Wild Bee and Honey Bee Response to Crop Production, Farm Diversity, and Native Habitat in an Agricultural Landscape

Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations

2019

Wild bee and honey bee response to crop production, farm diversity, and native habitat in an agricultural landscape

Ashley Louise St. Clair Iowa State University

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Recommended Citation St. Clair, Ashley Louise, "Wild bee and honey bee response to crop production, farm diversity, and native habitat in an agricultural landscape" (2019). Graduate Theses and Dissertations. 17790. https://lib.dr.iastate.edu/etd/17790

This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Wild bee and honey bee response to crop production, farm diversity, and native habitat in an agricultural landscape

Ashley Louise St. Clair

A dissertation submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Co-majors: Ecology and Evolutionary Biology; Entomology

Program of Study Committee: Amy Toth, Co-major Professor Matthew O’Neal, Co-major Professor Erin Hodgson Lisa Schulte Moore Brian Wilsey

The student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this dissertation. The Graduate College will ensure this dissertation is globally accessible and will not permit alterations after a degree is conferred.

Iowa State University

Ames, Iowa

2019

DEDICATION

To my husband, Coy St. Clair III, who has given me the strength I needed to complete

this degree. You have supported me with unwavering love, made me laugh, wiped away

countless tears, watched me succeed, seen me fail, and always kept me strong. While making

our way through this busy life, you have been a husband and a father our children can love,

respect, and admire. You have shown me that together we can accomplish anything. My

deepest and most intimate gratitude is reserved for you, I love you. And to my children,

Evangeline and Raylan St. Clair, who have been my everlasting light through my darkest of

times, your enthusiasm, passion, and love inspire me daily. I hope you always believe in your dreams and work together to make them happen.

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TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ...... vi

ABSTRACT ...... ix

CHAPTER 1. GENERAL INTRODUCTION ...... 1 Literature Review ...... 1 Dissertation Objectives ...... 4 Dissertation Organization ...... 5 References ...... 6

CHAPTER 2. AN ASSESSMENT OF COLORED PAN TRAPPING AS A METHOD TO ESTIMATE HONEY BEE ACTIVITY DENSITY IN SOYBEAN FIELDS ...... 10 Authors’ contributions ...... 10 Abstract ...... 10 Introduction ...... 11 Methods ...... 15 Experiment One: Does honey bee activity-density vary with the presence of an apiary, trap color, trap placement, and soybean phenology? ...... 15 Experiment Two: Does honey bee activity-density vary with distance from colonies and by site type? ...... 18 Combined Analysis: Does honey bee activity-density in soybeans predict number of colonies present? ...... 22 Results ...... 23 Experiment One: Does honey bee activity-density vary with the presence of an apiary, trap color, trap placement, and soybean phenology? ...... 23 Experiment Two: Does honey bee activity-density vary with distance from colonies and by site type? ...... 23 Combined Analysis: Does honey bee activity-density in soybeans predict number of colonies present? ...... 24 Discussion ...... 24 References ...... 29 Tables and Figures ...... 33

CHAPTER 3. LANDSCAPE DIVERSITY BUT NOT HONEY BEE PRESENCE SHAPES WILD BEE COMMUNITIES IN AN AGRICULTURAL LANDSCAPE ...... 37 Authors’ contributions ...... 37 Abstract ...... 37 Introduction ...... 38 Methods ...... 42 Selection of fields in landscapes with varying amounts of corn and soybean production ...... 42 Honey bee apiary placement at selected soybean fields ...... 44

Sampling the bee community ...... 45 Statistical analyses ...... 46 Results ...... 49 The effects of cultivation and honey bee presence on the wild bee community ...... 49 The effects of land cover in the surrounding landscape on wild bee community ...... 51 Discussion ...... 52 References ...... 57 Tables and Figures ...... 66

CHAPTER 4. NATIVE HABITAT MITIGATES FEAST/FAMINE CONDITIONS FACED BY HONEY BEES IN AGRICULTURAL LANDSCAPES ...... 74 Authors’ contributions ...... 74 Abstract ...... 74 Significance Statement ...... 75 Introduction ...... 76 Results ...... 79 Apiaries were heavier in landscapes with high cultivation than low cultivation ...... 79 Apiaries and individual bee health declined drastically in late summer ...... 80 The type of forage used by apiaries did not vary by location, but varied during the season ...... 81 Providing colonies access to prairie reverses late summer declines in weight and lipids ...... 83 Discussion ...... 84 Conclusions ...... 88 Methods ...... 89 Site Selection ...... 89 Hive source and apiary management...... 91 Apiary inspection regime ...... 92 Lipid content quantification ...... 93 Pollen collection and quantification ...... 93 Prairie access rescue experiment ...... 94 Statistical Analysis ...... 95 References ...... 99 Tables and Figures ...... 106

CHAPTER 5. DIVERSIFIED FARMING IN A MONOCULTURE LANDSCAPE: EFFECTS ON HONEY BEE HEALTH AND WILD BEE COMMUNTIES ...... 112 Authors’ contributions ...... 112 Abstract ...... 112 Introduction ...... 113 Methods ...... 117 Farm selection ...... 117 Comparing bee communities between farm type ...... 118 Comparing honey bee response between farm type ...... 119 Honey bee colony growth...... 121 Honey bee nutritional state ...... 122 Statistical analysis ...... 122 v

Results ...... 125 Bee community...... 125 Honey bee colony growth...... 127 Honey bee nutritional state ...... 128 Discussion ...... 129 References ...... 133 Tables and Figures ...... 141

CHAPTER 6. GENERAL SUMMARY AND CONCLUSIONS ...... 147 References ...... 154

APPENDIX A. CHAPTER 2 SUPPLEMENTAL INFORMATION ...... 157

APPENDIX B. CHAPTER 3 SUPPLEMENTAL INFORMATION ...... 159

APPENDIX C. CHAPTER 4 SUPPLEMENTAL INFORMATION ...... 161

APPENDIX D. CHAPTER 5 SUPPLEMENTAL INFORMATION ...... 174 vi

ACKNOWLEDGMENTS

I will start by thanking my committee co-chairs, Amy Toth and Matthew O’Neal, for without your support, love, and guidance, I could not be where I am today. Amy, thank you for showing me the meaning of patience and perseverance. I feel proud to have been mentored by such a strong and influential woman of science. You have, through your own example, demonstrated that it is possible to have a successful career and be a supportive wife and parent. You are an inspiration to me, and I am honored to follow in your footsteps. Matt, without you, I would not be the scientist I am today. You have taught me how to be my own critic, always taken the time to discuss my ideas (both scientific and otherwise), and pushed me to think outside the box. I often feel as though you were my compass, pointing me in the right direction, but allowing me to create the map. I could go on about the ways in which you have contributed to my success, but as you say “I am not paid by the word,” so instead I’ll simply say thank you. To be co-advised was no easy task, I am eternally grateful to the both of you for providing me the autonomy and atmosphere for my scientific ideas to prosper, for appreciating me, challenging me, and most of all being a friend to me throughout this exciting journey.

Thank you to my committee members, Erin Hodgson, Lisa Schulte Moore, and Brian

Wilsey, for your guidance and support throughout the course of this research. Each of you brought a valuable slice of knowledge and understanding to the table. I honestly feel that I could not have had a more perfect committee to guide me through this research. I would also like to extend thanks to Diane Debinski for her advice in the early stages of this research and for serving a short stint on my committee. Thank you to Adam Dolezal for being there to help no matter what. Without fail, you have always made yourself available to work in the vii

field, to bounce ideas off of, to proof read, and to lend a critical eye on any aspect of work I have sent your way. Through your example I have learned many valuable skills. You are an excellent mentor and a dear friend. The time you lent me never once went unappreciated.

Thank you to the hard-working undergraduates and lab technicians who assisted with

data collection Zoe Pritchard, Dave Stein, Frances Hunter and Edward Hsieh without whom,

this dissertation would not have been possible. Thank you to Zoe Pritchard for allowing me to be your mentor. You stuck with me through thick and thin. You have taught me so much about what it takes to be a great mentor. It was a truly special experience to watch you blossom into the brilliant young scientist you are today.

Thank you to all my friends for lending a supportive ear and being there for me during one of the most challenging, yet exciting, times of my life. Alex Walton, thank you

for the many great times we spent together. Over the past four years we have developed a friendship that I truly cherish. There was never a moment where we weren’t simpatico. It was refreshing to know that no matter the case, I always had you by my side. I will never watch

an episode of Frasier without stopping to think of you first. Caleb Corona, thank you for

helping ease the stress of grad school by wasting away so many Friday afternoons chatting

about, music, bugs, school, life, basically whatever came to mind. “Convulsion Friday” will

be a tradition I carry into my future, and I will always think fondly of you. Randall Cass,

without you I would not have remained sane in the final years of my degree. I needed only to

send you a glance or a smirk for you to know what I was thinking. You brought so much

humor and enjoyment to my life. Maura Hall, you are one of the funniest, craziest, and most

thoughtful people I know. I cannot thank you enough for all of the times you came and

listened to me ramble about whatever stress I may have been dealing with; always listening viii and never passing judgement. You are amazing! Thank you to Rebekah Reynolds, Kelsey

Fisher, and Abigail Kropf for your interminable positive attitudes. Never once did I see you

when you weren’t smiling. Both of you were always a beacon of positivity no matter the

circumstance and you brightened my day on countless occasions.

Thank you to my colleagues, the department faculty, and staff for making my time at

Iowa State University a wonderful experience. A special thank you to Kelly Kyle for your

passion in making sure that the graduate students in the department had all the resources they

needed in order to succeed. Not only were you always there to help me, but you have also

been a dear friend to me along the way. I appreciate everything you have done for me.

Donald Lewis, thank you for sharing so many of your wonderful stories with me throughout the course of my degree. It was always exciting to hear your odd and hilarious experiences in pollinators and other subjects you’ve encountered throughout the course of your career. I hope that one day, when I am as esteemed a scientist as you, I have just as many wonderful stories to tell.

Thank you to the United Soybean Board and Leopold Center for Sustainable

Agriculture for providing the funding that made this research possible. Thank you to the many farmers who allowed me to conduct experiments in their fields, and to Iowa State

University, Practical Farmers of Iowa, and Blomgren Seed Company for helping me connect with them.

ABSTRACT

Wild bee and managed honey bee populations are declining across the U.S., with

populations particularly at risk in the Upper Midwest where vast areas of land have been

converted into extensive row-crop agricultural systems, resulting in homogenous landscapes

with reduced forage availability. These declines are problematic as wild bees are an essential

part of maintaining natural ecosystems, and honey bees contribute to pollination of over 150

crops. The state of Iowa has been identified as a critical area for pollinator conservation and

is an ideal location to study agriculture-related bee declines. This area represents a model landscape for other parts of the world, can be used to understand how bees respond to

agricultural intensification, and may provide valuable insights into the future of pollinator

health. For my doctoral dissertation research, I examined the responses of both wild and

managed bees to row-crop agriculture, by investigating population, colony, and individual

metrics of health both longitudinally over time and spatially, across landscapes with different

extents of agricultural industrialization. In addition, I explored two ways in which landscape

diversity may help to mitigate bee health declines in monoculture crop landscapes:

diversified fruit and vegetable farming, and native perennial prairie habitat. Overall, I found

that landscape diversity, not honey bee presence, positively influences the wild bee

community. In contrast, managed honey bees had a positive response to row-crop agriculture

with higher populations and colony health in landscapes with more production of corn and

soybean; however, these colonies ultimately declined in the late season, i.e., post-crop

senescence. Diversified farming through fruit and vegetable production resulted in small

increases abundance and richness of a subset of the wild bee community during parts of the

season. Honey bee colony and individual bees were healthier on fruit and vegetable farms x

compared to monocrop soybeans; however, honey bees still declined in the late season.

Native perennial prairie habitat was able to mitigate late season honey bee declines and may

be a promising habitat type able to support both wild and managed bees in heavily cultivated

row-crop agricultural systems. These studies underline the importance of landscape and farm diversity in supporting the health of both managed and wild bees.

Keywords: Wild bee, native bee, honey bee, Apis mellifera, pollinators, agriculture, soybean

CHAPTER 1. GENERAL INTRODUCTION

Literature Review

Anthropogenic alterations of natural ecosystems have led to global impacts on

biodiversity (McNeely 1992, Pimm and Raven 2000, Ceballos and Ehrlich 2002, Wilson

2002, Gerstner et al. 2014, Wilson 2016), including insects (Sánchez-Bayo and Wyckhuys

2019), with one of the most extreme causes being the conversion of natural landscapes into row-crop agricultural systems (Foley et al. 2005, Sánchez-Bayo and Wyckhuys 2019). Some of the most notable declines in insect biodiversity have been observed with bees (Potts et al.

2010). In the U.S., declines of wild bees are well documented (Winfree et al. 2009, Cameron et al. 2011, Gardner and Spivak 2014, Koh et al. 2016), with the lowest abundance of bees observed in regions which are committed to extensive agricultural production of large monoculture commodity row-crops such as corn and soybeans (e.g., the Midwestern U.S.)

(Koh et al. 2016).

In addition to worldwide reductions in wild bee populations, the global stock of

managed honey bees is growing slower than the demand for agricultural production (Aizen

and Harder 2009). In the U.S., beekeepers are experiencing high annual losses of managed

honey bee colonies (Steinhauer et al. 2014, Seitz et al. 2016, Kulhanek et al. 2017), with

losses regularly exceeding the acceptable rate determined by the U.S. Department of

Agriculture (USDA 2017). In the past decade in the Midwest, beekeepers frequently lose as

many as 60% or more of their annual stock of honey bees, a level that is considered four

times higher than what is considered sustainable for beekeeping and substantially greater

than colony losses historically reported for this region (Steinhauer et al. 2014, Seitz et al.

2016, Kulhanek et al. 2017). 2

Declines in wild and managed bees are alarming as they provide an essential ecosystem service through pollination; the services provided support 35% of the global food

supply (Klein et al. 2007). In the U.S., honey bees contribute $14.6 billion dollars annually in

gross domestic product (Morse and Calderone 2000) by pollinating over 150 crops (Thapa

2006). In addition to honey bees, wild bees can be efficient pollinators of crops, contributing

to 20% of crop pollination requirements (Losey and Vaughan 2006). Despite their

contributions, the demand for agricultural production outweighs the supply (i.e., abundance)

of bees (Koh et al. 2016).

Multiple interacting factors drive wild and managed bee declines, including

pesticides, disease, and reduced nutritional resources, all of which stress bees in complex and

poorly understood ways (Goulson et al. 2015). In extensive agricultural systems where

resource and habitat abundance and diversity are limited, effects of these stressors are likely

exacerbated (Goulson et al. 2015). Simplified, large-scale agricultural systems have been

suggested to create one of the most inhospitable conditions for both wild and managed bees

(Spivak et al. 2011). The invention of herbicide tolerant crops as well as the widespread use of insecticides such as neonicotinoids has resulted in vast areas of land committed to crop production with very little presence of weeds or pests (Puricelli and Tuesca 2005, Pleasants and Oberhauser 2013, Douglas et al. 2015, Yang and Suh 2015). These types of systems have been popularized as “green deserts” or “agricultural deserts” for bees (Marcotty 2014,

Proesmans et al. 2019). Although aforementioned stressors are not independent in their

effects on bees, it has been argued that the main driver of decline in bee populations in

agricultural systems is caused by a reduction in resource availability as a result of habitat loss

and landscape conversion (Sánchez-Bayo and Wyckhuys 2019). 3

In addition, wild bees in agricultural landscapes already under stress from resource

limitation may be further impacted by the presence of managed honey bee colonies, which

are often integrated for pollination services (Morse and Calderone 2000). Although honey

bees are essential for crop pollination (Calderone 2012), in the U.S., they are a non-native

pollinator that can result in negative impacts on wild bee communities through competition

for floral resources (Thomson 2006, Mallinger et al. 2017) or transmission of disease

(Dolezal et al. 2016, Alger et al. 2019). Despite evidence for some negative effects of honey

bees on wild bees, the severity of their impact is not well-understood, and has been variable

across studies, particularly in agricultural landscapes (Mallinger et al. 2017). There is still a

critical knowledge gap concerning how the presence of managed bees affects wild bee

communities, specifically the interaction of honey bee presence within agricultural

landscapes on wild bee abundance, richness and diversity. Furthermore, throughout an entire

growing season, it is not clear how large scale agricultural systems effect honey bee health.

Longitudinal studies are important because floral resources are highly seasonally variable,

and many crops are in bloom for only a small fraction of the growing season. For efforts to

improve the health of wild and managed bees in agricultural landscapes to be effective, it is

vital to fully understand to what degree extensive agricultural land conversion impacts bees,

as well as the potential interaction between honey bees and wild bees in these settings.

The state of Iowa is an ideal location to study agriculture-related bee declines, as it has been identified a critical area for pollinator conservation (Grixti et al. 2009), and is centered in the heart of large scale Midwestern annual row-crop production. The landscape of

Iowa is 85.5% committed to farm operations with 65.5% in production of annual crops

(predominately corn and soybean) (NASS-USDA 2018), creating a unique opportunity to 4 study bee declines in the context of large scale farming. With demands for agricultural production expected to increase (Hertel 2011, Takle et al. 2013), many regions worldwide may undergo extensive conversion of the landscape, similar to what has already occurred in

Iowa (Otto et al. 2016). Iowa thus represents a futuristic or model landscape for other parts of the world. Results from these studies in Iowa can be used to understand how bees respond to extensive agricultural land conversion and may provide valuable insights into the future of pollinator health. The goal of this dissertation is to assess to what degree agricultural development and landscape diversity affect wild bee communities and honey bee health, as well as assess the how the presence of honey bees may affect wild bees in this type of agroecosystem.

Dissertation Objectives

Specific aims of this dissertation were as follows:

1) Determine whether honey bee activity-density in soybean agricultural systems can be accurately estimated with pan trapping collection methods.

2) Evaluate whether wild bee communities in agricultural landscapes are affected by the proportion of land in annual row-crops in the surrounding landscape, by the presence of honey bee colonies, or whether there is an interactive effect of land use and honey bee presence.

3) Understand to what degree the landscape surrounding honey bee colonies placed in soybean fields has an effect on the colony growth, productivity, and nutritional health of individual bees throughout the growing season, and whether access to native prairie habitat can mitigate nutritional stress. 5

4) Investigate whether diverse fruit and vegetable farms, when embedded within an extensive

agricultural landscape, affect the wild bee community and overall health and productivity of

honey bee colonies.

Dissertation Organization

This dissertation summarizes my research investigating the effects of extensive

agricultural industrialization on wild and managed bees. Chapter 1 serves as a general

introduction on wild and managed bee declines, economic value of bees, and contributing

stressors leading to bee declines. Additionally, my project objectives are summarized.

Chapter 2 is titled “An assessment of colored pan trapping as a method to estimate honey

bee activity-density in soybean fields,” and is an exploration of whether pan traps used in

different contexts within a soybean field, with and without the presence of managed honey

bee colonies, are an effective method for estimating honey bee activity-density. Chapter 3 is

titled “Landscape diversity but not honey bee presence shapes wild bee communities in an

agricultural landscape.” This chapter assesses how surrounding landscape diversity, a feature that has been shown to influence natural enemy populations in soybeans, affects the communities of wild bees within soybean fields. Additionally, in conjunction with surrounding landscape diversity, this chapter investigates the effects of honey bee colony presence and the potential interactions between honey bee presence and landscape on wild bee communities. Chapter 4 is titled “Native habitat mitigates feast/famine conditions faced by honey bees in agricultural landscapes,” and investigates how proportion annual row-crop in the surrounding landscape of soybean fields affects honey bee colony productivity and individual bee nutritional health. After discovering a critical period in the mid-season at 6

which colonies begin a steady and drastic decline, I assessed whether such colony declines

can be reversed by providing access to native, perennial prairie habitat. Chapter 5 is titled

“Diversified farming in a monoculture landscape: Effects on honey bee health and wild bee communities.” This chapter explores whether farm type has an effect on wild and managed

bees, specifically, whether local farm diversity through the production of fruit and vegetables

compared to monoculture soybean production can support a more diverse wild bee

community as well as heathier and more productive honey bee colonies. For this chapter, we

studied fruit and vegetable farms at multiple locations in Iowa, in addition to the soybean

fields from the previous two chapters, to assess wild and managed bee responses to a more

diverse agricultural farm type. Chapter 6 consists of a summary of my major findings from

each of the previous chapters, as well as general conclusions of the research, and lays out

suggestions for future directions in research based on the findings.

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CHAPTER 2. AN ASSESSMENT OF COLORED PAN TRAPPING AS A METHOD TO ESTIMATE HONEY BEE ACTIVITY DENSITY IN SOYBEAN FIELDS

Modified from a paper to be submitted to Journal of Apicultural Research

Ashley L. St. Clair1, 2, Adam G. Dolezal3, Matthew E. O’Neal2, Amy L. Toth1, 2

1Iowa State University, Department of Ecology, Evolution, and Organismal Biology

2Iowa State University, Department of Entomology

3Univeristy of Illinois Urbana-Champaign, Department of Entomology

Authors’ contributions

AGD, ALS, ALT, and MEO designed the study. ALS collected all the field data. ALS

analyzed the data and wrote the manuscript. All authors contributed critically to the drafts

and gave final approval for publication.

Abstract

Colored pan traps for monitoring the activity-density (bees per trap per sampling point) of a bee community can be cost-effective, simple, and efficient. In soybean fields, pan

traps have uncovered a community of over 50 bee species, but these estimates may underrepresent captures of honey bees. Previous studies suggest underrepresentation of honey bees could be due to a bias of the traps to collect smaller bees, ineffective use of traps to capture honey bees, or may actually reflect the true absence of honey bees. In areas where honey bees are not native and few feral colonies exist, the usefulness of pan traps as an accurate method of estimating honey bee activity-density is not well described. To better characterize the utility of this method for measuring activity-density of honey bees, we 11

examined if trap color, vertical placement, location within a field, and number of colonies present affect estimates of honey bee activity-density within soybean fields throughout the growing season. We also examined whether activity-density in soybeans differs from prairie by placing pan traps in fields of soybean or in restored prairies in central Iowa. Pan traps, especially those that are blue colored, captured more honey bees when colonies were placed in soybean field edges. Throughout the season, activity-density in soybeans was steady, but abundance in traps tripled after soybean blooming had ceased, perhaps reflecting increased searching by bees during a period of forage dearth. No variation in honey bee activity-density occurred with placement of traps within a field or distance from a colony. We conclude that pan traps may be useful for some applications related to assessing activity-density of honey bees, particularly for identifying times of forage scarcity and estimating colony presence in the nearby landscape.

Keywords: Honey bees, Apis mellifera, pan trap, activity-density

Introduction

The use of colored pan traps is a cost-effective, simple, and efficient technique to passively quantify insect communities, including bees (Cane et al. 2000, Droege 2006,

Droege et al. 2010, Bąkowski et al. 2013, Mitra et al. 2015, Mccravy and Ruholl 2017,

Skvarla and Dowling 2017). When used for studying pollinators, pan traps (or ‘bee-bowls’) can be a useful tool for monitoring bee communities (Prado et al. 2017), but honey bees may be underrepresented (Cane et al. 2000, Toler et al. 2005, Roulston et al. 2007, Popic et al.

2013), possibly due to a bias toward smaller bee species (Roulston et al. 2007, Baum and

Wallen 2011, Gonçalves et al. 2012). Honey bee presence in pan traps may be lower 12

compared to net samples and direct observations (Cane et al. 2000, Roulston et al. 2007);

however, the presence of smaller bees which are considered to be susceptible to pan traps

(e.g., Lasioglossum spp.) has also been observed to be lower compared to other sampling

methods (Roulston et al. 2007). Thus, the potential for using pan traps in assessing honey bee

activity may be unnecessarily underutilized.

Despite a potential bias, honey bees have appeared in colored pan traps, although in

low numbers, even in crops that do not require animal-mediated pollination (Gill and O'Neal

2015, Wheelock and O'Neal 2016, Wheelock et al. 2016). Studies conducted in regions

where honey bees are native or feral colonies are abundant have found pan traps effective at

capturing honey bees (Aizen and Feinsinger 1994, Baum et al. 2006, Westphal et al. 2008).

However, in regions where honey bees are not native or feral colonies absent, very small

numbers of honey bees are captured, likely due to an absence of a stable honey bee

population (Popic et al. 2013) or ineffective usage of traps to capture honey bees (e.g., traps

placed on the ground rather than elevated) (Brosi et al. 2007, Roulston et al. 2007). In

general, pan traps estimate activity-density, that is the movement of an insect through a

landscape coupled with its population density (Hokkanen and Holopainen 1986). Assuming

honey bees are present, factors that affect the activity of a honey bee within a sampled area

would affect the abundance of honey bees captured in a pan trap, therefore, the number of

honey bees captured per trap is an indicator of activity-density. The usefulness of pan trapping as an accurate method of estimating honey bee activity-density is not well understood.

Pan trapping has been identified as a method which captures the greatest activity- density of a pollinator community in agricultural fields compared to sampling methods used 13

by applied entomologists to study insect pests of crops (e.g., yellow sticky traps and non-

target sweep netting) (Nuttman et al. 2011, Gill and O'Neal 2015, Wheelock and O'Neal

2016). Although these studies confirmed the presence of honey bees in crop fields using pan

traps, they revealed a low level of honey bee activity-density, with honey bee foragers contributing a small percentage (0.005%) of the entire bee community (Gill and O'Neal 2015,

Wheelock and O'Neal 2016, Wheelock et al. 2016). However, these studies were conducted in areas in which it was not known whether honey bee colonies were present. Additionally, these studies sampled for a limited time period, potentially missing changes in seasonal activity-density of honey bees in relation to available flowering resources within or around the crop field.

Our goal was to evaluate the season-long activity-density of honey bees with colored pan traps in a crop field. We conducted this study in central Iowa, a region largely committed to the production of corn and soybean (NASS-USDA 2017), with a homogenous landscape comprised of low floral diversity (Brown and Schulte 2011). Although managed honey bee colonies are kept within this region, and sometimes are registered through a state registry,

feral honey bee colonies are uncommon. Contributing to a lack of feral honey bees is an

absence of non-cropped features in the landscape (Klopatek et al. 2009), including forest, a

primary choice for nesting habitat by honey bees in temperate climates (Seeley 1976). To

confirm that pan traps capture honey bees when they are in fact present in a landscape, we

placed colonies either adjacent to or within soybean fields. Soybean is a self-pollinated crop which does not require animal-mediated pollination; nonetheless, it is a moderately attractive crop for honey bees (USDA 2017), and a potentially valuable source of nectar (Ellis et al.

1998, Westphal et al. 2003). There may be potential for pollination by honey bees to increase 14

soybean yields (Milfont et al. 2013), however, in the present study we did not assess any

yield benefits as our goals were to evaluate the usefulness of pan traps to measure honey bee

activity-density. We designed a series of two experiments, and then conducted a comparative

analysis of both experiments, to answer seven core questions about the practicality of pan

traps to measure honey bee activity-density (Table 1). Through these experiments, we

sampled honey bees over a four-year period, within 44 soybean fields and three prairie fields

in central Iowa.

Our first experiment (Experiment 1) explored if the activity-density of honey bees

varied based on the presence of colonies, trap color, or trap placement, and how activity- density varied across the season (Table 1, questions one through four). Pan traps may be biased towards collecting more bees when floral diversity and abundance is low (Baum and

Wallen 2011, Popic et al. 2013, Adhikari et al. 2019), which may be a reason traps are more effective in agricultural landscapes. Tallgrass prairie habitat, which consists of a mixture of native grasses and flowering forbs, may offer greater floral abundance and diversity than soybean. Additionally, trap distance from a colony may affect the abundance of bees collected in the traps. Therefore, our next experiment (Experiment 2) compared how distance from the colony and a site’s floral diversity (soybean vs prairie) affect activity-density of honey bees (Table 1, questions five and six). Lastly, we conducted an analysis that combined pan trap data over four years and multiple soybean fields with number of colonies adjacent to or within them to investigate if activity-density of honey bees in a field predicts the number of colonies nearby (Table 1, question seven). Together, these experiments provide the first comprehensive investigation of the efficacy of pan traps for estimating honey bee activity- density. 15

Methods

Experiment One: Does honey bee activity-density vary with the presence of an apiary, trap color, trap placement, and soybean phenology?

Site selection

During the summers of 2015 and 2016, we identified 18 and 20 soybean fields in

central Iowa respectively. Soybean fields were a mix of Iowa State University (ISU)-owned and privately-owned commercial soybean fields. All fields were 20 km or greater in size and located at least 3.2 km from each other to maximize the probability that measurements of

honey bee activity-density could be considered independent observations (Couvillon et al.

2015). All soybean fields were planted with pesticidal seed treatments; ISU fields were

planted with a fungicidal seed treatment only (Fluopyram, ILeVO, Bayer, Pittsburgh PA),

while private fields were planted with an insecticide and fungicide (imidicloprid and ILeVo,

respectively; Acceleron seed treatment, Bayer, Pittsburgh PA). No insecticides were applied

to soybean foliage or in fields directly surrounding soybeans and weeds were managed with

glyphosate. Both years we surveyed soybean developmental growth stage to evaluate at what

time points flowers were present within the fields (Pedersen 2004, Hodgson et al. 2012).

Growth stages in which flowers were present spanned the R1 (at least one open flower at any

node on the main stem) to R4 (pods 2 cm at four uppermost nodes, flowers still present on

main stem) stages.

