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Theses and Dissertations--Entomology Entomology

2018

BEE CONSERVATION IN URBAN LANDSCAPES: ASSESSING BEE BEE CONSERVATION IN URBAN LANDSCAPES: ASSESSING BEE

ASSEMBLAGES, BEE–ATTRACTIVENESS, AND NUTRITRITIONAL ASSEMBLAGES, BEE–ATTRACTIVENESS, AND NUTRITRITIONAL

VALUE OF WOODY LANDSCAPE PLANTS AND MITIGATING VALUE OF WOODY LANDSCAPE PLANTS AND MITIGATING

POTENTIAL BEE HAZARD FROM NEONICOTINOID INSECTICIDES POTENTIAL BEE HAZARD FROM NEONICOTINOID INSECTICIDES

Bernadette Maria Mach University of Kentucky, bernadettemach@gmail.com Digital Object Identifier: https://doi.org/10.13023/etd.2018.295

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Recommended Citation Recommended Citation Mach, Bernadette Maria, "BEE CONSERVATION IN URBAN LANDSCAPES: ASSESSING BEE ASSEMBLAGES, BEE–ATTRACTIVENESS, AND NUTRITRITIONAL VALUE OF WOODY LANDSCAPE PLANTS AND MITIGATING POTENTIAL BEE HAZARD FROM NEONICOTINOID INSECTICIDES" (2018). Theses and Dissertations--Entomology. 46. https://uknowledge.uky.edu/entomology_etds/46

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REVIEW, APPROVAL AND ACCEPTANCE REVIEW, APPROVAL AND ACCEPTANCE

The document mentioned above has been reviewed and accepted by the student’s advisor, on

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changes required by the advisory committee. The undersigned agree to abide by the statements

above.

Bernadette Maria Mach, Student

Dr. Daniel A. Potter, Major Professor

Dr. Kenneth Haynes, Director of Graduate Studies

BEE CONSERVATION IN URBAN LANDSCAPES: ASSESSING BEE ASSEMBLAGES, BEE–ATTRACTIVENESS, AND NUTRITRITIONAL VALUE OF WOODY LANDSCAPE PLANTS AND MITIGATING POTENTIAL BEE HAZARD

FROM NEONICOTINOID INSECTICIDES

DISSERTATION

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

College of Agriculture, Food and Environment at the University of Kentucky

By Bernadette Maria Mach

Lexington, Kentucky

Director: Dr. Daniel A. Potter, Professor of Entomology

Lexington, Kentucky

2018

Copyright © Bernadette Maria Mach 2018

ABSTRACT OF DISSERTATION

BEE CONSERVATION IN URBAN LANDSCAPES: ASSESSING BEE ASSEMBLAGES, BEE–ATTRACTIVENESS, AND NUTRITRITIONAL VALUE OF WOODY LANDSCAPE PLANTS AND MITIGATING POTENTIAL BEE HAZARD

FROM NEONICOTINOID INSECTICIDES

Public awareness of declining pollinator populations has increased interest in creating “bee–friendly” urban landscapes. I quantified bee visitation and assemblages of 72 species of flowering woody plants common in urban landscapes. I found strong plant species effects and variation in seasonal activity of particular bee taxa but no overall differences in bee visitation or genus diversity between native versus nonnative species or trees versus shrubs. Analysis of pollen from a subset of these plants revealed small but statistically significant differences in total and essential amino acids between native and nonnative species and trees and shrubs, although each group had species with high quality pollen.

Uptake and dissipation of soil–applied imidacloprid and dinotefuran was measured in nectar and leaves of two woody plant species, Ilex × attenuata and Clethra alnifolia to assess concentrations to which pollinators might be exposed in landscape settings. Three application timings were evaluated. Residues in nectar and tissue were analyzed by HPLC–MS/MS in two successive years. Residues in nectar following autumn or spring applications exceed concentrations shown to adversely affect individual and colony–level traits of bees. Summer application mitigated concentrations of imidacloprid (8–31 ng/g), but not dinotefuran (235–1191 ng/g), in nectar.

KEYWORDS: Bee Conservation, Flowering Woody Ornamental Plants, Neonicotinoid Insecticides, Pollen Nutritional Quality, Pollinator Conservation, Urban Landscapes

MULTIMEDIA ELEMENTS USED: TIFF (.tif)

Bernadette Maria Mach

7/25/2018 Date

BEE CONSERVATION IN URBAN LANDSCAPES: ASSESSING BEE ASSEMBLAGES, BEE–ATTRACTIVENESS, AND NUTRITRITIONAL VALUE OF WOODY LANDSCAPE PLANTS AND MITIGATING POTENTIAL BEE HAZARD

FROM NEONICOTINOID INSECTICIDES

By

Bernadette Maria Mach

Dr. Daniel A. Potter Director of Dissertation

Dr. Kenneth Haynes Director of Graduate Studies

7/25/2018 Date

iii

ACKNOWLEDGEMENTS

I sincerely thank my advisor, Dr. Daniel A. Potter, for the boundless energy with which he has guided and supported me over the past four years. His enthusiasm and mentorship motivated me to achieve far more than I ever thought I could. I also thank my committee members, Drs. R. Bessin, W. Fountain, C. Rittschof, and J. White for their time and expertise as well as Dr. L. Vaillancourt who served as the external member of my committee for my defense.

Special thanks go to my current and former lab members A. Baker, T. D. McNamara, C. Redmond, and A. Saeed for their comradery and help collecting and curating bee samples as well as assisting with nectar extractions and all other fieldwork. I would also like to thank P. Douglas, R. Geneve, J. Hart, D. Leonard, and R. Webber for help locating sample sites for particular plant species. I am incredibly grateful to the staffs at the Arboretum and State Botanical Garden of Kentucky (Lexington, KY), Boone County Arboretum (Union, KY), the Lexington Cemetery (Lexington, KY), and Spring Grove Cemetery and Arboretum (Cincinnati, OH) for their cooperation, and to the many homeowners who gave permission for me to sample in their residential landscapes. My sincere thanks go to Dr. K. Urschel, A. Gerritsen, and Dr. D. Archbold for their lab space, expertise, and time spenthelping me analyze pollen and nectar. I am grateful to S. Bondarenko, T. Chen, andB. Kowalsky (Valent U.S.A. LLC) for assistance with residue analyses and J.Chamberlin (Valent U.S.A. LLC) for advice on treatment protocols.

Finally, I wish to extend special thanks to my friends, family, and furry nieces and nephews who helped keep me motivated and sane throughout my studies, and of course to Daniel McNamara whose love and level–headedness has guided me through this challenging and rewarding time.

This research was supported by grants from the Bayer N. American Bee Care Center, Valent U.S.A. LLC, Horticultural Research Institute, USDA–NIFA–SCRI grant 2016–51181–235399, and the University of Kentucky Nursery Research Endowment Fund.

iv

TABLE OF CONTENTS

Acknowledgements ............................................................ .......................................... iii List of Tables...................................................................... ........................................... vi List of Figures .................................................................... ..........................................vii Chapter One: Introduction .................................................. ............................................ 1 Chapter Two: Quantifying bee assemblages and attractiveness of flowering woody landscape plants for urban pollinator conservation .............. ............................................ 6

Introduction ............................................................ ............................................ 6 Methods .................................................................. ............................................ 9

Plant species ................................................ ............................................ 9 Sample sites................................................. .......................................... 10 Bee–attractiveness ratings ............................ .......................................... 10 Sample collection ........................................ .......................................... 11 Statistical analysis ....................................... .......................................... 12

Results .................................................................... .......................................... 13 Bee–attractiveness ratings ............................ .......................................... 13 Bee abundance by taxon and genus diversity .......................................... 13 Cultivar comparisons ................................... .......................................... 15

