Ann. Assoc. Am. Geogr. 67, 17–27. Vankat, J.L., 1970. Vegetation change in Sequoia National Park, California. University of California, Davis. Vankat, J.L., Major, J.L., 1978. Vegetation changes in Sequoia National Park, California. J. Biogeogr. 5, 377–402. Voegelin, E.W., 1938. Tubatulabal Ethnography, Anthropological Records. University of California Press, Berkeley, California. Webb III, T., Howe, S.E., Bradshaw, R.H.W., Heide, K.M., 1981. Estimating plant abundances from pollen percentages: The use of regression analysis. Rev. Palaeobot. Palynol. 34, 269– 300. doi:10.1016/0034‐6667(81)90046‐4 Whitlock, C., Anderson, R.S., 2003. Fire history reconstructions based on sediment records from lakes and wetlands, in: Veblen, T.T., Baker, W.L., Montenegro, G., Swetnam, T.W. (Eds.), Fire and Climatic Change in the Americas. Springer‐Verlag, New York, pp. 3–31. Whitlock, C., Knox, M.A., 2002a. Prehistoric burning in the Pacific Northwest: Human versus climatic influences, in: Vale, T.R. (Ed.), Fire, Native Peoples, and the Natural Landscape. Island Press, Washington, D.C., pp. 195–231. Whitlock, C., Knox, M.A., 2002b. Prehistoric burning in the Pacific Northwest: Human versus climatic influences, in: Vale, T.R. (Ed.), Fire, Native Peoples, and the Natural Landscape. Island Press, Washington D.C., pp. 195–231. doi:10.1017/CBO9781107415324.004 Whitlock, C., Larsen, C., 2001. Charcoal as a fire proxy, in: Smol, J.P., Birks, H.J.B., Last, W.M. (Eds.), Tracking Environmental Change Using Lake Sediments. Kluwer Academic Publishers, Dordrecht. Winkler, M.G., 1985. Charcoal analysis for paleoenvironmental interpretation: a chemical assay. Quat. Res. 23, 313–326. Wohlgemuth, E., 1996. Resource intensification in prehistoric California: Evidence from archaeobotanical data. J. Calif. Gt. Basin Anthropol. 18, 81–103. Xi, W., Xu, C., 2010. PnET‐II for LANDIS‐II 5.1 User Guide. 127

Yang, J., Weisberg, P.J., Dilts, T.E., Loudermilk, E.L., Scheller, R.M., Stanton, A., Skinner, C., 2015a. Predicting wildfire occurrence distribution with spatial point process models and its uncertainty assessment: A case study in the Lake Tahoe Basin, USA. Int. J. Wildl. Fire 24, 380–390. doi:10.1071/WF14001 Yang, J., Weisberg, P.J., Shinneman, D.J., Dilts, T.E., Earnst, S.L., Scheller, R.M., 2015b. Fire modulates climate change response of simulated aspen distribution across topoclimatic gradients in a semi‐arid montane landscape. Landsc. Ecol. 30, 1055–1073. doi:10.1007/s10980‐015‐0160‐1 Zobel, D.B., Roth, L.F., Hawk, G.M., 1985. Ecology, pathology, and management of Port Orford‐ cedar (Chamaecyparis lawsoniana), General Technical Report PNW‐184. Portland, OR.