Honey bee apiary placement

To determine if the activity-density of honey bees in fields varies with the presence of a nearby apiary, four colonies were placed 3 m from a field edge of a subset of soybean fields. Of our 18 fields in 2015, we randomly selected 10 soybean fields to receive an apiary 16

Hive (+). We compared honey bee activity-density in fields with apiaries to those without an

apiary Hive (-). To confirm that our Hive (-) sites did not have any other managed apiaries

present within 1.6 km of the field, we checked the state of Iowa’s voluntary registry for

beehives (DriftWatch Inc., West Lafayette, IN; https://ia.driftwatch.org/map). In addition to

the registry, we also scouted all fields directly neighboring our experimental field to validate

that no honey bee apiaries were present. These efforts resulted in eight Hive (-) locations. For

the Hive (+) sites, apiaries were transported to soybean fields on 6 June 2015 after 90% of

the corn and soybean had been planted in Iowa. Apiaries remained at soybean field edges

throughout the season until 12 October 2015 when they were moved to a separate ISU

research field for overwintering. Of the 20 fields in 2016, we repeated the design used in

2015, selecting 10 soybean fields to receive an apiary Hive (+) and 10 fields which were

Hive (-). Apiaries were transported to soybean fields on 22 May 2016 and moved for overwintering on 18 October 2016.

Sampling honey bee activity-density

To estimate honey bee activity-density, we used colored pan traps based on the design

of Droege (2010) with some modifications per Gill and O’Neal (2015). In 2015, traps were

deployed on posts, such that each post held three 3.2 oz. bowls (Solo® brand). We were

interested in which of the common trap colors were more attractive to honey bees, so each

post contained traps painted either fluorescent yellow, fluorescent blue, or white (Appendix

A, SI Figure 1 A). Each field had three posts with three traps of each color (nine traps total)

placed 10 m apart and 10 m into a soybean field in a row that ran parallel to the field edge

and was adjacent to the honey bee colonies when present (Appendix A, SI Figure 1 B). Traps

were deployed for 24 hr, every other week, on days with low cloud cover, no precipitation, 17 and low to no wind (<10 mph). During each collection, the pan traps were adjusted on the post so that their height was level with the soybean plant canopy. Each trap was filled with a soap-water solution consisting of 3% Dawn® dish soap and 97% water. We sampled bees for

13 weeks; 1 July through 24 September 2015. In 2016, we repeated the 2015 sampling design and added an additional three posts into the grassy perimeter of the field to test whether placement of the trap affected honey bee activity-density (Appendix A, SI Figure 1 B). We sampled bees for 13 weeks; 15 June through 9 September in 2016. All estimates of honey bee activity-density were calculated as honey bees per trap.

Statistical analysis

All statistical analyses were performed in SAS 9.4. To investigate whether honey bee activity-density varied with the presence of honey bee colonies, we performed a t-test with pooled variance (PROC TTEST) comparing Hive (+) sites to Hive (-) sites. The sampling days were combined such that the analysis is compares honey bees per trap per site. To explore whether activity-density of honey bees differed between pan traps 10 m inside the soybean field compared to traps placed 10 m in the exterior grassy perimeter, we performed a t-test with pooled variance (PROC TTEST). For this test, we used only data from the Hive

(+) sites pooled across the season, such that the analysis compared honey bees per trap per site. To explore which pan trap color was most attractive to honey bees we performed a mixed model analysis of variance (PROC GLIMMIX) with trap color as the main effect and site-year as a random effect. Data consisted of only trap collections from the interior of the soybean field at Hive (+) sites. Honey bee activity-density was pooled across the season, such that the analysis compared honey bees per trap color per site. We used least squared comparison of means with Tukey adjustment to evaluate post hoc comparisons of trap colors. 18

To examine how honey bee activity-density in soybean fields varied across the season and

with soybean phenology, we performed a mixed model analysis of variance (PROC

GLIMMIX) with date as a main effect and site-year as a random variable. For this analysis,

we analyzed honey bees per trap per site for Hive (+) sites only. We used least squared

comparison of means with Tukey adjustment to evaluate post hoc comparisons of sampling

dates.

Experiment Two: Does honey bee activity-density vary with distance from colonies and by site type?

Site selection

During the summer of 2017, we selected three soybean fields maintained by the ISU

Research Farm staff that were 20 ha or greater and at least 3.2 km apart to measure honey bee activity-density. Two fields were planted with a soybean aphid resistant variety (IA2010-

RA12, Rag1+Rag2) and did not have an insecticide or fungicide seed treatment. No foliar insecticides were applied and weeds were managed with clethodim, fomesafen, and pyroxasulfone. One field was planted with an insecticidal seed treatment (imidicloprid,

Pioneer Premium, Johnston IA) and when the fields were in full bloom (reproductive stage

R3) it was sprayed with a foliar application of Warrior II (lambda cyhalothrin, Syngenta,

Lone Tree IA) on 25 July 2017. In this field glyphosate was used for weed management.

Growth stage of soybeans was monitored weekly as described in experiment one from June through September.

We selected three remnant prairie fields that were 20 ha or greater in size to investigate whether honey bee activity-density varied with floral diversity. Prairies were located in the Chichaqua Bottoms Greenbelt of Iowa, Polk County. In the prairies, we 19 surveyed the richness of flowering forbs on a weekly basis from 11 August through 6

September 2017, the same weeks we surveyed soybean growth stage. The richness of blooming plants was measured by counting each species with at least one bloom present between the 30 m and 90 m pan traps (see below) and within 10 m on either side.

In 2018, we used the same guidelines as used to select soybean fields in 2017, to select three additional soybean fields. Two fields were planted with a soybean aphid resistant seed (IA2010-RA12, Rag1+Rag2) with no insecticide or fungicide applied to the seed and no applications of insecticides to foliage. Weeds were managed with clethodim, fomesafen, and pyroxasulfone. One field was planted with an insecticidal seed treatment (imidicloprid,

Pioneer Premium, Johnston IA) and sprayed with a foliar application of Warrior II on 17 July

2018 when the field reached reproductive stage R3. In this field glyphosate was used for weed management. Soybean growth stage was assessed weekly from June through

September. In 2018, we revisited the same three prairies as in 2017 and estimated floral richness from 10 August through 18 September in 2018.

Honey bee apiary placement

In 2017, each soybean field received an apiary of 16 colonies on 2 June after all fields were planted and the post-emergence herbicides were applied. To ensure honey bees would forage within the crop, colonies were placed in the interior of soybean fields rather than at the field edge. Within a field, apiaries were placed at two sub-sites such that each sub-site consisted of eight colonies, to reduce bees drifting between colonies. Sub-sites were at least

150 m from the closest field edge and 300 m from the adjacent sub-site (Appendix A, SI

Figure 1 C). All colonies remained in the soybean fields until 10 August 2017. At that time, half of the colonies from each sub-site (n=8) were moved out of the field, randomized, and 20

moved 20 m into one of the three remnant prairie fields. The remaining colonies (n=8) stayed

in the soybean fields. In soybeans, honey bee colonies utilize clover from the surrounding

landscape as a valuable pollen and nectar resource (Chapter 4, Dolezal et al. 2019). We chose

10 August to move colonies and compare honey bee activity-density between soybean and prairie fields because it is a time when clover is no longer blooming (Chapter 4, Dolezal et al.

2019), effectively reducing floral availability for apiaries in soybean to the soybean plants.

However, in early August, prairies continue to provide diverse and abundant forage (Dolezal et al. 2019). After the experiment, all colonies from apiaries were moved to an ISU research field for overwintering on 12 October 2017. In 2018, the same procedure from 2017 was repeated, with apiaries placed in soybean fields in June and a subset moved into prairies in

August. Colonies were transported to sites on 8 June 2018 and half of the colonies were randomized and moved to the same prairies as in 2017 on 9 August 2018. Colonies were moved out of soybean and prairie to overwinter on 13 October 2018.

Estimating honey bee activity-density

During 2017 and 2018, honey bee activity-density was measured using the same pan

trap design as described above in experiment one. In soybean, pan trap placement consisted

of six posts at each sub-site within a soybean field (12 posts per field, 6 posts per sub-site),

resulting in a total of 36 pan traps per field and 18 pan traps per sub-site. To evaluate whether

activity-density of honey bees decreased with increasing distance from an apiary, pan traps

were placed at two distances from the apiary in each soybean sub-site. Three posts, each

consisting of three pan traps (blue, yellow, and white), were placed adjacent to the apiary at a

distance of 30 m and 90 m. At each distance, individual posts were 10 m away from the next

closest post along a straight line (Appendix A, SI Figure 1 C). Because field was the 21

experimental unit, and sub-sites were not far enough in distance from each other to be an

independent sample, we took an average of honey bees collected in traps between the two

sub-sites. These averages resulted in one measure of honey bee activity-density for the 30 m and 90 m distance for each field. We standardized honey bee activity density by trap so that activity-density at each distance is represented as honey bees per trap per site. In soybeans, we sampled activity-density every other week from 16 June through 6 September 2017 and from 26 June through 18 September 2018. Pan traps in the prairies were set up with the same methods as in soybeans, with the exception that prairie fields had only one sub-site resulting in six total posts. In prairies, we sampled honey bee activity-density every other week from

11 August through 6 September in 2017, and 10 August through 18 September in 2018.

Statistical analyses

All statistical analyses were performed in SAS 9.4. To examine whether or not honey bee activity-density was affected by floral diversity (soybean or prairie fields) or distance from the apiary (30 m or 90 m) we performed a mixed model analysis of variance (PROC

GLIMMIX) with field type, trap distance, and collection date as the main effects. For this analysis, we only included sampling dates that took place after the colony move to prairies

(August and later), resulting in equal colony densities between soybean and prairie sites.

Sampling did not take place on the same dates across years, but there were three samples collected each year. Because our sampling was based on the soybean crop phenology rather than Julian date, each sampling date in 2017 corresponded to the same crop phenology period sampled in 2018. Therefore, we summed data across the two years such that there was an early August collection (11 August 2017 and 12 August 2018), a late August collection (22-

August 2017 and 25 August 2018), and a September collection (6 Sept. 2017 and 18 Sept. 22

2018). We performed post hoc comparisons of least squared means with Tukey adjustments

to compare differences between dates, field types, and trap distances.

Combined Analysis: Does honey bee activity-density in soybeans predict number of

colonies present?

To investigate if number of honey bee colonies present can be predicted by pan trap

estimates of activity-density within 90 m of an apiary, we compared pan trap data from the

previous two experiments. In our first experiment, we had 18 sites with no colonies and 20

sites with four colonies adjacent to a soybean field. In experiment two, we had six sites with

16 colonies present within soybean fields between June and August, and eight colonies

present in those same fields after August. Based on the results from experiment one, we

concluded that prior to and during soybean bloom (May to early September), there were no

differences in estimates of honey bee activity-density. Therefore, for the combined analysis, we included all dates from May through early September, excluding only the time point of 24

September 2015. During 24 September, soybean plants had ceased blooming and possibly over represented activity-density (see blow results for experiment one). Furthermore, based

on results from experiment two, we observed no difference in the activity-density of honey

bees between the two distances from the colony (see results below). Therefore, we combined

the two distances together. Due to the unequal number of pan traps in experiment one

compared to experiment two, we standardized all the data to be represented as number of

honey bees per trap per site. Additionally, because there are a different number of dates and

different dates represented for each of the colony densities, we further standardized the

activity-density of honey bees by the number of sampling days. Thus, for the combined 23

analysis honey bee activity-density is estimated as the number of honey bees per trap per site

per day. After all the data were standardized, we performed a linear regression analysis

(PROC REG) in SAS.

Results

Experiment One: Does honey bee activity-density vary with the presence of an apiary, trap color, trap placement, and soybean phenology?

Activity-density of honey bees was observed to be significantly higher at Hive (+) soybean fields compared to Hive (-) fields (T36=4.34, P=0.0001; Figure 1). At locations

where apiaries were placed, there was no observed difference in activity-density between

traps placed 10 m inside the soybean field compared to those placed 10 m inside the grassy

perimeter of the field (T18=0.51, P=0.62; Figure 2). The number of honey bees in the pan

traps varied significantly by color (F2, 38.39=6.41, P=0.004) with blue traps capturing

significantly more honey bees than both yellow and white traps (Figure 3). Activity-density

varied by date (F6, 86.88=9.32, P=<0.0001), with significantly more bees captured in traps on

24-Sept. (after soybeans ceased blooming) compared to all other dates including before and

during soybean bloom (Figure 4, Appendix A, SI Table 1).

Experiment Two: Does honey bee activity-density vary with distance from colonies and by site type?

There were no observable differences in honey bee activity-density based on field type (soybean vs prairie) (F1, 11.42=0.26, P=0.62), distance from the apiary (F1, 53.74=0.00,

P=0.98), or date (F2, 50.02=2.41, P=0.10) (Figure 5). There were no interactions between field

type and trap distance (F1, 53.74=1.14, P=0.29), field type and date (F2, 50.02=2.49, P=0.09), and 24

date and trap distance (F2, 50.02=0.24, P=0.78). Furthermore, there was no observable

interaction of field type by trap distance by date (F2, 50.02=1.15, P=0.33).

Combined Analysis: Does honey bee activity-density in soybeans predict number of colonies present?

Honey bee activity-density was significantly positively associated with the density of colonies nearby or within soybean fields (F1, 44=5.32, P=0.03); however, only 10.8% of the

variation in activity-density was explained by the number of colonies present (r2=0.1079;

Figure 6).

Discussion

Contrary to other studies suggesting honey bees are not captured in pan traps (Cane et

al. 2000, Toler et al. 2005, Roulston et al. 2007), we found that these traps can be an effective

method to measure honey bee activity-density in agricultural fields. These results suggest that, in regions such as Iowa where honey bees are not native and feral colonies are uncommon, pan traps can be used to measure activity-density of honey bee colonies placed within a focal crop field. Although we observed a positive association of honey bee activity-

density with the number of colonies present within 90 m of pan traps (Figure 6), the

relationship was not particularly strong, perhaps due to low replication (e.g., for sites with

eight and 16 colonies present). Nonetheless, our results suggest there may be some

usefulness in using pan traps to predict the density of colonies nearby and this relationship

deserves further investigation. The use of multiple colors (blue, yellow, and white) are

usually recommended to capture a diverse community of bee species (Toler et al. 2005, 25

Droege 2006, Grundel et al. 2011, Adhikari et al. 2019). We found blue traps captured the

most honey bees (Figure 3). For studies specifically targeting honey bee activity-density,

blue traps may be the most effective.

The placement of a pan trap can affect how many pollinators are captured (Droege et

al. 2010). For honey bees in soybeans, the placement of pan traps within the field or within

the grassy perimeter adjacent to the field did not affect the estimate of activity-density when

colonies were present at field edges (Figure 2). Furthermore, when colonies were embedded

within the field, trap distance from the colonies did not affect the estimate of activity-density

(Figure 5). These observations suggest that when honey bee colonies are present near or within a large crop field (>20 ha), the activity of foragers is spread evenly throughout the field. However, in our study, because we sampled only 30 and 90 m from the apiary, we cannot infer anything about the activity-density of honey bees in the entire field. Reduced activity-density may occur at greater distances than 90 m from an apiary. Alternatively, it is possible that a difference did occur and the pan traps just failed to capture those differences.

An additional caveat of our study is the reduced replication of soybean sites in experiment two, which may have failed to capture enough variation in trap distance to see significant effects. Further studies should aim to tease apart how honey bee activity-density estimates vary with trap distance from an apiary by examining multiple distances with increased site replication.

Although we found pan trapping to be a useful method to measuring honey bee activity-density in soybean fields, there were limitations to its effectiveness. Before soybean bloom and throughout the duration of soybean bloom honey bee capture rates in pan traps were consistent (Figure 4). Prior to soybean bloom, it is likely that there was an abundance of 26

alternative floral attractants (i.e., clover and tree pollen). In late September 2015 (24

September), we observed honey bee activity-density in soybeans nearly triple (Figure 4).

This is not likely due to an increase in the population of bees within the colony as colonies in

this region are typically declining in mass, developing bee, and adult bee populations after

the beginning of August (Chapter 4, Dolezal et al. 2019). Instead, it is probable that the

increased bee captures in late September were a result of decreased flowering resource

availability in soybean fields. In late September, soybeans have typically senesced to the

point of no longer having leaves. The high density of honey bees observed in a soybean field

at that time could be due to the pan traps being perceived as the only source of flowers and as

a highly attractive beacon to the bees at a time when no other floral attractants were present.

This explanation supports the idea that pan traps are biased towards collecting more bees

when floral diversity and abundance is low (Baum and Wallen 2011, Popic et al. 2013,

Adhikari et al. 2019). In this study, we were unable to keep pan traps in soybean fields into

late September after 2015 because our traps interfered with the crop harvest. To better

understand the relationship between activity-density estimates from pan traps and the resource availability in soybeans, future studies should focus on assessing activity into the late season. Although pan traps may overestimate the activity-density of honey bees when

floral resources decline, these data still serve as a valuable representation of honey bee

foraging behavior. Peaks in honey bee abundance in pan traps may provide useful

information about when honey bees face forage limitations in crops and other field types, but

additional studies are necessary to parse out the differences in activity due to forage

limitation compared to other possible reasons. 27

We further investigated if variation in foraging resources affects activity-density by comparing honey bee activity-density inside soybean fields (where plant diversity is limited to the crop) to prairies (where plant diversity varied between 10-12 species across sampling dates, APPENDIX A, SI Table 2). We did not observe a difference in activity-density of honey bees in pan traps in prairies compared to soybeans (Figure 5), suggesting floral diversity did not drive honey bee activity. At the time points we compared activity-density of honey bees in soybean to prairie (i.e., August and early September), soybean plants still had flowering resources available within the fields, meaning floral abundance may have been equivalent. Due to the generalist nature of honey bee foraging and honey bee preference for legumes (Giannini et al. 2015), it may be that honey bees were able to readily utilize the abundant resources available from monocultures of soybean. It is possible that in the Iowa cropping system, abundance rather than diversity of floral resources is a more important driver of honey bee activity-density. This justification would explain why pan traps only over estimated activity after crops ceased blooming, a time when both floral abundance and

diversity were low in soybean. Further studies are necessary to better understand the

interaction between resource availability and honey bee activity-density. Thus, it is important

to provide a word of caution about using pan-traps under conditions of extremely low

resource availability, as our data from soybeans in late September suggest inflated activity- density estimates under such extreme conditions.

Results from these experiments provide value on the usefulness of pan traps as a method of quantifying honey bee activity-density in extensive agricultural landscapes. With increases in crop production and the demand for honey bee pollination services (Aizen and

Harder 2009) occurring concurrent to widespread declines in managed honey bee colonies 28

(Steinhauer et al. 2014, Seitz et al. 2016, Kulhanek et al. 2017), there is a need for improved

methods to gain insight into the effects agricultural systems have on honey bees. In our

study, pan traps gave estimates of activity-density, but they were not necessarily synonymous with foraging activity. While some studies have used pan-trap collected bees to assess foraging resources (Gill and O’Neal 2015), for honey bees, we found this unlikely to be effective, because honey bee collected pollen was usually washed off in the traps, and the bees often regurgitated the contents of their honey stomachs when they drowned in the traps.

However, we were able to demonstrate that pan traps can be used as a tool to gain insights into honey bee activity-density within crop fields within 90 m of an apiary. Pan traps may be most useful in estimating local colony presence; however, when colonies are present, activity-density estimated by pan traps does not strongly correlate with number of colonies present. Our data also suggest restrictions associated with pan traps; at times when floral abundance and diversity are low, pan-traps can lead to inflated estimations of activity- density. Thus, we suggest that pan-trapping can have applications in easily and quantitatively estimating presence-absence of honey bee colonies as well as identifying times of extreme resource limitation. Such applications could be useful in identifying the presence of nearby honey bee colonies in studies estimating wild bee activity in the landscape, choosing landscape conservation enhancements that target critical resource gaps for bees, and identifying when honey bee colonies may need to be moved to landscapes with more resources or provided supplemental feed by beekeepers.

References

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Tables and Figures

Table 1. Hypotheses table of research questions to investigate the usefulness of pan traps as a method to estimate honey bee activity-density in soybeans.

a Question # Pan trap research objective Figure Result Does honey bee activity-density vary with colony 1 1 presence/absence? ✔ Does honey bee activity-density vary with placement 2 2 in the interior vs exterior of crop field? ✘ Does honey bee activity-density vary with pan trap 3 3 color? ✔ Does honey bee activity-density vary over the 4 4 soybean growing season? ✔/✘ Does honey bee activity-density vary with distance 5 5 from colonies? ✘ When colonies are present, does honey bee activity- 6 5 density in soybeans differ from prairies? ✘ Does honey bee activity-density vary with number of 7 6 colonies present? ✔ a ✔ represents a significant difference in honey bee activity-density occurred based on the result in the figure, ✘ indicates that no significant difference occurred. ✔/✘ indicates that activity-density of honey bees significantly differed for some factors but not all factors.

1.8 1.6 A 1.4 1.2 1 0.8 per site per 0.6 B 0.4 0.2 Mean honey Mean honey beestrap per 0 Hive+ Hive-

Figure 1. Honey bee activity-density (bees per trap per site) in pan traps inside soybean fields with an apiary of four honey bee colonies at the field edge compared to fields without an apiary at the field edge during 2015 and 2016 in central Iowa. Activity-density is reported as the mean ± one standard error of the mean of honey bees captured per trap per site. There was significantly higher activity-density of honey bees in soybeans when colonies were present at the field edge; Welch’s two sample t-test (T36=4.34, P=0.0001).

1.2 A

1 A 0.8

0.6

0.4

0.2

Mean honey Mean honey beestrap per per site 0 Interior Grassy Soybean Perimeter

Figure 2. Honey bee activity-density (bees per trap per site) at Hive (+) soybean fields with pan traps located 10 m inside soybean fields compared to traps placed 10 m into the grassy perimeter of the field during 2016 in central Iowa. Activity-density is reported as the mean ± one standard error of the mean of honey bees captured per trap per site. There was no significant difference in activity-density in the interior versus the grassy perimeter of soybean fields; Welch’s two sample t-test (T18=0.51, P=0.62).

2.5 A 2

1.5 B B 1 color per per sitecolor

0.5 Mean honey Mean honey trapbee per

0 Blue Yellow White

Figure 3. Honey bee activity-density (bees per trap color per site) in soybean fields in central Iowa during 2015 and 2016 by trap color. Activity-density is reported as the mean ± one standard error of honey bees captured blue, yellow, and white pan traps. There was a significant effect of trap color on honey bee activity-density; analysis of variance (F2, 38.39=6.41, P=0.004). Blue traps captured more honey bees than yellow traps (T35.66=3.20, P=0.003) and white traps (T39.94=2.95, P=0.005); however, no difference was observed between yellow and white traps (T39.94=0.14, P=0.89).

1.2 A

1 100

90 0.8 80 70 0.6 60 50 0.4 40 B B 30 B B 20 0.2 B B 10 % fields in soybean bloom in fields soybean % Mean honey Mean honey beestrap per per site 0 0 15-Jun 1-Jul 15-Jul 29-Jul 12-Aug 6-Sep 24-Sep

Figure 4. Seasonal honey bee activity-density inside Hive (+) soybean fields during 2015 and 2016. Activity-density was steady before and during soybean bloom and then increased post-bloom. Black bars represent mean honey bee activity-density (bees per trap per site). Green block represents the percentage of soybean fields in bloom (R1-R4) at each sampling week. Activity-density is reported as the mean ± one standard error of the mean of honey bees captured within pan traps. Mixed model analysis of variance (F6, 86.88=9.32, P=<0.0001). Letters signify P<0.05 for post hoc comparison of least squared means. 36

0.6 NS NS 0.5 0.4 0.3 NS

site 0.2 0.1 0

Mean honey Mean honey beestrap per per Early August Late August Early September Soybean 30m Soybean 90m Prairie 30m Prairie 90m

Figure 5. Mean seasonal honey bee activity-density (bees per trap per site) in pan traps in soybean and prairie fields with increasing distance from the colony in central Iowa during 2017 and 2018. Data represent mean ± one standard error of the mean. There was variation but no overall differences in mean honey bee activity-density between soybean and prairie fields (F1, 11.42=0.26, P=0.62) or between distance from the apiary (F1, 53.74=0.00, P=0.98); mixed model analysis of variance.

0.5 R² = 0.1079

0.4

0.3

0.2 site per day 0.1

0 Mean honey Mean honey trapbee per per 0 4 8 12 16 Number of colonies present within 90 m of pan traps

Figure 6. Correlation of colony number (0, 4, 8, or 16 colonies) with the observed honey bee activity-density per trap per soybean site per day in fields in central Iowa from 2015-2018. Honey bee activity-density was significantly correlated with the number of colonies within 90 m of the trap in soybean fields, linear regression (F1, 44=5.32, P=0.03), linear equation y=0.0092x +0.0957.

CHAPTER 3. LANDSCAPE DIVERSITY BUT NOT HONEY BEE PRESENCE SHAPES WILD BEE COMMUNITIES IN AN AGRICULTURAL LANDSCAPE

Modified from a paper to be submitted to Journal of Applied Ecology

Ashley L. St. Clair1, 2, Ge Zhang2, Adam G. Dolezal3, Matthew E. O’Neal2, Amy L. Toth1, 2

1Iowa State University, Department of Ecology, Evolution, and Organismal Biology

2Iowa State University, Department of Entomology

3Univeristy of Illinois Urbana-Champaign, Department of Entomology

Authors’ contributions

AGD, ALS, ALT, and MEO designed the study. ALS and GZ collected the field data. ALS

identified all insect specimens. ALS analyzed the data and wrote the manuscript. All authors

contributed critically to the drafts and gave final approval for publication.

Abstract

Declines in wild bee biodiversity are documented worldwide, with a major contributing factor being habitat conversion. These declines are problematic as bees are a critical component of the natural ecosystem, providing pollination to over 80% of wild plants. Long-term stability of pollination services will likely include integrating wild bees, in

addition to managed honey bees, into crop pollination management plans. However, it is

poorly understood whether honey bees near crop fields have an effect on wild bee

communities, and whether these effects are further exacerbated by extreme agricultural

production. Iowa, USA is a prime example of a landscape where natural habitat has been

converted into agricultural production, with 65.5% of the landscape committed to annual 38

production of corn and soybean. Surrounding landscape complexity affects non-pollinator

insect communities in soybean, but the extent to which landscape complexity affects wild

and managed pollinators is less well understood. To better understand the dynamics between

wild and managed pollinators and the surrounding landscape, we identified 38 commercial

soybean fields surrounded by an either high or low proportions of land committed to annual

production of corn and soybean in central Iowa in 2015 and 2016. At a subset of fields, we

placed honey bee colonies. Pan traps were used to estimate the diversity and abundance of

pollinators. We did not observe an effect of the presence of honey bees near soybean fields

on the wild bee community, however, soybean fields surrounded by low proportions of corn

and soybean production had a higher richness and diversity of bee species, specifically bees

which were classified as uncommon and rare. In fields surrounded by low proportion of corn

and soybean production, woodland and grasslands were positively associated with increases

in abundance and richness of uncommon and rare bees respectively, suggesting these cover

types may be valuable for pollinator conservation.

Keywords: Wild bee, honey bee, Apis mellifera, agriculture, land cover

Introduction

Declines in wild bee biodiversity are documented worldwide (Brown & Paxton 2009;

Potts et al. 2010; Levy 2011; Sánchez-Bayo & Wyckhuys 2019). These have been attributed to multifactorial stressors including environmental toxins, pathogens, reduced forage availability, and climate change (Potts et al. 2010; Goulson et al. 2015). Highly developed agricultural systems result in reduced landscape diversity (Tilman et al. 2001; Brown &

Schulte 2011), which may reduce diversity and abundance of wild bee communities 39

(Kremen, Williams & Thorp 2002; Steffan-Dewenter et al. 2002; Winfree et al. 2009). In the

U.S., wild bee populations are particularly at risk in regions of the Upper Midwest (Koh et al.

2016) where vast areas of the landscape have been converted for the annual production of row crop agriculture (primarily corn and soybeans) (Otto et al. 2016; 2018). In addition to reduced wild bee populations, honey bee (Apis mellifera L.) colony losses have mounted in this region (vanEngelsdorp et al. 2008; Kulhanek et al. 2017), with beekeepers in the

Midwest frequently losing 60% or more of their colonies annually (Steinhauer et al. 2014;

Seitz et al. 2016).

These declines are particularly problematic as wild bees are an essential part of maintaining natural ecosystems (Winfree et al. 2009) and honey bees contribute to pollination of over 150 crops (Thapa 2006). Increased demand in food supply (Calderone

2012) has resulted in greater dependence on honey bee pollination services. With unsustainably high colony losses (Steinhauer et al. 2014; Seitz et al. 2016), honey bees are unable to meet crop production needs (Aizen & Harder 2009). As a result, there is an increasing reliance on wild bees, in addition to managed bees, for pollination services

(Winfree, Griswold & Kremen 2007; Garibaldi et al. 2013), which are more efficient in some cropping systems (Klein, Steffan-Dewenter & Tscharntke 2003; Winfree et al. 2007;

Holzschuh, Dudenhöffer & Tscharntke 2012; Blitzer et al. 2016).

Crop management plans that integrate wild and managed bees can reduce pollination costs for farmers and ensure long term stability of pollination services (Kremen 2005;

Greenleaf & Kremen 2006; Garibaldi et al. 2013). However, in systems where wild bee communities and honey bees overlap there is potential for competition of floral resources

(Goulson 2003; Paini 2004; Moritz, Hartel & Neumann 2005; Stout & Morales 2009; Potts et 40

al. 2010; Burkle & Alarcon 2011) and transmission of disease (Oldroyd 2007; Dolezal et al.

2016). Many studies have investigated the effects of landscape intensification on wild bee

communities (Kremen, Williams & Thorp 2002; Kremen et al. 2007; Holzschuh, Steffan-

Dewenter & Tscharntke 2008; Carre et al. 2009; Gabriel et al. 2010; Garibaldi et al. 2011;

Kennedy et al. 2013), and the impacts honey bees can have on wild bees (Mallinger, Gaines-

Day & Gratton 2017; Henry & Rodet 2018; Valido, Rodríguez-Rodríguez & Jordano 2019).

However, a critical knowledge gap about how landscape and honey bee presence interact to

impact wild bee communities exists. Many species of wild bees already under stress from

agricultural industrialization could be negatively impacted by additional competition with

managed honey bees, further exacerbating population declines.

