Community Patterns and Plant Attractiveness to Pollinators in the Texas High Plains

Scale-Dependent Bee (Hymenoptera: Anthophila) Community Patterns and Plant Attractiveness to Pollinators in the Texas High Plains

Samuel Discua, B.Sc., M.Sc.

A Dissertation

Plant and Soil Science

Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of

DOCTOR OF PHILOSOPHY

Approved

Scott Longing Chair of the Committee

Nancy McIntyre

Robin Verble

Cynthia McKenney

Joseph Young

Mark Sheridan Dean of the Graduate School

May, 2021

Texas Tech University, Samuel Discua, May 2021

ACKNOWLEDGMENTS

There are many who helped me along the way on this long and difficult journey. I want to take a moment to thank them.

First, I wish to thank my dissertation committee. Without their guidance, I would not have made it. Dr. McIntytre, Dr. McKenney, Dr. Young and Dr. Verble served as wise committee members, and Dr. Longing, my committee chair, for sticking with me and helping me reach my goal.

To the Longing Lab members, Roberto Miranda, Wilber Gutierrez, Torie Wisenant, Shelby

Chandler, Bryan Guevara, Bianca Rendon, Christopher Jewett, thank you for all the hard work. To my family, my parents, my sisters, and Balentina and Bruno: you put up with me being distracted and missing many events.

Finally, and most important, to my wife, Baleshka, your love and understanding helped me through the most difficult times. Without you believing in me, I never would have made it. It is time to celebrate; you earned this degree right along with me. I am forever grateful for your patience and understanding. It is time to close this chapter of my life and start a new one.

Texas Tech University, Samuel Discua, May 2021

TABLE OF CONTENTS

ACKNOWLEDGMENTS ...... ii

LIST OF TABLES ...... vi

LIST OF FIGURES ...... xi ABSTRACT ...... xiii

I. INTRODUCTION ...... 1 Value of Pollination Services Provided by Bees ...... 1 Global Pollinator Declines ...... 5 Effects of Habitat Quality on Pollinators ...... 7 Ecological Restoration for Bees and Other Pollinators ...... 8 Agriculture and Pollination Services in the Texas High Plains ...... 10 Native Bees in the Texas High Plains ...... 12 Research Objectives ...... 15 Rationale and Significance ...... 16

LITERATURE CITED ...... 17

II. POLLINATOR ATTRACTIVENESS OF DROUGHT-TOLERANT PLANTS

IN THE TEXAS HIGH PLAINS ...... 24 Abstract ...... 24 Introduction ...... 25 Materials and Methods ...... 27 Site Description and Field Experimental Design ...... 27 Plant Selection ...... 29 Insect Floral Visitation Counts ...... 30 Plant Measurements ...... 31 Data Analysis ...... 31 Results...... 32 Bee Community ...... 32 Plant – Pollinator Associations ...... 34 Discussion ...... 36

LITERATURE CITED ...... 42 iii

Texas Tech University, Samuel Discua, May 2021

III. A MULTIVARIATE ANALYSIS OF NATIVE BEES AND FLORAL

COMMUNITIES ACROSS AGROECOSYSTEMS IN THE LLANO

ESTACADO REGION OF TEXAS ...... 56 Abstract ...... 56 Introduction ...... 57 Study Area ...... 61 Insect Sampling ...... 63 Sample Processing ...... 64 Local Habitat Sampling ...... 65 Pollinator Community Composition ...... 66 Data Analysis ...... 66 Results...... 69 Bee Richness and Abundance ...... 69 Floral Richness and Abundance ...... 71 Bee Habitat Data ...... 73 PCA Results ...... 73 NMDS Results ...... 73 Generalized Linear Mixed Model Results ...... 74 Bee Communities ...... 74 Bee Communities and Adjacency to CRP Lands ...... 75 Bee Communities and Farm Type ...... 75 Bee Communities and Playas ...... 76 Bee Communities and Soil Type ...... 76 Floral Communities and CRP Lands ...... 78 Floral Communities and Farm Type ...... 78 Floral Communities and Playas ...... 78 Floral Communities and Soil Types ...... 79 Discussion ...... 79

LITERATURE CITED ...... 85

Texas Tech University, Samuel Discua, May 2021

IV. HABITAT ANALYSIS OF NATIVE BEES AND FLORAL RESOURCES

IN THE LLANO ESTACADO AGRICULTURAL REGION OF TEXAS ...... 137 Abstract ...... 137 Introduction ...... 138 Materials and Methods ...... 143 Study Area ...... 143 Insect Sampling ...... 144 Land Cover data...... 144 Landscape Composition and Configuration Metrics ...... 145 Pollinator Community Composition ...... 146 Data Analysis ...... 147 Results...... 150 Land Cover within Buffers ...... 150 PCA Results ...... 150 NMDS Results ...... 150 Generalized Mixed Model Results ...... 151 Bee communities ...... 151 Floral communities and habitat data ...... 152 Discussion ...... 152

LITERATURE CITED ...... 157 V. AN ANNOTATED CHECKLIST OF THE BEES (HYMENOPTERA: APOIDEA: ANTHOPHILA) OF A 14-COUNTY REGION IN THE TEXAS HIGH PLAINS ...... 183 Abstract ...... 183 Introduction ...... 184 Materials and Methods ...... 189 Results...... 192 Discussion ...... 193

LITERATURE CITED ...... 197

VI. CONCLUSIONS ...... 249 v

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LITERATURE CITED ...... 254 APPENDICES ...... 277 A. List of additional plant varieties tested in the plant attractiveness study in Wildflower Plot 1, 2017, arranged in alphabetic order……………………..277 B. List of additional plant varieties tested in the plant attractiveness study in Wildflower Plot 2, 2017, arranged in alphabetic order……………………...278 C. List of additional plant varieties tested in the plant attractiveness study in Native grass and wildflower Plot 3, 2017, arranged in alphabetic order……279 D. Relative abundances of insect groups observed in three additional wildflower plots added in 2017 at Texas Tech University Quaker Avenue Research Farm……………………………………………………………………...... 280 E. Bees (Hymenoptera: Apoidea: Anthophila) of the Llano Estacado region of Texas and New Mexico…………………………………………………….287

Texas Tech University, Samuel Discua, May 2021

LIST OF TABLES

2.1. List of plant varieties used in the plant attractiveness study in 2016-2017, arranged in alphabetic order...... 51 2.2. Breakdown of insect groups observed the field plot at the Texas Tech University Quaker Avenue Research Farm in 2017...... 52 2.3. Plants ranked by species richness...... 54 3.1. Site descriptions, characteristics, and dates sampled in 2016-2017 of 43 farmland habitats across seven counties in the Llano Estacado region of Texas...... 99 3.2. Total number of bees collected by genera across 43 farmland habitats in the Llano Estacado region of Texas in 2016 and 2017...... 102 3.3. Bee abundance and richness collected per site across 43 farmland habitats in the Llano Estacado region of Texas in 2016 and 2017...... 104 3.4. Average number of bees collected by sampling season, generic richness, Shannon’s evenness index and Shannon’s diversity index across 43 farmland habitats in the Llano Estacado region of Texas in 2016 and 2017...... 107 3.5. Average number open of flowers/flower heads observed by sampling season, Species richness, Shannon’s evenness index and Shannon’s diversity index across 43 farmland habitats in the Llano Estacado region of Texas in 2016 and 2017...... 108 3.6. Percentage of bare ground, short grasses, tall grasses, and forbs; and number of ground nests, and woody stems within observed within transects across four sampling seasons in 2016 and 2017 on 43 farmland habitats across the Llano Estacado region of Texas...... 109 3.7. Number of open flowers/flower heads by plant species observed in standard transects in 2016 and 2017 across 43 farmland habitats in the Llano Estacado region of Texas...... 110 3.8. Correlation coefficients between PC1 and PC2 scores for local habitat variables of principal component across 43 farmland habitats in the Llano Estacado region of Texas during four sampling seasons. Season A - April- May 2016, Season B – Jul-Aug 2016, Season C – Sept-Oct, Season D – May-Jun 2017...... 114 3.9. Variance Inflation Factors (VIF) of local habitat variables during four sampling seasons used to fit Generalized Linear Mixed Models to predict bee abundance, richness, and diversity...... 119 3.10. Variance Inflation Factors (VIF) of local habitat variables during four sampling seasons used to fit Generalized Linear Mixed Models to predict floral abundance, richness, and diversity...... 120 vii

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3.11. Parameter estimates of Generalized Linear Mixed Models examining bee richness, abundance, and Shannon’s diversity indices at four collecting seasons and local habitat variables across 43 farmland habitats in the Llano Estacado region of Texas during 2016 and 2017...... 121 3.12. Parameter estimates of Generalized Linear Mixed Models examining floral richness, abundance, and Shannon’s diversity indices at four collecting seasons and local habitat variables across 43 farmland habitats in the Llano Estacado region of Texas during 2016 and 2017...... 123 3.13. Parameter estimates of Generalized Linear Mixed Models examining type III tests of fixed effects for bee richness, abundance, and Shannon’s diversity indices at four collecting seasons and local habitat variables across 43 farmland habitats in the Llano Estacado region of Texas during 2016 and 2017...... 125 3.14. Tukey-Kramer post-hoc pairwise comparisons of least square means of bee richness, abundance, and Shannon’s diversity indices across 18 CRP and 24 non-CRP sites during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017...... 126 3.15. Tukey-Kramer post-hoc pairwise comparisons of least square means of bee richness, abundance, and Shannon’s diversity indices across 43 agroecosystems grouped by farm types sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017...... 127 3.16. Tukey-Kramer post-hoc pairwise comparisons of least square means of bee richness, abundance, and Shannon’s diversity indices across 15 playa and 28 non-playa sites sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017...... 129 3.17. Tukey-Kramer post-hoc pairwise comparisons of least square means of bee richness, abundance, and Shannon’s diversity indices across 43 farmland habitats grouped by soil series sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017...... 130 3.18. Parameter estimates of Generalized Linear Mixed Models examining type III tests of fixed effects for floral richness, abundance, and Shannon’s diversity indices at four collecting seasons and local habitat variables across 43 farmland habitats in the Llano Estacado region of Texas during 2016 and 2017...... 132 3.19. Tukey-Kramer post-hoc pairwise comparisons of least square means of floral richness, abundance, and Shannon’s diversity indices across 18 CRP and 24 non-CRP sites during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017...... 133 3.20. Tukey-Kramer post-hoc pairwise comparisons of least square means of floral richness, abundance, and Shannon’s diversity indices across 43

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farmland habitats grouped by farm types sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017...... 134 3.21. Tukey-Kramer post-hoc pairwise comparisons of least square means of floral richness, abundance, and Shannon’s diversity indices across 15 playa and 28 non-playa sites sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017...... 135 3.22. Tukey-Kramer post-hoc pairwise comparisons of least square means of floral richness, abundance, and Shannon’s diversity indices across 43 agroecosystems grouped by soil series sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017...... 136 4.1. Percentage of land-use types, Edge density (in meters), Patch richness, Shannon’s diversity index, and Shannon’s evenness index within 200, 500, and 1000 m buffers on 43 farmland habitats across the Llano Estacado region of Texas in 2016 and 2017...... 168 4.2. Correlation coefficients between PC1 and PC2 scores for habitat variables at 200, 500 and 1000 m of principal component analyses of 43 farmland habitats in the Llano Estacado region of Texas during 2016 and 2017...... 169 4.3. Variance Inflation Factors (VIF) of land cover variables within 200, 500, and 1000 m buffers used to fit Generalized Linear Mixed Models to predict bee and floral abundance, richness, and diversity...... 178 4.4. Parameter estimates of Generalized Linear Mixed Models examining bee richness, abundance, Shannon’s diversity indices, and land-cover types at 200, 500, 1000 m buffers across 43 farmland habitats in the Llano Estacado region of Texas during 2016...... 179 4.5. Parameter estimates of Generalized Linear Mixed Models examining bee richness, abundance, Shannon’s diversity indices, and land-cover types at 200, 500, 1000 m buffers across 43 farmland habitats in the Llano Estacado region of Texas during 2017...... 180 4.6. Parameter estimates of Generalized Linear Mixed Models examining floral richness, abundance, Shannon’s diversity indices, and land-cover types at 200, 500, 1000 m buffers across 43 farmland habitats in the Llano Estacado region of Texas during 2016 ...... 181 4.7. Parameter estimates of Generalized Linear Mixed Models examining floral richness, abundance, Shannon’s diversity indices, and land-cover types at 200, 500, 1000 m buffers across 43 farmland habitats in the Llano Estacado region of Texas during 2017...... 182 5.1. Annotated checklist of bees in a 14-county portion of the Llano Estacado region of Texas...... 208 6.1. List of additional plant varieties tested in the plant attractiveness study in Wildflower Plot 1, 2017, arranged in alphabetic order...... 277 ix

Texas Tech University, Samuel Discua, May 2021

6.2. List of additional plant varieties tested in the plant attractiveness study in Wildflower Plot 2, 2017, arranged in alphabetic order...... 278 6.3. List of additional plant varieties tested in the plant attractiveness study in Native grass and wildflower Plot 3, 2017, arranged in alphabetic order...... 279 6.4. Bees (Hymenoptera: Apoidea: Anthophila) of the Llano Estacado region of Texas and New Mexico...... 287

Texas Tech University, Samuel Discua, May 2021

LIST OF FIGURES

2.1. A and C, Arrangement of experimental plots at Texas Tech University Quaker Avenue Research Farm. B, Russian Sage Salvia yangii Benth...... 47 2.2. Bloom intensity scores of 32 plant varieties planted at the Texas Tech University Quaker Avenue Research Farm 2017. Bloom intensity was measured as follows: 0 (absence of bloom), 1 (<⅓ of maximum), 2 (⅓–⅔ of maximum), 3 (full bloom,>⅔ of maximum); after Anderson and Hubricht (1940)...... 48 2.3. Relative abundance of insect groups observed in 2016 and 2017 in the experimental plot at the Texas Tech University Quaker Avenue Research Farm. Detailed taxonomic groups are given in Table 2.2...... 49 2.4. Daily mean numbers of insects per snapshot per plant varieties at the Texas Tech University Quaker Avenue Research Farm in 2017...... 50 3.1. Location of 43 sites sampled during 2016-2017 within the Llano Estacado region of Texas...... 98 3.2. Number of bees by family collected during sampling seasons across 43 farmland habitats in the Llano Estacado region of Texas in 2016 and 2017...... 106 3.3. Principal component analyses of local habitat associations across 43 farmland habitats in the Llano Estacado region of Texas during four sampling seasons: Season A - April-May 2016, Season B – Jul-Aug 2016, Season C – Sept-Oct, Season D – May-Jun 2017...... 113 3.4. Nonmetric multidimensional scaling ordinations of bee communities and local habitat associations across 42 farmland habitats in the Llano Estacado region of Texas during Season A - April-May 2016...... 115 3.5. Nonmetric Multidimensional Scaling ordinations of bee communities and local habitat associations across 43 agroecosystems in the Llano Estacado region of Texas during Season B – Jul-Aug 2016...... 116 3.6. Nonmetric Multidimensional Scaling ordinations of bee communities and local habitat associations across 43 agroecosystems in the Llano Estacado region of Texas during Season C – Sep-Oct 2016 ...... 117 3.7. Nonmetric Multidimensional Scaling ordinations of bee communities and local habitat associations across 43 agroecosystems in the Llano Estacado region of Texas during Season D – May-Jun 2017...... 118 4.1. Principal component analyses of habitat associations at 200, 500, and 1000 m across 43 farmland habitats in the Llano Estacado region of Texas during 2016...... 170

Texas Tech University, Samuel Discua, May 2021

4.2. Principal component analyses of habitat associations at 200, 500, and 1000 m across 43 farmland habitats in the Llano Estacado region of Texas during 2017...... 171 4.3. Nonmetric Multidimensional Scaling ordinations of bee communities and habitat associations at 200 m scale across 43 farmland sites in the Llano Estacado region of Texas during 2016...... 172 4.4. Nonmetric Multidimensional Scaling ordinations of bee communities and habitat associations at 500 m across 43 farmland sites in the Llano Estacado region of Texas during 2016...... 173 4.5. Nonmetric Multidimensional Scaling ordinations of bee communities and habitat associations at 1000 m across 43 farmland sites in the Llano Estacado region of Texas during 2016 ...... 174 4.6. Nonmetric Multidimensional Scaling ordinations of bee communities and habitat variables at 200 m across 43 habitats in the Llano Estacado region of Texas during 2017...... 175 4.7. Nonmetric Multidimensional Scaling ordinations of bee communities and habitat associations at 500 m across 43 farmland habitats in the Llano Estacado region of Texas during 2017...... 176 4.8. Nonmetric Multidimensional Scaling ordinations of bee communities and habitat associations at 1000 m across 43 farmland habitats in the Llano Estacado region of Texas during 2017...... 177 5.1. Numbers of bee species per county in 14 counties of the Llano Estacado region of Texas...... 248 6.1. Relative abundances of insect groups observed in three additional wildflower plots added in 2017 at Texas Tech University Quaker Avenue Research Farm...... 280

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ABSTRACT

Bees (Hymenoptera: Apoidea: Anthophila) are the most important pollinators; they pollinate 80% of all flowering plants worldwide, thus helping to maintain native plant communities while contributing to pollination services in agriculture. Loss of natural habitat, pesticide over-use, invasive species, diseases, and climate change have caused managed and wild bee populations to decline worldwide. The loss of natural land cover threatens pollinator populations, especially in agricultural regions where landscape change is geographically broad. In the US High Plains in western Texas, little is known about the influence of agricultural production on pollinator communities. Moreover, growing scientific evidence has shown the importance of enhancing the pollinator habitat and floral resources in agroecosystems, particularly wildflower plantings, to support pollinators and other wildlife.

However, empirical information is needed to better understand habitat associations at the local and landscape levels. The main objectives of my research were to 1) document the attractiveness of native and exotic drought-tolerant plants to foraging insect pollinators

(Chapter 2); 2) determine the variation in and relationships of local and landscape habitat and land cover factors with bee communities (Chapters 3 and 4); and 3) develop a checklist and report new records of the Apoidea in farmlands in the High Plains region (Chapter 5).

The attractiveness of 30 drought-tolerant plants to insect floral visitors located in 60 plant/patches was determined at an experimental plot and in restored habitats at Quaker

Avenue Research Farm (Plant and Soil Science, Texas Tech University). A total of 46 insect morphospecies were observed from the experimental plots. Bees dominated the total number of pollinators; honey bees (Apis mellifera) were the most frequently observed insect and

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Texas Tech University, Samuel Discua, May 2021 accounted for 31.1% of all observations. Russian sage (Salvia yangii) and Catmint (Nepeta x faassenii, ‘Walker's Low’) attracted the most insect visitors. Native plants attracted the highest diversity of insect species, with Indian blanket (Gaillardia pulchella) attracting 12 bees, three butterflies, and two fly species across sampling dates.

The relationships of local habitat structures and native bee communities in ruderal farm habitats, pivot corners, playa edges, and fallow fields adjacent to cultivated crop fields were investigated. Bee communities and habitats were sampled across several farm types: organic and conventional cotton farms, vegetable and fruit farms, Conservation Reserve

Program (CRP) lands, and vineyards. Selected habitat patches on farms were sampled during four seasons from 2016 to 2017. Pollinator communities were collected using pan traps and by hand netting across 43 habitat patches and 21 farms (i.e., homogenous habitat patches of various sizes). At each habitat patch, local floral resources (open flower heads) and the composition of vegetation along two 60m x 2m belt transects were quantified. Land cover/land use was determined for 200, 500, and 1000 m buffers surrounding each sampled habitat patch. Multivariate and mixed-effect models were used to assess the relationships of habitat, landscape variables, and wild bee and floral abundances and species richness.

Across farms and sampling dates, over 17,000 bees belonging to 106 species/morphospecies and 49 genera were identified, along with 95 wildflower species/morphospecies belonging to 49 genera. Non-metric multidimensional scaling results showed high similarities in the bee communities across land use and farm types. Mixed model results showed that sites adjacent to CRP had higher bee richness and abundance by sampling seasons or when combining the data by year. The percentage of forbs was a significant factor

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Texas Tech University, Samuel Discua, May 2021 in predicting floral and bee abundance, richness, and diversity across sampling seasons.

Across sites, bee richness increased in relation to the proportion of natural land-use type, while bee diversity was reduced by the proportion of highly developed urban areas. Using data from recent collections and the current study, a checklist of the Apoidea across a 14- county region of the Texas High Plains was developed and produced a total of 286 species and 603 new county records. Information from these studies advances what is known about regional bee biodiversity and environmental drivers of communities in the region, which should inform strategies for conservation and enhancing habitat for pollinators in this important grassland and agricultural region.

Texas Tech University, Samuel Discua, May 2021

CHAPTER I

INTRODUCTION

Value of Pollination Services Provided by Bees

Animal pollination is a vital ecosystem service, as it regulates the reproduction of angiosperms, or flowering plants. Up to 87% of all angiosperms depend on one or more of approximately 200,000 vertebrate and invertebrate species of animals for pollination (Ollerton et al. 2011, Vanbergen and Initiative 2013). Flowering plants are the primary producers in many ecosystems and provide resources and habitat for many other organisms, including large omnivores and herbivores. Moreover, more than three quarters of major global food crops (e.g., almonds (Prunus dulcis), apples (Malus domestica), blueberries (Vaccinium corymbosum), cacao (Theobroma cacao), coffee

(Coffea arabica), cotton (Gossypium hirsutum), melons (Cucurbitaceae), watermelons

(Citrullus lanatus)) benefit from animal pollination for crop production and yield stabilization (Klein et al. 2007). Pollination-dependent crops account for 35% of global crop production (Ingram et al. 1996, Klein et al. 2007). The global value of animal pollination services has been estimated at 5-8% of the current crop production, with a market value of $235 to $577 billion (Aizen and Harder 2009, Lautenbach et al.

2012). This value also translates to human health: Crops pollinated by animals supply a major proportion of the micronutrients in human diets (Eilers et al. 2011). Animal pollination affects human health and nutritional security, as many of the fruit, vegetable, seed, nut, and oil crops that provide essential micronutrients (e.g., vitamin

A, iron, and folate) are dependent on pollination (Chaplin-Kramer et al. 2014).

Texas Tech University, Samuel Discua, May 2021

Several insect groups (namely, bees (Anthophila), butterflies (Lepidoptera), moths (Lepidoptera), beetles (Coleoptera), and flies (Diptera)) contribute to the pollination requirements of over 308,000 species of flowering plants worldwide (Klein et al. 2007, Ollerton et al. 2011). Of these, bees are among the most important. As ubiquitous and abundant insect pollinators, bees play a major role in sustaining wildlife through the pollination and maintenance of flowering plant communities.

Bees are a monophyletic lineage within the superfamily Apoidea, which also includes four predatory wasp families (Heterogynaidae, Ampulicidae, Sphecidae, and

Crabronidae). Molecular data suggest that bees arose from within the family

Crabronidae (Debevec et al. 2012). Worldwide, there are more than 25,000 species of bees belonging to seven families, and in North America, there are approximately 4,000 bee species belonging to six families: Andrenidae, Apidae, Colletidae, Halictidae,

Megachilidae, and Melittidae (Ascher and Pickering 2020).

In addition to these native bee species in North America, a great deal of pollination is also performed by an exotic bee species. Honey bees (Apis mellifera) are the most important managed pollinator in the world. Globally, there are approximately

81 million honey bee hives producing approximately 1.6 million tons of honey annually (Potts et al. 2016). Thus, honey bees represent an important economic resource for many rural communities worldwide (Potts et al. 2016). Globally, worldwide stocks of honey bees have increased by 45% since 1961; however, the demand for pollination services has increased much more rapidly (+300%) than the supply of honey bees (Aizen and Harder 2009). Apart from the value derived from

Texas Tech University, Samuel Discua, May 2021 managed honey bees, wild (i.e., native) bees have significant roles in agricultural production. Given that the supply of honey bees has not met the demand for pollination worldwide, wild bees provide insurance against further potential losses of honey bees and other key pollinators (Winfree et al. 2007).

In agricultural production, there is a need for pollination services, and some studies have begun to assess what portion of the wild pollinator community is present in crops and contributes to crop pollination e.g. (Cusser 2016), While the contributions of wild pollinators to global crop production are difficult to estimate, Losey and

Vaughan (2006) have estimated that the value of pollination from wild bees annually in the U.S. was over $3 billion annually. Indeed, numerous studies have demonstrated that wild bee species contribute more to the global crop production of several crops than managed honey bees (Bohart 1972, Greenleaf and Kremen 2006, Garibaldi et al.

2013, Garibaldi et al. 2014). Crops such as alfalfa (Medicago sativa) apples (Malus domestica), blueberries (Vaccinium corymbosum), coffee (Coffea arabica), pumpkins

(Cucurbit pepo), and tomatoes (Solanum lycopersicum) are associated with native wild pollinators that have co-evolved with their native counterpart plants (Burkle et al.

2013). An example is the native squash bee (Peponapis pruinosa), which evolved in the region with the buffalo gourd (Cucurbita foetidissima) and is now the requisite pollinator in pumpkins. Similarly, wild bees have been shown to have greater pollination efficiency and result in a greater fruit set in sweet cherry compared to honey bees (Holzschuh et al. 2012). (Winfree et al. 2007) measured the pollination of watermelon across 23 farms and determined that native wild bees alone provided

Texas Tech University, Samuel Discua, May 2021 sufficient pollination services at >90% of the farms, and the total pollen deposition in flowers was strongly correlated with native bee visitations, but not honey bee visitations. (Blaauw and Isaacs 2014b) have found that fruit set, berry weight, and mature seeds per blueberry were significantly greater in fields adjacent to wildflower plantings three to four years after (wildflower) seeding, associated with annual increases in wild bee abundances. Across 41 cropping systems globally, (Garibaldi et al. 2013) found positive associations of wild bee visits and fruit set, whereas fruit set attributed to honey bees increased significantly in only 14% of the systems surveyed.

The results from this study further suggested that, because wild bees and honey bees promoted fruit set independently, practices to conserve both managed and wild bees would be beneficial to global crop yields (Garibaldi et al. 2013).

While a consideration of overall pollinator biodiversity should be central to strategies that promote conservation, it has been estimated that 80% of the pollination of global crops can be attributed to the activities of just 2% of wild bee species; thus, efforts to sustain pollination services (which is dominated by just a few species, including the honey bee) might not align with those focused on the conservation of overall pollinator biodiversity (Kleijn 2015). Therefore, conservation strategies could focus on the conservation of pollination services for crop production or on the conservation of species-level biodiversity and specialist plant-pollinator networks

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(Winfree 2015). Such strategies are crucial, given that declines in pollinators have become an important issue globally.

Global Pollinator Declines

Numerous studies have reported evidence of regional, national, and global declines of pollinators. For example, there is evidence of regional declines in honey bee populations in the U.S. and Europe. In the U.S., honey bee colonies declined by

59% between 1947 and 2005 (National Research Council 2007), and in central

Europe, 25% of honey bee colonies were lost between 1985 and 2005 (Potts 2010).

Similarly, wild pollinator declines have been well-documented in certain regions of northwest Europe (Nieto et al. 2017) and North America (National Research Council

2007). In Europe, Nieto et al. (2017) have reported that out of a total of 1,101 species,

9.2% of bees were threatened with extinction, and 5.2% were near-threatened. The

International Union for the Conservation of Nature and the Status and Trends of

European Pollinators program reported that nearly one in 10 wild bee species faces extinction in Europe, and that 56.7% of the 1,965 species were classified as data- deficient and therefore potentially facing unknown threats (Nieto‐Sánchez 2015). As of 2021, 93 insect species in the U.S. are listed as threatened or endangered by the

U.S. Fish and Wildlife Service (U.S. Fish and Wildlife Service 2020). Among them, eight bee species, consisting of the rusty patched bumble bee, Bombus affinis, which is native to the Great Lakes and eastern U.S., and seven bees in the genus Hylaeus that are endemic to the state of Hawaii, have been listed as endangered species. The Xerces

Society for Invertebrate Conservation currently lists 49 North American wild bees that

Texas Tech University, Samuel Discua, May 2021 are considered vulnerable, imperiled, critically imperiled, or possibly extinct in addition to those mentioned above.

Recent studies have shown that in addition to the losses of key pollinators of crops, the loss of plant-pollinator networks (i.e., plant-pollinator biodiversity) is an attribute of pollinator decline. (Burkle et al. 2013) quantified the degree to which global change has disrupted plant-pollinator interactions over 120 years in a forest understory community in Illinois; they found that 50% of the bee species had been extirpated (i.e., had gone locally extinct) and 407 pollinator-plant interactions were lost, including 183 that were lost because of bee extirpations. Moreover, this study found that the number of bee species declined from 109 in the 1800s to 54 in 2009-

2010. These losses are alarming because (Brosi and Briggs 2013) found that removing only one species from a pollinator community can have profound consequences on plant reproduction through reductions in floral fidelity among the remaining pollinators, and this can affect plant reproduction even when potentially effective pollinators remain in the ecosystem.