Discussion............................................................... .......................................... 16 Chapter Three: Pollen nutritional quality of native and nonnative flowering woody landscape plants .................................................................. .......................................... 32

Introduction ............................................................ .......................................... 32 Methods .................................................................. .......................................... 34

Plant species and study sites ........................ .......................................... 34 Pollen collection and analysis ...................... .......................................... 35 Nectar collection and analysis ...................... .......................................... 36 Statistical analysis ....................................... .......................................... 37

Results .................................................................... .......................................... 37 Discussion............................................................... .......................................... 38

Chapter Four: Uptake and Dissipation of Neonicotinoid Residues in Nectar and Foliage of Systemically–Treated Woody Landscape Plants . .......................................... 46

Introduction ............................................................ .......................................... 46 Methods .................................................................. .......................................... 49

Plant species and study sites ........................ .......................................... 49 Neonicotinoid soil applications .................... .......................................... 50 Collecting nectar and foliage samples .......... .......................................... 51 Chemicals and reagents ............................... .......................................... 52 Preparation of standard solutions ................. .......................................... 52 Nectar extraction ......................................... .......................................... 53

v

Leaf tissue extraction ................................... .......................................... 54 LC—MS/MS analyses ................................. .......................................... 55 Quality control ............................................ .......................................... 56 Statistical analysis ....................................... .......................................... 56

Results .................................................................... .......................................... 57 Imidacloprid, imidacloprid olefin, and 5–hydroxy–imidacloprid residues in Ilex nectar and leaves ................. .......................................... 57 Imidacloprid, imidacloprid olefin, and 5–hydroxy–imidacloprid residues in Clethra nectar and leaves ........... .......................................... 58 Dinotefuran residues in Ilex nectar and leaves ......................................... 58 Dinotefuran residues in Clethra nectar and leaves ................................... 59

Discussion............................................................... .......................................... 60

Chapter Five: Conclusions and Applications ....................... .......................................... 73 References .......................................................................... .......................................... 77 Vita .................................................................................... .......................................... 86

vi

LIST OF TABLES

Table 2.1a, Plant characteristics and snapshot counts of 36 flowering trees .................... 24 Table 2.1b, Plant characteristics and snapshot counts of 36 flowering shrubs ................. 25 Table 2.2, Bee–attractiveness rating, distribution of bee taxa, and bee genus diversity

of 45 species of bee–attractive flowering trees and shrubs ...................... 26 Table 2.3, Summary of analysis of variance for effects of plant species, plant type

(tree or shrub), provenance (native or nonnative), and Julian date number on bee genus diversity and snapshot counts ................................ 27

Table 2.4, Characteristics and distribution of bee species and genera identified on 45 species of flowering woody plants ............... .......................................... 28

Table 2.5, Summary of analysis of variance for effects of plant species, plant type (tree or shrub), provenance (native or nonnative), and Julian date number on bee taxa abundance .................... .......................................... 29

Table 3.1, Plant, pollen, and nectar characteristics of 11 flowering trees and shrubs sampled in 2017 .......................................... .......................................... 42

Table 3.2, Summary of analysis of variance for effects of plant species, family, plant type (tree or shrub), plant provenance (native or nonnative), Julian date number, snapshot count and bee genus diversity (Chapter 2, Mach and Potter 2018) on total amino acid content and essential amino acid content ....................................... .......................................... 43

Table 4.1, Dates on which woody plants received one—time soil treatment with imidacloprid or dinotefuran and on which nectar and foliage were sampled for residue analysis ........................ .......................................... 65

Table 4.2, Monitored transitions and their mass spectrometer specific parameters used in the residue analyses ......................... .......................................... 66

Table 4.3, Average recoveries (and standard deviations) of analytes in nectar and leaf tissues ................................................... .......................................... 67

Table 4.4, Average recoveries (and standard deviations) of analytes from concurrently analyzed fortified control nectar and leaf samples .............. 68

Table 4.5, Mean (range) concentrations of sugar (°Bx), imidacloprid (ng/g), its metabolites imidacloprid olefin and 5–hydroxy–imidacloprid in nectar of Ilex (Foster holly) or Clethra (summersweet) following systemic treatment by soil injection ............. .......................................... 69

Table 4.6, Summary of analysis of variance for effects of treatment date (November 2014, March 2015, or post–bloom 2015), year sampled (2015 or 2016), and leaf age class (current or 1–year old leaves, Ilex only) on residue levels (ng/g) imidacloprid or dinotefuran in nectar or foliage of two species of woody landscape plants .... .......................................... 70

vii

LIST OF FIGURES

Figure 2.1, Comparison of snapshot counts on trees and shrubs, native and nonnative plants ........................................................... ....................................... 30

Figure 2.2, Comparison of bee genus diversity on trees and shrubs and between native and nonnative plants .......................... ....................................... 31

Figure 3.1, Comparison of total amino acid content between trees and shrubs and native and nonnative plants .......................... ....................................... 44 Figure 3.2, Comparison of total essential amino acid content between trees and shrubs and native and nonnative plants ........ ....................................... 45 Figure 4.1, Mean (±SE) concentrations (ng/g) of imidacloprid in floral nectar of Ilex × attenuata (Foster holly) and Clethra alnifolia (summersweet) following systemic soil treatment in autumn (post–bloom, November

2014), spring (pre–bloom, March 2015), or summer (post–bloom, June or August 2015 for Ilex and Clethra, respectively) ....................... 71 Figure 4.2, Mean (±SE) concentrations (ng/g) of dinotefuran in leaves of Ilex ×

attenuata (Foster holly) and Clethra alnifolia (summersweet) following systemic soil treatment in autumn (post–bloom, November 2014), spring, (pre–bloom, March 2015), or summer (post–bloom, June or August 2015 for Ilex and Clethra, respectively) ....................... 72

1

CHAPTER ONE

Introduction

Bee populations are declining worldwide due to habitat loss and fragmentation,

exotic pests and pathogens, nutritional stress, pesticide exposure, loss of genetic diversity,

and other stressors (Potts et al. 2010, Goulson et al. 2015). Urban landscapes can be

refuges for bee populations (Goddard et al. 2010, Potts et al. 2016), and a growing body

of research indicates that native pollinator communities can persist and even thrive in

urban settings (Tommasi et al. 2004, McFrederick and LeBuhn 2006, Fetridge et al. 2008,

Matteson et al. 2008, Tonietto et al. 2011, Larson et al. 2014). Bees provide valuable

ecosystem services in urban landscapes by pollinating wild and ornamental plants that

provide food (fruit and seeds) for birds and other wildlife as well as increasing yield of

crops grown in residential and community gardens (Matteson and Langellotto 2009,

Lowenstein et al. 2015). Creating bee-friendly habitats and providing adequate floral

resources in urban landscapes can help conserve bees’ vital pollination services while

also promoting biodiversity by increasing connectivity between remnants of natural

habitat and other greenspace (Goddard et al. 2010).

While public interest in bee conservation has spurred the creation of many lists of

“bee-friendly” plants, these lists are generally not well-grounded in empirical data

(Garbuzova and Ratnieks 2014a), lack taxonomic detail on the pollinators observed, and

focus on native herbaceous plants. This lack of empirical data and taxonomic detail

mean we know very little about plant-pollinator interactions in urban landscapes.

Additionally, the focus on native herbaceous plants excludes flowering woody landscape

plants, which are key components of urban landscapes and can provide valuable and

2

plentiful food resources for urban bee populations (Hausmann et al. 2016, Somme et al.

2016). Relying exclusively on herbaceous ornamental flowers often results in seasonal

gaps in floral resource availability (Comba et al. 1999, Frankie et al. 2005), which could

be buffered by landscapes composed of a mix of woody plants with collective bloom

periods extending from early spring to autumn.