The Midwestern state of Iowa, USA, identified as a critical area for pollinator

conservation (Grixti et al. 2009), is an ideal location to study agriculture-related wild bee

declines and interactions with managed pollinators. Before European settlement, 80% of

Iowa was covered in tallgrass prairie, of which 0.1% remains (Samson & Knopf 1994; Smith

1998). Currently, 85.5% of the landscape is dedicated to agricultural production with 65.5%

committed to corn and soybean (NASS-USDA 2018) resulting in a landscape where

remaining natural habitat is embedded within an extensively cropped agricultural matrix.

This system represents the type of extensive landscape conversion that other parts of the

world are currently experiencing (e.g. large-scale monoculture cropping with chemical weed and pest control) (Otto et al. 2016; Smart et al. 2016a; Smart et al. 2016b; Otto et al. 2018), and can be used to understand how wild bees respond to extensive agricultural land conversion, providing valuable insights into the future of pollinator community dynamics. 41

Although corn and soybean do not require insect pollination studies have revealed

that Iowa corn and soybean fields can house over 50 species of pollinators, including honey

bees (Gill & O'Neal 2015; Wheelock & O'Neal 2016; Wheelock, Rey & O’Neal 2016). How

this community responds to variation within the surrounding landscape is not clear. In

soybeans, surrounding landscape has been shown to influence pest and beneficial insects

(Gardiner et al. 2009; Gardiner et al. 2010). In other agricultural systems, surrounding

landscape influences pollinators (Kremen, Williams & Thorp 2002; Steffan-Dewenter 2003;

Klein, Steffan-Dewenter & Tscharntke 2006; Julier & Roulston 2009; Klein 2009). Higher

plant diversity in a surrounding landscape can increase both pollinator and natural enemy

abundance and richness (Shackelford et al. 2013). If extensive farming is associated with

reductions in resource diversity and/or abundance, then it would be expected that in Iowa,

landscapes committed to high proportions of annual production of corn and soybean would

pose the highest risk of conflict between wild and managed bees compared to resource-rich areas. To advance efforts of conserving bee biodiversity and maintaining a sustainable future food supply it is vital to understand the impacts of surrounding land use on wild bees, particularly in areas where the landscape is dominated by agriculture, like Iowa.

Additionally, because of our reliance on honey bees for agricultural pollination, it is a necessity to understand how vulnerable wild bee populations are to impacts from honey bees in agricultural landscapes.

Here, we investigate the interactive effects of proportion annual production of corn and soybean in the surrounding landscape and honey bee presence on wild bee community dynamics in central Iowa soybean, using an on-farm study, replicated over multiple years and sites. We hypothesized that the abundance, richness, and diversity of wild bees would be 42

greater in fields within landscapes comprised of lower production of corn and soybean.

Secondly, we hypothesized that wild bee communities would suffer from competition

resulting in reduced abundance, richness, and diversity when honey bees were present. We

predicted that in fields surrounded by high corn and soybean production where honey bees are present, the potential for competition would be highest, resulting in the lowest wild bee community metrics. We predicted that fields surrounded by low corn and soybean production with honey bee colonies absent would provide a best-case scenario in Iowa, resulting in the

highest wild bee community metrics. To test these hypotheses, we measured wild bee

abundance, richness, and diversity within soybean fields surrounded with different landscape

proportions of annual corn and soybean production (designated as High- and Low-

cultivation) and at sites with and without managed honey bee colonies present. In addition,

we investigated which features of the surrounding landscape were associated with the highest

abundance, richness, and diversity of wild bee species, helping us identify landscape types

suitable for conserving wild bee communities.

Methods

Selection of fields in landscapes with varying amounts of corn and soybean production

We screened Iowa State University (ISU)-owned and privately-owned commercial soybean fields in Story, Boone, Marshall, and Hardin counties, Iowa, USA in 2015 and 2016 to locate fields which were greater than 20 ha. We classified land cover surrounding these

fields in ArcGIS, ArcMap 10.3.1 using a 1.6 km radius centered on the field edge. Land

cover features were based on the USDA-NASS cropland data layer for 2015 and 2016 at a 30

m x 30 m resolution (https://nassgeodata.gmu.edu/CropScape/). Using the ‘isecpolyrst’ 43 function in Geospatial Modeling Environment (Version 0.7.4.0) we quantified pixels within the buffer associated with each land cover type to measure the proportion of the landscape committed to each feature. Land cover types were then categorized into five groups (corn, soybean, other crops, developed, grassland, and woodland; Appendix B, SI Table 1). High and low production of corn and soybean was determined by the percentage of land in the 1.6 km radius committed to their production, and was then categorized into two distinct categories: High- and Low-cultivation. Although the term cultivation is commonly used to refer to land used to garden or grow crops, it can also reference the way in which soil is managed in preparation for growing crops (i.e., tillage). For this study, we use the former definition (i.e., we did not consider variation in tillage practices, noting that most of soybean fields are tilled in Iowa [USDA-NASS 2018]). We based cultivation categories solely on the percentage of land used to grow crops, specifically annual corn and soybean production, and did not assess how the soil was managed. Statewide, Iowa dedicates approximately 85.5% of its land area to farm operations with 65.5% planted with annual crops; Story County is 91% dedicated to farm use with 73% land area planted with annual crops (USDA 2017).

Therefore, we defined a soybean field as being in a landscape category of High-cultivation if it was surrounded by >73% corn or soybean production. Due to the dominance of crop production in this region, fields surrounded by Low-cultivation were difficult to find resulting in a wider range in the percentage of corn and soybean in the surrounding landscape

(range of 4% - 64% corn and soybean production). To ensure that fields were separated from each other such that measurements of the bee communities could be considered independent observations, we selected fields within a year that were at least 3.2 km apart from each other.

From this selection process, we identified a total of 38 soybean fields across the two years; 44

20 soybean fields surrounded by High-cultivation (10 in 2015 and 10 in 2016) and 18 soybean fields surrounded by Low-cultivation (8 in 2015 and 10 in 2016) (Figure 1). Fields within the High-cultivation landscapes were surrounded by 82.9% ± 1.5% (a mean ± SEM) corn/soybean while Low-cultivation fields were surrounded by 33.6% ± 4.6% (a mean ±

SEM) corn/soybean production.

At all soybean fields, weeds were managed with glyphosate. Soybeans were planted with seed-applied treatments; ISU fields were planted with a fungicide only (Fluopyram,

ILeVO, Bayer, Pittsburgh PA), while private fields were planted with an insecticide and fungicide (imidicloprid and ILeVo, respectively; Acceleron seed treatment, Bayer, Pittsburgh

PA). No insecticides were applied to soybean foliage or in fields directly surrounding

soybeans. All fields were in a corn and soybean rotation with corn planted in the previous

year.

Honey bee apiary placement at selected soybean fields

To determine if honey bees affected the wild bee community within a soybean field,

we randomly selected fields within each cultivation category to receive an apiary of honey

bees Hive (+). We compared fields with apiaries to those without an apiary Hive (-). To confirm that a field could fit the Hive (-) category, we checked if there were registered colonies within a 1.6 km radius of a field using the state-wide voluntary registry for beehives

(DriftWatch Inc., West Lafayette, IN https://ia.driftwatch.org/map). Once we confirmed that there were sufficient number of Hive (-) fields, we then randomly selected five fields in both cultivation categories each year in which to place an apiary consisting of four honey bee colonies. This resulted in a total of 10 High-cultivation/Hive (+), 10 Low-cultivation/Hive

(+), 10 High-cultivation/Hive (-), and 8 Low-cultivation/Hive (-) soybean fields across the 45 two years. At Hive (+) sites, colonies were transported to fields on 6 June 2015 and 22 May

2016 after 90% of the corn and soybean had been planted in Iowa, and were placed in a grassy perimeter 3 m from the field edge. In 2015, colonies were started from commercially obtained packages sourced from C.F. Koehnen & Sons LLC, Northern California, USA; all packages contained 0.9 kg of adult bees and an Italian (Apis mellifera ligustica) queen from the same source. In 2016, this protocol was repeated with the exception that colonies were derived from the overwintered bees from the 2015 experiment. Colonies were equalized to the same size as 2015 and each colony was provided with a new A. mellifera ligustica queen purchased from the same provider as used in 2015. Apiaries remained at the soybean field edges throughout the season until 12 October 2015 and 18 October 2016 when they were moved to an ISU research apiary for overwintering. Throughout the season, each colony was monitored for growth and provided a new hive box when the topmost existing hive box reached 75% capacity.

Sampling the bee community

To quantify wild bee abundance, richness, and diversity within soybean fields we used pan traps (bee-bowls) based on the design of Droege (2010) with some modifications per Gill and O’Neal (2015). Traps were deployed on posts, such that each post held three 3.2 oz. bowls (Solo® brand) painted either fluorescent yellow, blue, or white (i.e. unpainted).

Each field had 3 posts with 3 bowls (9 bowls total) placed 10 m apart and 10 m into the soybean field. At Hive (+) sites, bee-bowls were placed on the same field edge where honey bee colonies were present.

During each year, we sampled bees every other week for 13 weeks; 1 July through 24

September in 2015, and 15 June through 9 September in 2016. Collections were made on 46 days with low cloud cover, no precipitation, and low to no wind (<10 mph). During each collection, the bee-bowls were adjusted on the post so that their height was level with the soybean plant canopy. Bee-bowls were deployed for 24 hr, with each bowl filled with a 3% aqueous Dawn ® brand soap-water solution. Each field was considered the experimental unit, with bee-bowls as sub-samples, therefore all collections within a date were combined and all measures of abundance and richness are represented at the field level.

Specimens were processed using methodologies by Droege (2010) prior to identification. Individuals were identified to genus using the dichotomous key “The Bee

Genera of North and Central America” (Michener 1994) and to species using the online dichotomous key “Discover Life” (Ascher and Pickering 2015). All bees were identified to species, with the exception of the genus Lasioglossum which were identified to subgenus.

Statistical analyses

Because our sites varied in their proportion of surrounding land use, we created a nonmetric multidimensional scaling (NMDS) plot in R 3.4.1 (R Core Team 2017) to visually represent the dissimilarity in the distribution of land use types for High- and Low-cultivation categories using the ‘metaMDS’ function (Oksanen et al. 2018). The output from NMDS was used to create a two-dimensional plot indicating the dissimilarity of the counts for treatments in 2015 and 2016 combined. The resulting stress value of less than 0.1 confirmed that this analysis maintained the dissimilarities observed in the original data in the reduced dimensions (Buja et al. 2008). To confirm that the fields we selected in High- and Low- cultivation landscapes were significantly different in the proportion of land cover, we used permutated multivariate analysis of variance in R (R Core Team, 2017) with cultivation category as a fixed effect to compare land cover types (corn, soybean, other crops, grassland, 47 developed, and woodland). Additionally, we performed a two-tailed t-test with Satterthwaite variance to compare the proportion of individual land cover types surrounding High- and

Low-cultivation fields.

To ensure that we had sufficient sampling effort of the wild bee community at each of our cultivation and hive treatments, we created sample-size based rarefaction curves in R using the vegan package (Community Ecology Package V2.4-6) (Oksanen et al. 2018), the

SpadeR package (Species-Richness Prediction and Diversity Estimation with R V1.1.1)

(Chao et al. 2016), and the INext package (Interpolation and Extrapolation for Species

Diversity V2.0.12, 2016) (Hsieh et al. 2016) with data collected in bee-bowls. All samples were standardized to a common sample size of 2,500 individuals. Based on our species curves, our percent coverage of species captured at each cultivation and hive treatment was above 75% (Appendix B, SI Figure 1), confirming we had equal and high sampling effort at all locations.

To test whether or not cultivation category or honey bee presence had an effect on the overall wild bee abundance, richness, and diversity (as measured by Shannon-Wiener Index

[Shannon et al. 1950]), we created a repeated-measures mixed effect model (PROC

GLIMMIX) in SAS 9.4 with honey bee presence and cultivation category as fixed effects and siteyear as a random effect. To further assess whether specific groups of bees responded differently to a cultivation or hive presence category, the community of bees captured were divided into three categories: common, uncommon, or rare. To objectively separate species into these categories, placement was based on the relative abundance of each taxa compared to the entire community. We used cut-offs for these categories by those developed for a similar study conducted in central Iowa (Kordbacheh et al. 2019 in press). Species placed 48 into the common taxa category were collected at a proportion >0.01, uncommon taxa were collected at a proportion <0.01, and rare taxa occurred as either singletons (i.e., appeared once) or doubletons (i.e., appeared twice) within a cultivation and hive presence category.

Within these three groups, we used the same statistical model as above to investigate differences in wild bee abundance, richness, and diversity (as estimated by the Shannon-

Wiener index), with the exception for rare bees. Because rare bees only appeared as singleton or doubletons, it was not possible to measure aspects of evenness for the community of rare bees, therefore we could not report Shannon-Wiener index values. If significant interactions between cultivation category and honey bee hive presence were observed we performed post hoc least squared means comparisons of all groups with a Tukey adjustment for p-value.

To investigate which land use types specifically related to increased wild bee abundance, richness, and diversity, for the overall community of bees as well as common, uncommon, and rare bees, we performed multiple regressions with stepwise model selection

(PROC REG) in SAS. All variables required P<0.15 for inclusion within the model (Littell et al. 2002). Because land cover types are inherently related to each other, we first ran a

Pearson’s correlation (PROC COR) to ensure that there were no collinearities among variables (Pearson’s correlation coefficient <0.8; Appendix B, SI Table 2). In addition to land cover types, we also compared honey bee hive presence and absence within the model as a binary measure.

Results

The effects of cultivation and honey bee presence on the wild bee community

To compare how the amount of corn or soybean production in the surrounding landscape of soybean fields affected the wild bee community, we first confirmed our sampling locations were significantly different in the proportion of surrounding land committed to the production of corn or soybean. A pMANOVA considering all land use types in the surrounding landscape confirmed that High- and Low-cultivation fields were significantly different in corn and soybean production (Figure 2A; F1, 35=50.41; P=0.001).

When comparing the proportion of individual land cover types surrounding soybean fields,

we found corn and soybean production to be significantly higher in High-cultivation (Figure

2B; T22.52=10.02; P=<0.0001; T34.91=5.89; P=<0.0001 for corn and soybean respectively),

while proportion of other crops, grassland, developed land, and woodland were all

significantly greater in Low-cultivation (Figure 2B; T24.06=2.65; P=0.01; T32.81=5.01;

P=<0.0001; T17.44=3.35; P=0.004; T18.22=7.46; P=<0.0001; for other crops, grassland, developed land, and woodland respectively).

Honey bee activity-density was significantly lower in Hive (-) fields than Hive (+) fields (Figure 3; F1, 34 =13.56, P=0.0008), confirming we created a treatment category where

honey bees varied in their presence and activity-density within a soybean field. There was no effect of cultivation category on honey bee activity-density (F1, 34 =0.66, P=0.42) and no

interactions between cultivation category and hive presence were observed (F1, 34 =2.89,

P=0.10).

In total, we collected 4,301 wild bees from 54 taxa across all soybean fields in 2015

and 2016 (Table 1). Eight taxa were classified as common, and collectively accounted for 50

93% of all bees collected. Eighteen taxa were classified as uncommon, and collectively made

up 5.7% of all bees collected. Rare taxa consisted of 28 eight species, which were present as

only singletons or doubletons within a cultivation/hive presence treatment. Seventeen species

were collected exclusively in soybean fields surrounded by Low-cultivation while six species were exclusive to fields surrounded by High-cultivation (Table 1).

There were no observable effect of honey bee presence (colonies at the edge of the

soybean field, i.e., Hive (+) vs Hive (-)) on the overall wild bee community abundance

(Figure 4A; F1, 34=0.33; P=0.57), richness (Figure 4B; F1, 34=0.10, P=0.75), or diversity

(Figure 4C; F1, 34=3.23, P=0.08). There was no observable effect of cultivation category on the overall abundance of bees collected within soybean (Figure 4A; F1, 34=1.96, P=0.17).

However, overall richness (Figure 4B; F1, 34=6.72, P=0.01) and diversity of wild bees (Figure

4C; F1, 34=9.12, P=0.005) were significantly greater in fields surrounded by Low-cultivation compared to High-cultivation. We did not observe an interaction of honey bee presence and cultivation category on the overall wild bee abundance (F1, 34=0.10, P=0.08), richness (F1,

34=1.16, P=0.29), or diversity (F1, 34=3.65, P=0.06) of wild bees found within soybean fields.

For common wild bee taxa, there were no observable effects of honey bee presence

on the abundance (Figure 4D; F1, 34=0.35, P=0.56), richness (Figure 4E; F1, 34=0.00, P=0.98),

or diversity (Figure 4 F; F1, 34=0.24, P=0.46). Additionally, there were no observable effects

of cultivation category on common wild bee abundance (Figure 4 D; F1, 34=1.50, P=0.23),

richness (Figure 4E; F1, 34=0.08, P=0.78), or diversity (Figure 4F; F1, 34=0.01, P=0.92) within

soybean fields. We did not observe an interaction of honey bee presence and cultivation

category on common wild bee abundance (F1, 34=0.08, P=0.79), richness (F1, 34=0.28,

P=0.60), or diversity (F1, 34=0.41, P=0.53) found within soybean fields. 51

For uncommon bees, there were no observable effects of honey bee presence on the abundance (Figure 4G; F1, 34=0.05, P=0.82), richness (Figure 4H; F1, 34=0.08, P=0.77), or

diversity (Figure 4I; F1, 34=0.56, P=0.46) of uncommon bees captured inside soybean fields.

Uncommon bees had higher abundance (Figure 4G; F1, 34=7.70, P=0.009), richness (Figure

4H; F1, 34=4.08, P=0.05), and diversity (Figure 4I; F1, 34=5.01, P=0.03) in soybean fields

surrounded by Low-cultivation compared to fields surrounded by High-cultivation. We did

not observe an interaction of honey bee presence and cultivation category on the abundance

(F1, 34=0.47, P=0.50), richness (F1, 34=1.33, P=0.26), or diversity (F1, 34=1.66, P=0.21) of

uncommon bees captured inside soybean fields.

For rare bee species, honey bee presence had no observable effect on abundance

(Figure 4J; F1, 34=1.79, P=0.19) or richness (Figure 4K; F1, 34=1.35, P=0.25) of bees captured

in soybeans. Soybean fields surrounded by Low-cultivation had a significantly greater abundance (Figure 4J; F1, 34=6.73, P=0.01) and richness (Figure 4K; F1, 34=7.35, P=0.01) of

rare bee species compared to fields surrounded by High-cultivation. We did not observe an

interaction of honey bee presence and cultivation category on rare bee species abundance (F1,

34=0.08, P=0.77) or richness (F1, 34=0.08, P=0.78) found within soybean fields.

The effects of land cover in the surrounding landscape on wild bee community

We used model selection to compare how individual land use types and honey bee

hive presence impact the overall, common, uncommon, and rare bee abundance, richness,

and diversity of bees present within soybean fields. Proportion woodland in the surrounding

landscape was significantly positively associated with overall wild bee abundance, common

bee abundance, uncommon bee abundance, overall bee richness, uncommon bee richness,

and diversity of uncommon bees (Table 2). Proportion grassland in the surrounding 52

landscape was significantly positively associated with rare bee abundance and rare bee

richness (Table 2). While marginal, the presence of honey bee colonies was negatively

associated with rare bee abundance and richness, when there were higher proportions

grassland in the surrounding landscape (Table 2). Proportion soybean in the surrounding

landscape was significantly negatively associated with richness of common taxa and

diversity of the overall community (Table 2). Other crops were significantly negatively

associated with common taxa richness and marginally negatively associated with uncommon

bee richness and common bee diversity (Table 2). Proportion developed land in the

surrounding landscape of soybean fields was significantly positively associated with

increased common taxa diversity (Table 2).

Discussion

We investigated the effects of annual crop production in the landscape surrounding

soybean fields in conjunction with the presence or absence of managed honey bee colonies

on wild bee communities by measuring wild bee abundance, richness, and diversity using

bee-bowl pan traps. We observed that the abundance of the entire bee community (common,

uncommon and rare taxa combined) did not respond to variation in the production of corn

and soybean or the presence of a four-colony apiary (Figure 4A). The richness and diversity of this community responded to landscape variation, with both increased in soybean fields surrounded by less annual crop production (Figures 4B and C). Only overall bee diversity

(H’) responded marginally to the presence of honey bees, with a concurrent marginal interaction between the hive presence and cultivation category on the overall diversity of wild bees (Figure 4C). The highest diversity of bees was observed in Low-cultivation sites 53

without the presence of hives, compared to High-cultivation with and without honey bees

present (Figure 4C). These trends may be a result of the impact of agricultural

industrialization on resources available to bees when a potential competitor is present. As

resources become scare there is increasing potential for honey bees and wild bees to compete

(Hudewenz and Klein 2013, Hudewenz and Klein 2015). Soybean fields surrounded by a

high proportion of corn and soybean production with honey bee colonies also present

represent a worst-case scenario for wild bees, while sites with low production of corn and

soybean and an absence of honey bee colonies represents the best (or better) case scenario.

Regardless of the mechanism responsible for the trends observed in bee diversity, the impact

was marginal, which may be due to components of our experimental design.

Because we did not measure floral visitation rates we cannot say whether or not direct

competition for forage resources may have occurred between honey bees and wild bees.

Honey bee colony presence could have resulted in a reduction in the availability of

nutritional resources for bees, restricting the ability of bees to produce many offspring;

therefore, it is possible that shifts in wild bee community might not manifest until the years

following exposure. Since corn and soybean are rotated annually, our experimental design of

placing honey bee colonies next to a soybean field did not permit us to visit and monitor the

same location for multiple years. Additionally, the number of honey bee colonies may be an

important factor contributing to observed negative effects on wild bees. Previous studies that

have observed negative effects of honey bees on wild bees have used more colonies than

what we used (Henry and Rodet 2018, Ropars et al. 2019). In Iowa, the activity-density of honey bees in soybean fields may be directly related to the number of colonies nearby 54

(Chapter 2, Figure 6). The four honey bee colonies at the edges of the soybean fields used in

this study may not have created sufficient honey bee presence to negatively affect the overall

community.

Efforts to study the impact of honey bees on wild bees has produced mixed results

(Mallinger et al. 2017 and references therein). Many studies have observed resource

competition, including reduced floral visitation rates (Shavit et al. 2009, Wojcik et al. 2018,

Valido et al. 2019) and reduced nesting success by wild bees when honey bee colonies are

present (Hudewenz and Klein 2013, Hudewenz and Klein 2015). Further research addressing

the changes in wild bee community over several years and at varying colony densities can

help clarify if persistent honey bee presence has long term effects on wild bee communities.

When assessing the entire bee community, it was clear that some species were captured across all sites. Therefore, we broke the community down into three categories of bees; common, uncommon, and rare taxa. Common wild bee abundance, richness and diversity did not vary by cultivation category or with the honey bee colony presence (Figures

4D, E, and F). These bees were collected in every cultivation/hive treatment combination and have been commonly collected in Iowa corn and soybean fields (Gill and O'Neal 2015,

Wheelock and O'Neal 2016, Wheelock et al. 2016). Collectively common bees accounted for

93% of the bees collected across all sites. The lack of an effect of surrounding landscape variation on common bees suggests there is a community of bees which are potentially well adapted to living in disturbed landscapes and may have the ability to thrive on the resources available in an agricultural landscape. It is likely that these common bees not likely to respond significantly to variation in a landscape (Kleijn et al. 2015). 55

The surrounding landscape had a significant impact on our measurements of abundance, richness, and diversity of uncommon wild bees (Figures 4G, H, and I). Rare bee species also had higher abundance and richness in Low-cultivation landscapes (Figure 4J and

K). These bees may be ecotone species that require resources not provided by corn or soybeans, at some point in their lifecycle (Duelli and Obrist 2003). Less production of corn and soybean in the surrounding landscape may result in an increased availability of valuable resources (e.g. forage or nesting) necessary for wild bees to thrive. For example, seventeen species were collected exclusively in Low-cultivation fields with fifteen of them being rare bees. Conservation of these species may require additional access to uncultivated land.

Although the effects were marginal, in our models honey bee colony presence was found to be a factor which explained variation of rare bee abundance and richness in conjunction with proportion grassland in the surrounding landscape (Table 2). Rare bees may be more sensitive to the presence of potential competitors than other bees (Mallinger et al.

2017, Henry and Rodet 2018). Because activity-density of honey bees in soybeans increases with increasing number of colonies (Chapter 2 Figure 6), the effects we observed may be more severe in the presence of a greater number of colonies.

Not all bee species are active at the same time in the season, with some species emerging as adults as early as April or May (Michener 2007). Because we did not sample in that time frame, we may have missed some species that occur within central Iowa or misclassified a normally common bee as uncommon or rare. Our goal was not to understand which bees were present in the community across the season, but rather, which bees were present within Iowa soybean fields during the growing period of the crop. Moreover, bee- bowls may be biased towards collecting small bees (Cane et al. 2000, Roulston et al. 2007), 56 but in soybeans, bee-bowls have been shown to be an appropriate method for capturing bees compared to sticky traps and sweep nets (Gill and O'Neal 2015).

Soybean fields surrounded by low production of corn and soybean (i.e., Low- cultivation) were surrounded by significantly more woodland and grassland habitat than

High-cultivation fields (Figures 2A and B), which could provide valuable forage and nesting resources to wild bees (Smith 2007, Morandin and Kremen 2013, Mallinger et al. 2016,

Smith et al. 2016). We predicted these land use types could be valuable candidates for landscape enrichments and would be most positively correlated with increases in wild bee community metrics. We conducted model selection to see how the entire community of bees as well as common, uncommon, and rare bees responded to landscape variation. Proportion woodland in the surrounding landscape was most positively associated with overall community abundance and richness (Table 2). This effect was strongest for the uncommon bees, with woodland strongly positively associated with abundance, richness, and diversity of bees within soybeans. Rare bee abundance and richness on the other hand, was strongly positively associated with grassland in the landscape, suggesting not all types of bees respond the same to landscape variation. Pollinator targeted habitat enrichments in agricultural landscapes may need to be more directed depending on the ultimate goals for increasing bee biodiversity. To maximize bee biodiversity in agricultural landscapes, increases in both woodland and grassland habitat may be necessary. In addition to the response we have seen of bees to landscape variation, cropping systems incorporating prairie have shown promise for increasing bee biodiversity in corn and soybean systems (Gill and O’Neal 2015, Schulte et al. 2017, Kordbacheh et al. 2019 in press). 57

Together, these results enhance our understanding of how wild bees respond to land use within agricultural landscapes and the potential effects management plans that incorporate managed and wild bees for pollination services may have on the wild bee community. Our results reveal that honey bees, at least when present in small apiaries, do not have an overwhelmingly negative effect on the community of wild bees. However, some bees, particularly rare bees, may be more vulnerable to the presence of honey bees than other species. The interaction between managed honey bees and wild bees deserves more rigorous and longitudinal investigation to accurately understand how pollinator conservation might be most effective in extensive agricultural landscapes such as Iowa. We provide evidence that landscape diversity may be an important factor that influences wild bees in agricultural landscapes. Although not all groups of bees responded in the same way to landscape, overall, fields surrounded by less production of corn and soybean supported a greater community of bees, with uncommon and rare bees demonstrating the greatest response. In landscapes dominated by the production of corn or soybean, there is potential for increased woodland and grassland habitat in the surrounding landscape to support a greater richness and diversity of bees which are not as well adapted to living in agricultural landscapes or require non-crop habitat at some point in their life cycle.

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Tables and Figures

Table 1. Abundance of bee pollinators captured in bee-bowls in soybeans in High- and Low- cultivation and at locations with and without the presence of honey bee apiaries across the season in central Iowa over 2015 and 2016.

a Abundance Abundance in fields by cultivation and hive category High-cultivation High-cultivation Low-cultivation Low-cultivation Taxa Hive (+) Hive (-) Hive (+) Hive (-)

Common Lasioglossum (Dialictus) sp. 2273 428 597 616 632 Agapostemon virescens F. 518 143 125 130 120 Melissodes bimaculata (Lepeletier) 507 107 101 156 143 Halictus ligatus (Say) 272 72 51 73 76 Agapostemon texanus (Cresson) 174 68 36 49 21 Halictus confusus (Smith) 128 40 20 44 24 Augochlorella aurata (Smith) 85 19 27 19 20 Halictus parallelus (Say) 51 10 4 23 14

Uncommon Halictus rubicundus (Christ) 37 10 3 16 8 Lasioglossum (Evylaeus) sp. 28 2 5 12 9 Melissodes trinodis (Robertson) 20 6 2 6 6 Augochlora pura (Say) 18 3 1 7 7 Melissodes desponsa (Smith) 17 2 1 3 11 Melissodes agilis (Cresson) 16 4 4 6 2 Bombus impatiens (Cresson) 15 2 3 4 6 Eucera hamata (Bradley) 14 2 4 6 2 Melissodes communis (Cresson) 14 3 4 4 3 Peponapis pruinosa (Say) 12 7 1 3 1 Hylaeus affinis (Smith) 11 1 - 3 7 Bombus bimaculatus (Cresson) 9 - - 6 3 Bombus griseocollis (DeGeer) 9 - 2 3 4 Augochloropsis metallica F. 7 1 2 2 2 Dieunomia triangulifera (Vachal) 7 4 1 - 1 Bombus pensylvanicus (DeGeer) 5 1 3 - 1 Agapostemon angelicus (Cockerell) 4 3 1 - - Bombus vagans (Smith) 4 - - 4 -

Rare species Perdita halictoides (Smith) 4 - 2 1 1 Bombus citrinus (Smith) 3 - 1 - 2 Bombus perplexus (Cresson) 3 - - 1 2 Calliopsis andreniformis (Smith) 3 1 - 2 - Ceratina dupla (Say) 3 - 1 1 1 Hylaeus annulatus (Linnaeus) 3 - 1 2 - Melissodes boltoniae (Latreille) 3 1 - 1 1 Halictus tripartitus (Cockerell) 2 1 - - 1 aAbundance values are the number of individuals by species (includes females and males) summed across all 38 farms in 2015 and 2016 sampling seasons. bAbundance values are the number of individuals by species (includes females and males) summed across cultivation category and honey bee presence or absence. 67

Table 1 Continued.

a Abundance Abundance in fields by cultivation and hive category High-cultivation High-cultivation Low-cultivation Low-cultivation Taxa Hive (+) Hive (-) Hive (+) Hive (-)

Rare species Megachile rotundata (Fabricius) 2 - - - 2 Sphecodes davisii (Robertson) 2 - 2 - - Anthidium oblongatum (Illiger) 1 - - - 1 Bombus auricomus (Robertson) 1 - - - 1 Bombus fervidus (Fabricius) 1 - - - 1 Bombus variabilis (Cresson) 1 - - 1 - Ceratina calcarata (Robertson) 1 - 1 - - Ceratina strenua (Smith) 1 - - 1 - Colletes inaequalis (Say) 1 - - 1 - Megachile brevis (Say) 1 1 - - - Megachile centuncularis (Linnaeus) 1 - 1 - - Megachile inermis (Provancher) 1 - - - 1 Megachile parallela (Smith) 1 - - 1 - Nomada vegana (Cockerell) 1 - - 1 - Nomia universitatis (Cockerell) 1 1 - - - Osmia simillia (Panzer) 1 - - - 1 Protandrena bancrofti (Dunning) 1 - - - 1 Svastra compta (Cresson) 1 - - 1 - Triepeolus lunatus (Say) 1 - - - 1 Xylocopa virgifera (Latreille) 1 - 1 - - Total bees 4301 943 1008 1209 1140 aAbundance values are the number of individuals by species (includes females and males) summed across all 38 farms in 2015 and 2016 sampling seasons. bAbundance values are the number of individuals by species (includes females and males) summed across cultivation category and honey bee presence or absence.