The causes for these declines are not yet fully understood, but agricultural intensification, landscape fragmentation, pesticides, pathogen introductions, and climate change are some of the factors that influence pollinators (Wardell 1998;

National Research Council 2007; Potts 2010; Lebuhn 2013; Vanbergen 2013).

Although information for bee monitoring and threat assessments has improved, long- term national or international assessments of both pollinators and pollination are needed to provide information on status and trends for most species in most parts of

Texas Tech University, Samuel Discua, May 2021 the world (Potts et al. 2016). Our inability to quantify the biodiversity of native pollinators and our lack of understanding of the threats to native wild pollinators (i.e., their habitat resource needs) could underestimate threats and hinder conservation efforts.

Effects of Habitat Quality on Pollinators

Land-use changes that result in habitat loss, fragmentation, degradation, and reduced resources are persistent threats to pollinator communities. Evidence has emerged showing that landscape composition and farm management for landscape composition affect bee abundance and richness (Kennedy et al. 2013). Several studies have found that the proportion of natural land around an agroecosystem, size of native patches, and patch quality positively affect bee species richness and crop pollination

(Kremen et al. 2002). Indeed, Morandin and Winston (2006) used a cost-benefit model to determine that yield and profit could be maximized with 30% of land left uncultivated (within 750m of field edges) for pollinator habitat.

Although the proximity and cover of high-quality habitats appear to directly favor pollinators in agroecosystems, the flowering characteristics of wild plants and crops could have detrimental effects on each other based on species’ flowering phenologies and push-pull mechanisms between wild habitats and crop production systems (Blaauw and Isaacs 2014). For example, steady-state flowering affected wild and honey bee densities in coffee, with native bees potentially drawn from pollinator- dependent systems towards those with less reward (Peters et al. 2013).

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For crop production, it is important to understand how rewards and threats in crops influence population conditions and bee health across different crop cultivars.

Furthermore, pollinator-attractiveness could be silenced in mass-flowering crops that are not dependent on insects, especially where proximal insect-dependent crops or wild plants depend on wild pollinators. As these studies illustrate, landscape-scaled and whole-farm approaches that seek to understand the interplay among these effects are needed to better understand pollinator diversity and the management of habitat for pollination services.

Ecological Restoration for Bees and Other Pollinators

Applied conservation involving active restoration (generally, the increase in floral resources to improve habitat) and passive land management techniques (such as leaving intact areas of undisturbed, wild habitat) are two habitat restoration practices that could improve pollinator communities. A growing body of evidence shows the influence of semi-natural habitats on wild bee diversity in agroecosystems, and the consensus is that the proximity of high-quality, semi-natural habitats such as perennial native grasslands strongly promotes bee diversity (Kremen et al. 2004, Greenleaf and

Kremen 2006, Krewenka et al. 2011, Holzschuh et al. 2012, Kennedy et al. 2013,

Bennett and Isaacs 2014). Moreover, planted wildflower strips also represent potentially rich human-made habitats for bees in agriculturally dominated landscapes

(Wratten et al. 2012).

Texas Tech University, Samuel Discua, May 2021

In addition to agricultural systems, gardens in urban areas have been a focus for bee diversity studies and habitat conservation (Foltz Sweat et al. 2009, Pawelek et al. 2009, Werrell et al. 2009), and urban beekeeping has become a common activity in even some high-density urban areas. As in agroecosystems, local-level (e.g., presence of native plants, floral abundance, vegetation height, grass cover) and landscape-level

(land use/land cover composition) characteristics influence urban bee diversity (Pardee and Philpott 2014). Interestingly, however, Matteson and Langellotto (2011) found that native plant additions did not increase beneficial insects in New York City, and that beneficial insects heavily utilized exotic plants. Furthermore, contrary to their hypothesis, the amount of green space in the surrounding landscape was not a significant predictor of bee species richness; local within-garden floral area and sunlight availability were the factors included in the highly supported models.

Therefore, factors influencing bee diversity in urban areas may differ from those in agroecosystems.

Numerous initiatives and programs are available through governmental and non-governmental agencies that provide resources for enhancing and restoring pollinator habitat in agroecosystems and urban areas alike. In the US, for example, farmers can enroll in programs to establish conservation practices to sustain pollinator nesting and foraging resources, including planting annual and perennial wildflowers

(Winfree 2010). In agroecosystems, farmers can enroll in programs aimed at improving pollinator habitat, such as conservation practices that earn points for incentive-based programs (e.g., the pollinator habitat conservation practice of CP-42

Texas Tech University, Samuel Discua, May 2021 by the USDA NRCS Conservation Reserve Program). One of the regions in the U.S. with the highest acreage of land under CRP is the High Plains Region of Texas.

Agriculture and Pollination Services in the Texas High Plains

The U.S. High Plains is Level III Ecoregion (Region 25) that comprises the southern end of the Western Great Plains of the central United States (Omernik and

Griffith 2014). The High Plains is a relatively level high plateau that is separated from the Southwestern Tablelands ecoregion (Region 26, Level III) by the Caprock

Escarpment. The High Plains ecoregion is further subdivided into five smaller-scale

Level VI ecoregions: Rolling Sand Plains, Canadian/Cimarron High Plains, Llano

Estacado (“Staked Plains” in English), Shinnery Sands, and Arid Llano Estacado

(Omernik and Griffith 2014). Of these smaller-scale ecoregions, the Llano Estacado, which encompasses eastern New Mexico and northeastern Texas, is one of the largest mesas on the North American continent, with an area of 97,000 km2. The region is characterized by a cold, semi-arid climate (BSk Kopen climate classification) that features hot summers and mild winters. Elevations in this region range from 900 to

1,350 meters above sea level. Annual precipitation ranges from 380 to 560 m and is lowest during the winter and mid-summer months, and highest during April- or May and September- or October (Glenn et al. 2007). The surface texture of soils ranges from clay on hardland sites in the north to sandy soils in the southern portion of the region. Caliche generally underlies these surface soils at depths of two (60-153.96 cm) to five feet (152.4 cm). Predominant soil types in the region include Amarillo, Acuff,

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Berda, Bippus, Brownfield, Lofton, Nutivoli, Patricia, Pullman, Randall, and Olton

(Glenn et al. 2007).

Native vegetation in the Llano Estacado was once dominated by shortgrass prairie species, including buffalograss (Bouteloua dactyloides), blue grama (Bouteloua gracilis), and sideoats grama (Bouteloua curtipendula), along with a rich diversity of wildflowers. Little bluestem (Schizachyrium scoparium) and yucca (Yucca spp.) are common invaders in parts of the region, as are shinnery oak (Quercus havardii) and sand sage (Artemisia filifolia) in sandy regions. Juniper (Juniperus spp.) has spread from the breaks onto the plains in some areas in the region.

Approximately 80% of the Llano Estacado area is tilled for agriculture

(Schmidley 2002), with irrigation for crop production coming primarily from the

Ogallala Aquifer. Major crops in the region include upland cotton (Gossypium hirsutum), corn (Zea mays), sorghum (Sorghum bicolor), and winter wheat (Triticum aestivum) produced under dryland or irrigated agriculture. The Texas High Plains region has the second-largest contiguous planted cotton acreage in the world, and on a five-year average, it produces 66% of the Texas and 25% of the cotton crop annually in the U.S. (Plains Cotton Growers 2020). On average, 1.5 million hectares are planted with cotton every year, producing 3.7 million bales (Plains Cotton Growers, 2020).

Although cotton production is vast in the region, cotton does not depend on wild bee pollinators to provide outcross pollination. Despite this, Cusser et al. (2016) found increased cotton seed weight from farms containing a portion of wild land; this

Texas Tech University, Samuel Discua, May 2021 provided more wild bees to perform outcross pollination, which was estimated to provide a $266 per-hectare increase in crop value.

Specialty, cover, and hay crops produced in the Texas High Plains region include alfalfa, cucurbits (squash, watermelons, and pumpkin), apple, wine grapes, and sunflowers, and small vegetable farms. Many of these crops either benefit from or rely on pollination by both managed and wild bees. Jewett (2017) surveyed pollinators in Texas pumpkins fields and found that wild and native squash bees (Peponapis spp. and Xenoglossa spp.) were far more abundant in pumpkin fields than honey bees, even when honey bee hives were rented and placed close to pumpkin fields.

Koh et al. (2016) modelled the status, trends, and impacts of wild bee abundance in the United States. The study identified the Texas High Plains as one of the regions of major pollinator mismatches in the U.S., where pollination demand in cultivated areas was higher than the supply of wild bee pollinators. This is concerning since little is known about the effect of intensive agriculture on native bee populations in the Llano Estacado region of Texas. For example, the current studies discussed here show that the known bee genera in the region increased approximately two-fold. More studies are needed, and some are underway, to better understand pollinators in regional agriculture and wild lands.

Native Bees in the Texas High Plains

There are at least 900 native bee species belonging to six families in the state of Texas (Ascher and Pickering 2020). Based on historic museum records, there are

Texas Tech University, Samuel Discua, May 2021

286 bee species (belonging to five families and 59 genera) reported in the 43 counties of the Texas High Plains. Only 26 species/morphospecies are documented from the

Texas High Plains exist prior to 1949. Besides occasional collections and expeditions from bee taxonomists between 1950 and 1979 (e.g., Michener, LaBerge, and Rozen), few studies documented native bees in the Texas High Plains region during that period. Moffett (1980), Berger (1982), Maldonado (1993) collected and observed native bees in the Llano Estacado region in 1979, 1980-81, and 1991, respectively.

Moffett et al. (1980) conducted a preliminary survey of cotton pollinators in the Texas High Plains in 1979. They collected 35 species of wild bees and found that

Agapostemon angelicus Cockerell was the most important cotton pollinator. Berger’s

(1982) study builds on Moffet et al.’s (1980) findings and documents the potential of

A. angelicus and other wild bees as potential pollinators of male-sterile cotton on the

Texas High Plains. Berger (1982) sampled bee species across 13 counties (Lamb,

Hale, Floyd, Cochran, Hockley, Lubbock, Crosby, Dickens, Terry, Lynn, Garza,

Gaines, and Dawson) during the summers of 1980 and 1981, focusing on cotton, alfalfa, 34 species of wildflowers, and weeds; a total of 67 species/morphospecies of wild bees were found. Although this study did not provide many accurate species identifications, it was the most extensive collection of bees of the Texas High Plains until recent studies in the early 2010s. Finally, Maldonado (1993) collected 23 bee species/morphospecies in Lubbock county for four consecutive days in hybrid sunflower (Helianthus annus) seed production fields in July of 1991. These included

Texas Tech University, Samuel Discua, May 2021

15 species in seven genera of Apidae, seven species in three genera of Halictidae, and one genus and species of Megachilidae.

More recently, there has been renewed interest in studying bees in the Llano

Estacado, with four separate studies in the 2010s: those of Begosh (2017), Patridge

(2017), Auerbach et al. (2019), and the present study; all document bees and the impacts of land use on native bee communities. Begosh (2017) documented wild bees in the Texas High Plains in cropland, former cropland reclaimed as grassland, and native grasslands across nine counties (Bailey, Briscoe, Carson, Castro, Floyd, Gray,

Hockley, Lubbock, and Swisher) in 2013 and 2014; 127 species of wild bees representing 58 genera were collected. Begosh (2017) found that the reclaimed cropland sites had lower abundance, overall species richness, and diversity than did the native grassland and cropland sites. Auerbach et al. (2019) surveyed bee communities in the native prairies of Muleshoe and Buffalo Lake National Wildlife

Refuges in Bailey and Randall counties, respectively. Over a period of five months of sampling in 2013, a total of 180 bee species/morphospecies were collected in the presence/absence of prairie dog colonies. This study contains the most comprehensive number of bee species found to date and is used as a faunal reference for the present study.

The preceding studies on the Texas High Plains revealed a very diverse bee community, with large differences seen over relatively small geographic distances as a function of land use/land cover. More studies documenting native bees and the

Texas Tech University, Samuel Discua, May 2021 impacts of land use/land-cover type on bee communities clearly are needed for this region. The total number of bees in this region and the impacts of land-use change on pollinator biodiversity are unclear. The number of bee species currently reported in the

Texas High Plains is likely to be an underestimate. Considering that there have been few monitoring efforts until recently, changes in land use and native bee populations and their conservation status are likely, which provides a strong justification for the present study. My dissertation thus focuses on bee biodiversity and the environmental drivers of bee communities in the Llano Estacado region of the southwestern U.S.

Research Objectives

The present study seeks to better understand insect pollinator biodiversity

(with a focus on bees) and environmental drivers of bee communities in the Llano

Estacado region, focusing on both local and landscape factors. New information collected on the biodiversity of the Apoidea was compared to historical records and recent studies of bee communities in the Southern High Plains region (Berger 1982,

Texas Tech University, Samuel Discua, May 2021

Begosh 2018, and Auerbach et al 2019). The objectives of the current research include the following:

1. Quantify the attractiveness of drought-tolerant plants to insect floral

visitors (Chapter 2);

2. Investigate the variation in and relationships of pollinator communities

and local- and landscape-level habitat characteristics (Chapters 3 and 4);

3. Document the biodiversity of Apoidea in the region, including

comparisons with historical and contemporary datasets, with an additional

focus on non-native fauna and specialists (Chapter 5).

Rationale and Significance

Few studies have documented bee diversity in the Llano Estacado region of

Texas. Understanding this biodiversity and the environmental drivers of communities, including plant attractiveness, supports strategies that assist both species’ conservation and the conservation of important pollination services that these important insect communities provide.

Texas Tech University, Samuel Discua, May 2021

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1. CHAPTER II

POLLINATOR ATTRACTIVENESS OF DROUGHT-TOLERANT

PLANTS IN THE TEXAS HIGH PLAINS

Abstract

Urban and rural landscapes are important for providing floral resources to pollinating insects, yet determining attractiveness of specific plants to a variety of pollinators is warranted. The objective of this study was to determine the attractiveness of 30 different plants to insect floral visitors. On 14 dates in 2016 and

2017, floral abundances were measured and the number of insect visitors recorded. A total of 57 insect morphospecies were recorded, with bees (Apoidea: Anthophila) the most abundant pollinators and honey bees (Apis mellifera) the most frequently observed forager. Russian sage (Salvia farinacea) and Catmint (Nepeta x faassenii

‘Walker's Low’) attracted the greatest number of pollinators. Native plants adapted to the region attracted the highest diversity of pollinators. Thirteen plants, some that are considered good pollinator plants, attracted low numbers of insects. Five pollinator taxa (Anthophora californica/urbana, Apis mellifera, Agapostemon angelicus/texanus,

Lasioglossum spp., and Bombyliidae) were attracted to 10 or more different plants, while approximately 65 percent of the taxa were attracted to three or fewer plants.

Results support strategies for both grassland restoration and the selection of plants

Texas Tech University, Samuel Discua, May 2021 when resources and habitat for pollinators are considered in the management of urban green spaces.

Introduction

Insect pollinators are major components of global diversity and provide vital ecosystem services in managed and wild systems (Klein et al. 2007; Ollerton et al.

2011; Lautenbach et al. 2012). Approximately 87% of all flowering plants are pollinated by animals (Ollerton et al. 2011), with bees (Hymenoptera: Apoidea) being one of the most effective pollinators. However, insect pollinators are declining globally, with one of the major causes being the loss of natural habitat driven by land- use change (Potts et al. 2010). Changes in land use can result in habitat loss and degradation, habitat fragmentation, and reductions in habitat patch size and connectivity (Hendricks et al. 2007; Hadley and Betts 2012). Accordingly, pollinator habitat restoration aims to enhance and restore habitat for pollinators, including floral resources.

In recent years, the practice of urban gardening and landscape design has emerged to consider multiple benefits from using native plants as providing savings in yard maintenance and irrigation while promoting habitat for wildlife, including pollinators (Williams et al. 2011). In addition to the importance of determining which plant species that attract pollinators thrive in urban gardens, widespread threats associated with prolonged droughts and demand on freshwater resources emphasize a

Texas Tech University, Samuel Discua, May 2021 need to develop information to assist selection of plants for a variety of applications in urban environments.

Inclusion of flowering plants for enhancing green spaces and natural areas in urban environments to promote pollinators (Thompson et al. 2003) and enhancing floral resources in habitat fragments such as road edges, back-yard gardens, and flower beds supply habitat for bees in otherwise inhospitable environments (Westrich

1996). In rural or agricultural areas, enhancing floral resources for pollinators in ruderal (i.e., remnant wild land fragments not under production) can benefit multiple ecosystem services including pollination services provided by wild pollinators (Cusser et al. 2016). Urban green spaces, including parks and gardens, can house a wide diversity of insect pollinators (Matteson et al. 2008; Owen 2010; Hall et al. 2017), with private residential gardens often being the largest and probably the most important component of green spaces in urban ecosystems (Goddard et al. 2010).

Flowering plants in urban gardens are commonly selected based on aesthetics, local availability, or personal preference, and are often non-native. Although non-native plants in urban areas can be an important resource for pollinators (Matteson et al.

2008), native plants provide resources and an advantage over non-native plants because of co-evolution with diverse and native pollinator communities, and in some cases pollinators are highly specialized through plant-pollinator co-evolution

(Morandin and Kremen 2013; Harmon-Threatt and Kremen 2015). Accordingly, floral-specialist bees tend to be relatively scarce in urban habitats, as they no longer have access to native plants that they require for pollen and nectar (Frankie et al. 2009;

Texas Tech University, Samuel Discua, May 2021

Hernandez et al. 2009). However, the relative attractiveness of native and non-native flowering plants can depend on cues associated with plant nutritional quality, which can potentially override the importance of plant selection based on native or non- native status (Harmon-Threatt and Kremen 2015).

The ability to cultivate and manage pollinator resources in urban gardens and the more widespread adoption of native plantings for pollinators emphasize a need for determining the attractiveness of plants used in urban landscapes. Such information can be used to support recommendations and strategies to improve forage for pollinators in urban systems, which can support diverse pollinators (Banaszak-Cibicka and Zmihorski 2012; Hulsmann et al. 2015). The main objective of this study was to determine the attractiveness of 30 flowering plants to insect floral visitors (focusing on bees), considering plant bloom characteristics and the total anthophilous community observed foraging across the different plants.

Materials and Methods

Site Description and Field Experimental Design

The field experiment was conducted in Lubbock County in the western portion of the state of Texas and comprising the southern section of the broader U.S. High

Plains ecoregion (i.e., the Southern High Plains). Lubbock is the largest urban area in the region (population ca. 250,000) and is surrounded by over 400,000 hectares of farmland, dominated by cotton production. Being located in a semi-arid grassland region, the urban center has seen an interest of xeriscaping practices coupled with

Texas Tech University, Samuel Discua, May 2021 efforts to address major issues involving regional freshwater shortages. Accordingly, the use of drought-tolerant plants in urban gardens continues to be an area of focus, yet studies are needed to support the selection of drought-tolerant flowering plants based on benefits to pollinators and other wildlife.

The study plot was located at the Texas Tech University Quaker Avenue

Research Farm (33.59 N, -101.91 W). The field plot design was based on that of

Garbuzov and Ratnieks (2014a). The experimental plot consisted of two concentric circles with 30 1-m2 plant patches each, with the diameter of the inner circle 12.2 m and the diameter of the outer circle 18.2 m. Individual 1 m2 plant patches were separated by 0.5 m in the inner circle and 1.75 m in the outer circle. Plant varieties were replicated in each circle, and the position of plant varieties within circle patches was randomized (Figure 2.1). The field plot design was used to facilitate the strength of observational pollinator counts; affinity for a pollinator to a plant based on observations of two plants (over time) in both the outer circle and inner circle would be a robust estimate of the attraction of that plant to particular pollinators or pollinator groups. To compare the bee community found in the experimental plot and to ensure it was representative of the local bee community, three additional wildflower reference plots (i.e., “comparative plots”) proximal to the experimental plot (< 1 km) were sampled in 2017.

In July 2015, individual plants were transplanted to the center of each 1 m2 patch (60 patches total). Ambient soil in patches was mixed with 50% topsoil

(Timberline, Atlanta, GA) and used during transplanting. Plant patches were covered

Texas Tech University, Samuel Discua, May 2021 with landscape fabric and approximately 37.85 L of mulch were added to each patch.

A surface drip irrigation system was installed for the plot, with a 3.78 L emitter per plant. Plants were irrigated as necessary based on visually monitoring of moisture stress symptoms (i.e., wilting, leaf color, root zone soil moisture) during the duration of the experiment, with several prolonged periods of no supplemental irrigation needed. The total rainfall during the plot establishment and pollinator sampling was

118.11 mm in 2016 and 316.73 mm in 2017. Average annual precipitation for

Lubbock is ~480 mm.

Plant Selection

Thirty drought-tolerant flowering plant species (both native and non-native) were selected based on the following criteria: 1) they occurred in “pollinator friendly” plant list recommendations for the High Plains region provided through various entities e.g., Xerces Society, Lubbock Master Gardeners Association, Texas

SmartScape, and Ladybird Johnson Wildflower Center, 2) plants were abundant and/or diverse pollinating insects were noted in prior observations e.g., Salvia spp.), 3) general availability of the plant at home stores e.g., Russian Sage (Salvia yangii), coneflower (Echinacea spp.), Indian blanket, or (Gaillardia pulchella) and 4) the status of native to the Southern High Plains region e.g., blackfoot daisy

(Melampodium leucanthum), plains zinnia, (Zinnia grandiflora) and Tahoka daisy

(Machaeranthera tanacetifolia) (Table 2.1). Because I was interested in bloom characteristics and the attracted pollinators across growing seasons with limited rainfall, I biased selection towards native and drought-tolerant plants. Furthermore,

Texas Tech University, Samuel Discua, May 2021 due to its common occurrence and invasiveness in the field plot, two additional and separate patches of field bindweed (Convolvulus arvensis) that were delineated and managed in the experimental plot were additionally sampled on eight occasions in

2017.

Insect Floral Visitation Counts

On each sampling date, a 10-second snapshot observation was used to record the identity and number of insects visiting flowers. The “snapshot” method was chosen over other possible methods for its practicality and ease of implementation, allowing efficient sampling of all 60 plant patches while reducing potential re-counts of the same individuals. A foraging bout was recorded if an insect was observed to approach a flower through landing on petals and moving towards nectaries or actively gathering pollen (Garbuzov and Ratnieks 2014a).

Insects visiting flowers within the experimental plot were recorded at hourly intervals between 9:00 AM and 6:00 PM. The identity of each foraging insect was recorded to morphospecies and taxonomic name to the level of family, genus, or species (Table 2.1). In addition, the total numbers of individuals within each morphospecies or taxonomic group were recorded during each 10 s snapshot observation. Insect visitation data were collected from June to August on four occasions in 2016 and 10 occasions in 2017. Insect counts were made during days with favorable weather, consisting of sunny days (little cloud cover), average daily temperatures above 20 °C, and with winds less than 16.09 kph.

Texas Tech University, Samuel Discua, May 2021

Plant Measurements

Bloom intensity, i.e., the general bloom colors and floral abundances, has been shown to influence pollinator foraging (Balfour et al. 2013; Garbuzov and Ratnieks

2014a; Shackleton and Ratnieks 2016). On each day of data collection, the bloom intensity of plants was quantified by assigning a score 0 to 3, where 0 equaled absence of bloom, 1 denoted less than ⅓ of maximum, 2 denoted ⅓–⅔ of maximum and 3 denoted > ⅔ of maximum to full bloom (Anderson and Hubricht 1940) (Figure 2.2).

Furthermore, since corolla length is known to influence the type of pollinator visiting a flower and its ability to gather nectar (Balfour et al., 2013), corolla. Corolla length was estimated in each plant variety by measuring 20 randomly selected flowers (10 from each patch with a bloom intensity score of 2 or 3) using digital calipers and rounded to the nearest 0.1 mm (similar to Garbuzov and Ratnieks (2014a)).

Data Analysis

For plant attractiveness analyses, the total numbers of insects per snapshot observation were grouped into seven taxonomic groups: 1) honey bees (Apis mellifera), 2) Apidae (bumble bees, long-horned bees, and allies), 3) Halictidae (sweat bees), 4) other bees (Andrenidae and Colletidae), 5) butterflies and moths

(Lepidoptera), 6) flies (Diptera), and 7) all other insects (Coleoptera, Hemiptera,

Odonata). The mean number of visits per taxonomic group per plant variety was modeled as a generalized linear model (GLMM) using SAS 9.4 (PROC GLIMMIX

SAS Institute, Cary, NC). Plant variety, bloom intensity and corolla length were included in the model as fixed effects. To control for overdispersion due to the high

Texas Tech University, Samuel Discua, May 2021 number of zeroes in the data, a negative binomial probability distribution was used in the DIST (DIST=NB) option of the model statement. Goodness of fit was assessed with the Pearson Chi-Square test where a P-value in this test suggests a good fit for the model. Post-hoc pairwise comparisons of the mean number of insect groups per plant species were performed using the Tukey-Kramer grouping for Least Square Means (α

= 0.05).

The composition of the pollinator community visiting the different plants

(across all sampling dates) was analyzed using raw taxonomic counts to calculate diversity indices and metrics for comparison across plant species (two replicates for each plant species). Using pooled pollinator taxon counts, the Shannon-Weiner Index,

Simpson’s Diversity, and Shannon Evenness were calculated and compared across each plant species.

Results

Bee Community

Forty-six genera and 56 morphospecies were observed within the experimental plots across both years (Table 2.1). Across all sampling dates, anthophilous bees accounted for the majority of observations. Both native and exotic plants attracted a wide diversity of insect pollinators, with Hymenoptera accounting for the majority of morphospecies observed (29 of 56 morphospecies). Twenty-three hymenopteran morphospecies were bees from five families and 16 genera. The second-most

Texas Tech University, Samuel Discua, May 2021 abundantly observed order was Lepidoptera with 11 morphospecies, followed by

Diptera (6 morphospecies), Coleoptera (4), Hemiptera (4), and Odonata (3).

Honey bees were the most frequent and abundant pollinator observed, accounting for 31.1% of the total number of individuals recorded. The next-most commonly observed insect was the digger bee Anthophora californica/urbana, which accounted for 8% of the total observations, followed by the painted lady butterfly

Vanessa cardui), comprising 7.2% of the total number of individuals observed.

Sixteen morphospecies were observed only once, and an additional 23 morphospecies were observed less than 10 times. Insect pollinators visited on average 3.7 ± 3.83 plants per observing period. Agapostemon angelicus/texanus and Lassioglossum

(Dialictus) spp. visited the most plants at 14 species each, followed by Apis mellifera visiting 12 plant species. Twenty-five taxa visited only one plant variety, with 16 species of bees observed only once during the study (Table 2.2).

The composition of the bee community visiting plant varieties was similar in

2016 and 2017. In addition, the relative abundances of insect groups in the comparative plots in 2017 was similar to that recorded in the main study plot in both years, suggesting the bee community in the experimental plot was similar to that found in nearby wild, flowering plant habitat patches. In 2016, bees accounted for 79% of the total number of morphospecies observed during the sampling period. Insect groups compiled across each plant species occurred in the following order according to their relative abundances: honey bees (41%), sweat bees (24%), other bees (15%), flies

(9%), butterflies and moths (3%), and other insects (3%) (Figure 2.3). In 2017, bees 33

Texas Tech University, Samuel Discua, May 2021 accounted for 75% of the total number of individuals observed across all taxonomic groups and included honey bees (31%), sweat bees (15.2%), Apidae (13%), other bees

(15%), butterflies and moths (12.8%), flies (8.5%), and all other insects (4.3%)

(Figure 2.3).

Plant – Pollinator Associations

In 2017, each plant was visited an average of 32 times by insect pollinators across all dates, but there was large variation in the number of insect visits across the different plants. The top ten plant varieties (in terms of bee occurrences) accounted for

84% of all insect observations, whereas the bottom 10 plant varieties only accounted for 3% of all insect visits. Salvia yangii attracted the most insect visitors, including

75% of the total number of Apis mellifera observed during the study, followed by

Nepeta × faassenii), which attracted 17% of the total number of Apis mellifera observed (Figure 2.4). Lepidopera accounted for 12% of all observations, with 72% of the total number of lepidopteran individuals of Vanessa cardui observed on one plant variety, Buddleia davidii. Gaillardia pulchella had the highest diversity regarding insect morphospecies (17 morphospecies), followed by Nepeta × faassenii (16), Salvia farinacea ‘Saga Blue’ (15), and Salvia farinacea ‘Mealy blue sage’ (15).

The composition of the pollinator community varied across plant varieties

(Figure 2.4). Among plant varieties with more than 25 insect visitors across all sampling periods, Salvia microphylla attracted the highest proportion of Apidae at

61.76%. Salvia yangii attracted the highest proportion of Apis mellifera (79.2%).