128

Appendix A Raw Pollen Counts for Holey Meadow

Age (cal yr BP) ‐61 ‐40 1 22 43 66 113 160 220 278 340 372 403 466 501 538 Adjusted core 0 1 3 4 5 6 8 10 12 14 16 17 18 20 21 22 depth Core depth 2 3 5 6 7 8 10 12 14 16 18 19 20 22 23 24 Abies 114 73 102 70 76 58 47 64 42 40 42 44 45 47 63 50 Alnus 0 1 2 0 1 1 3 1 4 4 2 1 4 3 3 1 Amaranthaceae 3 2 2 0 2 0 2 2 4 1 5 1 2 5 5 8 Ambrosia 2 0 2 0 3 2 2 0 1 0 0 0 1 0 2 1 Apiaceae 0 1 2 2 1 3 2 2 2 5 1 0 3 4 2 2 Arceuthobium 0 1 0 0 0 0 0 0 0 1 0 1 1 1 1 1 Artemisia 1 1 2 0 1 1 0 1 1 2 3 0 0 0 3 0 Asteracea 6 2 2 3 2 3 3 4 4 2 4 1 8 7 5 4 Betula 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 Brassicaceae 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 Caryophyllaceae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ceanothus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Chrysolepis 1 2 3 1 3 2 2 1 0 1 4 1 2 5 1 1 Corylus 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 Cupressaceae 27 30 25 57 38 41 30 52 55 60 47 39 44 59 27 39 Cyperaceae 186 148 143 116 147 165 151 161 102 84 97 85 89 85 84 84 Fraxinus 5 1 4 3 2 3 3 1 4 3 4 5 2 1 2 2 Indeterminate 19 12 14 28 15 36 32 22 26 15 19 21 15 15 14 13 Lamiaceae 0 0 0 1 0 0 0 0 0 0 0 0 2 0 0 0 Liliaceae 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 Lycopodium 404 489 361 192 348 186 725 194 302 186 214 439 355 331 338 547 Pediastrum 0 0 1 1 0 0 2 0 0 2 4 4 17 8 1 4 Pinus 105 132 121 124 133 129 227 162 159 183 198 203 191 170 207 193 Poaceae 23 40 37 38 44 53 33 23 18 29 25 31 14 30 25 25 Polemoniaceae 0 0 0 0 0 0 1 0 2 0 0 0 2 3 0 1 Polygonaceae 0 3 5 3 4 3 2 7 5 3 3 2 2 1 2 3 Quercus 93 90 68 64 77 44 63 36 45 35 58 47 38 43 54 38 Rosaceae 9 5 9 5 6 6 9 19 21 17 13 11 11 10 1 12 Salix 2 1 4 4 4 5 4 1 0 4 2 5 3 4 8 1 Sarcobatus 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 Shepherdia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Typha.angustifolia 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 Typha.latifolia 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 9 Unknown 4 4 2 0 2 7 1 0 8 2 3 2 13 4 0 7

129

Age (cal yr BP) 574 611 647 680 746 812 893 975 1044 1103 1162 1310 1383 1457 Adjusted core 23 24 25 26 28 30 32 34 36 38 40 42 43 44 depth Core depth 25 26 27 28 30 32 34 36 38 40 42 44 45 46 Abies 53 51 53 50 59 49 52 42 51 39 50 42 58 51 Alnus 3 2 3 1 2 0 0 2 2 0 3 3 3 0 Amaranthaceae 3 6 3 4 4 5 4 1 1 5 6 5 4 3 Ambrosia 0 4 0 0 0 2 1 1 0 2 1 3 1 0 Apiaceae 4 5 4 5 3 3 3 4 4 5 2 3 3 4 Arceuthobium 0 0 0 1 0 0 0 0 0 1 0 0 1 0 Artemisia 2 1 2 0 0 3 1 2 5 2 1 2 0 0 Asteracea 6 7 6 9 8 11 7 9 6 5 5 13 4 3 Betula 1 0 1 0 1 1 0 0 0 0 0 0 1 0 Brassicaceae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Caryophyllaceae 0 0 0 0 0 0 0 0 0 3 0 0 0 0 Ceanothus 0 0 0 0 0 0 0 0 0 0 0 1 0 0 Chrysolepis 3 0 3 4 3 1 1 1 4 1 7 3 2 44 Corylus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cupressaceae 34 40 34 26 43 46 46 32 31 26 37 37 18 28 Cyperaceae 77 156 77 95 195 182 160 108 86 78 90 96 123 91 Fraxinus 1 1 1 0 0 1 0 1 3 1 1 0 0 3 Indeterminate 11 8 11 25 9 14 15 34 19 27 14 25 15 20 Lamiaceae 0 0 0 1 0 0 0 0 0 1 0 0 0 0 Liliaceae 0 3 0 0 0 4 0 1 0 0 0 0 1 0 Lycopodium 561 524 561 464 508 330 296 447 216 274 406 452 527 262 Pediastrum 1 2 1 3 1 8 2 9 11 16 14 19 0 5 Pinus 195 207 195 221 199 178 182 196 190 221 206 182 204 176 Poaceae 38 14 38 18 24 44 55 32 34 28 16 18 23 16 Polemoniaceae 0 0 0 0 0 0 0 2 1 0 1 0 0 0 Polygonaceae 2 2 2 0 3 4 2 5 2 4 0 1 1 2 Quercus 27 53 27 33 33 37 28 41 39 31 36 50 45 41 Rosaceae 8 15 8 11 10 11 9 8 16 10 6 11 10 8 Salix 4 3 4 1 1 2 0 2 0 3 7 6 4 2 Sarcobatus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Shepherdia 0 0 0 0 0 0 1 0 0 0 0 0 0 0 Typha.angustifolia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Typha.latifolia 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Unknown 0 0 0 1 11 5 8 0 2 9 5 12 1 1