Table 2. Multiple regression for wild bee abundance, richness, and diversity using landscape features corn, soybean, other crops, woodland, grassland, and developed land, as well as honey bee apiary presence as possible parameters. Stepwise model selection was used to obtain the final variables in each model (P<0.15 for inclusion in the model).

2 Dependent Variable Parameters Slope SE F P Model r Overall Community Woodland 173.6368 80.3033 4.68 0.0373 0.1149

Common Taxa Woodland 152.2237 77.3348 3.87 0.0568 0.0972

Uncommon Taxa Woodland 18.4658 4.8831 14.30 0.0006 0.2843

Rare Taxa Grassland 11.2278 3.5715 9.88 0.0034 Abundance Hive+/- -0.9128 0.4665 3.83 0.0584 0.2460

Overall Community Woodland 10.0871 3.577 7.95 0.0078 0.1809

Common Taxa Other Crops -31.4003 8.6978 13.03 0.0009

Soybeans -4.2504 1.4728 9.33 0.0066 0.3260

Uncommon Taxa Woodland 7.0996 2.3304 9.28 0.0044

Other Crop -3.6928 2.0456 3.26 0.0797 0.2284

Rare Taxa Grassland 10.5756 3.4211 9.56 0.0039 Richness Hive+/- -0.7812 0.4469 3.06 0.0892 0.2330

Overall Community Soybeans -0.4201 0.1471 8.15 0.0071 0.1846

Common Taxa Developed 0.6770 0.2990 5.13 0.0298

Other Crops -4.9423 2.5234 3.84 0.0582 0.1772

Diversity (H') Diversity Uncommon Taxa Woodland 1.6776 0.5507 9.28 0.0043 0.2050

Hardin

Boone Story Marshall

Sampling years

2015 2016

Treatments x High Cultivation/Hive+ High Cultivation/Hive-

Low Cultivation/Hive+ x Low Cultivation/Hive-

Figure 1. Overview of soybean sites sampled in central Iowa in 2015 and 2016. Circles represent sites from 2015 and square represent sites from 2016. Blue and open shapes represent High-cultivation/Hive (+), blue and closed shapes represent High-cultivation/Hive (-), green and open shapes represent Low-cultivation/Hive (+), and green and closed shapes represent Low-cultivation/Hive (-).

B 60% * 50% 40% * 30% * * 20% *

10% *

in 1.6 km Proportion use land Soybean Other Crops Corn Grassland Woodland Developed

High Cultivation Low Cultivation

Figure 2. (A) Nonmetric multidimensional scaling plot for NMDS 1 and 2 of land cover types surrounding soybean fields sampled in 2015 and 2016. Data based on a 1.6 km radius of field. High- and Low-cultivation sites were significantly different from each other in land cover composition (F1, 35=50.41, P=0.001), results based on pMANOVA. Sites with positive loadings on NMDS 1 were correlated with grassland and sites with negative loadings on NMDS 1 were correlated with corn and soybeans. Sites with positive loadings on NMDS 2 were correlated with developed land while sites with negative loadings were correlated with woodland. (B) Mean proportion of land cover types in high and low cultivation sites. Asterisks represent significant differences in two-tailed t-test, p <0.05. Error bars represent one standard error of the mean. 71

12 density - 10

2 Mean honey Mean honey bee activity 0 Hive+ Hive- Hive+ Hive- High Cultivation Low Cultivation

Figure 3. Mean honey bee activity-density (bees per site as measured by abundance in bee- bowls) in soybean farms. Significantly fewer honey bees were present in Hive (-) sites compared to Hive (+) sites (F1, 34 =13.56, P=0.0008). There was no effect of cultivation category on honey bee activity-density (F1, 34 =0.66, P=0.42) and no interactions between cultivation category and hive presence were observed (F1, 34 =2.89, P=0.10). Results based on mixed model analysis of variance.

Overall Community Common taxa Uncommon taxa Rare taxa A Hive: P=0.57 D Hive: P=0.56 Hive: P=0.82 J Hive: P=0.19 180 Cult: P=0.17 Cult: P=0.23 G Cult: P=0.009* Cult: P=0.01* 180 3.5 160 Hive*Cult: P=0.76 Hive*Cult: P=0.79 14 Hive*Cult: P=0.50 Hive*Cult: P=0.77 160 12 3 140 140 2.5 120 120 10 100 100 8 2 80 80 6 1.5 60 60 4 1 40 40 2 0.5 20 20 0 0 0 0 Meanwild bee abundance Hive- Hive- Hive- Hive- Hive- Hive- Hive- Hive- Hive+ Hive+ Hive+ Hive+ Hive+ Hive+ Hive+ Hive+ High Low High Low High Low High Low Cultivation Cultivation Cultivation Cultivation Cultivation Cultivation Cultivation Cultivation

Hive: P=0.75 Hive: P=0.98 Hive: P=0.77 Cult: P=0.01* Hive: P=0.25 B E Cult: P=0.78 H Cult: P=0.05* K 14 Hive*Cult: P=0.29 14 14 14 Cult: P=0.01* Hive*Cult: P=0.60 Hive*Cult: P=0.26 Hive*Cult: P=0.78 12 12 12 12 10 10 10 10 8 8 8 8 6 6 6 6 4 4 4 4

richness wild bee Mean 2 2 2 2 0 0 0 0

Hive- Hive- Hive- Hive- Hive- Hive- Hive+ Hive+ Hive- Hive- Hive+ Hive+ Hive+ Hive+ Hive+ Hive+ High Low High Low High Low High Low Cultivation Cultivation Cultivation Cultivation Cultivation Cultivation Cultivation Cultivation

Figure 4. Overall abundance, richness, and diversity of the entire wild bee community in soybeans in central Iowa over 2015 and 2016, reported in figure 4A, 4B, and 4C respectively. Sub sets of this community were created based on relative abundance (see text) and abundance, richness and diversity were reported for common bees (4D, 4E, and 4F, respectively), uncommon bees (4G, 4H, and 4I), and rare bees (J, K) Note that diversity could not be estimated for rare bees. Data are calculated as the average bees per site and error bars represent one standard error of the mean. Results based on mixed model analysis of variance.

Overall Community Common taxa Uncommon taxa Hive: P=0.62 Hive: P=0.46 Hive: P=0.08+ C F Cult: P=0.92 I Cult: P=0.03* Cult: P=0.005* 2 1.8 0.4 A Hive*Cult: P=0.53 Hive*Cult: P=0.20 Hive*Cult: P=0.06+ 1.8 0.35 1.6 1.6 1.4 0.3 1.4 1.2 0.25 AB 1.2 B 1 0.2 B 1 0.8 0.15 0.8 0.6 0.6 0.1 0.4 0.4

Meanwild bee diversity (H') 0.05 0.2 0.2 0 0 0

Hive- Hive- Hive- Hive- Hive- Hive- Hive+ Hive+ Hive+ Hive+ Hive+ Hive+ High Low High Low High Low Cultivation Cultivation Cultivation Cultivation Cultivation Cultivation

Figure 4 Continued.

CHAPTER 4. NATIVE HABITAT MITIGATES FEAST/FAMINE CONDITIONS FACED BY HONEY BEES IN AGRICULTURAL LANDSCAPES

Modified from a paper published in Proceedings of the National Academy of Sciences, USA

Adam G. Dolezal3*, Ashley L. St. Clair1, 2 *, Ge Zhang2, Amy L. Toth1, 2,

Matthew E. O’Neal2

1Iowa State University, Department of Ecology, Evolution, and Organismal Biology

2Iowa State University, Department of Entomology

3Univeristy of Illinois Urbana-Champaign, Department of Entomology

Authors’ contributions

AGD, ALS, GZ, ALT, and MEO designed the study. AGD, ALS, and GZ contributed

equally to data collection. ALS analyzed the data. AGD, ALS, ALT, and MEO wrote the

paper. All authors contributed critically to the drafts and gave final approval for publication.

*AGD and ALS contributed equally to this work and share co-first authorship on the

publication.

Abstract

Extensive production of agriculture can contribute to pollinator decline, exemplified by alarmingly high annual losses of honey bee colonies in regions dominated by annual crops

(e.g., Midwestern U.S.). As more natural or semi-natural landscapes are transformed into monocultures, there is growing concern over current and future impacts on pollinators. To forecast how landscape simplification can affect bees, we conducted a replicated, longitudinal assessment of honey bee colony growth and nutritional health in an extensively 75 farmed region where much of the landscape is devoted to production of corn and soybeans.

Surprisingly, colonies adjacent to soybean fields surrounded by more cultivated land grew more during mid-season than those in areas of lower cultivation. Regardless of the landscape surrounding the colonies, all experienced a precipitous decline in colony weight beginning in

August and ended the season with reduced fat stores in individual bees, both predictors of colony overwintering failure. Patterns of forage availability and colony nutritional state suggest that late season declines were caused by food scarcity during a period of extremely limited forage. To test if habitat enhancements could ameliorate this response, we performed a separate experiment in which colonies provided access to native perennials were rescued from weight loss and reduction in fat stores, suggesting the rapid decline observed in these agricultural landscapes is not inevitable. Overall, these results show that extensively farmed areas can provide a short-term feast that cannot sustain the long-term nutritional health of colonies; reintegration of biodiversity into such landscapes may provide relief from nutritional stress.

Keywords: honey bee, Apis mellifera, land use, agriculture, pollinators

Significance Statement

Industrial-scale production of crops through monocultures has resulted in “green deserts” of reduced biodiversity in many areas worldwide. Such simplified landscapes may impact ecosystem services such as pollination. Here, we present a large-scale, longitudinal study of managed honey bee colonies in the context of corn and soybean monocultures. Our results reveal a brief burst of colony growth during soybean bloom, followed by a longer period of forage dearth resulting in decline in several aspects of honey bee health at both 76

colony and individual levels. We demonstrate this decline is reversible when honey bees

have access to native, perennial plants (i.e. prairie). Our results suggest sustainable pollinator

management in landscapes dominated by monocultures can be achieved through reintegration

of native biodiversity.

Introduction

As human population grows (1), habitat loss from anthropogenic landscape changes

threaten the health and existence of many species (2). An ever-increasing demand for food and biofuels following human population expansion requires more land be dedicated to agricultural production (3, 4). Global land use has shifted to meet this demand, with natural areas and smaller scale agricultural enterprises transformed into high-yielding monocultures

(5-7), but with some cost (8). Monocultures can have substantial negative environmental effects on soil, water, and air quality, and when coupled with the removal of native, non-crop habitat, this form of agriculture is associated with declines in pollinator populations (9-13).

This conversion is provoking concerns for reduced pollination of crops and wild plants that could lead to reductions in agricultural production and ecosystem service delivery (14).

Worldwide, honey bees (Apis mellifera) are the most economically important pollinator of crops, with honey bee colonies in the United States alone responsible for over

$15 billion per year (10, 15). Like other bee species, honey bees are challenged by environmental stresses that reduce colony survival, with statewide losses as high 60% depending upon their location within the continental United States. This rate is higher than beekeepers consider sustainable (16-19), resulting in increased costs for contracted pollination services (15, 20). These losses are associated with multiple, potentially 77 interacting, stressors, including pest/pathogen pressure, pesticide exposure, and nutritional shortages (9, 11, 21, 22), all associated with anthropogenic influence (23, 24).

How do honey bees respond to landscapes that become increasingly dominated by extensive agriculture, particularly of crops considered to have limited nutritional benefit?

Nationwide surveys have shown some of the worst colony losses occur in the Midwestern

United States (16, 18, 25), a region of major agricultural production (5). Further, agricultural land use has been associated with lower amounts of protein in stored pollen (26), lower honey production (27, 28), and decreased physiological health of honey bees (29, 30).

Conversion of non-cropped land to crops has been linked to a decline in suitability for productive apiaries (4, 7) and several key metrics of honey bee health and productivity (31-

33) in the Northern Great Plains region of the U.S., where agricultural intensification has recently increased (4, 24, 34).

While the popular press has evocatively described regions that are agriculturally productive but devoid of biodiversity as “green deserts” (35), corn and soybean fields can host dozens of pollinator species (36). Further, increases in cropland can correlate with improvements in key honey bee growth metrics like food accumulation (37), as mass flowering crops or non-crop plants growing in field edges can provide forage for honey bees and wild bees (38-40). Thus, it remains unclear whether intensely farmed landscapes are overall net-positive or net-negative for managed pollinators such as honey bees. Studies of honey bees’ responses to crop production that do not explore seasonal exposure to landscape features may miss changes in phenology that can be significant for colony and individual 78

honey bee health. Determining the net effects of agriculture upon honey bee survival requires

multi-season, longitudinal studies of replicated, researcher-controlled colonies embedded in multiple agroecosystems.

Herein, we describe a comprehensive, longitudinal study of colony growth and bee nutrition in one of the most extensively farmed areas of the world, Iowa USA, a perennial leader in the production of corn and soybean (41), with 92.6% of the state dedicated to agriculture and 72.9% planted with annual crops (42). Despite this general lack of landscape diversity, variation in land use within the state can explain the abundance and diversity of key members of the insect community found within soybean fields (43-46). By placing bee colonies next to soybean fields and comprehensively studying their response to variation in land use surrounding these fields, we can understand how honey bees respond to a highly intensified agricultural landscape and begin to forecast the future of honey bee health in other regions undergoing similar agricultural intensification (4, 7, 24, 31, 32). Analogous longitudinal approaches can be used to assess intensification in other cropping systems.

We placed apiaries of four colonies adjacent to commercial soybean fields surrounded in a 1.6 km radius (7) by either a majority of cultivated cropland (average 83.9%

± 0.023 SEM corn and soybean; referred to as ‘high cultivation’) or minority of cropland

(average 38.2% ± 0.053 SEM corn and soybean; ‘low cultivation’). The remaining portions of these landscapes were comprised of more perennial, uncultivated features (i.e., woodland, grassland/pasture, urban development). We selected these two categories of land use as extremes within a range shown to affect the diversity and abundance of insect communities within soybean fields of Iowa (39-42). By mid-season apiaries within high cultivation landscapes had the greatest populations and heaviest hives. By the end of August, all 79

colonies, regardless of surrounding land use, declined precipitously, suggesting that, no

matter the surroundings, extensively farmed landscapes can be poorly suited for sustainable,

summer-long apiculture. We further demonstrate that this decline in colony health is

mitigated by providing colonies access to more diverse, native forage (i.e., prairie),

suggesting that the addition of flowering resources late in the growing season has the

potential to reverse some negative effects arising from the current landscape.

Results

Apiaries were heavier in landscapes with high cultivation than low cultivation

In both years, apiaries kept adjacent to soybean fields in high cultivation landscapes

were heavier (Figure 1a; F1, 17.38=5.66, P=0.0291), with marginally higher immature bee

populations (Figure 1b; F1, 17.33=3.63, P=0.0737), and higher adult bee populations (Figure

1c; F1, 8=6.53, P=0.0339) than those in low cultivation landscapes. All metrics of colony

growth varied significantly within a year (F12, 126.1=38.13, P=<0.0001; F12, 116.3=16.72,

P=<0.0001; F9, 72=31.12, P=<0.0001 for weight, immature population, and adult population respectively). We also detected interactions between cultivation category and sampling week for apiary weight (F12, 127.4=3.22, P=0.0005) and adult bee populations (F9, 72=5.74,

P=<0.0001), discussed below. However, weight (F1, 20.25=0.70, P=0.4121) and immature

population (F1, 34.79=3.08, P=0.0882) did not vary by year. We did not observe a significant

difference in nurse bee nutritional state, as estimated by lipid content (i.e. fat stores), between

cultivation categories (Figure 1d ; F1, 69=1.28, P=0.2625) or sampling years (F1, 69=1.22,

P=0.2735), and there was no interaction between landscape categories and sampling week

(F4, 69=0.65, P=0.6298). 80

Apiaries and individual bee health declined drastically in late summer

To further understand the temporal dynamics of colony growth and decline in light of

the interaction between cultivation category and weeks when weight was estimated, we

calculated rates at which apiaries gained weight (from initial weight to the seasonal

maximum), and lost weight (seasonal maximum to the end of our observations) (Figure 2).

Apiaries surrounded by high cultivation gained and lost weight at greater rates than those in

low cultivation landscapes (Figure 2). The rates of gain and loss were nearly identical within

a cultivation category (Figure 2). Apiaries in both cultivation categories began to lose weight

after 10 weeks at rates that were similar to the rates at which they gained weight, such that by

mid-October (week 43) all apiaries returned to their initial weight. Similar patterns of gains

and declines were observed in immature and adult bee populations (Appendix C, SI Table

S11). These declines began nearly two months before central Iowa normally experiences sub- freezing temperatures (47) that terminate all flowering resources; therefore, the significantly faster rate at which colonies lost weight in high cultivation landscapes may put them at an increased risk for nutritional deficit and overwinter starvation.

However, despite the differences in weight decline, lipid concentration of worker bees did not differ by cultivation category, but only by date (F4, 32.2=21.38, P=<0.0001).

Regardless of where apiaries were located, lipid content of nurse bees was highest at the

initiation of the experiment (week 26) and declined throughout the 22 weeks of our

monitoring (Figure 1d; Appendix C, SI Table S10). This is noteworthy as the final sampling

period occurred in mid-October, when honey bee colonies in temperate regions such as Iowa

enter a pre-overwintering stage commonly associated with increased lipid stores (48). 81

The type of forage used by apiaries did not vary by location, but varied during the season

Apiaries at every location began our experiment with the same average weight but reached different seasonal maximums, suggesting that variation in land use between the cultivation categories contributed to available forage. Honey stores are the greatest contributor to hive weight, derived from foragers focused on collecting nectar over other material (e.g., pollen, water, propolis) (49, 50). The design of this experiment does not allow us to determine how much a specific plant contributed to honey production, but there is indirect evidence suggesting several plants were nectar sources when colonies were gaining weight. Colonies in high cultivation landscapes were surrounded by significantly more soybean (and thus field edges) than those in low cultivation landscapes (Figure 3a;

T17.993=3.88, P=0.0011). Field edges are likely to contain a higher abundance of clover, a resource which has previously been identified as a significant source of nectar for honey production (37, 51). During our experiment, the period of greatest colony weight gain (Figure

1a) occurred when clover was in bloom (Figure 3b). However, this period also occurred when the majority of soybean fields adjacent to our apiaries were blooming (Figure 3b).

Although soybeans have been bred for self-pollination (52, 53), the flowers sometimes produce nectar used by honey bees for honey production (38, 54-56), though nectar production varies by cultivar and growing conditions (38, 57, 58). Nectar foragers incidentally come in contact with pollen during foraging (i.e., sticking to hairs (59)), and observations from stored honey within our colonies revealed traces of both soybean and clover pollen (Appendix C, SI Table S13). Although traces of both plants’ pollens were present in honey, these observations do not allow us to determine when and to what degree a 82

single plant contributed to overall honey production. Overall, these observations suggest that

colonies in the high cultivation landscapes may have grown heavier and at a faster rate

because more nectar forage was available.

Conversely, apiaries in the low cultivation landscapes may have had more alternative

sources of forage available later in the season such that their weight loss occurred at a slower

rate than those in the high cultivation landscapes. We tracked the collection of pollen by

colonies in these apiaries to determine if this type of forage provided insight into whether

flowering resources varied by cultivation category. We did not observe differences in the

amount of pollen collected between the landscape categories (Figure 3c; F1, 17=0.06,

P=0.8121), nor the pollen types collected (F2, 36=0.60, P=0.5525). No soybean pollen was detected in pollen traps at any apiary. Pollen was collected primarily from clover (Trifolium spp.) (61.9%) and secondarily from partridge pea (Chamaecrista fasciculata) (20.9%), with the remaining 17.2% comprised of 25 species (i.e., trace pollens).

Although our analysis of pollen collected by honey bees did not help explain potential differences in forage availability between the two landscape categories, they provided insight into why apiaries in both categories lost weight at the same time. Clover (Trifolium spp.) was the most common pollen source for our apiaries and is also a common nectar source for honey bees in the U.S. (51), and is likely to have contributed substantially to differences in colony weight. Flower production of both clover and soybean declined dramatically by week

33 (Figure 3b). Without a substantial source of flowering resources during late August and

September (weeks 33-38), honey bees would be left with only their stored honey and pollen as a food source. The larger colonies in the highly cultivated landscapes may have lost 83

weight at a faster rate than those in the lower cultivated landscapes simply because their

greater populations consumed their honey stores at a faster rate than smaller colonies.

Providing colonies access to prairie reverses late summer declines in weight and lipids

We conducted a separate experiment in summer 2016 to determine if declines in honey bee weight and health could be prevented by providing access to prairie habitat. We selected prairie because it is comprised of flowering plants that bloom during the late summer to early fall and are not commonly found in purely agricultural landscapes. Many prairie plant species are attractive to pollinators, and a subset bloom when we observed colony decline in our first experiment (60).

For this experiment, we focused on honey bee colonies as the experimental unit, and used ten colonies of similar population and weight, established at an agricultural location

(Bee and Wasp Research Facility, Iowa State University Horticulture Station, Ames, Iowa;

44% cropland in surrounding landscape). After three consecutive weeks of weight loss after the mid-summer mass peak, a random selection of five colonies were moved to a reconstructed tallgrass prairie, with the remaining colonies kept at the agricultural site. After the move to prairie, these colonies not only ceased losing weight (Figure 4a), but became heavier than those remaining at the agricultural site on weeks 38 (T72=2.87, P=0.0054), 39

(T72=3.18, P=0.0022), and 40 (T72=2.56, P=0.0127) (Appendix C, SI Table S15). Colonies

remaining at the agricultural site continued to decrease in weight and ended the season significantly lower than their summer maxima (Figure 4a; average 34.81 kg ±5.096 SEM,

T76=3.76, P=0.0209). In contrast, colonies with access to prairie ended the season with a

weight that reached their summer maxima (Figure 4a; average 50.08 kg ± 5.327 SEM,

T76=0.74, P=1.000; Appendix C, SI Table S16). In addition, colonies placed in prairie 84 contained nurse bees with significantly higher lipid content at week 40 than those that remained at the agricultural site (Figure 4b; T30=3.01, P=0.0053). While we cannot definitively tell what plants bees foraged on in the prairie, we report a qualitative list of flowering forbs observed at this site and their approximate bloom times in Appendix C, SI

Table S17.

Discussion

Overall, our results demonstrate that some highly cultivated landscapes can provide short-term gains in colony growth but can also fail to support colony health across the entire growing season, especially in the critical pre-overwintering period. This longitudinal perspective on honey bee health helps to clarify the dynamics of honey bee responses to landscape and forage availability, especially given previous, sometimes conflicting reports suggesting both positive and negative impacts of extensive farming on honey bee health (7,

33-36).

In the Midwestern US corn and soybean system, we found that apiaries located in landscapes with higher cultivation accrued greater weight, higher immature bee populations during peak season, and higher adult bee populations than those kept in areas with less cultivation (Figure 1a). The rate at which apiaries added weight was greatest in landscapes with more soybean (Figures 2, 3a), and occurred during the bloom period of soybean and clover (Figure 3b), suggesting that soybean and clover are sources of nectar for honey bees in central Iowa. Our observations of soybean and clover pollen in samples of honey collected at all locations provides further evidence that honey bees utilize both as nectar resource (59), although it is unclear what their relative contributions are (Appendix C, SI Table S13). It is 85

also notable that no corbicular soybean pollen was ever detected in pollen traps, suggesting

that any foraging on soybean was only for nectar; i.e., trace amounts of pollen are known to

fall into nectar or incidentally attach to nectar foragers, where it then is incorporated into

honey at low levels (59). Furthermore, we observed the greatest weight loss in apiaries within

high cultivation landscapes (Figure 2) after cessation of soybean and clover bloom. Although

apiaries in low cultivation landscapes had access to more grassland, woodland and developed

land (Figure 3a), which is more likely to contain alternative sources of forage (i.e., something

other than soybean or clover), this did not prevent a late season weight loss. These data therefore demonstrate a “critical period” of limited forage availability in late summer and early fall that is present in areas of both high and low cultivation.

In both high and low cultivation landscapes, colonies relied upon a startlingly limited number of plants, primarily clover (Figure 3c), for pollen, suggesting agricultural landscapes as a whole do not provide a diverse pollen resource for bees. Bees use pollen as their primary source of proteins, lipids, and micronutrients (50); further, honey bees are generalist pollinators and prefer mixed-pollen diets (61). Polyfloral pollen diets are associated with longer honey bee lifespan (62, 63), increased resilience against pathogens (64-66), and can interact with their response to pesticide exposure (67). Colony reliance on a limited pollen diet may contribute to honey bees stress in agroecosystems; first, access to pollen only occurs for part of the season; second, even when pollen is most abundant, the lack of diversity may produce colonies that are less tolerant of other stressors (21).

While we report evidence consistent with studies that reveal a positive response between annual crop production and colony health (25, 35-38), the uniform decline late in the growing season supports the findings of other studies suggesting that agricultural lands are 86 detrimental to bee health (24, 27-32). While honey bees can survive long periods of forage dearth, like winters in temperate climates (68), the responses we observed are not consistent with healthy colonies. By October, before the overwintering period has begun for central

Iowa, colonies had lost on average 53% of their total maximum weight, bringing their food stores to a dangerously low level unlikely to allow survival during the winter in a temperate climate (69), let alone produce a harvestable honey crop. Further, the lipid content of nurse bees at the end of the season was reduced (Figure 1d), suggesting individual bees were not transitioning to a physiological state for successful overwintering. By the end of the growing season, adult bees in an overwintering state should have high fat stores (68, 70); for example, experimentally-stimulated winter bees exhibit 43-59% higher lipid stores than summer controls (48). In contrast, the lipid concentration for bees kept in both of our cultivation categories changed in similar magnitude, but in the opposite direction, declining by 49% from June to October. Even if colonies were able to reduce populations to a level that could survive on the existing stored resources, or if supplemental food source (e.g., sugar solution) were added, the remaining bees may not be physiologically capable of surviving. To what extent the colonies we tracked in these experiments capture the physiological state of commercially-managed honey bees is not clear, as we did not provide a supplemental food source, a common practice for managed colonies experiencing a lack of forage.

Is decline during this period inevitable, or can land management practices be implemented to arrest or ameliorate the reduction in colony food stores and physiological health? Our colony relocation experiment revealed that providing declining colonies with access to tallgrass prairie reversed this trajectory. Although much of the upper Midwest was covered in tallgrass prairie before settlement by Europeans, very little currently remains. 87

Before European settlement, Iowa was approximately 80% prairie, but is now only 0.1%,

with most of this lost by early 20th century (71, 72). Plants native to prairies are highly

attractive to bee pollinators (60), and when grown in a mixture, attract a more abundant and

diverse community of pollinators than cultivated features of an agricultural landscape (73).

Small patches of prairie (1-4 hectares) embedded within annual crop fields increase

pollinator abundance along with improvement to other agriculturally-related ecosystem services (74). Previously, it was unclear how beneficial native plants are to the health of the exotic honey bee (75). Our results confirm that a habitat comprised of native plants can be used as forage by honey bees at least during this late-summer dearth period to counteract colony weight loss (Figure 4a) and reverse the changes in physiological health of putative nurse bees (Figure 4b).

Pesticide exposure is a significant stressor experienced by bees in agricultural landscapes (9), and since 2000, insecticide use on soybean has increased, due in part to the invasive soybean aphid (76, 77). Although we did not control for insecticide use within our experiments, we did not observe evidence of direct, lethal exposure to insecticides in any of our colonies. On the contrary, colonies performed better in areas of higher cultivation, particularly during a period when insecticide use to prevent aphid outbreaks is recommended

(i.e., the flowering period of soybeans (76)). Furthermore, no foliar insecticides were applied to any of the adjacent soybean fields, though applications could have occurred in the surrounding landscape, possibly leading to sub-lethal exposure. Thus, we cannot rule out a possible interaction between sub-lethal exposures to insecticides and forage availability 88 contributing to the nutritional deficiencies in nurse bees. Future experimental work is needed to better understand the interaction between nutritional stress and sub-lethal pesticide exposure in a field setting.