Urban landscapes contain a mix of native and nonnative woody plant species

(Collins et al. 2000, Dirr 2011, Salisbury et al. 2015), but recent debate on the role of

non–invasive nonnative plants has spurred support for landscapes composed

predominantly or exclusively of native plants (Tallamy 2007). Native plants support both

specialist and generalist native pollinators as well as insect herbivores, which in turn feed

insectivorous wildlife like birds (Tallamy 2007, Tallamy and Shropshire 2009, Burghardt

et al. 2010). However, supporting insect herbivores is not always a desirable trait in

urban landscapes where the presence of such insects may prompt insecticide treatments.

In addition, specialist foraging strategies in bees may be less common than previously

thought (Wasser et al. 1996). Data on the bee–attractiveness, bee assemblages, and

nutritional quality of flowering woody plants will help inform bee conservation efforts as

well as enhance our understanding of the plant characteristics and nutritional cues that

may influence bee attractiveness and assemblages.

Although flowering woody plants have potential for bee conservation, many

common species have key insect pests that often prompt the use of insecticides,

particularly neonicotinoid insecticides. Neonicotinoids are a widely used class of

insecticides that function by selectively binding to insect nicotinic acetylcholine receptors,

leading to paralysis and death. When neonicotinoids are applied to the soil, they are taken

3

up by the roots and move acropetally in xylem sap (Bonmatin et al. 2010), conferring

systemic protection to the plant which allows the neonicotinoids to reach pest feeding

sites (e.g., phloem–xylem interface under bark; upper–canopy foliage of trees) that are

impossible or impractical to protect with non–systemic application methods such as foliar

sprays. Systemic neonicotinoids can provide multiple years of protection from some

foliage–feeding pests of trees and shrubs (Cowles et al. 2006, Frank et al. 2007,

Szczepaniec and Raupp 2007, Benton et al. 2015), These systemic properties make them

valuable tools for controlling aphids, psyllids, scale insects, leaf miners, coleopteran

borers, leaf–feeding beetles, and other key pests of trees and shrubs in urban landscape

settings (Sclar and Cranshaw 1996, Gill et al. 1999, Cowles et al. 2006, Hubbard and

Potter 2006, Frank et al. 2007, Ugine et al. 2012, Smitley et al. 2015).

Although systemic neonicotinoid insecticides reduce hazard to beneficial insects

coming in direct contact with the residues when compared to foliar sprays, there is

concern over the extent to which bees and other pollinators may be exposed to

translocated residues in nectar and pollen (Blacquière et al. 2012, Goulson 2013, Godfray

et al. 2014, Bonmatin et al. 2015, Pisa et al. 2015). Laboratory and semi–field studies

have shown sublethal concentrations of neonicotinoids can adversely affect learning,

foraging ability, homing ability, and reproduction in bees (Blacquière et al. 2012,

Goulson 2013, Pisa et al. 2015), but it is unknown whether the concentrations that occur

in nectar and pollen of systemically–treated flowering trees or shrubs is sufficient to

cause these effects (Blacquière et al. 2012, Godfray et al. 2014, Lundin et al. 2015).

Information on the rates of uptake and dissipation of neonicotinoid residues in nectar and

pollen of woody landscape plants is needed to assess the range of concentrations to which

4

bees might be exposed in urban landscape settings. Such information is needed by

regulatory agencies for pesticide risk assessment and to support extension

recommendations to nursery growers and landscape managers to enable them to manage

insect pests without harming bees.

The main theme of this dissertation is actionable science to help inform bee

conservation efforts in urban landscapes, namely by making empirically-based

recommendations for pest-free, flowering woody plants as well as informing the

treatment timing of woody plants with systemic neonicotinoids. The target audiences for

this research include landscape professionals, nurseries and garden centers that sell

flowering woody landscape plants, regulatory agencies, extension educators, and the

general public. This dissertation includes three main research–based chapters. Chapter 2

describes a study quantifying bee visitation to 72 species of flowering trees and shrubs in

urban landscapes in central and northern Kentucky and southern Ohio, USA. I also

sampled the bee assemblages associated with 45 of the most bee–attractive plant species

and compared overall attractiveness and bee genus richness and diversity between native

and non–native plant species, trees and shrubs, and early–, mid–, and late–season

blooming species. Patterns of preference and seasonal activity of different bee taxa based

on their abundance in collections from each plant species were quantified. To further

enhance our understanding of the value of these plants for bee conservation, I

investigated the nutritional quality of pollen and nectar from a subset of these plants,, the

results of which are reported in Chapter 3. Chapter 4 focuses on the uptake and

dissipation of residues of two soil–applied neonicotinoids, imidacloprid and dinotefuran,

in nectar and foliage of two species of established woody landscape plants using autumn

5

(post–bloom), spring (pre–bloom), and summer (post–bloom) timings, with the end goal

to support protocols for integrating pest and pollinator management for urban landscapes.

The final chapter is a general summary of the key findings, with discussion of their

applications for urban bee conservation and integrated pest and pollinator management.

6

CHAPTER TWO

Quantifying bee assemblages and attractiveness of flowering woody landscape

plants for urban pollinator conservation

Introduction

Many wild bee species, including important crop pollinators such as bumble bees

(Bombus spp.), are declining in abundance or range (Beismeijer et al. 2006, Potts et al

2010, Cameron et al. 2011, Goulson et al 2015, Koh et al. 2016, Potts et al. 2016). Loss

of floral resources, associated with agricultural intensification and habitat loss, is one of

the major drivers of pollinator decline (Roulston and Goodell 2011, Koh et al. 2016).

Protecting natural areas and restoring agricultural lands are important strategies for

pollinator conservation, but urban landscapes, which offer a variety of forage and nesting

sites, can also be refuges for bees (Baldock et al. 2015, Hernandez et al. 2009, Hall et al.

2017). Indeed, substantial portions of native bee communities can persist and even thrive

in urban and suburban areas with support from gardens (Tommasi et al. 2004, Frankie et

al. 2005, Fetridge et al. 2008, Matteson et al. 2008, Tonietto et al. 2011, Garbuzov and

Ratnieks 2014b), parks (McFrederick and LeBuhn 2006), low–input lawns (Larson et al.

2014, Lerman and Milam 2016), and other properly designed and managed urban green

spaces.

Bees are keystone species in urban environments, where their pollination services

help propagate both wild and ornamental plants that in turn support birds and other urban

wildlife by providing fruit and seeds as well as harboring insect prey (Cane 2005,

Beismeijer at al. 2006, Gardiner et al. 2013, Hausmann et al. 2016). Urban bees directly

7

benefit people by pollinating crops grown in residential and community gardens

(Matteson and Langellotto 2009, Lowenstein et al. 2015), but they also present

opportunities to interact with nature and engage in conservation (Miller et al. 2005,

Colding et al. 2006, Goddard et al. 2010, Bellamy et al. 2017). The rise in urban

beekeeping (Castillo 2014) and initiatives such as the "Million Pollinator Garden

Challenge" (National Pollinator Garden Network 2017), the Monarch Waystation

program (Monarch Watch 2017), and the Certified Wildlife Habitat program (National

Wildlife Federation 2017) have spurred public interest and participation in gardening or

landscaping to help conserve pollinators, and many garden centers and websites now

promote certain species or varieties of ornamental plants as "friendly" to bees, butterflies,

and other flower–visiting insects (Garbuzov and Ratnieks 2014a, Garbuzov et al. 2017).

Numerous lists of "pollinator friendly" plants have been compiled by conservation

organizations (Pollinator Partnership 2017, Royal Horticultural Society 2017, Xerces

Society 2017), or produced by individuals and published in books (Fleming 2015, Frey

and LeBuhn 2015) or on websites. Those lists, for the most part, are not well–grounded in

empirical data (Garbuzov and Ratnieks 2014a) or do not cite published sources of such

data, nor do they specify, except in general terms (e.g. "bees", "butterflies", or "flies"),

the taxonomic composition of pollinator assemblages attracted to particular plant species.