130

Age (cal yr BP) 1554 1599 1601 1662 1760 1828 1928 1993 2082 2137 2212 2266 2340 3183 Adjusted core 46 47.9 48 50.5 54.6 57.3 61.3 63.9 68 70.7 75 78.1 82.2 114 depth Core depth 48 64 50 66 69 71 74 76 79 81 87 90 94 125 Abies 49 41 54 34 22 31 24 32 40 62 52 27 14 52 Alnus 1 0 1 1 0 2 1 3 1 2 1 2 4 2 Amaranthaceae 3 0 1 2 2 4 2 6 4 4 6 7 7 4 Ambrosia 0 3 1 0 1 2 0 0 0 1 1 1 1 1 Apiaceae 3 39 4 34 5 2 1 0 0 1 8 2 4 3 Arceuthobium 0 4 0 0 0 1 1 0 0 0 0 0 0 0 Artemisia 1 1 1 3 0 1 1 0 2 1 1 1 0 0 Asteracea 4 19 2 24 10 5 8 6 6 6 5 8 8 10 Betula 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Brassicaceae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Caryophyllaceae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Ceanothus 1 0 0 0 0 0 0 0 0 0 0 1 0 1 Chrysolepis 1 0 3 2 0 1 0 4 1 3 1 0 0 1 Corylus 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Cupressaceae 25 12 38 14 36 25 37 17 17 21 28 28 24 28 Cyperaceae 169 98 104 94 205 260 165 114 218 193 145 66 23 153 Fraxinus 0 0 0 0 0 1 0 0 5 4 2 0 1 0 Indeterminate 20 10 19 8 4 9 6 6 10 14 12 4 13 6 Lamiaceae 0 1 0 0 0 0 0 0 0 0 0 0 0 0 Liliaceae 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Lycopodium 368 142 289 80 148 219 270 132 315 234 363 472 573 210 Pediastrum 0 0 11 0 0 10 12 0 0 0 0 3 1 0 Pinus 221 185 160 196 229 205 237 238 163 170 248 178 137 266 Poaceae 41 37 44 44 46 68 32 47 95 56 48 78 141 19 Polemoniaceae 0 0 0 0 0 0 0 0 0 0 0 0 1 0 Polygonaceae 1 5 5 4 2 1 1 2 0 0 0 3 0 7 Quercus 28 37 36 41 42 70 37 30 37 49 42 34 40 38 Rosaceae 3 12 12 17 8 4 11 11 11 38 18 11 12 7 Salix 4 2 0 1 3 3 1 2 1 4 15 35 4 2 Sarcobatus 0 0 0 0 1 0 0 0 0 0 0 0 0 0 Shepherdia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Typha.angustifolia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Typha.latifolia 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Unknown 2 2 4 1 0 0 0 0 0 0 0 2 6 0

131

Appendix B Raw Pollen Counts for Trout Meadow

Age (cal yr BP) ‐63 4 75 138 195 255 326 376 406 432 457 483 509 Adjusted core depth 0 4 8 12 16 20 25 28.6 31 33.4 35.8 38.2 40.6 Core depth 0 4 8 12 16 20 35 38 40 42 44 46 48 Abies 29 19 16 14 18 11 9 14 9 10 17 15 9 Alnus 0 1 2 0 1 0 0 0 1 0 4 1 0 Amaranthaceae 5 2 0 1 0 0 0 3 3 1 1 2 0 Ambrosia 1 1 0 0 1 0 0 0 1 0 0 0 1 Apiaceae 0 0 0 0 0 2 1 0 0 0 0 2 2 Arceuthobium 0 0 3 3 2 1 4 1 1 1 2 0 0 Artemisia 3 0 1 2 2 0 1 2 0 0 0 0 1 Asteracea 2 4 4 3 0 6 8 20 8 11 5 1 6 Brassicaceae 0 0 0 0 3 0 1 0 1 0 0 0 0 Chrysolepis 0 2 0 1 1 0 0 0 0 0 0 0 0 Corylus 0 0 0 2 0 0 0 0 0 0 0 0 0 Cupressaceae 9 12 14 12 14 10 4 5 10 11 19 10 2 Cyperaceae 45 42 27 19 23 4 7 7 16 5 10 7 9 Fraxinus 0 2 1 0 0 2 0 1 0 0 0 2 0 Fungal.Unknown.1 0 0 0 13 13 0 0 0 0 0 0 0 4 Fungal.Unknown.2 8 2 2 12 3 1 0 0 0 0 0 0 2 Fungal.Unknown.3 4 3 6 3 9 1 0 0 0 1 0 0 3 Gaeumannomyces 39 149 121 54 43 10 0 0 1 0 0 4 30 Indeterminate 13 9 8 10 17 30 22 33 25 23 17 45 28 Liliaceae 0 0 1 0 0 0 0 0 0 0 0 0 0 Lycopodium 299 300 371 580 621 741 412 1074 1051 658 562 171 478 Pediastrum 0 0 2 0 0 0 0 0 0 0 0 1 0 Pinus 270 296 292 250 251 270 271 246 214 265 270 205 255 Poaceae 21 31 33 30 41 17 34 24 57 24 28 10 16 Polemoniaceae 2 2 1 1 0 1 1 0 0 0 2 0 0 Polygonaceae 0 4 0 2 2 1 2 11 1 1 0 2 2 Quercus 24 12 10 24 23 9 32 25 42 13 15 6 32 Rhamnaceae 0 0 0 0 0 0 0 2 1 2 0 0 0 Rosaceae 8 5 5 10 15 13 3 10 8 5 14 3 10 Salix 2 3 2 3 2 0 0 0 0 0 0 0 0 Unknown 0 0 0 0 0 4 2 3 0 2 3 1 3