Leucanthemum × superbum attracted the highest proportion of Diptera (61.5%), and

Texas Tech University, Samuel Discua, May 2021

Buddleia davidii attracted the highest proportion of Lepidoptera (77.5%). Thymophylla tenuiloba attracted the highest percentage of sweat bees (33.3%), and Xanthisma texanum attracted the highest percentage of non-Halictidae bees (59.3%).

Two plant varieties, Ipomopsis rubra and Centaurea americana had no insect pollinator visitors during both 2016 and 2017 sampling periods. No insect visitors to the latter species could be a result of the isolation of individual plants in cultivation, as

Centaurea americana typically is observed in relatively large, contiguous patches in the region. An additional six plant species had very low pollinator counts (3 visits or fewer): Achillea millefolium, Coreopsis grandiflora, Lantana camara, Oenothera macrocarpa, Scabiosa columbaria, and Simsia calva. All plant varieties, with the exception of Oenothera macrocarpa, had relative weak bloom intensities during the majority of 2017 because of an extended drought period, or in some cases bloomed later during the summer (Figure 2.3). Due to the low counts of insects, these plants were excluded from GLMM models.

Results from GLMM showed significant differences in the mean number of insects per snapshot per day and per plant variety (Figure 2.4). However, bloom intensity and date were not significant covariates in the model but potentially contributed to the commonness of blooms occurring during observations periods (i.e., during the growing season when most blooms persisted outside of drought periods).

Likewise, there were no significant differences in the total numbers of insects among the inner and outer circle patches. Furthermore, corolla length was not a significant covariate in models. The results of the Tukey-Kramer groupings for Least Square

Texas Tech University, Samuel Discua, May 2021

Means (α = 0.05) were not significant and had low power to differentiate between varieties due to the large number of pairwise comparisons and relatively low sample sizes. However, GLMM model results revealed trends in how different plants attracted different insect pollinator groups. Russian sage attracted the highest average number of insect pollinators per snapshot per day (14.15), followed by catmint (5.55) and butterfly bush (4). In contrast, chocolate daisy, prairie verbena, and coneflower attracted the lowest numbers of insect pollinators per snapshot per day, with fewer than 0.2 insect visitors per day (Figure 2.4).

Diversity and evenness indices offer another view of the pollinator community composition attracted by plant varieties (Table 2.3). Senecio flaccidus and Gaillardia pulchella had the most diverse pollinator communities, with a Shannon’s diversity index (S) of 2.47 each. Senecio flaccidus also had the most even composition of pollinator visitors, with a Shannon’s Evenness index (E) of 0.94, followed by

Gaillardia pulchella with an E of 0.87. Salvia yangii and Nepeta × faassenii were among the lowest evenness indices, 0.38, and 0.61, likely skewed because of the large proportion honey bees attracted by these plants.

Discussion

Although regional pollinator-friendly plant lists provide recommendations to enhance resources for pollinators, many of them are based on expert opinion and often lack empirical data (Garbuzov and Ratnieks 2014b). The main outcomes of this study include empirical results detailing the numbers and types of insect foragers visiting

Texas Tech University, Samuel Discua, May 2021 different plants. Many of the plants used in this study are recommended as garden plants for their value to wildlife and additionally because of low water need and drought tolerance. Although the exotic plants, Salvia yangii and Nepeta × faassenii, attracted a relatively high abundance of insects due to their sustained blooms, many of the native plants (e.g., Gaillardia pulchella, and Salvia farinacea) attracted a more diverse pollinator community. Furthermore, native plants generally attracted more diverse and even pollinator communities when compared to the exotic plants used in this study.

Native and non-native plants can provide floral resources for a wide variety of generalist pollinators. However, it is important to understand how different plant species attract a variety of pollinators, and which taxonomic groups are associated with flowering plant species or plant characteristics. In my study, the proportions of major insect groups were similar across both years, with bees the most abundant pollinator group observed. Moreover, similar dominance by bees was observed in the three supplemental and comparative plots. In these plots, wild bees (not honey bees) were the most abundant pollinators observed, driven by two native plant species,

Xanthisma texanum and Nama hispidum. Xanthisma texanum was the only plant were bees in the genus Perdita were observed. Species of the genus Perdita are known to have oligolectic behavior, and plants from the genera Xanthisma and Nama have been associated with this genus (Timberlake 1971; Ascher and Pickering 2020).

Even with my results, care must be taken when using and recommending exotic plants as floral resources for pollinators. Exotic plants that can easily naturalize

Texas Tech University, Samuel Discua, May 2021 outside gardens can rapidly invade and colonize disturbed areas. For example, although I found Buddleja davidii to be attractive to pollinators, it is considered an invasive species and noxious weed in the United Kingdom and in some U.S. states

(Tallent-Halsell and Watt 2009).

It is likely that honey bees visited the exotic Salvia yangii and Nepeta × faassenii in higher proportion than native bees because of their shared co-evolutionary history in their native regions. This is an important consideration when selecting native or exotic plants for urban gardens and planting floral strips around agroecosystems. For example, using flowering plants that attract honey bees in proximity to crops could assist in pollination (Decourtye et al. 2010), whereas limiting plants that are known to be highly attractive to honey bees in their non-native range could mitigate negative behavioral interactions between honey bees and native bees

(Paini 2004). Similarly, these plant selections can reduce exposure of humans to honey bees, minimizing sting potential.

Some of the plants used in this study that occur commonly on recommended plant lists for pollinators, but attracted low numbers of insect pollinators, including

Oenothera macrocarpa and Scabiosa columbaria, the latter occurring in the comparative plots. This in part could be because of the setting of the experimental unit and field conditions at the research farm, where my experimental plot contained potentially lower patch sizes than might occur naturally. In the case of Oenothera macrocarpa, two additional patches were observed in the comparative plots, yet even after all additional observations, only one insect visitor was recorded from this plant

Texas Tech University, Samuel Discua, May 2021 species. Although the native bee Lasioglossum oenotherae is an evening primrose

(Onagraceae) specialist with known floral records in Oenothera macrocarpa

(McGinley 2003), this bee species was not observed in my study. It is possible that oligolectic pollinators of Oenothera macrocarpa are not present in my study region.

In Chapter III of this document, Machaeranthera tanacetifolia was found to be the most common wildflower across 43 different patches in a seven-county region in the Southern High Plains. However, findings from the current study show that

Machaeranthera tanacetifolia attracted a low number of insect visitors.

Machaeranthera tanacetifolia was observed to be consumed in part by mammalian herbivores including black-tailed jackrabbits (Lepus californicus) and Eastern cottontails (Sylvilagus floridanus) that were consistently present in the experimental plot throughout the duration of the study. Scabiosa columbaria, Xanthisma texanum and Leucanthemum x superbum were also observed with either apparent damage likely attributed to rabbit feeding.

It is important to note that this study only evaluated a small fraction of the commonly used garden plants in Texas High Plains region. To maintain and provide high-quality habitat for insect pollinators, it is important to provide a constant and wide variety of floral resources. Most pollinators will utilize several plant varieties to meet their nutritional demands (they are polylectic generalists). However, especially in the diverse southwestern U.S. and semi-arid grasslands, many native bee specialists require resources from a narrow range of host plants. The plants used in my study

Texas Tech University, Samuel Discua, May 2021 mostly are visited by insect generalists (i.e., besides Missouri Primrose there were no plants associated with known specialist bees).

Results from this study add to a growing body of literature that tries to quantity attractiveness of flowers to pollinators and tries to develop recommendations for

“pollinator friendly plants” (Rollings and Goulson 2019, Anderson et al. 2020).

Additionally, there is growing interest in developing systems on managed turfgrass areas to provide for pollinator-friendly habitats (Davis et al. 2017 and Wisdom et al.

2019).

It is important to notice that this study did address how floral characteristics such as shape, resource quality and color affect insect visitation. Floral color is known to be an important factor in insect floral visits (for example see Campbell et al. 2010).

Likewise, while I tried to standardize patch size, the number of flowers/flower head per plant variety was different. This could have affected the results, as evidenced by

Fowler et al. (2016), which found that floral abundance and nectar quality (total sugar per inflorescence) positively affect insect visitation.

Native plants are becoming increasingly available to gardeners in the Texas

High Plains and improved wildflower varieties are being released (e.g., Raider

Wildflower Collection, McKenney et al. 2012), with local greenhouses increasing the supply and availability of native plants adapted to the region. It is important that horticulturalists and entomologists interact to address the challenges associated with urban landscape designs that aim to integrate native habitat for wildlife in landscapes.

Texas Tech University, Samuel Discua, May 2021

Information provided by this study supports a framework for plant recommendations by considering specific plants and the pollinators they attract.

Texas Tech University, Samuel Discua, May 2021

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Thompson, K., K. C. Austin, R. M. Smith, P. H. Warren, P. G. Angold, and K. J. Gaston. 2003. Urban domestic gardens (I): Putting small-scale plant diversity in context. Journal of Vegetation Science 14: 71-78.

Timberlake, P. H. P. H. 1971. Supplementary studies on the systematics of the genus Perdita (Hymenoptera, Andrenidae), University of California Press.

Westrich, P. 1996. Habitat requirements of central European bees and the problems of partial habitats, pp. 1-16. In B. S. Matheson A, O’Toole C, Westrich P, Williams and IH (eds.), The Conservation of Bees, vol. 18. Academic Press Limited.

Williams, N. M., D. Cariveau, R. Winfree, and C. Kremen. 2011. Bees in disturbed habitats use, but do not prefer, alien plants. Basic and Applied Ecology 12: 332-341.

Wisdom, M.M., Richardson, M.D., Karcher, D.E., Steinkraus, D.C. and McDonald, G.V., 2019. Flowering persistence and pollinator attraction of early-spring bulbs in warm-season lawns. HortScience, 54(10), pp.1853-1859.

Texas Tech University, Samuel Discua, May 2021

Figure 2.1. A and C, Arrangement of experimental plots at Texas Tech University Quaker Avenue Research Farm. B, Russian Sage Salvia yangii Benth.

Texas Tech University, Samuel Discua, May 2021

Achillea millefolium Anisacanthus quadrifidus Berlandiera lyrata Buddleja davidii 3

Campanula rotundifolia Centaurea americana Coreopsis grandiflora Echinacea x hybrida 3

Eustoma exaltatum Gaillardia pulchella Glandularia bipinnatifida Goniolimon tataricum 3

Ipomopsis rubra Lantana camara Leucanthemum x superbum Machaeranthera tanacetifolia 3

Melampodium leucanthum Nepeta × faassenii Oenothera cinerea Oenothera macrocarpa 3

Bloom Intensity Score Intensity Bloom 1

Penstemon x mexicali Perovskia atriplicifolia Ratibida columnifera Salvia farinacea 3

Salvia farinacea Saga Blue Salvia microphylla Scabiosa columbaria Senecio flaccidus 3

Simsia calva Simsia calva Xanthisma texanum Zinnia grandiflora 3

Apr May Jun Jul Aug Sep Apr May Jun Jul Aug Sep Apr May Jun Jul Aug Sep Apr May Jun Jul Aug Sep Figure 2.2. Bloom intensity scores of 32 plant varieties planted at the Texas Tech University Quaker Avenue Research Farm 2017. Bloom intensity was measured as follows: 0 (absence of bloom), 1 (<⅓ of maximum), 2 (⅓–⅔of maximum), 3 (full bloom,>⅔of maximum); after Anderson and Hubricht (1940).

Texas Tech University, Samuel Discua, May 2021

2016

3% 9%

9% 41%

15%

23%

Apis mellifera Sweat bees Other bees Diptera Other Insects Lepidoptera

2017

4% 9%

31% 13%

13%

15% 15%

Apis mellifera Sweat bees Other bees Apidae Lepidoptera Diptera Other insects

Figure 2.3. Relative abundance of insect groups observed in 2016 and 2017 in the experimental plot at the Texas Tech University Quaker Avenue Research Farm. Detailed taxonomic groups are given in Table 2.2.

Texas Tech University, Samuel Discua, May 2021

16 a Apis mellifera Apidae 14 Other bees Halictidae Diptera Other insects

8 a b 6 b b c c b b b b c b 4 c c c c c c c c c

Daily mean insects per per snapshot insects Daily mean c d d d d d d d d d d 2 d

Salvia yangii Salvia

Buddleja davidii Buddleja

Senecio flaccidus Senecio

Zinnia grandiflora Zinnia

Berlandiera lyrata Berlandiera

Salvia microphylla Salvia

Nepeta faassenii × Nepeta

Oenothera cinerea Oenothera

Gaillardia pulchella Gaillardia

Eustoma exaltatum Eustoma

Echinacea x hybrida x Echinacea

Xanthisma texanum Xanthisma

Convolvulus arvensis Convolvulus

Ratibida columnifera Ratibida

Penstemon x x mexicali Penstemon

Goniolimon tataricum Goniolimon

Thymophylla tenuiloba Thymophylla

Campanula rotundifolia Campanula

Glandularia bipinnatifida Glandularia

Anisacanthus quadrifidus Anisacanthus

Salvia farinacea 'wildtype' farinacea Salvia

Melampodium leucanthum Melampodium

Salvia farinacea 'Saga Blue' 'Saga farinacea Salvia

Leucanthemum x superbum x Leucanthemum Machaeranthera tanacetifolia Machaeranthera

Figure 2.4. Daily mean numbers of insects per snapshot per plant varieties at the Texas Tech University Quaker Avenue Research Farm in 2017. Letters above bars represent significant differences based on Tukey-Kramer grouping for Least Square Means (Alpha=0.05) comparisons; varieties sharing a common letter are not significantly different from each other.

50 Texas Tech University, Samuel Discua, May 2021

Table 2.1. List of plant varieties used in the plant attractiveness study in 2016-2017, arranged in alphabetic order. Native to Scientific Name Family Common Name Plant Variety U.S.?US? Achillea millefolium Asteraceae Yarrow Colorado Adapted Anisacanthus Acanthaceae Flame acanthus Wildtype Native quadrifidus Berlandiera lyrata Asteraceae Chocolate daisy Wildtype Native Buddleja davidii Scrophulariaceae Butterfly bush Buzz Magenta Exotic Campanula Bluebell Campanulaceae Wildtype Native rotundifolia bellflower Centaurea americana Asteraceae Basket flower Wildtype Native Common Coreopsis grandiflora Asteraceae Early Sunrise Native coreopsis Sombrero Salsa Echinacea x hybrida Asteraceae Purple coneflower Native Red Eustoma exaltatum Gentianaceae Texas blue bell Wildtype Native Gaillardia pulchella Asteraceae Indian blanket Arizona Sun Native Glandularia Verbenaceae Prairie verbena Wildtype Native bipinnatifida Unknown Goniolimon tataricum Plumbaginaceae German statice Exotic parentage Ipomopsis rubra Polemoniaceae Standing cypress Wildtype Native Lantana camara Verbenaceae Lantana Dallas Red Exotic Leucanthemum x Asteraceae Shasta daisy White Breeze Exotic superbum Machaeranthera Asteraceae Tahoka daisy Wildtype Native tanacetifolia Melampodium Asteraceae Blackfoot daisy Wildtype Native leucanthum Nepeta × faassenii Lamiaceae Catmint Walkers low Exotic Oenothera cinerea Onagraceae High plains gaura Wildtype Native Oenothera Onagraceae Missouri primrose Wildtype Native macrocarpa Penstemon x mexicali Plantaginaceae Penstemon Red Rocks Native Ratibida columnifera Asteraceae Mexican hat Wildtype Native Salvia farinacea Lamiaceae Mealy blue sage Saga Blue Native Salvia farinacea Lamiaceae Mealy blue sage Wildtype Native Salvia microphylla Lamiaceae Salvia 'hot lips' Hot lips Exotic Salvia yangii Lamiaceae Russian sage Blue Spire Exotic Scabiosa columbaria Dipsacaceae Scabiosa Butterfly Blue Exotic (Senecio) Senecio flaccidus Asteraceae Threadleaf Wildtype Native ragwort Awnless Simsia calva Asteraceae Wildtype Native bushsunflower Thymophylla tenuiloba Asteraceae Dahlberg daisy Wildtype Native

51 Texas Tech University, Samuel Discua, May 2021

Table 2.1. Continued Xanthisma texanum Asteraceae Sleepy daisy Wildtype Native Zinnia grandiflora Asteraceae Plains zinnia Wildtype Native

Table 2.2. Breakdown of insect groups observed the field plot at the Texas Tech University Quaker Avenue Research Farm in 2017. Plant Observations varieties Most visited plant Order Family/Taxa Tribe/Genera/Species visited # %

Hymenoptera Andrenidae Calliopsis 1 (0.1) 1 Eustoma exaltatum Perdita 5 (0.5) 1 Xanthisma texanum

3 Unidentified Andrenidae (4) Gaillardia pulchella 8 Anthophora 7 Salvia microphylla and Salvia Apidae (8) 16 californica/urbana 7 farinacea Anthophorula 1 (0.1) 1 Zinnia grandiflora compactula 2 Apis mellifera 9 (31.1) 12 Salvia yangii 8 Bombus pensilvanicus 5 (0.5) 3 Salvia farinacea

Diadasia 2 (0.2) 1 Eustoma exaltatum

Exomalopsini 1 (0.1) 1 Glandularia bipinnatifida

Nepeta x faassenii and Gaillardia Exomalopsis solani 2 (0.2) 1 pulchella 3 Melissodes (3.4) 6 Salvia yangii 3 Nomada 3 (0.3) 2 Goniolimon tataricum

Unidentified Apidae 2 (0.2) Gaillardia pulchella

Goniolimon tataricum and Colletidae Colletes 2 (0.2) 2 Leucanthemum x superbum Agapostemon 7 Halictidae (7.5) 14 Nepeta x faassenii angelicus/texanus 2 Goniolimon tataricum and Senecio Augochlorini 2 (0.2) 2 flaccidus Augochloropsis 5 (0.5) 3 Gaillardia pulchella sumptuosa/metallica Halictus ligatus 7 (0.7) 3 Gaillardia pulchella

5 Lasioglossum (5.2) 14 Thymophylla tenuiloba 0 1 Unidentified Halictidae (1) Ratibida columnifera 0 1 Salvia farinacea and Gaillardia Megachilidae Megachile (1.1) 5 1 pulchella 2 Unidentified Megachilidae (2.1) Salvia farinacea 0 6 Unidentified bees (Anthophila) (7) Convolvulus arvensis 7 Braconidae Braconidae 7 (0.7) 3 Thymophylla tenuiloba

Chrysididae Chrysididae 1 (0.1) 1 Salvia farinacea

Crabronidae Bembicinae 2 (0.2) 1 Buddleja davidii

Crabronidae 2 (0.2) 1 Senecio flaccidus and Simsia calva

52 Texas Tech University, Samuel Discua, May 2021

Table 2.2. Continued Sphecidae Sphecidae 1 (0.1) 1 Convolvulus arvensis

Aculeata 5 (0.5) Thymophylla tenuiloba

Lepidoptera Hesperiidae 7 (0.7) 4 Buddleja davidii

2 Lycaenidae (2.2) 7 Salvia yangii 1 1 Noctuidae (1.3) 4 Buddleja davidii 2 Nymphalidae Euptoieta claudia 1 (0.1) 1 Senecio flaccidus

Junonia coenia 1 (0.1) 1 Nepeta x faassenii

Unidentified Nymphalidae 1 (0.1) 1 Salvia farinacea

6 Vanessa Cardui (7.2) 9 Buddleja davidii 9 Nepeta x faassenii, Gaillardia Pieridae Pontia protodice 5 (0.5) 5 pulchella, Oenothera cinerea,Salvia farinacea, and Salvia microphylla Unidentified Pieridae 1 (0.1) 1 Senecio flaccidus Pterophoridae 1 (0.1) 1 Melampodium leucanthum

Unidentified Lepidoptera 3 (0.3) Melampodium leucanthum Diptera Asilidae Mallophora 2 (0.2) 1 Oenothera cinerea 2 Bombyliidae (2.5) 11 Buddleja davidii 4 Calliphoridae 1 (0.1) 1 Thymophylla tenuiloba

Sarcophagidae 1 (0.1) 1 Leucanthemum x superbum

1 Syrphidae (1) 7 Buddleja davidii 0 4 Unidentified Diptera (4.7) Leucanthemum x superbum 5 Coleoptera Anthicidae 3 (0.3) 1 Xanthisma texanum

Coccinelidae Coccinelidae 1 (0.1) 1 Salvia farinacea

Merylidae Collops 3 (0.3) 1 Xanthisma texanum

Unidentified Coleoptera 3 (0.3) Oenothera cinerea

Hemiptera Berytidae 1 (0.1) 1 Oenothera macrocarpa

Geocoridae 3 (0.3) 2 Coreopsis grandiflora

Pentatomidae 1 (0.1) 1 Salvia farinacea

Rhopalidae 1 (0.1) 1 Salvia farinacea

Odonata Coenagrionidae 1 (0.1) 1 Salvia farinacea Enallagma 2 (0.2) 1 Oenothera cinerea

Unidentified Zygoptera 3 (0.3) 2 Oenothera cinerea

53 Texas Tech University, Samuel Discua, May 2021

Table 2.3. Plants ranked by species richness. Rank Name Sum S E H D` Most common insect visitor Gaillardia 1 57 17 0.87 2.47 0.88 Andrenidae pulchella 2 Nepeta × faassenii 111 16 0.61 1.7 0.72 Apis mellifera 3 Salvia farinacea 34 15 0.89 2.41 0.89 Agapostemon 4 Salvia farinacea 63 15 0.83 2.25 0.85 Anthophora 5 Senecio flaccidus 32 14 0.94 2.47 0.9 Anthophila 6 Salvia yangii 283 12 0.38 0.93 0.37 Apis mellifera 7 Xanthisma texanum 27 11 0.9 2.15 0.86 Andrenidae 8 Buddleja davidii 80 9 0.62 1.37 0.58 Vanessa cardui Anthophora, Coleoptera, Enallagma, 9 Salvia microphylla 34 9 0.67 1.46 0.62 Mallophora, and Zygoptera 10 Oenothera cinerea 13 8 0.98 2.03 0.86 Anthophora Leucanthemum x 11 39 8 0.66 1.37 0.61 Diptera superbum Melampodium 12 19 7 0.89 1.73 0.8 Diptera leucanthum Thymophylla 13 45 7 0.88 1.71 0.79 Lasioglossum tenuiloba Ratibida 14 21 7 0.89 1.73 0.8 Anthophila columnifera 15 Convolvulus arvensis 28 7 0.83 1.62 0.76 Anthophila Goniolimon 16 14 6 0.75 1.35 0.63 Agapostemon tataricum 17 Zinnia grandiflora 10 6 0.95 1.7 0.8 Lasioglossum Campanula 18 8 5 0.97 1.56 0.78 Anthophora, Anthophila Andrenidae rotundifolia 19 Eustoma exaltatum 7 5 0.96 1.55 0.78 Lasioglossum and Diadasia Penstemon x 20 9 3 0.85 0.94 0.57 Apis mellifera mexicali Glandularia Bombyliidae, Vanesssa cardui, and Apis 21 3 3 1 1.1 0.67 bipinnatifida mellifera Scabiosa Bombyliidae, Vanesssa cardui, and 22 3 3 1 1.1 0.67 columbaria Exomalopsini 23 Simsia calva 4 3 0.95 1.04 0.63 Hesperiidae Machaeranthera 24 4 3 0.95 1.04 0.63 Megachilidae tanacetifolia 25 Berlandiera lyrata 2 2 1 0.69 0.5 Agapostemon and Melissodes Echinacea x 26 2 2 1 0.69 0.5 Lycaenidae and Braconidae hybrida Anisacanthus 27 2 2 1 0.69 0.5 Lasioglossum and Apis mellifera quadrifidus Coreopsis 28 3 2 0.92 0.64 0.44 Geocoridae grandiflora 29 Lantana camara 1 1 0 0 0 Syrphidae Oenothera 30 1 1 0 0 0 Berytidae macrocarpa

54 Texas Tech University, Samuel Discua, May 2021

Table 2.3. Continued Average 32 6.9 0.8 1.38 0.65

Sum = Sum of all insect visits, S = Species richness, E = Species evenness, H = Shannon’s diversity index, D` = Inverse Simpson’s diversity index

55 Texas Tech University, Samuel Discua, May 2021

1. CHAPTER III

A MULTIVARIATE ANALYSIS OF NATIVE BEES AND FLORAL COMMUNITIES ACROSS AGROECOSYSTEMS IN THE LLANO ESTACADO REGION OF TEXAS

Abstract

Agricultural intensification and loss of natural habitat threaten pollinator populations across agroecosystems. In the Llano Estacado region of western Texas, little is known about the broad influence of broad agricultural production on localized habitat structure and thence on pollinator communities. Recent studies have documented the diversity of bee communities in the Llano Estacado, but these studies have focused primarily on native grasslands and CRP lands, with little emphasis on studying ruderal lands and surrounding fallow lands within agroecosystems. The objective of this study was to determine the relationships of localized habitat structure on native bee and wildflower communities on different agroecosystems across a seven-county region in western Texas. Agroecosystems were categorized as cotton farms, vegetable and fruit farms, Conservation Reserve Program (CRP) lands, organic cotton farms, vineyards, and other land- use types. Ruderal lands, pivot corners, playas, and fallow land adjacent to agroecosystems were sampled across four collecting seasons in 2016 and 2017. Pollinator communities were collected using pan traps and hand netting across 43 locations (i.e. homogenous habitat patches of various sizes). At each location, local habitat related to floral resources and the composition of vegetation was quantified along two 60 m x 2 m belt transects. Multivariate and mixed-effect models were used to assess the relationships of habitat variables with

56 Texas Tech University, Samuel Discua, May 2021 wild bee and floral abundances and species richness. Over 17,000 bees belonging to

106 species/morphospecies and 49 genera were collected, and 95 wildflower species/morphospecies belonging to 49 genera were recorded. Although the composition of the bee community varied across sampling seasons, non-metric multidimensional scaling results showed little difference between bee communities and land- use types. Mixed- model results showed that sites adjacent to CRP had higher bee richness and abundance either by sampling seasons or when combining the data over all years of the study. The percent of forbs within transects was a significant factor in predicting floral and bee abundance, richness, and diversity across sampling seasons. The findings of the present study support growing evidence that underscore the importance of field margins and nearby uncultivated patches of land for supporting pollinators in areas of intensive agricultural production.

Introduction

Honey bees (Apis mellifera) are the most important pollinators in agriculture, contributing USD $15 billion USD annually in pollination services to agriculture in the United States (Calderone 2012). Despite their importance, a combination of stressors, including colony collapse disorder, diseases, mites, and lack of adequate floral resources threaten the continued use of honey bees for pollination (Goulson et al. 2015). Although data and estimates are lacking, native bees provide USD $3.07 billion USD in pollination services annually in the United States (Losey and Vaughan

2006). Furthermore, native bees, such as bumblebees, are better pollinators of several crops, including blueberries and cranberries (Javorek et al. 2002). Given the decline of

57 Texas Tech University, Samuel Discua, May 2021 both honey bees and native bees, there is renewed interest in understanding factors that support native bee diversity (Klein et al. 2007).

Habitat loss caused by land-use change and agricultural intensification is among the most important factors driving native bee declines (Potts et al. 2010). Land- use change results in habitat loss, fragmentation, degradation, and reduced resource diversity, thus affecting bee community structure and composition. A growing body of evidence shows that wild bee communities in agroecosystems benefit from high- quality habitats around farms, as well as from organic farming and local-scale field diversity (Kennedy et al. 2013).

Bee conservation practices, such as planting floral strips, can be used to increase pollination services around agroecosystems (Blaauw and Isaacs 2014). An increasing number of studies have demonstrated the effectiveness of establishing pollinator habitat (i.e., floral strips) around agroecosystems in increasing pollinator diversity (Diekötter and Crist 2013; Petersen and Nault 2014; Burkle et al. 2017;

Lichtenberg et al. 2017). Among government initiatives in the United States, the

Conservation Reserve Program (CRP) is the most widely adopted program for providing habitat for wildlife and has considerable potential for providing floral resources and habitat for bees.

As of January 2021, there are 8.09 million hectares under existing CRP contracts across the United States, including 0.93 million hectares in Texas (11% of the nation’s total and more than in any other state) (Farm Service Agency 2021). In

2008, pollinators became a high-priority wildlife taxon for CRP projects with the

58 Texas Tech University, Samuel Discua, May 2021 creation of the CRP Pollinator Habitat Initiative (CP-42). There is evidence that butterflies benefit from CRP restorations, however, evidence is lacking on the effects of CRP restorations on bees. In addition, the practice of sowing CRP restorations with non-native grasses is widespread and can likely diminish the value of these habitats for bees (Winfree 2010).