With >4000 species of native bees in North America (Moisset and Buchmann 2011), each

with unique life history and feeding preferences, such lists have limited conservation

value. Another shortcoming is that existing lists invariably focus on native herbaceous

plants, omitting or largely ignoring woody plants. Several scientific studies have

documented the genera or species of bees associated with native eastern North American

8

herbaceous perennials (Tuell et al. 2008) and selected herbaceous native and nonnative

garden plants (Frankie et al. 2005, 2009; Garbuzov and Ratnieks 2014b, Garbuzov et al.

2015, 2017), but no comparable studies have documented the bee assemblages associated

with a broad array of woody landscape plants anywhere in North America.

Flowering woody plants can provide valuable food resources for urban bee

populations (Hausmann et al. 2016, Somme et al. 2016). A single tree or large shrub can

produce thousands of flowers, far more per unit area than in an equivalent patch of

garden plants or meadow, and offer copious pollen and nectar with high sugar content

(Somme et al. 2016). Landscapes with a mix of woody plants whose collective bloom

periods extend from early spring to autumn can buffer bee populations from seasonal

gaps in floral resource availability that can occur with herbaceous ornamental flowers in

urban gardens (Comba et al. 1999, Frankie et al. 2005). Such landscapes also promote

bee species richness and diversity by sustaining early–emerging seasonal specialists (e.g.,

Andrena spp.) as well as eusocial species (e.g., honey bees and bumble bees) whose

colony development and reproduction require large amounts of pollen and nectar

throughout the growing season (Dicks et al. 2015, Somme et al. 2016). Establishing

sustainable woody landscape plants to provide more and better food for bees should be

part of any strategy to conserve and restore urban pollinators.

About 75% of all U.S. households engage in yard and garden activities

(GardenResearch.com 2017), so there is a need for actionable science to help city

foresters, landscapers, and a larger, interested public make informed decisions in creating

bee–friendly landscapes. To that end, I quantified bee visitation to a wide range of

established flowering trees and shrubs at 368 urban and suburban sites in central and

9

northern Kentucky and southern Ohio, USA, and sampled the bee assemblages associated

with 45 of the most bee–attractive plant species. I compared overall attractiveness and

bee genus richness and diversity between native and non–native plant species, trees and

shrubs, and early–, mid–, and late–season blooming species. Patterns of preference and

seasonal activity of different bee taxa based on their abundance in collections from each

plant species were quantified. I identified numerous bee–attractive species of woody

landscape plants and documented clear differences in the assemblages of bees attracted to

different plant species.

Materials and methods

Plant species

In total, 72 species of flowering woody plants were sampled from 2014–2017

(Table 2.1). Sampling took place from February to November each year. Plant species

were selected based on recommendations from land care professionals, their suitability

for planting within the Ohio River Valley region, and availability and frequency of use in

urban landscapes. Both native and nonnative plant species were included in order to

compare their usage by bees. Plants listed as an invasive or nuisance species by the

USDA National Invasive Species Information Center (USDA NISIC 2018) or by the state

governments of Kentucky, Tennessee, Missouri, Illinois, Indiana, Ohio, West Virginia, or

Virginia were not included. Wind–pollinated plants were also excluded from sampling in

order to focus on plants with showy floral displays that would be attractive to both

consumers. Additionally, I sampled three sets of plant species (Hydrangea spp., Ilex spp.,

and Rosa spp.) to compare bee–attractiveness and bee genus diversity among cultivars

10

differing in horticultural characteristics, and between closely–related native and

nonnative plants.

Sample sites

All 318 sample sites were located within the urban landscape and were separated

by at least 1 km for same–species sites to ensure minimal overlap of bee populations.

Sample sites included street–side and municipal plantings, commercial and residential

landscapes, campuses, parking lots, and urban arboreta and cemeteries. Most (93%) of

the sample sites were within the Lexington, Kentucky metropolitan area; all were within

145 km of Lexington city limits. . The few sites outside the Lexington metropolitan area

were located in urban or peri–urban cemeteries and arboreta near Cincinnati, Ohio.

Individual sample sites ranged from single trees or large shrubs, to groupings or hedges

of a single plant species. Given the variability of the size, floral density, and floral

resources (e.g. nectar and pollen) at each site, we did not standardize floral density or

plant size. Instead, we chose to capture the real–world variation in bee–attractiveness

and bee diversity across each woody plant species.

Bee–attractiveness ratings

Each plant’s relative bee–attractiveness was rated based on two 30–second

“snapshot” counts per site , with three to five sites per plant species for a total of six to

ten snapshot counts per plant species (Garbuzov and Ratnieks 2014b). Snapshot counts

were taken during or close to peak bloom. During each 30–second period, bees actively

foraging on the flowers of the target plant(s) were counted, taking care to avoid counting

the same insects more than once. For plantings with variable floral density, snapshot

counts were concentrated in the areas of the plant or planting with the highest floral

11

density. Snapshot counts were taken while walking slowly around the vegetation for sites

with long, continuous plantings (> 2.5 m), while snapshot counts at smaller trees and

plantings were taken while stationary. Snapshot counts were taken between 9:00 to 18:00

EST during non–inclement weather (> 10 °C, winds < 16 kph, sunny to overcast)

immediately before collecting each 50–bee sample (see Sample collection below?) to

minimize disturbance of the bees. I chose this snapshot method as it is quick to

implement and practical for assessing many sites and can be applied to a wide range of

plants having very different floral densities and forms.

Sample collection

I sampled the bee assemblages associated with 45 of the 72 aforementioned plant

species, excluding relatively non–attractive ones with average snapshot counts of < 5

bees. Samples were collected from five sites for 35 of the plant species, four sites for

eight species, and three sites for two of the rarer plants (213 total sites). Bees were

collected immediately after taking snapshot counts and represented the first 50 bees

observed on the flowers after the counts were finished (250 total bees collected for most

plant species). Most samples were collected using aerial insect nets that could be

extended to about 4 m. Some plants with fragile flowers were sampled by knocking

individual bees into plastic containers filled with 75% EtOH. Sampling time ranged from

< 15 min to more than 2 h per site. Bee samples were washed with water and dish soap,

rinsed, then dried using a fan–powered dryer for 30–60 min, and pinned. All bees were

identified to genus (Packer et al 2007, Williams et al 2014). Bumble bees (Bombus spp.)

and honey bees (Apis mellifera L.) were identified to species. Reference specimens are

deposited in the University of Kentucky Department of Entomology Insect Collection.

12

Statistical analysis

Snapshot counts, bee genus diversity, and and abundance of select bee taxa (five

bee families, Bombus, and A. mellifera) were pooled across all sampling years and

analyzed for main effects of plant species, plant family (as a proxy for plant species due

to limited degrees of freedom), provenance (native or nonnative), plant type (tree or

shrub), site type (park–like or other), and Julian date number using General linear models

procedure (SAS, Version 9.4; SAS Institute, Cary, NC, USA), with snapshot counts

coded as repeated measures and with mean separation by Least Square Means. Species

diversity was based on the inverse of Simpson’s D (hereafter 1/D), which calculates a

number between 0 and 1, with higher numbers indicating more species–rich and even

samples. For analysis of bee taxa abundance, I counted the number of individuals in each

sample belonging to one of five North American bee families (Apidae, Andrenidae,

Colletidae, Halictidae, Megachilidae) and two additional taxa, Apis mellifera and Bombus

spp. and analyzed abundance of each for main effects. Sampling date was standardized

by converting to a Julian date number, which assigns each calendar date a unique integer

starting from 0 on January 1. I also attempted to analyze main effects of flower color,

flower type, and inflorescence type on bee snapshot counts, taxa abundance, and diversity

but was unable to do so due to the uneven distribution of the data among class variable

levels.