132

Age (cal yr BP) 536 562 603 644 687 730 776 807 853 906 968 Adjusted core depth 43 45.4 49 52.6 56.3 59.9 63.5 65.9 69.5 73.1 76.7 Core depth 50 52 55 58 61 64 67 69 72 75 78 Abies 6 6 5 10 2 15 13 3 16 6 1 Alnus 0 0 0 0 2 3 0 2 1 7 0 Amaranthaceae 2 3 3 0 0 2 0 0 4 3 3 Ambrosia 0 0 0 0 0 0 0 1 1 0 0 Apiaceae 0 0 0 0 0 0 0 0 2 1 2 Arceuthobium 1 0 0 1 2 1 2 2 3 8 3 Artemisia 0 0 0 0 1 0 1 1 1 1 4 Asteracea 2 1 8 2 3 7 2 8 3 7 6 Brassicaceae 0 0 0 0 0 0 0 0 3 12 7 Chrysolepis 0 0 0 0 0 2 0 3 0 0 0 Corylus 0 0 0 0 0 0 0 0 0 0 0 Cupressaceae 4 0 5 6 14 20 10 12 16 23 20 Cyperaceae 10 4 15 10 14 25 14 15 12 4 6 Fraxinus 2 1 1 0 0 0 0 0 0 0 0 Fungal.Unknown.1 7 0 7 14 2 2 2 1 4 3 3 Fungal.Unknown.2 2 0 0 3 0 1 0 0 0 0 0 Fungal.Unknown.3 0 10 7 9 0 0 0 0 0 1 0 Gaeumannomyces 18 31 48 59 52 3 0 2 19 20 18 Indeterminate 15 8 14 4 14 22 8 13 21 12 26 Liliaceae 1 0 0 0 0 0 1 1 0 0 0 Lycopodium 229 372 473 243 909 736 495 608 452 733 685 Pediastrum 0 0 0 0 0 0 0 0 0 0 0 Pinus 140 178 120 145 111 245 208 176 263 177 142 Poaceae 10 11 16 15 30 33 11 20 34 40 40 Polemoniaceae 0 1 0 0 3 2 0 1 0 1 0 Polygonaceae 2 1 2 3 0 2 0 1 6 0 1 Quercus 13 11 19 8 15 29 10 17 15 14 11 Rhamnaceae 1 1 0 0 0 0 0 0 0 0 1 Rosaceae 11 7 7 5 5 12 4 9 7 7 3 Salix 3 0 0 0 0 2 0 0 1 5 1 Unknown 1 0 0 1 1 1 1 2 0 0 0