Conclusions

In 2016, Iowa was planted with 5.62 million ha of corn and 3.84 million ha of soybean (78), making it the top producer of both crops by dedicating the highest percentage of its landscape (72.9%) to their production compared to any other US state (79). This extreme example of crop production represents a worst-case scenario for studying how landscape transformation can affect the food supply for bees, with a majority of the landscape taken up by crops that provide limited floral resources. This transformation is occurring elsewhere, with important beekeeping states like North and South Dakota increasing their production of both corn and soybean in the last decade (4). While our focus is on honey bees, many other insect fauna are likely impacted by the conversion of perennial grassland for annual crop production (80).

With a loss of floral resources and increased risk of insecticide exposure associated with the production of many annual crops, is such a landscape no longer tenable for honey bees or other pollinators? Our results show that agricultural intensification can result in honey bee colonies experiencing poor nutritional conditions, particularly in the late summer and autumn, and dependency on a limited number of floral resources that grow primarily in field edges around agricultural farms. We addressed these deficiencies by providing honey bees access to prairies; exposure to a diverse, late summer forage successfully reversed the sharp decline in weight and improved bee lipid stores, a key to successful overwintering (48, 89

68, 81, 82), suggesting that even in the most extreme landscapes, colony decline is not inevitable if patches of suitable habitat are available (24).

Our data do not allow us to determine the relative contribution of overall forage availability or increased forage diversity to these benefits. While honey bees can survive on low diversity diets, they perform best on mixed plant sources (50, 62, 83, 84), and more diverse pollen may improve resilience to pathogens (64, 65) and pesticides (67, 85). Thus, providing honey bees late summer forage in the form of prairie could improve the food accumulation (as witnessed by increased colony weight), the physiological health of their bees (increased lipids), and potentially increase their resilience to other stressors. Efforts to apply these findings should address to what extent the amount and diversity of forage independent of each other affect honey bee productivity and health. Further elucidation of nutritional deficiencies within agricultural landscapes can help inform efforts to efficiently improve pollinator habitats within extensively farmed regions. Moreover, moving honey bee colonies to the limited patches of prairie available in these systems is likely not a sustainable remedy; however, efforts to integrate native vegetation into and around agricultural fields may improve honey bee health while providing other benefits (24, 74).

Methods

Site Selection

To determine how honey bees respond to varying levels of crop cultivation (high vs low), we conducted a landscape-level study in which apiaries of four colonies were assigned to soybean fields embedded within landscapes of varying amounts of crop production. For this experiment, each site was an experimental unit. Because soybean is annually rotated with 90 corn, new sites had to be selected each year, for a total of ten site-years for each cultivation category. In 2015 and 2016, we screened Iowa State University (ISU) and privately-owned commercial soybean farms in Story, Boone, Marshall, and Hardin counties, Iowa, USA to locate farms which were greater than 20 ha and did not have honey bee colonies within 1.6 km (Appendix C, SI Table S1). Land use surrounding each farm was quantified in ArcGIS,

ArcMap 10.3.1 using a 1.6 km radius centered on the apiary location. Land use features were based on the USDA-NASS cropland data layer for 2015 and 2016 at a 30 m x 30 m resolution (https://nassgeodata.gmu.edu/CropScape/). Using the ‘isecpolyrst’ function in

Geospatial Modeling Environment (Version 0.7.4.0) the proportion of all landscape feature classes were identified by counting pixels associated with each land category (Appendix C,

SI Table S2). Land use types were categorized into four groups (cropland, developed, grassland, and woodland; Appendix C, SI Table S3). On average, corn or soybean was 95.4% of crop cover in the cropland category. We selected a subset of farms classified into two distinct categories – high cultivation and low cultivation, defined by the percentage of land in the 1.6 km radius dedicated to corn and soybean production. Farms surrounded by >73%

(average 83.9% ± 0.023 SEM) corn and soybean were considered in a landscape of ‘high cultivation’, while those surrounded by <53% (average 38.2% ± 0.053 SEM) corn and soybean production were considered in a landscape of ‘low cultivation’. For comparison,

91% of the land in Story County is dedicated to farm use with 73.2% planted with crops (86);

Iowa as a whole dedicates approximately 92.6% to farm use with 72.9% planted with crops

(42). Therefore, our low cultivation sites were surrounded by substantially less crop production than the region as a whole. Additionally, the proportions of developed land, grassland, and woodland were all significantly higher in low cultivation sites compared to 91 high cultivation sites (Figure 3a). Each year we randomly chose fields that fit both categories, and then we randomly selected a subset of five fields per category, to serve as sites for our experimental apiaries. Each site was separated by at least 3.2 km to help ensure that honey bees foraged only within the landscape defined by that category, allowing use to assume each site was an independent experimental unit.

All soybean fields managed weeds with glyphosate and were planted with seed- applied pesticides; ISU-managed fields were planted with a fungicide only (Fluopyram,

ILeVO, Bayer, Pittsburgh PA), while privately managed fields were planted with an insecticide and fungicide (imidicloprid and Fluopyram, respectively; Acceleron seed treatment, Bayer, Pittsburgh PA). We transported apiaries to farms in June after 90% of the corn and soybean had been planted in Iowa, which would reduce honey bee exposure to dust contaminated with neonicotinoids originating from seed treatments. No insecticides were applied to foliate at any of the soybean farms or farms directly surrounding our apiaries. All fields were in a corn and soybean rotation with corn planted in the previous year.

Hive source and apiary management

In 2015, all colonies were started from packages sourced from C.F. Koehnen & Sons

LLC, Northern California, USA and purchased via a local honey bee broker. All packages contained 0.9 kg of adult bees and an Italian (Apis mellifera ligustica) queen from the same source, and were started on bare plastic foundation in a 10 frame Langstroth hive, on 24

April 2015. All packages were installed at the same day at the Bee and Wasp Research

Apiary, Iowa State University Horticulture Station, Ames, Iowa. Each colony was kept on a four-colony pallet, similar to those used in the migratory beekeeping industry. Each pallet represents an apiary that could be moved to a given soybean farm. After 4 weeks, colonies 92

were inspected for growth, and then equalized such that each apiary received the same

quantity of colonies, bees, drawn frame, and pupae, and that every colony had a healthy laying queen. In the first week of June, each pallet was randomly assigned and transported to a site within a single day. The day before transportation each colony was inspected to determine starting metrics (see below). All apiaries were placed 3 m from the edge of a soybean field. In 2016, this protocol was repeated with the exception that colonies were derived from the overwintered colonies from the 2015 experiment and colonies were started on fully drawn comb from the previous year rather than bare foundation. In 2016 apiaries were fully equalized to the same size as 2015 and each colony was provided with a new A. mellifera ligustica queen purchased from the same provider used in 2015.

Apiary inspection regime

At each site, apiaries were inspected on a biweekly basis from June-October in 2016, and in 2015, biweekly during June-August and monthly during August-October. During each inspection, each colony within an apiary was weighed and additional hive boxes were added when those present reached approximately 75% capacity. The mass of these additional hive boxes was weighed before inspection, allowing the calculation of weight added by bee- forage only. Immature bee population was estimated by capped pupae area (cm2) in each colony via photography in 2015 and with a Plexiglas grid screen in 2016 (87). In 2016, adult bee populations were estimated based on fractional estimates of sides of a frame covered in bees (i.e., ‘frame sides’; (87). At each inspection, queen presence was determined by observation of the queen or eggs in a colony; if the queen was determined to be absent, a new queen from the same source was provided within 1 week. Monthly quantification of Varroa desctructor mites was performed via alcohol wash (88). At the beginning of all experiments, 93 mite load (mites per 300 bees) for every colony was zero. Mite levels remained below this threshold throughout the season, but thymol (Apilife Var; Mann Lake, LTD) was applied beginning in the last week of August to prevent mite infestation from confounding the effects of our experimental (30). During each inspection, a 15 mL tube was filled with worker bees collected from frames of exposed larvae (i.e., putative nurse bees), placed on ice and transported back to the laboratory and frozen at -80° C until further processing. In addition to assessing each colony at an apiary, the adjacent soybean field was assessed for its growth and development using methods developed by (Appendix C, SI Figure S1), to determine when and to what extent the crop was blooming.

Lipid content quantification

To measure colony lipid levels of nurse bees, sampled bees from each date were processed via the protocol of Toth and Robinson (89) as modified in Dolezal et al. (30).

Approximately 50 nurse bees, by mass, were homogenized in liquid nitrogen, and approximately 0.25 g of homogenate was subsampled and weighed. Lipid content was quantified via phosphor-vanilin spectrophotometric assay and lipid calculated as mg lipid/mg bee mass.

Pollen collection and quantification

To quantify pollen collected by honey bees in each cultivation category, a colony was randomly chosen within each apiary to receive a pollen trap (Brushy Mountain Bee Supply,

Wilsonville, OR). This trap was attached to the front of the hive and requires foraging bees to pass through a plastic plate which releases pollen from the bees and is collected in a pan.

Although pollen collection may vary by colony (32), pollen traps were only added to one 94 colony per apiary to reduce overall stress to colony growth at an apiary. Each trap was open for 24 h each week during June-October.

A sub-sample of 2 g was extracted from each pollen sample collected on each day and sorted by pellet color. The sorted pellets were weighed, dissolved in Caberla’s solution with fuschin dye and mounted onto glass slides (90). Pollen was identified to the lowest possible taxonomic unit or morphospecies using light microscopy to observe morphological features. To validate pollen identification, pollen was also collected from all flowering plants found near each site during collection days and compared to mounted specimens.

In 2015, we found clover pollen to be the most abundant pollen collected by honey bees (60.4%) in the pollen traps. To assess when clover was blooming, we created two 10 m2 plots around a patch of white clover (Trifolium repens) at the Bee and Wasp Research

Apiary. We sampled blooms per m2 once per week starting 12 July (week 29), when clover blooms were at maximum abundance, and continued through 6 September (week 37).

Prairie access rescue experiment

To evaluate if the decline in honey bee health metrics (see results) could be prevented or reversed, we kept a separate set of colonies (n=10) at an independent agricultural site (ISU

Bee and Wasp Research Apiary) in 2016 monitored changes in weight beginning 15 July.

Unlike our first experiment, in which the site was the experimental unit, herein the colony was the experimental unit, with the treatment being the availability of late-summer forage.

Colonies were sourced and maintained as described above with the exception that inspections occurred weekly and did not include brood or bee assessments. A sample of putative nurse bees was collected (see apiary inspection above) biweekly to assess individual lipid content.

Three weeks after colonies reached their peak weight (28 July), half (n=5) were randomly 95

selected and moved to a reconstructed tallgrass prairie located in the Chichaqua Bottoms

Greenbelt, Polk County, Iowa, approximately 48 km from the ISU Bee and Wasp Research

Apiary. This location was selected because it is 55.8 ha of contiguous prairie that contain

species that bloom during August and September, with several species considered highly

attractive to pollinators, including honey bees (60). This location was not insulated from crop

production, as 36% of the land within 1.6 km from the colonies was comprised of corn and

soybean. Colonies were inspected weekly until 29 September when all were moved back to

the research apiary in preparation for overwintering. Though it is not quantifiably

comparable to pollen trap data from the other experiments, we qualitatively assessed the

presence and blooming status of flowering forbs present along a 60 m linear transect at this

site on a weekly basis from 26 July to 19 September 2017. A blooming forb was considered

any plant with at least one stem in anthesis within 10 m on either side of the transect.

Statistical Analysis

Apiary growth and lipid content in cultivated landscapes

All statistical analyses were performed using SAS 9.4 to investigate how apiaries

responded to the two levels of cultivation, by performing repeated measures analysis within a

linear mixed effect model (PROC GLIMMIX). To include site-years in one analysis, we

binned all inspection days by calendar week across the two years because measurements

taken during 2015 and 2016 did not always take place on the same calendar date and

frequency. We avoided pseudo-replication in estimating the impact of cultivation category on honey bees by averaging individual colony metrics for each apiary, creating a single site- level metric (91) as site was the unit of replication. 96

We created models to estimate the response of weight, immature bee population, and

adult bee population (2016 only) with cultivation category, week, year, and cultivation

category/week interaction as predictor variables and site as a random variable. Due to crop

rotation, we selected new sites each year within the two cultivation categories preventing us

from exploring year by site interactions. To ensure that neither mite load or queen loss during

the season had an effect on colony growth metrics, we first ran mixed models including mite

values and a binary measure of queen presence along with the predictor variables listed

above. We did not observe an effect of mite load or queen presence on any growth metrics

(Appendix C, SI Table S4), therefore, we removed them from the models to avoid rank

deficiency. The effect of year was removed from all analyses of adult bee population because

bee population was measured in 2016 only. Colony weight, immature bee population, and

adult bee population were analyzed within a linear mixed-models to identify which dates

differed using a simple effects comparisons of least square means (weight, Appendix C, SI

Table S5; immature bee population, Appendix C, SI Table S6; adult bee population,

Appendix C, SI Table S7). We performed additional repeated measures analyses within a linear mixed effect model investigating the response of apiary weight, immature bee population, and adult bee population (2016 only) using percent landscape categories (e.g. cropland, grassland, woodland, developed) as continuous variables. Due to collinearity not all features could be examined in the same model. Cropland was most correlated with other

features of the landscape therefore we ran a model with cropland, week, year, and

cropland/week interaction as predictor variables and site as a random variable. We performed

a separate model with grassland, woodland, developed land, week, year, and all interactions

of landscape type and week as predictor variables and site as a random variable. Results from 97 this analysis were analogous to cultivation category, with percent cultivation in the landscape being positively correlated with weight, immature bee, and adult bee populations (Appendix

C, SI Table S8).

To explore the effects of high and low cultivation on colony level lipid content of nurse honey bees, we created a similar model as above with cultivation category, week, year and the cultivation category/week interaction as predictor variables and site as a random factor. From this model, least square means were used to make multiple comparisons with

Tukey HSD adjustment to identify which weeks and cultivation categories exhibited significantly different colony nurse bee lipid levels (Appendix C, SI Table S9). Lipids from every date were not evaluated in the model; rather, key dates were chosen to represent starting level, two mid-season time points, and end of season lipid content. One colony from each apiary was randomly chosen to analyze lipid content. Lipids from the month of June

(starting level) were sampled from week 24, July (early-season) lipids were sampled from week 28, August (mid-season) lipids were sampled from weeks 32 and 34, and October (end of season) lipids were sampled from week 43 for 2015 and 2016. Due to the fact that sampling periods across years did not always line up, samples from the month of August did not occur on the same weeks; rather, week 32 lipids are from 2015 and week 34 lipids are from 2016. All lipid content is represented as percent lipid (mg of lipid per mg of bee mass).

Honey bee colony growth and lipid content over time

To identify when colony growth began to decline for both high and low cultivation categories, post-hoc multiple comparisons of least square means from cultivation category and week were performed on the respective linear mixed effects models as described above with a Tukey HSD adjustment (Appendix C, SI Tables S5-S8). In addition to post-hoc 98

comparisons, we were also interested in the overall rate of weight growth and decline from

the initiation of the experiment to peak growth and from peak growth to pre-overwintering at

the end of the season. To investigate the difference in these rates over the season in colonies

in high and low cultivation landscapes, we adjusted our linear mixed effects model from

above to include month as the binning system as a replacement for week; i.e., all weeks

within a month were combined for one month average of colony weight. Using estimates from the respective model we calculated rates of growth as the slopes of the linear trend from

May to July and rates of decline as the slopes of the linear trends from August to October.

We used absolute values of slopes to perform multiple contrasts to compare growth rates vs the decline rates within and across cultivation categories (Appendix C, SI Table S11). To explore changes in colony lipid content over time regardless of cultivation category we made multiple comparisons of least square means of weeks from the mixed linear model above

(Appendix C, SI Table S10).

Forage resources used by honey bees (pollen availability and prairie access rescue experiment)

To confirm that apiaries placed at sites of either high and low cultivation landscapes had access to different amounts of landscape features, we performed t-tests (PROC TTEST) comparing each of the land use types (corn, soybean, grassland, woodland, developed). To test the effects of cultivation category on total pollen and type of pollen collected, a linear mixed effect model was performed with cultivation category, pollen type (clover, partridge pea, trace pollens), year, and the cultivation category/pollen type interaction as fixed effects with site as a random factor. 99

To test the effects that prairie had on colony weight and on lipid content in the late

season, linear mixed models were performed (PROC GLIMMIX) as listed above with

landscape (prairie vs agricultural site) and week as predictor variables and with change in

weight from maximum summer weight as the response variable.

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106

Tables and Figures

35 A 30 ** ** ** 25 ** 20 15 ** 10

5 Mean weight (kg) 0

500 B ** ** 400

300

200

Population (cm²) 100

Mean immature bee 0 22 24 26 28 30 32 34 36 38 40 42 Calendar Week May October Low-Cultivation High-Cultivation

Figure 1. High cultivation landscapes result in better bee health metrics, but all experience late season declines. Apiary-averaged hive weight (A), immature bee population (i.e. capped pupae) (B), adult bee population (C), and percent lipid content (D) of colonies kept in soybeans surrounded by high (N=10; solid red lines and triangles) and low cultivation (N=10; dotted purple lines and circles) landscapes over the growing season, mean ±SEM. Weight and adult bee population were significantly higher overall in high cultivation landscapes while immature bee population was only marginally higher. Results based on repeated mixed model ANOVA with post hoc Tukey comparisons. For lipid content all weeks include one colony from each site from each year (totaling 10 per cultivation category) with the exception of week 32 which includes only 2015 (N=5 per category) and week 34 which includes only 2016 (N=5 per category) while weight, immature population, and adult population include four colonies per location (totaling 40 colonies per cultivation category) on each week. Results based on mixed model ANOVA with post hoc Tukey comparisons; ** P<0.05, *** P<0.0001 for statistical difference between cultivation category at a specific week (Appendix C, SI Table S5, S6, S7, S8 for weight, immature population, and adult population, as well as nurse bee lipid content respectively).

107

35 C *** ** 30 ** 25 ** 20

15 10 5 0 (frame sidesof bees) 22 24 26 28 30 32 34 36 38 40 42 population Mean adult bee 4.5 4 D 3.5 3 2.5 2

lipid content 1.5 1 Meannurse % bee 0.5 0

Calendar Week May October High Cultivation Low Cultivation Figure 1 Continued. 108

16 A 14 A 12

B 10 B 8 (kg/month) 6 4 Absolute rate of weight change 2 0 Rate of Growth Rate of Decline

High Cultivation Low Cultivation

Figure 2: Apiaries in high cultivation landscapes grow and decline at a faster rate Apiary-averaged absolute rate of weight growth and decline in colonies surrounded by high (red solid bars) and low cultivation (purple and pattern bars) landscapes in 2015 and 2016, mean ±SEM. Rate of growth includes all time points from week 22 - 30 calculated by months (May, June, July). Rate of decline includes all time points from week 32 - 43 calculated by months (August, September, October). Results based on mixed model ANOVA with post- hoc contrasts within and across treatments; letters represent significance between cultivation categories P<0.05 (Appendix C, Table S11).

109

A 60%

50% ***

in 1.6 km 40% ** 30% ** ** ** landscape landscape 20%

10%

0% % Mean Corn Soybeans Other Crops Developed Grassland Woodland High Cultivation Low Cultivation 100 120

90 B 80 100 70 80 60 50 60 40

m² bloom/

40 clover in flowers 30 20 20 No. % fields in soybean bloom soybean in fields % 10 0 0 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Calendar Week June September

Figure 3. Even though land use differs significantly, pollen collection is driven by few plants. (A) Site averages of percent landscape features within 1.6 km radius of apiaries in high (red solid bars) and low cultivation (purple pattern bars) landscapes in 2015 and 2016, mean ±SEM. There was significantly more corn and soybean in high cultivation landscapes. Low cultivation landscapes consisted of significantly more developed land, low forb diversity grasslands, and woodland. Results based on t-tests; ** P<0.05, *** P<0.0001 for statistical difference between cultivation categories for each landscape feature. (B) Percentage of fields in soybean bloom (green and filled area) across the season in 2015 and 2016 (represented in left axis). Number of clover blooms present per meter square (solid lines and circles) in 2016 transect (represented in right axis). (C) Mean colony collected pollen grams in high (red solid bars) and low cultivation (purple pattern bars) landscapes in 2015 and 2016, mean ±SEM. There were no significant differences in total pollen collected by cultivation category, however, significantly more clover pollen was collected in both landscapes compared to partridge pea (T36=4.68, P=0.0001) and trace pollens (T36=5.57, P=<0.0001). Partridge pea and trace pollen amounts did not differ (T36=1.97, P=0.6513). Results based on mixed model ANOVA with post hoc Tukey comparisons; no significance was detected between cultivation categories for each pollen type collected (Appendix C, Table S12).

110

120 C A AB

) 100 80 collected collected BC 60 C C C pollen 40

per location (g 20 Mean Mean 0 Clover Partridge Pea Trace Pollen High Cultivation Low Cultivation Figure 3 Continued.

111

8 A 6 ** 4 ** 2 ** 0 -2 32 33 34 35 36 37 38 39 40 -4 -6 from maximum (kg) -8 -10 Mean change in colony colony in change Mean

weight -12 -14 Move to prairie

3.5 ** 3 B

2.5

content 1.5 lipid 1 bee nurse % Mean 0.5 0 32 33 34 35 36 37 38 39 40

Prairie Site Agricultural Site Calendar Week July September

Figure 4. Access to prairie arrests and reverses late season declines in hive weight and body quality. Change in weight of colonies from maximum summer mass (A) and colony percent lipid content of nurse bees (B) moved to a prairie or remaining in an agricultural site from July to September of 2016, mean ±SEM. Results based on repeated measures mixed model ANOVA with post hoc Tukey comparisons; ** P<0.05 for significance between landscape at a specific week.

112

CHAPTER 5. DIVERSIFIED FARMING IN A MONOCULTURE LANDSCAPE: EFFECTS ON HONEY BEE HEALTH AND WILD BEE COMMUNTIES

Modified from a paper in first revision at Environmental Entomology

Ashley L. St. Clair1, 2, Adam G. Dolezal3, Matthew E. O’Neal2, Amy L. Toth1, 2

1Iowa State University, Department of Ecology, Evolution, and Organismal Biology

2Iowa State University, Department of Entomology

3Univeristy of Illinois Urbana-Champaign, Department of Entomology

Authors’ contributions

AGD, ALS, ALT, and MEO designed the study. ALS collected the data. ALS analyzed the

data. AGD, ALS, ALT, and MEO wrote the paper. All authors contributed critically to the

drafts and gave final approval for publication.

Abstract

In the last century, a global transformation of Earth’s surface has occurred due to human activity, with extensive agriculture replacing huge areas of natural ecosystems.

Concomitant declines in wild and managed bees are occurring, in part due to a lack of floral resources and inadequate nutrition, caused by the conversion of land into monoculture-based farming. Diversified fruit and vegetable farms may provide enhanced floral resources with a wider variety of crops and weedy plants which have the potential to sustain human and bee nutrition. We hypothesized fruit and vegetable farms can enhance honey bee (Hymenoptera:

Apidae, Apis mellifera) colony growth and nutritional state over a monoculture of soybean, as well as support a more diverse bee community. We tracked managed honey bee colony 113

growth, nutritional state, and wild bee abundance and richness in both farm types. Honey

bees kept in diversified farms had increased colony weight and pre-overwintering nutritional

state. Regardless of colony location, bees still experienced precipitous declines in weight

during autumn, and were thus not completely buffered from the stressors of living in a matrix

dominated with monocultures. There were no overwhelming effects of diversified farming on

the wild bee community. A group of agriculturally adapted bees were found commonly

throughout both farm types. Bee which were classified as “uncommon” were more speciose on fruit and vegetable farms. Overall, these results suggest diversified fruit and vegetable farms may provide some benefits for a subset of bees, including honey bees, living in regions where annual crop production is widely practiced. However, incorporation of more natural habitat may be necessary to support bees into the late season.

Keywords: wild bee, honey bee, Apis mellifera, diversified farming

Introduction

Bees are an essential component of ecosystems providing a pivotal service through the pollination of a wide variety of plants, including economically important crops (Winfree et al. 2008, 2009; Potts et al. 2010a, Ollerton et al. 2011). However, wild bee populations have declined at local and regional scales (Banaszak 1992, Steffan-Dewenter et al. 2002,

Kremen et al. 2004), and managed honey bees are also facing high colony losses (Aizen and

Harder 2009, Potts et al. 2010b, Steinhauer et al. 2014).

Wild and managed bees are affected by interacting environmental stressors, such as diseases, inadequate nutrition, and exposure to pesticides as a result of agricultural intensification (Oldroyd 2007, Naug 2009, Goulson et al. 2015). Worldwide, habitat 114

conversion due to transformation of landscapes into row-crop agricultural systems is cited as a primary driver of wild and managed bee declines (Koh et al. 2016, Sánchez-Bayo and

Wyckhuys 2019). For example, conversion of natural habitat into extensive row crop agriculture in the Midwestern U.S. is associated with reduced wild bee populations (Koh et al. 2016) and reduced habitat suitability for honey bees (Otto et al. 2016). In the U.S., beekeepers lose up to 40% of their honey bee colonies annually. In the Midwest these losses can exceed 60% (Seitz et al. 2016).

Land used for agriculture can reduce natural and semi-natural habitat creating a scarcity in floral diversity and abundance that affects pollinator abundance (Kremen et al.

2007, Isaacs et al. 2009, Potts et al. 2010a) and health (Naug 2009, Winfree 2010). Although mass-flowering monocultures may provide transient forage for some bee species (Westphal et al. 2003, Jauker et al. 2012, Holzschuh et al. 2013, Todd et al. 2016), the simplified landscape and post-crop bloom results in a paucity of floral abundance (Kremen et al. 2002,

Klein et al. 2007). Such loss of resource diversity can lead to sub-optimal bee nutrition resulting in a compromised bee immune system and poor overall health (Dolezal et al. 2019).

The Midwestern USA has been identified as a critical focus region for pollinator declines (Koh et al. 2016), due to the extreme simplification that came with the production of corn and soybean in monocultures. In the Midwestern state of Iowa, 85.5% of the landscape is committed to farming (NASS-USDA 2017), primarily to produce corn and soybean

(Brown and Schulte 2011). Despite this, a diverse community of wild bees (at least 40 species of Apoidea) has been observed in corn and soybean within Iowa (Gill and O'Neal

2015, Wheelock and O'Neal 2016, Wheelock et al. 2016). Historical records of bee diversity in this region are limited; thus, it is unclear to what extent this community represents the 115

possible bee diversity in this region. To date, 300 bee species have been reported to currently

reside in the state of Iowa (DNR 2018), suggesting a richer community is present. Given this

extant community and the extreme form of agriculture practiced within Iowa, this is an ideal

location to study bee responses to human-driven ecological change.

Pollinator responses to landscape complexity and non-crop resources have been a

subject of several studies (Ricketts et al. 2008, Batary et al. 2011, Garibaldi et al. 2011,

Shackelford et al. 2013, Crist and Peters 2014). However, few studies have investigated the

response of managed honey bees and wild bees within the same context (Shackelford et al.

2013, Mallinger et al. 2017). Although wild and managed bees are affected by similar

stressors, wild bees encompass thousands of species with varying life histories (Michener

2007); therefore, their responses to stress may differ from managed bees depending on their

individual foraging preferences within a specific landscape. Understanding how managed

and wild bees cope with different types of agricultural landscapes is necessary for the

creation of effective conservation plans.

Historically, farming included the production of several crops within a single parcel

of land (Foley et al. 2005). The use of more diverse farming practices has the potential to not

only produce human sustenance, but also enhance biodiversity, ecosystem services, and

pollinator health (Garibaldi et al. 2017). In general, greater plant diversity increases

pollinator diversity (Shackelford et al. 2013), and more non-crop habitat at the local and landscape level are associated with greater availability of floral and nesting resources that are, in turn, associated with greater pollinator richness (Ricketts et al. 2008, Shackelford et al. 2013). To what extent wild and managed bees utilize floral resources in more diversified farms (e.g., fruit and vegetable farms) is not well understood. Diverse farms that grow a 116 multifarious mix of cultivars may provide more floral resources throughout a growing season than farms with a few large monocultures. Diversified farms are likely to be composed of exotic species of crops and weeds, which the introduced honey bees, generalists (Giannini et al. 2015) that forage on a variety of crops, may benefit from more readily than native, wild bees (Thapa 2006, Calderone 2012). While some wild pollinators also forage on certain crops

(Michener 2007), others are more specialized foragers with varying nesting site requirements, and may not benefit from diversified farms. Our goal was to investigate the responses of wild and managed bees (considering common and rare species) in a monoculture (i.e., soybeans) and more diverse farms (i.e., fruit and vegetables).

We incorporated a “landscape physiology” approach (Alaux et al. 2017), measuring not only the response of managed honey bee colonies and the bee community, but also individual nutritional state in honey bees. We hypothesized that diverse farms would support increased honey bee colony growth (i.e., weight, capped brood production, and adult bee population), nutritional state (i.e., lipid content), and a more diverse community of wild bees compared to farms committed to soybean production. To test these hypotheses we deployed sentinel honey bee colonies at selected farms in central Iowa that were either committed to conventional soybean production or produced fruits and vegetables. Regardless of the type of farm, all were embedded in the same landscape matrix consisting of corn and soybean production. Throughout the growing season, we monitored honey bee colony growth and used pan traps to sample the wild bee community. Overall, our study aims to provide insights into the potential of diversified farming to foster bee abundance, diversity, and health. 117

Methods

Farm selection

We identified farms as the experimental unit to test our hypotheses regarding the

impact of farm diversity on honey bees and wild bees. We selected two types of farms

located within central Iowa that either grew only soybean (Mono-SOY) or grew fruits and

vegetables (Div-FV), in 2015 and 2016. From those farms, we randomly selected a subset of

farms which did not have honey bee colonies placed within 1.6 km to reduce resource

competition with our sentinel colonies (see below), and were at least 3 km or more from one

another. In total, 4 Div-FV and 10 Mono-SOY farms were selected in 2015 and 5 Div-FV and 10 Mono-SOY farms in 2016 (Appendix D, SI Table 1). All Mono-SOY and Div-FV farms were independent of each other with the exception of two Div-FV farms which were visited in both years (Appendix D, SI Table 1). The number of crops produced on Div-FV farms ranged from 12 to 50 (29.86; ± 4.91 SEM) and farms ranged in size from 1.2 to 16.2 ha

(6.2 ha; ± 2.42 SEM) (Appendix D, SI Table 2). Two Div-FV farms were certified organic, and all participating Div-FV farmers reported use of the following pesticides that are approved for use in organic farms: foliar Bt, organic insecticidal soaps, and diatomaceous earth. On two of the non-organic farms, the only non-organic approved pesticide applied was glyphosate. On all Mono-SOY farms glyphosate was used for weed management and there were no applications of foliar insecticides. Soybean seed planted in the Mono-SOY farms were treated with only a fungicide (Fluopyram, ILeVO, Bayer, Pittsburgh PA).