13

Results

Bee—attractiveness ratings

Snapshot counts, which were obtained for all 72 plant species, ranged from 0 to

103 with an average count of 12.8 bees per 30–sec observation per site (Tables 2.1a, b;

Table 2.2). The plants with the five highest average snapshot counts were Rhus

copallinum, Tetradium daniellii, Maackia amurensis, Heptacodium miconioides, and

Hydrangea paniculata (65.3, 50.1, 42.2, 33.2, and 31.4, respectively). I did not observe

any bees during the snapshot counts for Calcycanthus floridus, Hydrangea arborescens

‘Annabelle’, Hydrangea macrophylla, Magnolia liliiflora, and Sassafras albidum at any

of the sites sampled. Plant species and family, plant type, and Julian date number had

significant effects on snapshot counts (Table 2.3). There were small but statistically

significant differences in snapshot counts between trees and shrubs, with trees having

higher snapshot counts than shrubs (Fig 2.1). Snapshot counts increased slightly as the

growing season progressed. There were no significant differences in snapshot counts

between either native or nonnative species or between site types (Fig 2.1).

Bee abundance by taxon and genus diversity

Overall, 11,275 bees were collected from 45 species of flowering woody plants

that attracted, on average, ≥ 5 bees in the snapshot counts. Apid bees comprised 44.0%

of all bees sampled and were present on all 45 plant species sampled (Table 4; Table 2).

Halictid bees were similarly ubiquitous on all 45 plant species and accounted for 23.6%

of total bees. Andrenid bees accounted for 21.4% of total bees and often dominated the

bee assemblages of early blooming plants. Colletid and megachilid bees were the least

abundant bees overall, comprising only 5.0 and 5.9%, respectively, of the total bees in

14

my samples. Apis mellifera and Bombus spp. were collected from 44 and 39 of the

sampled plant species, respectively, and accounted for 21.4 and 11.9% of the total bees.

Plant species, and by extension plant family, played a key role in abundance of all

bee taxa analyzed (Table 2.5) and both were the only significant factors for Andrenidae,

Apidae, and A. mellifera. Most woody plants attracted bees from at least four families;

one exception was mock orange (Philadelphus) from which >95% of the bees collected

were Chelostoma philadelphi, a small megachilid. Andrenidae, Colletidae, Halictidae,

and Bombus spp. all showed strong seasonal patterns in abundance, with the proportion

of Andrenidae and Colletidae in my samples declining sharply with increasing Julian date,

while proportionate abundance of Halictidae and Bombus increased. Colletidae were

proportionately more abundant on trees than on shrubs, and on native as opposed to non–

native plant species (Table 2.5). All other bee taxa, including non–native A. mellifera and

native Bombus, were equally proportionately abundant on native and non–native plants.

Twenty–four bee genera were represented in my samples (Table 2.4), the most

abundant being Apis (22.1% of total bees), Andrena (21.4%), Lasioglossum (19.6%), and

Bombus (12.2%). Bee genus diversity index values ranged from 0 to 0.85 with an

average of 0.52 (Table 2.2). The plants with the highest average genus diversity (1/D)

were Abelia x grandiflora (0.74), Aesculus parviflora (0.71), Aesculus × carnea (0.70),

Rosa setigera (0.70), and Oxydendrum arboreum (0.69). Plant species and plant family

played a key role in genus diversity (Table 2.3), but there were no overall significant

differences in genus diversity between trees and shrubs or natives or nonnatives (Fig 2.2).

15

Cultivar comparisons

Snapshot counts were compared among four Hydrangea species, H. arborescens

‘Annabelle’ (native, shrub), H. macrophylla (nonnative, shrub), H. paniculata (nonnative,

shrub), and H. quercifolia (native, shrub) which differ in their floral characteristics (Table

2.1b). Most notably, H. paniculata has exposed fertile flowers while the other three

species lack fertile flowers or have them hidden beneath showy sterile outer sepals (Dirr

2011). Nonnative hydrangeas had higher average snapshot counts than native hydrangeas

(14.0 and 2.8, respectively, P = 0.02), but this was entirely because H. paniculata, a

nonnative, was the only species that was highly attractive to bees. Bee genus diversity

was not analyzed because H. arborescens, H. macrophylla, and H. quercifolia had

extremely low bee visitation rates, and were not sampled for bees.

Snapshot counts and bee assemblages were compared between four Ilex species: I.

× attenuata (native, shrub), I. × meserveae (nonnative, tree), I. opaca (native, tree), and I.

verticillata (native, shrub). All four had similar floral characteristics (Tables 2.1a, b) and

differed mainly by height and spread of the plant. There were no significant differences

between the average snapshot counts of native and nonnative Ilex (18.8 and 15.5,

respectively, P = 0.39), nor were there significant differences between the average genus

diversity of native and nonnative Ilex species (0.56 and 0.60, respectively, P = 0.60).

Two Rosa species were analyzed: R. setigera (native, n = 5) and several cultivars

of hybrid tea roses (nonnative, n = 35). Rosa setigera is a single–flowered rose with

pollen prominently displayed during most of its bloom. Hybrid tea roses are typically

double– or triple–flowered and either lack stamens and pollen, or have pollen that is

concealed by many layers of petals during bloom. I sampled a variety of hybrid tea roses

16

which I divided into seven categories based on color and flower form: light pink single–

petaled, dark pink single–petaled, red single–petaled, white double– or triple–petaled,

light pink double– or triple–petaled, dark pink double– or triple–petaled, and red double–

or triple–petaled. Rosa setigera had a significantly higher average snapshot count than

all hybrid tea roses sampled (16.1 and 0.1, respectively, P < 0.001). Bee genus diversity

was not analyzed because the hybrid tea roses had extremely low bee visitation rates and

were not sampled for bees.

Discussion

To my knowledge, this is the first scientific study to quantify variation in bee–

attractiveness and bee assemblages across a wide range of flowering woody landscape

plants. I identified 45 species of trees and shrubs that could be useful for augmenting

floral resources for bees in urban and suburban settings. Although all of my sampling

took place in Kentucky and southern Ohio, most of the bee–attractive plants on my list

should grow satisfactorily throughout USDA Plant Hardiness Zone 6, which covers

extensive regions of the United States (USDA 2018).

As with all studies assessing diversity of bees (Westphal et al. 2008), my

sampling methodology has limits and biases. Most notably, the large volume of bee

specimens (11,275) was prohibitive to identifying each to species and limits our

understanding of the extent to which native and non–native bee species, excepting A.

mellifera,utilize native and non–native plant species. While A. mellifera were equally

abundant on native and nonnative plants, we did observe certain non–native bee species

(e.g. Megachile sculpturalis, Smith) predominantly on non–native plants (Authors’

observations). This suggests that some non–native, invasive bees may benefit from

17

landscapes with plentiful non–native plants. Further analysis and identification of our

samples may yield more subtle patterns not observed in the analysis contained herein. As

for the snapshot counts, As for the snapshot counts, counting bees on the wing, as in my

snapshot counts, leaves room for misidentification (e.g., counting bee mimics as bees)

and miscounting, but I attempted to reduce this by replicating counts and using skilled

observers with strong backgrounds in bee identification. Ultimately, the snapshot counts

are an inherently variable measure influenced by numerous abiotic and biotic factors (e.g.

temperature, wind, local flora, etc) with limited usefulness outside of identifying bee–

attractive plant species and determining general trends in bee–attractiveness. Snapshot

counts and 50 bee samples were based on one visit to each site because of the large

number of sample sites, the distances between them (up to 90 km), and the relatively

short bloom periods of some plants. While it is unlikely that a sample of 250 bees

collected from five sites would capture the full bee species richness and diversity of a

given plant species during the entirety of its bloom, my data do provide a measure of

which tree and shrub species attract and support robust bee assemblages. Although some

studies have used replicated plots with similar–aged plants to compare bee visitation rates

(Tuell et al. 2008, Garbuzov and Ratnieks 2014b), establishing 72 species of trees and

shrubs in a replicated common garden plot for eventual pollinator sampling would have

been impractical because of the cost, space, and time required for establishment.