133

Age (cal yr BP) 1038 1111 1174 1234 Adjusted core depth 80.3 83.9 87 90 Core depth 81 84 87 90 Abies 3 9 4 2 Alnus 0 1 4 0 Amaranthaceae 2 10 5 3 Ambrosia 0 1 1 2 Apiaceae 0 0 1 0 Arceuthobium 0 1 1 1 Artemisia 0 3 1 2 Asteracea 7 8 14 25 Brassicaceae 14 12 23 33 Chrysolepis 0 0 0 0 Corylus 0 0 0 0 Cupressaceae 8 22 8 6 Cyperaceae 5 5 2 4 Fraxinus 0 0 0 0 Fungal.Unknown.1 3 16 16 1 Fungal.Unknown.2 1 0 8 0 Fungal.Unknown.3 0 0 0 2 Gaeumannomyces 17 11 15 6 Indeterminate 20 23 19 22 Liliaceae 0 0 0 0 Lycopodium 602 623 666 679 Pediastrum 0 0 0 0 Pinus 109 209 74 117 Poaceae 29 42 44 32 Polemoniaceae 0 2 0 0 Polygonaceae 0 1 0 0 Quercus 15 15 13 13 Rhamnaceae 0 0 0 0 Rosaceae 2 8 9 8 Salix 0 2 4 4 Unknown 0 0 0 0

134

Appendix C Charcoal and Loss‐on‐Ignition for Holey Meadow

Age Depth Adjusted Charcoal count Loss‐on‐ignition Core id (cal yr BP) (cm) depth (cm) (250 µm) %OM %TIC HLY‐12‐4 22 6 4 0 83.15 4.27 HLY‐12‐4 43 7 5 1 82.14 3.35 HLY‐12‐4 66 8 6 0 68.84 1.02 HLY‐12‐4 90 9 7 0 70.80 1.84 HLY‐12‐4 113 10 8 0 73.06 1.44 HLY‐12‐4 136 11 9 0 56.49 1.57 HLY‐12‐4 160 12 10 2 40.48 1.19 HLY‐12‐4 190 13 11 1 41.57 1.38 HLY‐12‐4 220 14 12 1 42.93 0.70 HLY‐12‐4 250 15 13 0 43.14 1.42 HLY‐12‐4 278 16 14 0 40.75 1.35 HLY‐12‐4 308 17 15 8 48.30 1.46 HLY‐12‐4 340 18 16 2 49.72 1.67 HLY‐12‐4 372 19 17 7 52.47 1.57 HLY‐12‐4 403 20 18 14 49.17 1.41 HLY‐12‐4 434 21 19 9 48.39 0.85 HLY‐12‐4 466 22 20 16 48.82 1.81 HLY‐12‐4 501 23 21 13 53.34 0.71 HLY‐12‐4 538 24 22 21 51.36 1.28 HLY‐12‐4 574 25 23 11 52.22 0.72 HLY‐12‐4 611 26 24 8 51.60 1.75 HLY‐12‐4 647 27 25 18 57.43 1.25 HLY‐12‐4 680 28 26 108 47.59 1.09 HLY‐12‐4 713 29 27 49 54.43 2.60 HLY‐12‐4 746 30 28 29 48.75 2.05 HLY‐12‐4 779 31 29 19 24.93 4.42 HLY‐12‐4 812 32 30 35 26.07 3.48 HLY‐12‐4 852 33 31 71 17.98 1.85 HLY‐12‐4 893 34 32 78 15.32 2.71 HLY‐12‐4 934 35 33 128 32.05 3.67 HLY‐12‐4 975 36 34 186 58.78 2.79 HLY‐12‐4 1015 37 35 205 49.78 1.97 HLY‐12‐4 1044 38 36 22 43.78 2.20 HLY‐12‐4 1073 39 37 14 41.63 2.78 HLY‐12‐4 1103 40 38 8 43.68 2.23 HLY‐12‐4 1133 41 39 7 51.90 1.27

135

Age Depth Adjusted Charcoal count %OM %TIC Core id (cal yr BP) (cm) depth (cm) (250 µm) HLY‐12‐4 1236 43 41 28 60.29 1.43 HLY‐12‐4 1310 44 42 17 55.94 0.49 HLY‐12‐4 1383 45 43 23 39.98 0.23 HLY‐12‐4 1457 46 44 17 61.68 2.22 HLY‐12‐4 1530 47 45 17 57.42 3.64 HLY‐12‐4 1554 48 46 16 52.75 4.07 HLY‐12‐4 1577 49 47 13 40.75 0.03 HLY‐11‐1 1630 65 49.2 347 44.07 0.90 HLY‐11‐1 1662 66 50.5 266 45.74 0.60 HLY‐11‐1 1696 67 51.9 127 40.10 0.76 HLY‐11‐1 1727 68 53.2 18 34.95 0.95 HLY‐11‐1 1760 69 54.6 22 31.62 1.26 HLY‐11‐1 1793 70 55.9 46 33.31 1.45 HLY‐11‐1 1828 71 57.3 54 33.26 1.56 HLY‐11‐1 1860 72 58.6 66 31.20 2.50 HLY‐11‐1 1893 73 59.9 23 32.25 3.82 HLY‐11‐1 1928 74 61.3 56 35.75 2.73 HLY‐11‐1 1960 75 62.6 36 37.57 3.01 HLY‐11‐1 1993 76 63.9 116 40.52 3.71