The present study is focused on the impacts of agricultural land-use types on native bee communities in the Llano Estacado region of Texas. The Llano Estacado

(Staked Plains) occupies the largest portion of the Texas High Plains (Type III ecoregion). The Llano Estacado stretches for 400 km north to south and 240 km east to west, with a total area of 97,000 km2, making it the largest contiguous non- mountainous area in the United States. It covers all or part of 33 Texas counties and four New Mexico counties (Figure 3.1). This region has the largest contiguous planted cotton area in the United States and the second-largest in the world, producing approximately 66% of the state’s and 25% of the nation’s cotton crop (Plains Cotton

Growers 2020). On average, 1.5 million hectares are planted with cotton every year, producing 3.7 million bales.

Specialty crops, cover crops, and hay crops are also grown in the Texas High

Plains, including relatively small plantings of alfalfa (Medicago sativa), squash

(Cucurbita spp.), watermelon (Citrullus lanatus), pumpkin (Cucurbita pepo), apple

(Malus domestica), wine grape (Vitis vinifera), sunflower (Helianthus spp.), and vegetables. Many of these crops either benefit from or are reliant on pollination by both managed and native bees. Despite the potential benefits of increasing pollinator

59 Texas Tech University, Samuel Discua, May 2021 abundance around agroecosystems, the Llano Estacado is a region with one of the highest pollinator deficiencies in the United States (i.e., where the need for pollination services exceeds the current supply of pollinators; Koh et al. 2016).

Cotton, the major cash crop in the Llano Estacado, is a pollen-limited crop that can be benefited by animal pollination. Cusser et al. (2016) found that cotton farms in south Texas that were surrounded by more natural habitat were visited by more native bees and had 18% higher seedcotton weight, equivalent to USD $266 per hectare increase in value compared with farms surrounded by less wild habitat. This is an important consideration for helping implement pollinator conservation programs along farmland.

The increase in pollinator habitat across agroecosystems may be achieved by the implementation of CRPs, particularly those that focus on pollinators (CP-42) and the establishment of wildflower meadows. Farmers can increase or maintain agricultural productivity by increasing the amount of natural land surrounding cotton crop production, thus helping maintain bee communities. There may also be secondary benefits to establishing and increasing pollinator habitat via increased biocontrol services, increased soil moisture, and improved aesthetic quality of farm land

(Haaland et al. 2011).

In order to better implement any conservation program in the Llano Estacado, it is important to understand the bee and floral community composition around agroecosystems in this region. With concerns of pollinator declines in the U.S., there has been a resurgence of studies documenting wild bee communities in the Llano

60 Texas Tech University, Samuel Discua, May 2021

Estacado (Partridge 2017, Begosh 2018, and Auerbach et al. 2019), including the present study. Based on historical museum records and contemporary studies, there are

188 bee species in the Llano Estacado region of Texas and New Mexico. These are further described in Chapter V.

Given the importance of agricultural production in the Llano Estacado and the potential benefits of increasing pollinator richness and abundance around agroecosystems, the present study seeks to determine the relationships between local habitat and native bee and floral abundance and richness across different agroecosystems in a seven-county section of the Llano Estacado region in Texas. The objectives of this study are to: 1) document the anthophilous pollinator and floral community across a seven-county region in the Llano Estacado region of Texas; and

2) determine the relationships between local habitat resources and bee communities.

Materials and Methods

Study Area

The area investigated in the present study included seven counties (Crosby,

Floyd, Hale, Hockley, Lubbock, Lynn, and Terry) with a combined area of 16,884.4 km2 in the Llano Estacado region of Texas (Figure 3.1). All seven counties in the study area are among the top 10 cotton-producing counties in the United States

(USDA, 2018). United States Department of Agriculture National Agricultural

Statistics Service data on land cover and land use state that cultivated cropland in 2018 covered at least 60% (10,130.64 km2) of the study area, with cotton covering at least

40% (6874 km2) of the combined area of the counties. Additional crops cultivated in

61 Texas Tech University, Samuel Discua, May 2021 the seven-county region include alfalfa, apples, corn, grapes, oats, peanuts, pecans, pumpkins, rye, sorghum, and winter wheat (USDA, 2018).

Bee communities were sampled in 43 habitat areas occurring in agroecosystems located on a west-to-east gradient across the seven-county study area.

Pivot corners, playas, and non-cultivated habitats including native grassland remnants and CRP lands were sampled; land-use types adjacent to these sites included conventional and organic cotton farms, apple orchards, vegetable farms, and CRP

(Table 3.1). Average area of the sites sampled was 31.33 ha, ranging from 0.19 ha to

131.60 ha. Sampling sites included existing CRP lands (n = 18), playas (n = 15) or both (n = 10) (Table 3.1).

Eleven different soil series were found across the study sites: Amarillo, Berda,

Bippus, Estacado, Mansker, Olton, Patricia, Potter, Pullman, and Spur (Table 3.1).

The Amarillo soil series was the dominant soil series across the study sites (n = 34)

(Table 3.-1). These soils consist of deep, well drained, and moderately permeable soils

(Soil Survey Staff, 2014). Taxonomically, these are fine-loamy, mixed, superactive, thermic Aridic Paleustalfs. The similar and geographically associated soils, Olton (30 sites) and Acuff (21 sites) (Table 3.1). Olton series soils are fine, mixed, superactive, thermic Aridic Paleustolls consisting of very deep, well-drained, moderately slowly permeable soils (Soil Survey Staff 2014). The Acuff series soils are very deep, well- drained, moderately permeable soils (Soil Survey Staff 2014). Most of the soils found in the study sites share similar characteristics (depth, drainage, and permeability); in addition, they were nearly level to gently sloping (slopes ranging from 0% to 8%),

62 Texas Tech University, Samuel Discua, May 2021 with annual mean precipitation ranging from 457 mm to 610 mm and air temperatures ranging from 15 °C to 16.7 °C (Soil Survey Staff 2014).

Insect Sampling

Bees and other pollinating insects were collected during four sampling seasons: three in 2016 (April−May, July−August, and Sep−Oct) and once in 2017

(April−May). Sampling methods included sweep netting (using random and standard transects) and pan trapping. Standard transects consisted of a 60 m long × 2 m wide linear belt, where bees were collected via sweeping the net through vegetation in the linear transect for 15 minutes. Sweep netting was paced at a constant steady speed at the center of each transect. Sweeping covered an area of 1 m on either side of the transect, and the 120 m2 area of the transect received equal netting time. Random transects consisted of sweep netting for 15 minutes in a random pattern across each site. Netting was focused to cover as many flowering plant patches and vegetation as possible. For both transect types, sweep-net samples were labeled and transferred to

3.785 L freezer bags and stored in a cooler with ice. Sweep netting occurred during the daytime between 8:00 AM to 5:00 PM CDT. A sweep net with a 121.92 cm long handle and 38.1 cm in diameter (Rose Entomology, Rancho Dominguez, CA) was used. Standard transects and random transects were replicated twice in each site.

In addition to sweep netting, pan traps were placed across the length of the standard transects. Pan traps consisted of 96.11 ml cups painted with silica white base paint mixed with blue fluorescent and yellow fluorescent pigment (New Horizons

Entomology Services, Upper Marlboro, MD). To maximize bee richness collected by

63 Texas Tech University, Samuel Discua, May 2021 pan traps, four each of blue, yellow, and white (unpainted) pan traps (12 in total) were placed 5 m apart along the transect, alternating pan colors. Tuell and Isaacs (2009) found that elevating pan traps to canopy height in different crops maximized bee abundance and diversity collected by pan trapping. To adjust for vegetation height across sites, pan traps were taped to 30.48 cm, 60.96 cm, or 121.92 cm wooden landscape stakes using industrial-strength tape (VELCRO Manchester, NH) based on the vegetation height within the transect. Pan traps were filled with a soapy water solution at a ratio of 14.78 ml of dishwashing soap (Procter and Gamble, Cincinnati,

OH) per 3.78 L of water (LeBuhn et al. 2003). Pan traps were placed during morning hours (9:00 AM to 12:00 PM CDT) and picked up 4−6 hours after placement. During collection, pan-trap samples within a transect were combined and placed in 250−500 ml screw-cap polypropylene histology containers (Universal Medical, Walpole, MA).

Pan-trap samples were labeled and stored in 70% ethanol for preservation.

Sample Processing

Samples were taken to the Texas Tech University Department of Plant and Soil

Science for processing. Pan-trap samples were placed in a 20 mesh 203.2 mm diameter test sieve (Advantech, New Berlin, WI) and rinsed with water to remove ethanol and soapy water solution. Bee specimens were dried using a bee dryer, which consisted of a computer fan attached to a wooden frame; bees were placed on a 10.16 cm PVC pipe fitting covered with cotton cheesecloth that was then placed on the bee dryer. Bees and other insect pollinators from sweep-net and dried pan-trap samples were pinned and pointed, and additional by-catch specimens were placed separately in

64 Texas Tech University, Samuel Discua, May 2021

14.79 ml borosilicate glass tubing vials (Bioquip Products, Rancho Dominguez, CA).

Specimens were identified to genera, species, or morphospecies using published dichotomous keys, and problematic specimens were identified by comparing them to museum specimens and by taxonomist review. This study follows taxonomic keys from Michener (2007), with exceptions from groups with more recent taxonomic revisions. The primary identifiers for specimens were Samuel Discua (Texas Tech

University), Jack Neff (Central Mettiological Institute), and Karen Wright (Texas

A&M University). Specimens from this study are housed at the Texas Tech University

Plant and Soil Science Entomology Laboratory.

Local Habitat Sampling

Existing habitat attributes, including crop type, non-crop flowering vegetation, temperature, wind speed, relative humidity, ground habitat, as well as farm management were recorded at each farm in each sampling season. Local habitat variables were estimated within 0.5 m2 quadrats at 10 m intervals (totaling five quadrat counts) along each standard transect. Local habitat variables measured within quadrats included flowering plant richness, floral abundance (counted as the total number of open flowers or flower heads per plant species), percent bare ground, percent grasses, percent forbs, number of woody stems, and number of observable ground nests. Placement of transects was representative of the overall habitat within the site sampled. Habitat variables per transect were averaged (percent bare ground, percent grasses, and percent forbs) or added (flowering plant richness and floral abundance) for subsequent analyses.

65 Texas Tech University, Samuel Discua, May 2021

Pollinator Community Composition

Pollinator abundance was determined as the total number of specimens per genera combined for sampling seasons in 2016 and 2017. Pollinator richness was determined as the total generic richness of specimens pooled across sampling seasons and trapping methods. For comparisons across habitats, data were relativized by maxima by site to equalize the influence of common and rare species (McCune and

Medford 2011). Shannon diversity and species evenness indexes for all sites per season were calculated using PC-ORD (Version 6.22, MJM Software, Gleneden

Beach, OR) and compared across sites.

Data Analysis

Principal component analysis (PCA) was used to find patterns in habitat data.

An outlier analysis was conducted in PC-ORD to remove sites that had extreme values

(more than two standard deviations from the mean) on one or more of the measured variables. The habitat matrix was analyzed using a variance/covariance cross-products matrix, which allowed for reducing the influence of outliers while allowing the expression of gradients in the habitat data. Randomization tests (999) were used to evaluate the statistical significance of the PCA solutions, and the Rnd-Lambda stopping rule was used to determine the number of useful axes for interpretation

(McCune and Grace 2002).

Multivariate ordinations were used to explore data structure and seek patterns between bee communities and environmental variables. Non-metric multidimensional scaling (NMDS) ordinations were used to visualize differences in bee community

66 Texas Tech University, Samuel Discua, May 2021 composition between sites, site characteristics (farm type, presence of CRP, presence of playas, and soil series), and habitat variables. To calculate distance matrices in

NMDS, the Sorensen (Bray-Curtis) distance measure was used, which compares sites using the identity and relative abundance of species (Winfree et al. 2017). A Monte

Carlo test with 50 randomizations was used to determine significance and stress in relation dimensionality (Peck 2017). To determine the significance and verify the consistency of the NMDS solution, multiple NMDS were run, and a stress value < 20 was deemed appropriate for interpretation (Clarke 1993). Ordinations were graphed as joint biplots using the habitat data in the second matrix. Vectors of association of habitat variables to the ordination axes were calculated using an r2 value of 0.100 as a cutoff value for modelling the strength of the associations. Convex hulls were drawn to visualize the grouping variables (farm type, CRP, playa, and soil series) in the ordination space.

To determine whether spatial distance between study sites significantly affected bee abundance, richness, and diversity, Mantel and Moran’s I tests were used to determine the degree of spatial autocorrelation between site distance and bee abundance, generic richness, and diversity. Mantel and Moran’s I tests were conducted using the statistical program R, version 3.5 (R Core Team 2018), using the packages ade4 for Mantel tests (Dray and Dufour 2007) and ape for Moran’s I tests (Paradis et al. 2004). Based on the Mantel and Moran’s I test results, there was no spatial autocorrelation with bee abundance and sites (Mantel test, p = 0.58; Moran’s I test, p =

0.06) or bee diversity and sites (Mantel test, p = 0.255; Moran’s I test, p = 0.402), but

67 Texas Tech University, Samuel Discua, May 2021 there was significant autocorrelation with bee generic richness and sites (Mantel test, p

= 0.0007; Moran’s I test, p = 0.00002).

Because the local habitat variables and land-use variables are inherently related to one another, variables were screened for multi-collinearity. To do this, variance inflation factors (VIF) and tolerance values (TOL) were calculated using the TOL and

VIF (PROC REG) statements in SAS 9.4. A theta value of VIF < 2, similar to Cusser et al. (2016), and a tolerance value > 0.1 were used as cutoff values to eliminate variables with collinearity from the models. At the local habitat level and across seasons, only six variables were found to be non-collinear: percent short grasses, forbs, ground nests, woody stems, floral abundance, and floral diversity (Tables 3.9 and 3.10).

To determine the relationships among habitat and floral and pollinator abundance, richness, and diversity, Generalized Linear Mixed Models (GLMMs) were fitted using the SAS 9.4 statistical software (PROC GLIMMIX). Sites were modelled as R-side random effects (RANDOM = _RESIDUAL_) with land-use variables at local and landscape scales as fixed effects. Poisson distributions (LINK=LOG, DIST =

POISSON) were used to model abundance, richness, and diversity. Abundance, richness, and diversity data were log-transformed prior to analysis to meet the assumption of normality of residuals. Data were analyzed in two separate ways. First, to determine the relationship between non-collinear predictors and dependent variables by sampling season and local habitat variables, GLMMs were fitted without using categorical (farm type, CRP, playa, soil series) variables. Second, to determine

68 Texas Tech University, Samuel Discua, May 2021 whether farm type, the presence or absence of CRP lands, playas, and soil types influenced pollinator and floral richness, abundance, and diversity, GLMMs were fitted using farm type, CRP, playa, and soil series as categorical (CLASS) variables.

Least mean squares (LSMEANS) and the Tukey-Kramer HSD (ADJ=TUKEY) mean separation procedure were used to compare differences in the model estimates of bee and floral abundance, richness, and diversity by season and scale. No interactions among variables were modeled. Preliminary analyses showed that models including interactions among predictor variables lacked appropriate fit, and most algorithms were unable to converge. This is likely due to the relatively small sample size, unbalanced number of replicates, and insufficient degrees of freedom.

Results

Bee Richness and Abundance

A total of 17,725 bees belonging to five families, 49 genera, and 106 species/morphospecies were collected in 2016 and 2017 (Table 3.2). Additional details about new county records and comparisons of these collection events to previous collections are discussed in Chapter V. Averaged across both years, 412.2 ± 156.59 bees and 17.5 ± 3.34 genera were collected per site. The Shannon’s diversity index was 1.56 ± 0.29 and the Shannon’s evenness index was 0.55 ± 0.09 (Table 3.4).

Halictidae was the most abundant bee family collected across all sampling seasons, accounting for 72.21% (12,578 specimens) of all bees collected in 2016−2017, followed by Apidae (21.49%; 3,746 specimens), Andrenidae (4.98%; 868 specimens),

69 Texas Tech University, Samuel Discua, May 2021

Megachilidae (1.1%; 192 specimens), and Colletidae (0.2%; 35 specimens) (Table

3.6).

Bee abundance was highest in the Apr–May 2016, followed by the May–Jun

2017, Jul-Aug 2016, and lowest in 2016 Sep–Oct 2016 sampling season (Table 3.6).

Generic richness was highest in May–Jun 2017, followed by Jul-Aug 2016, Apr-May

2016 and lowest in the Sep-Oct 2017 sampling season (Table 3.6). There was a significant turnover of genera across seasons, from the 49 genera collected only 17 were collected across all months (April-Oct) sampled.

Pan traps were the most effective sampling method, collecting 83.43% of the total specimens (14,533) and 43 different genera. A total of 2,439 bees in 41 genera were collected in the random transects. Standard transects collected the lowest number of specimens (447) and genera (23). Since the objective of this study was to accurately represent bee communities across different land-use types and in relation to local habitat, samples from pan traps and random and standard transects from each site were pooled and analyzed as a single sample from each site for all subsequent analyses.

Three bee genera, Agapostemon, Lasioglossum (Dialictus), and Melissodes, accounted for 75.21% (13,110 specimens) of the total specimens collected. These three genera were also collected in all 43 sampled sites. There were eight genera where only one singleton specimen was collected and for ten genera, less than ten specimens were collected (Table 3.2).

70 Texas Tech University, Samuel Discua, May 2021

Bees of the subgenus Lasioglossum (Dialictus) spp. were the most common bees collected in every season and across sites, accounting for 53.39% (9,463 specimens) of the total bees collected in 2016 and 2017. At least seven Lasioglossum

(Dialictus) morphospecies were collected; among these, Lasioglossum (Dialictus) sp.

1 and Lasioglossum hudsoniellum were the most commonly collected bees.

Abundance of Lasioglossum was greatest in the April–May sampling season in 2016

(Table 3.2).

Melissodes was the second most common genera collected, accounting for

11.84% (2,098 specimens) of all bees collected. These included nine Melissodes species/morphospecies, with Melissodes communis and Melissodes tristis being the most abundant. Abundance of Melissodes was greatest in May–June 2017 (Table 3.2).

Agapostemon accounted for 10.24% (1,815 specimens) of all bees collected

(third most common). Six species of Agapostemon were collected; yet, 99.4% of the specimens belonged to the species Agapostemon angelicus/texanus (two species with females that cannot be separated to species by morphological characteristics alone).

Abundance of Agapostemon was greatest in April–May 2016 (Table 3.2).

Floral Richness and Abundance

A total of 67,335 flowers/flower heads from 95 flowering plant species/morphospecies belonging to 18 orders, 22 families, and 52 genera were observed (Table 3.7). Nine non-native flowering plant species were found across sites which accounted for 16.94% (11,404) of the total open flower heads observed.

71 Texas Tech University, Samuel Discua, May 2021

Asteraceae was the most abundant family observed across sites (33 species).

The most observed plant species across sites were Machaeranthera tanacetifolia (25 sites), Xanthisma texanum (20 sites), and Solanum elaeagnifolium (20 sites). There were 42 plant species that were observed in only one site (Table 3.7), whereas five plant species (Machaeranthera pinnatifida, Verbena bracteata, Erodium cicutarium,

Rapistrum rugosum, and Aphanostephus skirrhobasis) accounted for 43.85% (29,524) of all flowers observed, with all having peak bloom during spring (Table 3.7).

On average, each site had 8.44 ± 3.88 flowering plant species and 1,565.93 ±

2,009.29 flowers. The Shannon’s diversity index was 1.25 ± 0.53 the Shannon’s evenness index was 0.62 ± 0.23 (Table 3.5). The low number of flowering species and flowers observed across some sites was partly because of local farm practices in those sites, as these sites were mowed or plowed at times when the data was collected. Sites with missing flowering abundance/species data were excluded from floral community analyses.

There was a high turnover of flowering species blooming across sampling seasons, the dominant flowering plant species changed at every sampling season. Only two species (Machaeranthera tanacetifolia and Xanthisma texanum) remained blooming through all months and sampling seasons in 2016 and 2017 (April to

October), and an additional 38 plant species were found blooming in at least two different sampling seasons. Floral diversity and abundance decreased through the 2016 sampling seasons. The highest floral diversity and abundance was observed in April–

May and lowest in Sept-Oct 2016 sampling season (Table 3.7).

72 Texas Tech University, Samuel Discua, May 2021

Bee Habitat Data

There were no significant differences (α = 0.05) in habitat variables and farm types, presence or absence of CRP, playas, soil types, and seasons, this is likely because of large variation in local habitat variables across sites (Table 3.6). Mean values for local habitat data by season are presented in Table 3.11.

PCA Results

P-values from randomizations and Rnd-Lambda stopping rules determined that only the first two axes were useful for interpretation. For all analyses, correlations >

0.5 or < -0.5 were deemed important. Across all seasons, the bee habitat variables: number of ground nests, percent bare ground, floral abundance, and floral diversity were consistently among the most strongly correlated variables. Sites typically fell on gradients where the percentage of observed ground nests and bare ground were negatively correlated with the percentage of short and tall grasses (Table 3.8). In most principal components, floral abundance and floral diversity were negatively correlated to ground nests and bare ground (Figure 3.3).

NMDS Results

Across most sampling seasons, there was substantial overlap in community composition and most landscape and habitat grouping variables, suggesting that the site groupings are not compositionally distinct, which could be attributed to the dominance of common, abundant taxa across sampled habitats. Figures 3.4 through

3.7 show the results of NMDS ordinations of bee communities and local habitat associations (farm type, presentence of CRP, playas, and soil series).

73 Texas Tech University, Samuel Discua, May 2021

Vineyard farm type and the Amarillo-Patricia soil type showed differentiation in April–May 2016 sampling season, this is partly because the vineyard farm type sites predominantly have the Amarillo-Patricia soil type. Four variables were significant

(percent ground nests, bare ground, forbs and floral abundance), (Figure 3.4). Floral abundance, floral diversity, and percent forbs were significant variables in the July–

August 2016 sampling season. These results seem to be influenced by Sites 15 and 41, which had the most and third-most percentage of forbs and among the highest values of floral abundance and diversity (Figure 3.5). In September–October 2016 season, only two variables (floral diversity and percent grasses) had significant associations

(Figure 3.6).

There were differences in vegetable and organic cotton farm types in the May–

June 2017 season. Vectors of association of habitat variables to the ordination axes were weak (r2 = 0.05) and showed that two variables (percent forbs and floral abundance) had significant associations (Figure 3.7).

Generalized Linear Mixed Model Results

Bee Communities

Variables for predicting bee communities changed across sampling seasons. In the April–May 2016 season, only forbs and floral richness were significant predictors for bee richness. For the July–August 2016 season, short grasses, percent forbs, and floral abundance were the only significant predictors for bee abundance. In the

September–October 2016 season, only percent short grasses was significant in predicting bee diversity (Table 3.11). When combining all 2016 sampling seasons, the

74 Texas Tech University, Samuel Discua, May 2021 number of ground nests was the only significant predictor of bee richness, and floral richness.

Four variables were significant for predicting bee richness in May-Jun 2016: percent short grasses, percent ground nests, and floral abundance were significant predictors for bee richness. Floral abundance and floral richness were significant for predicting bee abundance (Table 3.11).

Bee Communities and Adjacency to CRP Lands

Sites adjacent to CRP lands had higher model estimates on either bee richness or abundances than sites not adjacent to CRP lands across sampling seasons in 2016.

In the Apr-May 2016 sampling season, sites adjacent to CRP lands had a higher model estimate for bee richness (Estimate = 0.0062) than non-adjacent sites (Estimate =

−0.1044). Similarly, in the July–August 2016 season, CRP sites had a higher model estimate for bee abundance (Estimate = 0.7181) than non-adjacent CRP sites (Estimate

= 0.6070). In the September–October 2016 season, sites adjacent to CRP lands had a higher model estimate for bee abundance (Estimate = 0.5218) than non-adjacent CRP sites (Estimate = 0.3689). When combining all 2016 sampling seasons CRP adjacent sites had a higher model estimate for abundance (Estimate = 0.9309) than sites that were not (Estimate = 0.8544). No significant models for predicting bee richness, abundance, and diversity and adjacency to CRP lands were found in the May–June

2017 sampling season (Table 3.14).

Bee Communities and Farm Type

75 Texas Tech University, Samuel Discua, May 2021

In April–May 2016 Cotton farms and CRP sites had a higher estimate for richness than vineyard sites, but were not significantly different from the other land cover types (Table 3.15). In July–August 2016 Vineyard sites had a higher bee richness (Estimate = 0.134) than vegetable farms (Estimate = −0.139), whereas cotton,

CRP lands, organic cotton, and other were not different from any of the land cover types compared (Table 3.15). In September–October 2016 vineyard (Estimate =

−0.788) sites had a higher bee diversity compared to CRP lands (Estimate = −1.035) and other land-use types (Estimate = −1.189), whereas the other farm types did not show significant effects (Table 3.15). No significant effects were found for bee abundance, richness, and diversity, and farm type when combining all 2016 data

(Table 3.15). In May–June 2017 organic cotton sites had lower richness estimate

(Estimate = −0.233) than all other land-use types. No significant effects for bee abundance and diversity and farm type for the May–June 2017 sampling season were found (Table 3.15).

Bee Communities and Playas

The presence of playas was a significant predictor (p = 0.0210) of bee richness in the April–May 2016 sampling season (Table 3.13), sites with playas had a higher model estimate for bee richness (Estimate = 0.003) compared to sites without playas

(Estimate = −0.081). No further significant differences were found in subsequent sampling seasons (Table 3.16).

Bee Communities and Soil Type

76 Texas Tech University, Samuel Discua, May 2021

April–May 2016 sampling season: Sites that were located in Acuff-Amarillo-

Olton (Estimate = −0.011) and Olton-Pullman (Estimate = −0.007) soil types had a higher model estimate for bee richness than sites located in the Amarillo-Patricia soil type (Estimate = −0.233). There were no differences in bee richness for sites located in the Amarillo and Mansker-Potter-Spur soil types (Table 3.17). Sites that had Olton-

Pullman soil had a higher bee diversity estimate (Estimate = −0.955) than Amarillo soil type (Estimate = −1.264); no differences were found for the other soil types.

In September–October 2016 Amarillo-Patricia sites had a higher bee diversity

(Estimate = −0.783) compared to Berda-Bippus-Estacado-Mansker (Estimate = -

1.403) and Olton-Pullman (Estimate = −1.096), whereas the other soil types showed no significant effects (Table 3.17).

Soil type had no significant effect in predicting bee abundance, richness, or diversity for the July–August 2016 sampling season, all 2016 sampling seasons combined, or the May–June 2017 sampling season (Table 3.17).

Floral Communities

Similar to bee communities, variables for predicting floral communities changed across sampling seasons. Yet, percent forbs, was consistently a significant variable for predicting either floral abundance or richness in 2016. In April−May

2016, percent forbs and short grasses were significant predictors for floral richness.

Bee abundance, short grasses and forbs were significant predictors for floral abundance. In July–August 2016, percent forbs was significant in predicting floral abundance. In September–October 2016, percent forbs was significant in predicting

77 Texas Tech University, Samuel Discua, May 2021 floral richness. When combining all 2016 sampling seasons: percent forbs and short grasses were significant in predicting floral abundance (Table 3.12). During May–June

2017, percent woody stems was a significant variable for predicting floral richness

(Table 3.12).

Floral Communities and CRP Lands

In the April–May 2016 sampling season (Table 3.18); sites that were located in

CRP lands had a lower model estimate for floral diversity (Estimate = −2.112) than sites that were not (Estimate = −1.610) (Table 3.19). CRP had no significant effect in predicting floral abundance, richness, and diversity for the July–August 2016,

September–October 2016, and May–June 2017 sampling seasons in any of the models tested (Table 3.19).

When combining all 2016 sampling seasons, CRP had significant effect in floral diversity (p = 0.0094) (Table 3.19); sites that were located in CRP lands had a lower model estimate for floral diversity (Estimate = −1.470) than sites that were not

(Estimate = −1.111, Table 3.19). No other significant effects for floral abundance and richness in CRP were found.

Floral Communities and Farm Type

Farm type was not a significant predictor for floral richness, abundance, and diversity for any of the sampling seasons in any of the models tested (Tables 3.8 and

3.20).

Floral Communities and Playas

78 Texas Tech University, Samuel Discua, May 2021

Playas were a significant predictor for floral diversity (p = 0.0418) in the year

2016 (Table 3.18); sites that were located in playas had a lower model estimate for floral diversity (Estimate = −1.4684) than sites that were not located in playas

(Estimate = −1.1671) (Table 3.21). No other significant effects for floral abundance and richness and playas in playa lakes were found for the year 2016. Playas had no significant effect in predicting floral abundance, richness, and diversity for all other sampling seasons (Table 3.21).

Floral Communities and Soil Types

Soil type was not a significant predictor for floral richness, abundance, and diversity for any of the sampling seasons in any of the models tested (Tables 3.18 and

3.22).

Discussion

A large diversity of bee species and pollinators across the different habitats sampled was found in this study, with 119 bee species and morphospecies identified.