Moreover, results from common garden experiments can be location–specific, reflecting

the relative abundance of different pollinator taxa at that particular site. My sampling

from multiple (in most cases five) plantings of each species across hundreds of existing

urban landscape sites doubtless encompassed more of the variation in soil conditions,

18

potential nesting sites, and other landscape–level factors that would affect bee diversity

than if all sampling had been done at a single location.

Overall, my goal was to capture the variation in bee–attractiveness and bee

assemblages for a wide range of woody landscape plants growing in diverse real–world

settings. I observed no distinct differences in the bee–attractiveness, bee assemblages, or

bee taxa abundance between native and nonnative species or trees and shrubs. Instead,

the strongest predictor of bee–attractiveness and bee assemblage diversity was plant

species which indicates that flowering trees and shrubs should be evaluated on a case–

by–case, species–by–species basis to determine their suitability for use in bee–friendly

landscapes.

The premise that augmenting floral resources benefits bees is based on the

assumption that local bee populations are often food–limited. Floral resource availability

is thought to be a major driver of population abundance and diversity of wild bees

(Roulston and Goodell 2011). Long–term abundance of bumble bees and other wild bees

has declined in parallel with widespread declines in floral abundance and diversity in

Europe (Biesmeijer et al. 2006, Goulson et al. 2015), and populations of solitary bees are

enhanced by mass–flowering crops, suggesting that floral resources are indeed limiting

(Holzchuh et al. 2013, Diekötter et al. 2014). My results, together with studies

documenting that four common city tree species attracted a fifth of all native bee species

occurring in Berlin, Germany (Hausmann et al. 2016), and that nine of the main tree

species planted along streets of European cities provide nectar and pollen of high

nutritional suitability for pollinators (Somme et al. 2016) indicate urban landscapes

19

already support diverse pollinator assemblages and can be made more bee–friendly

through continued planting of bee-attractive woody plant species.

Urban and suburban landscapes typically consist of a diverse mix of native and

non–native plant species (Collins et al. 2000, Dirr 2011, Garbuzov and Ratnieks 2014,

Garbuzov et al. 2015, Harrison and Winfree 2015, Salisbury et al. 2015, Mayer et al.

2017). Recently, the long–standing debate (Davis et al. 2011, Simberloff 2011) about

whether or not there is any role for non–invasive exotic plants in conservation biology

has spurred a fervent movement in gardening circles advocating that urban landscapes be

constructed predominantly or exclusively with native plants (Tallamy 2007). One of the

main arguments against landscaping with non–native plants, i.e., ones that do not occur

naturally in a particular region, ecosystem, or habitat, is their potential to become

invasive. Although ornamental horticulture has been a major pathway for plant invasions

(Reichard and White 2003, La Sorte et al. 2014), many non–native ornamentals are either

sterile hybrids or are considered non–invasive with a low risk of escaping cultivation

(Dehnen–Schmutz 2011, Shackleford et al. 2013). None of the woody plants included in

my study is listed as invasive in Kentucky or surrounding states (USDA–NISIC 2018).

Another argument for landscaping with native as opposed to nonnative plant

species is that the former tend to support higher diversity and numbers of endemic

caterpillars and other coevolved plant–feeding insects that in turn help to support

populations of insectivorous birds and other urban wildlife (Tallamy 2007, Tallamy and

Shropshire 2009, Burghardt et al. 2010, Clem and Held 2015). However, supporting

insect herbivores is not always desirable in urban and suburban landscapes. Plants in

urban landscapes not only provide ecosystem services such as supporting wildlife, they

20

also provide socioeconomic services, aesthetic value, and recreational and mental health

benefits (Nowak et al. 2010). The presence of insect herbivores and their damage to

ornamental plants can cause tension between pest management and maximizing

ecological value; e.g. a plant that supports large numbers of caterpillars or other plant–

feeding insects may prompt an insecticide application that could be hazardous to

pollinators. Moreover, some native North American woody plants are far more

susceptible to invasive pests than their exotic congeners originating from the pest’s natal

region (Rebek et al. 2008, Potter and Redmond 2013) and are at a higher risk of being

treated with pesticides. I identified a number of non–invasive, non–native woody plants

(e.g., Abelia, Aralia, Cornus mas, Heptaconium miconioides, Hydrangia paniculata,

Maackia amurensis, Tetradium daniellii, Vitex agnus–castus, and others), that are highly

bee–attractive and relatively pest free, making them good candidates for use in bee–

friendly urban landscapes. The present study adds to a growing body of evidence that

both native and non–native plants can be valuable in helping to support bees and other

pollinators in urbanized habitats (Comba et al. 1999, Frankie et al. 2005, Matteson and

Langellotto 2011, Garbuzov and Ratnieks 2014a, Larson et al. 2014, Harrison and

Winfree 2015, Salisbury et al. 2015, Hausmann et al. 2016, Somme et al. 2016). Because

most urban bees are polylectic (Fetridge et al. 2008, Matteson et al. 2008) and will forage

on a wide variety of plant species, bees will readily incorporate a non–native plant into

their diet, as long as it provides sufficient quantity and quality of pollen and nectar

(Harmon–Threatt and Kremen 2015).

Phenology of bloom is important when considering the value of plants for bees.

Bloom time tends to be conserved by geographic origin, with cultivated non–native

21

plants generally retaining the phenology of their source region (Godoy et al. 2009,

Harrison and Winfree 2015). My study identified 15 bee–attractive plant species that

typically bloom before April or after mid–July, of which only 3 were native. Early or late

blooming plants can be especially valuable to seasonal specialists by providing floral

resources during critical times of nest establishment in the spring and winter provisioning

in the fall (Memmott and Waser 2002, Salisbury et al. 2015). Bumble bees, which do not

store substantial amounts of pollen and nectar, require a consistent supply of floral

resources throughout the growing season including in early spring when post–wintering

queens are foraging alone to establish their colony, and late in the growing season to

provision the developing queen brood, and as food for new queens that feed heavily in

preparation for hibernation in overwintering sites (Bowers 1986, Beekman and Van

Stratum 1998, Pelletier and McNeil 2003). In my study, bumble bees constituted a large

proportion of the samples from Aesculus × carnea in early spring, and from Abelia x

grandiflora, Clethra alnifolia, Heptacodium miconioides, and Vitex agnus–castus late in

the growing season. Honey bees, which will forage year–round if weather permits, also

benefit from season–long floral resources. I identified a number of trees and shrubs that

are highly attractive to honey bees including some that bloom early (e.g., Cornus mas and

Prunus subhirtella ‘Autumnalis’) or late (e.g., Rhus copallinum and Tetradium daniellii)

in the growing season. Urban landscapes can be enhanced by including both native and

non–native trees and shrubs to ensure succession of overlapping bloom periods and

extend the flowering season for pollinators.

