University of Nevada, Reno a Mechanistic and Landscape Scale

University of Nevada, Reno

A Mechanistic and Landscape Scale Approach Quantifying Habitat Suitability of Cheatgrass (Bromus tectorum) Engineered Habitats for Great Basin Reptiles

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

Gareth D. Blakemore

Dr. Chris Feldman/Thesis Advisor

August, 2018

THE GRADUATE SCHOOL

We recommend that the thesis prepared under our supervision by

GARETH D BLAKEMORE

Entitled

A Mechanistic and Landscape Scale Approach Quantifying Habitat Suitability of Cheatgrass (Bromus tectorum) Engineered Habitats for Great Basin Reptiles

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

MASTER OF SCIENCE

Chris Feldman, Ph.D., Advisor

Elizabeth Leger, Ph.D., Committee Member

Jack Hayes, Ph.D., Committee Member

Peter Weisberg, Ph.D., Graduate School Representative

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

August, 2018

Abstract Invasive species are recognized as key drivers of global change and can have significant impacts on ecosystem functioning and biodiversity. Yet, a detailed understanding of the impacts of invasion on native wildlife is lacking. Furthermore, the driving mechanisms behind those adverse impacts of invasion that have been quantified are rarely investigated. An understanding of such mechanisms is needed to predict wildlife responses to plant invasion and to design targeted ecological restoration practices. In the Great Basin Desert of North America, habitat degradation has led to widespread invasion by a highly successful plant species, cheatgrass

(Bromus tectorum). The result has been the alteration of landscape structure and ecosystem function. This novel habitat has been implicated in biodiversity losses for multiple wildlife taxa in the Great Basin. Yet, an understanding of the mechanisms driving these losses is lacking. Reptiles are an important component of this system and model organisms for elucidating the impacts of desert habitat modification. We established six paired study sites across three heavily invaded landscapes of northwest Nevada to quantify differences between cheatgrass invaded habitat and the adjacent native shrub habitat. All cheatgrass habitats had a depauperate or non-existent reptile community as compared to shrub habitat. We then assessed four possible mechanisms driving these differences in reptile biodiversity: 1) plant community composition, 2) physical habitat structure, 3) arthropod prey community composition and 4) the thermal environment. Cheatgrass habitats had significantly less plant diversity, a homogenized habitat structure and a significantly less diverse arthropod prey base. Lastly, cheatgrass invasion has likely rendered vast expanses of the Great Basin Desert thermally unsuitable for at least one reptile species, Sceloporus occidentalis. We suggest that suitable habitat for Great Basin Desert reptiles is altered, or wholly erased, by cheatgrass invasion. Our mechanistic approach to understanding biodiversity loss in the Great Basin Desert will provide a knowledge base that is urgently needed to help mitigate the rapidly advancing invasion of Bromus tectorum and other detrimental invasives across the globe.

Acknowledgements I would first like to thank my advisor, Dr. Chris Feldman, for the opportunity to acquire a valuable skill set and for his patient and insightful guidance while conducting research towards my thesis. I thank my committee members (Dr. Elizabeth Leger, Dr. Peter Weisberg and Dr. Jack

Hayes) who offered practical research design and other constructive advice both before and during implementation of our project.

I was fortunate to have much help during my 3 seasons of field work. To start, I thank

Robbie Irvin for his dedication to a crucial component of the project (arthropods) and for his positive attitude in harsh field settings. I thank my mom, Sandra Blakemore, my Uncle, Todd

Shewbridge, and colleague, Vicki Thill, for their field help while facing the extremes of the Great

Basin Desert. Robert and Peggy Burton played an instrumental role in accomplishing field goals by providing practical knowledge of the land and logistical support, as well as offering kind hospitality and I am very grateful to them. I thank Dr. Oren Shelef for the cheerful time we had gathering drone imagery at our study sites and to Tom Dilts for his insights into the processing and application of this data. Dr. Chris Gienger readily provided his knowledge of reptile thermal biology, which guided chapter 2, and I am grateful for his time. I thank Jerry Tiehm for his plant identifications, the EvolDoers lab group for their research design and presentation advice and Dr.

Dyer and Dr. Forister for their statistical analysis advice. Lastly, I thank my partner, Dr. Monica

Arienzo, for her help in the field and with life in general. I would not have accomplished my goals without her.

Support for this research was provided by grants from the UNR Department of Biology

(Diana-Hadley Lynch & Kevin D. Freeman Scholarships), the UNR Graduate Student

Association (travel awards), the Joint meeting of Ichthyologists and Herpetologists (travel award) and from the American Society of Ichthyologists and Herpetologists (Helen T. and Frederick M.

Gaige Award).

iii

Table of Contents Abstract ...... i Acknowledgements ...... ii Table of Contents ...... iii List of Tables ...... v List of Figures ...... vi

Chapter 1: A divided landscape? A step towards quantifying the habitat suitability of cheatgrass (Bromus tectorum) engineered landscapes for Great Basin Desert reptiles...... 1 Abstract ...... 1 1. Introduction ...... 2 1.1 Overview ...... 2 1.2 The Great Basin Desert ...... 3 1.3 Great Basin sagebrush habitats ...... 3 1.4 Cheatgrass as a driver of reduced habitat quality ...... 4 1.5 Importance of habitat complexity to Great Basin Desert reptiles ...... 6 1.6 Knowledge gap ...... 7 1.7 Project aims ...... 7 2. Methods ...... 8 2.1 Study area ...... 8 2.1.1 Study site overview ...... 8 2.1.2 Study sites ...... 9 2.1.2a The Lahontan Sagebrush Slopes...... 9 2.1.2b The Upper Lahontan Basin...... 10 2.1.2c The Sierra Nevada-Influenced Semiarid Hills and Basins ...... 10 2.2 Field surveys ...... 11 2.2.1 Reptile community surveys ...... 11 2.2.2 Plant community surveys ...... 14 2.2.3 Arthropod community surveys ...... 16 2.3 Analyses ...... 16 2.3.1 Reptile community data ...... 16 2.3.2 Diversity estimates (plant and arthropod data) ...... 17 2.3.3 Habitat structural characteristics ...... 19 2.3.4 NMDS ordination ...... 19 3. Results ...... 20 3.1 Reptile community ...... 20 3.2 Plant community ...... 20 3.2.1 Richness ...... 20 3.2.2 Evenness ...... 21 3.2.3 Effective diversity ...... 22 3.3 Arthropod community ...... 23 3.4 Habitat structure...... 24 3.5 NMDS ordination ...... 26 4. Discussion ...... 27 4.1 Overview ...... 27 4.2 Reptile richness, abundance and density ...... 27 4.3 Plant and arthropod diversity ...... 28

4.3.1 Plant and arthropod richness ...... 28 4.3.2 Plant and arthropod diversity and evenness ...... 30 4.4 Habitat structural characteristics ...... 31 5. Conclusion...... 34 References ...... 35 Tables ...... 43 Figures ...... 48

Chapter 2: The heat is on. Cheatgrass (Bromus tectorum) engineered habitats are thermally unsuitable for shrub dependent Great Basin reptiles ...... 57 Abstract ...... 57 1. Introduction ...... 58 1.1 Overview ...... 58 1.2 The Great Basin Desert, a case study ...... 58 1.3 Importance of thermoregulation ...... 59 1.4 Thermoregulation in the Great Basin Desert ...... 59 1.5 Invasive plant engineered thermal regimes: a global issue ...... 60 1.6 Study aims ...... 61 2. Methods ...... 61 2.1 Study area ...... 61 2.2 Study species ...... 62 2.3 Measuring habitat thermal quality ...... 63 2.3.1 Operative temperature models ...... 63 2.3.2 Model array and Te measurements ...... 64 2.4 Habitat structure...... 65 3. Analyses ...... 65 3.1 Average thermal quality ...... 66 3.2 Potential for activity and risk of death ...... 67 3.3 Statistical analyses ...... 68 3.4 Habitat structure...... 68 4. Results ...... 68 4.1 Thermal quality...... 68 4.1.1 Within habitat, monthly thermal regimes ...... 69 4.1.2 Contrasts between habitat types, seasonal and monthly thermal regimes ...... 70 4.2 Habitat structure...... 72 5. Discussion ...... 72 5.1 Overview ...... 72 5.1.1 Declines in Great Basin Desert reptile biodiversity ...... 72 5.1.2 Loss of the thermal resource ...... 73 5.2 Altered thermal regimes ...... 74 5.2.1 Reduced ease of thermoregulation (de, de and deMax) ...... 74 5.2.2 Reduction in potential activity (VTactive) ...... 75 5.2.3 Increased risk of thermally induced death (CTmax) ...... 77 References ...... 78 Tables ...... 83 Figures ...... 87 Appendix A ...... 93 Appendix B ...... 95

List of Tables Table 1.1: Distance sampling survey effort, reptile abundance and species richness ...... 43 Table 1.2: Reptile community data ...... 43 Table 1.3: Vegetation composition ...... 44 Table 1.4: Plant diversity indices ...... 45 Table 1.5: Arthropod diversity indices ...... 46 Table 1.6: Arthropod abundance ...... 46 Table 1.7: Habitat structure ...... 47 Table 2.1: Terms and indices ...... 83 Table 2.2: Metrics of habitat structure important for Great Basin Desert reptiles ...... 84 Table 2.3: Magnitude of thermal change...... 84 Table 2.4: Number of hours in which activity is possible per month ...... 85 Table 2.5: Number of hours in which thermally induced death is likely per month ...... 86 Table A1.1: Plant inventory ...... 93 Table A1.2: Detection function models ...... 94 Table A2.1: Thermal quality of cheatgrass and shrub habitats of the Sierra Nevada Influenced ecoregion ...... 95 Table A2.2: Thermal quality of cheatgrass and shrub habitats of the Lahontan Sagebrush Slopes ecoregion ...... 96 Table A2.1: Thermal quality of cheatgrass and shrub habitats of the Upper Lahontan Basin ecoregion ...... 97

List of Figures Figure 1.1: Study region ...... 48 Figure 1.2: Study overview ...... 49 Figure 1.3: Barplots of cumulative reptile abundance and richness ...... 50 Figure 1.4: Plant diversity indices (habitat scale) ...... 51 Figure 1.5: Plant diversity indices (ecoregion scale) ...... 52 Figure 1.6: Arthropod diversity indices (habitat and ecoregion scale) ...... 53 Figure 1.7: Habitat structure (habitat scale) ...... 54 Figure 1.8: Habitat structure (ecoregion scale) ...... 55 Figure 1.9: NMDS ordination ...... 56 Figure 2.1: Project components ...... 87 Figure 2.2: Stacked barplots showing ‘ease’ of thermoregulation for S. occidentalis ...... 88 Figure 2.3: Reproductive season activity plots; barplots and thermal maps ...... 89 Figure 2.4: Hot season activity plots; barplots and thermal maps ...... 90 Figure 2.5: Cool season activity plots; barplots and thermal maps...... 91 Figure 2.6: Barplots showing hours above upper CTmax for S. occidentalis ...... 92

Chapter 1: A divided landscape? A step towards quantifying the habitat suitability of cheatgrass (Bromus tectorum) engineered landscapes for Great Basin Desert reptiles

Abstract Invasive plant species are recognized as key drivers of global change and can have significant impacts on ecosystem functioning and biodiversity. In the Great Basin Desert, habitat degradation has led to widespread invasion by a highly successful plant species, cheatgrass

2) physical habitat structure and 3) arthropod prey community composition. Cheatgrass habitats had significantly less plant diversity and a homogenized habitat structure. The arthropod prey base was likewise less diverse in cheatgrass habitats. We suggest that suitable habitat for Great

Basin Desert reptiles is altered, or wholly erased, by cheatgrass invasion. We echo the call among biologists to further investigate mechanisms by which invasive plants drive reduced habitat suitability for wildlife. Design and implementation of conservation plans requires an understanding of these mechanisms, providing a knowledge base that is urgently needed to help mitigate the rapidly advancing invasion of Bromus tectorum.

1. Introduction 1.1 Overview Invasive plant species are recognized as key drivers of global change and can have significant impacts on ecosystem functioning and biodiversity (Vila et al. 2011). In the Great

Basin, habitat degradation and land use practices have led to widespread invasion by a highly successful and damaging plant species, cheatgrass (Bromus tectorum) (Young and Clements

2009). The result has been the loss of native plant communities and the alteration of landscape structure and ecosystem function (With 2002). The flammable nature of cheatgrass has led to decreased fire return intervals throughout the region (Knick et al. 2005) resulting in the conversion of open and complex heterogeneous shrub habitats to simpler and more homogeneous grasslands (Billings 1994). Cheatgrass is now the dominant species in approximately 10% of the

Great Basin (Balch et al. 2013) and threatens to displace 40% of the regions sagebrush habitat in the next few decades (Suring et al. 2005). Such reductions in habitat heterogeneity have negative implications for biodiversity at multiple spatial scales in western landscapes (Weisberg et al.

2014). Consequently, the altered structure and function of cheatgrass habitats has been implicated in biodiversity reductions for multiple taxa in the Great Basin (Litt and Pearson 2013). This includes small mammals (Larrison and Johnson 1973, Gano and Rickard 1982, Yensen et al.

1992, Brandt and Rickard 1994, Ostoja and Schupp 2009, Hall 2012, Freeman et al. 2014), birds

(Holmes and Miller 2010, Earnst and Holmes 2012, Lockyer et al. 2015), arthropods (Rickard and Haverfield 1965, Rickard and Cline 1974, Rogers et al. 1988, Looney and Zack 2008,

Gardner et al. 2009) and reptiles (Newbold 2005, Hall et al. 2009). Though a pattern of biodiversity loss resulting from cheatgrass invasion is clear, the mechanisms for many species remain unknown. In particular, there is little information on whether and how cheatgrass impacts

Great Basin Desert reptile communities. Here, we examine how the loss of Great Basin sagebrush habitat, due to cheatgrass invasion, may negatively impact habitat complexity (plant composition

3 and structure) and prey base, ultimately resulting in the loss of reptile communities in the Great

Basin Desert.

1.2 The Great Basin Desert The Great Basin Desert is the northernmost and coldest of North Americas deserts. The desert is bound to the west by the Sierra Nevada and Cascade Mountains, and to the east by the

Wasatch Range of central Utah. These ranges block wet and warm air masses originating in the

Pacific Ocean and the Gulf of Mexico (Commission for Environmental Cooperation 1997), respectively, resulting in the most extensive arid region in the United States. Precipitation in this region comes primarily in the form of winter snow or summer convective thunderstorms (Young and Clements 2009). The topography of the region is dominated by a Basin and Range configuration, with alternating mountain ranges and intervening xeric basins and valleys

(DeCourten and Biggar 2017), generally aligned north-south. Valley floors consist of alkaline playas and are bordered by alluvial fans and piedmont slopes that grade gently downwards from the bordering ranges (DeCourten and Biggar 2017). Plant communities grade upwards in elevation from salt-tolerant shrub species (e.g. Atriplex sp., Sarcobatus sp.) to shrub-steppe communities composed of shrubs (e.g. Artemisia sp., Chrysothamnus sp., Purshia sp.), and perennial bunchgrasses (e.g. Poa sp., Elymus sp., Achnatherum sp.).

1.3 Great Basin sagebrush habitats The varied topography and sheer size of the Great Basin result in a diversity of shrub habitat types, each with its own unique community structure and endemic plant and animal species (Ricketts et al. 1999). Sagebrush (Artemisia sp.) habitat is by far the dominant type in the

Great Basin Desert and the focus of our study efforts. This habitat is characteristically dominated by one or two species of Artemisia, with a variety of subdominant shrubs (e.g. Ephedra sp.,

Chrysothamnus sp., Purshia sp., Atriplex sp.). The shrub interspaces are occupied by perennial bunchgrasses (e.g. Elymus sp., Poa secunda) and a variety of annual and perennial forbs (e.g.

Lupinus sp., Balsamorhiza sp., Phlox sp. Erigonum sp.). This general habitat type covers an

4 estimated 29% of the total Great Basin landcover (~8 million hectares), representing one of North

Americas largest expanses of shrub habitat (Rowland et al. 2010).

Despite their expansive range, sagebrush ecosystems are considered one the most imperiled in the United States (Noss et al. 1995), due to several stressors (Connelly et al. 2004,

Wisdom et al. 2005). To start, 40% of North America’s sagebrush ecosystem has been lost since

European settlement began in the 1800’s (Connelly et al. 2004). Currently, major land uses in the region comprise livestock grazing, mining, recreation and military bases (Commission for

Environmental Cooperation 1997). The near ubiquity and cumulative effect of these and other stressors means that only a small fraction of sagebrush habitat is unaltered by anthropogenic disturbance (West 1999), placing at least 20% of species within this system at risk of extirpation

(The Heinz Center 2008). Of new concern is the ability of certain exotics (especially Bromus tectorum) to alter disturbance regimes of invaded systems (D’Antonio and Vitousek 1992) and engineer irreversible ecosystem level changes of structure and function (D’Antonio and Vitousek

1992, Brooks et al. 2004, Balch et al. 2013).

1.4 Cheatgrass as a driver of reduced habitat quality Modified fire regimes due to the introduction of exotic plants have become cosmopolitan, with nearly every continent impacted (D’Antonio and Vitousek 1992, Rossiter et al. 2003, Milton

2004). This phenomenon is especially pronounced in arid regions of the world and is primarily driven by exotic annual grasses (Hobbs and Atkins 1988, D’Antonio and Vitousek 1992, Brooks et al. 2004, Milton 2004). Cheatgrass has established itself as one of North America’s most destructive invasive grasses, largely because extensive degradation of Great Basin sagebrush habitat has permitted it to invade millions of hectares (Chambers et al. 2007). Once established, cheatgrass can substantially alter the natural fire regime of Great Basin sagebrush habitats (Knapp

1996), increasing fire frequencies and quickly establishing as the dominant plant.

Secondary compounds in sagebrush reduce its palatability as livestock forage, thereby focusing grazing pressure onto native bunchgrasses (Young and Sparks 1985). With the competitive balance between shrubs and bunchgrasses relaxed, shrub cover increases substantially, with a subsequent reduction in open habitat (Young and Clements 2009).

Historically, a layer of lichen-moss biocrust was established in shrub interspaces (Chambers et al.

2016). Native grasses have adapted methods for germination through this layer, while germination by Bromus sp. is inhibited by it (Chambers et al. 2016). Livestock trampling of biocrust and removal of the competitive advantage that established perennial grasses have over cheatgrass through overgrazing, releases cheatgrass to colonize the shrub interspaces (Young and

Clements 2009). Once established, cheatgrass weaves a dense thatch of dried vegetation into the reduced open habitat between shrubs, further increasing fuel continuity (Davies and Nafus 2013).

Accumulation of this combustible biomass increases both wildfire potential (Davies and Nafus

2013) and fire intensity, so that most or all plant matter is consumed (Davies et al. 2011).

Cheatgrass is then afforded a competitive advantage over native species given its early ‘green-up’ phenology (Harris 1967), allowing it to quickly recolonize and dominate the burned habitat

(Pellant 1989). The resultant positive feedback cycle between cheatgrass establishment and fire has decreased the natural fire return interval in sagebrush habitats of 50 to 100 + years, to 3-5 years in some areas (Stewart and Hull 1949, Whisenant 1989). These regular and catastrophic cheatgrass fueled fires are destroying habitat complexity at an astounding rate. For example, fires between 1994 and 2001 consumed more than 500,000 ha, or 6.4%, of shrub habitat in the Great

Basin (Wisdom et al. 2005). This phenomenon is nowhere more pronounced than in northwest

Nevada (the focus of our study effort), given several factors.

Northwest Nevada has a long and storied history of human migration and land use

(Young and Sparks 1985), permitting the initial introduction and spread of cheatgrass within the region. The Interstate 80 corridor, which traces the original California Trail and Central Pacific

Railroad line, runs through the region and has acted as a highly efficient vector for invasive plant species (Gelbard and Belnap 2003). The mean distance from Interstate 80 to our study sites was just under 21 miles. Second, the xeric sagebrush habitats of the region have low resilience to disturbance and resistance to invasion (Rodhouse et al. 2014). Lastly, the introduction of large herds of livestock to this fragile system, coupled with ~150 years of intensive grazing practices, have further facilitated invasion throughout the region (Young and Sparks 1985, Chambers et al.

2007, Young and Clements 2009).

1.5 Importance of habitat complexity to Great Basin Desert reptiles In deserts too arid to support trees, the dominant overstory and major source of habitat structural heterogeneity are shrubs. Accordingly, desert species, both plants and animals, have evolved direct and indirect interactions and dependencies on shrubs (Filazzola et al. 2017).

Consequently, reptile abundance and diversity in the Great Basin Desert is strongly linked to shrub cover (Pianka 1986), on which they depend for a variety of functions, including thermoregulation (Bauwens et al. 1996), escape from predation (Germano and Hungerford 1981) and as a resource for prey species (Parmenter and Macmahon 1983, Wiens and Rotenberry 1985,

Greenfield et al. 1989, Yensen et al. 1992). Complex habitat structure, both horizontally (shrub, forb and perennial grass spacing) and vertically (shrub stratification), is essential for maintenance of reptile richness and diversity (Pianka 1966) by providing multiple microhabitats utilized by different species (Germano and Lawhead 1986). Desert reptiles are adapted to and prefer open habitat between shrubs (Baltosser and Best 1990, Steffen and Anderson 2006, Davidson et al.