136

Appendix D Charcoal and Loss‐on‐Ignition for Trout Meadow

Age Depth Adjusted Core id (cal yr BP) (cm) depth (cm) %Charcoal %OM %TIC TRT‐13‐2 ‐63 0 0 3.01 53.38 1.07 TRT‐13‐2 ‐46 1 1 2.96 51.81 1.61 TRT‐13‐2 ‐30 2 2 2.9 50.53 1.88 TRT‐13‐2 ‐13 3 3 2.77 52.02 1.70 TRT‐13‐2 4 4 4 2.93 49.38 1.62 TRT‐13‐2 20 5 5 2.82 50.37 2.06 TRT‐13‐2 38 6 6 3.27 48.65 1.76 TRT‐13‐2 56 7 7 3.07 51.51 1.94 TRT‐13‐2 75 8 8 2.74 48.52 1.96 TRT‐13‐2 93 9 9 2.43 50.19 2.10 TRT‐13‐2 110 10 10 2.26 54.15 0.38 TRT‐13‐2 124 11 11 2.44 55.18 0.46 TRT‐13‐2 138 12 12 3.02 52.19 0.70 TRT‐13‐2 152 13 13 2.59 48.61 0.66 TRT‐13‐2 166 14 14 3.61 51.92 0.67 TRT‐13‐2 180 15 15 3.15 59.69 0.84 TRT‐13‐2 195 16 16 2.88 44.76 1.12 TRT‐13‐2 211 17 17 2.19 24.90 0.79 TRT‐13‐2 226 18 18 2.43 20.12 0.64 TRT‐13‐2 240 19 19 2.32 13.15 0.79 TRT‐13‐2 255 20 20 2.13 6.77 1.11 TRT‐13‐2 270 21 21 2.13 7.14 1.08 TRT‐13‐2 284 22 22 2.75 8.89 1.04 TRT‐13‐2 298 23 23 2.53 12.28 1.07 TRT‐13‐2 326 35 25 2.73 11.81 0.22 TRT‐13‐2 342 36 26.2 2.13 10.40 0.59 TRT‐13‐2 359 37 27.4 2.19 5.98 0.60 TRT‐13‐2 376 38 28.6 2.54 6.48 1.17 TRT‐13‐2 393 39 29.8 2.41 6.45 0.54 TRT‐13‐2 406 40 31 3.09 4.99 0.44 TRT‐13‐2 419 41 32.2 2.68 8.21 0.53 TRT‐13‐2 432 42 33.4 3.16 6.51 2.70 TRT‐13‐2 444 43 34.6 3.25 7.88 0.68 TRT‐13‐2 457 44 35.8 4.08 10.63 1.30 TRT‐13‐2 470 45 37 4.04 25.87 0.35 TRT‐13‐2 483 46 38.2 4.15 61.15 0.07 TRT‐13‐2 496 47 39.4 3.92 60.84 0.89 TRT‐13‐2 509 48 40.6 3.53 58.92 1.45 137