Although some plant varieties with double flowers or showy sterile outer sepals

that inhibit access to central, fertile flowers may not provide sufficient floral rewards to

22

attract bees (Comba et al. 1999, Corbet et al. 2001), many horticulturally–modified plants,

including hybrids, can be as attractive, or more attractive, than their wild–type

counterparts (Garbuzov and Ratnieks 2014b, Garbuzov et al. 2015). In my study, neither

of the native Hydrangea species, having been bred for large clusters of showy sterile

sepals, was bee–attractive whereas the open–flowered, non–native H. paniculata had the

highest average snapshot count of the 36 shrub species I sampled. Similarly, R. setigera,

a native single–flowered rose, was highly attractive to bees, whereas none of the double–

and triple–flowered hybrid tea rose cultivars attracted more than a single bee. All four of

the Ilex species I compared, representing a mix of native, non–native, and hybrid species,

offer easily accessible floral rewards, and all four were attractive to bees. This further

illustrates that cultivars and nonnative species can be equally attractive to bees as long as

floral rewards have not been bred out or obscured. Similar patterns were seen within

other plant genera; e.g., Prunus subhirtella and P. virginiana that have single, open

flowers, were highly bee–attractive, whereas P. kanzan, a double–flowered species,

attracted almost no bees.

In conclusion, this study identified many species of flowering trees and shrubs

that are highly attractive to bees and documented the types of bees that visit them. Even

so, I did not come close to capturing the enormous diversity of flowering woody

landscape plants available in the marketplace (Dirr 2011), so there is great potential for

identifying additional plants that could have value for urban bee conservation.

Recommendations for bee–attractive plants that are based on empirical data are

preferable to the large number of plant lists available to the public that are based only on

informal observations or anecdotes (Garbuzov and Ratnieks 2014a). My data should help

23

to inform and augment existing lists of bee–attractive plants in addition to encouraging

the use of sustainable, bee–attractive woody landscape plants to conserve and restore

resources for urban pollinators.

24

Table 2.1a. Plant characteristics and snapshot counts of 36 flowering trees. Snapshot counts are presented as mean (range).

Species Common Name n Snapshot

Count Plant Family Native Statusa Bloom Period

Aesculus flava Yellow buckeye 3 4.5 (1-8) Sapindaceae nat Apr-May Aesculus x carnea Red horsechestnut 5 11.5 (7-14) Sapindaceae non Apr-May Amelanchier spp. Serviceberry 4 16.9 (9-24) Rosaceae nat Apr-May Aralia elata, spinosa Devil's walking stick 5 22.5 (18-29) Araliaceae non Jul-Aug Catalpa speciosa Catalpa 3 4.2 (3-6) Bignoniaceae nat May-Jun Cercis canadensis Eastern redbud 5 28.8 (8-48) Fabaceae nat Apr-May Chionanthus virginicus White fringetree 5 0.3 (0-2) Sapindaceae nat May-Jun Cladrastis kentukea American yellowwood 5 10.2 (4-17) Fabaceae nat May-Jun Cornus drummondii Roughleaf dogwood 5 2.7 (0-8) Cornaceae non May-Jun Cornus florida Flowering dogwood 5 2.1 (0-8) Cornaceae nat Apr-May Cornus kousa Kousa dogwood 5 1 (0-2) Cornaceae non May-Jun Cornus mas Cornelian cherry 5 18.1 (10-38) Cornaceae non Mar Crateagus viridus Winter king hawthorn 5 15.7 (0-26) Rosaceae nat Apr-May Heptacodium miconioides Seven-son flower 5 33.2 (19-48) Caprifoliaceae non Aug-Sep Ilex opaca American holly 5 13 (7-21) Aquifoliaceae nat Apr-May Ilex x meserveae Blue/China holly 5 15.5 (9-19) Aquifoliaceae non Apr-May Koelreuteria paniculata Golden raintree 5 25.4 (16-38) Sapindaceae non Jun-Jul Lagerstroemia sp. Crape myrtle 4 10.4 (0-19) Lythraceae non Jul-Sep Maackia amurensis Amur maackia 3 42.2 (6-73) Fabaceae non Jul Magnolia liliiflora Mulan magnolia 5 0 (0-0) Magnoliaceae non Apr-May Magnolia stellata Star magnolia 5 0.6 (0-3) Magnoliaceae non Mar Malus spp. Flowering crabapple 5 30.7 (14-65) Rosaceae varies Mar-Apr Nyssa sylvatica Black gum, Tupelo 5 13.5 (2-27) Cornaceae nat Apr-Jun Oxydendrum arboreum Sourwood 5 19.5 (9-36) Ericaceae nat Jun-Jul Prunus 'Kanzan' Japanese cherry 5 0.1 (0-1) Rosaceae non Apr Prunus spp. Flowering cherry 4 26.8 (14-61) Rosaceae non Mar-Apr Prunus subhirtella 'Autumnalis' Higan cherry 4 24.4 (16-30) Rosaceae non Mar-Apr Prunus subhirtella 'Pendula' Higan weeping cherry 5 16.3 (3-30) Rosaceae non Mar-Apr Prunus virginiana Chokecherry 4 21.0 (7-64) Rosaceae nat Apr-May Rhus copallinum Winged sumac 5 65.3 (23-103) Anacardiaceae nat Jul-Aug Sambucus canadensis American elderberry 5 3.7 (1-6) Adoxaceae nat Jun Sassafras albidum Sassafras 3 0 (0-0) Lauraceae nat Apr Syringa reticulata Japanese tree lilac 5 6.7 (2-12) Oleaceae non May-Jun Tetradium daniellii Bee bee tree 4 50.1 (24-70) Rutaceae non Jul-Aug Tilia cordata Linden 5 21.9 (10-60) Tiliaceae non Jun-Jul Vitex agnus-castus Chaste tree 5 13 (3-18) Lamiaceae non Aug-Sep aNat = native; Non = nonnative; Varies = both native and nonnative species

25

Table 2.1b. Plant characteristics and snapshot counts of 36 flowering shrubs. Snapshot counts are presented as mean (range).

Species Common Name n Snapshot

Count Plant Family Native Statusa

Bloom Period

Abelia x grandiflora Abelia 5 17.5 (7-32) Caprifoliaceae non Jul-Sep Aesculus parviflora Bottlebrush buckeye 5 20.5 (11-29) Sapindaceae nat Jun-Jul Amorpha fruticosa False indigo 5 16.3 (8-23) Sapindaceae nat May-Jun Rhododendron spp. Azalea 5 0.2 (0-1) Ericaceae non Apr-May Buxus sempervirens European boxwood 5 0.1 (0-1) Buxaceae non Mar-Apr Calycanthus floridus Carolina allspice 3 0 (0-0) Calycanthaceae nat Apr-Jun Cephalanthus occidentalis Buttonbush 4 12.8 (3-27) Rubiaceae nat Jun Clethra alnifolia Summersweet 5 26.3 (10-38) Clethraceae nat Jul-Aug Deutzia scabra Fuzzy deutzia 5 7.6 (0-23) Hydrangeaceae non May Forsythia spp. Forsythia 5 0.1 (0-1) Oleaceae non Mar Fothergilla gardenii Dwarf fothergilla 5 9.3 (4-15) Hamamelidaceae nat Mar-Apr Hamamelis vernalis Ozark witch hazel 4 0.7 (0-2) Hamamelidaceae nat Jan-Mar Hydrangea arborescens Smooth hydrangea 5 0 (0-0) Hydrangeaceae nat Jun Hydrangea macrophylla Bigleaf hydrangea 5 0 (0-0) Hydrangeaceae non Jun Hydrangea paniculata PG Hydrangea 5 31.4 (12-50) Hydrangeaceae non Jul-Aug Hydrangea quercifolia Oakleaf hydrangea 5 5 (0-9) Hydrangeaceae nat Jun-Jul Hypericum frondosum St. John's wort 5 28.2 (14-43) Hypericaceae nat Jun-Aug Hypericum 'hidcote' St. John's wort 'hidcote' 4 1.8 (0-4) Hypericaceae non Jun Ilex verticillata Common winterberry 5 28 (10-46) Aquifoliaceae nat Jun Ilex x attenuata Foster's holly 5 14.6 (5-28) Aquifoliaceae nat Apr-May Itea virginica Sweetspire 5 10 (5-13) Iteaceae nat Jun-Jul Lindera benzoin Spicebush 5 2.7 (0-11) Lauraceae nat Mar Lonicera fragrantissima Winter honeysuckle 5 15.8 (1-24) Caprifoliaceae non Mar-Apr Philadelphus spp. Mock orange 5 13.2 (5-32) Hydrangeaceae non May-Jun Physocarpus opulifolius Ninebark 3 13.8 (5-23) Rosaceae nat May-Jun Prunus laurocerasus Cherry laurel 5 9.5 (6-18) Rosaceae non Apr-May Pyracantha spp. Pyracantha 4 11.5 (5-20) Rosaceae non May Rhodedendron spp. PJM rhodedendron 5 1.7 (0-8) Ericaceae non Mar-Apr Rosa setigera Climbing rose 5 16.1 (5-41) Rosaceae nat Jun Rosa spp. Hybrid tea rose 35 0.1 (0-5) Rosaceae non May-Oct Spiraea x vanhouttei Vanhouttespiraea 5 4.1 (1-9) Rosaceae non Apr-May Spirea japonica Japanese spirea 5 3.5 (0-9) Rosaceae non May-Jun Spirea virginiana Virginia spiraea 5 16.8 (5-35) Rosaceae nat May-Jun Syringa vulgaris Lilac 5 2.1 (0-11) Oleaceae non Apr-May Viburnum burkwoodii Burkwood viburnum 5 15.6 (4-37) Adoxaceae non Apr Viburnum carlesii Koreanspice viburnum 5 0.2 (0-2) Adoxaceae non Apr-May aNat = native; Non = nonnative; Varies = both native and nonnative species