2008), which facilitates effective movement for foraging and predator avoidance (Newbold 2005,

Rieder et al. 2010). Great Basin reptiles also require a reliable and diverse prey base. Small mammals are a principal prey item for snakes, including the Great Basin rattlesnake (Crotalus lutosus) and gopher snake (Pituophis catinefer) (Yensen et al. 1992, Stebbins 2003), while

7 arthropods are the principal prey item for nearly all lizards and juveniles of some snake species

(Stebbins 2003).

1.6 Knowledge gap Despite the global issue of invasive plant mediated habitat alteration, a detailed understanding of the impacts on native wildlife is lacking (Pysek et al. 2012). Furthermore, the driving mechanisms behind those adverse impacts of invasion that have been quantified (e.g. biodiversity loss) are rarely investigated (DeVore and Maerz 2014, Hacking et al. 2014). Thus, there has been a resounding call among biologists to unravel such mechanisms so as to better implement management actions that counteract biodiversity loss and ecosystem collapse (DeVore and Maerz 2014, Hacking et al. 2014). The mechanisms by which cheatgrass invasion is negatively impacting biodiversity in the Great Basin remains largely speculative at this point, including for reptiles (Wiens 1985, Hall et al. 2009). Authors working with Great Basin wildlife and cheatgrass have proposed many mechanisms, which can generally be placed in two broad groups: 1) cheatgrass invasion changes the structure and diversity of native plant communities, thereby reducing habitat complexity, and altering resource and microhabitat availability (Kelrick et al. 1986, Yensen et al. 1992, Brandt and Rickard 1994, Newbold 2005, Ostoja and Schupp

2009, Freeman et al. 2014); and 2) cheatgrass converts shrubland habitat to thick stands of grass thereby inhibiting movements through and within it, with negative implications for reproduction and foraging (Newbold 2005, Hall et al. 2009, Rieder et al. 2010). The second group has received some attention (Newbold 2005, Hall et al. 2009, Rieder et al. 2010), while the impacts of reduced habitat complexity and altered resource availability have not been addressed.

1.7 Project aims Reptiles are a model group for inferring local and landscape scale implications of cheatgrass invasion due to their relative abundance and ease of studying them (Huey 1991).

Furthermore, given their important role in desert ecosystems at various trophic levels (Pianka

1986, Ayal 2007) (primary and secondary predators), and sensitivity to changes in habitat

8 structure (Heatwole and Taylor 1987, Zeng et al. 2014), any deviation in their abundance or diversity will serve as an indication that appropriate management may be needed. Thus, the aim of this study is to assess how cheatgrass engineered habitat modification has impacted this crucial component of Great Basin Desert sagebrush communities. We aim to accomplish this by: 1) quantifying reptile abundance and richness in relation to B. tectorum invasion and 2) assessing plausible mechanisms by which B. tectorum reduces habitat quality for reptiles at the local scale.

To that end, we established paired study sites throughout northwest Nevada, allowing for comparisons of community composition (reptile, plant and arthropod) between habitat types

(cheatgrass vs. shrub). To start, we conducted reptile community surveys (1) to quantify differences in abundance and richness between habitat types. Next, we conducted plant community surveys (2) to examine differences in plant community composition, diversity and physical structure. Finally, we conducted arthropod community surveys (3) to assess one potential mechanism driving differences in reptile community structure between invaded and shrub habitats, that of an altered prey base.

2. Methods 2.1 Study area 2.1.1 Study site overview We chose six study sites (fig 1) on public lands across northwest Nevada; five on Bureau of Land Management (BLM) and one on US Forest Service (USFS) lands. When choosing study sites, we sought to minimize differences between cheatgrass and shrub sampling locations (e.g. edaphic, vegetative, climatic, disturbance history, etc.) to maximize our ability to isolate the effects of cheatgrass invasion on reptile communities. Thus, we sought out cheatgrass invasion boundaries along historical fire edges to establish paired study sites (fig 2). This paired study design allows us to compare reptile, plant and arthropod communities in cheatgrass-invaded habitat to the adjacent and more “intact” native shrub habitat that serve as reference habitats.

A major objective of this project is to extend our findings to as large a scale as feasible.

To that end, we intentionally chose sites within three different EPA Level IV ecoregions (Bryce et al. 2003) that have physiographic, climatic, soil and disturbance factors that have permitted cheatgrass invasion and persistence (fig 1). These landscapes represent the upper resolution

(extent) of this study and will allow us to better understand how reptile, plant and arthropod communities within these distinct ecological communities are responding to cheatgrass invasion.

We sampled two sites within each of three ecoregions: Lahontan Sagebrush Slopes, Upper

Lahontan Basin and Sierra Nevada-Influenced Semiarid Hills and Basins.

2.1.2 Study sites 2.1.2a The Lahontan Sagebrush Slopes (LSS) This ecoregion occurs on the hills, low mountains, alluvial fans and piedmont slopes of the Lahontan Basin (Pleistocene Lake Lahontan). Slopes are dominated by Wyoming big sagebrush (Artemisia tridentata spp. wyomingensis), with an understory of perennial grasses.

Cheatgrass is prevalent and summer lightning storms are common, maintaining extensive cheatgrass-dominated landscapes.

1. Eden Valley (EV) Eden Valley is located in Humboldt County and sits between the Osgood Range to the east and the Hot Springs Range to the west. The study site is situated on the WNW facing slope of the Osgood Range on BLM lands at an average elevation of 5,200’. The cheatgrass habitat

(41°8’26.04” N, 117°23’8.01” W) is located at the mouth of Goughs Canyon and has an average slope of 5°. The shrub habitat (41°8’35.34” N, 117°23’16.56” W) is 350m to the NW with an average slope of 7°.

2. Grass Valley (GV) Grass Valley is located in Pershing county and lies between the Tobin Range to the east and the East Range to the west. The study site is situated on the west facing slope of the Tobin

Range on BLM lands at an average elevation of 5,000’. Both cheatgrass and shrub habitats are

10 located on the lower reaches of the piedmont slope formed by Pollard and Jim Creeks. The cheatgrass habitat (40°29'44.43" N, 117°34'27.78" W) has an average slope of 1°. The shrub habitat (40°29'43.82" N, 117°33'59.10" W) is 650m to the east with an average slope of 2°.

2.1.2b The Upper Lahontan Basin (ULB) A northerly latitude lends to cooler temperatures than the other two ecoregions, while benefiting from increased rainfall than LSS due to a reduced Sierra Nevada rain shadow effect.

This ecoregion has a similar plant composition to LSS, with an increase in perennial grasses.

Lightning storms maintain a positive cheatgrass-wildfire cycle in large parts of the ecoregion.

1. Buffalo Canyon (BC) Buffalo Canyon is located in Humboldt County and flows out of the western versant of the Santa Rosa Range. The cheatgrass habitat (41°28'27.09" N, 117°46'53.12" W) is situated on the lower reaches of the piedmont slope formed by Buffalo Creek at an average elevation of

4,450’ and a slope of 3°. Due to lack of suitable shrub habitat directly adjacent to the cheatgrass habitat, the shrub habitat (41°28'25.48" N, 117°45'45.55" W) is located 1,200m to the east and upslope at an average elevation of 4,700’ and a slope of 6°. Both habitats are on BLM lands.

2. Paradise Valley (PV) Paradise Valley is located in Humboldt County and lies between the Santa Rosa Range to the west and the Hots Springs range to the East. The study site is situated on the ESE slope of the

Santa Rosas on BLM lands and at an average elevation of 4,650’. Both habitats are located midslope on the piedmont slope formed by Provo Creek. The cheatgrass habitat (41°21'30.25" N,

117°36'31.04" W) has an average slope of 3.5°. The shrub habitat (41°21'40.79" N,

117°36'41.99" W) is 350m to the NNW with an average slope of 3.5°.

2.1.2c The Sierra Nevada-Influenced Semiarid Hills and Basins (SNI) The basins and lower mountain slopes just east of the central Sierras are similar to the above ecoregions, while benefiting from higher precipitation. Major population centers (Reno,

Carson City) and heavy land use lends to increased cheatgrass prevalence and risk of wildfires.

1. Peavine (PE) Peavine Peak is located in Washoe County at the northern margins of the city of Reno.

The study site is situated on the NE slope of Peavine Peak on USFS lands at an average elevation of 5,750’. The cheatgrass habitat (39°35'33.16" N, 119°52'38.50" W) is located on an exposed

ENE trending ridge and has an average slope of 10°. The shrub habitat (39°35'37.02" N,

119°52'36.86" W) is 115m downslope with an average slope of 11°.

2. Red Rock (RR) The Red Rock site is in the Sand Hills which are located in Washoe County and rise up from Bedell Flats, bordered by the Dogskin Mountains to the NE and the Petersen Mountains to the West. The study site is situated on the NE facing slope of the Sand Hills on BLM lands at an average elevation of 5,015’. The cheatgrass habitat (39°52'37.13" N, 119°52'16.55" W) is located on an exposed NE trending ridge and has an average slope of 13°. The shrub habitat

(39°52'28.39" N, 119°52'5.45" W) is 350m to the SE with an average slope of 9°.

2.2 Field surveys 2.2.1 Reptile community surveys As the goal of our survey effort was to quantify reptile communities across highly disparate habitat types, we employed a transect methodology that is robust across species and habitat types (McDiarmid 2012). The use of classical transects as a means of standardizing survey effort and inferring reptile populations has been used for decades when mark-recapture methods are not feasible (McDiarmid 2012). We used a distance sampling method along transects, as modified from Buckland et al. (2001), to quantify reptile abundance, richness and density. Line

Transect Sampling (LTS) uses a detection function, calculated from survey effort and measured distances to objects along a transect, to relax the assumption that all individuals are encountered in the survey area. If certain assumptions are met, LTS can produce unbiased abundance and density estimates (Buckland et al. 2001). The assumptions of distance sampling are that: 1) objects directly on the line are always detected; 2) objects are detected at their initial location,

12 before moving in response to surveyor; and 3) distances are measured accurately. Lastly, because object density varies spatially, it is vital that transects are randomly placed in the study area to infer densities representative of the study area, not just the surveyed lines. We developed a LTS survey protocol for use at our sites with these assumptions in mind.

To minimize any detection bias due to time of day, season or weather we randomized the order in which surveys were conducted in each habitat type with a coin toss. To satisfy the requirement that transects are randomly placed, we selected the start point for the first transect with a randomly generated compass bearing and distance from site center. We then conducted a set of surveys successively between habitat types in grids of systematically spaced transects (fig

2). In each set, transect number and length were equal between habitats. During a survey session, the surveyor shuttled between habitat types to conduct these survey sets. The number of transects surveyed in each set varied in number but was typically from 4 to 6. During each survey session we surveyed a minimum of 25 to 30 transects (done in several sets) to provide a basis for an adequate variance of the encounter rate. We placed transects 10 m apart, which is sufficient to avoid double detection (Steffen and Anderson 2006). To start a transect, a 50m tape was anchored at the start of the transect and drawn out while surveying to quantify survey effort. At the first and last transects of every survey grid we recorded surface temperature (C°, in shade) and wind speed

(mph, 60 sec. average) with a Kestrel 3500 (Nielsen-Kellerman, Boothwyn, PA), cloud cover

(%), and coordinates (Lat-Long), taken with a Garmin Etrex 30 (Garmin International, Inc,

Olathe, KS).

Survey protocol varied slightly between habitat types to maximize detection rates. In shrub habitat, search behavior involved scanning in front of, then to either side of the transect, emphasizing search effort on the centerline (to satisfy the first assumption) and moving slowly to minimize disturbing animals ahead (to satisfy the second assumption). The open nature of shrub habitat allowed the observer to survey to greater distances from the transect than in cheatgrass

13 habitat, often into the space of subsequent transects. As a result, we ‘truncated’ scans to ~ 4 m on either side of the centerline to minimize differences in effort between habitat types and rule out double detection. When a shrub was encountered on the line, we made a reasonable effort to search within for reptiles. Large woody debris encountered on the line was overturned and searched for reptiles. Search behavior in the cheatgrass habitat varied in several ways. The high basal cover made detection beyond ~ 4 m of the centerline a challenge, hence the 4m truncation used in shrub habitat. As with shrub habitat, we emphasized search effort on the centerline yet, unlike shrub habitat, the thick cover of cheatgrass restricted a full and complete view of the ground. To maximize detections, a ‘shuffle and stir’ method was adapted from herpetofauna survey techniques used to dislodge cryptic and wary individuals from stationary positions in grassland and wetland habitats (McDiarmid 2012). While scanning to both sides of the transect, the surveyor shuffled through and stirred the grass cover within 1.5m of the centerline in an attempt to stir up reptiles. Observing individuals moving away from the centerline due to surveyor disturbance violates the second assumption yet was determined as the best means of surveying this habitat based on several merits. First, the issue of detectability inherent in thick grassland habitats necessitated the need to ‘dislodge’ cryptic individuals, allowing the surveyor to utilize the visual and auditory cues of a fleeing reptile. Second, the first assumption (complete detection on centerline) trumps the second assumption in survey constraints and maximizing detections along the centerline was therefore the main goal. And lastly, it was noted that many of the individuals detected in shrub habitat were observed as a result of their movement (or associated sounds) away from the approaching surveyor (~1/3 of observations).

After detecting a reptile, we took a radial distance (r) and sighting angle (θ) from the point at which the observer first detected the reptile. We identified all individuals to species to quantify species richness and abundance. We gathered the following ancillary data after each detection: time, species, location (e.g. shrub dripline, open patch, etc.), behavior (basking, fleeing,

14 etc.) and latitude-longitude. Surveys were diurnal and generally completed during the first 3 hours after sunrise and before sunset. However, survey times varied on occasion to conform to weather conditions suitable for reptile activity. Appropriate survey conditions were dictated by several factors (temperature, wind, cloud cover, precipitation) and survey sessions generally spanned two days due to these constraints. We conducted two survey sessions at each site between May 20th and July 10th, 2017.

In 2018, survey methodology was modified slightly to take advantage of field assistance.

Rather than one surveyor surveying sets of transects and shuttling between habitat types, instead both habitats were surveyed concomitantly with two surveyors. Transects were surveyed along a compass bearing and survey effort was tracked with a GPS, permitting us to survey substantially more habitat. Only sites within the ULB and SNI ecoregions were surveyed in 2018, the LSS ecoregion was not re-surveyed. Surveys were conducted between April 23rd and June 24th, 2018.

2.2.2 Plant community surveys We quantified differences in plant community composition, diversity and physical structure between cheatgrass and shrub habitats. We measured these variables using the line-point intercept (LPI) method. Elzinga et al. (2001) and Bonham (1989) regard this method as the least biased and most objective of vegetative cover measures. Our methodology was modified from that described in Herrick et al. (2005). To measure cover with this method a pin is dropped at regular intervals along a transect and the resulting number of “hits” for each plant species is divided by the total number of points measured. We conducted surveys in both habitat types, at all study sites. We randomly selected the starting point and compass bearing for the first transect and surveyed four additional transects, spaced 10m apart and parallel to the first transect, for a total of 5 transects per habitat type. To start a transect we pulled a 50m tape measure out along the designated random compass bearing and anchored at both ends. The surveyor began at the “0” end of the line and dropped the 1.6 mm diameter pin vertically from a consistent height of 40 cm

15 above the ground at 1-meter intervals, for a total of 50 readings per transect. The pin was dropped freely and not guided to the ground exactly at the meter mark, providing for a degree of randomness (Elzinga et al. 2001). Once the pin was dropped and flush with the ground every plant intercepted by the pin was recorded. Three successive layers were recorded, from top to bottom: 1) top canopy, 2) lower canopy and 3) soil surface. We identified plants to genus and, when possible, to species, using field references by Taylor (1992) and Perryman (2014). The first plant intercepted was recorded under the “top canopy” layer. If no plant was intercepted, the “top canopy” was recorded as “none”, indicating an open canopy at that reading. Each subsequent intercepted plant was recorded under the “lower canopy” layer. Canopy species were recorded only once, even if contacted multiple times. If contacted, herbaceous litter (< ¾ cm in diameter) or woody debris (> ¾ cm in diameter) were also recorded in the “lower canopy” layer. Lastly, if the pin contacted a plant base, that species was recorded for the “soil surface” layer and counted as basal cover. If, however, a plant base was not contacted one of several codes was recorded for the “soil surface” layer as follows: rock, duff, biological crust or bare soil.

Data gathered from LPI surveys not only include plant cover and composition, but also the physical structure of the habitat. These data allow us to characterize differences in habitat structure and complexity that might be important in contributing to habitat suitability for Great

Basin reptiles. For both shrub and cheatgrass sites, LPI surveys provide data on percent canopy cover, percent basal cover, percent cheatgrass cover, percent open habitat, percent shrub cover and percent woody litter cover.

One possible limitation of the LPI method is that it may miss (or underrepresent) species with low cover values (Elzinga et al. 2001). We therefore confirmed LPI estimates of plant richness using ocular estimates from square quadrats (Coulloudon et al. 1999). We randomly placed five 1 x 1m quadrats along each LPI transect for a total of 25 quadrats per habitat type, at

16 each study site. Because LPI and quadrat estimates of plant richness were nearly identical, we simply present data from the LPI method for this index.

2.2.3 Arthropod community surveys We measured the abundance and richness of arthropods to quantify differences in community composition and diversity between habitats. We surveyed sites when arthropods were active, as well as their reptile predators (June 2017), to provide an accurate measure of the prey base available during the active season for reptiles. We used a standard 38 cm diameter

“American-type” sweep net (BioQuip Products, Rancho Dominguez, CA). Within a given study site, we surveyed both cheatgrass and shrub habitats on the same day and immediately following the other to minimize sampling bias. A coin toss was used to determine in which habitat type to start the survey. We conducted sweep net surveys along the LPI transects (as mentioned above) to allow direct comparisons between vegetation parameters and arthropod abundance and richness.

Two individuals were required to complete surveys. The ‘netter’ walked the transect at a constant pace while sweeping the upper vegetation, whether grass or shrub, in an arc ~ 2.5m wide while an observer followed to keep the netter on the proper bearing and within 2 m of transect centerline.

Netter and observer were the same individuals for all surveys to minimize sampling bias. Survey effort was quantified by conducting sweep net surveys for exactly 2 minutes per transect. Once a transect was completed, we placed all arthropods into a 1-gallon freezer bag which was then frozen for transport. We sorted and identified arthropods to order and family in the lab using the field reference by Evans (2007).

2.3 Analyses 2.3.1 Reptile community data We used reptile survey data from 2017 and 2018 to: 1) compare cheatgrass and shrub habitats in terms of reptile abundance and species richness, and 2) to calculate estimates of abundance and density within each ecoregion. To determine if reptile abundance and richness differ across habitat types, we grouped all survey data by habitat type and compared these counts

17 with a t-test. To calculate abundance and density estimates, we used measured distances to detected individuals to fit detection functions which model the probability of detecting an individual, given distance from the surveyor (Buckland et al. 2001, Thomas et al. 2002). This allows for an inference of how many individuals were not detected, thereby producing density and abundance estimates for the study area (Buckland et al. 2001). Detection distances (r) must be perpendicular distances (x) from the line and we converted those distances taken with a detection angle (θ) to perpendicular distances (푥 = 푟 sin 휃). Before modeling densities, truncation of the data should be considered (Buckland et al. 2001, Thomas et al. 2002, Miller et al. 2017).

Outliers provide little information for estimating f(0), the density function at x = 0. Furthermore, fitting a model to account for outliers may require additional adjustment terms, thereby increasing sampling variance of the density estimate. A histogram of the distance data showed no prominent outliers and models were thus formulated on the untruncated at w = 4 m.

By utilizing the R package ‘Distance’ (Miller 2017) and following the guidelines of

Miller et al. (2017), we used x to estimate detection functions using the ‘key plus adjustment terms’ series of models (K + A). We formulated models with a “key” function, to which optional adjustments (cosine, polynomial, Hermite) were used in an attempt to improve model fit. We

x2 x used three key functions: half-normal, exp(− ); hazard-rate, 1 − exp⁡((− )−b) and uniform, 2σ2 σ

1/w; where x represents perpendicular distance from transect, w is truncation distance, σ is the scale parameter and b is the shape parameter. To assess model fit, we compared the best detection functions, as determined by AIC values, visually with Q-Q plots and then with a Cramér-von

Mises test (Miller et al. 2017). The chosen detection function produces an average probability of detection, allowing for corrected counts of individuals and estimates of the of individuals in the area covered by surveys. Lastly, we calculated density estimates in the 3 study landscapes, which are the 3 EPA Level IV ecoregions.

2.3.2 Diversity estimates (plant and arthropod data)

To characterize the biotic differences in habitat types, we estimated several diversity indices from plant and arthropod survey data. For these analyses we treated all LPI and sweep-net transects within the same habitat type as replicates (n = 30 transects/habitat type; 5 transects per site x 6 sites). Diversity measures for plants were calculated at the genus level for three groups

(all, native, exotic), and for arthropods at the family level, using the ‘vegan’ package (v. 2.4-1)

(Oksanen et al. 2018) in RStudio (v. 1.1.383) (R Core Team 2016). We calculated richness (R =

푅 ∑ number of taxa); Shannon diversity index: H⁡= − i=1 piln⁡( pi), where pi equals the proportion of observations at a site belonging to taxa i; and Pielou’s evenness from the diversity index: 퐽 =

퐻⁡ ⁡ . H is not itself a true diversity value and is highly non-linear, making comparisons between ln(푅) habitat types difficult (Jost 2006). Therefore, to understand the ‘magnitude of change’ in diversity

(ΔE) from shrub to cheatgrass habitat we calculated effective diversity (E) from H. Effective diversity, 퐸⁡ = exp(퐻), places H on a linear scale allowing for comparison between habitats. We used E as our index of diversity and calculated ΔE between habitat types by dividing the smaller

E value by the larger.