Iowa is dedicated primarily to corn and soybean production, producing a uniform landscape matrix. However, elements of diversity exist within the state and have been shown to affect insect diversity within soybean (Gardiner et al. 2009, 2010). To ensure the 118

surrounding landscape matrix of each experimental farm was similar, land cover within a 1.6

km radius centered on the honey bee colonies was measured. Land cover was quantified in

ArcGIS, ArcMap 10.3.1 using the 2015 and 2016 USDA-NASS cropland data layer at a 30 m x 30 m resolution (https://nassgeodata.gmu.edu/CropScape/). The proportion of pixels associated with each land cover type were measured by using ‘isecpolyrst’ function in

Geospatial Modeling Environment (Version 0.7.4.0). Land cover types were categorized into four groups (cropland, developed, grassland, and woodland) (Appendix D, SI Table 3). We only considered annual crops in the cropland category. The only perennial crops present in the landscape were apples, which were only present at one farm across the two years and at

<0.001% of the total land area. Because apples are a permanent woody feature of the landscape, which may provide nesting resources for bees, we considered them in the woodland category. Using two tailed t-tests with Satterthwaite variance, we compared each landscape category between Div-FV and Mono-SOY. We found no differences in proportion cropland, developed, grassland, or woodland between Div-FV and Mono-SOY farms

(Appendix D, SI Figure 1).

Comparing bee communities between farm type

We used modified pan traps (bee-bowls) to measure bee abundance and richness in

Div-FV and Mono-SOY farms. Traps were deployed on posts (Droege et al. 2010, Gill and

O'Neal 2015), to hold three 3.2 oz. bowls (Solo® brand). Because different colors have varying levels of attractiveness to distinctive bee species (Droege 2006) we used bowls painted fluorescent yellow, blue, or left white to maximize the diversity captured. Because each farm was considered an experimental unit, individual bee-bowls at a farm were subsamples. Each field had three bowls of each color (total of nine bowls), deployed on three 119

posts, and each post was placed 10 m apart from each other. In Mono-SOY farms, traps were

placed parallel to honey bee colonies and 10 m inside of the field (Appendix D, SI Figure

2A). To avoid disturbing the cropping area in Div-FV farms, traps were placed in an area

planted with grass directly adjacent to the crops and also parallel to honey bee colonies

(Appendix D, SI Figure 2B).

The sampling period spanned 13 weeks each year, from 1-VII through 24-IX in 2015,

and 15-VI through 9-IX in 2016. Collections were made on a bi-weekly basis. During each collection, the bee-bowls were adjusted on the post so that their height was level with the adjacent plant canopy. A collection was made by filling each bowl one quarter full with soapy water solution made from a 0.2% aqueous soap solution (Dawn® brand). Bee-bowls

were deployed when weather conditions were considered favorable to pollinator foraging

behavior and remained in the field for 24 hours. When collecting samples, all bowls for a

field at a specific date were combined resulting in one collection for each time point.

Specimens were processed using methodologies by Droege (2010) prior to identification. Individuals were identified to genus using the dichotomous key “The Bee

Genera of North and Central America” (Michener 1994) and to species using the online dichotomous key “Discover Life” (Ascher and Pickering 2015). Bees were identified to species or the lowest taxonomic unit possible, with the exception of the genus Lasioglossum which were identified to subgenus.

Comparing honey bee response between farm type

In 2015, all honey bee colonies were started from commercially-obtained packages of

Italian (Apis mellifera ligustica) honey bees provided by C.F. Koehnen & Sons LLC,

Northern California. Packages contained 0.9 kg of adult bees and a queen. All packages were 120

initiated on bare plastic foundation in a standard 10 frame Langstroth hive and were installed

at the Bee and Wasp Research Farm, Ames, Iowa, on 24-IV-2015. After four weeks, colonies were inspected and ranked by size based on weight. Colonies were randomly selected to go to farms such that each farm had the same average weight of colonies (6.66 kg ±0.35 SEM).

On 6-VI-2015, colonies were transported to farms within a single day. All colonies were placed in the exterior grassy perimeter, 3 m from the edge of the soybean or fruit/vegetable and 10 m from the bee-bowls. In 2015, four honey bee colonies were placed at Mono-SOY

farms and two were placed at Div-FV farms, totaling 48 colonies. All colonies were left at

these farms until 15-X-2015, after which they were returned to the Bee and Wasp Research

Farm, where they were kept through the winter.

In 2016, this protocol was repeated with the exception that colonies were derived

from those used in 2015 that survived the winter. Colonies were on created on 3-V-2016 on

fully drawn comb from the previous year rather than bare foundation. Before colonies were

randomly assigned a farm, each was manipulated so that they had the same weight, adult bee,

and brood populations as colonies of 24-IV-2015. Each colony was provided with a new Apis

mellifera ligustica queen purchased from the same source as 2015. Colonies were initiated in

fields on 22-V-2016. We created enough colonies to increase the number at both Div-FV and

Mono-SOY farms to four, for a total of 60 colonies.

All colonies were managed using the following practices. Every month, Varroa

destructor populations were monitored within each colony using the alcohol wash method

(Shimanuki 1991). At the beginning of the experiment (6-VI-2015 and 22-V-2016), the mite

load (estimated based on mites per 300 bees) in colonies was zero. Although mite levels

throughout the season remained below the 10% of adult bees infested threshold (Lee et al. 121

2010), thymol (Apilife Var; Mann Lake, LTD, Hackensack, MN) was applied per label instructions beginning the last week in month VIII 2015 and 2016 to prevent mite infestation from confounding the effects of landscape (Dolezal et al. 2016). During the experiment, if no queen or sign of queen presence (recently laid eggs) was observed, a new A. mellifera ligustica queen was introduced.

Honey bee colony growth

In 2015, colonies were inspected on a biweekly basis from month VI through VIII, and monthly from month VII through X. Colony growth was quantified by measuring weight; an indicator of colony honey and pollen stores (Klein et al. 2019) and overall colony productivity. Hive equipment was weighed prior to the experiment allowing the calculation of added mass only. Additional hive boxes were added to the colonies when those present reached approximately 75% capacity. Colony inspections occurred on alternate weeks as bee- bowl sampling to reduce the influence that disturbed honey bee colonies may pose for estimating bee activity/density. In 2016, inspections followed the same practice as 2015 with the exception that we maintained a biweekly inspection regime starting 22-V through 18-X resulting in four additional seasonal measurements. In addition to colony weight, we estimated the area of capped brood (i.e., pupae), adult bee population, and collected bees for measurements of nutritional state (see below). Capped brood was quantified with a Plexiglas grid screen (Delaplane et al. 2013), allowing calculation of brood area in cm2. Bee population was estimated in terms of frame sides of bees, that is, fractional estimates of the sides of a frame covered in bees (Delaplane et al. 2013). Because farm is the experimental unit, colonies are subsamples, and all metrics of colony growth are reported at the farm level. 122

Honey bee nutritional state

In 2016, we evaluated changes in nutritional state of honey bees by estimating whole

body lipid content. At each inspection date a 15 mL tube of putative nurse bees (i.e., worker

bees collected from frames of open larvae) was collected and stored at -80°C until assayed.

Lipid content was measured via the protocol of Toth and Robinson (2005) as modified in

Dolezal et al. (2016). Fifty nurse bees were homogenized; from this, 0.25 g of homogenate

was subsampled and weighed. Lipid content was quantified via sulphophospho-vanillin

spectrophotometric assay, mg lipid per sample were calculated based on a standard curve of

pure cholesterol, and lipid content calculated as percent of total bee mass.

Statistical analysis

We had unequal sample size between farm treatment types. To determine if our sampling effort was sufficient to compare species richness between each farm type, we constructed species accumulation curves in R 3.4.1 (R Core Team 2017) using the vegan package (Community Ecology Package V2.4-6) (Oksanen et al. 2018) and the SpadeR package (Species-Richness Prediction and Diversity Estimation with R V1.1.1) (Chao et al.

2016), and the INext package (Interpolation and Extrapolation for Species Diversity V2.0.12,

2016) (Hsieh et al. 2016) from bee bowl data. Because our data contain many species that are singletons and doubletons, we assume there were many undetectable or “invisible” species.

We used the Chao1 estimator to derive a lower bound of undetected species richness in terms of the numbers of singletons and doubletons (Chao 1984, 1987; Colwell and Coddington

1994). Sample-size-based rarefaction and extrapolation curves represent an estimator of the expected species accumulation curve, which depicts richness with respect to the sample size

(Chao and Chiu 2016). All samples were standardized by estimating the expected species 123 richness for a common sample size. Curves were reported by farm type (Div-FV versus

Mono-SOY) for 2015 and 2016 combined.

We used nonmetric multidimensional scaling (NMDS) to visually represent the bee community from both farm types using the ‘metaMDS’ function (Oksanen et al. 2018). We used Bray-Curtiss metric in NMDS scaling because it takes into consideration abundance rather than just presence/absence. The output from NMDS was used to create a two- dimensional plot indicating the dissimilarity of the counts for Div-FV versus Mono-SOY for

2015 and 2016 combined. The resulting stress value of less than 0.1 confirmed that this analysis maintained the dissimilarities observed in the original data in the reduced dimensions (Buja et al. 2008).

We used permutated MANOVA to test for significance between the bee communities in Div-FV and Mono-SOY farms by creating a model with farming type, year, and the interaction as predictor variables. In some cases, permutated MANOVA can report false significances between treatments if there is a strong lack of homogeneity in the variability of the data between groups. We wanted to examine the variability of these data in the different farm types. To check this assumption, we used the ‘betadisper’ function (Oksanen et al.

2018). The results of this test indicated that there was not a significant difference in the homogeneity between Div-FV and Mono-SOY (F1, 27=0.0024; P=0.9611), therefore we are confident in the results of the pMANOVA.

Because bee community sampling took place at different time points across the two years, to include all sampling dates, we binned sampling points by month to test if there was a difference in abundance and richness of bees between Div-FV and Mono-SOY. To further assess whether specific groups of bees responded differently to farming diveristy, the 124 community of bees captured were divided into three categories: common, uncommon, or rare. To objectively separate species into these categories, placement was based on the relative abundance of each taxa compared to the entire community. We used cut-offs for these categories by those developed for a similar study conducted in central Iowa

(Kordbacheh et al. 2019 in press). Species placed into the common taxa category were collected at a proportion >0.01, uncommon taxa were collected at a proportion <0.01, and rare taxa occurred as either singletons (i.e., appeared once) or doubletons (i.e., appeared twice) within a farm type. We created a repeated measures mixed effect model (PROC

GLIMMIX) in SAS 9.4 with farm type, month, farm type by month interaction, and year as predictor variables and site as a random variable. Least square means comparisons with

Tukey adjustments were used to look for differences in abundance and richness of the total community of bees, as well as common, uncommon, and rare bees between farm types on specific months.

We determined if honey bee colony weight varied by farm type by combining data taken in both sampling years by using dates in 2016 that aligned with those from 2015. We created a repeated measures mixed effect model with year, date, and farm type as predictor variables and colony and site as a random variables in SAS. To account for variation in land use surrounding each farm, we used land use categories as covariates in the model (Appendix

D, SI Table 1). Cropland correlated heavily with other landscape categories (woodland, developed, and grassland) and was therefore not included in the model. Using post hoc paired comparisons of least square means with Tukey adjustment for multiple comparison, we compared all dates to look for significance between farm types at each time point. Because we increased our sampling effort in 2016 to include more time points and additional 125

measures of colony growth we also analyzed the 2016 colony data (weight, brood, frame

sides of bees, and lipid content) separately using the same model as above.

Results

Bee community

In total, 3,086 bees were collected across all farms; 2,151 in Mono-SOY, and 935 in

Div-FV, for a total of 47 species from 22 genera and two subgenera of Lasioglossum (Table

1). Based on species accumulation curves generated from these data, our sampling efforts accounted for 77.5% of the potential species that could be found in Mono-SOY and 83.3% of the potential species in Div-FV (Figure 1A). The NMDS plots produced polygons (i.e., hulls) connecting the perimeter distributions of sites in the NMDS plots constructed from the pollinators collected inside Div-FV and Mono-SOY farms (Figure 1B). The pMANOVA indicated no significant difference in bee communities between the farm types (F1, 25=1.05;

P=0.351; Figure 1B), although several species were unique to the different farm types. There was a significant difference in the bee communities across years (F1, 25=3.07; P=0.02). There

was no interaction between farm type and year (F1, 25=1.13; P=0.317). Species that were

collected in only one farm type were often rare species (i.e. singletons or doubletons). We

observed a total of 42 taxa in Mono-SOY and 36 taxa in Div-FV farms (Table 1). We

observed 29 taxa shared between the farm types, meaning they appeared in at least one site in

each farm type. Shared taxa consisted primarily of solitary, ground nesting bees. Thirteen

taxa were collected exclusively in Mono-SOY, and seven exclusively in Div-FV.

There were no observable differences in the total abundance (F1, 29.39=0.18; P=0.6745)

or total richness (F1, 29.43=0.05; P=0.821) in the overall community of bees collected between 126

Div-FV and Mono-SOY. Across the entire season an average of 117 individuals (±17 SEM)

for an average of 13 species (±0.60 SEM) per farm were collected in Mono-SOY. Within

Div-FV, an average of 120 individuals (±26.0 SEM) for an average of 14 species (±1.10

SEM) were collected per farm. Bee abundance and richness did not vary by year, however both varied significantly by month (F3, 121.8=3.34; P=0.0217 and F3, 121.8=9.53; P<0.0001 for

abundance and richness respectively; Appendix D, SI Table 4). There were no significant

interactions of farm type and month with bee abundance (F3, 126.3= 1.43; P=0.2366); however,

there were significant interactions with bee richness (F3, 126.2= 3.73; P=0.0131). In month VI,

we observed greater total richness in Div-FV farms than Mono-Soy (T113=2.07; P=0.0412;

Appendix D, SI Table 5).

We further investigated whether groups of bees differed in their responses to farm

type by subdividing the bee community into common, uncommon, and rare taxa. We

classified nine taxa as common, 19 taxa as uncommon, and 21 taxa as rare. Collectively,

common taxa comprised 92.8% of the entire wild bee community, with uncommon and rare

taxa comprising 6.1% and 1.1% respectively. We did not observe a difference in abundance

or richness of common taxa between farm types (abundance, F1, 29.83=0.25; P=0.6177; Figure

2A; richness, F1, 30.28=0.43; P=0.5185; Figure 2B). A higher richness of common bee taxa

was observed in Mono-SOY compared to Div-FV farms in the month of August (T70.03=3.12;

P=0.0026; Figure 2B; APPENDIX D, SI Table 5). Richness, but not abundance, of common

taxa varied significantly across years (Appendix D, SI Table 4). Abundance and richness of

common taxa varied across sampling months (Appendix D, SI Table 4).

There were no observable differences in the abundance of uncommon taxa (F1,

31.44=2.88, P=0.0998; Figure 2C); however, richness of uncommon taxa was significantly 127

higher in Div-FV farms compared to Mono-SOY (F1, 32.12=3.99, P=0.0542; Figure 2D).

Uncommon bee taxa abundance and richness were significantly greater in Div-FV farms compared to Mono-SOY during the month of June (abundance; T121.4=2.44, P=0.0161;

richness, T121.6= 2.09, P= 0.0388; Figures 2C, D; Appendix D, SI Table 5). Uncommon bee

taxa abundance and richness did not vary by year; however, abundance and richness did vary

across months (Appendix D, SI Table 4). There were no observable interactions of farm type

and month on uncommon bee abundance or richness (Appendix D, SI Table 4).

Rare bee taxa abundance and richness did not differ between farm types (abundance,

F1, 144=1.99, P=0.1605; richness, F1, 144=1.14, P=0.2869; Figure 2E, F). However, abundance

and richness of rare bees was significantly greater in Div-FV farms compared to Mono-SOY

during August (abundance, T144=2.74, P=0.0068; richness, T144=2.51, P=0.0133; Figure 2E,

F). Abundance and richness of rare bee taxa varied across sampling months, but did not vary

by year (Appendix D, SI Table 4). There were no interactions of farm type and month on the

abundance or richness of rare wild bees (Appendix D, SI Table 4).

Honey bee colony growth

Colony weight did not vary significantly by farm type (F1, 21.04=2.40; P=0.1359).

Colony weight varied significantly by date, year, and all interactions of date, year, and farm

type for data combined from both years, with the exception of a farm type by year interaction

(Appendix D, SI Table 6). The farm type by date interaction was observed on 8-July when

colonies were significantly heavier in Div-FV farms (T45.76=3.07; P=0.0026; Figure 3A;

Appendix D, SI Table 7) and at the end of the season on 18-October (T50.98=2.83; P=0.0055;

Figure 3A). The heavier weight in Div-FV prior to overwintering sparked an interest in

investigating the late season changes in colonies more closely, as we may have overlooked 128

subtle changes by only using a subset of the colony data from 2016 in the combined analysis.

Therefore, we analyzed the 2016 data separately and observed significant differences in

several honey bee colony metrics between the two farm types. Colonies in Div-FV farms had

higher weight (F1, 10=6.92; P=0.0251), with weight varying significantly by date (F9,

117=43.89; P<.0001; Figure 3B). Colonies were significantly heavier in Div-FV farms

compared to Mono-SOY starting 3-Augusst and remained heavier than Mono-SOY through

18-October (Figure 3B; Appendix D, SI Table 9). No overall difference in brood production between the farm types was observed (F1, 10= 3.91; P= 0.0763; Figure 3B); however, brood

production varied by date (F9, 117=51.27; P<.0001; Figure 3C). Brood production was significantly higher on 22-July and 3-August, marginally higher on 17-August, and significantly higher 31-VIII in colonies in Div-FV farms compared to Mono-SOY (Figure

3C; Appendix D, SI Table 9). There were no interactions of date and farm type with brood

production (Appendix D, SI Table 8). More frame sides of bees were produced in colonies in

Div-FV farms (F1, 10=4.89; P=0.0514) and varied by date (F9, 117=40.94; P<.0001; Figure 3D),

with no interactions of date and farm type (Appendix D, SI Table 8). There were

significantly more frame sides of bees in colonies in Div-FV farms on 8-July, and from 3-

August through 31-August (Figure 3D; Appendix D, SI Table 9).

Honey bee nutritional state

There were no overall differences in total lipid content of honey bees between colonies in Mono-SOY and Div-FV in 2016 (F1, 12.97=0.02; P=0.8778; Figure 4A; Appendix

D, SI Table 9); however, prior to overwintering (18-October) lipid content was significantly

higher in honey bees in colonies in Div-FV farms (T24.28=1.97; P=0.0511; Figure 4A). Honey

bee lipid content varied by date (F3, 100.4= 8.5; P<0.0001) with lipids highest at the start of the 129 season (7-June) and then decreasing on 8-July with no change throughout the remainder of the season regardless of farm type (Figure 4A; Appendix D, SI Table 10). There were no interactions of treatment and date (F3, 100.4= 1.52; P= 0.2138).

Discussion

This study presents novel insights into whether there is potential for bee conservation through diverse farming in a monoculture landscape. Our data support the hypothesis that diversified fruit and vegetable farms, even when found in a landscape that consists of extensive monoculture crops, can benefit honey bee health. Specifically, our results show

Div-FV farms supported increased colony growth and individual nutritional state from honey bees collected from within a managed colony. Because the fruit and vegetable farms we studied are characterized by increased plant diversity and abundance throughout the season

(in the form of both crops and weedy plants), our data suggest that diversified farming may benefit honey bees through increased forage availability.

In addition to a nutritional enhancement of honey bees in fruit and vegetable farms over soybean monocultures, we observed differences in the abundance and diversity of some wild bees between the two farm types. Increased plant diversity through cropping of exotic fruit and vegetables hosted a more species rich community of uncommon bees found within central Iowa (Figure 2D). The most pronounced increases in uncommon bee biodiversity in

Div-FV farms were seen during month of June, where abundance and richness were greater.

It may be that the plants present on Div-FV farms (weeds and crops) during the early season provided sufficient nesting and/or forage resources for more specialized or sensitive wild bee species at a time when corn and soybean fields were not yet planted or in an early growth 130 stage. However, not all bees responded the same to diversified farming. Although there were no main effects of diversified farming on rare bees, abundance and richness of rare bees was greater in Div-FV farms during the month of August (Figures 2E, F). It may be that during late season forage limitations, Div-FV farms provide a necessary requirement to support species which are rare in Iowa’s agricultural systems.

Common bees were near ubiquitous in our study and are regularly found in the U.S., often associated with landscapes impacted by human disturbance (Kremen et al. 2002,

Winfree et al. 2007, Winfree et al. 2008, Pardee and Philpott 2014). These common bees were found in high abundance (collectively totaling 92.8% of all bees) in both farm types throughout the season. Surprisingly, there was a higher richness of common bee species found within Mono-SOY farms compared to Div-FV during the month of August. This is a time when soybeans are flowering and potentially provide a forage resource for bees, suggesting a subset of bees may have become habitat and dietary generalists that may be well adapted to living in highly disturbed landscapes and thrive on resources available in agricultural systems. If declines in bee populations continue as they have in recent years

(Kremen et al. 2002, Steinhauer et al. 2014) the future of crop pollination success will likely depend upon incorporating both wild and managed bees into pollination management plans

(Greenleaf and Kremen 2006, Garibaldi et al. 2013). These results suggest cropping diversity through fruit and vegetable farming can increase bee biodiversity beyond common agricultural species, although the effects are not overwhelming. In heavily cultivated systems such as the Midwestern USA, addition of landscape diversity through small scale cropping diversity has the potential to support increased biodiversity of less common but not truly rare species. We suggest that diversified farming, in addition to an increase in more native, 131 perennial habitat may be required in both farm types to support rare ecotone species which require resources from non-crop habitat at some point in their development (Duelli and

Obrist 2003).

With respect to managed honey bees, our data suggest on-farm diversity can have subtle, but significant impact on the health and fitness of honey bees. Across both years, honey bee colonies in Div-FV farms had higher colony weight at the end of the season prior to overwintering. In 2016, colonies in Div-FV farms had higher colony weight, bee populations throughout much of the season, and also produced more brood at individual dates throughout the season. Although the surrounding agricultural landscape can provide abundant resources (e.g., soybean nectar), these data suggest honey bees benefit from being housed in the Div-FV farms. The morphology and behavior of honey bees allows them to more readily utilize many plant species as forage, making them a ‘supergeneralist’ compared to other bee species (Giannini et al. 2015). Unlike many wild bee species which only forage roughly 500 meters from their nesting site (Gathmann and Tscharntke 2002, Zurbuchen et al.

2010a, Zurbuchen et al. 2010b), honey bees are capable of foraging long distances (Beekman and Ratnieks 2000) and utilizing both the resources directly available from the diversity of

Div-FV farms and in the soybean surrounding those farms. Taken together with the varied results from wild bees with respect to common, uncommon, and rare bee taxa, this study suggests that farm practices that benefit honey bees are not necessarily a good indicator of how wild bee communities in general will respond. As other studies have previously suggested, honey bees cannot be indiscriminately used as an “indicator species” to extrapolate wild bee response to anthropogenic landscape change (Heard et al. 2017), 132

especially in areas such as the U.S. where honey bees are exotic to the landscape and likely

utilizing different foraging resources compared to many wild bees.

Although honey bees gained some measurable benefits from being in Div-FV farms compared to Mono-SOY, there was nonetheless a precipitous decline in weight of colonies in the late summer regardless of farm type. This resulted in colonies from both farm types entering the winter with honey stores below what is considered adequate to sustain them

(Brodschneider and Crailsheim 2010, Caron and Conner 2013). An additional challenge for honey bees kept at either farm type is indicated by our lipid analysis. Honey bees store fat in the form of vitellogenin in preparation for overwintering (Fluri et al. 1977, Döke et al. 2015), therefore, lipid stores of bees in the colony are an indicator of colony overwintering potential

(Dolezal et al. 2016). Although honey bee total lipid content was higher in Div-FV farms than Mono-SOY, by October, even the highest lipid levels observed were below what would be considered adequate for successful overwintering (Dolezal et al. 2016) indicating that neither farm type is ideal for long term success of honey bee colonies.

In our study sites, all farms were surrounded by a matrix of extensive monoculture.

Fruit and vegetable farms can have a measurable, though modest impact on a few key health indicators for honey bees, and support elevated richness of uncommon species of wild bees through additional resources. Our results suggest there is potential for positive effects of increased forage and habitat through the floral resources found in diversified farms, but for greater benefits to be realized, the land area in diversified farming and type of resources provided may need to be more extensive and pollinator-targeted. Integration of native, perennial habitat (e.g., prairie, (Schulte et al. 2017) is a promising possibility for enhancing forage in an agricultural landscape. 133

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Tables and Figures

Table 1. Abundance of bee pollinators captured in bee-bowls by crop type summed across the entire season for Div-FV and Mono-SOY sites sampled in central Iowa during 2015 and 2016. Abundancea Abundance in fields by farming typeb

Taxa Div-FV Mono-SOY Common Taxa Lasioglossum (Dialictus) spp . 1575 532 1043 Agapostemon virescens F. 368 95 273 Melissodes bimaculata (Lepeletier) 310 47 263 Halictus ligatus (Say) 160 15 145 Agapostemon texanus (Cresson) 142 25 117 Halictus confusus (Smith) 95 11 84 Lasioglossum (Evylaeus) spp. 80 66 14 Halictus parallelus (Say) 74 41 33 Augochlorella aurata (Smith) 61 23 38 Uncommon Taxa Halictus rubicundus (Christ) 34 8 26 Augochlora pura (Say) 21 11 10 Peponapis pruinosa (Say) 16 6 10 Melissodes trinodis (Robertson) 15 3 12 Melissodes agilis (Cresson) 13 3 10 Bombus impatiens (Cresson) 13 7 6 Bombus bimaculatus (Cresson) 11 5 6 Eucera hamata (Bradley) 9 1 8 Ceratina dupla (Say) 7 6 1 Augochloropsis metallica F. 7 4 3 Melissodes communis (Cresson) 7 - 7 Bombus griseocollis (DeGeer) 6 3 3 Bombus vagans (Smith) 6 2 4 Melissodes desponsa (Smith) 6 1 5 Hylaeus affinis (Smith) 5 2 3 Dieunomia triangulifera (Vachal) 4 - 4 Agapostemon angelicus (Cockerell) 3 - 3 Calliopsis andreniformis (Smith) 3 - 3 Ceratina calcarata (Robertson) 3 3 - Rare Taxa Melissodes boltoniae (Latreille) 4 2 2 Hylaeus annulatus (Linnaeus) 4 2 2 Bombus pensylvanicus (DeGeer) 3 2 1 Perdita halictoides (Smith) 2 1 1 aAbundance values are the number of individuals by species (includes females and males) summed across all 14 fields in 2015 and 15 fields in the 2016 sampling season. bAbundance values are the number of individuals by species (includes females and males) summed across farm type; Div-FV and Mono-SOY.

142

Table 1 Continued.

Abundancea Abundance in fields by farming typeb

Div-FV Mono-SOY Taxa Rare Taxa

Nomada vegana (Cockerell) 2 1 1 Bombus variabilis (Cresson) 2 1 1 Ashmeadiella bucconis (Say) 1 1 - Bombus citrinus (Smith) 1 1 - Bombus perplexus (Cresson) 1 - 1 Ceratina strenua (Smith) 1 - 1 Colletes inaequalis (Say) 1 - 1

Eucera atriventris (Smith) 1 - 1 Halictus tripartitus (Cockerell) 1 - 1 Megachile brevis (Say) 1 - 1 Megachile parallela (Smith) 1 - 1 Melissodes vernoniae (Robertson) 1 1 - Nomia universitatis (Cockerell) 1 - 1 Perdita boltoniae (Robertson) 1 1 -

Protandrena bancrofti (Dunning) 1 1 - Sphecodes davisii (Robertson) 1 1 - Svastra compta (Cresson) 1 - 1 Total bees 3086 935 2151 aAbundance values are the number of individuals by species (includes females and males) summed across all 14 fields in 2015 and 15 fields in the 2016 sampling season. bAbundance values are the number of individuals by species (includes females and males) summed across farm type; Div-FV and Mono-SOY.

143

Figure 1. (A) Sample-size-based rarefaction (solid lines) and extrapolation (dashed lines) sampling curves with 95% confidence intervals (shaded areas, based on a bootstrap method with 200 replications) comparing wild bee species richness collected from bee-bowls for data of two farming types (Div-FV and Mono-SOY) in central Iowa in 2015 and 2016. Observed samples are denoted by the solid circle (Div-FV) and open circle (Mono-SOY). The estimated asymptote for each curve is 1870 and 4302 individuals for Div-FV and Mono-SOY respectively. (B) Nonmetric, multidimensional scaling plot of the pollinator community found in Div-FV and Mono-SOY farms in central Iowa in 2015 and 2016. The hulls (black and closed circles for Div-FV, grey and open circles for Mono-SOY) are constructed from a representation of the pollinator community found at each of the 9 Div-FV and 20 Mono-SOY farms across the two years. No significant difference in the community of bees was detected; results based on permutated multivariate analysis of variance (F1, 25=1.05, P=0.351).