Observed species composition was similar to that observed by Berger (1982) and

Begosh (2017). Species diversity and community composition observed during this study, updated records, and comparisons with bee communities found in agroecosystems and urban landscapes are further discussed in Chapter V.

This study demonstrates that agroecosystems can provide habitat for a large number of species. Indeed, compared to highly developed urban areas in the study

79 Texas Tech University, Samuel Discua, May 2021 region, more bee genera (38) were collected in this study than Partridge et al. (2017), who found 20 genera while sampling urban gardens in the city of Lubbock. However, it is important to note that the area sampled across agroecosystems was larger and the sampling methods used were different across these two studies. The impacts of urban land on bee diversity are further explored in Chapter IV.

This study did not sample bees by flower type and did not examine bee-floral associations. Therefore, it is likely that rare or oligolectic bees were under-sampled.

For example, Nama hispidum, bristly nama, is known to be the host of oligolectic bees, including Sphecodosoma pratti. Nama hispidum was found across some of the study sites, yet Sphecodosoma pratti was not collected in all sites in which Nama hispidum was observed. It is likely that bee sampling methods that do not specifically focus on these specialist systems (e.g., through “hunt” sampling) could underestimate occurrences and abundances of these specialist taxa.

Bee abundance and richness increase with increasing number of years since land has been under restoration (Griffin et al. 2017). Further studies could address the impact of the number of years under the CRP program on bee communities. For the present study, it is unknown how long some of the study sites have been under CRP; thus, future studies could address this question. Similarly, it is important to distinguish sites under conventional CRP and pollinator habitat enhancement CP-42 designation, as the former typically contains a large dominance of native and non-native grass species.

80 Texas Tech University, Samuel Discua, May 2021

There was a small proportion of cleptoparasitic bees in this study. These accounted for approximately 1% of the total specimens collected. Relative abundance and diversity of cleptoparasitic bees can be used as an indicator of bee community health (Sheffield et al. 2013). While there is no baseline with which to compare bee diversity prior to conversion to agricultural land use in this region, museum records show that diversity of cleptoparasitic bees in the Texas High Plains could be potentially greater than that reported in this study.

When comparing bee diversity across different land-use types, there were seasonal differences in bee richness and diversity. Vineyard sites had the lowest bee richness in our early-season sampling (April–May 2016) but had the highest richness in our second sampling (July–August 2016). Vineyard sites also had the highest bee diversity in the late-season sampling of September–October 2016. However, it is important to notice that the number of study sites per land-use type were not balanced, thus limiting the ability to accurately compare bee communities across my a priori land-use type classifications (cotton farms, vineyards, other land-use types, vegetable farms, organic cotton farms, and CRP lands). This also limited the ability to model interactions (i.e., land-use type and soil).

The results of the present study contrast with some of the findings from

Begosh (2019), where CRP had a negative influence on pollinator abundance and richness. Although the lower floral diversity found in CRP lands was similar to

Begosh (2019), the higher bee richness and abundance I found may be related to the type of CRP sampled in my study (sampling included CRP sites under CP-42 for

81 Texas Tech University, Samuel Discua, May 2021 pollinator habitat designation) as well as to the landscape context surrounding sites.

There is little research investigating the impact of CRP and native bees; while studies have documented the benefits of CRP lands in honey bees (Ricigliano et al. 2019,

McMinn-Sauder et al. 2020), native bee floral and nesting requirements are different than honey bees and could be differently affected by CRP plantings.

The number of ground nests was a significant factor in predicting bee richness for the pooled 2016-2017 data. Bare ground and ground nests have been shown to be an important factor for predicting bee diversity (Morandin and Kremen, 2013; Begosh,

2020). Percent of forbs was a significant factor in predicting either bee abundance, richness, or diversity across all sampling seasons. The presence of forbs was also a significant predictor of floral abundance and richness. This is likely because flowering forb species were the dominant floral resources in the sites sampled. Floral richness was a significant predictor of bee diversity in April–May 2016 and bee richness and abundance in May–June 2017. Floral abundance was a significant predictor of bee abundance in July–August 2016 and bee richness in May–June 2017. This finding is similar to work by Moorhouse (2016) in Iowa, who found that native bee abundance increased as forb diversity increased in CRP contour buffer and filter strips within row cropped fields in Iowa.

Information on some bee species groups are still incomplete, species identifications on certain genera collected from this study were difficult to resolve, particularly on the more abundant species groups such as Lasioglossum, Perdita, and

82 Texas Tech University, Samuel Discua, May 2021

Eucerini. Identifications from this species will likely reveal associations on the influence of land cover type and species within this groups.

It is unclear from this study and from historic museum records how bee communities have changed over time. Acres under crop production in the Llano

Estacado region have remained relatively constant for the past four decades, and as far as is known to the author’s knowledge, there were no significant bee-collecting efforts made before intensive agriculture began in this region.

Although the current study focused on bees as pollinators, the importance of non-bee insect pollinators should not be ignored and merits further examination

(Rader et al. 2016). Further studies could build on this work to investigate the significant number of other pollinator groups collected in this study. In the samples collected were numerous groups of other insect pollinators such as hoverflies (Diptera:

Syrphidae), bee flies (Diptera: Bombyliidae), longhorned beetles (Coleoptera:

Cerambycidae), metallic woodboring beetles (Coleoptera: Buprestidae), and leaf beetles (Coleoptera: Chrysomelidae), similar to Begosh (2018). In addition, a relatively large number of flower crab spiders (Misumena spp.) was collected; this is an interesting observation, because many of these spiders are predators of pollinators.

The presence of pollinator predators could also be an indicator of healthy pollinator communities, but this has not been investigated.

The findings of the present study support studies that underscore the importance of field margins, field edges and paths, headlands, fence-lines, rights of way, and nearby uncultivated patches of land for providing habitat for pollinators in

83 Texas Tech University, Samuel Discua, May 2021 areas of intense agriculture (Nicholls and Altieri 2013). Moreover, this study adds to the growing body of research documenting the anthophilous communities in the Llano

Estacado region of Texas. Many of the community patterns and dominant genera found were similar to contemporary studies (Chapter V), as well as to the findings of

Berger (1982).

Additional studies related to this work could focus on understanding the contribution of native bees on pollinator-dependent crops grown in this region or studying the effects of establishing wildflower strips on bee communities and their benefits on crop production, soil, and habitat conservation. The benefits of planting and establishing wildflower meadows on wild bee abundance and diversity have been well-documented (Williams et al. 2015); likewise, the potential benefits to cotton and other crops from bee pollination can support potential strategies for increasing pollinator habitat in the Llano Estacado region of Texas. This study provides important baseline data to address the conservation and monitoring of bee communities in the region, and to support the conservation of the important ecosystem service of pollination in wild plant communities and agricultural crops.

84 Texas Tech University, Samuel Discua, May 2021

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Figure 3.1. Location of 43 sites sampled during 2016-2017 within the Llano Estacado region of Texas.

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Table 3.1. Site descriptions, characteristics, and dates sampled in 2016-2017 of 43 farmland habitats across seven counties in the Llano Estacado region of Texas. Dates Sampled 2016 2017 Site Size Farm Site Name County Site Description CRP Playa Soil Series # ha Type Apr- Jul- Sep- May- May Aug Oct Jun Bingham Uncultivated land next to 1 Terry 106.06 Vineyard No No Mansker, Potter, Spur 4/7 7/20 10/5 5/15 Wildland (UW) organic cotton/vineyard Uncultivated land adjacent to 2 Bingham Clint Terry 7.41 Vineyard No No Amarillo, Patricia 4/7 7/20 10/5 5/15 vineyard 3 Bingham Playa Terry Cultivated Playa on pivot corner 3.12 Vineyard No Yes Amarillo, Patricia 4/7 7/20 10/5 5/15 Uncultivated land adjacent to 4 BING-FV-CM Terry 0.55 Vineyard No No Amarillo, Patricia 4/7 7/20 10/5 5/15 vineyard BING Buena Uncultivated land adjacent to 5 Terry 11.59 Vineyard No No Amarillo, Patricia 4/7 7/20 10/5 5/15 Juerta vineyard Uncultivated land adjacent to 6 SunVeg Lubbock 0.19 Vegetable No No Amarillo 4/14 7/25 10/26 5/26 vegetable farm 7 Apple Country Lubbock Apple orchard 9.55 Vegetable No No Olton, Pullman 4/14 7/25 10/26 5/29 8 SPFB Garden Lubbock Vegetable farm 1.42 Vegetable No No Amarillo 4/14 7/25 10/31 5/26 Acuff, Amarillo, 9 Arboretum Lubbock City park 5.86 Other No No 4/21 7/27 10/26 5/26 Olton 10 Juicy Lucy Lubbock Vegetable farm 1.98 Vegetable No No Amarillo 4/21 8/4 10/26 5/29 Acuff, Amarillo, 11 AgriLife Tunnels Lubbock Vegetable farm 0.90 Vegetable No No 4/21 7/25 10/31 5/29 Olton CRP Pivot corner adjacent to 12 Hicklen CRP Hockley 4.53 Cotton Yes No Amarillo 4/26 7/26 9/19 5/5 cotton farm P. Harrist Acuff, Amarillo, 13 Lubbock Wetland adjacent to cotton farm 7.34 Cotton No Yes 4/26 7/26 10/3 5/11 Wetland Olton Acuff, Amarillo, 14 P. Harrist Upland Lubbock Terrace corner 66.67 Cotton No No 4/26 7/26 10/3 5/11 Olton Acuff, Amarillo, 15 P. Harrist Playa Lubbock Terrace corner 66.67 Cotton No Yes 4/26 7/26 10/3 5/11 Olton Uncultivated land next to Acuff, Amarillo, 16 L. Harrist House Lubbock 1.74 Cotton No No 4/28 7/19 9/21 5/19 cropland Olton Acuff, Amarillo, 17 L. Harrist Upland Lubbock CRP 58.10 CRP Yes No 4/28 7/19 9/21 5/19 Olton L. Harrist CRP Acuff, Amarillo, 18 Lubbock Playa on CRP land 58.10 CRP Yes Yes 4/28 7/19 9/21 5/19 Playa Olton L. Harrist Small Acuff, Amarillo, 19 Lubbock CRP 26.62 CRP Yes No 4/28 7/19 9/21 5/19 CRP Olton

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Table 3.1 Continued Acuff, Amarillo, 20 Onion Shed Hockley Vegetable farm 1.73 Vegetable No No 4/28 7/27 9/21 5/19 Olton 21 Conner Upland Crosby Cotton farm, grassland, CRP 85.48 CRP Yes Yes Olton, Pullman 5/3 8/2 10/28 6/7 22 Conner East Playa Crosby Cotton farm, grassland, CRP 85.48 CRP Yes Yes Olton, Pullman 5/3 8/2 10/28 6/7 Conner West 23 Crosby Cotton farm, grassland, CRP 85.48 CRP Yes Yes Olton, Pullman 5/3 8/2 10/28 6/7 Playa Wilmeth CRP Acuff, Amarillo, 24 Crosby Cotton farm, grassland, CRP 83.91 CRP Yes Yes 5/3 8/2 10/28 6/7 Upland Olton Wilmeth CRP Acuff, Amarillo, 25 Crosby Cotton farm, grassland, CRP 83.91 CRP Yes Yes 5/3 8/2 10/28 6/7 Playa Olton Acuff, Amarillo, 26 J. Pate South CRP Lubbock Cotton farm, CRP Corners 3.64 Cotton Yes No 5/5 8/4 10/24 5/29 Olton Acuff, Amarillo, 27 J. Pate North CRP Lubbock Cotton farm, CRP Corners 3.33 Cotton Yes No 5/5 8/4 10/24 5/29 Olton 28 Boseman CRP PL Lubbock Wildlife, Grass Corner 65.05 CRP Yes Yes Olton, Pullman 5/5 8/3 10/24 6/8 Boseman CRP 29 Lubbock Wildlife, Grass Corner 65.05 CRP Yes Yes Olton, Pullman 5/5 8/3 10/24 6/8 UL Organic 30 Wilkes CRP Terry CRP 119.49 No No Amarillo 5/10 7/27 9/19 5/5 cotton Organic 31 Wilkes East Farm Terry Pivot Corner 2.99 No No Amarillo 5/10 7/27 9/19 5/5 cotton Wilkes West Organic 32 Terry Pivot Corner 3.00 No No Amarillo 5/10 7/27 9/19 5/5 Farm cotton Acuff, Amarillo, 33 Thomas 111 CRP Lynn Terraces 131.06 Cotton Yes Yes 5/13 5/13 10/10 5/31 Olton Thomas Native Native strip surrounded by Acuff, Amarillo, 34 Lynn 3.18 Cotton No No 5/13 5/13 10/10 5/31 Strip farmland Olton Thomas 160 Acuff, Amarillo, 35 Lynn Cotton farm 131.60 Cotton Yes Yes 5/13 5/13 10/10 5/31 Upland Olton Thomas 160 Acuff, Amarillo, 36 Lynn Playa adjacent to upland cotton 131.60 Cotton Yes Yes 5/13 5/13 10/10 5/31 Playa Olton Acuff, Amarillo, 37 Thomas 62 CRP Lynn Playa adjacent to cotton farm 65.85 Cotton Yes Yes 5/13 5/13 10/10 5/31 Olton Acuff, Amarillo, 38 Crazy Hoe Lubbock Vegetable farm 6.40 Vegetable No No 5/24 7/26 9/19 5/11 Olton SPFB-Apple 39 Lubbock Apple orchard 8.04 Vegetable No No Amarillo 5/24 7/25 10/26 5/26 Orchard Hopper Home 40 Hale Pond no till 19.32 Cotton Yes No Olton, Pullman 5/27 8/3 9/23 6/8 Place Hopper Trashy 41 Floyd Pond no till 28.01 Cotton Yes No Olton, Pullman 5/27 8/3 9/23 6/8 Playa

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Table 3.1 Continued Hopper Sketchy 42 Floyd Pond no till 20.93 Cotton Yes No Olton, Pullman 5/27 8/3 9/23 6/8 Playa Lubbock Lake Berda, Bippus, Not 43 Lubbock City park, rangeland 135.94 Other No No 7/25 10/26 5/26 Landmark Estacado, Mansker sampled

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Table 3.2. Total number of bees collected by genera across 43 farmland habitats in the Llano Estacado region of Texas in 2016 and 2017.

2016 2017 Total Genera/Date Apr-May July-Aug Sept-Oct May-Jun # (%) Sites coll. # (%) Sites coll. # (%) Sites coll. # (%) Sites coll. # (%) Sites coll. Agapostemon 1090 (16.54) 41 224 (5.33) 39 197 (9.4) 38 304 (6.28) 41 1815 (10.24) 43 Ancyloscelis 45 (0.68) 1 142 (3.38) 22 55 (2.63) 10 41 (0.85) 15 283 (1.6) 30 Andrena 2 (0.03) 2 5 (0.12) 1 2 (0.1) 2 0 0 9 (0.05) 4 Anthidiellum 0 0 0 0 1 (0.05) 1 0 0 1 (0.01) 1 Anthidium 1 (0.02) 1 1 (0.02) 1 0 0 3 (0.06) 2 5 (0.03) 4 Anthophora 13 (0.2) 8 9 (0.21) 5 10 (0.48) 7 8 (0.17) 7 40 (0.23) 18 Anthophorula 7 (0.11) 4 164 (3.91) 19 45 (2.15) 11 14 (0.29) 9 230 (1.3) 28 Apis mellifera 20 (0.3) 11 10 (0.24) 6 26 (1.24) 9 74 (1.53) 17 130 (0.73) 25 Ashmeadiella 1 (0.02) 1 8 (0.19) 5 1 (0.05) 1 3 (0.06) 3 13 (0.07) 10 Augochlorella 150 (2.28) 12 50 (1.19) 15 31 (1.48) 9 54 (1.12) 16 285 (1.61) 28 Augochloropsis 101 (1.53) 19 56 (1.33) 16 9 (0.43) 4 150 (3.1) 20 316 (1.78) 31 Bombus 0 0 13 (0.31) 7 20 (0.95) 13 1 (0.02) 1 34 (0.19) 16 Calliopsis 4 (0.06) 1 0 0 6 (0.29) 6 38 (0.78) 17 48 (0.27) 25 Centris 0 0 1 (0.02) 1 0 0 3 (0.06) 3 4 (0.02) 3 Ceratina 43 (0.65) 15 50 (1.19) 6 8 (0.38) 5 16 (0.33) 6 117 (0.66) 14 Coelioxys 0 0 1 (0.02) 1 0 0 0 0 1 (0.01) 1 Colletes 7 (0.11) 5 0 0 12 (0.57) 7 15 (0.31) 9 34 (0.19) 18 Diadasia 30 (0.46) 16 226 (5.38) 25 212 (10.12) 22 115 (2.38) 29 583 (3.29) 39 Dianthidium 0 0 6 (0.14) 3 6 (0.29) 6 2 (0.04) 2 14 (0.08) 11 Dieunomia 0 0 3 (0.07) 3 1 (0.05) 1 4 (0.08) 2 8 (0.05) 5 Epeolus 1 (0.02) 1 2 (0.05) 1 0 0 7 (0.14) 6 10 (0.06) 8 Ericrocis 1 (0.02) 1 0 0 1 (0.05) 1 2 (0.04) 1 4 (0.02) 3 Eucera 0 0 0 0 0 0 1 (0.02) 1 1 (0.01) 1 Exomalopsis 0 0 15 (0.36) 6 10 (0.48) 5 0 0 25 (0.14) 10 Halictus 217 (3.29) 39 164 (3.91) 28 28 (1.34) 17 450 (9.3) 28 859 (4.85) 43 Heriades 0 0 0 0 0 0 2 (0.04) 2 2 (0.01) 2 Holcopasites 1 (0.02) 1 3 (0.07) 2 1 (0.05) 1 0 0 5 (0.03) 4 Hoplitis 2 (0.03) 1 0 0 0 0 0 0 2 (0.01) 1 Hyaleus 1 (0.02) 1 0 0 0 0 0 0 1 (0.01) 1 Lasioglossum 4355 (66.08) 42 2020 (48.11) 43 895 (42.72) 42 2193 (45.3) 43 9463 (53.39) 43 Lithurgus 11 (0.17) 5 0 0 0 0 0 0 11 (0.06) 5 Megachile 18 (0.27) 13 38 (0.9) 21 31 (1.48) 14 37 (0.76) 19 124 (0.7) 35 Melissodes 148 (2.25) 26 722 (17.19) 41 331 (15.8) 37 897 (18.53) 38 2098 (11.84) 43

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Table 3.2. Continued Nomada 41 (0.62) 16 3 (0.07) 1 3 (0.14) 3 12 (0.25) 6 59 (0.33) 21 Osmia 15 (0.23) 9 2 (0.05) 1 0 0 2 (0.04) 2 19 (0.11) 12 Panurginus 1 (0.02) 1 3 (0.07) 3 4 (0.19) 4 5 (0.1) 3 13 (0.07) 9 Peponapis 0 0 14 (0.33) 3 0 0 0 0 14 (0.08) 3 Perdita 186 (2.82) 23 150 (3.57) 19 92 (4.39) 23 313 (6.47) 37 741 (4.18) 43 Protandrena 0 0 0 0 1 (0.05) 1 1 (0.02) 1 2 (0.01) 2 Protoxea 0 0 0 0 1 (0.05) 1 0 0 1 (0.01) 1 Pseudopanurgus 3 (0.05) 3 2 (0.05) 1 40 (1.91) 17 10 (0.21) 7 55 (0.31) 22 Sphecodes 56 (0.85) 17 3 (0.07) 2 4 (0.19) 4 37 (0.76) 12 100 (0.56) 27 Sphecodosoma 14 (0.21) 5 0 0 0 0 0 0 14 (0.08) 5 Svastra 0 0 78 (1.86) 22 6 (0.29) 5 4 (0.08) 4 88 (0.5) 23 Tetraloniella 1 (0.02) 1 4 (0.1) 4 1 (0.05) 1 19 (0.39) 12 25 (0.14) 15 Triepeolus 3 (0.05) 3 6 (0.14) 5 4 (0.19) 3 3 (0.06) 3 16 (0.09) 13 Triopasites 0 0 1 (0.02) 1 0 0 0 0 1 (0.01) 1 Xenoglossa 1 (0.02) 1 0 0 0 0 0 0 1 (0.01) 1 Xeromelecta 0 0 0 0 0 0 1 (0.02) 1 1 (0.01) 1 Total Bees 6590 4199 2095 4841 17725 Total Genera 34 35 34 36 49

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Table 3.3. Bee abundance and richness collected per site across 43 farmland habitats in the Llano Estacado region of Texas in 2016 and 2017.

2016 2017 Total Site Apr-May Jul-Aug Sept-Oct May-Jun Sum S E H D` Sum S E H D` Sum S E H D` Sum S E H D` Sum S E H D` 1 302 7 0.27 0.53 0.21 78 14 0.75 1.99 0.78 28 7 0.92 1.79 0.81 48 8 0.82 1.70 0.78 456 20 0.44 1.33 0.50 2 271 4 0.56 0.77 0.51 69 11 0.50 1.21 0.52 24 8 0.86 1.79 0.79 127 12 0.75 1.87 0.80 491 20 0.53 1.58 0.69 3 192 5 0.66 1.06 0.60 41 11 0.76 1.83 0.78 10 7 0.97 1.89 0.84 32 8 0.89 1.85 0.82 275 18 0.59 1.70 0.72 4 119 6 0.55 0.99 0.55 68 14 0.67 1.76 0.68 37 10 0.84 1.94 0.82 164 16 0.59 1.62 0.65 388 26 0.61 2.00 0.78 5 71 6 0.56 1.00 0.52 109 15 0.73 1.99 0.80 17 8 0.89 1.84 0.81 81 13 0.68 1.76 0.71 278 22 0.71 2.20 0.85 6 57 5 0.49 0.78 0.38 58 8 0.50 1.03 0.46 29 7 0.68 1.32 0.61 175 9 0.36 0.79 0.33 319 15 0.38 1.04 0.40 7 295 6 0.54 0.97 0.51 21 4 0.85 1.18 0.64 72 8 0.54 1.13 0.54 169 10 0.43 1.00 0.41 557 15 0.42 1.14 0.50 8 78 5 0.49 0.78 0.38 43 8 0.43 0.90 0.37 39 6 0.71 1.28 0.63 93 12 0.68 1.69 0.72 253 17 0.53 1.51 0.59 9 26 6 0.80 1.43 0.70 104 6 0.55 0.98 0.51 31 7 0.67 1.31 0.59 118 15 0.61 1.65 0.70 279 17 0.57 1.62 0.66 10 157 7 0.51 0.99 0.47 79 8 0.60 1.24 0.58 29 8 0.82 1.71 0.78 244 10 0.57 1.32 0.65 509 18 0.53 1.54 0.66 11 149 8 0.61 1.27 0.57 67 6 0.64 1.14 0.60 13 5 0.93 1.50 0.76 107 9 0.42 0.92 0.38 336 15 0.48 1.31 0.56 12 180 7 0.46 0.90 0.53 107 10 0.69 1.58 0.71 70 16 0.77 2.13 0.82 64 10 0.72 1.67 0.73 421 22 0.56 1.72 0.72 13 80 10 0.61 1.40 0.65 105 9 0.82 1.81 0.81 16 8 0.88 1.84 0.80 39 9 0.79 1.74 0.76 240 17 0.72 2.03 0.83 14 66 6 0.50 0.90 0.50 80 8 0.68 1.41 0.63 14 5 0.94 1.51 0.77 13 5 0.82 1.31 0.67 173 11 0.69 1.65 0.76 15 42 9 0.69 1.52 0.70 79 6 0.54 0.97 0.44 19 6 0.83 1.48 0.71 12 6 0.91 1.63 0.78 152 13 0.69 1.77 0.76 16 121 6 0.32 0.57 0.24 58 8 0.91 1.89 0.83 55 11 0.77 1.84 0.74 87 13 0.75 1.91 0.78 321 19 0.63 1.86 0.71 17 384 9 0.33 0.73 0.35 60 10 0.81 1.85 0.80 137 8 0.51 1.06 0.49 180 11 0.73 1.76 0.77 761 17 0.51 1.44 0.60 18 81 8 0.56 1.17 0.60 92 5 0.81 1.30 0.68 157 9 0.55 1.21 0.53 73 11 0.85 2.04 0.83 403 17 0.61 1.71 0.70 19 321 6 0.29 0.53 0.25 142 10 0.74 1.70 0.73 33 9 0.89 1.96 0.83 69 8 0.85 1.76 0.80 565 14 0.57 1.49 0.62 20 139 9 0.53 1.17 0.60 53 8 0.71 1.47 0.70 25 5 0.60 0.97 0.49 55 9 0.54 1.19 0.50 272 18 0.51 1.48 0.63 21 61 7 0.59 1.15 0.52 115 9 0.67 1.48 0.69 37 6 0.52 0.93 0.41 84 9 0.77 1.69 0.75 297 15 0.63 1.71 0.71 22 90 10 0.63 1.46 0.67 103 10 0.57 1.32 0.64 6 4 0.90 1.24 0.67 57 9 0.67 1.46 0.66 256 16 0.63 1.75 0.74

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Table 3.3. Continued 23 96 11 0.66 1.59 0.65 206 11 0.62 1.47 0.66 9 5 0.95 1.52 0.77 112 14 0.79 2.09 0.83 423 21 0.64 1.95 0.74 24 91 9 0.60 1.31 0.57 125 7 0.61 1.18 0.62 7 3 0.91 1.00 0.61 118 5 0.55 0.88 0.46 341 12 0.51 1.28 0.58 25 81 7 0.44 0.85 0.37 232 9 0.59 1.31 0.63 23 6 0.87 1.56 0.76 168 8 0.35 0.74 0.32 504 15 0.46 1.25 0.53 26 134 9 0.76 1.67 0.75 142 7 0.55 1.07 0.53 127 10 0.60 1.37 0.69 73 12 0.80 1.99 0.82 476 20 0.60 1.79 0.76 27 162 10 0.58 1.34 0.62 47 8 0.68 1.42 0.66 117 10 0.61 1.40 0.60 191 9 0.52 1.15 0.51 517 17 0.54 1.52 0.62 28 168 9 0.77 1.69 0.77 58 4 0.73 1.01 0.56 42 5 0.54 0.87 0.40 25 5 0.83 1.34 0.69 293 13 0.68 1.74 0.73 29 83 8 0.59 1.22 0.60 98 9 0.48 1.06 0.45 71 9 0.61 1.34 0.64 65 8 0.68 1.41 0.65 317 17 0.50 1.42 0.60 30 259 6 0.34 0.61 0.26 198 11 0.41 0.97 0.43 21 7 0.83 1.62 0.74 53 6 0.69 1.24 0.62 531 14 0.38 1.01 0.40 31 43 8 0.72 1.50 0.71 53 8 0.79 1.65 0.77 27 7 0.75 1.47 0.69 33 5 0.65 1.05 0.52 156 14 0.66 1.75 0.75 32 84 9 0.57 1.25 0.54 115 7 0.64 1.24 0.58 14 7 0.98 1.91 0.85 87 5 0.59 0.95 0.53 300 14 0.60 1.58 0.68 33 171 14 0.57 1.49 0.65 103 8 0.62 1.29 0.64 63 10 0.62 1.43 0.67 41 11 0.76 1.82 0.76 378 21 0.55 1.67 0.71 34 128 9 0.65 1.42 0.66 89 10 0.77 1.78 0.77 49 13 0.77 1.97 0.78 105 12 0.66 1.65 0.72 371 22 0.62 1.93 0.76 35 536 8 0.31 0.65 0.30 63 10 0.71 1.64 0.69 16 5 0.82 1.32 0.67 98 11 0.64 1.54 0.71 713 18 0.37 1.08 0.45 36 116 11 0.43 1.02 0.41 206 15 0.55 1.48 0.64 79 13 0.71 1.82 0.78 232 11 0.44 1.06 0.49 633 23 0.51 1.59 0.69 37 371 14 0.35 0.91 0.36 94 8 0.48 0.99 0.43 78 13 0.62 1.58 0.70 113 12 0.64 1.58 0.68 656 25 0.39 1.25 0.49 38 205 12 0.58 1.43 0.64 48 9 0.82 1.80 0.77 61 7 0.79 1.53 0.73 29 6 0.78 1.40 0.65 343 16 0.65 1.81 0.74 39 328 9 0.31 0.68 0.29 114 3 0.09 0.10 0.03 28 7 0.67 1.30 0.59 192 12 0.45 1.12 0.55 662 16 0.32 0.88 0.37 40 121 12 0.64 1.59 0.68 211 8 0.53 1.11 0.53 61 7 0.62 1.20 0.62 257 14 0.48 1.28 0.55 650 19 0.50 1.47 0.61 41 74 9 0.77 1.69 0.76 90 6 0.61 1.09 0.56 217 6 0.49 0.88 0.47 152 13 0.49 1.27 0.54 533 17 0.52 1.48 0.70 42 57 9 0.73 1.60 0.70 157 14 0.45 1.17 0.46 42 7 0.73 1.42 0.68 389 13 0.45 1.15 0.52 645 20 0.47 1.41 0.57 43 Not Sampled 19 8 0.83 1.73 0.77 45 6 0.43 0.76 0.35 217 11 0.55 1.32 0.63 281 16 0.55 1.53 0.69 Average 156.90 8.21 0.54 1.13 0.53 97.65 8.79 0.64 1.36 0.62 48.72 7.70 0.74 1.46 0.68 112.60 9.90 0.65 1.45 0.65 412.20 17.50 0.55 1.56 0.65 *Sum = Abundance, S = Richness, E = Evenness, H = Shannon’s Diversity index, D` = Inverse Simpson’s diversity index

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Andrenidae Apidae Colletidae Halictidae Megachilidae

6000

5000

4000

3000

2000

1000

0 Apr-May Jul-Aug Sept-Oct May-Jun

Figure 3.2. Number of bees by family collected during sampling seasons across 43 farmland habitats in the Llano Estacado region of Texas in 2016 and 2017.