We compared the diversity indices (R, J, E), for plants and arthropods, at two scales: 1) habitat scale (cheatgrass versus shrub), using a one-way ANOVA to compare the effect of habitat type on the diversity indices; and 2) ecoregion scale, using a two-way ANOVA to compare the influence of the two main effects, habitat type and ecoregion (SNI, LSS, ULB), on diversity indices. Tukeys Honest Significant Difference (HSD) studentized range tests were used to conduct post-hoc comparisons of means. All analyses were conducted in RStudio.

Along with p-values for test results, we provide effect sizes and their 95% confidence intervals (CI) for significant effects. We calculated effect sizes for indices and metrics as the raw mean differences (ΔM), which are in the same units as the response variable. The ‘magnitude of change’ in E (ΔE) is an additional measure of effect size for that index.

2.3.3 Habitat structural characteristics To quantify differences in habitat complexity between cheatgrass and shrub habitats, we recorded structural characteristics during LPI transects that are potentially important to reptiles

(e.g. percent shrub cover, percent basal cover, open habitat, etc.). These metrics of habitat structure provide information on the amount and type of microhabitats available to reptiles and their arthropod prey base. We compared these metrics at the habitat and ecoregion scales, as described above. For these analyses we treated all LPI transects within the same habitat type as replicates (n = 30 transects/habitat type; 5 transects per site x 6 sites). Along with p-values, effect sizes and their 95% confidence intervals are provided for significant effects.

2.3.4 NMDS ordination To visualize similarity of study sites with respect to plant functional group composition, we used non-metric multidimensional scaling (NMDS) (Kruskal 1964). NMDS produces a biplot showing how study sites, differing in habitat type, compare in terms of plant functional group composition. NMDS makes few assumptions about the nature of the data and is thus well suited to measuring ecological community dissimilarities as it can handle non-linear species responses to effectively represent underlying gradients (McCune and Grace 2002). We completed the ordination in RStudio, using the ‘vegan’ package. To quantify community dissimilarities, and then ‘map’ these differences in an ordination, we used the Bray-Curtis dissimilarity metric, due to its strength with ecological datasets (McCune and Grace 2002), within the metaMDS function which follows the ordination technique described by (Minchin 1987). Data were first square root transformed and submitted to Wisconsin double standardization (Legendre and Gallagher 2001), then run iteratively until a global solution of low stress was reached. Axes of the final ordination were centered, scaled to half-change units and rotated to PCA so that the first axis (x-axis) explains the largest variation between habitat types. To provide ecological meaning to the final ordination we used the envfit function to fit vectors of habitat structure metrics and plant diversity

20 indices to the ordination. A vector of reptile presence and abundance data was also fitted to the ordination to visualize correlations with these metrics and indices. We assessed correlation of fitted vectors to site locations in ordination space with permutation tests, producing a squared correlation coefficient (R2) and P-value for each variable (Oksanen et al. 2018). Only those variables near significance (p < 0.10) were displayed on the final ordination.

3. Results 3.1 Reptile community In 2017, we surveyed 70 sets (140 grids) of transects, for a total of 353 fifty-meter transects per habitat type, or 17.65 km of survey effort per habitat type. We observed 27 individuals in shrub habitat and 2 in cheatgrass habitat. In 2018, we surveyed 33.2 km of shrub habitat and 33.9 km of cheatgrass habitat and observed 51 individuals in shrub habitat and 3 in cheatgrass habitat. In total, we surveyed 50.8 km of shrub habitat and observed 78 individuals, comprised of 7 species (fig 3; tables 1 and 2). Reptiles were more scarce in cheatgrass dominated habitats, where 51.6 km of transects produced 5 individuals, of 3 species. Reptile abundance (t9.3

= 3.61, p = 0.005) and richness (t14.3 = 4.61, p < 0.001) were significantly reduced in cheatgrass habitat, when compared to the adjacent shrub habitat (fig 1).

To formulate a detection function, we ran six K + A models on the untruncated data

(appendix A, table 2). The models showed little difference in terms of AIC values. We chose the model with the fewest parameters and lowest AIC value (half-normal key, no adjustments) to estimate density and abundance. A Cramér-von Mises test for this model was non-significant (p =

0.63). The model produced density estimates for each ecoregion (table 1) as follows: the Sierra

Nevada Influenced Hills and Basins (SNI) had a density of 238.0 individuals/km2, the Lahontan

Sagebrush Slopes (LSS) had a density of 354.0 individuals/km2 and the Upper Lahontan Basin

(ULB) had a density of 351.4 individuals/km2.

3.2 Plant community 3.2.1 Richness

We grouped plant genera into 3 categories for comparisons: all plants, native plants and exotic plants. Total plant richness (R) pooled by habitat type was 21 and 26 genera for cheatgrass and shrub habitat types, respectively. At the habitat scale (fig 4; table 4), cheatgrass and shrub habitats were not significantly different in terms of all plant R (F1,58 = 3.7, p = 0.060). Despite both habitats possessing comparable all plant R, we found that community composition differed dramatically between habitat types when we grouped plants based on their status (native vs. exotic). Mean native R in cheatgrass habitat was 50% lower (R = 2.47, SD = 2.15) and significantly less than in shrub habitat (F1,58 = 19.1, p < 0.001, ΔM = 2.46, CI = 1.33 to 3.60). In contrast, mean exotic R in cheatgrass habitat was 54.5% higher (R = 3.3, SD = 1.47) and significantly more than in shrub habitat (F1,58 = 24.8, p < 0.001, ΔM = 1.50, CI = 0.90 to 2.10).

At the ecoregion scale (fig 5; table 4), the effect of habitat type on all plant R differed according to ecoregion and produced a significant interaction effect (F2,54 = 6.8, p = 0.002). This interaction effect was driven by the significantly lower all plant R in cheatgrass habitats, compared to shrub habitats, of the SNI ecoregion (Tukeys HSD, p < 0.05; ΔM = 2.7, CI = 0.9 to

4.51). Native R was significantly lower in cheatgrass habitat (F1,54 = 50.7, p < 0.001, ΔM = 2.47,

CI = 1.77 to 3.16), at an equal magnitude across ecoregions. Lastly, the effect of habitat type on exotic plant R differed according to ecoregion and produced a significant interaction effect (F2,54 =

6.3, p = 0.003). Exotic R was significantly higher in cheatgrass habitats, compared to shrub habitats, of the LSS (Tukeys HSD, p < 0.05; ΔM = 1.8, CI = 0.64 to 3.0) and ULB (Tukeys HSD, p < 0.05; ΔM = 2.3, CI = 1.14 to 3.46) ecoregions.

3.2.2 Evenness At the habitat scale (fig 4; table 4), all plant evenness in cheatgrass habitat was 22.7% lower (J = 0.49, SD = 0.12) and significantly less than in shrub habitat (F1,58 = 15.5, p < 0.001,

ΔM = 0.15, CI = 0.07 to 0.22), indicating dominance by just a few plant species. Yet, we did not find significant differences between habitat types in terms of native J (F1,49 = 2.15, p = 0.149;

16% less in cheatgrass, J = 0.67, SD = 0.39) or exotic J (F1,47 = 0.003, p = 0.954; 0.6% less in cheatgrass, J = 0.59, SD = 0.17). At the ecoregion scale (fig 5; table 4), all plant J was significantly lower in cheatgrass habitat (F1,54 = 16.8, p < 0.001, ΔM = 0.15, CI = 0.07 to 0.22), at an equal magnitude across ecoregions.

3.2.3 Effective diversity At the habitat scale (fig 4; table 4), cheatgrass habitat had 30.1% lower all plant effective diversity (E = 2.95, SD = 0.64) which was significantly less than in shrub habitat (F1,58 = 24.0, p

< 0.001, ΔM = 1.27, CI = 0.75 to 1.80). Native E in cheatgrass habitat was 36.6% lower (E =

2.26, SD = 1.47) and significantly less than in shrub habitat (F1,58 = 10.5, p = 0.002, ΔM = 1.31,

CI = 0.50 to 2.11), while cheatgrass habitat had 71.1% higher exotic E (E = 2.07, SD = 0.76) which was significantly more than in shrub habitat (F1,58 = 13.1, p < 0.001, ΔM = 0.60, CI = 0.27 to 0.93). E values were used to calculate ΔE at each site (table 4). On average, there was 26.3% less all plant E, 38.7% less native E and 22.4% more exotic E in cheatgrass habitat.

At the ecoregion scale (fig 5; table 4), the effect of habitat type on all plant E differed according to ecoregion and produced a significant interaction effect (F2,54 = 5.1, P = 0.010). This interaction effect was driven by the significantly lower all plant E in cheatgrass habitats, compared to shrub habitats, of the SNI ecoregion (Tukeys HSD, p < 0.05; ΔM = 2.28, CI = 1.14 to 3.43). The effect of habitat type on native plant E also differed according to ecoregion and produced a weak but significant interaction effect (F2,54 = 3.2, p = 0.048). This interaction effect was driven by the significantly lower native plant E in cheatgrass habitats, compared to shrub habitats, of the SNI (Tukeys HSD, p < 0.05; ΔM = 1.85, CI = 0.65 to 3.05) and ULB (Tukeys

HSD, p < 0.05; ΔM = 1.59, CI = 0.39 to 2.78) ecoregions, with the concurrent non-significant difference between habitats of the LSS ecoregion (Tukeys HSD, p > 0.05). Exotic E was significantly higher in cheatgrass habitat (F1,54 = 18.7, p < 0.001, ΔM = 0.60, CI = 0.32 to 0.88), at an equal magnitude across ecoregions.

3.3 Arthropod community We collected a total of 8,596 arthropods, representing 10 orders and 50 families (table 6).

Of the total, 7,034 individuals (77.8%) were collected from the cheatgrass habitats while 1,562 individuals (22.2%) were collected from the shrub habitats. Cumulative family R, pooled by habitat type, was 42 and 38 for cheatgrass and shrub habitat types, respectively. At the habitat scale (fig 6; table 5), shrub and cheatgrass habitats did not differ in R of arthropod prey (F1,58 =

1.5, p = 0.221; 11.6% more in cheatgrass, R = 11.53, SD = 4.53). Evenness in cheatgrass habitat was 26.2% lower (J = 0.62, SD = 0.24) and significantly less than in shrub habitat (F1,58 = 23.0, p

< 0.001, ΔM = 0.22, CI = 0.13 to 0.31). Likewise, effective diversity in cheatgrass habitat was

19.7% lower (E = 5.35, SD = 2.89) and significantly less than in shrub habitat (F1,58 = 4.56, p <

0.037, ΔM = 1.32, CI = 0.08 to 2.55). E values were used to calculate ΔE at each site (table 5).

On average, there was a 23.3% decrease in arthropod E in cheatgrass habitat.

At the ecoregion scale (fig 6; table 5), arthropod R did not differ between habitat types

(F1,54 = 3.0, p = 0.091), but did differ significantly between ecoregions (F2,54 = 27.9, p < 0.001). In particular, the mesic and more plant diverse communities (both shrub and cheatgrass) of the SNI ecoregion had significantly higher arthropod R than both the LSS (Tukeys HSD, p < 0.05; ΔM =

5.45, CI = 3.16 to 7.74) and ULB (Tukeys HSD, p < 0.05; ΔM = 6.65, CI = 4.36 to 8.94) ecoregions. The effect of habitat type on arthropod evenness differed according to ecoregion and produced a significant interaction effect (F2,54 = 3.6, p = 0.033). This interaction effect was primarily driven by the significantly lower J in cheatgrass habitats, compared to shrub habitats, of the LSS ecoregion (Tukeys HSD, p < 0.05; ΔM = 0.35, CI = 0.13 to 0.58). J in shrub habitats did not differ significantly across ecoregions (Tukey’s HSD, p > 0.05), as seen in the equitable spread of abundance of orders (table 6). In contrast, J in cheatgrass habitats differed significantly across ecoregions (Tukey’s HSD, p < 0.05; SNI > LSS), driven by the dominance of orthopteran and

24 hemipteran families in the LSS ecoregion (fig 6). E was significantly lower in cheatgrass habitat

(F1,54 = 5.8, p = 0.020, ΔM = 1.32, CI = 0.22 to 2.41), at an equal magnitude across ecoregions.

E in shrub habitats did not differ significantly across ecoregions (Tukey’s HSD, p > 0.05), whereas E in cheatgrass habitats differed significantly across ecoregions (Tukey’s HSD, p < 0.05;

SNI > ULB, LSS).

3.4 Habitat structure At the habitat scale (fig 7; table 7), the vegetation state change from shrub to cheatgrass habitat was evident with the significant reduction in shrub cover (F1,58 = 243.3, p < 0.001, ΔM =

25.0, CI = 22.8 to 28.2; 88.2% less in cheatgrass, M = 3.3, SD = 5.3), significant increase in basal cover (F1,58 = 66.8, p < 0.001, ΔM = 31.6, CI = 23.9 to 39.3; 59.8% more in cheatgrass, M = 52.9,

SD = 19.0) and subsequent reduction of open habitat (F1,58 = 35.9, p < 0.001, ΔM = 20.3, CI =

13.5 to 27.0; 60.7% less in cheatgrass, M = 13.1, SD = 14.6). The substantial increase in basal cover and loss of open habitat in cheatgrass habitat was largely due to the significant increase of cheatgrass cover (F1,58 = 59.3, p < 0.001, ΔM = 42.1, CI = 31.1 to 53.0; 56.3% more in cheatgrass, M = 74.7, SD = 19.7). Above the dense basal layer, there was also a significant increase in canopy cover (F1,58 = 29.0, p < 0.001, ΔM = 20.7, CI = 13.0 to 28.4; 25.1% more in cheatgrass, M = 82.6, SD = 17.0) in cheatgrass habitats. The lack of wood bearing plants (shrubs) in cheatgrass habitat resulted in a significant reduction in the amount of woody litter (F1,58 = 28.6, p < 0.001, ΔM = 3.53, CI = 2.21 to 4.86) by 79.1% (M = 0.9, SD = 1.3).

At the ecoregion scale (fig 8; table 7), percent shrub cover was significantly lower in cheatgrass habitats, compared to shrub habitats, within each ecoregion (Tukey’s HSD, p < 0.05).

However, the effect of habitat type on shrub cover differed according to ecoregion and produced a significant interaction effect (F2,54 = 5.0, p = 0.010). This interaction effect was largely driven by the smaller mean difference in percent shrub cover between habitats of the LSS ecoregion

(ΔM = 18.6) as compared to the larger mean differences in percent shrub cover in the SNI (ΔM =

27.2) and ULB ecoregions (ΔM = 29.2). The effect of habitat type on open habitat differed according to ecoregion and produced a significant interaction effect (F2,54 = 8.0, p < 0.001). This interaction effect was driven by the non-significant (Tukey’s HSD, p > 0.05) difference in percent open habitat between habitats of the SNI ecoregion, due to the large amount of open habitat in the

Red Rock cheatgrass study habitat (44%). Furthermore, there were significant differences in percent open habitat between habitats of the LSS (Tukeys HSD, p < 0.05; ΔM = 33.0, CI = 18.2 to 47.8) and ULB (Tukeys HSD, p < 0.05; ΔM = 22.8, CI = 7.96 to 37.6) ecoregions. Percent basal cover was significantly higher in cheatgrass habitat (F1,54 = 89.9, p < 0.001, ΔM = 31.6, CI =

24.9 to 38.3), at an equal magnitude in each ecoregion. The effect of habitat type on percent cheatgrass cover differed according to ecoregion and produced a significant interaction effect

(F2,54 = 17.9, p < 0.001). This interaction effect was largely driven by the non-significant (Tukey’s

HSD, p > 0.05) difference in percent cheatgrass cover between habitats of the SNI ecoregion, due to low cheatgrass cover in the Red Rock cheatgrass study habitat (37.6%). Furthermore, there were significant differences in percent cheatgrass cover between habitats of the LSS (Tukeys

HSD, p < 0.05; ΔM = 75.6, CI = 54.9 to 96.3) and ULB (Tukeys HSD, p < 0.05; ΔM = 31.4, CI =

10.7 to 52.1) ecoregions. The effect of habitat type on percent canopy cover differed according to ecoregion and produced a significant interaction effect (F2,54 = 9.8, p < 0.001). This interaction effect was largely driven by the non-significant (Tukey’s HSD, p > 0.05) difference in percent canopy cover between habitats of the SNI ecoregion, due to low canopy cover in the Red Rock cheatgrass study habitat (46.8%). Furthermore, there were significant differences in percent cheatgrass cover between habitats of the LSS (Tukeys HSD, p < 0.05; ΔM = 37.4, CI = 20.7 to

54.1) and ULB (Tukeys HSD, p < 0.05; ΔM = 22.6, CI = 5.88 to 39.3) ecoregions. The effect of habitat type on percent woody litter differed according to ecoregion and produced a significant interaction effect (F2,54 = 3.7, p = 0.03). This interaction effect was largely driven by the non-

26 significant (Tukey’s HSD, p > 0.05) difference in percent woody litter between habitats of the

SNI ecoregion.

3.5 NMDS ordination The final NMDS (fig 9) showed a stable 2-dimensional solution (stress = 0.102) with a strong agreement between site dissimilarity and ordination distances (non-metric R2 = 0.99, linear

R2 = 0.94). Of the habitat structure metrics tested, basal cover (R2 = 0.504, p = 0.060) and shrub cover (R2 = 0.822, p = 0.002) were significantly correlated with the ordination (p < 0.10). Of the plant diversity indices tested, all plant R (R2 = 0.561, p = 0.020), exotic R (R2 = 0.789, p = 0.003), native R (R2 = 0.864, p = 0.001), native J (R2 = 0.646, p = 0.010), exotic E (R2 = 0.692, p = 0.005) and native E (R2 = 0.864, p = 0.001) were significantly correlated with the ordination (fig 9).

These vectors provide ecological meaning to the axes by describing the major gradients that define vegetation community. Axis 1 was defined by shrub cover, basal cover, exotic richness and effective diversity, while axis 2 was primarily defined by all plant richness and native effective diversity. The reptile abundance data showed a correlation to the ordination (R2 = 0.405, p = 0.097) and were strongly and positively associated with axis 1.

Study sites showed clear groupings based on habitat type and ecoregion. Shrub habitats of the ULB and LSS ecoregions were clustered and separated along axis 1 (towards increased shrub cover) from their respective cheatgrass habitats. ULB and LSS cheatgrass sites showed a tight cluster, with the Eden Valley site an outlier given its higher shrub cover, higher native plant diversity and reduced exotic plant diversity. Both habitat types of the SNI ecoregion separated from ULB and LSS ecoregions along axis 1, due to increased plant richness and diversity.

Peavine and Red Rock shrub sites clustered and the Peavine cheatgrass site showed strong similarities to this cluster due to its increased shrub cover, while the Red Rock cheatgrass site separated along axis 1 due to its lack of shrub cover.

4. Discussion 4.1 Overview By significantly altering the complexity of shrub habitats across vast swathes of the Great

Basin Desert, cheatgrass invasion is responsible for the loss of biodiversity across various taxa

(Roberts Jr 1991, Wisdom et al. 2005, Litt and Pearson 2013). Whether and how cheatgrass engineered habitats impact native reptiles remains an open question. Here we quantified differences in reptile communities (richness, abundance and density) between paired cheatgrass and reference (“intact”) native-shrub habitats. We then discuss how differences between habitat types, in terms of vegetation and arthropod diversity and habitat structure, may have direct and indirect impacts on local reptile populations. Based on these data, we suggest that suitable habitat for Great Basin Desert reptiles is substantially reduced by cheatgrass engineered habitat modification.

4.2 Reptile richness, abundance and density Over the course of two field seasons (2017 – 2018) we found few reptiles in cheatgrass dominated habitat, where 51.6 km of transects produced only 5 individuals, of 3 species. This novel habitat type displays a roughly 15-fold lower total reptile abundance compared to the adjacent shrub habitat (fig 3). In fact, the only reptiles we detected in cheatgrass (one Gambelia wislizenii and four Pituophis catenifer) (table 2) were found at sites with relatively low cheatgrass cover (37.6%, Red Rock) or where small mammal burrows were in abundance, which permit predator avoidance and thermoregulation opportunities in place of those offered by shrubs

(Filazzola et al. 2017). Regardless, we found so few reptiles in cheatgrass sites that we could not accurately estimate reptile densities using distance sampling analyses, where at least 60 detections are recommended for suitable estimates (Buckland et al. 2001, Thomas et al. 2002).

Overall, we found significantly lower richness and abundance (and likely density) of reptiles in cheatgrass engineered landscapes of the Great Basin. This is supported by similar findings elsewhere in the Great Basin showing a preference by reptiles for native shrub habitat over

28 cheatgrass dominated habitats (Newbold and MacMahon 2004, Hall et al. 2009). Yet an understanding of the mechanisms driving these losses, which are likely many and act synergistically to reduce habitat suitability, is still lacking (Newbold and MacMahon 2004,

Newbold 2005, Hacking et al. 2014). Design and implementation of conservation plans requires an understanding of these local scale mechanisms (Hacking et al. 2014) and we thus assessed several possible mechanisms.

4.3 Plant and arthropod diversity Desert arthropod diversity is facilitated by increasing habitat complexity (Wisdom 1991) and plant diversity (Hunter and Price 1992, Siemann et al. 1998, Knops et al. 1999, Ayal 2007,

Haddad et al. 2009) and is thus highly sensitive to habitat modification (Gardner et al. 2009, Zeng et al. 2014). Indeed, arthropod community structure is substantially altered in cheatgrass dominated habitats, with shifts towards reduced diversity (Rogers et al. 1988, Looney and Zack

2008, Gardner et al. 2009). Such reductions in arthropod diversity can cause considerable upward cascading effects to higher trophic levels (Hunter and Price 1992, Price 2002, Haddad et al.