144

50 A 5 B * Common Taxa Common Taxa 40 4 30 3 2

abundance 20 richness 1 Mean common bee common Mean 10 0 bee common Mean 0 5 C Uncommon Taxa 2.5 D Uncommon Taxa 4 * * 2 3 1.5 2

richness 1 abundance 1 0.5 Mean uncommon bee uncommon Mean Mean uncommon bee uncommon Mean 0 0

Rare Taxa Rare Taxa 0.8 F * 0.9 E * 0.7 0.8 0.6 0.7 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 Mean rare bee richness 0 0 Mean Mean rare bee abundance June July August September June July August September Div-FV Mono-SOY Div-FV Mono-SOY

Figure 2. Mean abundance and richness of common (A, B), uncommon (C, D), and rare bee taxa (E, F) bee taxa in Div-FV (solid line and closed circles) and Mono-SOY (dotted line and open circles) in central Iowa across the season during 2015 and 2016. Error bars represent one standard error of the mean. Values represent the mean of bees collected at each farm per month. Asterisks signify post hoc least square means comparison for differences between farm types at each time point.

145

40 A

kg ) 30 * 20 *

( weight Mean colony 10 0 27-Jun 8-Jul 22-Jul 3-Aug 21-Sept. 18-Oct

40 B * * 30 * *

colony colony 20 *

weight (kg) Mean Mean 10

0 500 C * * ) * 2 400 300

200

brood area (cm 100

Mean capped colony 0

40 D * * 30 * * 20

sides of bees 10

frame colony Mean 0 22-May 7-Jun 27-Jun 8-Jul 22-Jul 3-Aug 17-Aug 31-Aug 21-Sep 18-Oct

Div-FV Mono-SOY

Figure 3. Honey bee colony weight in Div-FV (black line and closed circles) and Mono- SOY (dotted line and open circles) farms in central Iowa for 2015 and 2016 combined (A). Honey bee colony weight (B), capped brood area (C), and frame sides of honey bees (D) in colonies in Div-FV and Mono-SOY farms in central Iowa for 2016 only. Error bars represent one standard error of the mean. Asterisks signify post hoc least square means comparison for differences between farm types at each time point. Results based on repeated measures linear mixed effect model. 146

4.5 4 A 3.5 B B B 3 * 2.5 2 1.5 1

Mean honey bee Mean honey content%lipid 0.5 0 June July August October Div-FV Mono-SOY

Figure 4. Mean percent honey bee lipid content (lipid mg bee-mass mg-1) for colonies in Div-FV (solid line and closed circles) and Mono-SOY (dotted line and open circles) farm types in central Iowa in 2016. Lipid levels were higher than all other dates on June-2016 regardless of farm type. In October, lipid levels in honey bees were higher in colonies maintained in Div-FV farms compared to Mono-SOY. Error bars represent one standard error of the mean. Asterisks signify post hoc least square means comparison for differences between farm types at each time point. Letters signify post hoc least square means comparison for differences between sampling dates. Results based on repeated measures linear mixed effect model.

147

CHAPTER 6. GENERAL SUMMARY AND CONCLUSIONS

Wild bees and honey bee populations and health are at risk due to multiple interacting environmental stressors, including parasites, pathogens, pesticides, and habitat loss (Goulson et al. 2015). In the U.S., declines in wild bees have been associated with the conversion of natural landscapes into row-crop agriculture (Koh et al. 2016). For honey bees, there have been mixed results on whether agricultural landscapes have a positive or negative effect

(Sponsler and Johnson 2015, Otto et al. 2016). In habitats where managed honey bee colonies overlap with existing wild bee communities there is potential for competition over finite resources such as pollen and nectar (Mallinger et al. 2017), which can be further exacerbated by the already limited resource availability in agricultural systems. Although many studies have investigated the effects of agricultural systems on wild and managed bees

(Kremen et al. 2002, Winfree et al. 2009, Dolezal et al. 2016, Otto et al. 2016, Evans et al.

2018), very few have investigated both in the same context within landscapes as extensively cropped as Iowa. The research presented in this thesis investigates bee responses to land cover diversity within a landscape dominated by row-crop agriculture by assessing both wild bees and honey bees, including potential interactions, side by side, in highly replicated, longitudinal studies. The results provide clarity on several previously unanswered questions in this active area of research, as summarized below.

In Chapter 2, I aimed to assess the usefulness of pan traps as an accurate method of assessing honey bee activity-density in an agricultural landscape. Despite previous research suggesting pan traps are not effective at capturing honey bees (Cane et al. 2000, Roulston et al. 2007), I found evidence that pan traps can be used to assess honey bee activity-density and these estimates provide a representation of the presence of honey bee colonies with a 148

potential to estimate number of colonies nearby. I also demonstrate limitations to the

sensitivity of this method; I did not detect differences in activity-density in field types

(soybean fields vs prairies), in different locations within a field (within the field vs grassy perimeter), nor based on distance from the colony. It is possible that activity-density of honey bees across these contexts truly did not differ. Further studies are necessary to tease apart whether the lack of difference is attributable to a lack of trap effectiveness vs a true lack of difference in honey bee activity level in the landscape. However, I did find that season had a strong effect on activity-density of honey bees with a spike in abundance of honey bees in traps later in the season, during a period of low forage availability. This suggests that pan trapping can provide estimates of honey bee activity-density in a crop field, but that these estimates may be greatly affected by forage availability. The main contribution of this chapter is that I demonstrate the viability of this method, and list potential applications for pan trapping of honey bees in agricultural and bee research.

In Chapter 3, I investigated whether proportion of corn and soybean production in the surrounding landscape of a soybean field affected the community of wild bees present within the field. Additionally, because there has been growing concern about the impact of managed honey bees on wild bee populations, I explored how the presence of honey bee colonies in fields surrounded by high and low proportions of corn and soybean affected wild bee communities. I found that richness and diversity of the overall bee community were greater in fields surrounded by landscapes with lower proportions of corn and soybean.

However, not all bee taxa responded in the same way. Common bee species had no response to proportion crop production in the surrounding landscape. Bees classified as uncommon and rare were more abundant, species rich, and diverse in soybean surrounded by a low 149

proportion crop production. Specifically, uncommon bees were positively associated with

increasing amounts of woodland in the surrounding landscape, whereas rare bees were

positively associated with proportion grassland in the surrounding landscape. Overall, there

was no observable effect of honey bee presence on the wild bee community. The wild bee

community was also not affected by an interaction of honey bee presence with proportion

crop production in the surrounding landscape. These results are important because they may

help to inform conservation management decisions, suggesting that interactions with honey

bees are less important than landscape composition in shaping the wild bee community in extensive agroecosystems.

Considering the variable responses of different groups of bees to landscape composition in Chapter 3, this begs the important question—how do non-native honey bees, often utilized in crop pollination, respond to production of row-crop agriculture? Honey bees represent a model insect for assessing the effects of landscape on bee decline because sentinel colonies can be placed at different sites, monitored, and there are well-established methods for quantifying many different honey bee health indicators (Delaplane et al. 2013).

The focus of Chapter 4 was to assess the health and productivity of honey bee colonies in soybean fields surrounded by high and low proportions of corn and soybean production.

Overall, the response of honey bees was the opposite of what was observed with wild bees in

Chapter 3. Honey bee colonies had more adult bees, immature bees, and heavier weight in fields surrounded by higher production of corn and soybean. Despite honey bee colonies being more productive when surrounded by row-crop, colony population, size, and nutritional state declined precipitously in the late season, a time corresponding to the senescence of soybean and clover blooms. This suggests that while areas of high corn and 150 soybean production may support bursts of colony growth during certain “feast” periods of the season, colony health cannot be maintained throughout the season, with colonies ending in a

“famine” state that indicated they were too weak to survive.

In a follow-up study, I showed these declines are not inevitable in an agricultural landscape when bee colonies are given access to prairie forage. Providing honey bee colonies access to native perennial habitat during this critical time reversed late season decline of honey bee colonies and individual bee nutritional state. These results are significant because they provide clarity on previously conflicting reports about both positive and negative responses of honey bees to extensive agricultural production. This study reveals the subtle and complex feast/famine dynamics of honey bee colonies experiencing seasonal forage fluctuations in highly cultivated landscapes. These results also suggest landscape enhancements with native habitat can provide benefits to non-native honey bees, which is an important finding because it opens the door to a way forward that can address both conservation and agricultural priorities.

In Chapter 5, I examined whether local farm diversity through the production of fruits and vegetables can also serve as a viable option for boosting wild bee community and honey bee colony health. I focused on fruit and vegetable farms that grew a diverse mixture of crops, some of which bloomed in the late season and could potentially provide valuable resources to wild and managed bees. For wild bee community, the largest responses observed were with uncommon bee species, which were more species rich in fruit and vegetable farms, especially in June. Honey bee colonies in fruit and vegetable farms had higher adult and immature bee populations, and were heavier compared to colonies in soybean farms.

Although colonies were more productive in diverse fruit and vegetable farms, colonies still 151 declined precipitously in population, size, and nutritional health during the late season critical period, suggesting both farm types do not offer necessary late season resources for honey bees. Overall, these results suggest diversified farming can offer some modest benefits to both wild and managed bees.

Altogether, the findings of this research demonstrate the importance of landscape composition and forage availability to the health and populations of both wild and managed bees. I demonstrated that within the context of extensive agroecosystems such as the vast corn and soybean monocultures of Iowa, pockets of landscape diversity, in the form of woodlands, grasslands, native prairies, and even small diversified farms, can provide tangible benefits to bees. Although these general conclusions can be broadly applied to the question of bee declines, my research also clearly showed that not all bees respond to land use in the same way. Within wild bee communities, the response to surrounding landscape and local farm diversity differed for different subsets of the wild bee community. Rare and uncommon bees are of special concern in conservation (Senapathi et al. 2015). In agreement with this assessment, I found that general land use did not affect common bee species, however, bees classified as uncommon and rare were more abundant, species rich, and communities were more diverse in areas surrounded by less production of corn and soybean. Uncommon wild bees were more species rich on diversified fruit and vegetable farms, suggesting these farms may provide some valuable resources to a subset of the wild bee community, but not enough resources for rare bee species. In contrast, we did not see similar trends with honey bees, in fact, honey bee colonies produced more bees and were able to gain more weight when they were in landscapes with higher production of corn and soybean. These results suggest that honey bees are not a good indicator for how wild bees respond to agricultural land use. This 152 could be a result of honey bees being a highly generalist species (Giannini et al. 2015), or because they tend to favor legumes (including soybeans and clover, which are highly abundant in the Iowa landscape), and they are thus able to thrive on floral resources provided by agricultural production systems in a way that wild bees are not. Regardless, efforts to conserve bees should note these differences in response to land use in an effort to optimize landscape enhancements to support both wild and managed bees.

To date, multiple solutions have been proposed to reverse the declines of bee populations, many of which involve the addition of diversity back into agricultural and urban systems to provide forage for wild and managed bees as well as nesting resources for wild bees. Hedgerows consisting of wild shrubs and trees bordering fields have been shown to promote both wild and managed bee populations (Kremen et al. 2018) and when adjacent to agricultural fields can export bees into fields (Morandin and Kremen 2013). Hedgerows can be particularly valuable in agricultural systems because they provide flowering resources in the early spring when many crops are not yet planted or blooming (Hannon and Sisk 2009).

Although I did not experimentally manipulate woodland habitat and quantify the response of bees in these experiments, I did observe that uncommon wild bees had a positive response to the proportion woodland in the surrounding landscape.

Agricultural plant diversification within farms and within fields can have strong positive effects on pollinators (Lichtenberg et al. 2017), with organic cropping systems especially supporting increased wild bee diversity (Holzschuh et al. 2008). In this research, agricultural plant diversification through the production of fruits and vegetables subtly boosted a subset of the wild bee community compared to communities in soybeans. Honey bee colony productivity and health was significantly greater in fruit and vegetable farms, but 153

colonies still declined in the late season, suggesting diversification of crops in small areas of

an already extensively cropped landscape is not sufficient to show large gains for both

managed and wild bees.

Native plantings of diverse wildflower mixes within or near agricultural landscapes are another promising solution shown to support wild bee abundance and diversity (Williams

et al. 2015, Schulte et al. 2017). In some cropping systems, the addition of native plants can

bolster the pollination services provided to crops (Isaacs et al. 2009, Blaauw et al. 2014).

Native plantings in residential areas support abundant and species rich communities of bees,

however, bee communities can be highly variable (Hinners et al. 2012). In Iowa’s

agricultural system, native prairie was a resource capable of rescuing honey bee colony

declines. It is unlikely that large areas of land will be returned to the native state; however, a

possible solution to mitigate stress faced by bees and support diverse wild bee communities

is to incorporate strips of prairie within agricultural systems (Schulte et al. 2017). In

agreement with this, I found that rare bees were more abundant and species rich in

landscapes surrounded by more grassland. Combined with the finding that honey bees can

also benefit from late season prairie habitat, my research suggests a potential for restored

grasslands and/or prairie strips as a type of habitat that has promise to support wild and

managed bees. Further research should focus on understanding to what degree the

incorporation of native perennial habitat within extensive agricultural systems can support

both wild and managed bees.

154

References

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APPENDIX A. CHAPTER 2 SUPPLEMENTAL INFORMATION

Pan trap post contains 1 yellow, 1 blue, and 1 white bowl

SI Figure 1. (A) Example of trap design for a single post. Posts were constructed to hold three 3.2 oz bowls painted fluorescent yellow, fluorescent blue, or left white. (B) Example of experimental design of soybean fields in 2015 and 2016. Four colonies were placed 3 m from the field edge. In 2015, three posts were placed in the interior of the field adjacent to the colonies. In 2016, three posts were placed in the interior and the exterior of the field adjacent to the colonies. (C) Experimental design for experiment two. An apiary of 16 colonies was moved into the fields (8 at each sub-site). In August one half of the colonies were moved to the prairie resulting in 4 colonies per sub-site remaining. Pan trap posts were placed 30 m and 90 m from the colonies at each sub-site. 158

SI Table 1. Least square means comparisons with Tukey adjustment of honey bee activity- density in pan traps across the season in soybean fields in central Iowa during 2015 and 2016.

Date Date Estimate SE DF t Value P value

1-Jul 12-Aug -0.4000 0.8262 81.94 -0.48 0.6296 1-Jul 15-Jul -0.3000 0.8262 81.94 -0.36 0.7175 1-Jul 15-Jun 0.3446 1.0150 91.93 0.34 0.7350 1-Jul 24-Sep -6.7446 1.0150 91.93 -6.65 <.0001

1-Jul 29-Jul -0.9000 0.8262 81.94 -1.09 0.2792 1-Jul 6-Sep -1.2554 1.0150 91.93 -1.24 0.2193

12-Aug 15-Jul 0.1000 0.8262 81.94 0.12 0.9040 12-Aug 15-Jun 0.7446 1.0150 91.93 0.73 0.4651

12-Aug 24-Sep -6.3446 1.0150 91.93 -6.25 <.0001

12-Aug 29-Jul -0.5000 0.8262 81.94 -0.61 0.5467

12-Aug 6-Sep -0.8554 1.0150 91.93 -0.84 0.4015

15-Jul 15-Jun 0.6446 1.0150 91.93 0.64 0.5270

15-Jul 24-Sep -6.4446 1.0150 91.93 -6.35 <.0001

15-Jul 29-Jul -0.6000 0.8262 81.94 -0.73 0.4698

15-Jul 6-Sep -0.9554 1.0150 91.93 -0.94 0.3490

15-Jun 24-Sep -7.0891 1.1790 102.9 -6.01 <.0001

15-Jun 29-Jul -1.2446 1.0150 91.93 -1.23 0.2233

15-Jun 6-Sep -1.6000 1.1685 81.94 -1.37 0.1746

24-Sep 29-Jul 5.8446 1.0150 91.93 5.76 <.0001 24-Sep 6-Sep 5.4891 1.1790 102.9 4.66 <.0001 29-Jul 6-Sep -0.3554 1.0150 91.93 -0.35 0.7270

SI Table 2. Mean plant species richness observed at native tallgrass prairies in central Iowa during 2017 and 2018.

Collection Date Location Richness1 Early August Barrer 16 Darnell 6.5 Sandhill 12.5 Late August Barrer 13 Darnell 7.5 Sandhill 11.5 Early September Barrer 12 Darnell 7.5 Sandhill 12 1Richness based on the average species observed between sampling dates in 2017 and 2018. 159

APPENDIX B. CHAPTER 3 SUPPLEMENTAL INFORMATION

SI Table 1. Individual landscape classifications used to estimate land cover types surrounding soybean farms in central Iowa in 2015 and 2016.

Corn Soybean Other Crop Grassland Developed Woodland Corn Soybeans Sorghum Clover/Wildflower Open Water Deciduous Forest Sweet Corn Fallow crop Developed open space Evergreen Forest Rye Grass/ Pasture Developed Low intensity Mixed Forest Oats Developed Med intensity Shrubland Alfalfa Developed High intensity Woody Wetlands Other Hay Barren Herbaceous Wetlands Other Crops

SI Table 2. Pearson correlation coefficients of land cover types surrounding soybean farms in central Iowa in 2015 and 2016. No correlations exceeded the 0.80 threshold, therefore, all land cover types were included in model selections, as well as honey bee presence. Woodland Developed Corn Soybeans Grassland Other crop Woodland 1 0.26397 -0.78015 -0.67483 0.58017 0.14572 Developed 0.26397 1 -0.71338 -0.66598 0.13007 0.19219 Corn -0.78015 -0.71338 1 0.69775 -0.57392 -0.32464 Soybeans -0.67483 -0.66598 0.69775 1 -0.57694 -0.27345 Grassland 0.58017 0.13007 -0.57392 -0.57694 1 0.33775 Other crop 0.14572 0.19219 -0.32464 -0.27345 0.33775 1

160

SI Figure 1. Sample-size-based rarefaction (solid lines) and extrapolation (dashed lines) sampling curves with 95% confidence intervals (shaded areas, based on a bootstrap method with 200 replications) comparing wild bee species richness collected from bee-bowls for data in soybean fields surrounded by High-cultivation and no honey bee colonies Hive (-) (blue line and circle), High-cultivation fields with honey bee colonies Hive (+) (blue line and triangle), Low-cultivation fields with no honey bee colonies Hive (-) (green line and square), and Low-cultivation fields with honey bee colonies present Hive (+) (green line and diamond) in central Iowa in 2015 and 2016. Observed samples are denoted by the solid shape and solid line, extrapolated community denoted by the dashed line. The estimated asymptote for each curve is 1715 and 1681 individuals in High-cultivation/Hive (-) and High- cultivation/Hive (+) respectively, and 1785 and 1821 individuals in Low-cultivation/Hive (-) and Low-cultivation/Hive (+) respectively. Based on the accumulation curves, the percent sampling coverage of species diversity of each site is 77.5%, 84.8%, 84.8%, and 76.6% for High-cultivation/Hive (-), High-cultivation/Hive (+), Low-cultivation/Hive (-), and Low- cultivation/Hive (+) respectively. 161

APPENDIX C. CHAPTER 4 SUPPLEMENTAL INFORMATION

SI Figure S1. Measurement guide from Hodgson et al. (2012) used to quantify soybean growth stage for all sites in 2015 and 2016.

SI Table S1. Iowa farms surveyed in 2015 and 2016.

Site Cultivation Year County Percent Land use in 1.6 km Radius of Field¹ Farm Coordinates Name Category

Cropland Developed Grassland Woodland 2015 JUL High Story 78% 7% 9% 6% 41.9648972° -93.62951389° 2015 OFAR High Marshall 75% 6% 17% 2% 42.033889° -93.0285389° 2015 WDAI High Story 88% 8% 4% 0% 41.97079167° -93.658989° 2015 GLI High Marshall 93% 5% 2% 0% 42.145356° -93.1175° 2015 LIPP High Boone 74% 8% 11% 7% 42.0476361° -93.72373889° 2015 LIOP Low Marshall 44% 21% 27% 8% 42.0072472° -92.9658083°

2015 CAT Low Story 56% 9% 15% 20% 42.0583167° -93.68207778°

2015 CURT Low Story 30% 31% 21% 19% 42.00281° -93.6715194°

2015 HOR Low Story 44% 5% 26% 25% 42.1069694° -93.5810972°

162 2015 IARIV Low Marshall 55% 5% 19% 20% 42.1402083° -93.04970278° 2016 OAK High Story 91% 5% 3% 0% 41.929764° -93.639831° 2016 IVI High Boone 93% 5% 3% 0% 41.994122° -93.751983° 2016 TRA1 High Boone 79% 4% 16% 1% 42.137892° -93.831206° 2016 EDAI High Story 82% 8% 10% 1% 41.972141° -93.629994° 2016 UTH High Story 87% 3% 9% 1% 41.921955° -93.747422° 2016 KOST Low Boone 13% 29% 15% 43% 42.050572° -93.918092°

2016 HAG Low Story 52% 4% 13% 32% 42.090914° -94.011750°

2016 CURT Low Story 27% 34% 20% 19% 42.003816° -93.668273°

2016 TRA3 Low Boone 50% 6% 14% 29% 42.123217° -93.912994° 2016 ONT Low Story 12% 46% 11% 31% 42.037406° -93.665611° ¹Landscape categories based on individual features of landscape listed in table 2

SI Table S2. Individual landscape features surrounding soybean fields within a 1.6 km buffer in Iowa in 2015 and 2016.

Land use categories1

Site Year Cult 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

JUL 2015 High 43.77 32.96 0.00 0.00 0.05 0.69 0.43 0.00 0.00 0.00 0.00 0.13 5.53 0.96 0.48 0.01 0.00 5.99 0.00 0.00 0.00 8.81 0.14 0.04 OLD 2015 High 46.92 26.87 0.00 0.00 0.07 0.58 0.22 0.00 0.00 0.00 0.00 0.00 5.59 0.74 0.08 0.00 0.00 2.02 0.00 0.00 0.00 16.89 0.00 0.01 WDAI 2015 High 56.08 31.20 0.00 0.00 0.34 0.47 0.07 0.00 0.00 0.01 0.00 0.00 5.35 0.96 0.99 0.25 0.00 0.14 0.00 0.00 0.00 4.10 0.00 0.04 GLI 2015 High 40.24 52.55 0.00 0.00 0.00 0.17 0.00 0.00 0.00 0.00 0.00 0.00 5.17 0.02 0.02 0.00 0.04 0.08 0.00 0.00 0.00 1.70 0.00 0.00 LIP 2015 High 53.46 19.89 0.00 0.00 0.00 0.08 0.08 0.00 0.00 0.00 0.00 0.18 6.34 0.85 0.23 0.02 0.00 7.12 0.00 0.00 0.00 11.50 0.06 0.17 LPAR 2015 Low 28.72 14.49 0.00 0.00 0.00 0.54 0.26 0.00 0.00 0.00 0.00 0.54 14.40 4.10 1.72 0.01 0.00 7.79 0.02 0.00 0.00 27.10 0.18 0.12 CATT 2015 Low 18.45 31.99 0.04 0.00 0.05 2.26 3.22 0.01 0.00 0.01 0.00 0.23 7.05 1.65 0.12 0.00 0.00 18.54 0.00 0.00 0.00 14.56 1.66 0.17 CUR1 2015 Low 11.72 12.91 0.01 0.00 0.22 2.62 2.26 0.02 0.04 0.01 0.00 0.41 14.04 7.67 6.77 1.79 0.01 18.08 0.02 0.00 0.02 20.98 0.24 0.13 HOR 2015 Low 18.01 24.58 0.00 0.00 0.01 0.41 0.49 0.00 0.00 0.00 0.00 1.60 3.08 0.54 0.02 0.00 0.00 16.88 0.00 0.00 0.00 26.25 7.77 0.35 IRIV 2015 Low 25.03 28.44 0.00 0.00 0.88 0.29 0.07 0.00 0.01 0.00 0.00 2.41 2.91 0.11 0.00 0.00 0.00 12.82 0.00 0.00 0.00 19.36 7.29 0.38 OAK 2016 High 60.70 29.07 0.00 0.00 0.16 0.53 0.63 0.00 0.00 0.00 0.00 0.00 4.91 0.24 0.11 0.00 0.00 0.24 0.00 0.00 0.00 3.40 0.00 0.02 163 IVI 2016 High 53.28 39.53 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 4.32 0.19 0.02 0.00 0.00 0.08 0.00 0.00 0.01 2.50 0.00 0.04 TRA1 2016 High 49.46 25.11 0.00 0.00 0.08 1.60 3.14 0.01 0.01 0.00 0.00 0.02 3.63 0.06 0.00 0.00 0.00 0.54 0.00 0.00 0.00 16.30 0.00 0.02 EDAI 2016 High 46.52 34.09 0.00 0.01 0.07 0.40 0.49 0.00 0.00 0.00 0.00 0.17 5.48 1.35 0.93 0.02 0.00 0.63 0.00 0.00 0.01 9.65 0.05 0.13 UTH 2016 High 50.78 35.84 0.01 0.00 0.00 0.08 0.18 0.00 0.00 0.05 0.00 0.00 3.25 0.12 0.04 0.00 0.00 0.57 0.04 0.00 0.00 8.98 0.01 0.05 KOS 2016 Low 6.47 5.36 0.00 0.00 0.05 0.35 0.58 0.01 0.00 0.00 0.00 1.29 16.96 9.22 0.94 0.18 0.02 41.49 0.08 0.01 0.02 15.34 1.30 0.32 HAG 2016 Low 30.65 17.87 0.00 0.00 0.37 0.98 1.63 0.00 0.00 0.00 0.00 0.34 3.50 0.14 0.00 0.00 0.00 30.66 0.07 0.00 0.06 12.83 0.42 0.46 CUR2 2016 Low 8.56 12.44 0.01 0.00 0.22 3.29 2.45 0.02 0.04 0.07 0.00 0.30 16.93 8.41 6.71 1.53 0.01 19.06 0.05 0.00 0.05 19.53 0.22 0.11 TRA3 2016 Low 32.15 17.71 0.00 0.00 0.00 0.54 0.02 0.00 0.00 0.00 0.00 0.01 6.21 0.14 0.00 0.00 0.00 29.39 0.00 0.00 0.00 13.75 0.04 0.04 ONT 2016 Low 6.44 4.42 0.00 0.00 0.00 0.89 0.20 0.04 0.00 0.00 0.00 0.00 21.65 17.94 5.19 1.42 0.00 29.49 0.00 0.00 0.01 10.63 1.54 0.14 HOR 2015 Ag Site 18.01 24.58 0.00 0.00 0.01 0.41 0.49 0.00 0.00 0.00 0.00 1.60 3.08 0.54 0.02 0.00 0.00 16.88 0.00 0.00 0.00 26.25 7.77 0.35 SAND 2016 Prairie 29.12 7.15 0.00 0.00 0.00 0.36 1.23 0.00 0.00 0.00 0.04 1.17 11.09 0.74 0.01 0.00 0.01 6.03 0.00 0.00 0.00 34.23 8.19 0.63 1Land use features are numbered as follows 1:Corn, 2:Soybeans, 3:Sweet Corn, 4:Rye, 5:Oats, 6:Alfalfa, 7:Other Hay, 8:Other Crops, 9:Wildflower/Clover, 10:Fallow crop, 11:Apples, 12:Open Water, 13:Develped open space, 14:Developed low intensity, 15:Developed med intensity, 16:Developed High intensity, 17:Barren, 18:Deciduous Forest, 19:Evergreen Forest, 20:Mixed Forest, 21:Shrubland, 22:Grass/ Pasture, 23:Woody Wetlands, 24:Herbaceous wetlands

164

SI Table S3. Land use classifications combined to create four land use¹ categories in 2015 & and 2016.

Cropland Developed Grassland Woodland

Soybean Open Water Clover/Wildflower Deciduous Forest

Corn Developed Open Space Fallow Crop Evergreen Forest

Sweet Corn Developed Low Intensity Grass/Pasture Mixed Forest

Rye Developed Medium Intensity Shrub land

Oats Developed High Intensity Woody Wetland

Alfalfa Barren Herbaceous Wetland

Other Hay Apples

Other Crops

¹Land use features were based on the USDA-NASS cropland data layer for 2015 and 2016 within a 1.6 km radius of each soybean farm.

SI Table S4. Mite load and queen presence as fixed factors in 2015 & 2016. Mite Load¹ Queen Presence² F Df p-value F Df p-value

Mass 0.84 1, 38.05 0.3638 0.12 4, 37.78 0.9746

Immature bee 0.34 1, 38 0.5638 0.41 4, 37.45 0.7968 population

Adult bee 0.1 1, 25.97 0.7566 0.28 4, 23.92 0.8886 population

1Mites per 300 bees as measured monthly during 2015 and 2016 2Queen presence is measured as binary 1 (present) and (1) absent at each inspection in 2015 and 2016

165

SI Table S5. Simple effect comparisons of interaction between cultivation category and sampling week least square means for apiary mass in 2015 and 2016. Adjustment for multiple comparisons: Tukey-Kramer.