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Table 3.4. Average number of bees collected by sampling season, generic richness, Shannon’s evenness index and Shannon’s diversity index across 43 farmland habitats in the Llano Estacado region of Texas in 2016 and 2017.

Year Date Avg. bees Richness Evenness Diversity Apr-May 156.9 ± 110.7 8.21 ± 2.30 0.54 ± 0.14 1.13 ± 0.35 2016 Jul-Aug 97.65 ± 51.20 8.79 ± 2.77 0.64 ± 0.15 1.36 ± 0.37 Sept-Oct 48.72 ± 43.81 7.7 ± 2.61 0.74 ± 0.15 1.46 ± 0.34 2017 May-Jun 112.6 ± 77.30 9.9 ± 2.89 0.65 ± 0.15 1.45 ± 0.35 Total 412.2 ± 156.59 17.5 ± 3.34 0.55 ± 0.10 1.56 ± 0.29

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Table 3.5. Average number open of flowers/flower heads observed by sampling season, Species richness, Shannon’s evenness index and Shannon’s diversity index across 43 farmland habitats in the Llano Estacado region of Texas in 2016 and 2017.

Year Date Avg. flowers Richness Evenness Diversity Apr-May 1037.83 ± 1931.89 3.30 ± 1.85 0.38 ± 0.31 0.51 ± 0.43 2016 Jul-Aug 163.56 ± 282.98 2.91 ± 1.87 0.40 ± 0.31 0.49 ± 0.04 Sep-Oct 145.47 ± 274.34 1.35 ± 1.65 0.23 ± 0.33 0.27 ± 0.41 2017 May-Jun 194.51 ± 204.78 3.33 ± 2.13 0.47 ± 0.34 0.63 ± 0.50 Total 1565.93 ± 2009.29 8.44 ± 3.88 0.62 ± 0.23 1.25 ± 0.53

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Table 3.6. Percentage of bare ground, short grasses, tall grasses, and forbs; and number of ground nests, and woody stems within observed within transects across four sampling seasons in 2016 and 2017 on 43 farmland habitats across the Llano Estacado region of Texas.

Year Date %Bare ground %Short grasses %Tall grasses %Forbs #Ground Nests #Woody Stems Apr-May 31.62 ± 24.10 13.73 ± 15.05 31.75 ± 26.01 22.45 ± 21.36 7.14 ± 7.97 0.76 ± 3.54 2016 Jul-Aug 29.02 ± 24.72 23.79 ± 22.67 24.16 ± 26.18 17.71 ± 16.3 3.33 ± 3.9 0.84 ± 2.53 Sept-Oct 33.69 ± 25.93 6.81 ± 8.62 34.14 ± 29.13 19.45 ± 18.28 1.74 ± 2.22 0.37 ± 1.46 2017 May-Jun 29.9 ± 20.84 17.62 ± 17.45 27.86 ± 26.79 24.43 ± 22 2.42 ± 2.54 0.56 ± 1.98 Total 31.05 ± 24.11 15.55 ± 17.97 29.4 ± 27.45 20.96 ± 19.79 3.64 ± 5.17 0.63 ± 2.51

109 Texas Tech University, Samuel Discua, May 2021

Table 3.7. Number of open flowers/flower heads by plant species observed in standard transects in 2016 and 2017 across 43 farmland habitats in the Llano Estacado region of Texas.

2016 2017 Total Flowering plants/Date April-May Jul-Aug Sept-Oct May-Jun # (%) Sites obs. # (%) Sites obs. # (%) Sites obs. # (%) Sites obs. # (%) Sites obs. Allium spp. 110 (0.25) 1 0 0 0 0 0 0 110 (0.16) 1 Aphanostephus skirrhobasis 3064 (7.03) 10 53 (0.58) 2 320 (5.12) 2 257 (3.07) 2 3694 (5.49) 12 Argemone albiflora 0 0 0 0 0 0 1 (0.01) 1 1 (0.01) 1 Asteraceae sp. 1 1032 (2.37) 2 319 (3.5) 5 725 (11.59) 1 0 0 2076 (3.08) 7 Asteraceae sp. 2 0 0 251 (2.75) 8 0 0 308 (3.68) 4 559 (0.83) 12 Brassicaceae spp. 4644 (10.65) 10 0 0 0 0 0 0 4644 (6.9) 10 Capsella bursa-pastoris 60 (0.14) 1 0 0 0 0 0 0 60 (0.09) 1 Centaurea americana 0 0 0 0 0 0 5 (0.06) 1 5 (0.01) 1 Chloracantha spinosa 0 0 0 0 0 0 34 (0.41) 2 34 (0.05) 2 Cirsium ochrocentrum 0 0 1 (0.01) 1 0 0 0 0 1 (0.01) 1 Cirsium spp. 10 (0.02) 1 4 (0.04) 2 0 0 46 (0.55) 3 60 (0.09) 4 Convolvulus arvensis 263 (0.6) 2 2210 (24.21) 6 10 (0.16) 1 902 (10.78) 7 3385 (5.03) 13 Convolvulus equitans 0 0 0 0 0 0 122 (1.46) 1 122 (0.18) 1 Coreopsis spp. 820 (1.88) 2 160 (1.75) 2 40 (0.64) 1 509 (6.09) 5 1529 (2.27) 5 Coreopsis tinctorial 0 0 0 0 0 0 25 (0.3) 1 25 (0.04) 1 Cucurbitaceae spp. 0 0 11 (0.12) 1 0 0 0 0 11 (0.02) 1 Dalea purpurea 0 0 0 0 0 0 3 (0.04) 1 3 (0.01) 1 Erigeron annuus 0 0 0 0 0 0 84 (1) 2 84 (0.12) 2 Erigeron canadensis 0 0 433 (4.74) 5 0 0 0 0 433 (0.64) 5 Erigeron strigosus 0 0 0 0 0 0 1265 (15.12) 11 1265 (1.88) 11 Eriogonum multiflorum 0 0 20 (0.22) 1 0 0 0 0 20 (0.03) 1 Erodium cicutarium 6061 (13.9) 8 5 (0.05) 1 0 0 0 0 6066 (9.01) 9 Gaillardia pinnatifida 80 (0.18) 1 5 (0.05) 1 0 0 0 0 85 (0.13) 2 Gaillardia pulchella 0 0 0 0 0 0 135 (1.61) 3 135 (0.2) 3 Glandularia bipinnatifida 0 0 93 (1.02) 2 0 0 210 (2.51) 3 303 (0.45) 3 Glandularia pumila 210 (0.48) 1 0 0 0 0 0 0 210 (0.31) 1 Grindelia papposa 0 0 3 (0.03) 2 8 (0.13) 3 0 0 11 (0.02) 4 Gutierrezia sarothrae 0 0 0 0 939 (15.01) 4 0 0 939 (1.39) 4 Helianthus ciliaris 4 (0.01) 1 4 (0.04) 3 1 (0.02) 1 175 (2.09) 6 184 (0.27) 9

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Table 3.7. Continued Helianthus spp. 0 0 146 (1.6) 5 25 (0.4) 3 4 (0.05) 1 175 (0.26) 7 Heterotheca villosa 0 0 819 (8.97) 5 0 0 0 0 819 (1.22) 5 Hibiscus trionum 0 0 11 (0.12) 1 0 0 0 0 11 (0.02) 1 Hymenoxys odorata 7 (0.02) 1 0 0 216 (3.45) 2 0 0 223 (0.33) 3 Leucanthemum x. superbum 0 0 0 0 0 0 250 (2.99) 1 250 (0.37) 1 Lythrum californicum 80 (0.18) 1 0 0 0 0 0 0 80 (0.12) 1 Machaeranthera pinnatifida 8521 (19.55) 2 100 (1.1) 1 375 (6) 1 0 0 8996 (13.36) 4 Machaeranthera spp. 0 0 0 0 450 (7.19) 2 0 0 450 (0.67) 2 Machaeranthera tanacetifolia 1393 (3.2) 20 209 (2.29) 15 267 (4.27) 7 272 (3.25) 12 2141 (3.18) 25 Malva spp. 0 0 0 0 0 0 56 (0.67) 5 56 (0.08) 5 Malvaceae 0 0 696 (7.63) 2 0 0 0 0 696 (1.03) 2 Malvella leprosa 78 (0.18) 2 203 (2.22) 2 0 0 0 0 281 (0.42) 3 Melilotus officinalis 225 (0.52) 2 5 (0.05) 1 0 0 130 (1.55) 3 360 (0.53) 6 Mentzelia spp. 0 0 306 (3.35) 3 504 (8.06) 7 0 0 810 (1.2) 9 Mimosa spp. 0 0 0 0 0 0 13 (0.16) 1 13 (0.02) 1 Monarda spp. 0 0 0 0 0 0 40 (0.48) 1 40 (0.06) 1 Nama hispidum 25 (0.06) 1 0 0 0 0 0 0 25 (0.04) 1 Oenothera sp. 1 130 (0.3) 3 0 0 0 0 0 0 130 (0.19) 3 Oenothera sp. 2 2 (0.01) 1 0 0 0 0 0 0 2 (0.01) 1 Oenothera sp. 3 1051 (2.41) 9 0 0 0 0 275 (3.29) 1 1326 (1.97) 9 Oenothera canescens 312 (0.72) 5 35 (0.38) 6 0 0 107 (1.28) 7 454 (0.67) 1 Oenothera macrocarpa 1595 (3.66) 1 0 0 0 0 0 0 1595 (2.37) 1 Oenothera suffrutescens 214 (0.49) 3 0 0 0 0 0 0 214 (0.32) 3 Phacelia tanacetifolia 8 (0.02) 1 0 0 0 0 0 0 8 (0.01) 1 Phyla nodiflora 45 (0.1) 1 0 0 0 0 0 0 45 (0.07) 1 Polygonum pensylvanicum 0 0 0 0 0 0 80 (0.96) 1 80 (0.12) 1 Polygonum spp. 545 (1.25) 1 656 (7.19) 3 538 (8.6) 3 5 (0.06) 1 1744 (2.59) 6 Proboscidea louisianica 1 (0.01) 1 19 (0.21) 1 0 0 0 0 20 (0.03) 2 Quincula lobate 3 (0.01) 1 0 0 0 0 0 0 3 (0.01) 1 Rapistrum rugosum 461 (1.06) 3 0 0 0 0 120 (1.43) 2 581 (0.86) 4 Ratibida columnifera 0 0 0 0 0 0 1 (0.01) 1 1 (0.01) 1 Rudbeckia hirta 0 0 0 0 0 0 7 (0.08) 1 7 (0.01) 1 Senecio flaccidus 568 (1.3) 4 21 (0.23) 1 403 (6.44) 3 1020 (12.2) 11 2012 (2.99) 13 Senecio vulgaris 0 0 0 0 67 (1.07) 2 458 (5.48) 7 525 (0.78) 8 Solanum elaeagnifolium 7 (0.02) 4 657 (7.2) 13 0 0 359 (4.29) 16 1023 (1.52) 20

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Table 3.7. Continued Solanum rostratum 0 0 0 0 63 (1.01) 2 0 0 63 (0.09) 2 Species 1 0 0 0 0 79 (1.26) 1 0 0 79 (0.12) 1 Species 2 0 0 0 0 100 (1.6) 1 0 0 100 (0.15) 1 Species 3 16 (0.04) 1 0 0 0 0 0 0 16 (0.02) 1 Species 4 0 0 0 0 118 (1.89) 2 0 0 118 (0.18) 2 Species 5 1 (0.01) 1 0 0 0 0 0 0 1 (0.01) 1 Species 6 26 (0.06) 1 0 0 0 0 0 0 26 (0.04) 1 Species 7 7 (0.02) 1 0 0 0 0 0 0 7 (0.01) 1 Species 8 481 (1.1) 1 0 0 0 0 0 0 481 (0.71) 2 Species 9 121 (0.28) 2 1 (0.01) 1 0 0 0 0 122 (0.18) 2 Species 10 3500 (8.03) 1 0 0 0 0 0 0 3500 (5.2) 1 Species 11 1 (0.01) 1 0 0 0 0 0 0 1 (0.01) 1 Species 12 109 (0.25) 1 0 0 0 0 0 0 109 (0.16) 2 Species 13 77 (0.18) 1 0 0 0 0 0 0 77 (0.11) 3 Species 14 8 (0.02) 3 0 0 0 0 0 0 8 (0.01) 1 Species 15 0 0 0 0 0 0 127 (1.52) 2 127 (0.19) 2 Species 16 0 0 0 0 0 0 3 (0.04) 1 3 (0.01) 3 Species 17 0 0 0 0 0 0 2 (0.02) 1 2 (0.01) 1 Species 18 0 0 0 0 0 0 1 (0.01) 1 1 (0.01) 1 Sphaeralcea coccinea 116 (0.27) 2 0 0 0 0 50 (0.6) 1 166 (0.25) 3 Stephanomeria pauciflora 0 0 3 (0.03) 1 0 0 0 0 3 (0.01) 1 Symphyotrichum tenuifolium 0 0 12 (0.13) 1 0 0 0 0 12 (0.02) 12 Taraxacum spp. 129 (0.3) 1 6 (0.07) 1 0 0 4 (0.05) 1 139 (0.21) 1 Thelesperma spp. 215 (0.49) 1 486 (5.32) 5 0 0 0 0 701 (1.04) 5 Tribulus terrestris 0 0 86 (0.94) 2 1 (0.02) 1 0 0 87 (0.13) 3 Verbena bracteate 6100 (13.99) 1 24 (0.26) 2 0 0 0 0 6124 (9.09) 3 Verbena spp. 0 0 0 0 0 0 12 (0.14) 1 12 (0.02) 1 Verbenaceae spp. 8 (0.02) 1 0 0 0 0 0 0 8 (0.01) 1 Verbesina encelioides 0 0 266 (2.91) 1 326 (5.21) 2 278 (3.32) 2 870 (1.29) 3 Vernonia fasciculata 0 0 3 (0.03) 1 0 0 0 0 3 (0.01) 1 Xanthisma texanum 1045 (2.4) 9 785 (8.6) 9 680 (10.87) 6 609 (7.28) 8 3119 (4.63) 20 Total flowers 43589 9127 6255 8364 67335 Flowering Species 50 39 23 42 95

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Apr-May 2016 Jul-Aug 2016

2016 – Sep-Oct May-Jun 2017

Figure 3.3. Principal component analyses of local habitat associations across 43 farmland habitats in the Llano Estacado region of Texas during four sampling seasons: Season A - April-May 2016, Season B – Jul-Aug 2016, Season C – Sept-Oct, Season D – May-Jun 2017. *Circles represent sites, vectors represent strength of correlations with habitat variables along the axes, and percentages in parentheses along the axes represent the amount of variance explained by each axis. Habitat variables are abbreviated as follows: %BG = Percent bare ground, %F = Percent forbs, FA = Floral abundance, Fl-Div = Floral diversity, %G = Percent tall grasses, #GN = Number of ground nests, %L = Percent short grasses, #WS = Number of woody stems

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Table 3.8. Correlation coefficients between PC1 and PC2 scores for local habitat variables of principal component across 43 farmland habitats in the Llano Estacado region of Texas during four sampling seasons. Season A - April-May 2016, Season B – Jul-Aug 2016, Season C – Sept-Oct, Season D – May-Jun 2017. Variable Season A Season B Season C Total 2016 Season D PC1 PC2 PC1 PC2 PC1 PC2 PC1 PC2 PC1 PC2 #GN -0.456 -0.561 -0.588 -0.587 -0.420 -0.311 -0.678 -0.467 0.682 0.488 #WS 0.041 -0.350 0.244 -0.234 -0.471 0.096 0.082 0.142 -0.533 0.244 %BG -0.637 -0.473 -0.736 -0.320 0.030 -0.894 -0.828 -0.386 0.528 0.733 %F -0.402 0.786 -0.555 0.238 -0.614 -0.215 -0.465 0.648 -0.272 0.344 %G 0.738 -0.064 0.618 0.418 0.275 0.820 0.838 -0.152 -0.142 -0.842 %L 0.590 0.463 0.293 -0.230 -0.364 0.237 0.400 0.428 -0.093 0.054 FA -0.554 0.379 -0.485 0.659 -0.870 0.155 -0.420 0.469 -0.727 0.446 FL-Div -0.709 0.343 -0.385 0.681 -0.887 0.221 -0.304 0.638 -0.736 0.388

Habitat variables are abbreviated as follows: %BG = Percent bare ground, %F = Percent forbs, FA = Floral abundance, Fl-Div = Floral diversity, %G = Percent tall grasses, #GN = Number of ground nests, %L = Percent short grasses, #WS = Number of woody stems

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Figure 3.4. Nonmetric Multidimensional Scaling ordinations of bee communities and local habitat associations across 42 farmland habitats in the Llano Estacado region of Texas during Season A - April-May 2016. *Triangles represent study sites, circles represent bee genera, convex hulls represent grouping variables (Farm type, Presence or absence of Conservation Reserve Program lands, Presence or absence of playas, Soil series). Biplots show relationship between ordination scores and local environmental variables (cutoff r2 value = 0.1). Stress 19.353. 3-Dimensional solution. Data relativized by genera.

115 Texas Tech University, Samuel Discua, May 2021

Figure 3.5. Nonmetric Multidimensional Scaling ordinations of bee communities and local habitat associations across 43 agroecosystems in the Llano Estacado region of Texas during Season B – Jul-Aug 2016. *Triangles represent study sites, circles represent bee genera, convex hulls represent grouping variables (Farm type, Presence or absence of Conservation Reserve Program lands, Presence or absence of playas, Soil series). Biplots show relationship between ordination scores and local environmental variables (cutoff r2 value = 0.1). Stress 10.417. 3-Dimensional solution. Data relativized by genera.

116 Texas Tech University, Samuel Discua, May 2021

Figure 3.6. Nonmetric Multidimensional Scaling ordinations of bee communities and local habitat associations across 43 agroecosystems in the Llano Estacado region of Texas during Season C – Sep-Oct 2016 *Triangles represent study sites, circles represent bee genera, convex hulls represent grouping variables (Farm type, Presence or absence of Conservation Reserve Program lands, Presence or absence of playas, Soil series). Biplots show relationship between ordination scores and local environmental variables (cutoff r2 value = 0.1). Stress 14.625. 2-Dimensional solution. Data were not relativized.

117 Texas Tech University, Samuel Discua, May 2021

Figure 3.7. Nonmetric Multidimensional Scaling ordinations of bee communities and local habitat associations across 43 agroecosystems in the Llano Estacado region of Texas during Season D – May-Jun 2017. *Triangles represent study sites, circles represent bee genera, convex hulls represent grouping variables (Farm type, Presence or absence of Conservation Reserve Program lands, Presence or absence of playas, Soil series). Biplots show relationship between ordination scores and local environmental variables (cutoff r2 value = 0.05). Stress 13.89. 2-Dimensional solution. Data was relativized by site.

118 Texas Tech University, Samuel Discua, May 2021

Table 3.9. Variance Inflation Factors (VIF) of local habitat variables during four sampling seasons used to fit Generalized Linear Mixed Models to predict bee abundance, richness, and diversity.

2016 2017 Variable / Season April-May Jul-Aug Sep-Oct Total 2016 May-Jun Short Grasses 1.44 1.09 1.14 1.14 1.28 Forbs 1.48 1.24 1.42 1.19 1.24 Ground Nests 1.37 1.45 1.19 1.08 1.10 Woody Stems 1.17 1.26 1.40 1.20 1.48 Floral Abundance 1.30 1.59 3.45 1.10 2.06 Floral Diversity 1.82 1.40 3.52 1.29 1.83 Size 1.14 1.53 1.12 1.19 1.36 *VIF cutoff value < 2; only variables found to be non-collinear are presented.

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Table 3.10. Variance Inflation Factors (VIF) of local habitat variables during four sampling seasons used to fit Generalized Linear Mixed Models to predict floral abundance, richness, and diversity.

2016 2017 Variable / Year Scale (m) April-May Jul-Aug Sep-Oct Total 2016 May-Jun Bee Abundance 1.15 1.41 1.37 1.20 1.39 Bee Richness 1.19 1.28 1.42 1.43 1.59 Short Grasses 1.31 1.22 1.20 1.18 1.31 Forbs 1.16 1.11 1.06 1.01 1.27 Ground Nests 1.33 1.43 1.06 1.32 1.18 Woody Stems 1.14 1.35 1.19 1.22 1.16 Size 1.16 1.59 1.10 1.13 1.27 *VIF cutoff value < 2; only variables found to be non-collinear are presented.

120 Texas Tech University, Samuel Discua, May 2021

Table 3.11. Parameter estimates of Generalized Linear Mixed Models examining bee richness, abundance, and Shannon’s diversity indices at four collecting seasons and local habitat variables across 43 farmland habitats in the Llano Estacado region of Texas during 2016 and 2017.

Bee richness Bee abundance Bee diversity Season Effect DF Estimate Std. Error F Value Pr > F Estimate Std. Error F Value Pr > F Estimate Std. Error F Value Pr > F Intercept 29 -0.0793 0.0604 -1.31 0.200 0.652200 0.08347 7.81 <.0001 -1.02850 0.12440 -8.27 <.0001 Short grasses 29 -0.0008 0.0014 -0.61 0.550 0.002349 0.00184 1.27 0.213 -0.00170 0.00285 -0.60 0.555 Forbs 29 0.0011 0.0010 1.15 0.259 -0.002220 0.00136 -1.63 0.113 0.00629 0.00194 3.24 0.003 Apr-May Ground nests 29 0.0010 0.0044 0.23 0.818 -0.000790 0.00612 -0.13 0.898 0.00399 0.00900 0.44 0.661 2016 Woody stems 29 -0.0061 0.0144 -0.42 0.674 0.006260 0.01924 0.33 0.747 -0.01604 0.02950 -0.54 0.591 Floral abundance 29 0.0000 0.0000 -1.51 0.142 0.000002 0.00001 0.15 0.885 -0.00002 0.00002 -0.81 0.425 Floral richness 29 0.0058 0.0125 0.47 0.645 0.022150 0.01718 1.29 0.208 -0.05380 0.02586 -2.08 0.046 Size 29 0.0007 0.0004 1.66 0.109 0.000682 0.00058 1.18 0.249 -0.00074 0.00088 -0.84 0.407 Intercept 35 -0.1003 0.0523 -1.92 0.063 0.676700 0.04213 16.06 <.0001 -1.07700 0.08424 -12.78 <.0001 Short grasses 35 0.0005 0.0009 0.55 0.585 -0.001960 0.00078 -2.51 0.017 0.00272 0.00145 1.88 0.069 Forbs 35 0.0005 0.0014 0.33 0.743 0.002345 0.00114 2.06 0.047 -0.00163 0.00227 -0.72 0.476 Jul-Aug Ground nests 35 0.0030 0.0062 0.49 0.627 -0.001480 0.00510 -0.29 0.774 0.00968 0.00987 0.98 0.333 2016 Woody stems 35 0.0138 0.0085 1.63 0.113 -0.000150 0.00735 -0.02 0.983 0.01374 0.01352 1.02 0.317 Floral abundance 35 -0.0002 0.0001 -1.87 0.070 -0.000180 0.00007 -2.64 0.012 0.00001 0.00012 0.06 0.951 Floral richness 35 0.0209 0.0123 1.70 0.098 0.005517 0.01027 0.54 0.595 0.00027 0.01989 0.01 0.989 Size 35 0.0000 0.0005 -0.01 0.991 0.000322 0.00046 0.70 0.488 -0.00025 0.00087 -0.29 0.777 Intercept 35 -0.0384 0.0436 -0.88 0.384 0.428100 0.07611 5.63 <.0001 -0.87360 0.04451 -19.63 <.0001 Short grasses 35 -0.0018 0.0027 -0.65 0.518 0.004768 0.00442 1.08 0.288 -0.00672 0.00286 -2.35 0.025 Forbs 35 -0.0013 0.0014 -0.91 0.367 -0.000070 0.00238 -0.03 0.976 -0.00133 0.00146 -0.91 0.371 Sept-Oct 2016 Ground nests 35 0.0004 0.0104 0.04 0.970 -0.002780 0.01814 -0.15 0.879 0.00105 0.01067 0.10 0.922 Woody stems 35 0.0146 0.0167 0.87 0.391 -0.005960 0.02982 -0.20 0.843 0.03290 0.01666 1.97 0.056 Floral abundance 35 0.0001 0.0001 0.62 0.537 -0.000090 0.00025 -0.37 0.713 0.00015 0.00015 0.97 0.340 Floral richness 35 0.0004 0.0238 0.02 0.987 0.017430 0.03958 0.44 0.662 -0.00038 0.02490 -0.02 0.988

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Table 3.11. Contiunued Size 35 -0.0007 0.0005 -1.29 0.207 -0.000510 0.00087 -0.58 0.565 -0.00101 0.00053 -1.90 0.066 Intercept 35 0.1343 0.0363 3.70 0.001 0.8644 0.0450 19.21 <.0001 -0.9015 0.0745 -12.10 <.0001 Short grasses 35 -0.0003 0.0009 -0.29 0.774 -0.0015 0.0011 -1.34 0.188 0.0015 0.0019 0.80 0.430 Forbs 35 0.0002 0.0007 0.34 0.736 -0.0005 0.0009 -0.53 0.601 0.0028 0.0015 1.86 0.072 Total Ground nests 35 0.0085 0.0033 2.56 0.015 0.0004 0.0042 0.10 0.924 0.0106 0.0069 1.54 0.131 2016 Woody stems 35 0.0088 0.0045 1.96 0.058 0.0022 0.0057 0.38 0.704 0.0001 0.0101 0.01 0.993 Floral abundance 35 0.0000 0.0000 0.86 0.398 0.0000 0.0000 0.46 0.651 0.0000 0.0000 0.25 0.808 Floral richness 35 -0.0011 0.0039 -0.29 0.772 0.0065 0.0048 1.37 0.181 -0.0209 0.0081 -2.59 0.014 Size 35 -0.0001 0.0002 -0.30 0.766 0.0003 0.0003 0.89 0.378 -0.0010 0.0005 -1.95 0.060 Intercept 35 0.0261 0.0499 0.52 0.605 0.623600 0.07298 8.54 <.0001 -0.89820 0.07792 -11.53 <.0001 Short grasses 35 0.0026 0.0011 2.37 0.023 0.001625 0.00159 1.02 0.314 0.00128 0.00174 0.74 0.466 Forbs 35 0.0019 0.0008 2.25 0.031 0.001466 0.00121 1.21 0.233 0.00110 0.00133 0.83 0.412 May-Jun 2017 Ground nests 35 -0.0155 0.0070 -2.20 0.034 -0.010240 0.01020 -1.00 0.322 -0.01890 0.01112 -1.70 0.098 Woody stems 35 0.0058 0.0106 0.54 0.590 -0.000720 0.01559 -0.05 0.964 0.01520 0.01596 0.95 0.347 Floral abundance 35 -0.0003 0.0001 -2.46 0.019 -0.000570 0.00019 -3.06 0.004 0.00001 0.00020 0.06 0.954 Floral richness 35 0.0084 0.0112 0.75 0.457 0.041270 0.01606 2.57 0.015 -0.02015 0.01772 -1.14 0.263 Size 35 -0.0008 0.0004 -1.90 0.065 -0.000690 0.00063 -1.09 0.282 -0.00018 0.00069 -0.27 0.792

*Pr>|t| highlighted in bold indicate significant differences (α = 0.05)

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Table 3.12. Parameter estimates of Generalized Linear Mixed Models examining floral richness, abundance, and Shannon’s diversity indices at four collecting seasons and local habitat variables across 43 farmland habitats in the Llano Estacado region of Texas during 2016 and 2017.