2009). Therefore, because desert lizards (Pianka 1986) and some snakes (Stebbins 2003) are partially or wholly reliant on arthropod prey, cheatgrass invasion may have indirect impacts on

Great Basin Desert reptile diversity through a bottom-up cascade of reduced prey base diversity

(Hunter and Price 1992, Price 2002, Kagata and Ohgushi 2006). We quantified substantial shifts in both plant and arthropod diversity in cheatgrass habitat and discuss how they are interrelated and their potential impacts on Great Basin Desert reptile communities.

4.3.1 Plant and arthropod richness Arthropod richness (at the family level) did not differ between habitats (fig 6), while overall abundance increased in cheatgrass habitats (table 6). This could partly be a relic of sampling methodology in that sweep net surveys miss part of the arthropod community living in inaccessible shrub microhabitats (Haddad et al. 2009). However, the trend of comparable (or

29 higher) arthropod richness and abundance in cheatgrass dominated landscapes has been documented elsewhere in the Great Basin, using a variety of sampling methodologies (Looney and Zack 2008, Gardner et al. 2009, Ostoja et al. 2009). As arthropod richness is fostered by plant richness (Siemann et al. 1998, Knops et al. 1999), a potential explanation for the comparable richness between habitat types is the comparable all plant richness we likewise documented between habitat types (ΔM = 0.9) (fig 5). This can partly be explained by the fact that reference shrub habitats are heavily degraded and composed of 38.5% exotic plant material (primarily cheatgrass, 31.7%) (table 3), the presence of which suppresses native species (Beyers 2004), while heavy grazing pressure at our sites (Patrick Champa, BLM, personal communication) further reduces native species richness (Reynolds and Trost 1980). Whereas, cheatgrass habitats were composed of a variety of weedy exotic forbs (e.g. Erodium cicutarium, Lepidium perfoliatum, Sisymbrium altissimum), resulting in a surprisingly rich and novel habitat (appendix

A, table 1).

Despite comparable all plant richness at the habitat scale, of note is the homogenization of habitats across landscapes in terms of this index. For example, while the all plant richness of shrub habitats differed across ecoregions, indicative of the diversity of Great Basin shrub communities, the respective cheatgrass habitats did not differ, suggesting a cheatgrass driven homogenization of shrub habitats, across landscapes, to similar habitats of reduced all plant richness (fig 6). As arthropods depend on, and are adapted to, the native plant community for their specific feeding, shelter and oviposition ‘resources’ (Lawton 1983), of more concern is the significant reduction (ΔM = 2.4), and near homogenization across ecoregions, of native plant richness in cheatgrass habitats (fig 6). Yet there is a disconnect between this homogenization of plant richness and the apparently unaltered arthropod richness between habitat types within each ecoregion. It is possible that we are unable to discern the implications of altered plant richness on arthropod richness at the family level. Identifying arthropods beyond family level and applying a

30 different sampling methodology (e.g. pit-fall traps) would help to better understand the impacts of cheatgrass invasion on arthropod richness.

4.3.2 Plant and arthropod diversity and evenness Just as desert lizards differ in their use of microhabitat and thermal resources (Pianka

1966, 1986), so they differ in their choice of prey (Pianka 1986, Vitt 1991). The overall diversity of the arthropod prey base is thus likely of more importance than richness for maintaining a diverse reptile community. We documented nearly one quarter lower arthropod diversity available to reptiles in cheatgrass habitat (ΔE = 23.3%) (table 5). The substantial change in plant composition and reduction of plant diversity is a likely mechanism for this difference in arthropod diversity, with potential indirect implications for reptile diversity. To provide scale: cheatgrass study sites were composed of an average 83.5% exotic plants (two sites were over 96%) (table 3) and experienced a near 40% decline in native plant effective diversity (table 4).

The state change from a community composed of shrubs (30.4%) and perennial grasses

(25.6%) to one dominated by cheatgrass (63.8%) and exotic forbs (17.7%) (table 3) represents a habitat type of reduced plant evenness (ΔM = 0.15) (fig 5). A plant community dominated by one or two species is likely to be coopted by a few generalist and herbivorous arthropod orders

(Siemann et al. 1998, Haddad et al. 2009). Rogers et al. (1988) observed that arthropod diversity was dominated by coleopterans and orthopterans in cheatgrass communities, while sagebrush- bunchgrass communities had higher arthropod evenness. The abundance of cheatgrass plant matter at our cheatgrass sites was likewise coopted by generalist and herbivorous arthropod orders (orthopterans, hemipterans). These two orders collectively accounted for 84.5% of the individuals netted in cheatgrass habitats (versus 61.0% in shrub) (table 6) and are largely responsible for the significant decline of arthropod evenness (ΔM = 0.22) and diversity (ΔM =

23.3%) in cheatgrass habitat (fig 6).

Lastly, plant species diversity is important for facilitating a diverse arthropod community

(Hunter and Price 1992, Siemann et al. 1998, Knops et al. 1999, Haddad et al. 2009). Most herbivorous arthropods have a limited preference for host plants and are thus limited in diversity by the plant community (Bernays and Graham 1988), while the diversity of a given arthropod trophic level is theoretically linked to that of the level below it (Hutchinson 1959, Hunter and

Price 1992). Thus, an increase in plant diversity should promote a higher diversity of herbivorous arthropods, and subsequently higher trophic levels. For example, prey generalists such as

Gambelia wislizenii may not be impacted (or may even benefit) from the pulse in the abundance of orthopterans, whereas prey specialists (e.g. Phrynosoma sp.) would suffer given a loss of their specific prey requirements. Thus, the 26.3% decline in all plant diversity, the homogenization of this metric across landscapes, and the subsequent 23.3% decline in arthropod diversity is one potential mechanism indirectly and negatively impacting Great Basin Desert reptile diversity.

4.4 Habitat structural characteristics Given the limited vertical and horizontal structure of desert shrublands, there is a finite number of ways sagebrush habitat may be partitioned by reptiles, thereby regulating species richness and diversity (Pianka 1966). Pianka (1966) compellingly notes how niche partitioning by desert lizards is accomplished via temporal and spatial subdivisions of the habitat, with the spatial component being the more important for lizard diversity. Thus, cheatgrass driven reductions in these habitat ‘subdivisions’ will have direct consequences (Langellotto and Denno 2004) on the abundance, richness and diversity of Great Basin reptiles at the local scale.

Desert reptiles are adapted to and prefer an open habitat type with scattered shrubs

(Baltosser and Best 1990, Steffen and Anderson 2006, Davidson et al. 2008). This shrub habitat facilitates effective movement for predator avoidance, foraging and thermoregulation (Germano and Hungerford 1981, Pianka 1986, Newbold 2005, Rieder et al. 2010). We quantified several habitat characteristics that represent these components of a ‘structurally suitable’ habitat for

32 reptiles and found them all to be significantly altered by cheatgrass invasion (fig 7 and 8; table 7).

In particular, we documented substantially less shrub cover (ΔM = 25.0%; 28.3 - 3.3%) and open habitat (ΔM = 20.3%; 33.4 - 13.1%), with concurrent significantly higher cheatgrass (ΔM =

42.1%; 32.6 - 74.7%) and basal cover (ΔM = 31.6%; 21.3 - 52.9%) in cheatgrass habitats. Thus, cheatgrass invaded habitats represent a significantly less open and likely less maneuverable habitat type for reptiles, with a near complete loss of shrub cover.

The loss of open habitat, from a reptile’s perspective, is driven by the thick cheatgrass basal cover. This dense habitat is less suitable to terrestrial Great Basin species, especially those with an affinity to open habitat (Newbold 2005). For example, Phrynosoma platyrhinos is an ant specialist and thus frequents open habitats, utilizing the ‘terrestrial open-sun’ microhabitat nearly

93% of the time (Pianka 1986). Thick cheatgrass cover substantially hinders movements of this species, limiting its ability to forage and avoid predators (Newbold 2005). Other terrestrial species are also likely to be impacted by the infilling of shrub habitat. For example, Gambelia wislizenii requires open habitat (Parker and Pianka 1976) with shrub cover (Steffen and Anderson

2006) from which they hunt passing prey, namely lizards and orthopterans (Pianka 1966). This key predator of lizards (Steffen and Anderson 2006) alters reptile community structure throughout its range by limiting competitive interactions between smaller species of lizards

(Parker and Pianka 1976). Their loss on the landscape, due to cheatgrass invasion, may thus result in further cascading effects to reptile diversity (Parker and Pianka 1976).

Given their importance for thermoregulation (see chapter 2), predator avoidance and facilitation of prey items, desert shrubs are arguably the structural habitat characteristic of most importance to Great Basin reptiles. Indeed, reptile abundance was strongly correlated to the

NMDS along the floristic gradient best described by increasing shrub cover (axis 1) (fig 9). The loss of shrub cover is likely to decrease reptile abundance and richness, given the strong correlation between these measures and habitat structural complexity (Pianka 1966, Germano and

Hungerford 1981, Germano and Lawhead 1986). Furthermore, loss of the habitat spatial

‘subdivisions’ provided by shrubs are likely to decrease reptile diversity by limiting the presence of certain functional groups, such as arboreal species. For example, in the Great Basin Desert

Sceloporus occidentalis is generally found in association with shrubs (Pianka 1966) on which it depends for its specific autecology. This species is semi-arboreal and actively forage for prey in shrubs (Burkholder and Tanner 1974, Pianka 1986) or use the vertical structure as a perch to hunt arthropod prey moving on the ground below (Pianka 1986). Furthermore, the diversity of thermal microhabitats provided by shrubs is required by this species to maintain its precise thermoregulation requirements (Pianka 1986). The cheatgrass habitats of the ULB ecoregion (fig

8), given their complete lack of shrubs, are thus not capable of supporting the thermoregulatory and foraging requirements of at least S. occidentalis. Though some shrub structure was present in the Peavine (8.4%) and Eden Valley (10.4%) cheatgrass sites, the absence of S. occidentalis and other arboreal species there (table 2) suggests that other mechanisms are reducing habitat suitability.

The last metric of note is the woody litter microhabitat. The lack of shrubs in cheatgrass habitat explains the significantly lower woody litter cover (ΔM = 3.53) available to reptiles.

Shrub litter provides an important element of habitat structure in the desert, providing cover from predation, crypsis for hunting, thermoregulatory opportunities and facilitates prey species for reptiles (Smith et al. 1996, Ayal 2007, Bateman and Ostoja 2012, McDonald et al. 2012).

In contrast to cheatgrass habitats, shrub habitats differed across ecoregions in various important habitat features such as bare ground, woody debris and shrub cover (fig 8). As reptiles often choose habitat structure characteristics independent of vegetation composition (Singh et al.

2002, Kanowski et al. 2006), this diversity of habitat complexity available to Great Basin reptiles is extremely important for maintaining regional reptile biodiversity. Yet, cheatgrass engineered homogenization of these structurally different shrub habitats has reduced landscape

34 heterogeneity, with subsequent local reductions in reptile diversity across the 3 landscapes we surveyed.

5. Conclusion The hot and arid conditions characteristic of desert regions reduces primary productivity

(Ayal 2007) and habitat complexity, as compared to more mesic habitats. Yet, despite their low productivity and outward simplicity, desert shrub communities support a high diversity of trophic interactions. A 4-trophic level scheme for desert communities (Ayal 2007) can be applied to the

Great Basin Desert. Primary producers in these shrub habitats include shrubs, forbs and perennial bunchgrasses whose seeds, plant material and litter support a diversity of primary consumers

(small mammals, herbivorous arthropods). This base of small bodied prey items supports ectothermic primary predators (small lizards, insectivorous juvenile snakes, predatory arthropods), which in turn support large secondary predators (large lizards, snakes, birds, mammals). This generalized food web illustrates findings by researchers that desert reptiles and arthropods hold various key positions in the food chain as predators and prey items for higher trophic levels (Pianka 1986, Zeng et al. 2014). Yet, these components of the food web are highly sensitive to habitat modification (Pianka 1986, Zeng et al. 2014), making this an unstable system.

Invasive plant driven declines of biodiversity has become a global issue (Jones et al.

1994, 1997). In arid regions, this phenomena is primarily driven by the life history of exotic annual grasses (D'Antonio and Vitousek 1992). We studied one such system in North America and quantified substantial differences between invaded and native habitat. We propose that various aspects of these habitat differences act as mechanisms driving reduced habitat suitability and the subsequent absence of reptiles in invaded landscapes. Lastly, we suggest that these mechanisms may have similar impacts on reptiles in other arid systems and hope that our research will aid in mitigating declines of this crucial component of desert ecosystems.

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Tables Table 1: Distance sampling survey effort, reptile abundance and reptile species richness from spring/summer 2017 and spring 2018 surveys, combined. Data are grouped by habitat type within each ecoregion. Also shown are reptile community density estimates with % coefficient of variance, as calculated from distance sampling data (see methods). Surveys were not conducted in the LSS ecoregion in 2018. Habitat Eco-region Total effort (km) Abundance Species richness Density (per km²) cv (%) Cheatgrass Sierra Nevada Influenced (SNI) 23.0 3 3 NA NA Shrub Sierra Nevada Influenced (SNI) 23.1 38 5 238.0 30.0 Cheatgrass Lahontan Sagebrush Slopes (LSS) 6.2 0 0 NA NA Shrub Lahontan Sagebrush Slopes (LSS) 6.2 9 4 354.0 33.3 Cheatgrass Upper Lahontan Basin (ULB) 22.4 2 1 NA NA Shrub Upper Lahontan Basin (ULB) 21.5 31 6 351.4 17.2 Table 2: Reptile community data. All individuals encountered during spring/summer 2017 and spring 2018 surveys, combined. Data are grouped by habitat type within each ecoregion. Habitat Eco-region CNTI CRVI GAWI PHPL PICA SCGR SCOC Cheatgrass Sierra Nevada Influenced (SNI) -- -- 1 -- 1 -- 1 Shrub Sierra Nevada Influenced (SNI) 2 -- 14 3 -- 6 13 Cheatgrass Lahontan Sagebrush Slopes (LSS) ------Shrub Lahontan Sagebrush Slopes (LSS) 1 1 -- 5 -- 2 -- Cheatgrass Upper Lahontan Basin (ULB) ------2 -- -- Shrub Upper Lahontan Basin (ULB) 11 1 7 6 2 4 -- CNTI: Cnemidophorus tigris tigris, Great Basin whiptail CRVI: Crotalus lutosus, Great Basin Rattlesnake GAWI: Gambelia wislizenii, Long-nosed leopard lizard PHPL: Phrynosoma platyrhinos, Desert horned lizard PICA: Pituophis catenifer, Great Basin gopher snake SCGR: Sceloporus graciosus, Sagebrush lizard SCOC: Sceloporus occidentalis, Western fence lizard -- Not detected

Table 3: Vegetation composition. Values represent percent composition of each functional group. Grasses are represented by 4 functional groups (cheatgrass, native perennial grass, exotic perennial grass, native annual grass); forbs by 3 (native forb, exotic forb, unknown forb); and shrubs by one (shrubs). Percent composition was calculated from line-point intercept data (see methods) by summing the times each functional group was ‘hit’ on a transect, using the ‘all- hit’ average; divided by the total pin drops at that site (5 transects x 50 pin drops/transect = 250 pin drops) and lastly, these cover values of each functional group were divided by the sum of all plant components, giving percent vegetation composition for that given functional group. Native Exotic Native Native Exotic Unknown % % Habitat Eco-region Site Grasses Forbs Shrubs Cheatgrass perennial perennial annual forb forb forb Exotic Native grass grass grass Cheatgrass SNI Peavine (PE) 74.8 18.8 6.4 65.8 9.1 0 0 6.4 10.9 1.5 76.7 23.3 Shrub SNI Peavine (PE) 53 18.9 28.1 28.5 24.4 0 0 13.7 1.9 3.3 30.4 69.6 Cheatgrass SNI Red Rock (RR) 67.3 30.7 2 61.4 4.6 0 1.3 11.8 13.1 5.9 74.5 25.5 Shrub SNI Red Rock (RR) 61.4 3 35.6 58.5 3 0 0 2.1 0.8 0 59.3 40.7 Cheatgrass LSS Eden Valley (EV) 88.5 2.4 9.1 76 9.4 0 3.1 0 2.1 0.3 78 22 Shrub LSS Eden Valley (EV) 51.1 15.2 33.7 2.2 45.5 0 3.4 2.8 11.2 1.1 13.5 86.5 Cheatgrass LSS Grass Valley (GV) 77.1 22.9 0 62.3 2.9 11.9 0 0.3 22.3 0.3 96.5 3.5 Shrub LSS Grass Valley (GV) 68.4 0 31.6 15.5 52.4 0.5 0 0 0 0 16 84 Cheatgrass ULB Buffalo Canyon (BC) 70.7 29.3 0 46.5 0 0.5 23.6 0 28.8 0.5 76 24 Shrub ULB Buffalo Canyon (BC) 65.1 13.5 21.4 46.5 15.7 0 2.8 1.9 11.6 0 58.2 41.8 Cheatgrass ULB Paradise Valley (PV) 71.2 28.8 0 70.6 0.6 0 0 0 28.8 0 99.4 0.6 Shrub ULB Paradise Valley (PV) 51.7 16.1 32.2 38.8 12.8 0 0 0.4 14.9 0.8 53.7 46.3 Cheatgrass habitat mean 74.9 22.2 2.9 63.8 4.4 2.1 4.7 3.1 17.7 1.4 83.5 16.5 Shrub habitat mean 58.5 11.1 30.4 31.7 25.6 0.1 1 3.5 6.7 0.9 38.5 61.5

Table 4: Plant diversity indices. Mean richness (R), evenness (J) and effective diversity (E) indices for all plant, native plant and exotic plant genera groups (± standard deviation); calculated from line-point intercept transects (see methods) completed at each site (n=5/habitat type). Bolded values indicate a significant difference between habitat types at that site (p ≤ 0.05, 1-way ANOVA). Significant two-way ANOVA test results (p ≤ 0.05), with habitat type and ecoregion as the main effects, are noted beside each index as follows: 1 = differs between habitat types, 2 = differs between ecoregions, 3 = interaction effect. For indices that differ between ecoregion, a post-hoc test (Tukey HSD) was used to see which ecoregions differ for that index; Tukey HSD results are noted beside each index as follows: α = SNI vs. LSS, β = ULB vs. LSS and Χ = ULB vs. SNI. The right 3 columns show the effective diversity magnitude of change (ΔE) at each site. ΔE represents the percent change in E resulting from the state change from shrub to cheatgrass habitat (see methods). A negative value indicates a reduction in E for that plant group, whereas a positive value indicates an increase in E for that plant group. An asterisk indicates that ΔE is significant (1-way ANOVA, p value ≤ 0.05) between habitat types at that site. Bottom 2 rows show index means at the habitat scale; bolded values indicate a significant difference between habitat types (1-way ANOVA, p value ≤ 0.05), for that index. Magnitude Richness (R) Evenness (J) Effective Diversity (E) of change (ΔE) Eco- Habitat Site Native¹²ᵅᵡ Exotic¹²³ All¹²³ Native¹²³ Exotic²³ All¹ Native¹²³ Exotic¹²ᵅᵝᵡ All¹²³ Native Exotic All region Cheatgrass SNI PE 5.8 ± 1.92 2.0 ± 0 7.8 ± 1.92 0.85 ± 0.11 0.54 ± 0.20 0.49 ± 0.06 4.50 ± 1.57 1.47 ± 0.21 3.05 ± 0.48 -20.9 14.3 -53.2* Shrub SNI PE 8.0 ± 1.58 1.8 ± 0.45 9.8 ± 1.79 0.83 ± 0.07 0.40 ± 0.13 0.83 ± 0.17 5.69 ± 1.24 1.26 ± 0.18 6.51 ± 1.15 Cheatgrass SNI RR 3.2 ± 0.45 2.0 ± 0 5.2 ± 0.45 0.92 ± 0.04 0.65 ± 0.13 0.50 ± 0.09 2.92 ± 0.39 1.57 ± 0.14 2.90 ± 0.30 -46.2* +31.8* -27.7* Shrub SNI RR 7.2 ± 1.30 1.4 ± 0.55 8.6 ± 1.14 0.85 ± 0.05 0.23 ± 0.004 0.62 ± 0.15 5.43 ± 1.34 1.07 ± 0.10 4.01 ± 0.54 Cheatgrass LSS EV 3.6 ± 0.89 1.8 ± 0.84 5.4 ± 1.14 0.85 ± 0.05 0.24 ± 0.05 0.36 ± 0.14 2.94 ± 0.48 1.13 ± 0.12 2.24 ± 0.61 3.7 -38.3 -42.7* Shrub LSS EV 4.4 ± 1.14 2.0 ± 1.0 6.4 ± 1.52 0.71 ± 0.03 0.90 ± 0.11 0.60 ± 0.16 2.83 ± 0.52 1.83 ± 0.80 3.91 ± 0.82 Cheatgrass LSS GV 0.8 ± 0.84 4.8 ± 0.45 5.6 ± 0.55 0.33 ± 0.58 0.69 ± 0.06 0.55 ± 0.10 1.20 ± 0.45 2.96 ± 0.30 3.24 ± 0.43 -47.4* +62.5* 7.4 Shrub LSS GV 3.0 ± 0.71 1.0 ± 0.71 3.8 ± 1.10 0.76 ± 0.09 0.33 ± 0.46 0.52 ± 0.14 2.28 ± 0.47 1.11 ± 0.25 3.00 ± 0.79 Cheatgrass ULB BC 1.0 ± 0 4.6 ± 0.55 5.6 ± 0.55 NA 0.67 ± 0.09 0.63 ± 0.09 1 2.82 ± 0.46 3.77 ± 0.47 -65.6* +42.2* -5 Shrub ULB BC 4.0 ± 0.71 2.0 ± 0 6.0 ± 0.71 0.78 ± 0.04 0.69 ± 0.16 0.63 ± 0.14 2.91 ± 0.35 1.63 ± 0.18 3.97 ± 0.27 Cheatgrass ULB PV 0.4 ± 0.55 4.6 ± 0.55 5.0 ± 1.0 0 0.60 ± 0.10 0.43 ± 0.08 1 2.47 ± 0.25 2.54 ± 0.24 -55.8* +21.5* -36.5* Shrub ULB PV 3.0 ± 0.71 2.6 ± 0.55 5.6 ± 0.55 0.77 ± 0.13 0.71 ± 0.04 0.63 ± 0.07 2.26 ± 0.35 1.94 ± 0.25 4.00 ± 0.28 Cheatgrass mean 2.5 ± 2.1 3.3 ± 1.5 5.8 ± 1.4 0.67 ± 0.39 0.59 ± 0.17 0.49 ± 0.12 2.26 ± 1.48 2.07 ± 0.76 2.96 ± 0.64 -38.7 22.4 -26.3 Shrub mean 4.9 ± 2.2 1.8 ± 0.8 6.7 ± 2.3 0.78 ± 0.09 0.59 ± 0.26 0.64 ± 0.16 3.57 ± 1.64 1.47 ± 0.49 4.23 ± 1.27