Cultivation Cultivation Week Estimate SE DF t Value Adj P Category Category Week 22 Low Cultivation High Cultivation 2.2116 2.7961 79.58 0.79 0.4304 Week 24 Low Cultivation High Cultivation -0.2988 2.2546 43.3 -0.13 0.8948 Week 26 Low Cultivation High Cultivation -2.8491 2.7961 79.58 -1.02 0.3102 Week 27 Low Cultivation High Cultivation -0.9209 2.7961 79.58 -0.33 0.7424 Week 28 Low Cultivation High Cultivation -3.4988 2.2546 43.3 -1.55 0.1232 Week 30 Low Cultivation High Cultivation -3.9157 2.2546 43.3 -1.74 0.0848 Week 32 Low Cultivation High Cultivation -9.3333 2.2546 43.3 -4.14 <.0001 Week 34 Low Cultivation High Cultivation -7.7574 2.7961 79.58 -2.77 0.0064 Week 36 Low Cultivation High Cultivation -7.3504 2.7961 79.58 -2.63 0.0096 Week 38 Low Cultivation High Cultivation -7.8591 2.7961 79.58 -2.81 0.0057 Week 39 Low Cultivation High Cultivation -3.7749 2.7961 79.58 -1.35 0.1794 Week 42 Low Cultivation High Cultivation -3.8926 2.7961 79.58 -1.39 0.1663 Week 43 Low Cultivation High Cultivation -5.5410 2.7961 79.58 -1.98 0.0497 166

SI Table S6. Simple effect comparisons of interaction between cultivation category and sampling week least square means for apiary immature bee population (i.e., Brood) in 2015 and 2016. Adjustment for multiple comparisons: Tukey-Kramer. Cultivation Cultivation Week Estimate SE DF t Value Pr > |t| Category Category Week 22 Low Cultivation High Cultivation 3.5952 57.7091 122.1 0.06 0.9504 Week 24 Low Cultivation High Cultivation -3.2992 57.7091 122.1 -0.06 0.9545 Week 26 Low Cultivation High Cultivation 7.2962 57.7091 122.1 0.13 0.8996 Week 27 Low Cultivation High Cultivation -26.9660 57.7091 122.1 -0.47 0.6411 Week 28 Low Cultivation High Cultivation -47.8189 41.9798 95.58 -1.14 0.2575 Week 30 Low Cultivation High Cultivation -99.8669 41.9798 95.58 -2.38 0.0194 Week 32 Low Cultivation High Cultivation -28.4920 41.9798 95.58 -0.68 0.4990 Week 34 Low Cultivation High Cultivation -71.2019 57.7091 122.1 -1.23 0.2196 Week 36 Low Cultivation High Cultivation -183.90 57.7091 122.1 -3.19 0.0018 Week 38 Low Cultivation High Cultivation -54.8881 57.7091 122.1 -0.95 0.3434 Week 39 Low Cultivation High Cultivation -88.7360 57.7091 122.1 -1.54 0.1267 Week 42 Low Cultivation High Cultivation -18.3270 57.7091 122.1 -0.32 0.7513 Week 43 Low Cultivation High Cultivation -0.3410 57.7091 122.1 -0.01 0.9953

SI Table S7. Simple effect comparisons of interaction between cultivation category and sampling week least square means for apiary adult bee population in 2016. Adjustment for multiple comparisons: Tukey-Kramer. Week Cultivation Category Cultivation Category Estimate SE DF t Value Adj P Week 22 Low Cultivation High Cultivation 0.8500 3.3249 23.84 0.26 0.7990 Week 24 Low Cultivation High Cultivation -0.3000 3.3249 23.84 -0.09 0.9284 Week 27 Low Cultivation High Cultivation -3.4250 3.3249 23.84 -1.03 0.3064 Week 28 Low Cultivation High Cultivation -3.9000 3.3249 23.84 -1.17 0.2447 Week 30 Low Cultivation High Cultivation -3.4000 3.3249 23.84 -1.02 0.3099 Week 32 Low Cultivation High Cultivation -15.9250 3.3249 23.84 -4.79 <.0001 Week 34 Low Cultivation High Cultivation -14.2000 3.3249 23.84 -4.27 <.0001 Week 36 Low Cultivation High Cultivation -8.5750 3.3249 23.84 -2.58 0.0120 Week 39 Low Cultivation High Cultivation -9.2000 3.3249 23.84 -2.77 0.0072 Week 43 Low Cultivation High Cultivation -5.5250 3.3249 23.84 -1.66 0.1009

SI Table S8. Type III fixed effects of continuous measures of landscape on apiary weight, immature bee population, and adult bee population in 2015 and 2016.

Weight (Kg) Immature bee population Adult bee population Num Den Num Den Num Den Effect F Value Pr > F F Value Pr > F F Value Pr > F DF DF DF DF DF DF

Grassland 1 17.5 0.01 0.939 1 18.6 0.13 0.7202 1 6 0.65 0.4524

Woodland 1 15.9 5.27 0.0356 1 15.7 0.19 0.6675 1 6 3.47 0.1117

Developed 1 15.4 0 0.9704 1 14.7 0.59 0.4557 1 6 0.23 0.6478 Week 12 103 11.78 <.0001 12 93.5 4.69 <.0001 9 54 16.42 <.0001 Year 1 17.4 1.44 0.2463 1 27.9 3.94 0.0571 . . . . Grassland*Week 12 104 0.26 0.9942 12 92.4 0.44 0.9421 9 54 1.35 0.2335 Woodland*Week 12 103 2.93 0.0016 12 94.3 0.55 0.8778 9 54 2.19 0.0368 Developed*Week 12 103 0.27 0.992 12 94 0.55 0.8751 9 54 1.03 0.4254 Cropland 1 17.5 5.84 0.0268 1 17.5 3.62 0.0735 1 8 5.15 0.0529 167 Week 12 128 1.92 0.0375 12 119 3.15 0.0006 9 72 3.12 0.0032

Year 1 20.6 1.1 0.3059 1 34.7 3.91 0.0561 . . . .

Cropland*Week 12 128 2.7 0.0028 12 118 1.71 0.0734 9 72 6.31 <.0001

Grassland, Woodland, and Developed were all ran in one model while cropland was ran in a separate model due to collinearity of landscape features. Adult bee population was measured in 2016 only.

168

SI Table S9. Simple effect comparisons of interaction between cultivation category and sampling week least square means for apiary nurse bee lipid percent in 2015 and 2016. Adjustment for multiple comparisons: Tukey-Kramer. Week Cultivation Category Cultivation Category Estimate SE DF t Value Adj P Week 24 Low Cultivation High Cultivation -0.1828 0.2858 69 -0.64 0.5246 Week 28 Low Cultivation High Cultivation 0.2410 0.2858 69 0.84 0.4021 Week 32 Low Cultivation High Cultivation -0.2712 0.4042 69 -0.67 0.5046 Week 34 Low Cultivation High Cultivation -0.4791 0.4042 69 -1.19 0.2400 Week 43 Low Cultivation High Cultivation -0.1623 0.2858 69 -0.57 0.5720

SI Table S10. Differences of least square means for apiary nurse bee lipid content between weeks in 2015 and 2016. Week Week Estimate SE DF t Value Adj P

24 28 1.0074 0.2021 69 4.98 <.0001 24 32 1.2566 0.2609 69 4.82 <.0001 24 34 1.1298 0.2609 69 4.33 0.0005 24 43 1.8425 0.2021 69 9.12 <.0001 28 32 0.2492 0.2609 69 0.96 0.874 28 34 0.1224 0.2609 69 0.47 0.9899

28 43 0.835 0.2021 69 4.13 0.0009

32 34 -0.1268 0.3301 69 -0.38 0.9953

32 43 0.5858 0.2609 69 2.25 0.1757

34 43 0.7126 0.2609 69 2.73 0.0595 169

SI Table S11. Rates of apiary growth and decline in high and low cultivation in 2015 and 2016.

Cultivation Rate Standard Estimate DF t Value Pr > |t| Category Comparison Error Rate of gain High Cultivation vs - 0.9414 1.8966 592.8 -0.5 0.6198 Rate of loss Rate of gain Low Cultivation vs 0.763 1.8966 592.8 0.4 0.6876 Rate of loss Low Cultivation Weight vs Rate of gain -5.5938 2.0978 594 -2.67 0.0079 High Cultivation Low Cultivation vs Rate of Loss 3.8893 1.6025 591.7 2.43 0.0155 High Cultivation Rate of gain

High Cultivation vs - 56.47 46.739 553 -1.21 0.2275 Rate of loss Rate of gain Low Cultivation vs - 32.728 46.739 553 -0.7 0.4841 Rate of loss Low Cultivation vs Rate of gain -87.433 51.571 556 -1.7 0.0906 High Cultivation Low Cultivation Immature bee population vs Rate of Loss 63.691 39.447 550.2 1.61 0.107 High Cultivation Rate of gain -2.8458 2.1949 350 -1.3 0.1956 High Cultivation vs Rate of loss Rate of gain Low Cultivation vs 0.02917 2.1949 350 0.01 0.9894 Rate of loss Low Cultivation vs Rate of gain -4.5 2.2585 350 -1.99 0.0471 High Cultivation

Adult bee population Low Cultivation vs Rate of Loss 7.375 2.1294 350 3.46 0.0006 High Cultivation

Rate of gain calculated from colony initiation to peak weight, weeks (22-30) May, June, and July Rate of loss calculated from peak weight to the end of the season, weeks (32-42) August, September, and October

SI Table S12. Differences of least square means for colony pollen collection type in high and low cultivation landscapes in 2015 and 2016. Cultivation Cultivation Pollen Type Pollen Type Estimate DF t Value Adj P Category Category High Clover High Partridge Pea 67.2209 36 4.03 0.003 High Clover High Trace 67.5546 36 4.05 0.003 High Clover Low Clover 12.0419 51.14 0.68 0.983 High Clover Low Partridge Pea 55.1787 51.14 3.12 0.039 High Clover Low Trace 75.7701 51.14 4.28 0.002 High Partridge Pea High Trace 0.3338 36 0.02 1 High Partridge Pea Low Clover -55.179 51.14 -3.12 0.039 High Partridge Pea Low Partridge Pea -12.0422 51.14 -0.68 0.983 High Partridge Pea Low Trace 8.5492 51.14 0.48 0.997 High Trace Low Clover -55.5128 51.14 -3.13 0.037 170 High Trace Low Partridge Pea -12.3759 51.14 -0.7 0.981 High Trace Low Trace 8.2155 51.14 0.46 0.997 Low Clover Low Partridge Pea 43.1368 36 2.59 0.126 Low Clover Low Trace 63.7282 36 3.82 0.006 Low Partridge Pea Low Trace 20.5914 36 1.24 0.817

SI Table S13. Pollen grains identified in honey sampled from colonies biweekly throughout soybean bloom in 2016.

Mean percent pollen found in honey Percent Rest (including 8 Soybean Cultivation Total pollen Red+white Birdsfoot minor species and Week Soybean Fields Category pellets sampled clover trefoil other unknown Blooming species) 28 100 High Cultivation 1500 38.93% 3.73% 0.40% 57.33% 30 100 High Cultivation 1500 61.93% 22.73% 0.00% 15.33% 32 100 High Cultivation 1100 52.53% 17.53% 0.73% 29.93% 34 0 High Cultivation 720 36.50% 5.25% 0.33% 58.25% 28 100 Low Cultivation 1500 25.67% 1.20% 0.00% 73.13% 30 100 Low Cultivation 1500 40.87% 4.53% 0.20% 54.60% 32 100 Low Cultivation 1200 50.83% 0.92% 0.08% 48.25% 34 0 Low Cultivation 860 23.00% 11.08% 0.79% 65.92%

171

172

SI Table S14. Growth stage¹ of each apiary soybean farm in 2015 and 2016.

Site Cultivation Year Calendar week Name Category 22 24 26 28 30 32 34 2015 JUL High . V2 V4 R1 R2 R3 . 2015 OFAR High . V3 R1 R2 R3 R4 .

2015 WDAI High . V3 R1 R2 R3 R4 . 2015 GLI High . V3 R1 R2 R3 R4 . 2015 LIPP High . V1 V3 R1 R2 R4 . 2015 LIOP Low . V1 V4 R1 R2 R3 .

2015 CAT Low . V3 V5 R1 R2 R4 . 2015 CURT Low . V4 R1 R2 R3 R4 . 2015 HOR Low . V5 R1 R2 R3 R4 . 2015 IARIV Low . V1 V3 R1 R3 R4 .

2016 OAK High VE V2 R1 R2 R3 R4 R5 2016 IVI High VE V1 V3 R1 R3 R4 R5 2016 TRA1 High VE V1 R1 R2 R3 R4 R5 2016 EDAI High VC V4 R1 R1 R3 R4 R5

2016 UTH High VE VC V3 R2 R3 R4 R5 2016 KOST Low VE VC R1 R2 R3 R4 R5 2016 HAG Low VC V1 R1 R2 R3 R4 R5 2016 CURT Low VE V5 R1 R2 R3 R4 R5

2016 TRA3 Low VC V2 R1 R2 R3 R4 R5 2016 ONT Low VE V1 R1 R2 R3 R4 R5 ¹Growth stage based on Hodgson et al. 2012 (SI Figure S1).

173

SI Table S15. Simple effect comparisons of interaction between landscape type and sampling week least square means for colony weight in prairie project during 2016. Adjustment for multiple comparisons: Tukey-Kramer. Week Landscape Landscape Estimate SE DF t Value Adj P

Week 36 Agricultural Site Prairie 0.01000 2.6561 76 0.00 0.9970

Week 37 Agricultural Site Prairie -0.3400 2.6561 76 -0.13 0.8985

Week 38 Agricultural Site Prairie -7.8000 2.6561 76 -2.94 0.0044

Week 39 Agricultural Site Prairie -8.6400 2.6561 76 -3.25 0.0017

Week 40 Agricultural Site Prairie -6.9500 2.6561 76 -2.62 0.0107

SI Table S16. Simple effect comparison of least square means of lipid content in honey bees in prairie and agricultural sites by month.

Week Landscape Landscape Estimate Df t Value p-value 32 Agricultural Site Prairie 0.00117 30 0.41 0.6864 34 Agricultural Site Prairie 0.00278 30 0.97 0.3394 36 Agricultural Site Prairie 0.00393 30 1.37 0.1796 38 Agricultural Site Prairie -0.0033 30 -1.15 0.2579 40 Agricultural Site Prairie -0.00861 30 -3.01 0.0053

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APPENDIX D. CHAPTER 5 SUPPLEMENTAL INFORMATION

SI Table 1. Farms selected for bee community sampling in central Iowa during 2015 and 2016. Farm Site Year County Percent land-use in 1.6 km radius of farm Type Cropland Developed Grassland Woodland JUL Mono-Soy 2015 Boone 78% 7% 9% 6% LIP Mono-Soy 2015 Boone 74% 8% 11% 7% CAT Mono-Soy 2015 Boone 56% 9% 15% 20% WDAR Mono-Soy 2015 Story 88% 8% 4% 0% CUR Mono-Soy 2015 Story 30% 30% 21% 19% HOR Mono-Soy 2015 Story 44% 5% 26% 25% OFAM Mono-Soy 2015 Hardin 75% 6% 17% 2% GLI Mono-Soy 2015 Hardin 93% 5% 2% 0% IR Mono-Soy 2015 Hardin 55% 5% 19% 20% LP Mono-Soy 2015 Hardin 44% 21% 27% 8% IVI Mono-Soy 2016 Boone 93% 5% 3% 0% KOS Mono-Soy 2016 Boone 13% 29% 15% 43% TRA1 Mono-Soy 2016 Boone 79% 4% 0.16 1% TRA3 Mono-Soy 2016 Boone 50% 6% 14% 29% CUR Mono-Soy 2016 Story 27% 34% 20% 19% EASD Mono-Soy 2016 Story 82% 8% 10% 1% HAG1 Mono-Soy 2016 Story 52% 4% 13% 32% OAK Mono-Soy 2016 Story 91% 5% 3% 0% ONT Mono-Soy 2016 Story 12% 46% 11% 31% UTH Mono-Soy 2016 Story 87% 3% 9% 1% SEV Div-FV 2015, 2016 Story 83% 6% 7% 4% RIV Div-FV 2015, 2016 Polk 40% 10% 20% 29% MUS Div-FV 2016 Story 70% 5% 15% 10% BER Div-FV 2016 Polk 58% 5% 21% 16% BLA Div-FV 2016 Polk 56% 3% 15% 25% NFAM Div-FV 2016 Polk 40% 10% 20% 30% MESK Div-FV 2016 Tama 83% 6% 7% 4%

175

SI Table 2. Div-FV (diverse fruit and vegetable) farm land area and number of crops in production in 2015 and 2016.

Farm Sampling Year Hectare Number of Crops Pesticide Regime NFAM 2015 2 30 Conventional MESK 2015 2.8 15 Conventional SEV 2015, 2016 1.8 36 Certified Organic RIV 2015, 2016 4.8 31 Conventional MUS 2015 1.2 35 Conventional BER 2015 16.2 12 Conventional BLA 2015 14.6 50 Certified Organic

SI Table 3. Land use classifications combined to create four land use categories for all Mono-SOY and Div-FV farms in 2015 and 2016.

Cropland Developed Grassland Woodland Soybean Open Water Clover/Wildflower Deciduous Forest Corn Developed Open Space Fallow Crop Evergreen Forest Sweet Corn Developed Low Intensity Grass/Pasture Mixed Forest Winter Wheat Developed Medium Intensity Shrub Land Rye Developed High Intensity Woody Wetland Oats Barren Herbaceous Wetland Alfalfa Apples Other Hay Other Crops Winter Wheat

176

SI Table 4. Abundance and richness of bee community in Div-FV and Mono-SOY farms in central Iowa in 2015 and 2016.

Abundance Richness

Effect DF F Value Pr > F DF F Value Pr > F Year 1, 32.15 0.22 0.6448 1,32.09 3.83 0.0591 Farm Type 1, 29.39 0.18 0.6745 1,29.43 0.05 0.821

Total Total Month 3, 121.8 3.34 0.0217 3,121.8 9.53 <.0001

community Farm Type*Month 3, 126.3 1.43 0.2366 3,126.2 3.73 0.0131 Year 1, 32.76 0.00 0.9744 1,30.83 0.02 0.8852

Farm Type 1, 29.66 0.01 0.9195 1,28.44 4.72 0.0383

Taxa Month 3, 122.1 3.50 0.0177 3,121 4.51 0.0049 Common Farm Type*Month 3, 126.8 0.69 0.5570 3,125.3 2.98 0.0339 Year 1, 32.29 4.95 0.0333 1,33.15 12.37 0.0013

Farm Type 1, 29.04 4.97 0.0337 1,29.87 6.57 0.0156

Taxa Month 3, 121.7 2.50 0.0627 3,122.3 4.72 0.0038

Uncommon Farm Type*Month 3, 126.6 7.05 0.0002 3,127.1 3.00 0.0333 Year 1, 36.84 0.01 0.9311 1,37.15 0.18 0.6713 Farm Type 1, 31.27 0.80 0.3789 1,31.09 0.36 0.5538 Month 3, 123.7 5.02 0.0026 3,123.6 5.06 0.0024

Rare Taxa Rare Farm Type*Month 3, 128.9 0.70 0.5564 3,128.8 0.62 0.6057

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SI Table 5. Post hoc comparisons of least square means comparing bee abundance and richness between Div-FV and Mono-SOY farms in central Iowa during each month of the sampling season in 2015 and 2016.

Abundance Richness Month Farm Type Estimate SE DF t Value Pr > |t| Estimate SE DF t Value Pr > |t|

June Div-FV Mono-SOY 14.325 10.325 114 1.39 0.168 1.9426 0.941 113 2.07 0.0412

July Div-FV Mono-SOY 5.0057 8.7523 97.8 0.57 0.5687 0.3013 0.798 96.2 0.38 0.7067

August Div-FV Mono-SOY -7.4062 7.5227 70.2 -0.98 0.3282 -1.3339 0.688 68.9 -1.94 0.0565 Total Community Bee September Div-FV Mono-SOY -1.9451 9.9783 124 -0.19 0.8458 -0.4158 0.908 123 -0.46 0.6479

June Div-FV Mono-SOY 3.1183 9.1541 116 0.34 0.734 -0.05715 0.555 110 -0.1 0.9182

July Div-FV Mono-SOY 4.5524 7.7355 103 0.59 0.5575 -0.5479 0.473 91.5 -1.16 0.2494

August Div-FV Mono-SOY -6.9123 6.6048 74.8 -1.05 0.2987 -1.6128 0.409 64.6 -3.94 0.0002 Common Bee Taxa Bee Common September Div-FV Mono-SOY -2.8227 8.8703 129 -0.32 0.7508 -0.6159 0.536 119 -1.15 0.2524

June Div-FV Mono-SOY 11.1205 2.3836 117 4.67 <.0001 1.8863 0.5487 117.1 3.44 0.0008

July Div-FV Mono-SOY 0.2191 2.0112 105 0.11 0.9135 0.7218 0.4631 105.1 1.56 0.1220

August Div-FV Mono-SOY -0.6981 1.7111 76.3 -0.41 0.6844 0.08786 0.3942 77.1 0.22 0.8242

Uncommon Bee Taxa Bee Uncommon September Div-FV Mono-SOY 0.9266 2.3136 131 0.4 0.6894 0.3753 0.5324 130.8 0.70 0.4821

June Div-FV Mono-SOY 0.1576 0.2447 122 0.64 0.5208 0.1105 0.2226 121.3 0.50 0.6204

July Div-FV Mono-SOY 0.2042 0.2055 124 0.99 0.3223 0.1138 0.1870 126.1 0.61 0.5438

August Div-FV Mono-SOY 0.2256 0.1706 101 1.32 0.1889 0.1909 0.1547 105.1 1.23 0.2199 Rare Bee Taxa

September Div-FV Mono-SOY -0.1688 0.2424 142 -0.7 0.4874 -0.1656 0.2216 143 -0.75 0.4561

178

SI Table 6. Type III tests of fixed effects of honey bee colony weight compared between Div-FV and Mono-SOY farms in central Iowa for 2015 and 2016 combined. Effect Num DF Den DF F Value Pr > F Year 1 39.9 30.04 <.0001 Date 5 122.7 78.98 <.0001 Year*Date 5 122.7 13.16 <.0001 Treatment 1 21.04 2.40 0.1359 Year*Treatment 1 42.53 0.17 0.6864 Date*Treatment 5 122.7 4.67 0.0006 Year*Date*Treatment 5 122.7 10.40 <.0001 Developed 1 20.9 0.01 0.9186 Grassland 1 21.02 0.01 0.9197 Woodland 1 20.81 4.76 0.0407

SI Table 7. Simple effects comparisons of least square means with Tukey adjustment comparing colony weight between Div-FV and Mono-SOY farm across the season in central Iowa during 2015 and 2016.

Month Farm Type Estimate SE DF t Value Adj P 24-Jun Div-FV Mono-SOY 2.0177 2.3692 45.76 0.85 0.3961 8-Jul Div-FV Mono-SOY 7.2726 2.3692 45.76 3.07 0.0026 22-Jul Div-FV Mono-SOY -0.3589 2.3692 45.76 -0.15 0.8799 4-Aug Div-FV Mono-SOY 0.03469 2.3692 45.76 0.01 0.9883 12-Sep Div-FV Mono-SOY 2.1069 2.4477 50.98 0.86 0.3911 12-Oct Div-FV Mono-SOY 6.9169 2.4477 50.98 2.83 0.0055

179

SI Table 8. Type III tests of fixed effects of honey bee colony growth metrics and nutritional state in colonies in Div-FV and Mono-SOY farms in central Iowa during 2016 only.

Effect Num DF Den DF F Value Pr > F Date 9 117 43.89 <.0001

Farm Type 1 10 6.92 0.0251 Date*Farm Type 9 117 1.88 0.0618 Developed 1 10 0.68 0.4291 Weight (kg) Grassland 1 10 0.72 0.4154 Woodland 1 10 6.36 0.0303 Date 9 117 51.27 <.0001

) 2 Treatment 1 10 3.91 0.0763 Date*Treatment 9 117 1.18 0.3151 Developed 1 10 0.04 0.8517 Brood (cmBrood Grassland 1 10 0.56 0.4712 Woodland 1 10 2.47 0.1470 Date 9 117 40.94 <.0001 Treatment 1 10 4.89 0.0514

Date*Treatment 9 117 0.77 0.6444 Bees Developed 1 10 0.22 0.6474 Grassland 1 10 1.31 0.2782 Frame Sides of Woodland 1 10 4.24 0.0664 Date 3 100.4 8.5 <.0001

Treatment 1 12.97 0.02 0.8778 Date*Treatment 3 100.4 1.52 0.2138 Developed 1 16.48 0.03 0.8589 Grassland 1 10.23 0.67 0.4314 Percent Lipid Woodland 1 13.41 0.03 0.8691

180

SI Table 9. Simple effects comparisons of least square means using Tukey adjustment comparing colony growth metrics and nutritional state between Div-FV and Mono-SOY farms across the season in central Iowa during 2016 only.

Date Farm Type Estimate SE DF t Value Pr > |t| Adj P 22-May Div-FV Mono-SOY 2.7495 3.1615 23.75 0.87 0.3932 0.3863 7-Jun Div-FV Mono-SOY 3.6595 3.1615 23.75 1.16 0.2586 0.2494 27-Jun Div-FV Mono-SOY 4.8707 3.1615 23.75 1.54 0.1366 0.1261 8-Jul Div-FV Mono-SOY 4.733 3.1615 23.75 1.5 0.1475 0.1371 22-Jul Div-FV Mono-SOY 6.2016 3.1615 23.75 1.96 0.0616 0.0522 3-Aug Div-FV Mono-SOY 10.1089 3.1615 23.75 3.2 0.0039 0.0018

Weight (kg) 17-Aug Div-FV Mono-SOY 10.1605 3.1615 23.75 3.21 0.0037 0.0017 31-Aug Div-FV Mono-SOY 7.893 3.1615 23.75 2.5 0.0199 0.0139 21-Sep Div-FV Mono-SOY 9.8712 3.1615 23.75 3.12 0.0047 0.0023 18-Oct Div-FV Mono-SOY 6.3095 3.1615 23.75 2 0.0576 0.0483 22-May Div-FV Mono-SOY 21.8775 44.2837 33.22 0.49 0.6245 0.6222 7-Jun Div-FV Mono-SOY 37.8985 44.2837 33.22 0.86 0.3982 0.3939 27-Jun Div-FV Mono-SOY 45.5478 44.2837 33.22 1.03 0.3111 0.3058

) 8-Jul Div-FV Mono-SOY 28.1525 44.2837 33.22 0.64 0.5293 0.5262 2 22-Jul Div-FV Mono-SOY 88.3037 44.2837 33.22 1.99 0.0544 0.0485 d (cm 3-Aug Div-FV Mono-SOY 111.58 44.2837 33.22 2.52 0.0167 0.0131

Broo 17-Aug Div-FV Mono-SOY 85.4836 44.2837 33.22 1.93 0.0621 0.056 31-Aug Div-FV Mono-SOY 110.03 44.2837 33.22 2.48 0.0182 0.0144 21-Sep Div-FV Mono-SOY 78.5057 44.2837 33.22 1.77 0.0854 0.0789 18-Oct Div-FV Mono-SOY 30.7298 44.2837 33.22 0.69 0.4926 0.4891 22-May Div-FV Mono-SOY 2.0476 3.0969 35 0.66 0.5128 0.5098 7-Jun Div-FV Mono-SOY 3.0726 3.0969 35 0.99 0.3279 0.3232

27-Jun Div-FV Mono-SOY 3.7101 3.0969 35 1.2 0.239 0.2333 8-Jul Div-FV Mono-SOY 6.1976 3.0969 35 2 0.0532 0.0477 22-Jul Div-FV Mono-SOY 4.5226 3.0969 35 1.46 0.1531 0.1469 3-Aug Div-FV Mono-SOY 7.6351 3.0969 35 2.47 0.0187 0.0151 es Sidesof Bees 17-Aug Div-FV Mono-SOY 7.0726 3.0969 35 2.28 0.0286 0.0242 Fram 31-Aug Div-FV Mono-SOY 7.1851 3.0969 35 2.32 0.0263 0.0221 21-Sep Div-FV Mono-SOY 2.9976 3.0969 35 0.97 0.3397 0.3351 18-Oct Div-FV Mono-SOY 4.7351 3.0969 35 1.53 0.1353 0.129

-0.0026 0.00314 82.39 -0.84 0.4043 0.4038 id 7-Jun Div-FV Mono-SOY 8-Jul Div-FV Mono-SOY -0.0008 0.00314 82.39 -0.24 0.8099 0.8098 21-Sep Div-FV Mono-SOY 0.00053 0.00314 82.39 0.17 0.8662 0.8661 Percent Lip 18-Oct Div-FV Mono-SOY 0.00391 0.00198 24.28 1.97 0.0599 0.0511

181

SI Table 10. Simple effects comparisons of least square means using Tukey adjustment comparing honey bee nutritional state (lipid content) across sampling dates in central Iowa during 2016. Date Date Estimate SE DF t Value Adj P 7-June 8-July 0.006722 0.002110 100.4 3.19 0.0102 7-June 17- August 0.007235 0.002110 100.4 3.43 0.0048 7-June 18 October 0.008668 0.001723 100.4 5.03 <.0001 8-July 17- August 0.000513 0.002110 100.4 0.24 0.9949 8-July 18 October 0.001945 0.001723 100.4 1.13 0.6724 17- August 18 October 0.001432 0.001723 100.4 0.83 0.8394

SI Figure 1. Mean proportion of each landscape category found within a 1.6 km radius of colonies in Div-FV (black) and Mono-SOY (grey) farms in central Iowa over 2015 and 2016.

Data represent 4 Div-FV and 10 Mono-SOY farms in 2015 and 5 Div-FV and 10 Mono-SOY farms in 2016. Two tailed t-tests of each landscape category showed no difference in proportion cropland (T18.17= 0.04; P=0.9699), developed (T22.61=1.91; P=0.0687), grassland (T18.17=0.65; P=0.5211), or woodland (T18.132 =0.72; P=0.4814) between Div -FV and Mono- SOY farms.

182

A B

= 10 m

SI Figure 2. Placement of honey bee colonies and bee-bowl pan traps in Mono-SOY (A) and Div-FV (B) farms in central Iowa. In Mono-SOY farms, traps were placed parallel to honey bee colonies and 10 m inside of the field. In Div-FV farms, traps were placed in an area planted with grass directly adjacent to the crops and also parallel to honey bee colonies. Honey bee colonies were placed in the exterior grassy perimeter, 3 m from the edge of the crop and 10 m from the bee-bowls.