TRT‐13‐2 523 49 41.8 3.6 56.68 1.09 TRT‐13‐2 536 50 43 3.52 51.58 0.31 TRT‐13‐2 549 51 44.2 4.19 61.45 0.58 TRT‐13‐2 562 52 45.4 4.09 58.01 0.46 TRT‐13‐2 576 53 46.6 2.96 67.33 1.66 TRT‐13‐2 590 54 47.8 3.51 72.38 2.62 TRT‐13‐2 603 55 49 3.1 67.84 2.00 TRT‐13‐2 617 56 50.2 3.23 63.28 3.21 TRT‐13‐2 630 57 51.4 3.34 50.35 2.96 TRT‐13‐2 644 58 52.6 2 58.31 1.89 TRT‐13‐2 658 59 53.8 1.42 44.35 1.71 TRT‐13‐2 687 61 56.3 1.25 5.99 0.70 TRT‐13‐2 701 62 57.5 1.98 6.31 0.68 TRT‐13‐2 716 63 58.7 1.23 5.71 0.55 TRT‐13‐2 730 64 59.9 1.87 7.37 0.80 TRT‐13‐2 745 65 61.1 1.36 6.32 0.94 TRT‐13‐2 760 66 62.3 1.94 6.38 0.78 TRT‐13‐2 776 67 63.5 2.84 7.18 0.55 TRT‐13‐2 791 68 64.7 2.92 12.26 0.79 TRT‐13‐2 807 69 65.9 3.15 10.04 0.72 TRT‐13‐2 822 70 67.1 4.67 70.50 0.00 TRT‐13‐2 837 71 68.3 6.72 15.61 1.39 TRT‐13‐2 853 72 69.5 5.54 39.89 1.12 TRT‐13‐2 870 73 70.7 6.7 49.85 2.87 TRT‐13‐2 888 74 71.9 5.42 62.74 2.13 TRT‐13‐2 906 75 73.1 4.54 56.71 1.66 TRT‐13‐2 924 76 74.3 4.74 64.16 2.42 TRT‐13‐2 944 77 75.5 5.3 67.67 2.25 TRT‐13‐2 968 78 76.7 5.04 67.97 2.16 TRT‐13‐2 991 79 77.9 4.52 67.78 2.02 TRT‐13‐2 1014 80 79.1 4.44 78.51 0.82 TRT‐13‐2 1038 81 80.3 4.97 63.22 2.18 TRT‐13‐2 1062 82 81.5 4.45 58.88 2.08 TRT‐13‐2 1087 83 82.7 4.61 60.23 3.55 TRT‐13‐2 1111 84 83.9 3.51 64.99 2.50 TRT‐13‐2 1134 85 85 4.44 70.77 2.35 TRT‐13‐2 1154 86 86 4.23 74.77 1.59 TRT‐13‐2 1174 87 87 4.51 73.88 2.48 TRT‐13‐2 1194 88 88 3.9 73.65 2.88 TRT‐13‐2 1214 89 89 4.54 73.53 1.36 TRT‐13‐2 1234 90 90 3.5 67.42 0.00

138

Appendix E Alternative Native American‐Set Fires Modeling Scenario

Alternative parameters for LANDIS‐II Harvest module: No WHI o Study area harvested (by scenario) regardless of fire region 100% patch cutting

Harvesting all of the study area equally resulted in statistically negative relationships for all treatments during the MCA (Table E‐1). Overall performance of these modeled scenarios was weak. Statistical significance over the full modeling period is only achieved when 12% of the study area is burned by surface fire (H2‐A3(b) and A4(b)). Using the WHI in the H2 scenarios

(Chapter 3) resulted in the most statistically significant approximations between mVRI and pVRI

(Table E‐1; Figure E‐1).

Table E‐1. Correlation values between mVRI and pVRI for various periods through the simulation. Bolded values show statistically significant correlations.

Scenario H2‐A3(a) H2‐A3(b) H2‐A4(a) H2‐A4(b) 750‐100 cal yr BP 1550‐1000 cal yr BP; 750‐500 cal yr BP 6% area 12% area 6% area 6%; 12% area MCA r/p‐value ‐0.93/0.006 ‐0.93/0.006 ‐0.79/0.06 ‐0.79/0.06 ρ /p‐value ‐0.93/0.008 ‐0.93/0.008 ‐0.93/0.008 ‐0.93/0.008 LIA r/p‐value 0.48/0.09 0.79/0.001 0.15/0.62 0.54/.06 ρ/p‐value 0.32/0.28 0.74/0.004 0.17/0.58 0.39/.18 1050‐100 cal yr BP r/p‐value 0.33/0.16 0.48/0.04 0.27/0.25 0.47/0.04 ρ /p‐value 0.38/0.11 0.58/0.01 0.28/0.25 0.41/0.08

139

Table E‐2. Number of climatic lightning‐caused crown fires and percent study area burned by crown fires per cumulative 50 years (average). Scenarios describe the timing and percent area of applied prescribed burns (Native American‐set fires). Darker cells represent years with prescribed burns.