Table 5: Arthropod diversity indices. Mean arthropod family richness (R), evenness (J) and effective diversity (E) indices (± standard deviation); calculated from sweep net transects (see methods) completed at each site (n=5/habitat type). Asterisks indicate a significant difference between habitat types at that site; p- values: (***) < 0.001, (**) < 0.01, (*) ≤ 0.05. Significant two-way ANOVA results (p ≤ 0.05), with habitat type and ecoregion as the main effects, are noted beside each index as follows: 1 = differs between habitat types, 2 = differs between ecoregions, 3 = interaction effect. For indices that differ between ecoregion, a post-hoc test (Tukey HSD) was used to see which ecoregions differ for that index; results are noted beside each index as follows: α = SNI vs. LSS, β = ULB vs. LSS and Χ = ULB vs. SNI. The right column shows effective diversity magnitude of change (ΔE) at each site. ΔE represents the percent change in arthropod E resulting from the state change from shrub to cheatgrass habitat (see methods). A negative value indicates a reduction in E, whereas a positive value indicates an increase in E. An asterisk indicates that ΔE is significant (1-way ANOVA, p value ≤ 0.05) between habitat types at that site. Bottom 2 rows show index means at the habitat scale; asterisks indicate a significant difference between habitat types (as above). Eco- Effective Habitat Site Richness (R)²ᵅᵡ Evenness (E)¹³ ΔE region Diversity (E)¹²ᵅᵡ Cheatgrass SNI Peavine (PE) 18.4 ± 1.95 0.74 ± 0.06 8.20 ± 2.10 -4 Shrub SNI Peavine (PE) 16.4 ± 2.41 0.79 ± 0.02 8.56 ± 0.98 Cheatgrass SNI Red Rock (RR) 13.2 ± 2.17 0.73 ± 0.07 6.62 ± 1.55 -8 Shrub SNI Red Rock (RR) 11.6 ± 2.97 0.82 ± 0.05* 7.22 ± 2.26 Cheatgrass LSS Eden Valley (EV) 12.0 ± 3.54 0.85 ± 0.02 7.77 ± 1.75 16 Shrub LSS Eden Valley (EV) 9.4 ± 1.67 0.83 ± 0.06 6.55 ± 1.53 Cheatgrass LSS Grass Valley (GV) 9.4 ± 1.67* 0.15 ± 0.06 1.41 ± 0.18 -75* Shrub LSS Grass Valley (GV) 7.0 ± 1.22 0.87 ± 0.05*** 5.57 ± 1.24*** Cheatgrass ULB Buffalo Canyon (BC) 11.2 ± 1.30 0.70 ± 0.06 5.56 ± 1.05 -14 Shrub ULB Buffalo Canyon (BC) 9.8 ± 2.17 0.82 ± 0.05** 6.47 ± 1.63 Cheatgrass ULB Paradise Valley (PV) 5.0 ± 1.22 0.53 ± 0.17 2.55 ± 0.97 -55* Shrub ULB Paradise Valley (PV) 7.0 ± 1.73 0.90 ± 0.05** 5.64 ± 1.24** Cheatgrass mean 11.5 ± 4.5 0.62 ± 0.24 5.35 ± 2.89 -23.3 Shrub mean 10.2 ± 3.8 0.84 ± 0.06*** 6.67 ± 1.74* Table 6: Arthropod abundance. Abundance of each arthropod order collected during sweep net surveys (see methods) and their percent composition, grouped by habitat type. Order Count in cheatgrass Count in shrub % in cheatgrass % in shrub Araneae 622 115 84.4 15.6 Coleoptera 125 71 63.8 36.2 Diptera 82 54 60.3 39.7 Hemiptera 5596 752 88.2 11.8 Hymenoptera 229 307 42.7 57.3 Lepidoptera 22 47 31.9 68.1 Mantodea 5 1 83.3 16.7 Neuroptera 0 3 0.0 100.0 Orthoptera 352 201 63.7 36.3 Raphidioptera 1 11 8.3 91.7 Habitat scale 7034 1562 77.8 22.2

Table 7: Habitat structure. Important habitat structure metrics for desert lizards, calculated from line-point intercept transects (see methods) completed at each site (n=5/habitat type). Asterisks indicate a significant difference between habitat types at that site; p-values: (***) < 0.001, (**) < 0.01, (*) ≤ 0.05. Significant two-way ANOVA results (p ≤ 0.05), with habitat type and ecoregion as the main effects, are noted beside each metric as follows: 1 = differs between habitat types, 2 = differs between ecoregions, 3 = interaction effect. For metrics that differ between ecoregion, a post-hoc test (Tukey HSD) was used to see which ecoregions differ for that metric; results are noted beside each index as follows: α = SNI vs. LSS, β = ULB vs. LSS and Χ = ULB vs. SNI. Bottom 2 rows show metric means at the habitat scale; asterisks indicate a significant difference between habitat types (as above), for that metric. 1 2 Canopy cover Basal cover Cheatgrass cover3 Open Shrub Woody Habitat Eco-region Site 4 5 6 (% cheatgrass) (% cheatgrass) 123 Habitat Cover litter 123 12 βΧ 123 13 13 Cheatgrass SNI Peavine (PE) 93.2 (69.1)*** 62.0 (91.6)*** 86.8*** 5.6 8.4 1.2 Shrub SNI Peavine (PE) 69.2 (23.7) 19.6 (34.7) 30.8 28.8*** 30.4*** 1.6 Cheatgrass SNI Red Rock (RR) 46.8 (65.8) 17.6 (65.9) 37.6 44.0* 1.2 2.4 Shrub SNI Red Rock (RR) 66.4 (52.4)** 19.2 (91.7) 55.2** 30.8 33.6*** 4.8 Cheatgrass LSS Eden Valley (EV) 87.6 (80.4)*** 51.6 (88.4)*** 87.2*** 9.2 10.4 0.8 Shrub LSS Eden Valley (EV) 46.8 (2.6) 15.2 (2.6) 1.6 45.6*** 24.0** 6.8** Cheatgrass LSS Grass Valley (GV) 88.8 (59.9)*** 55.2 (89.1)*** 77.2*** 8.4 0 0.4 Shrub LSS Grass Valley (GV) 54.8 (13.1) 17.6 (25.0) 11.6 38.0*** 23.6*** 5.6** Cheatgrass ULB Buffalo Canyon (BC) 87.6 (64.4)* 68.4 (56.1)*** 68.4 6.0 0 0 Shrub ULB Buffalo Canyon (BC) 79.6 (49.2) 38.4 (43.8) 59.2 15.6** 27.2*** 4.0** Cheatgrass ULB Paradise Valley (PV) 91.6 (81.2)*** 62.4 (93.6)*** 91.2*** 5.6 0 0.8 Shrub ULB Paradise Valley (PV) 54.4 (26.5) 17.6 (61.4) 37.6 41.6*** 31.2*** 4.0 Cheatgrass habitat mean 82.6 (70.1)*** 52.9 (80.8)*** 74.7*** 13.1 3.3 0.9 Shrub habitat mean 61.9 (27.9) 21.3 (43.2) 32.6 33.4*** 28.3*** 4.5*** ¹ Percent canopy cover: # of canopy intercepts (percent of canopy cover that is composed of cheatgrass in parentheses). ² Percent of ground occupied by base of plants: # of live/dead plant base intercepts (percent of basal cover that is composed of cheatgrass in parentheses). ³ Percent cheatgrass cover: all-hit average. 4 Percent of habitat for which there is no canopy cover or basal cover, and where the surface layer is composed of either rock, soil, biocrust, duff or litter. 5 Percent shrub cover: all-hit average. 6 Percent of soil surface covered with woody litter: >3/4 cm diameter.

Figures

Figure 1: Study region. Map of northwest Nevada, showing study site locations (green stars). Two sites were located within each of three ecoregions (EPA level IV) that are heavily impacted by cheatgrass invasion. Highway 80, a main thoroughfare for westward human migration, parallels the original California Trail and Central Pacific Railroad. This route has acted as a highly efficient vector for invasive plant species, permitting the introduction, establishment and near ubiquity of cheatgrass in this region.

Figure 2: Study overview; a) aerial view of the Paradise Valley study site, where a grazing allotment fence line and associated dirt track were used as a fire break by wildland firefighters and now represents a prominent invasion boundary. Shrub habitat (with pronounced bare ground) is west of the fence line, with cheatgrass (senescent) habitat to the east. White rectangles represent habitat surveyed for reptiles; each rectangle is a set of distance sampling transects (3 to 10 transects each; see methods); b) ground level view of Paradise Valley (PV) and Eden Valley (EV) sites whose cheatgrass habitats represent the range of cheatgrass dominated habitats in our study: a near cheatgrass monoculture at PV, to presence of pioneer shrub species (e.g. Chrysothamnus sp.) at the EV cheatgrass site; c, d and e) represent the three components of the project: reptile abundance and richness, plant diversity and habitat structure and arthropod prey base diversity; c) a commonly encountered lizard species (Phrynosoma platyrhinos, desert horned lizard); d) a native plant (Calochortus sp.); and e) a mantid (Order Mantodea).

Figure 3: Barplots of cumulative reptile abundance and richness, by habitat type. Abundance (t9.3 = 3.61, p = 0.005) and richness (t14.3 = 4.61, p < 0.001) of Great Basin Desert reptiles are both significantly reduced in the invaded cheatgrass habitat, as compared to native-shrub habitat.

Figure 4: Plant diversity indices (habitat scale). Boxplots of plant richness (R) evenness (J) and effective diversity (E) indices for all plant, native plant and exotic plant genera groups with standard deviation, calculated from line-point intercept transects (see methods) completed at

each site (n=5/habitat type). One-way ANOVA results noted below boxplots as follows: non-significant difference (ns) or significant 51

difference between habitat types; p-values (***) < 0.001, (**) < 0.01, (*) ≤ 0.05.

Figure 5: Plant diversity indices (ecoregion scale). Boxplots of plant richness (R) evenness (J) and effective diversity (E) indices for all plant, native plant and exotic plant genera groups with standard deviation, calculated from line-point intercept transects (see methods) completed at each site (n=5/habitat type). Significant two-way ANOVA results (p ≤ 0.05), with habitat type and ecoregion as the main effects, are noted in grey boxes as follows: 1 = differs between habitat types, 2 = differs between ecoregions, 3 = interaction effect. For indices that differ between ecoregion, a post-hoc test (Tukey HSD) was used to see which ecoregions differ for that index, results are noted as follows: 1) in grey boxes as α = SNI vs. LSS, β = ULB vs. LSS and Χ = ULB vs. SNI; 2) letters

above boxplots indicate how that habitat compares across ecoregions, as indicated by a different letter; and 3) comparisons between habitat types, within 52 each ecoregion, are noted below boxplots, as non-significant (ns) or significant difference; p-values (***) < 0.001, (**) < 0.01, (*) ≤ 0.05.

Figure 6: Arthropod diversity indices (habitat scale, top panel). Boxplots of richness (R) evenness (J) and effective diversity (E) indices with standard deviation, calculated from sweep net transects (see methods) completed at each site (n=5/habitat type). One-way ANOVA results noted below boxplots as follows: non-significant difference (ns) or significant difference between habitat types; p-values (***) < 0.001, (**) < 0.01, (*) ≤ 0.05. Arthropod diversity indices (ecoregion scale, bottom panel). Boxplots of richness (R) evenness (J) and effective diversity (E) indices with standard deviation, calculated from sweep net transects (see methods) completed at each site (n=5/habitat type). Significant two-way ANOVA results (p ≤ 0.05), with habitat type and ecoregion as the main effects, are noted in grey boxes as follows: 1 = differs between habitat types, 2 = differs between ecoregions, 3 = interaction effect. For indices that differ between ecoregion, a post-hoc test (Tukey HSD) was used to see which ecoregions differ for that index, results are noted as follows: 1) in grey boxes as α = SNI vs. LSS, β = ULB vs. LSS and Χ = ULB vs. SNI; 2) letters above boxplots indicate how that habitat compares across ecoregions, as indicated by a different letter; and 3) comparisons between habitat types, within each ecoregion, are noted below boxplots, as non-significant (ns) or significant difference; p-values (***) < 0.001, (**) < 0.01, (*) ≤ 0.05. 53

Figure 7: Habitat structure (habitat scale). Boxplots with standard deviation, calculated from line-point intercept transects (see methods) completed at each site (n=5/habitat type). Percent shrub and cheatgrass cover plots show the percent of ground covered by each (all-hit average); basal cover is the percent of ground occupied by the base of plants; percent canopy cover is the total foliar cover of all plants; open habitat is the percent of habitat for which there is no canopy or basal cover, and where the surface layer is composed of either rock, soil, crust, duff or litter; woody litter is the percent of surface covered with woody litter (>3/4 cm diameter). One-way ANOVA results noted below boxplots as follows: non-significant difference (ns) or significant difference between habitat types; p-values (***) < 0.001, (**) < 0.01, (*) ≤ 0.05.

Figure 8: Habitat structure (ecoregion scale). Boxplots with standard deviation, calculated from line-point intercept transects (see methods) completed at each site (n=5/habitat type). Percent shrub and cheatgrass cover plots show the percent of ground covered by each (all-hit average); basal cover is the percent of ground occupied by the base of plants; percent canopy cover is the total foliar cover of all plants; open habitat is the percent of habitat for which there is no canopy or basal cover, and where the surface layer is composed of either rock, soil, crust, duff or litter; woody litter is the percent of surface covered with woody litter (>3/4 cm diameter). Significant two-way ANOVA results (p ≤ 0.05), with habitat type and ecoregion as the main effects, are noted in grey boxes as follows: 1 = differs between habitat types, 2 = differs between ecoregions, 3 = interaction effect. For metrics that differ between ecoregion, a post- hoc test (Tukey HSD) was used to see which ecoregions differ for that metric, results are noted as follows: 1) in grey boxes as α = SNI vs. LSS, β = ULB vs. LSS and Χ = ULB vs. SNI; 2) letters above boxplots indicate how that habitat compares across ecoregions, as indicated by a different letter; and 3)

comparisons between habitat types, within each ecoregion, are noted below boxplots, as non-significant (ns) or significant difference; p-values (***) < 55

0.001, (**) < 0.01, (*) ≤ 0.05.

BC Buffalo Canyon Habitat structure metrics EV Eden Valley 2 GV Grass Valley Code Variable R P-value PE Peavine A Canopy cover 0.261 0.254 PV Paradise Valley B Open habitat 0.323 0.180 RR Red Rock C Basal cover 0.504 0.060 D Cheatgrass cover 0.377 0.125 E Shrub cover 0.822 0.002

Diversity indices Code Index R2 P-value F Richness (all) 0.561 0.020 G Effective diversity (all) 0.198 0.401 H Evenness (all) 0.145 0.509 I Richness (exotic) 0.789 0.003 J Effective diversity (exotic) 0.692 0.005 K Evenness (exotic) 0.156 0.447 L Richness (native) 0.864 0.001 M Effective diversity (native) 0.864 0.001 N Evenness (native) 0.646 0.010

Figure 9: NMDS ordination. Final ordination solution showing similarity of study sites with respect to plant functional group composition. Axes were centered, scaled to half-change units and rotated to PCA so that axis 1 explains the largest variation between habitat types. Circles represent each plant genera encountered, placed in its respective functional group (see legend). Study sites are represented by a two-letter code and habitat type (S for shrub, B for Bromus). For example, BC-S is the Buffalo Canyon shrub site. The greater the distance between sites, the greater the difference in floristic composition and structure. Axes were correlated with environmental variables (see table) to provide environmental meaning. Vectors represent those environmental

variables that strongly explain (p < 0.10, in bold) the gradients of floristic similarity and show direction of gradient, while vector length indicates degree of 56 2 significance. Reptile abundance data was fitted to the ordination and showed a strong correlation (R = 0.405, p = 0.097), strengthening along axis 1.

Chapter 2: The heat is on. Cheatgrass (Bromus tectorum) engineered habitats are thermally unsuitable for shrub dependent Great Basin reptiles

Abstract The altered structure and function of cheatgrass (Bromus tectorum) engineered habitats have been implicated in biodiversity reductions throughout the Great Basin. Yet, an understanding of the mechanisms driving these losses is still lacking. Design and implementation of conservation plans requires an understanding of these local scale mechanisms. We have previously quantified significant declines in reptile abundance and diversity across cheatgrass dominated landscapes in the northwestern Great Basin Desert (chapter 1). Here, we examine a likely mechanism behind these declines: that the state change from Great Basin sagebrush habitat to cheatgrass dominated landscapes has substantially altered the thermal regimes required by desert lizards. We deployed arrays of operative temperature models across NW Nevada to quantify the thermal regimes of both native-shrub and cheatgrass-invaded habitat types. These data were analyzed using several indices of habitat thermal quality and compared against published values of the thermal tolerances for a common and well-studied lizard species of the region, the western fence lizard (Sceloporus occidentalis). As a whole, cheatgrass habitat is significantly warmer than shrub habitat and represents a landscape in which S. occidentalis is likely to be thermally stressed. In particular, there are fewer optimal thermal patches and a significant reduction in the amount of activity S. occidentalis may achieve in cheatgrass habitat.

The loss of shrub cover in cheatgrass landscapes represents a substantially increased risk of thermally induced death for this species. Our data show that cheatgrass habitat is thermally unsuitable for at least one species (Sceloporus occidentalis) and suggest that the altered thermal regime of cheatgrass engineered habitats is a mechanism driving the declines in Great Basin reptile biodiversity.

1. Introduction 1.1 Overview Despite the global issue of invasive plant mediated habitat alteration, a detailed understanding of the impacts on native wildlife is lacking (Pysek et al. 2012). Furthermore, the driving mechanisms behind those adverse impacts of invasion that have been quantified (e.g. biodiversity loss) are rarely investigated (DeVore and Maerz 2014, Hacking et al. 2014). An understanding of such mechanisms is needed to predict wildlife responses to plant invasion and to design targeted and functionally relevant ecological restoration practices (Valentine et al. 2007,

DeVore and Maerz 2014, Hacking et al. 2014, Carter et al. 2015).

1.2 The Great Basin Desert, a case study In the Great Basin Desert of North America, habitat degradation and land-use have led to widespread invasion by a highly successful plant species, cheatgrass (Bromus tectorum) (Young and Clements 2009). The flammable nature of cheatgrass has decreased fire return intervals in shrub habitats of the region (Knick et al. 2005), converting these heterogeneous habitats to homogeneous grasslands (Billings 1994) across landscapes (see chapter 1). This alteration of landscape structure and ecosystem function (With 2002) has been implicated in biodiversity losses of multiple taxa in the Great Basin (Litt and Pearson 2013). The driving mechanisms behind these taxon wide collapses remain largely speculative at this point (Wiens 1985, Hall et al.

2009). In particular, there is little information on whether and how cheatgrass impacts Great

Basin Desert reptile communities.

Reptiles are a model group for inferring local and landscape scale implications of cheatgrass invasion due to their abundance and ubiquity in Great Basin shrub habitats, the ease of studying them (Huey 1991), as well as their sensitivity to habitat modification (Tracy and

Christian 1986, Heatwole and Taylor 1987, Zeng et al. 2014). We have previously quantified significant declines in reptile abundance and diversity across 3 landscapes in the northwestern

Great Basin Desert (chapter 1). Here, we examine a likely mechanism behind these declines: that

59 the change from Great Basin sagebrush habitat to cheatgrass dominated landscapes has substantially altered the thermal regimes required by desert lizards.