Floral richness Floral abundance Floral diversity Season Effect DF Estimate Std. Error F Value Pr > F Estimate Std. Error F Value Pr > F Estimate Std. Error F Value Pr > F Intercept 29 -0.6101 0.3093 -1.97 0.058 1.229 0.252 4.88 <.0001 -2.015 0.713 -2.83 0.009 Bee abundance 29 0.0009 0.0006 1.47 0.153 0.001 0.000 3.19 0.0034 0.001 0.001 0.47 0.640 Bee richness 29 -0.0110 0.0320 -0.34 0.733 -0.052 0.027 -1.94 0.0624 -0.015 0.074 -0.20 0.840 Apr- Short grasses 29 -0.0123 0.0057 -2.15 0.040 -0.020 0.005 -4.06 0.0003 -0.004 0.013 -0.29 0.778 May 2016 Forbs 29 0.0087 0.0031 2.81 0.009 0.010 0.003 4.05 0.0004 0.009 0.007 1.22 0.233 Ground nests 29 0.0058 0.0167 0.35 0.730 -0.015 0.014 -1.06 0.2986 0.032 0.038 0.85 0.403 Woody stems 29 -0.0601 0.0645 -0.93 0.359 -0.001 0.040 -0.03 0.9763 -0.143 0.200 -0.72 0.479 Size 29 -0.0017 0.0016 -1.07 0.292 -0.002 0.001 -1.35 0.1870 -0.004 0.004 -1.00 0.324 Intercept 35 -0.9852 0.2982 -3.30 0.002 0.108 0.320 0.34 0.7370 -2.457 0.463 -5.31 <.0001 Bee abundance 35 -0.0008 0.0018 -0.44 0.660 -0.002 0.002 -0.94 0.3521 -0.002 0.003 -0.78 0.439 Bee richness 35 0.0412 0.0309 1.33 0.191 0.056 0.033 1.72 0.0948 0.083 0.047 1.76 0.087 Jul- Short grasses 35 -0.0013 0.0038 -0.33 0.743 -0.002 0.004 -0.57 0.5749 -0.005 0.006 -0.83 0.413 Aug 2016 Forbs 35 0.0073 0.0048 1.53 0.135 0.013 0.005 2.58 0.0144 0.014 0.007 1.92 0.064 Ground nests 35 -0.0179 0.0239 -0.75 0.459 -0.011 0.025 -0.42 0.6806 0.003 0.037 0.07 0.941 Woody stems 35 -0.0273 0.0361 -0.76 0.454 0.007 0.035 0.2 0.8440 -0.063 0.064 -0.99 0.331 Size 35 0.0010 0.0022 0.43 0.669 -0.001 0.002 -0.34 0.7330 -0.001 0.004 -0.24 0.813 Intercept 35 -2.7122 0.7212 -3.76 0.001 -1.482 0.694 -2.14 0.0397 -4.280 1.102 -3.88 0.000 Bee abundance 35 0.0004 0.0038 0.11 0.912 -0.001 0.004 -0.36 0.7211 0.001 0.005 0.22 0.827 Sept- Bee richness 35 0.0734 0.0670 1.10 0.280 0.108 0.064 1.67 0.1034 0.083 0.098 0.85 0.403 Oct Short grasses 35 0.0247 0.0190 1.30 0.203 0.028 0.018 1.54 0.1315 0.032 0.028 1.15 0.256 2016 Forbs 35 0.0176 0.0083 2.11 0.042 0.016 0.008 1.93 0.0622 0.024 0.012 2.03 0.050 Ground nests 35 0.1095 0.0661 1.66 0.106 0.112 0.063 1.78 0.0838 0.089 0.101 0.88 0.384 Woody stems 35 0.1234 0.0751 1.64 0.109 0.098 0.078 1.26 0.2151 0.164 0.099 1.66 0.106

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Table 3.12. Continued Size 35 -0.0023 0.0042 -0.55 0.587 -0.003 0.004 -0.7 0.4891 -0.002 0.006 -0.25 0.804 Intercept 35 -0.2659 0.2632 -1.01 0.319 0.624 0.285 2.19 0.0350 -0.9458 0.3975 -2.38 0.023 Bee abundance 35 0.0005 0.0004 1.13 0.265 0.001 0.000 1.24 0.2235 0.0003 0.0006 0.45 0.654 Bee richness 35 -0.0044 0.0171 -0.26 0.800 0.009 0.018 0.49 0.6282 -0.0246 0.0264 -0.93 0.357 Total Short grasses 35 -0.0062 0.0043 -1.43 0.161 -0.011 0.005 -2.27 0.0292 -0.0067 0.0065 -1.03 0.311 2016 Forbs 35 0.0059 0.0029 2.02 0.052 0.008 0.003 2.49 0.0175 0.0047 0.0044 1.07 0.293 Ground nests 35 0.0045 0.0166 0.27 0.788 0.005 0.018 0.28 0.7807 0.0165 0.0248 0.66 0.511 Woody stems 35 0.0262 0.0194 1.35 0.185 0.006 0.023 0.27 0.7869 0.0532 0.0279 1.90 0.065 Size 35 -0.0021 0.0011 -1.83 0.076 -0.002 0.001 -1.35 0.1862 -0.0036 0.0018 -2.02 0.051 Intercept 35 -0.4157 0.3319 -1.25 0.219 0.975 0.319 3.06 0.0043 -1.338 0.559 -2.39 0.022 Bee abundance 35 0.0012 0.0010 1.17 0.251 0.000 0.001 0.05 0.9616 0.003 0.002 1.71 0.097 Bee richness 35 -0.0364 0.0314 -1.16 0.255 -0.039 0.030 -1.29 0.2058 -0.073 0.052 -1.39 0.173 May- Short grasses 35 0.0049 0.0047 1.04 0.305 0.006 0.005 1.23 0.2254 0.011 0.008 1.40 0.170 Jun 2017 Forbs 35 0.0050 0.0035 1.40 0.169 0.006 0.003 1.85 0.0731 0.006 0.006 0.97 0.341 Ground nests 35 -0.0405 0.0326 -1.24 0.222 -0.065 0.032 -2.01 0.0517 -0.064 0.056 -1.15 0.260 Woody stems 35 0.0548 0.0321 1.71 0.097 0.055 0.031 1.76 0.0879 0.100 0.048 2.08 0.045 Size 35 -0.0020 0.0018 -1.08 0.288 -0.003 0.002 -1.91 0.0643 -0.005 0.003 -1.61 0.117

*Pr>|t| highlighted in bold indicate significant differences (α = 0.05)

124 Texas Tech University, Samuel Discua, May 2021

Table 3.13. Parameter estimates of Generalized Linear Mixed Models examining type III tests of fixed effects for bee richness, abundance, and Shannon’s diversity indices at four collecting seasons and local habitat variables across 43 farmland habitats in the Llano Estacado region of Texas during 2016 and 2017.

Bee Richness Bee Abundance Bee Diversity Year Season Effect Num DF Den DF F Value Pr > F F Value Pr > F F Value Pr > F Apr-May 1 40 12.20 0.0012 0.47 0.4979 2.57 0.1170 Jul-Aug 1 41 0.14 0.7125 10.85 0.0020 0.05 0.8224 2016 Sep-Oct CRP 1 41 0.14 0.7145 5.68 0.0219 3.33 0.0754 Total 2016 1 41 2.58 0.1161 11.67 0.0014 0.07 0.7973 2017 May-Jun 1 41 0.64 0.4297 1.95 0.1701 0.28 0.5985 Apr-May 5 36 3.88 0.0065 1.79 0.1404 1.09 0.3817 Jul-Aug 5 37 3.67 0.0085 3.23 0.0161 1.80 0.1379 2016 Sep-Oct Farm 5 37 2.19 0.0764 0.91 0.4843 4.92 0.0015 Total 2016 5 37 3.32 0.0142 0.99 0.4365 1.33 0.2714 2017 May-Jun 5 37 2.98 0.0235 1.23 0.3157 3.21 0.0167 Apr-May 1 40 5.77 0.0210 0.22 0.6443 2.11 0.1543 Jul-Aug 1 41 0.00 0.9819 2.92 0.0951 0.00 0.9811 2016 Sep-Oct Playa 1 41 1.34 0.2534 2.00 0.1649 0.63 0.4305 Total 2016 1 41 0.60 0.4443 0.23 0.6332 0.63 0.4319 2017 May-Jun 1 41 1.16 0.2870 3.08 0.0866 0.87 0.3566 Apr-May 4 37 5.04 0.0024 0.71 0.5904 4.22 0.0065 Jul-Aug 5 37 2.26 0.0686 1.95 0.1100 2.61 0.0407 2016 Sep-Oct Soil 5 37 0.75 0.5941 0.60 0.7022 5.86 0.0004 Total 2016 4 38 0.90 0.4740 0.97 0.4335 1.70 0.1709 2017 May-Jun 5 37 0.90 0.4903 0.70 0.6240 1.49 0.2166 *Pr>F highlighted in bold indicate significant differences (α = 0.05)

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Table 3.14. Tukey-Kramer post-hoc pairwise comparisons of least square means of bee richness, abundance, and Shannon’s diversity indices across 18 CRP and 24 non-CRP sites during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017.

Bee Richness Bee Abundance Bee Diversity Season CRP DF Estimate Std. Error Estimate Std. Error Estimate Std. Error No 40 -0.1044b 0.0225 0.7290- 0.0301 -1.1853- 0.0493 April -May 2016 Yes 40 0.0062a 0.0223 0.7586- 0.0311 -1.0740- 0.0489 No 41 -0.0350- 0.0284 0.6070b 0.0237 -0.9952- 0.0439 Jul-Aug 2016 Yes 41 -0.0196- 0.0303 0.7181a 0.0240 -1.0098- 0.0475 No 41 -0.0893- 0.0289 0.3689- 0.0455 -0.9086- 0.0321 Sep-Oct 2016 Yes 41 -0.0738- 0.0307 0.5218- 0.0452 -0.9966- 0.0360 No 41 0.1547- 0.0147 0.8544b 0.0156 -0.9539- 0.0314 Total 2016 Yes 41 0.1890- 0.0155 0.9309a 0.0161 -0.9421- 0.0335 No 41 0.0053- 0.0263 0.6323- 0.0359 -0.9690- 0.0359 May-Jun 2017 Yes 41 0.0358- 0.0277 0.7044- 0.0371 -0.9413- 0.0380

*LS-means with different letters are significantly different (α = 0.05) *LS-means with “-” are not significantly different (α = 0.05)

126 Texas Tech University, Samuel Discua, May 2021

Table 3.15. Tukey-Kramer post-hoc pairwise comparisons of least square means of bee richness, abundance, and Shannon’s diversity indices across 43 agroecosystems grouped by farm types sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017.

Bee Abundance Bee Richness Bee diversity Season Farm DF Estimate Std. Error Estimate Std. Error Estimate Std. Error Cotton 36 0.767- 0.039 0.020a 0.029 -1.084- 0.067 CRP 36 0.714- 0.036 -0.026a 0.026 -1.081- 0.059 April - Organic Cotton 36 0.691- 0.078 -0.069ab 0.058 -1.143- 0.132 May 2016 Other 36 0.359- 0.160 -0.168ab 0.105 -0.953- 0.208 Vegetable 36 0.782- 0.046 -0.082ab 0.036 -1.205- 0.083 Vineyard 36 0.801- 0.057 -0.206b 0.048 -1.314- 0.111 Cotton 37 0.683- 0.033 -0.010ab 0.035 -0.974- 0.061 CRP 37 0.721- 0.028 -0.035ab 0.032 -0.998- 0.055 Jul-Aug Organic Cotton 37 0.709- 0.062 -0.021ab 0.068 -1.033- 0.121 2016 Other 37 0.508- 0.084 -0.106ab 0.087 -1.004- 0.146 Vegetable 37 0.558- 0.041 -0.139b 0.044 -1.169- 0.079 Vineyard 37 0.615- 0.050 0.134a 0.049 -0.827- 0.085 Cotton 37 0.508- 0.065 0.013- 0.037 -0.875ab 0.039 CRP 37 0.478- 0.059 -0.135- 0.035 -1.035b 0.038 Sep-Oct Organic Cotton 37 0.279- 0.140 -0.102- 0.074 -0.857ab 0.075 2016 Other 37 0.460- 0.157 -0.135- 0.092 -1.189b 0.108 Vegetable 37 0.425- 0.080 -0.131- 0.046 -1.001ab 0.049 Vineyard 37 0.298- 0.108 -0.050- 0.056 -0.788a 0.056 Cotton 37 0.913- 0.024 0.213- 0.019 -0.898a 0.043 Total 2016 CRP 37 0.910- 0.022 0.157- 0.017 -0.933a 0.039 Organic Cotton 37 0.862- 0.048 0.145- 0.037 -0.973a 0.085

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Table 3.15. Continued Other 37 0.793- 0.086 0.076- 0.066 -1.015a 0.151 Vegetable 37 0.878- 0.029 0.121- 0.023 -1.064a 0.055 Vineyard 37 0.849- 0.034 0.216- 0.025 -0.912a 0.059 Cotton 37 0.593- 0.053 0.022a 0.034 -0.903a 0.044 CRP 37 0.707- 0.044 0.026a 0.030 -0.943a 0.040 May- Organic Cotton 37 0.552- 0.104 -0.223b 0.073 -1.150a 0.096 Jun 2017 Other 37 0.792- 0.112 0.133a 0.074 -0.932a 0.106 Vegetable 37 0.717- 0.058 0.019ab 0.039 -1.094a 0.057 Vineyard 37 0.636- 0.077 0.077a 0.048 -0.820a 0.063 *LS-means with different letters are significantly different (α = 0.05) *LS-means with “-” are not significantly different (α = 0.05)

128 Texas Tech University, Samuel Discua, May 2021

Table 3.16. Tukey-Kramer post-hoc pairwise comparisons of least square means of bee richness, abundance, and Shannon’s diversity indices across 15 playa and 28 non-playa sites sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017.

Bee Abundance Bee Richness Bee Diversity Season Playa DF Estimate Std. Error Estimate Std. Error Estimate Std. Error No 40 0.751- 0.027 -0.081b 0.021 -1.170- 0.045 April -May 2016 Yes 40 0.730- 0.036 0.003a 0.027 -1.064- 0.057 No 41 0.637- 0.023 -0.027- 0.026 -1.001- 0.040 Jul-Aug 2016 Yes 41 0.702- 0.030 -0.028- 0.035 -1.003- 0.055 No 41 0.478- 0.042 -0.064- 0.026 -0.934- 0.030 Sep-Oct 2016 Yes 41 0.374- 0.060 -0.116- 0.036 -0.976- 0.042 No 41 0.886- 0.016 0.165- 0.014 -0.962- 0.028 Total 2016 Yes 41 0.899- 0.021 0.182- 0.018 -0.924- 0.038 No 41 0.699- 0.031 0.034- 0.023 -0.974- 0.032 May-Jun 2017 Yes 41 0.604- 0.044 -0.009- 0.032 -0.924- 0.043

*LS-means with different letters are significantly different (α = 0.05) *LS-means with “-” are not significantly different (α = 0.05)

129 Texas Tech University, Samuel Discua, May 2021

Table 3.17. Tukey-Kramer post-hoc pairwise comparisons of least square means of bee richness, abundance, and Shannon’s diversity indices across 43 farmland habitats grouped by soil series sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017.

Bee Abundance Bee Richness Bee Diversity Season Soil DF Estimate Std. Error Estimate Std. Error Estimate Std. Error Amarillo 37 0.731- 0.050 -0.111ab 0.035 -1.264- 0.076 Acuff-Amarillo-Olton 37 0.753- 0.031 -0.011a 0.021 -1.126- 0.045 April -May 2016 Amarillo-Patricia 37 0.772- 0.069 -0.233b 0.053 -1.238- 0.106 Olton-Pullman 37 0.698- 0.048 -0.007a 0.032 -0.955- 0.061 Masker-Potter-Spur 37 0.909- 0.130 -0.102ab 0.100 -1.695- 0.266 Amarillo 37 0.662- 0.041 -0.072- 0.046 -1.181- 0.077 Acuff-Amarillo-Olton 37 0.672- 0.026 -0.040- 0.028 -0.965- 0.043 Berda-Bippus-Estacado-Mansker 37 0.263- 0.141 -0.047- 0.128 -0.829- 0.182 Jul-Aug 2016 Amarillo-Patricia 37 0.608- 0.059 0.126- 0.059 -0.849- 0.092 Olton-Pullman 37 0.693- 0.038 -0.058- 0.043 -1.069- 0.068 Masker-Potter-Spur 37 0.641- 0.116 0.162- 0.115 -0.743- 0.174 Amarillo 37 0.392- 0.083 -0.059- 0.048 -0.890ab 0.044 Acuff-Amarillo-Olton 37 0.476- 0.050 -0.064- 0.031 -0.936ab 0.029 Berda-Bippus-Estacado-Mansker 37 0.509- 0.222 -0.168- 0.145 -1.403b 0.161 Sep-Oct 2016 Amarillo-Patricia 37 0.277- 0.125 -0.037- 0.068 -0.783a 0.059 Olton-Pullman 37 0.480- 0.075 -0.155- 0.048 -1.096b 0.046 Masker-Potter-Spur 37 0.380- 0.237 -0.102- 0.140 -0.809ab 0.120 Amarillo 38 0.872- 0.029 0.151- 0.026 -1.049- 0.054 Acuff-Amarillo-Olton 38 0.906- 0.018 0.167- 0.016 -0.934- 0.032 Total 2016 Amarillo-Patricia 38 0.875- 0.042 0.229- 0.035 -0.852- 0.069 Olton-Pullman 38 0.900- 0.027 0.165- 0.024 -0.920- 0.048

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Table 3.17. Continued Masker-Potter-Spur 38 0.794- 0.061 0.189- 0.050 -1.045- 0.107 Amarillo 37 0.688- 0.061 -0.037- 0.046 -1.068- 0.062 Acuff-Amarillo-Olton 37 0.632- 0.040 0.012- 0.028 -0.941- 0.037 Berda-Bippus-Estacado-Mansker 37 0.850- 0.159 0.076- 0.122 -1.008- 0.170 May-Jun 2017 Amarillo-Patricia 37 0.662- 0.087 0.105- 0.060 -0.815- 0.077 Olton-Pullman 37 0.715- 0.057 0.045- 0.041 -0.970- 0.056 Masker-Potter-Spur 37 0.525- 0.187 -0.047- 0.130 -0.841- 0.157 *LS-means with different letters are significantly different (α = 0.05) *LS-means with “-” are not significantly different (α = 0.05)

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Table 3.18. Parameter estimates of Generalized Linear Mixed Models examining type III tests of fixed effects for floral richness, abundance, and Shannon’s diversity indices at four collecting seasons and local habitat variables across 43 farmland habitats in the Llano Estacado region of Texas during 2016 and 2017.

Floral Richness Floral Abundance Floral Diversity Year Season Effect Num DF Den DF F Value Pr > F F Value Pr > F F Value Pr > F Apr-May 1 40 1.66 0.2049 0.96 0.3339 4.21 0.0467 Jul-Aug 1 41 0.23 0.6322 0.63 0.4322 0.03 0.8678 2016 Sep-Oct CRP 1 41 1.08 0.3055 1.44 0.2372 0.82 0.3691 Total 2016 1 41 2.20 0.1460 0.13 0.7209 7.42 0.0094 2017 May-Jun 1 41 0.19 0.6614 0.51 0.4773 0.67 0.4192 Apr-May 5 36 1.04 0.4106 0.75 0.5909 2.42 0.0546 Jul-Aug 5 37 2.11 0.0862 2.02 0.098 1.59 0.1879 2016 Sep-Oct Farm 5 37 0.44 0.8163 0.42 0.8284 0.26 0.9326 Total 2016 5 37 1.05 0.4058 0.38 0.859 1.15 0.3532 2017 May-Jun 5 37 1.73 0.1525 1.85 0.1279 1.45 0.2300 Apr-May 1 40 0.68 0.4137 0.32 0.5733 2.92 0.0950 Jul-Aug 1 41 0.43 0.5176 0.09 0.7623 0.47 0.4990 2016 Sep-Oct Playa 1 41 3.93 0.0542 3.58 0.0655 4.03 0.0512 Total 2016 1 41 1.98 0.1666 0.00 0.9799 4.41 0.0418 2017 May-Jun 1 41 7.11 0.0109 6.78 0.0128 8.09 0.0069 Apr-May 4 37 0.19 0.9399 0.29 0.8798 0.81 0.5267 Jul-Aug 5 37 0.24 0.9416 0.65 0.6645 0.2 0.9622 2016 Sep-Oct Soil 5 37 0.64 0.6708 0.54 0.7429 0.41 0.8409 Total 2016 4 38 0.45 0.7723 0.95 0.4440 1.37 0.2616 2017 May-Jun 5 37 1.41 0.2446 1.91 0.1169 1.05 0.4041 *Pr>F highlighted in bold indicate significant differences (α = 0.05)

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Table 3.19. Tukey-Kramer post-hoc pairwise comparisons of least square means of floral richness, abundance, and Shannon’s diversity indices across 18 CRP and 24 non-CRP sites during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017.

Floral Richness Floral Abundance Floral Diversity Season CRP DF Estimate Std. Error Estimate Std. Error Estimate Std. Error April - No 40 -0.482- 0.083 0.924- 0.084 -1.610a 0.146 May 2016 Yes 40 -0.643- 0.094 0.801- 0.094 -2.112b 0.196 Jul- No 41 -0.599- 0.098 0.620- 0.106 -1.828- 0.159 Aug 2016 Yes 41 -0.669- 0.109 0.492- 0.121 -1.867- 0.174 Sep- No 41 -1.148- 0.206 0.242- 0.203 -2.312- 0.286 Oct 2016 Yes 41 -1.493- 0.262 -0.160- 0.267 -2.745- 0.381 Total No 41 -0.152- 0.061 0.897- 0.072 -1.111a 0.081 2016 Yes 41 -0.288- 0.070 0.858- 0.079 -1.470b 0.104 May- No 41 -0.533- 0.094 0.653- 0.094 -1.576- 0.149 Jun 2017 Yes 41 -0.594- 0.103 0.551- 0.106 -1.764- 0.176 *LS-means with different letters are significantly different (α = 0.05) *LS-means with “-” are not significantly different (α = 0.05)

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Table 3.20. Tukey-Kramer post-hoc pairwise comparisons of least square means of floral richness, abundance, and Shannon’s diversity indices across 43 farmland habitats grouped by farm types sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017.

Floral Abundance Floral Richness Floral diversity Season Farm DF Estimate Std. Error Estimate Std. Error Estimate Std. Error Cotton 36 0.741- 0.133 -0.807- 0.139 -2.631- 0.336 CRP 36 0.875- 0.110 -0.524- 0.107 -1.907- 0.207 April - Organic Cotton 36 0.817- 0.244 -0.335- 0.210 -1.239- 0.321 May 2016 Other 36 0.843- 0.418 -0.507- 0.396 -1.293- 0.570 Vegetable 36 1.076- 0.131 -0.454- 0.136 -1.557- 0.230 Vineyard 36 0.765- 0.194 -0.485- 0.175 -1.499- 0.283 Cotton 37 0.562- 0.155 -0.718- 0.149 -1.908- 0.220 CRP 37 0.493- 0.143 -0.600- 0.124 -1.832- 0.188 Jul- Organic Cotton 37 -0.262- 0.449 -1.349- 0.390 -3.022- 0.736 Aug 2016 Other 37 -0.324- 0.568 -1.201- 0.444 -12.693- 113.490 Vegetable 37 0.855- 0.158 -0.306- 0.142 -1.362- 0.197 Vineyard 37 0.755- 0.209 -0.671- 0.215 -1.940- 0.332 Cotton 37 0.003- 0.334 -1.448- 0.350 -2.599- 0.485 CRP 37 0.093- 0.283 -1.225- 0.278 -2.558- 0.421 Sep- Organic Cotton 37 -0.410- 0.787 -1.349- 0.638 -2.095- 0.721 Oct 2016 Other 37 -12.693- 448.150 -12.693- 227.080 -12.693- 176.740 Vegetable 37 0.115- 0.371 -1.395- 0.400 -2.601- 0.569 Vineyard 37 0.585- 0.371 -0.762- 0.368 -1.968- 0.524 Cotton 37 0.812- 0.112 -0.339- 0.096 -1.373- 0.141 CRP 37 0.920- 0.094 -0.214- 0.080 -1.412- 0.127

Total Organic Cotton 37 0.785- 0.218 -0.165- 0.169 -1.107- 0.236 2016 Other 37 0.758- 0.383 -0.507- 0.348 -1.303- 0.451 Vegetable 37 0.989- 0.121 -0.051- 0.098 -1.015- 0.138 Vineyard 37 0.804- 0.153 -0.209- 0.122 -1.198- 0.175 Cotton 37 0.286- 0.165 -0.942- 0.166 -2.280- 0.309 CRP 37 0.696- 0.119 -0.433- 0.114 -1.615- 0.197 May- Organic Cotton 37 0.794- 0.245 -0.429- 0.246 -1.310- 0.365 Jun 2017 Other 37 -0.011- 0.449 -0.795- 0.362 -2.068- 0.652 Vegetable 37 0.653- 0.161 -0.519- 0.158 -1.429- 0.237 Vineyard 37 0.889- 0.181 -0.343- 0.182 -1.352- 0.289 *LS-means with different letters are significantly different (α = 0.05) *LS-means with “-” are not significantly different (α = 0.05)

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Table 3.21. Tukey-Kramer post-hoc pairwise comparisons of least square means of floral richness, abundance, and Shannon’s diversity indices across 15 playa and 28 non-playa sites sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017.

Floral Abundance Floral Richness Floral Diversity Season Playa DF Estimate Std. Error Estimate Std. Error Estimate Std. Error April - No 40 0.8932- 0.07681 -0.5177- 0.0758 -1.6781- 0.136 May 2016 Yes 40 0.8184- 0.107 -0.6262- 0.1074 -2.1337- 0.2291 Jul- No 41 0.5799- 0.09887 -0.5969- 0.08861 -1.7891- 0.1409 Aug 2016 Yes 41 0.5281- 0.1386 -0.6981- 0.1273 -1.9615- 0.2098 Sep- No 41 0.2763- 0.1766 -1.0854- 0.174 -2.1988- 0.2304 Oct 2016 Yes 41 -0.4649- 0.3496 -1.8603- 0.3502 -3.4914- 0.6009 Total No 41 0.8777- 0.06612 -0.1671- 0.05502 -1.1671a 0.07641 2016 Yes 41 0.8805- 0.09021 -0.3044- 0.08052 -1.4684b 0.1214 May- No 41 0.7302a 0.07974 -0.436a 0.07884 -1.4405a 0.1269 Jun 2017 Yes 41 0.3256b 0.1334 -0.8466b 0.1323 -2.2699b 0.2625 *LS-means with different letters are significantly different (α = 0.05) *LS-means with “-” are not significantly different (α = 0.05)

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Table 3.22. Tukey-Kramer post-hoc pairwise comparisons of least square means of floral richness, abundance, and Shannon’s diversity indices across 43 agroecosystems grouped by soil series sampled during four collecting seasons in the Llano Estacado region of Texas during 2016 and 2017.