Scenario H1‐C1 H2‐A3 H2‐A4 Climatic (a) (b) (a) (b) 750‐100 cal yr BP 1550‐1000 cal yr BP; . 750‐500 cal yr BP 6% area 12% area 6% area 6%; 12% area cal yr BP # % # % # % # % # % 1050 10 24 23 23 9 23 11 25 11 25 1000 11 37 9 42 13 42 10 27 10 27 950 10 34 13 16 8 16 9 39 9 39 900 7 22 8 28 8 28 9 32 9 32 850 6 24 8 19 6 19 5 21 5 21 800 9 19 6 30 9 30 9 19 9 19 750 11 28 9 35 10 29 11 35 12 37 700 9 17 10 29 9 19 9 26 9 23 650 6 19 9 14 8 35 8 29 7 23 600 5 18 7 33 6 20 6 15 5 15 550 6 18 7 11 6 21 6 19 7 19 500 6 14 6 17 7 23 6 13 7 13 450 6 14 6 17 6 26 7 17 6 17 400 5 21 6 22 5 12 5 16 6 15 350 5 8 5 18 6 21 5 14 5 10 300 2 10 5 5 2 6 2 9 2 2 250 2 12 2 5 2 1 2 4 2 6 200 2 5 2 7 2 5 2 16 2 9 150 1 4 2 18 2 11 2 12 2 3 100 3 5 2 10 2 2 3 5 2 9 50 2 3 3 10 3 7 3 5 2 13 0 2 4 2 7 3 7 2 11 3 8

140

Figure E‐1. Standardized modeled VRI (mVRI) vs paleo VRI (pVRI) values. Dark grey line represents the standardized mVRI, grey dots mVRI per run, and grey band a 95% confidence interval. Green line represents 50‐year splined pVRI values to which mVRI is compared.

Figure E‐2. Amount of hectares burned by background “natural” climatic crown fires per year. Does not include prescribed burns. Dark grey line represents the average number of annual fires, grey dots modeled averages per run, and grey band a 95% confidence interval. Red line is a 50‐year splined average of fires, dark red line a 50‐year mean average. 141

Figure E‐3. Alternative H2 scenario of climatic crown fires per year. Thick red line represents average number of annual fires, white band a 10x exaggeration, and grey dots each iteration.

Figure E‐4. Alternative H2 scenario boxplots of actual modeled vs paleo VRI values for all iterations. Green dots represents 50‐year splined pVRI values to which mVRI is compared.

142

Figure E‐5. Alternative H2 scenario scatterplots of standardized values for modeled VRI (averaged) vs paleo VRI, symbolized by time periods. Square represents MCA (1050‐750 cal yr BP), “*” the LIA (750‐100 cal yr BP), and “X” modern (100 cal yr BP – A.D. 2011).

143

Appendix F Additional Figures for Chapter 3 Modeling Scenarios

H1 Scenarios (Climatic lightning‐caused fires)

Figure F‐1. H1 scenario boxplots of actual mVRI values vs pVRI (green dots) for climatic crown‐ fire only scenarios (H1‐C1 to C5).

Figure F‐2. H1 scenario boxplots of actual mVRI values vs pVRI (green dots) for climatic crown‐ fire plus 6% surface fire scenarios (H1‐C6 and C7).

144

Figure F‐3. H1 climatic crown fires per year (crown fire‐only scenarios). Thick red line represents the average number of annual fires, white band a 10x exaggeration, and grey dots modeled averages per run.

Figure F‐4. H1 climatic crown fires per year (crown fire‐plus 6% surface fire scenarios). Thick red line represents the average number of annual fires, white band a 10x exaggeration, and grey dots modeled averages per run.

145

Figure F‐5. H2 scenario scatterplots of standardized values for modeled VRI (averaged) vs paleo VRI, symbolized by time periods for crown fire‐only scenarios. Square represents MCA (1050‐ 750 cal yr BP), “*” the LIA (750‐100 cal yr BP), and “X” modern (100 cal yr BP – A.D. 2011).

Figure F‐6. H2 scenario scatterplots of standardized values for modeled VRI (averaged) vs paleo VRI, symbolized by time periods for crown fire‐only plus 6% surface fire scenarios. Square represents MCA (1050‐750 cal yr BP), “*” the LIA (750‐100 cal yr BP), and “X” modern (100 cal yr BP – A.D. 2011).

146

H2 Scenarios (Climatic baseline + Native American‐set fires)

Figure F‐7. H2 scenario boxplots of actual mVRI values vs pVRI (green dots).

Figure F‐8. H2 climatic crown fires per year. Thick red line represents the average number of annual fires, white band a 10x exaggeration, and grey dots modeled averages per run.

147

Figure F‐9. H2 scenario scatterplots of standardized values for modeled VRI (averaged) vs paleo VRI, symbolized by time periods. Square represents MCA (1050‐750 cal yr BP), “*” the LIA (750‐100 cal yr BP), and “X” modern (100 cal yr BP – A.D. 2011).