1.3 Importance of thermoregulation Biochemical and physiological reactions are temperature dependent (Bennett 1980,

Prosser 1991), and the body temperature (Tb) of an animal is thus intrinsically linked to the performance of vital physiological processes and functions, such as digestion, reproduction and locomotion (Bennett 1980, Huey 1982, Christian et al. 2016). While the physiology of endotherms allows them to stabilize their Tb across a range of thermal regimes (Bennett 1980), that of ectotherms is subject to the varying temperatures of their chosen environment (Christian et al. 2016). Most terrestrial reptiles must actively thermoregulate to attain an optimal Tb by exploiting the spatial and temporal variation of environmental temperatures available (Cowles and Bogert 1944, Porter et al. 1973). The risks associated with this ‘behavioral thermoregulation’

(e.g. exposure to predation and lethal temperatures), and the range of body temperatures a reptile can tolerate, place constraints on activity (Porter and James 1979, Waldschmidt and Tracy 1983,

Grant and Dunham 1988). The amount of time in which an individual is active translates into its immediate ability to perform such functions as prey acquisition and courtship, which ultimately affects its long-term survival and fitness (Huey 1982, Dunham et al. 1989, Huey 1991). In short, the ability of an ectotherm to attain its preferred Tb, in its selected macrohabitat, has direct and pronounced ecological implications for both the individual and the community (Huey 1982,

Christian et al. 2016).

1.4 Thermoregulation in the Great Basin Desert Most habitats are thermally heterogeneous (Huey 1991), with various microhabitats providing a wide distribution of potential body temperatures to a thermoregulating reptile. In extreme environments such as deserts, where ectotherm activity is constrained to environmental temperatures that avoid lethal extremes (Hertz et al. 1993), the availability of suitable microclimates are of obvious importance to the thermal ecology of reptiles (Tracy and Christian

1986, Huey 1991). In deserts too arid to support trees, the dominant overstory and major source of habitat structural heterogeneity are shrubs. Accordingly, desert species, both plants and animals, have evolved means to exploit shrub-dominated habitats, and may thus be dependent on this important functional group (Pianka 1966, Filazzola et al. 2017). In particular, abundance and diversity of Great Basin Desert reptiles are strongly linked to shrub cover (Pianka 1986), on which reptiles depend for thermoregulation (Bauwens et al. 1996).

The focus of our study efforts are Sagebrush (Artemisia sp.) dominated shrub communities, a vegetation type that encompasses a full 29% of the total Great Basin landcover

(~8 million hectares) (Rowland et al. 2010). Sagebrush, along with other shrub species, provide wildlife with microclimates buffered from temperature extremes (Pianka 1966, Werschkul 1982,

Pianka 1986, Filazzola et al. 2017). For example, Wyoming big sagebrush (Artemisia tridentata spp. wyomingensis) subcanopy soil temperatures (1 – 10cm) are substantially moderated during diurnal extremes relative to sun exposed shrub interspaces, which exhibit higher maxima and lower minima temperatures (Pierson and Wight 1991, Pellant 1996, Davies et al. 2007). Reptiles utilize the thermal gradient between shrubs and the sparsely vegetated shrub-interspaces, characteristic of this habitat type, to maintain a consistent and optimal Tb by shuttling between these microclimates (Cowles and Bogert 1944, Heatwole and Taylor 1987). Thus, despite being a land of thermal extremes, the Great Basin Desert provide reptiles with the distribution of temperatures required to effectively thermoregulate.

1.5 Invasive plant engineered thermal regimes: a global issue Changes in thermal regimes following plant invasion, and the subsequent impacts to reptiles, are increasingly being recognized as a global issue (Leslie and Spotila 2001, Valentine et al. 2007, Bolton and Brooks 2010, Carter et al. 2015, Schreuder and Clusella-Trullas 2016). In these invaded systems, thermoregulatory opportunities and incubation temperatures were negatively impacted as a result of the dense growth pattern of exotic vegetation reducing

61 temperatures and temperature variability. Cheatgrass invasion and loss of shrub cover in the Great

Basin are likely to result in the inverse: a habitat with higher and more variable temperatures

(Pellant 1996) given the loss of thermal refugia. In either case, the ecological result is the same: an exotic plant mediated reduction in the potential for ectotherm activity, which ultimately translates to decreased fitness (Tracy and Christian 1986).

1.6 Study aims We tested the hypothesis that cheatgrass invasion has substantially affected the thermal regimes available to Great Basin Desert reptiles for effective thermoregulation, thus driving the significant declines previously quantified in reptiles across three characteristic landscapes of the

Great Basin Desert (see chapter 1). We deployed arrays of operative temperature models (Te models) at 6 paired study sites (shrub vs cheatgrass) throughout northwest Nevada to study thermal regimes in both habitat types and how they differ in terms of their thermal suitability. We collected operative temperatures data 24 hrs a day throughout the active season (May – October) at a resolution of 15 min. These data were then analyzed using several indices of habitat thermal quality and compared against published values of the thermal tolerances for a common lizard species of the region, the western fence lizard (Sceloporus occidentalis). This allows us to draw biologically relevant inferences of the thermal quality of cheatgrass dominated landscapes and whether reptiles are able to persist in cheatgrass given the altered thermal regime.

2. Methods 2.1 Study area When choosing study sites, we sought out cheatgrass invasion boundaries along historical fire edges to establish paired study sites. This paired design allows us to compare the thermal regime in cheatgrass-dominated sites to the adjacent and more “intact” native shrub sites that serve as a thermal reference.

A major objective of this project is to extend our findings to as large a scale as possible.

To that end, we intentionally chose sites within three different EPA Level IV ecoregions (Bryce

62 et al. 2003) that have physiographic, climatic, soil and disturbance factors that have permitted cheatgrass invasion and persistence. These landscapes represent the upper resolution (extent) of this study and will allow us to better understand how reptile communities within these distinct ecological communities are responding to potentially altered thermal regimes. We sampled two sites within each of three ecoregions, for a total of six sites. The Lahontan Sagebrush Slopes ecoregion (LSS) is represented by the Eden Valley (EV) and Grass Valley (GV) sites and is characterized by an overstory of Wyoming big sagebrush (Artemisia tridentata spp. wyomingensis), with an understory of perennial grasses. The Upper Lahontan Basin ecoregion

(ULB) is represented by the Buffalo Canyon (BC) and Paradise Valley (PV) sites and is comprised of similar vegetation to the LSS ecoregion, with an increase in perennial grasses. The

Sierra Nevada-Influenced Semiarid Hills and Basins ecoregion (SNI) is represented by the

Peavine (PE) and Red Rock (RR) sites and is characterized by plants with higher moisture requirements (e.g. Prunus sp., Purshia sp., etc.) associated with xeric-adapted shrub communities similar to those of the LSS and ULB ecoregions.

2.2 Study species The western fence lizard (Sceloporus occidentalis) is a common and wide-ranging species throughout the western United States (Stebbins 2003). It occupies a variety of open and arid habitat types (Adolph 1990, Stebbins 2003). In the Great Basin Desert, S. occidentalis is generally found in association with shrubs (Pianka 1966) on which it depends. This species is semi-arboreal and actively forages for prey within shrubs (Burkholder and Tanner 1974, Pianka

1986) or uses the vertical structure as a perch to hunt arthropod prey moving on the ground below

(Pianka 1986). S. occidentalis has a relatively narrow thermal performance breadth (Pianka

1986), within which physiological performance is optimized (Huey and Stevenson 1979), and it requires the microclimates provided by shrubs to maintain its precise thermoregulation requirements (Pianka 1986). Thus, cheatgrass mediated loss of shrub cover is likely to negatively

63 impact this species. The thermoregulatory behavior and thermal physiology of this species is well studied (e.g. Brattstrom 1965, McGinnis 1966, 1970, Adolph 1990, Asbury and Adolph 2007) making it an excellent candidate for inferring the mechanistic impacts of cheatgrass invasion on desert reptiles. We used thermal preference and tolerance values for S. occidentalis from the literature (discussed below) as reference points to assess the thermal suitability of shrub and cheatgrass study sites based on data we collected from operative temperature models (see below).

2.3 Measuring habitat thermal quality 2.3.1 Operative temperature models To estimate the distribution of potential body temperatures and the thermal quality of both habitat types, we constructed operative temperature models (Te; Bakken 1992, Christian et al. 2016). Te models integrate the convective, conductive and radiative heat transfer properties between the focal species and their environment to measure the thermal environment at the same spatial scale as the animal (Bakken 1992, Dzialowski 2005). Model dimensions were based on S. occidentalis but will permit inferences of habitat thermal suitability for similar sized lizard species (e.g. Sceloporus graciosus). Specimens of S. occidentalis (n = 50) from the University of

Nevada, Reno, Museum of Natural History (UNRMNH), were measured to attain mean snout-to- vent length (6.8 cm) and mean circumference at the widest point of the body (1.8 cm diameter).

Models were constructed from ¾” (1.9 cm) diameter copper pipe, cut to 5.8 cm in length (6.8 cm total length with endcaps). Models were spray-painted with Krylon (Cleveland, OH, USA) No.

1314 All Purpose Platinum Primer (reflectance 17.1%, total absorptivity 82.9%; Peterson et al.

1993) to approximate the reflectivity of S. occidentalis (Peterson et al. 1993). A temperature logger (Maxim DS1912G iButton®, San Jose, CA, USA) was incorporated into each model.

Three zip ties were first secured to each iButton (1 around the lip, 2 in an ‘X’ pattern across the face). This design held the iButton off the inner wall, to reduce conductive heat exchange, while also holding the iButton secure in the center of the model to measure the average Te value given

64 any internal thermal gradient (Bakken 1992). Models were secured on each end with spray- painted copper endcaps and weatherproofed with silicone caulk.

Given the geographic scale of this project, this simple ‘lizard’ model design was chosen for its ease of construction and low cost, allowing for a large number of replicates (Bakken 1992,

Christian et al. 2016). Though this design only approximates the shape of a lizard (e.g. disregards appendages), numerous studies have successfully applied cylindrical models to estimate Te of lizards (VanBerkum et al. 1986, Adolph 1990, Diaz 1994, Belliure et al. 1996, Diaz 1997,

Schauble and Grigg 1998, Shine and Kearney 2001, Dzialowski 2005, Asbury and Adolph 2007).

Asbury and Adolph (2007) constructed similar sized cylindrical copper models (7.0 cm long, 1.5 cm diameter) to measure Te for S. occidentalis.

2.3.2 Model array and Te measurements

Te models were randomly deployed within sites by choosing a random distance (1 – 50m) and compass bearing to walk from site center. Within shrub sites, Te models were then systematically placed by selecting the largest Artemisia sp. shrub within 5 paces for placement of an array of 4 models. One Te model was placed in each of 4 microhabitats: ‘full shade’ (under shrub), ‘full sun’ (nearest patch of open habitat) and one each at shrub dripline on the NE and SW aspects of the shrub. In this sense, Te models capture the range of available Te in the habitat

(Bakken and Angilletta 2014, Christian et al. 2016). Three arrays were placed in each shrub habitat, for a total of 12 Te models per site. In the cheatgrass habitat of four sites (BC, PV, GV and RR) Te models (n = 6) were randomly placed (as above) within the one ‘microhabitat’ available (full sun), given the lack of shrubs and homogenous grassland structure. Two cheatgrass sites (PE and EV) had sparse shrub cover and Te models were placed in arrays (as with shrub habitat) to assess whether the shrub cover offered potential thermal refugia for lizards. These shrubs were generally pioneer Chrysothamnus sp. or Tetradymia sp., as Artemisia sp. were nearly non-existent. All Te models were placed on the ground, along an East – West axis. In shrub

habitat, full sun Te models were placed on bare soil and full shade Te models rested on leaf litter.

In cheatgrass habitat, loggers were placed as close to ground surface as possible. Given the semi- arboreal nature of S. occidentalis and their ability to behaviorally thermoregulate by shifting orientation to solar radiation (Adolph 1990), we are limited in characterizing the numerous dimensions of their thermal habitat and acknowledge this as a limitation of the study.

Te models recorded at 15 min intervals, over a 24 hr period, and were synched to record simultaneously with all other loggers (to within 15 sec.). We recorded Te from mid-May through mid-October, to capture the thermal regime of the active season for S. occidentalis and other

Great Basin lizard species. Te models were downloaded at 2-3 week intervals and checked for functionality. Small gaps in recorded Te resulted from malfunctioning iButton loggers or logistical constraints.

2.4 Habitat structure To quantify differences in the physical structure between cheatgrass and shrub habitats, we recorded vegetation metrics that are potentially important to reptiles (e.g. percent shrub cover, percent basal cover, open habitat, etc.) using the line-point intercept (LPI) method. Elzinga et al.

(2001) and Bonham (1989) regard this method as the least biased and most objective of vegetative cover measures. Our methodology was modified from that described in Herrick et al.

(2005) and conducted along five 50m transects per habitat type, at each site (n = 30 transects per habitat type). These metrics of habitat structure provide information on the amount and type of microhabitats available to reptiles (e.g. the relative patchiness of sun and shade).

3. Analyses The thermal physiology of S. occidentalis is well studied and we gathered thermal preference and tolerance values from peer-reviewed literature. Only values obtained in controlled laboratory gradients were chosen, given their preference over field gathered values (Hertz et al.

1993). We used metrics that describe both the thermal attributes of the lizard itself, as well as the thermal resources of the habitats that the lizard relies on (summarized in table 1). The preferred

temperature range (Tset, 32 – 36°C; Keeley and McGinnis 2007 and reviewed in Adolph 1990) is the range of Tbs over which physiological processes and functions are maximal (Huey 1982,

Stevenson 1985). The voluntary minimum (VTmin) to voluntary maximum (VTmax) temperature

(30.5 – 38.3°C; McGinnis 1970) is the range of Tbs an ectotherm behaviorally restricts itself to during activity (Cowles and Bogert 1944, Heatwole and Firth 1982). Both the lower (CTmin,

4.0°C; Tsuji 1988) and upper critical thermal tolerances (CTmax, 46.0°C; Larson 1961) are temperatures that lead to immobility and swift death (Cowles and Bogert 1944). These values were collected from S. occidentalis populations across a range of latitudes, elevations and habitats. Yet, this species is evolutionarily conservative in its thermal biology (Adolph 1990), showing consistent thermal preferences and tolerances in both field (Bogert 1949, Brattstrom

1965, Mayhew 1968) and laboratory settings (Wilhoft and Anderson 1960, McGinnis 1966, 1970,

Mueller 1970). We used these values to quantify the thermal properties of both habitat types by calculating several commonly applied indices (Hertz et al. 1993, Christian et al. 2016) from the Te data, at all study sites (Appendix B, tables 1, 2 and 3). All analyses are of the complete 24 hr cycle of Te readings. As our aim is to quantify the extent of thermal change between the

‘reference’ shrub habitat and novel cheatgrass habitat, we calculated and discuss the magnitude of change (effect size) of the indices. These effect sizes are calculated as the raw mean differences and are given in the same unit of the given index (table 2).

3.1 Average thermal quality If we assume that, as a general rule, field-active individuals will behaviorally maintain their Tb within Tset as often as possible (Licht et al. 1966), then the deviation between the distribution of available Tes from Tset (de, Hertz et al. 1993) in a habitat can be used as an index to quantify the thermal quality of that habitat from the perspective of the organism (Hertz et al.

1993). A de of zero represents a thermally ideal habitat, in which Te is always within Tset, thus minimizing the risks associated with behavioral thermoregulation (Huey 1974, 1982). To quantify

the ‘ease’ at which S. occidentalis could maintain Tb within Tset in both habitat types we calculated mean de (d̅e), mean max de ( d̅eMax), as well as the percent of Te readings within (de=Tset), above (de>Tset) and below (de

(T̅e) and the mean maximum Te (T̅eMax) and mean minimum Te (T̅eMin) for comparisons of overall thermal regimes. We calculated the latter two indices by taking the maximum (or minimum) Te available in the habitat at each 15 min timestamp and averaged these readings over the month.

3.2 Potential for activity and risk of death As discussed by Grant and Dunham (1988), it may be more appropriate to view the thermal quality of extreme environments in terms of the constraints they place on ectotherm activity. The last two indices we calculated use S. occidentalis thermal tolerances to quantify the potential for activity and risk of thermally induced death in each habitat type.

If we assume that a lizard is capable of behaviorally accessing a diversity of microclimates required for thermoregulation and avoidance of extreme temperatures, we can quantify a biologically relevant index (Heatwole and Firth 1982, Tracy and Christian 1986, Grant and Dunham 1988, Van Damme et al. 1989) of the ‘potential hours of activity’ a lizard can be active in each habitat type. The VTmin to VTmax range represents the range of Tbs an ectotherm behaviorally operates within and restricts itself to during activity (Cowles and Bogert 1944). For ease of discussion, we define this range of the ‘potential for activity’ as VTactive. We calculated

VTactive as the number of hours per month in which activity is possible, by assuming a lizard could be active during a given 15 min timestamp when the minimum (TeMin) and upper (TeMax) Tes for that timestamp encompassed the VT range (TeMin <= 38.3°C, TeMax >= 30.5°C). Bracketing the

VTactive range are the critical thermal tolerances at which an exposed animal would quickly perish

(CTmin and CTmax; Cowles and Bogert 1944). We calculated the number of hours per month in

which death would be likely, by summing the timestamps when TeMin crossed the upper threshold

(TeMin > = 46.0°C).

3.3 Statistical analyses Monthly means of all thermoregulatory indices were grouped by treatment (habitat type; n = 6 sites) and compared for significant differences (p < 0.05) across habitat type (shrub vs. cheatgrass) using a two-way repeated measures ANOVA, and across months (p < 0.05) using a post-hoc Tukeys Honest Significant Difference test. Both tests used functions from the ‘afex’ package (v. 0.21-2) (Singmann et al. 2018). The Red Rock shrub site had 1 logger continuously fail throughout the season and analyses were thus based on n = 5 Te models. The two cheatgrass sites (EV, PE) with Te models in the full shade microhabitat were analyzed at n = 3 (full sun microhabitat) when grouped with the other cheatgrass sites; indices that include readings from the shrub microhabitat are shown in tables 4 and 5. All statistical analyses were conducted in RStudio

(v. 1.1.383) (R Core Team 2016).

3.4 Habitat structure Habitat structure metrics were compared at the habitat scale (shrub vs cheatgrass), using a one-way ANOVA. For these analyses we treated all LPI transects within the same habitat type as replicates (n = 30 transects/habitat type; 5 transects per site x 6 sites). We calculated effect sizes as the raw mean difference in percent cover (ΔM) for each metric (see chapter 1).

4. Results 4.1 Thermal quality

We measured a total of 1,012,862 usable Te readings from 71 S. occidentalis Te models over the active season (mid-May to mid-October). For models in shrub habitat, only the full sun and full shade microhabitats are used for analyses within this chapter (n = 6). The two additional microhabitats (shrub dripline NE and SW) were measured to gather a higher resolution of Te data in shrub habitats for use in developing a thermal model with 3D drone imagery (future work).

With the large amount of data, at a high temporal resolution (15 min) and across 6 paired sites,

69 we found it useful to use a more coarse temporal (month) and spatial resolution (habitat type; shrub vs cheatgrass) to view the overall thermal quality of each habitat type. Even at this resolution, the differences between the thermal environments of each habitat type were substantial.

4.1.1 Within habitat, monthly thermal regimes

In shrub habitat, T̅e, T̅eMin and T̅eMax increased significantly each month from May to July

(Tukey’s, p < 0.05), while starting in July to August these measures declined through the end of the season (Oct). The declines of T̅e and T̅eMin from Jul to Aug were not significant (Tukey’s, p >

0.05), while T̅eMax declined significantly during this time (Tukey’s, p < 0.05). In cheatgrass habitat, T̅e, T̅eMin and T̅eMax likewise rose significantly from May to July (Tukey’s, p < 0.05), while differing from shrub habitat in that all declined significantly from July through end of season (Tukey’s, p < 0.05).

In shrub habitat, d̅e declined from May to Aug (Jul to Aug: Tukey’s, p > 0.05), before increasing significantly through end of season (Tukey’s, p < 0.05). In cheatgrass habitat, d̅e did not begin to decline significantly until Jul to Aug (Tukey’s, p < 0.05), after which it rose significantly each month through end of season (Tukey’s, p < 0.05). In shrub habitat, d̅eMax declined from May to Aug (Jun to Jul: Tukey’s, p > 0.05), then increased significantly through the season (Tukey’s, p < 0.05). In cheatgrass habitat, d̅eMax did not begin to decline significantly until Jul to Aug, after which it rose through the season (Aug to Sep: Tukey’s, p > 0.05).

In shrub habitat, de=Tset increased significantly from May to Jul (Tukey’s, p < 0.05), then declined from Jul through end of season (Jul to Aug: Tukey’s, p > 0.05). In cheatgrass habitat, de=Tset remained constant throughout the season, with no significant changes from month to month

(Tukey’s, p > 0.05). In both shrub and cheatgrass habitats, de>Tset increased significantly from

May to Jul, then declined significantly through end of season (Tukey’s, p < 0.05). In shrub habitat, de

season (Tukey’s, p < 0.05). In cheatgrass habitat, de

May to Jul, but then remained stable from Jul to Aug (Tukey’s p > 0.05) before increasing significantly through end of season (Tukey’s, p < 0.05).

In shrub habitat, VTactive increased from May to Jul (Jun to Jul: Tukey’s, p > 0.05), before declining through end of season (Jul to Aug: Tukey’s, p > 0.05). Whereas, in cheatgrass habitat

VTactive remained constant throughout the season, with no significant changes from month to month (Tukey’s, p > 0.05). In shrub habitat, the number of hours above CTmax totaled 3 hours for the entire season and month to month differences for this index were trivial. In cheatgrass habitat the number of hours above CTmax increased significantly from May to Jul (Tukey’s, p < 0.05), before declining significantly through end of season (Tukey’s, p < 0.05).