Floral Abundance Floral Richness Floral Diversity Seaso D Soil n F Estimat Std. Estimat Std. Estimat Std. e Error e Error e Error Amarillo 37 0.956- 0.140 -0.469- 0.142 -1.566- 0.249

Acuff-Amarillo-Olton 37 0.822- 0.094 -0.602- 0.096 -1.956- 0.192 April - May Amarillo-Patricia 37 0.779- 0.216 -0.479- 0.201 -1.561- 0.352 2016 Olton-Pullman 37 0.934- 0.133 -0.575- 0.141 -2.013- 0.294

Masker-Potter-Spur 37 0.704- 0.448 -0.507- 0.408 -1.286- 0.613

Amarillo 37 0.386- 0.207 -0.689- 0.180 -1.657- 0.248 Acuff-Amarillo-Olton 37 0.557- 0.120 -0.616- 0.110 -1.895- 0.177 Berda-Bippus-Estacado- Jul- 37 -0.507- 0.917 -1.201- 0.657 -11.693- 105.950 Aug Mansker 2016 Amarillo-Patricia 37 0.743- 0.245 -0.654- 0.250 -1.857- 0.387

Olton-Pullman 37 0.667- 0.170 -0.547- 0.158 -1.767- 0.247

Masker-Potter-Spur 37 0.805- 0.476 -0.740- 0.522 -2.371- 1.002

Amarillo 37 0.077- 0.386 -1.395- 0.406 -2.554- 0.574

Acuff-Amarillo-Olton 37 0.059- 0.246 -1.359- 0.252 -2.545- 0.362 Berda-Bippus-Estacado- Sep- 37 -11.693- 392.240 -11.693- 197.920 -11.693- 156.720 Oct Mansker 2016 Amarillo-Patricia 37 0.453- 0.452 -0.944- 0.459 -2.244- 0.695

Olton-Pullman 37 -0.182- 0.414 -1.349- 0.374 -2.594- 0.552 Masker-Potter-Spur 37 0.987- 0.692 -0.251- 0.648 -1.294- 0.865

Amarillo 38 0.842- 0.124 -0.130- 0.106 -1.048- 0.142

Acuff-Amarillo-Olton 38 0.859- 0.078 -0.250- 0.071 -1.297- 0.102 Total Amarillo-Patricia 38 0.955- 0.166 -0.126- 0.150 -1.059- 0.202 2016 Olton-Pullman 38 0.996- 0.109 -0.212- 0.104 -1.462- 0.165

Masker-Potter-Spur 38 0.409- 0.309 -0.399- 0.243 -1.555- 0.366 Amarillo 37 0.796- 0.148 -0.414- 0.150 -1.425- 0.242

Acuff-Amarillo-Olton 37 0.372- 0.116 -0.757- 0.112 -1.920- 0.196 Berda-Bippus-Estacado- May- 37 0.246- 0.552 -0.507- 0.443 -1.375- 0.667 Jun Mansker 2017 Amarillo-Patricia 37 0.851- 0.204 -0.433- 0.214 -1.516- 0.358 Olton-Pullman 37 0.732- 0.144 -0.453- 0.144 -1.621- 0.251

Masker-Potter-Spur 37 1.028- 0.373 -0.047- 0.352 -0.880- 0.521 *LS-means with different letters are significantly different (α = 0.05) *LS-means with “-” are not significantly different (α = 0.05)

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2. CHAPTER IV

SCALAR HABITAT ANALYSIS OF NATIVE BEES AND FLORAL RESOURCES IN THE LLANO ESTACADO AGRICULTURAL REGION OF TEXAS

Abstract

Agricultural intensification and associated loss of natural habitats are factors that threaten pollinator populations. On the Llano Estacado Region of Texas, little is known about the influence of broad agricultural production in highly fragmented landscapes on pollinator communities. This study was conducted to determine the relationships of local habitat variables and landscape structure on native bee abundance and richness across different agroecosystems in a six-county portion of the

Llano Estacado Region of Texas. In 2016 and 2017, pollinator communities were sampled using pan traps and hand netting across 43 farmland areas that serve as pollinator habitats (i.e., patches of various sizes occurring adjacent to crop lands).

Land cover was determined within 200, 500, and 1000 m buffers surrounding each sampled habitat. Mixed-effect models were used to assess the relationships of land cover within sites and in surrounding landscapes with wild bee and floral abundances and species richness. Over 17,000 bees from 106 species/morphospecies were collected. In 2016, across all scales, bee richness increased across sites with a higher proportion of natural land cover, whereas sites with higher proportions of urban areas had lower bee diversity. In 2017, partly because of a shorter sampling season, there were no significant variables in the models. Floral diversity increased with patch richness in the 500 and 1000 m scales in 2016, and in 2017 the proportion of natural

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Texas Tech University, Samuel Discua, December 2018 land cover increased floral richness in the 200 m scale. Results from this study improve our understanding of the influence of fragmented landscapes on bee communities and supports strategies to protect wild lands and conserve resources for pollinators in agricultural landscapes.

Introduction

Habitat loss caused by land-cover change is one of the most important factors driving wild bee declines (Potts et al. 2010). Land-over change results in habitat loss, fragmentation, degradation, and reduced resource diversity and availability.

Agricultural intensification is one of the main causes for habitat loss and homogenization. Although reversing habitat loss on agricultural land is difficult, increasing the farmland configurational heterogeneity (higher field border density) and farmland compositional heterogeneity (higher crop diversity) has been proposed to counter some habitat loss (Kennedy et al. 2013). The amount of high-quality remnant habitat around farms and regional land-cover diversity are important factors in enhancing wild bee communities in agroecosystems (Kennedy et al. 2013).

Bee diversity and abundance are scale-dependent (Steffan-Dewenter et al.

2002), with patterns of diversity and abundance dependent on the highly variable differences in dispersal capacities among species (Krewenka et al. 2011) and the setting of the system studied (e.g., agricultural versus urban systems). Wild habitats close to cropland increase visitation rates of native bees to crops (e.g., Cusser et al.

2016). Furthermore, studies have demonstrated pollinator richness and visitation rates

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Texas Tech University, Samuel Discua, December 2018 to crops decline with increasing distance from natural habitats (Ricketts et al. 2008).

Thus, the presence of natural habitats close to cropland is important for maintaining bee richness.

Wild bee abundance is reduced by anthropogenic disturbances (Carre et al.

2009; Potts et al. 2010b; Winfree et al. 2011; Hadley and Betts 2012; Kennedy et al.

2013; Cariveau and Winfree 2015) and the reduction of natural and wild habitats.

Although bee abundance and richness are affected by disturbances, there is evidence that bees are resilient and able to tolerate habitat reduction (Winfree et al. 2019).

Winfree et al. (2009) found that wild bee abundance and richness were significantly reduced by habitat loss only where very little habitat remained (i.e., less than 5% natural habitat cover or location > 1 km from the nearest natural habitat). Furthermore, some bee species are adaptable to land-cover change and are able to utilize small habitat remnants (Winfree et al. 2009).

The benefits of wild bee pollination are independent from those of honey bee pollination. Garibaldi et al. (2013) studied landscape effects on the stability of ecosystem services and found that isolation from natural areas reduced the stability of flower visitor richness, visitation rates, and fruit set. The authors found a negative association between isolation from natural areas and crop fruit set. Similar trends were not observed for related honey bee visitation. This suggests that pollination services provided by other (wild) insects are important even in the presence of honey bees.

Furthermore, strategies to improve or sustain pollination services in crops should account for both the native bee fauna and the habitat resources in landscapes that

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Texas Tech University, Samuel Discua, December 2018 influence their abundance and diversity. Indeed, studies have only begun to explore the relationships between habitat composition and configuration of habitats and fields within landscapes (Fahrig 2013; Haddadet al.2017).

Landscape composition (the proportion of land-use types) and configuration

(size, shape and spatial arrangement of land-use patches) are increasingly being recognized for their influence on biodiversity and ecosystem services in agriculture dominated landscapes (Fahrig 2013). According to Martin et al. (2019), landscape configuration can be measured as the density of edges between farmland and their surroundings, including neighboring crops and non-crop areas. Complex landscapes with higher density of edges can support spillover of dispersal-limited populations between patches (Smith et al.2014; Fahrig 2017) and likely enhance a populations’ persistence of disturbances and potential to provide services in crops (Boetzlet al.2019).

Farmland heterogeneity in two components: configurational and compositional heterogeneity. Farmland configuration heterogeneity refers to the spatial arrangement of farmland, which can be measured as mean field size or density of field borders.

Similarly, farmland compositional heterogeneity refers to the diversity of crops in a landscape, which can be expressed with diversity indexes (e.g. Shannon diversity index) of different crops. Landscape compositional heterogeneity effects on pollinators and associated pollination services have been measured in a variety of ways. Most studies simply used the percentage of non-crop or semi-natural cover in the landscape as their measure of landscape composition

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An increasing number of studies have demonstrated the importance of establishing pollinator habitat around agroecosystems in supporting pollinator diversity (Diekötter and Crist 2013, Petersen and Nault 2014, Burkle et al. 2017,

Lichtenberg et al. 2017). Among large-scale initiatives, the Conservation Reserve

Program (CRP) is among the most widely adopted for providing habitat for wildlife in the United States. Established in 1985, this program has saved tons of topsoil and has helped to provide habitat for wildlife, including pollinators. The U.S. state of Texas has 0.93 million hectares under CRP programs, which represents 11.5% of the nation’s total and more than in any other state (Farm Service Agency 2021). In 2008, pollinators became a high-priority wildlife taxon for CRP projects. Studies have demonstrated that butterflies and bees benefit from CRP restorations, but there have been few studies investigating the effects of CRP on native bees. However, the practice of sowing CRP fields with non-native grasses is widespread and likely diminishes the value of these habitats for bees (Winfree 2010).

The Llano Estacado is the largest contiguous non-mountainous area in the

United States. It lies south of the Canadian River in northwest Texas and is to the east by the Caprock Escarpment and occupies the largest portion of the Texas High Plains.

This region covers all or part of 33 Texas counties and four New Mexico counties

(Figure 3.1). The Llano Estacado has the largest contiguous planted cotton acreage in the United States, producing 66% of the state’s and 25% of the nation’s cotton crop

(Plains Cotton Growers 2020). On average, 1.5 million hectares are planted with cotton every year, producing 3.7 million bales. Additional crops produced in the

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Texas Tech University, Samuel Discua, December 2018 region include alfalfa (Medicago sativa), squash (Cucurbita sp.), watermelon

(Citrullus lanatus), pumpkin (Cucurbita pepo), apple (Malus domestica), wine grape

(Vitis vinifera), sunflower (Helianthus annuus), and small vegetable farms. Even though the main crop in the region, cotton, does not rely upon animal pollination, it is however a pollen-limited crop that benefits from animal pollination (Cusser et al.

2016). Indeed, Cusser et al. (2016) found that cotton farms in south Texas that were surrounded by more natural habitat were visited by more native bees and had 18% higher seedcotton weight (equivalent to USD $266 per hectare) compared with farms surrounded by less natural habitat. This study provides valuable supporting information for implementing conservation programs that increase habitat for pollinators.

The increase of natural lands across agroecosystems may be achieved by the implementation of CRPs, particularly those that focus on pollinators (e.g., the CP-42 pollinator habitat) and the establishment of wildflower plantings. Cotton farmers can increase or maintain agricultural productivity by increasing the amount of wild, untilled land surrounding cotton crop production, thus helping to sustain bee communities. There may also be secondary benefits to establishing and increasing pollinator habitat via increased biocontrol services, increased soil moisture, and improved aesthetic quality of farm land (Haaland et al. 2011).

To better implement any conservation program in the Llano Estacado, it is important to understand the bee community composition and habitats for pollinators around agroecosystems in this region. There has been a renewed interest in

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Texas Tech University, Samuel Discua, December 2018 documenting wild bees in the Texas High Plains (Partridge 2017, Begosh 2018, and

Auerbach et al. 2019). Despite these recent efforts, relatively little is known about native bee communities in the Llano Estacado and the impact of land management practices in their habitats. Given the importance of cotton in the Llano Estacado and the potential benefits of increasing pollinator richness and abundance around cotton- dominated agroecosystems, the present study seeks to determine the relationships between landscape structure and native bee abundance and richness across different agroecosystems in a seven-county section of the Llano Estacado region of Texas. The objective of the present study is to determine local and landscape-scaled habitat relationships on bee communities in the Llano Estacado region of Texas.

Materials and Methods

Study Area

Pollinator communities were sampled across a seven-county area (Crosby,

Floyd, Hale, Hockley, Lubbock, Lynn, and Terry) covering 16,884.4 km2 in the Llano

Estacado region of Texas (Figure 3.1). All counties in the study area are among the top

10 cotton-producing counties in the United States (USDA 2018). At the time of sampling, cultivated cropland covered at least 60% (10,131 km2) of the study area, with cotton covering at least 40% (6874 km2) of the combined area of the counties.

Additional crops cultivated in the seven-county region included alfalfa, apples, corn, grapes, oats, peanuts, pecans, pumpkins, rye, sorghum, and winter wheat (USDA

2018).

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Bee communities were sampled at 43 sites located on a west-to-east gradient across the seven-county study region. Areas sampled within sites included pivot corners, playas, non-cultivated habitats including native grassland remnants, and CRP lands. Land-use types adjacent to study sites included conventional and organic cotton, apple orchards, vegetable farms, and CRP lands (Table 3.1). The average area of the sites sampled was 31.33 ha, ranging from 0.19 ha to 131.60 ha. Sites sampled were on existing CRP lands (n =18) playa lakes (n = 15), and 15 were on playas, including 10 that were both CRP lands and near playas (n = 10).

Insect Sampling

Bees and other pollinating insects were collected during four sampling seasons across the study sites: three in 2016 (April–May, July–August, and September–

October) and once in 2017 (April–May). Sampling methods included sweep netting

(using random and standard transects) and pan trapping. Collecting methods and sample processing are further described in Chapter III.

Land Cover data

The percentage of different land-cover types within nested 200 m, 500 m, and

1000 m buffers around farm polygons was determined. Buffer sizes were chosen based on bee foraging distances (Winfree et al. 2008), according to differences between long-distance flyers such as honey bees and native bees that generally forage for only a few hundred meters. Geographic information system data were analyzed using

ArcMap (Version 10.6.1, Esri, Redlands, CA). Land-use data within buffers for 2016 and 2017 were obtained from the United States Department of Agriculture National

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Agricultural Statistics Service Cropland Data Layer (NASS CropScape). Land-use types within the Cropland Data Layer (CDL) were reclassified into four categories: 1) cropland (alfalfa, corn, cotton, cucurbits, millet, oats, peanuts, peas, rye, sorghum, sunflowers, winter wheat, triticale, and other crops); 2) developed; 3) grassland

(fallow/idle cropland, grass/pasture, other hay/non alfalfa, and sod/grass seed); and 4) other (barren, deciduous forest, herbaceous wetlands, open water, shrubland, and woody wetlands).

Landscape Composition and Configuration Metrics

Landscape composition and configuration metrics were calculated using

FRAGSTATS v4 (McGarigal et al. 2012). For each site and buffer size, metrics for land-cover patches within each site (class metrics) and at the total site and buffer size scale (landscape metrics) were calculated. The following composition metrics were calculated at the land-cover type (class) scale (McGarigal et al. 2012): 1) percentage of landscape (PLAND), the proportion of different landscape occupied by each land- cover type. This metric was chosen as it allowed for comparing among the different scales used. 2) Number of patches (NP). Defined as the number of patches in the landscape of patch type (with a patch defined as contiguous adjacencies). Number of patches were calculated using the 8-neighbor rule. 3) Mean patch area (MN). Equal to the sum of all patches a corresponding patch type divided by the number of patches of the same type. In addition to the composition metrics, the configuration metric, Total

Edge (TE), was calculated. Total Edge is measure of total edge length of a particular patch type and allows for comparing landscapes of identical size. In addition to the

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Texas Tech University, Samuel Discua, December 2018 composition metrics, the Patch Cohesion Index (COHESION), was calculated. Patch cohesion index measures the physical connectedness of patch types.

At the landscape scale, edge density was calculated (Similar to Cusser et al.

2016, and Martin et al. 2019) Edge density is a configuration metric similar to total edge, but allows for the comparison of landscapes of different sizes. In addition, the following diversity metrics were calculated at the landscape scale (McGarigal et al.

2012): 1) Patch Richness (PR). Patch richness is equal to the number of different patch types present within the landscape boundary. 2) Shannon’s diversity index (SHDI).

The Shannon’s diversity index equals minus the sum, across all patch types, of the proportional abundance of each patch type multiplied by that proportion. This index was chosen over other diversity indexes as it is more sensitive to rare patch types

(McGarigal et al. 2012). (3) Shannon’s evenness index (SHEI). This index is equal to the minus the sum, across all patch types, of the proportional abundance of each patch type multiplied by that proportion, divided by the logarithm of the number of patch types. It is expressed such that an even distribution of area among patch types results in maximum evenness.

Pollinator Community Composition

Pollinator abundance was determined as the total number of specimens per genus combined for sampling seasons in 2016 and 2017. Pollinator richness was determined as the total generic richness of specimens pooled across sampling seasons and trapping methods. Data were relativized by maxima by site to equalize the influence of common and rare species (McCune and Medford 2011). Data summaries,

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Texas Tech University, Samuel Discua, December 2018 including Shannon’s diversity and species evenness indexes, for all sites per season were calculated using PC-ORD (Version 6.22, MJM Software, Gleneden Beach, OR).

Data Analysis

Principal component analysis (PCA) was used to find patterns across the sampled sites related to habitat data. An outlier analysis was conducted in PC-ORD to remove sites that had extreme values (more than two standard deviations from the mean) on one or more of the measured variables. The habitat matrix was analyzed using a variance/covariance cross-products matrix; this allowed for reducing the influence of outliers while allowing the expression of gradients in the habitat data.

Randomization tests (999) were used to evaluate the statistical significance of the PCA solutions, and the Rnd-Lambda stopping rule was used to determine the number of useful axes for interpretation (McCune and Grace 2002).

Multivariate ordinations were used to explore data structure and seek patterns between bee communities and landscape variables. Non-metric multidimensional scaling (NMDS) ordinations were used to visualize differences in bee community composition between sites and scales. To calculate distance matrices in NMDS, the

Sorensen (Bray-Curtis) distance measure was used, which compares sites using the identity and relative abundance of species (Winfree et al. 2017). To determine significance and stress in relation to dimensionality, a Monte Carlo test with 50 randomizations was used (Peck 2017). To determine the significance and to verify the consistency of the NMDS solution, multiple NMDS solutions were run and a stress value < 20 was deemed appropriate for interpretation (Clarke 1993). Ordinations were

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Texas Tech University, Samuel Discua, December 2018 graphed as joint biplots using the habitat data in the second matrix. Vectors of association of habitat variables to the ordination axes were calculated using an r2 value of 0.100 as a cutoff value for modelling the strength of the associations. Convex hulls were drawn to visualize the grouping variables (farm type, CRP, playas, and soil series) in the ordination space.

To determine whether spatial distance between study sites significantly affected bee abundance, richness, and diversity, Mantel and Moran’s I tests were used to determine the degree of spatial autocorrelation between site distance and bee abundance, generic richness, and diversity. Mantel tests and Moran’s I tests were conducted with the statistical program R, version 3.5 (R Core Team 2018), using the packages ade4 for Mantel tests (Dray and Dufour 2007) and ape for Moran’s I tests

(Paradis et al. 2004). Based on Mantel and Moran’s I test results, there was no spatial autocorrelation with bee abundance and sites (Mantel test, p = 0.58; Moran’s I test, p =

0.06) or bee diversity and sites (Mantel test, p = 0.255; Moran’s I test, p = 0.402), but there was significant autocorrelation with bee generic richness and sites (Mantel test, p

= 0.0007; Moran’s I test, p = 0.00002).

Because the land-cover variables measured are inherently related to one another, variables were screened for multi-collinearity. To do this, variance inflation factors (VIF) and tolerance values (TOL) were calculated using the TOL and VIF

PROC REG statements in SAS 9.4. A theta value of VIF < 2, similar to Cusser et al.

(2016), and a tolerance value > 0.1 were calculated as a cutoff value to eliminate variables with collinearity from models. Only five variables were found to be non-

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Texas Tech University, Samuel Discua, December 2018 collinear: developed, grassland, and other land-use types; patch richness; and farm size.

To determine the relationships between land-cover types at different scales and floral and pollinator abundance, richness, and diversity, Generalized Linear Mixed

Models (GLMMs) were fitted using the SAS 9.4 statistical software (PROC

GLIMMIX). Sites were modelled as R-side random effects (RANDOM =

_RESIDUAL_), and land-cover variables at local and landscape scales were treated as fixed effects. Poisson distributions (LINK=LOG, DIST = POISSON) were used to model abundance, richness, and diversity. Abundance, richness, and diversity data were log-transformed prior to analysis to meet the assumption of normality of residuals. GLMMs were fitted to determine the relationship between non-collinear predictors and dependent variables by scale. Least mean squares (LSMEANS) and the

Tukey-Kramer HSD (ADJ=TUKEY) mean separation procedure were used to compare differences in the model estimates of bee and floral abundance, richness, and diversity by season and scale. Preliminary analyses showed that models including interactions among predictor variables lacked appropriate fit, and most algorithms were unable to converge; therefore, no model interactions were tested. This was likely due to the relatively small sample size, unbalanced number of replicates, and insufficient degrees of freedom.

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Results

Land Cover within Buffers

Cropland was the dominant land-cover type within buffers across scales, years, and most sites. The percent cropland and patch richness increased with scale, whereas developed land, grassland, Shannon’s diversity and Shannon’s evenness indexes decreased. “Other” land cover type was lowest at the 500 m scale and highest at the

1000 m scale. Across scales, land cover surrounding sites averaged 63.25-71.44% cropland, 14.66-22.38% grassland, 10.75-11.82% developed land, and 2.10-2.89%

“other” land cover types. The Shannon’s diversity index averaged 1.16-1.24, the

Shannon’s evenness index averaged 0.45-0.58, and patch richness averaged 8.8-13.17

(Table 4.1).

PCA Results

For all analyses, correlations > 0.5 or < −0.5 were deemed as important.

Across both years and all scales, the first principal component (PC1) variables fell on a gradient moving from a high percentage of cropland to a high percentage of grassland, developed land, patch richness, patch diversity, patch evenness, and edge density. The second principal component (PC2) variables fell on a gradient of high floral abundance, floral diversity, patch evenness, and patch diversity to high bee diversity and evenness (Table 4.2).

NMDS Results

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In 2016, across scales, vectors of association of habitat variables to the ordination axes showed that three variables: cropland, bee diversity, and evenness had significant associations. In 2017, six variables (cropland; developed land; and bee abundance, richness, diversity, and evenness) had significant associations across scales. Figures 4.3 through 4.8 show the results of NMDS ordinations of bee communities and scale (200, 500 and 1000 m).

Generalized Mixed Model Results

Bee communities

200 m scale: In 2016, model parameter results for bee richness showed that only three variables were significant (p < 0.05) in predicting bee richness: developed land (Estimate = −0.0022), grassland (Estimate = −0.0012), and other land-use type

(Estimate = 0.0043). There were no significant coefficients for predicting bee abundance, and only developed land-use type (Estimate = −0.0042) was a significant predictor for bee diversity (Table 4.4). No significant variables were found for 2017 models (Table 4.5).

500 m scale: In 2016, two parameters had a significant effect in predicting bee richness: “other” land-use type (Estimate = 0.0068) and patch richness (Estimate =

−0.0095). Only farm size was significant for predicting bee abundance (Estimate =

0.0007). The percent of developed land (Estimate = −0.0042) was the only significant predictor for bee diversity (Table 4.4). There were no significant predictors for any of the independent variables analyzed in 2017 (Table 4.5).

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1000 m scale: In 2016, only “other” land-use type (Estimate = 0.0033) had a significant effect in predicting bee richness. Farm size (Estimate = 0.0006) was the only significant variable for predicting bee abundance. The percent of developed land

(Estimate = −0.0033) was the only significant predictor for bee diversity, showing a negative relationship (Table 4.4). There were no significant predictors for any of the independent variables analyzed in 2017 (Table 4.5).

Floral communities and habitat data

200 m scale: No significant predictors for floral abundance, richness, and diversity were found for any of the independent variables analyzed in 2016 (Table

4.6). In 2017, only “other” land-use type had a significant effect on floral richness

(Estimate = 0.029) and floral diversity (Estimate = 0.0414, Table 4.7).

500 m scale: In 2016, only patch richness (Estimate = 0.0535) had a significant effect in predicting floral diversity (Table 4.6). There were no significant predictors for any of the independent variables analyzed in 2017 (Table 4.7).

1000 m scale: Patch richness (Estimate = 0.0471) was the only significant variable for predicting floral diversity in 2016 (Table 4.6). For 2017, there were no significant predictors for any of the independent variables analyzed (Table 4.7).

Discussion

This study found that the proportion of the “other” land-cover type was a significant predictor for bee richness across all scales in 2016. This land-cover type was primarily made up of natural land, which suggests that sites with more natural

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Texas Tech University, Samuel Discua, December 2018 land were able to support a higher richness of bee genera. This increase in generic richness could be driven by the interaction of local habitat and landscape-level habitat composition. These results support a growing body of evidence that show the importance of native land and habitat heterogeneity around farmland to increase or sustain bee communities (exemplified by Kennedy et al. 2013, Hass et al. 2019,

Martin et. al. 2019, and Ballare et. al 2019).

Although the proportion of the “other” land-cover type (i.e., wild landscapes) was a significant predictor for bee richness, within the data set, a majority of the sites that had a high proportion of natural land were classified as vineyards. Some of the vineyard sites had 28-30% natural land cover compared to the average of slightly more than 2% across sites, including multiple sites that had 0% natural land cover. This suggests that the bee richness observed in the vineyard sites (also see Chapter III) might have been more influenced by the proportion of natural land surrounding them rather than their local land-use type. One way to better assess the effects and the interactions of the surrounding land-cover type (primarily the proportion of natural land) and the local land-cover type would be by including an assortment of sites with varying proportions of natural land ranging from a low percentage to a high percentage, similar to Cusser et al. (2016) and Ballare et al. (2019). However, I was unable to do so due to the dominance of cropland in the region, uneven number of sites of each land-cover type, and varying proportions of natural lands across sites.

Bee abundance was influenced by habitat size at the 500 m and 1000 m scales in 2016. This is consistent with other studies in which habitat size has been found to

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Texas Tech University, Samuel Discua, December 2018 be an important factor influencing bee communities (Steffan-Dewenter 2003, Hass et al. 2019). For example, Knauss et al. (2009) found that habitat size was a strong predictor of bee species richness in highly modified European landscapes.

A higher percentage of developed land had a negative impact on bee diversity across all scales in 2016. These findings support evidence documented in other studies demonstrating the negative effects of urbanization gradients on bee communities

(Winfree et al. 2009, Fortel et al. 2014; Cardoso and Goncalves 2018). A local habitat study by Fortel et al. (2014) found that along an urbanization gradient in Lyon, France, there were fewer bees in sites with higher levels of urbanization and more species in sites with an intermediate proportion of impervious surface. This finding has important implications for this region, because broad and intensive agricultural region surrounds multiple urban areas, and urban development often encroaches on farm lands or is in close proximity to farm lands.

In 2016, patch richness had a significant effect in predicting floral diversity across the 500 m and 1000 m scales. Patch richness, an indicator of landscape heterogeneity, is an important indicator of species diversity (Tuner and Gardner 2015).

No other significant factors for predicting floral richness and abundance were found for 2016. In 2017, the only significant factor found in the models was other land-use type at the 200 m scale. Data for bee communities for 2017 were limited compared to the 2016 dataset, as it consisted of a single collecting season compared to the three separate sampling seasons included in the 2016 dataset. Thus, the 2017 sampling season data offers an incomplete assessment of the bee communities across all seasons

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Texas Tech University, Samuel Discua, December 2018 that year, because different species have different phenologies. On the other hand, the land-cover data were measured on an annual basis.

2016 data suggests that bee richness was higher in sites that had a higher proportion of natural land surrounding the sites, bee abundance was higher in sites of larger size, and bee diversity was negatively affected by the percentage of urban land surrounding the sites. Higher patch richness at 500 m and 1000 m scales was associated with increased floral diversity. In 2016, regardless of scale, the proportion of natural land influenced bee richness. These results are consistent with other regional surveys and community studies conducted across the United States showing positive relationships of areal coverage of wild land and bee communities (Winfree et al. 2007;

Mallinger et al. 2016).

Future studies could focus on determining how increasing heterogeneity of crop production practices, either by with the reduction of intensive crop management or the adoption of conservation practices such as planting floral strips and the use of

CRP, increase habitat connectivity support pollinators and pollination services.

Additionally, studies that demonstrate economic benefits of habitat heterogeneity around farmlands to growers in the Llano Estacado are necessary to better implement conservation practices.

This study provides information on how the structure and composition of landscapes affects bee communities across farmland habitats in the Llano Estacado region of Texas and underscores the importance of wild lands on farms in supporting bee diversity. Findings from this study should provide a baseline for subsequent

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Texas Tech University, Samuel Discua, December 2018 studies and support recommendations for increasing native bee habitat and pollinator services in the region.

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LITERATURE CITED

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Ascher, J. S., and J. Pickering. 2020. Discover Life bee speces guide and world checklist (Hymenoptera: Apoidea: Anthophila).

Auerbach, E. S., W. P. Johnson, J. R. Smith, and N. E. McIntyre. 2019. Wildlife Refuges Support High Bee Diversity on the Southern Great Plains. Environmental entomology 48: 968-976.

Ballare, K.M., Neff, J.L., Ruppel, R. and Jha, S., 2019. Multi‐scalar drivers of biodiversity: Local management mediates wild bee community response to regional urbanization. Ecological Applications, 29(3), p.e01869.

Banaszak-Cibicka, W., and M. Zmihorski. 2012. Wild bees along an urban gradient: winners and losers. Journal of Insect Conservation 16: 331-343.

Bartomeus, I., J. S. Ascher, J. Gibbs, B. N. Danforth, D. L. Wagner, S. M. Hedtke, and R. Winfree. 2013. Historical changes in northeastern US bee pollinators related to shared ecological traits. Proceedings of the National Academy of Sciences of the United States of America 110: 4656-4660.

Bennett, A. B., and R. Isaacs. 2014. Landscape composition influences pollinators and pollination services in perennial biofuel plantings. Agriculture, Ecosystems and Environment 193: 1-8.

Berger, L. A. 1982. Agapostemon angelicus Cockerell and other wild bees as potential pollinators of male-sterile cotton on the Texas High Plains. M.S. Thesis. Oklahoma State University.

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