4.1.2 Contrasts between habitat types, seasonal and monthly thermal regimes

Across the full season, both T̅e (F1,5 = 116.0, p < 0.001; ΔT̅e = 3.8°C) and T̅eMin (F1,5 =

81.7, p < 0.001; ΔT̅eMin = 6.5°C) were significantly higher in cheatgrass than shrub habitat. Both

T̅e and T̅eMin were significantly higher in cheatgrass habitat during all months of the season

(Tukey’s, p < 0.05) (table 3). The mean maximum operative temperature (T̅eMax) in cheatgrass habitat remained comparable to shrub habitat across the season (F1,5 = 4.2, p = 0.1; ΔT̅eMax =

1.0°C), and within each month (but Jun) (table 3), despite markedly different daily cycles of TeMax between habitats (figures 3, 4 and 5). Viewing a 24 hr thermal cycle (figures 3, 4 and 5) shows that TeMax readings in shrub habitat tended to be lower than, or comparable to, cheatgrass by day, yet slightly higher by night. The amplitude of TeMax readings in cheatgrass habitat is thus greater than in shrub habitat. The issue of using means of environmental temperatures to infer habitat thermal quality has been noted by others (e.g. Hertz et al. 1993, Camacho et al. 2015) given that means may hide relevant thermal variation. The measure of T̅eMax thus might be misleading if viewed at the seasonal scale and we suggest this metric be used with caution.

Mean deviation from Tset, (d̅e), was significantly higher in cheatgrass habitat across the season (F1,5 = 29.5, p = 0.003; Δd̅e = 2.4°C) and within the months of Jun, Jul and Aug (Tukey’s, p < 0.05) (table 3). In May, at the start of the reproductive season for S. occidentalis (Asbury and

Adolph 2007), mean deviation from Tset (d̅e) was comparable between habitats (Δd̅e = 0.8), partly due to the mild spring of 2017. Yet, as the reproductive season progressed d̅e declined (improved) significantly (Tukeys, p < 0.05) in shrub habitat, while remaining unchanged in cheatgrass habitat

(table 3). Cheatgrass and shrub habitats converge in d̅e once again in Sep and Oct. While d̅eMax remained comparable between habitat types through the season (F1,5 = 0.53, p = 0.5; Δd̅eMax =

0.5°C), the interaction between month and habitat was significant (F2.1,10.3 = 28.0, p < 0.001).

There were no within month significant differences in d̅eMax between habitat types (Tukey’s, p >

0.05) (table 3).

By using the metric of de=Tset (percent of Te readings within Tset) we can gauge the relative availability of optimal thermal patches to S. occidentalis in each habitat type, across all months of the season (figure 2). As with d̅e, both habitats are comparable at the start of the reproductive season (Δde=Tset = 0.7). Yet, as mentioned above, de=Tset remains constant in cheatgrass habitat through the entire season (Tukey’s, p > 0.05). Whereas, in shrub habitat the availability of these optimal thermal patches increases significantly (Tukey’s, p < 0.05) through July in shrub habitat and remains constant through Aug before once again converging with cheatgrass habitat in Sep and Oct. In cheatgrass habitat, de

Jul (Tukey’s, p > 0.05). The percent of Te readings within (F1,5 = 21.6, p = 0.01; Δde=Tset = -2.1%) and below Tset (F1,5 = 46.7, p = 0.001; ΔdeTset) was significantly higher in cheatgrass habitat (F1,5 = 134.8, p < 0.001; Δde>Tset = 10.8%), across all months of the season (Tukeys, p < 0.05).

The potential for activity was substantially reduced in cheatgrass habitat (F1,5 = 256.1, p <

0.001; ΔVTactive = -130.6 hrs), in all months but Oct (Tukeys, p > 0.05) (table 4; figures 3,4 and

5). Lastly, the hours above CTmax significantly increased in cheatgrass habitat (F1,5 = 154.7, p <

0.001; ΔCTmax = 103.3 hrs), in all months but Oct (Tukeys, p > 0.05) (table 5; figure 6).

4.2 Habitat structure We quantified several habitat characteristics that represent suitable habitat for reptiles and found them all to be significantly altered by cheatgrass invasion (table 2). The vegetation state change from shrub to cheatgrass habitat is evident given the significant reduction in shrub cover (F1,58 = 243.3, p < 0.001; ΔM = -25.0%; 28.3 - 3.3%), significant increase in basal cover

(F1,58 = 66.8, p < 0.001; ΔM = 31.6%; 21.3 - 52.9%) and subsequent reduction of open habitat

(F1,58 = 35.9, p < 0.001; ΔM = -20.3%; 33.4 - 13.1%). The substantial increase in basal cover and loss of open habitat in cheatgrass habitat was largely due to the significant increase of cheatgrass cover (F1,58 = 59.3, p < 0.001; ΔM = 42.1%; 32.6 - 74.7%). Above the dense basal layer, there was a significant increase in total canopy cover (F1,58 = 29.0, P < 0.001; ΔM = 20.7%; 61.9 – 82.6%) in cheatgrass habitats (see chapter 1).

5. Discussion 5.1 Overview 5.1.1 Declines in Great Basin Desert reptile biodiversity Over the course of two field seasons (2017 – 2018) we gathered reptile richness and abundance data at 6 paired native-shrub and invaded-cheatgrass (Bromus tectorum) sites, across 3 heavily invaded ecoregions in northwest Nevada (chapter 1). The contrast between habitat types in terms of their herpetofaunal communities was stark: during 50.9 km of transects in shrub habitat we observed 78 individuals, comprised of 7 species. While in cheatgrass dominated habitats, 51.7 km of transects produced only 5 individuals, of 3 species. The mechanisms driving such substantial reductions in Great Basin biodiversity remain largely speculative and untested

(Wiens 1985, Hall et al. 2009). Here, we test a likely mechanism driving declines of Great Basin

Desert herpetofauna: a decrease in habitat suitability due to dramatic changes in the availability of thermal resources in cheatgrass altered habitats.

5.1.2 Loss of the thermal resource Desert reptiles require an open habitat type with scattered shrubs (Baltosser and Best

1990, Steffen and Anderson 2006, Davidson et al. 2008). This general habitat structure facilitates effective behavioral thermoregulation by allowing reptiles to shuttle between a variety of thermal microclimates (Cowles and Bogert 1944, Heatwole and Taylor 1987). It is useful to view these elements of the ‘thermal environment’ as a resource, that varies in space and time, to be utilized by reptiles for the purpose of achieving an optimal Tb (Roughgarden et al. 1981, Tracy and

Christian 1986). The complexity of Great Basin sagebrush habitats provides these resources, while cheatgrass invasion has substantially altered these thermal resources (table 2). Mean shrub cover in native-shrub habitat was 28.3% and reduced to a mean of 3.3% in cheatgrass dominated landscapes, with many sites entirely devoid of shrub cover (ΔM = -25%). Likewise, the amount of open (un-vegetated and sun-exposed) habitat was reduced from a mean of 33.4% in shrub habitat to 13.1% in cheatgrass dominated landscapes (ΔM = -20.3%). The result is a habitat type of sparsely spaced shrubs, if any, separated by a thatch of accumulated cheatgrass and other exotic plant material (Young and Clements 2009). Thick stands of cheatgrass are known to hinder movements of wide-bodied horned lizards (Phrynosoma sp.), placing them at an increased risk of predation and exposure to lethal temperatures (Newbold 2005, Rieder et al. 2010). Yet, given our observation of a taxon-wide collapse in reptile diversity, we hypothesize that multiple stressors may contribute to the loss of reptiles. Here we propose that reductions in thermal resources in cheatgrass engineered sites is a driving mechanism that renders these landscapes unsuitable for reptiles. Our data show that cheatgrass habitat is thermally unsuitable for at least one species,

Sceloporus occidentalis, and potentially for many other Great Basin reptiles.

5.2 Altered thermal regimes Most habitats offer an ever changing availability of thermal patches, across temporal scales (Tracy and Christian 1986). Here, we discuss how the thermal patches required by S. occidentalis for thermoregulation differ in cheatgrass dominated landscapes in relation to reference native-shrub habitat, across days, months and the season.

5.2.1 Reduced ease of thermoregulation (de, d̅e and d̅eMax)

The preferred temperature range of reptiles (Tset) is strongly correlated with Tbs that optimize physiological functions (Bennett 1980, Huey 1982, Stevenson 1985). The ‘ease’ at which an individual may attain optimal Tbs within its habitat is thus of ecological importance. For example, readily accessible optimal thermal patches (a microclimate in which Te = Tset) will minimize risky movements between patches (Huey 1974, 1982), while permitting optimal performance within that patch (e.g. sprint speed for predator avoidance and hunting). S. occidentalis has a narrow thermal performance breadth and depends on shrubs to maintain its precise thermoregulation requirements in the Great Basin Desert (Pianka 1986). In particular, the complex structure of shrubs allows an individual to attain Tset with ease, by shuttling short distances between the abundant thermal patches (Pianka 1986). To quantify how the loss of this structural complexity in cheatgrass landscapes has increased the difficulty at which S. occidentalis can thermoregulate, we calculated deviations of Te from Tset (de, Hertz et al. 1993)

(table 3).

As the reproductive season progressed, the overall thermal quality of shrub habitats improved (d̅e declined), while remaining unchanged in cheatgrass (figure 3). Likewise, the relative availability of optimal thermal patches, or the percent of Te readings within Tset, remained constant in cheatgrass through the entire season, while the availability of these optimal thermal patches increased through July in shrub habitat. Thus, the availability of optimal thermal patches to S. occidentalis increased in shrub habitat during a crucial time of year for activity in terms of

75 foraging and reproduction (Asbury and Adolph 2007), while remaining consistently low in cheatgrass. S. occidentalis actively select microhabitats where Tset is most easily attainable

(Adolph 1990). From the perspective of S. occidentalis, as the abundance of optimal thermal patches decreases, so too does their access to resources and mates. Furthermore, the reduction in optimal thermal patches will force movements into and through a thermally unsuitable cheatgrass matrix, increasing exposure to predation and lethal temperatures (see below). In other words, as optimal thermal patches decrease in availability, the risks of exposure in searching for them begin to outweigh the benefits of finding them (Tracy and Christian 1986).

5.2.2 Reduction in potential activity (VTactive) In environments of thermal extremes an ectotherm’s activity is constrained by the range of Tbs it can tolerate (Heatwole and Firth 1982, Grant and Dunham 1988). The amount of activity an individual is capable of translates into its immediate ability to acquire prey and mates (Huey

1982, Dunham et al. 1989, Huey 1991), ultimately affecting its long term fitness and survival.

Thus, activity has direct and pronounced ecological implications for populations of ectotherms

(Huey 1982, Christian et al. 2016), at different temporal scales. We quantified a biologically relevant index of activity (Heatwole and Firth 1982, Tracy and Christian 1986, Grant and

Dunham 1988, Van Damme et al. 1989) by using the range of Tbs S. occidentalis behaviorally restricts itself to during activity (VTactive) and found substantial declines in the amount of time in which S. occidentalis may be active in cheatgrass habitats (table 4; figures 3, 4 and 5).

To start, cheatgrass habitat is substantially warmer than the adjacent shrub habitat (ΔT̅e =

3.8°C), given the consistently higher T̅e in cheatgrass across all months (table 3). This increase in potential Tbs is driven by the lack of shrub cover in cheatgrass habitats and its temperature moderating effects. In particular, TeMin spikes along with TeMax in cheatgrass habitat during daylight hours (figures 3, 4 and 5). However, TeMin is moderated in shrub habitat, given the presence of shaded microhabitats, and remains below or within the activity range (VTactive) of S.

76 occidentalis throughout the season (figures 3, 4 and 5). As a result, S. occidentalis is afforded a mean of 1,306 hrs of activity for the season, or a full 54 days, in shrub habitat. Whereas in cheatgrass habitat the potential for activity across the season is decreased by more than half that in shrub, to 522.1 hrs, or a mere 21.8 days.

Some desert reptiles have a broad thermal niche, tolerating a wide variance in Tb during activity. For example, Phrynosoma sp. have an affinity to open sun-exposed microhabitats given their prey specialization (ants) and thus are capable of tolerating a larger range of Tbs to effectively forage (Pianka and Parker 1975). However, the stenothermic nature of S. occidentalis means they must maintain their Tbs within a more restricted range during activity. Their choice of habitat in the Great Basin Desert (shrubs) allows them to remain active throughout the day, given the variety of easily accessible microclimates. For example, viewing the thermal maps (figures 3,

4 and 5) shows how, with the availability of buffered Tes within shrubs, an individual S. occidentalis is capable of activity during most of the daylight hours, throughout the season.

However, the loss of shrubs in cheatgrass produces a pattern of bimodal potential activity throughout most of the season. It is not until Oct that S. occidentalis is afforded a comparable amount of activity between habitat types (table 3). Yet, by this time TeMin has begun to consistently drop below CTmin, coinciding with the time of year in which most Great Basin reptiles become inactive (Pianka 1986).

Heat flux patterns are substantially altered in cheatgrass dominated landscapes, given the loss of shrub cover (Prater and DeLucia 2006). An observation of interest is the difference between habitat types in their seasonal pattern of minimum Te readings. In particular, T̅eMin in cheatgrass habitat begins to decline significantly from Jul to Aug, whereas shrub habitat T̅eMin does not decline significantly until Aug to Sep. We suspect that shrub cover acts to insulate against nocturnal heat flux to the atmosphere, thus maintaining the higher T̅eMin. The significant

cooling of potential Tbs in cheatgrass habitat by mid-season could translate to a shortened season of activity for Great Basin reptiles, especially nocturnal species.

5.2.3 Increased risk of thermally induced death (CTmax) The ability to escape lethal thermal extremes is of immediate importance to the survival of ectotherms living in desert environments (Huey 1991, Hertz et al. 1993). Midday Te readings in the full sun microhabitat of both habitat types regularly exceeded CTmax of S. occidentalis

(figures 3,4 and 5). The comparable TeMax between habitats (table 3) is driven by the daily temperature extremes of this microhabitat. If unable to avoid these temperature extremes, S. occidentalis would quickly perish (Cowles and Bogert 1944). The full shade microhabitat of shrubs offers a distribution of Tes below CTmax (figures 3, 4 and 5) representing an ever present refugia from lethal temperature extremes throughout the day and entire season (figure 6). TeMin readings crossed the CTmax threshold a mean of 0.5 hrs across the entire season in shrub habitat, while the loss of shrubs in cheatgrass habitat places S. occidentalis at risk of being exposed to lethal temperatures for a mean of 619 hrs. (or nearly 26 full days) throughout the season (table 5; figure 6).

Shrub cover appears to be a vital thermal resource to Great Basin Desert reptiles in the form of buffered daily and seasonal temperature extremes. Given the role shrubs play in offering thermal refugia from extreme temperatures (Pianka 1986) cheatgrass invasion has likely rendered vast expanses of the Great Basin Desert thermally unsuitable for S. occidentalis. In particular, the loss of this resource is likely to impact S. occidentalis at various temporal scales: daily, through exposure to lethal temperatures, and seasonally, through reduced activity. The loss of shrub cover and subsequent reduction in potential activity and increased risk of thermally induced death is a likely mechanism driving declines of at least one Great Basin Desert reptile species, S. occidentalis, and potentially others.

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Tables Table 1: Terms and indices used throughout this paper. Thermal preference and tolerance values, where shown, are for the western fence lizard, Sceloporus occidentalis Index or symbol Definition

Tb Body temperature Te Operative temperature TeMin / TeMax Minimum and maximum Te (°C) T̅e, T̅eMin , T̅eMax Grand-mean, mean minimum and mean maximum Te (°C) Tset The preferred temperature range; range of Tbs where physiological processes are maximal (32 – 36°C) VTmin / VTmax Voluntary minimum and maximum temperatures; range of Tbs an ectotherm accepts during activity (30.5 and 38.3°C, respectively) CTmin / CTmax Lower and upper critical thermal tolerances; temperatures that lead to immobility and swift death (4.0 and 46.0°C, respectively) de Deviation of Te from Tset d̅e Mean de (°C) d̅eMax Mean max de (°C) de>Tset Percent of Te readings above Tset de

Table 2: Metrics of habitat structure important for Great Basin Desert lizards, averaged over 6 paired sites (shrub vs cheatgrass). Shrub Open Canopy cover3 Basal cover4 Cheatgrass Habitat Cover1 Habitat2 (% cheatgrass) (% cheatgrass) cover5 Cheatgrass 3.3 13.1 82.6 (70.1) 52.9 (80.8) 74.7 Shrub 28.3 33.4 61.9 (27.9) 21.3 (43.2) 32.6 ¹ Percent shrub cover (all-hit average). ² Percent of habitat for which there is no canopy cover or basal cover, and where the surface layer is composed of either rock, soil, biocrust, duff or litter. ³ Percent canopy cover (# of canopy intercepts); percent of canopy cover that is composed of cheatgrass in parentheses. 4 Percent of ground occupied by base of plants (# of live/dead plant base intercepts); percent of basal cover that is composed of cheatgrass in parentheses. 5 Percent cheatgrass cover (all-hit average).

Table 3: Magnitude of thermal change from the reference shrub habitat to the invaded cheatgrass habitat type (effect size) for each thermoregulatory index (from the perspective of Sceloporus occidentalis). Effect sizes were calculated as the raw mean differences and are presented in the same unit as the given index (in parentheses). An asterisks indicates a significant difference between habitat types for that month (2-way repeated measures ANOVA, p < 0.05); p represents a value rounded to significance level.

̅ ̅ ̅ ̅ ̅ Month ΔTe ΔTeMax ΔTeMin Δde ΔdeMax Δde=Tset ΔdeTset ΔVTactive ΔCTmax (°C) (°C) (°C) (°C) (°C) (% of Te readings) (% of Te readings) (% of Te readings) (hrs within) (hrs above) May 3.6* 0.5 6.6* 0.8 -0.8 0.7 -11.4* 10.7* -81* 44.3* Jun 5.4* 2.5p 8.2* 3.4* 1.7 -2.9* -11.6* 14.5* -191* 141* Jul 5.3* 1.9 8.7* 5.8* 2.8 -6.6* -5.1 11.7* -215* 205* Aug 3.9* 1.0 7.1* 4.0* 1.8 -4.9* -7.6* 12.5* -211* 167* Sep 2.5* 0.3 4.8* 0.9 -0.6 -0.6 -8.8* 9.3* -91* 61.8* Oct 2.0* -0.2 3.7p -0.8 -2.2 1.8 -7.6* 5.8* 5.6 0.58 Season mean 3.8 1.0 6.5 2.4 0.5 -2.1 -8.7 10.8 -130.6 103.3

Table 4: Number of hours in which activity is possible per month, based on TeMin and TeMax readings that encompass the VT range of Sceloporus occidentalis (Te = 30.5 – 38.3 °C). Te models were placed in the shrub microhabitat of two cheatgrass sites (Peavine and Eden Valley), hours of potential activity that include TeMin and TeMax readings from these models are shown in parentheses.

Ecoregion Sierra Nevada Influenced Lahontan Sagebrush Slopes Upper Lahontan Basin Study site Peavine Red Rock Eden Valley Grass Valley Buffalo Canyon Paradise Valley

Habitat type Shrub Cheat (+ shrub) Shrub Cheat Shrub Cheat (+ shrub) Shrub Cheat Shrub Cheat Shrub Cheat

May 177.0 75.0 (177.75) 62.25 22.5 144.75 46.75 (148.5) 151.25 83.75 149.0 60.5 169.25 82.0 Hours Jun 296.5 98.75 (305.25) 307.25 88.75 298.0 75.5 (300.75) 287.25 125.5 236.0 81.0 284.0 96.0 per Jul 315.0 91.0 (302.0) 324.25 83.75 345.0 71.75 (334.75) 265.75 103.75 267.25 78.5 292.5 89.25 month Aug 299.75 84.75 (303.75) 272.0 79.25 310.5 68.75 (300.5) 312.25 121.25 295.0 77.75 316.5 104.5 activity possible Sep 192.75 93.25 (196.5) 202.5 120.5 202.25 75.25 (211.25) 199.75 163.75 200.25 109.25 226.0 113.75 Oct 77.0 81.75 (84.5) 110.25 125.0 59.5 52.0 (72.75) 66.25 77.75 61.5 69.25 58.75 60.75 Season 1,358 524.5 (1,369.75) 1,278.5 519.75 1,360 390 (1,368.5) 1,282.5 675.75 1,209 476.25 1,347 546.25 total

# of Te loggers 6 3 (6) 5 6 6 3 (6) 6 6 6 6 6 6 Date Range 5/9 – 10/16 5/25 – 10/26 5/12 – 10/13 5/10 – 10/14 5/13 – 10/14 5/10 – 10/13

Table 5: Number of hours per month in which the minimum temperature available in the habitat is above the upper critical thermal tolerance for Sceloporus occidentalis (CTmax = 46°C and above). Te models were placed in the shrub microhabitat of two cheatgrass sites (Peavine and Eden Valley), hours above CTmax that include Te readings from these models are shown in parentheses.

Ecoregion Sierra Nevada Influenced Lahontan Sagebrush Slopes Upper Lahontan Basin Study site Peavine Red Rock Eden Valley Grass Valley Buffalo Canyon Paradise Valley Habitat type Shrub Cheat (+ shrub) Shrub Cheat Shrub Cheat (+ shrub) Shrub Cheat Shrub Cheat Shrub Cheat May 0 62.5 (0) 0 25.5 0.75 59.0 (0) 0 25.0 0 63.75 0 30.75 Hours Jun 0 161.5 (0) 0 154.5 0 175.5 (0) 0.25 97.75 0 127.75 0 128.75 per Jul 0.25 192.25 (0) 0 202.5 0 247.0 (0) 0.25 151.75 0 247.25 0 188.25 month Aug 0 171.25 (0) 0 128.0 0.5 201.5 (0.75) 0.25 135.75 0 206.5 0.75 160.75 above Sep 0 57.25 (0) 0 49.25 0 90.25 (0) 0 30.0 0 80.75 0 63.5 CTmax Oct 0 0 (0) 0 0 0 1.5 (0) 0 0 0 2.0 0 0 Season 0.25 644.75 (0) 0 559.75 1.25 774.75 (0.75) 0.75 440.25 0 728 0.75 572 total

# of Te loggers 6 3 (6) 5 6 6 3 (6) 6 6 6 6 6 6 Date Range 5/9 – 10/16 5/25 – 10/26 5/12 – 10/13 5/10 – 10/14 5/13 – 10/14 5/10 – 10/13

Figures

Figure 1: Project components: a) characteristic Artemisia shrub habitat of the Great Basin Desert; b) thermoregulating Sceloporus occidentalis; c) Te model in typical shrub “full shade” microhabitat; d) painted copper pipe Te model; and e) Te model in typical cheatgrass “full sun”

87 microhabitat.

Figure 2: Stacked barplots showing the ‘ease’ at which S. occidentalis could maintain its body temperature (Tb) within its preferred temperature range (Tset) in each habitat type. Values represent the percent of Te readings above (orange), below (blue) and within (green) Tset by month and for each habitat type.

Figure 3: Reproductive season plots. Above - barplots showing the total hours of potential activity by site and month, grouped by ecoregion. Below - thermal maps showing a typical sunny 24 hr period in May (left) and June (right), to view the extent to which S. occidentalis could potentially exploit their environment during the reproductive season. One representive site per ecoregion is shown. Shaded bar shows the VTactive range of S. occidentalis (30.5 to 38.3°C), and bars at bottom of plot represent the times of day S. occidentalis may be active in each habitat type (cheatgrass above, shrub below).

Figure 4: Hot season plots. Above - barplots showing the total hours of potential activity by site and month, grouped by ecoregion. Below - thermal maps showing a typical sunny 24 hr period in July (left) and August (right), to view the extent to which S. occidentalis could potentially exploit their environment during the hot season. One representive site per ecoregion is shown. Shaded bar shows the VTactive range of S. occidentalis (30.5 to 38.3°C), and bars at bottom of plot represent the times of day S. occidentalis may be active in each habitat type (cheatgrass above, shrub below).

Figure 5: Cool season plots. Above - barplots showing the total hours of potential activity by site and month, grouped by ecoregion. Below - thermal maps showing a typical sunny 24 hr period in September (left) and October (right), to view the extent to which S. occidentalis could potentially exploit their environment during the cool season. One representive site per ecoregion is shown. Shaded bar shows the VTactive range of S. occidentalis (30.5 to 38.3°C), and bars at bottom of plot represent the times of day S. occidentalis may be active in each habitat type (cheatgrass above, shrub below).

Figure 10

Figure 6: Barplots showing hours above the upper critical thermal tolerance for S. occidentalis (CTmax = 46°C) by site and month, grouped by ecoregion. Values represent the number of hours per month in which an exposed S. occidentalis would quickly perish, calculated from TeMin readings that crossed the CTmax threshold (TeMin > = 46.0°C).

Appendix A Table 1: Plant Inventory. Functional Sites Encountered Sites Encountered Family Genus Species group (Cheatgrass) (Shrub)

ASTERACEAE Artemisia¹ᶜ²ᶜ Shrub EV, PE BC, EV, GV, PE, PV, RR Balsamorhiza¹ᵇ²ᶜ Native forb PE PE, RR

Chrysothamnus¹ᶜ²ᶜ Shrub EV, PE, RR BC, EV, GV, PE, PV, RR

Crepis¹ᶜ²ᶜ Native forb PE, RR BC, EV, PE Erigeron¹ᵇ²ᵃ aphanactis Native forb RR RR Pleiacanthus¹ᶜ²ᶜ spinosus Native forb PE, RR PE

Tetradymia¹ᶜ²ᶜ Shrub PE PE, RR

BORAGINACEAE Amsinckia¹ᶜ Native forb GV

BRASSICACEAE Cardaria¹ᶜ Exotic forb PV Lepidium¹ᶜ²ᶜ perfoliatum Exotic forb BC, GV, PV PV Sisymbrium¹ᶜ²ᶜ altissimum Exotic forb BC, EV, GV, PE, PV EV, PE, RR CARYOPHYLLACEAE Holosteum¹ᶜ²ᶜ umbellatum Exotic forb BC, PV BC, EV, PV

EPHEDRACEAE Ephedra¹ᶜ Shrub PE, RR FABACEAE Astragalus¹ᶜ²ᶜ filipes Native forb PE, RR BC, EV, PE, PV

Lupinus¹ᶜ²ᶜ Native forb PE, RR PE, RR GERANIACEAE Erodium¹ᶜ²ᶜ cicutarium Exotic forb BC, EV, GV, PE, PV, RR EV, PE, RR

LILIACEAE Calochortus²ᶜ Native forb BC

Zygadenus²ᵇ venesosus Native forb PV

PAPAVERACEAE Argemone²ᵇ munita Native forb PE POACEAE Agropyron¹ᶜ²ᶜ cristatum Exotic perennial grass BC, GV GV

Pseudoroegneria²ᶜ spicata Native perennial grass GV Bromus¹ᶜ²ᶜ tectorum Cheatgrass BC, EV, GV, PE, PV, RR BC, EV, GV, PE, PV, RR

Elymus¹ᶜ²ᶜ Native perennial grass EV, PE, RR BC, EV, GV, PE, PV, RR Poa¹ᶜ²ᶜ secunda Native perennial grass EV, GV, PE, PV, RR BC, EV, GV, PE, PV, RR

Stipa¹ᵃ²ᵃ comata Native perennial grass PE RR 93

Vulpia¹ᶜ²ᶜ octoflora Native annual grass BC, EV, GV, RR BC, EV, PE

POLEMONIACEAE Linanthus²ᶜ pungens Shrub RR

Phlox²ᵇ Native forb PV

POLYGONACEAE Erigonum²ᶜ Native forb EV, RR

RANUNCULACEAE Ceratocephala²ᵃ testiculata Exotic forb PE, PV ROSACEAE Prunus¹ᶜ²ᶜ andersonii Shrub PE, RR PE, RR Purshia²ᶜ tridentata Shrub PE, RR ¹ Indicates presence of genus in BRTE sites. ² indicates presence of genus in shrub sites. ᵅ Indicates detection by LPI methodology. ᵇ Indicates detection by quadrat methodology quadrats ᶜ Indicates detection by both methods.

Table 2: Detection function models. Models use measured perpendicular detection distances from transects (x), obtained during distance sampling transects, to estimate reptile community density for each of the 3 ecoregions. Six detection functions were assessed for model fit. All models were assessed on untruncated data (w = 4.0m). The model with the fewest parameters and lowest AIC value was chosen (in bold) to estimate density. No. of parameters Model (key + adjustment) Key Adjustments AIC Uniform + cosine 0 1 186.92 Uniform + simple polynomial 0 2,4 189.03 Half-normal + cosine 1 0 186.49 Half-normal + Hermite polynomial 1 4 188.43 Hazard-rate + cosine 2 2 187.11 Hazard-rate + simple polynomial 2 2 187.14

Appendix B Table 1: Thermal quality of cheatgrass and shrub habitats of the Sierra Nevada Influenced ecoregion, represented by Peavine and Red Rock sites. Above: grand mean, mean maximum and mean minimum operative temperatures, with standard error; by site, habitat type and month. Below: mean deviation, mean maximum deviation from Tset (32 – 36 °C) and the percentage of observations equal to Tset for Sceloporus occidentalis, with standard error; by site, habitat type and month.

Mean Te (°C) Mean Max Te (°C) Mean Min Te (°C) Peavine Red Rock Peavine Red Rock Peavine Red Rock Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat May 18.4 ± 0.1 22.6 ± 0.2 22.6 ± 0.3 26.0 ± 0.3 23.3 ± 0.1 24.5 ± 0.2 29.8 ± 0.4 27.8 ± 0.3 13.5 ± 0.1 20.7 ± 0.2 15.7 ± 0.2 23.8 ± 0.3 Jun 23.2 ± 0.1 30.3 ± 0.2 24.9 ± 0.1 28.4 ± 0.1 29.0 ± 0.1 33.2 ± 0.3 31.8 ± 0.2 30.0 ± 0.2 17.6 ± 0.1 27.2 ± 0.2 18.0 ± 0.1 26.4 ± 0.1 Jul 28.1 ± 0.1 34.8 ± 0.2 28.4 ± 0.1 32.3 ± 0.1 33.7 ± 0.1 37.4 ± 0.3 35.9 ± 0.2 34.1 ± 0.2 22.4 ± 0.1 31.9 ± 0.2 20.9 ± 0.1 30.2 ± 0.1 Aug 26.5 ± 0.1 30.6 ± 0.2 26.3 ± 0.1 29.1 ± 0.1 31.3 ± 0.1 32.4 ± 0.2 31.5 ± 0.1 30.5 ± 0.1 21.6 ± 0.1 28.9 ± 0.2 20.1 ± 0.1 27.8 ± 0.1 Sep 19.2 ± 0.1 21.5 ± 0.2 18.5 ± 0.1 21.2 ± 0.1 24.0 ± 0.1 22.8 ± 0.2 23.1 ± 0.1 22.8 ± 0.1 14.8 ± 0.1 20.4 ± 0.2 13.6 ± 0.1 19.5 ± 0.1 Oct 10.3 ± 0.1 11.6 ± 0.2 9.5 ± 0.1 12.5 ± 0.1 15.6 ± 0.1 14.1 ± 0.3 14.3 ± 0.1 14.0 ± 0.1 5.6 ± 0.1 9.2 ± 0.2 5.3 ± 0.1 10.4 ± 0.1

Mean de (°C) Mean Max de (°C) % Within Tset (de=Tset) Peavine Red Rock Peavine Red Rock Peavine Red Rock Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat May 16.3 ± 0.1 17.5 ± 0.1 16.1 ± 0.1 16.4 ± 0.1 19.3 ± 0.1 19.2 ± 0.1 20.9 ± 0.1 18.2 ± 0.1 2.6 ± 0.005 4.1 ± 0.001 3.4 ± 0.01 5.1 ± 0.02 Jun 13.8 ± 0.1 18.1 ± 0.1 15.0 ± 0.1 16.1 ± 0.1 17.6 ± 0.05 20.8 ± 0.1 20.2 ± 0.1 17.7 ± 0.1 5.0 ± 0.005 3.6 ± 0.004 5.9 ± 0.01 4.0 ± 0.01 Jul 10.7 ± 0.04 16.7 ± 0.1 13.2 ± 0.1 15.6 ± 0.1 15.2 ± 0.05 19.3 ± 0.1 19.6 ± 0.1 17.5 ± 0.1 10.0 ± 0.01 3.9 ± 0.004 11.3 ± 0.01 3.9 ± 0.01 Aug 10.7 ± 0.04 14.8 ± 0.1 12.3 ± 0.05 13.3 ± 0.1 14.5 ± 0.04 16.5 ± 0.1 16.0 ± 0.1 14.6 ± 0.1 8.2 ± 0.01 4.0 ± 0.005 6.5 ± 0.01 4.9 ± 0.01 Sep 15.4 ± 0.1 16.2 ± 0.1 16.5 ± 0.07 15.7 ± 0.1 18.7 ± 0.1 17.3 ± 0.1 19.9 ± 0.1 17.2 ± 0.1 4.6 ± 0.01 5.4 ± 0.01 5.0 ± 0.01 6.3 ± 0.01 Oct 22.3 ± 0.1 22.4 ± 0.2 23.0 ± 0.1 20.7 ± 0.1 26.4 ± 0.1 24.4 ± 0.1 26.7 ± 0.1 22.6 ± 0.1 3.0 ± 0.01 4.7 ± 0.01 3.4 ± 0.004 7.5 ± 0.01

Table 2: Thermal quality of cheatgrass and shrub habitats of the Lahontan Sagebrush Slopes ecoregion, represented by Eden Valley and Grass Valley sites. Above: grand mean, mean maximum and mean minimum operative temperatures, with standard error; by site, habitat type and month. Below: mean deviation, mean maximum deviation from Tset (32 – 36 °C) and the percentage of observations equal to Tset for Sceloporus occidentalis, with standard error; by site, habitat type and month.

Mean Te (°C) Mean Max Te (°C) Mean Min Te (°C) Eden Valley Grass Valley Eden Valley Grass Valley Eden Valley Grass Valley Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat May 17.4 ± 0.1 22.2 ± 0.2 17.5 ± 0.1 18.8 ± 0.1 22.4 ± 0.2 23.8 ± 0.3 21.8 ± 0.2 21.9 ± 0.2 12.3 ± 0.1 20.6 ± 0.2 12.8 ± 0.1 15.9 ± 0.1 Jun 24.1 ± 0.1 30.7 ± 0.2 24.4 ± 0.1 28.1 ± 0.2 29.3 ± 0.1 33.2 ± 0.2 29.2 ± 0.1 32.2 ± 0.2 18.2 ± 0.1 28.6 ± 0.2 19.0 ± 0.1 23.4 ± 0.1 Jul 31.4 ± 0.1 37.9 ± 0.2 30.0 ± 0.1 34.4 ± 0.2 37.1 ± 0.1 40.1 ± 0.2 35.4 ± 0.1 38.3 ± 0.2 25.0 ± 0.1 35.9 ± 0.2 24.2 ± 0.1 30.3 ± 0.2 Aug 28.4 ± 0.1 33.4 ± 0.2 27.6 ± 0.1 31.1 ± 0.1 33.1 ± 0.1 34.8 ± 0.2 32.7 ± 0.1 35.0 ± 0.2 23.2 ± 0.1 32.1 ± 0.2 22.3 ± 0.1 27.4 ± 0.1 Sep 19.8 ± 0.1 23.3 ± 0.2 18.6 ± 0.1 20.3 ± 0.1 23.7 ± 0.1 24.9 ± 0.2 22.5 ± 0.1 24.3 ± 0.1 15.7 ± 0.1 21.9 ± 0.2 14.5 ± 0.1 16.7 ± 0.1 Oct 10.4 ± 0.1 13.6 ± 0.3 9.3 ± 0.1 9.5 ± 0.1 14.6 ± 0.2 15.5 ± 0.3 13.4 ± 0.1 14.6 ± 0.2 6.0 ± 0.1 11.9 ± 0.2 5.3 ± 0.1 4.7 ± 0.1

Mean de (°C) Mean Max de (°C) % Within Tset (de=Tset) Eden Valley Grass Valley Eden Valley Grass Valley Eden Valley Grass Valley Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat May 17.8 ± 0.1 18.2 ± 0.1 18.0 ± 0.1 18.4 ± 0.1 21.4 ± 0.1 19.6 ± 0.1 21.4 ± 0.1 21.3 ± 0.1 3.9 ± 0.01 3.8 ± 0.01 4.3 ± 0.01 3.9 ± 0.01 Jun 13.7 ± 0.1 17.4 ± 0.1 14.1 ± 0.1 17.3 ± 0.1 17.9 ± 0.1 19.5 ± 0.1 18.4 ± 0.1 21.5 ± 0.1 7.4 ± 0.01 3.7 ± 0.005 10.0 ± 0.01 4.5 ± 0.01 Jul 11.0 ± 0.04 17.7 ± 0.1 11.5 ± 0.05 16.9 ± 0.1 16.3 ± 0.1 19.7 ± 0.1 16.7 ± 0.1 20.7 ± 0.1 11.1 ± 0.01 3.4 ± 0.004 10.9 ± 0.01 4.8 ± 0.01 Aug 10.7 ± 0.04 15.8 ± 0.1 11.7 ± 0.04 15.3 ± 0.1 15.1 ± 0.1 17.0 ± 0.1 16.1 ± 0.1 19.5 ± 0.1 10.6 ± 0.01 3.6 ± 0.01 9.9 ± 0.01 4.9 ± 0.01 Sep 15.6 ± 0.1 17.2 ± 0.1 16.1 ± 0.1 16.6 ± 0.1 18.9 ± 0.1 18.5 ± 0.1 19.3 ± 0.1 20.3 ± 0.1 5.6 ± 0.01 4.0 ± 0.01 5.6 ± 0.01 5.0 ± 0.01 Oct 22.2 ± 0.1 21.4 ± 0.2 23.0 ± 0.1 23.1 ± 0.1 26.0 ± 0.1 23.0 ± 0.2 26.7 ± 0.1 27.4 ± 0.1 3.8 ± 0.01 5.4 ± 0.01 5.0 ± 0.01 2.0 ± 0.004

Table 3: Thermal quality of cheatgrass and shrub habitats of the Upper Lahontan Basin ecoregion, represented by Buffalo Canyon and Paradise Valley sites. Above: grand mean, mean maximum and mean minimum operative temperatures, with standard error; by site, habitat type and month. Below: mean deviation, mean maximum deviation from Tset (32 – 36 °C) and the percentage of observations equal to Tset for Sceloporus occidentalis, with standard error; by site, habitat type and month.

Mean Te (°C) Mean Max Te (°C) Mean Min Te (°C) Buffalo Canyon Paradise Valley Buffalo Canyon Paradise Valley Buffalo Canyon Paradise Valley Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat May 18.8 ± 0.1 23.5 ± 0.2 18.2 ± 0.1 21.6 ± 0.2 24.1 ± 0.2 26.3 ± 0.2 23.7 ± 0.1 23.9 ± 0.2 13.3 ± 0.1 20.5 ± 0.2 13.3 ± 0.1 19.1 ± 0.1 Jun 24.5 ± 0.1 30.6 ± 0.2 24.9 ± 0.1 30.5 ± 0.2 30.7 ± 0.2 34.0 ± 0.2 31.7 ± 0.1 33.8 ± 0.2 18.2 ± 0.1 27.1 ± 0.2 19.5 ± 0.1 26.8 ± 0.1 Jul 33.3 ± 0.1 38.3 ± 0.2 31.7 ± 0.1 37.2 ± 0.2 40.4 ± 0.2 41.5 ± 0.2 38.6 ± 0.1 40.8 ± 0.2 26.2 ± 0.1 35.0 ± 0.2 26.1 ± 0.1 33.5 ± 0.2 Aug 30.4 ± 0.1 34.3 ± 0.2 29.0 ± 0.1 33.0 ± 0.1 36.0 ± 0.1 36.7 ± 0.2 34.8 ± 0.1 35.8 ± 0.1 24.4 ± 0.1 31.8 ± 0.1 24.2 ± 0.1 30.1 ± 0.1 Sep 20.8 ± 0.1 23.3 ± 0.1 20.9 ± 0.1 23.3 ± 0.1 25.4 ± 0.1 25.9 ± 0.2 26.1 ± 0.1 25.7 ± 0.1 16.2 ± 0.1 20.9 ± 0.1 16.6 ± 0.1 20.8 ± 0.1 Oct 10.1 ± 0.1 12.4 ± 0.2 10.6 ± 0.1 12.3 ± 0.2 14.9 ± 0.1 14.8 ± 0.2 15.8 ± 0.1 14.6 ± 0.2 5.7 ± 0.1 10.1 ± 0.2 6.3 ± 0.1 9.9 ± 0.1

Mean de (°C) Mean Max de (°C) % Within Tset (de=Tset) Buffalo Canyon Paradise Valley Buffalo Canyon Paradise Valley Buffalo Canyon Paradise Valley Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat Shrub Cheat May 17.2 ± 0.1 19.1 ± 0.1 16.5 ± 0.1 16.8 ± 0.1 20.8 ± 0.1 21.7 ± 0.1 19.8 ± 0.1 19.0 ± 0.1 3.2 ± 0.01 3.3 ± 0.01 3.1 ± 0.005 4.4 ± 0.01 Jun 13.9 ± 0.1 17.7 ± 0.1 12.5 ± 0.05 16.8 ± 0.1 18.5 ± 0.1 21.1 ± 0.1 17.5 ± 0.1 19.9 ± 0.1 6.2 ± 0.01 3.9 ± 0.01 6.8 ± 0.01 4.1 ± 0.01 Jul 11.8 ± 0.04 18.9 ± 0.1 9.6 ± 0.04 16.8 ± 0.1 18.5 ± 0.1 21.9 ± 0.1 15.9 ± 0.1 20.1 ± 0.1 7.7 ± 0.01 3.7 ± 0.01 13.0 ± 0.01 4.6 ± 0.01 Aug 11.2 ± 0.04 16.5 ± 0.1 9.6 ± 0.03 14.2 ± 0.1 16.5 ± 0.1 18.9 ± 0.1 14.4 ± 0.1 17.0 ± 0.1 8.6 ± 0.01 3.6 ± 0.01 11.0 ± 0.01 4.2 ± 0.01 Sep 15.4 ± 0.1 17.0 ± 0.1 14.3 ± 0.1 15.7 ± 0.1 19.1 ± 0.1 19.4 ± 0.1 18.1 ± 0.1 18.0 ± 0.1 5.1 ± 0.01 3.9 ± 0.01 6.6 ± 0.01 4.5 ± 0.01 Oct 22.6 ± 0.1 22.0 ± 0.1 21.8 ± 0.1 20.8 ± 0.1 26.3 ± 0.1 24.0 ± 0.1 25.7 ± 0.1 23.1 ± 0.1 2.8 ± 0.01 6.1 ± 0.01 2.6 ± 0.01 5.5 ± 0.01