26

Table 2.2. Bee–attractiveness ratinga, distribution of bee taxab, bee genus diversityc, and provenanced of 45 species of bee–attractive flowering trees and shrubs. Plant species are arranged in order of bloom period.

Species Sites Bees Bloom Period Prov Rating Apid

A. mellifera

Bombus spp. Andr Coll Hali Mega 1/D

Cornus mas 5 247 Mar non *** 45.7 45.7 0 43.7 2.4 6.5 1.6 0.34 Fothergilla gardenii 5 267 Mar–Apr nat * 18 1.1 0.7 70 1.9 9.7 0.4 0.43 Malus spp. 5 258 Mar–Apr varies **** 10.1 7.4 1.2 65.1 2.7 16.3 5.8 0.47 Prunus spp. 4 194 Mar–Apr varies **** 19.1 11.9 0.5 66 4.1 3.6 7.2 0.49 Prunus subhirtella 'Autumnalis'

4 213 Mar–Apr non *** 84.5 83.1 0.9 3.8 2.8 2.8 6.1 0.27

Prunus subhirtella 'Pendula'

5 285 Mar–Apr non ** 22.5 18.6 1.8 18.2 39.6 6.3 13.3 0.62

Viburnum burkwoodii

5 284 Apr non ** 13.7 9.5 1.4 18.3 1.1 46.8 20.1 0.58

Aesculus × carnea 5 282 Apr–May non * 63.1 40.8 14.2 28.7 2.8 1.8 3.5 0.7 Amelanchier spp. 4 215 Apr–May nat *** 4.7 2.8 0 80.5 0.9 14 0 0.23 Cercis canadensis 5 274 Apr–May nat **** 8 0 1.1 1.5 79.2 2.2 9.1 0.31 Cornus florida 5 155 Apr–May nat * 4.5 2.6 0.6 79.4 11.6 4.5 0 0.35 Crateagus viridus 5 345 Apr–May nat ** 21.7 15.1 2.3 51.3 4.6 22 0.3 0.57 Ilex opaca 5 242 Apr–May nat ** 60.3 21.1 2.1 30.2 0.4 8.3 0.8 0.65 Ilex × attenuata 5 302 Apr–May nat ** 65.6 46 3.3 5.6 3.3 23.8 1.7 0.51 Ilex × meserveae 5 254 Apr–May non ** 51.2 39.4 0 16.9 9.1 17.7 5.1 0.6 Nyssa sylvatica 5 268 Apr–May nat ** 28 21.3 3.4 42.9 4.9 23.9 0.4 0.32 Prunus laurocerasus

5 273 Apr–May non * 4.4 0.7 1.1 61.5 0 33.3 0.7 0.47

Prunus virginiana 4 220 Apr–May nat *** 4.1 1.8 0 76.8 1.8 17.3 0 0.35 Deutzia scabra 5 245 May non * 65.3 14.7 8.6 27.3 1.2 4.1 2 0.57 Pyracantha spp. 4 238 May non * 8.4 1.3 3.8 69.7 4.2 17.2 0.4 0.47 Amorpha fruticosa 5 302 May–Jun nat ** 76.8 25.5 27.8 10.9 1 7.6 3.6 0.66 Cladrastis kentukea 5 268 May–Jun nat * 88.8 67.9 14.9 9.7 0 1.1 0.4 0.48 Philadelphus spp. 5 253 May–Jun varies ** 5.9 0.8 3.6 1.2 0.8 5.5 86.6 0.24 Physocarpus opulifolius

3 167 May–Jun nat ** 20.4 7.8 1.2 57.5 6 16.2 0 0.6

Spirea virginiana 5 277 May–Jun nat *** 33.9 0.7 12.3 49.5 6.1 10.5 0 0.67 Syringa reticulata 5 221 May–Jun non * 31.2 7.2 3.6 48 0.5 20.4 0 0.64 Cephalanthus occidentalis

4 199 Jun nat ** 48.7 3.5 45.2 0 0.5 49.7 1 0.5

Ilex verticillata 5 267 Jun nat **** 59.6 54.3 2.6 2.2 1.5 19.1 17.6 0.53 Rosa setigera 5 160 Jun nat ** 59.4 16.3 41.3 0.6 6.3 31.3 2.5 0.7 Aesculus parviflora 5 260 Jun–Jul nat *** 64.2 25.8 8.1 0.4 1.2 32.3 1.9 0.71 Hypericum frondosum

5 268 Jun–Jul nat **** 70.1 33.6 35.8 0 1.9 28 0 0.51

Itea virginica 5 270 Jun–Jul nat * 80.4 19.6 17 9.6 4.1 3.7 2.2 0.65 Koelreuteria paniculata

5 282 Jun–Jul non *** 57.8 42.6 9.6 1.1 0 39 2.1 0.59

Oxydendrum arboreum

5 228 Jun–Jul nat *** 59.6 4.4 51.8 0 0.4 31.1 8.8 0.69

Tilia cordata 5 264 Jun–Jul non *** 80.7 48.1 15.9 1.5 0 17.4 0.4 0.6 Maackia amurensis 3 165 Jul non **** 26.7 6.7 12.1 0 0 44.8 28.5 0.63 Aralia elata, spinosa

5 270 Jul–Aug varies *** 26.3 22.2 1.1 0 4.1 68.5 1.1 0.34

Clethra alnifolia '16 candles'

5 260 Jul–Aug nat **** 48.5 2.7 39.6 0 2.7 46.2 2.7 0.52

Hydrangea paniculata

5 283 Jul–Aug non **** 26.5 25.1 0.7 0.4 0.7 72.4 0 0.42

Lagerstroemia sp. 4 220 Jul–Aug non * 37.3 26.4 5 0 0.9 61.8 0 0.41 Rhus copallinum 5 269 Jul–Aug nat **** 68 60.6 6.3 0 0.7 31.2 0 0.41 Tetradium daniellii 4 258 Jul–Aug non **** 70.5 64.7 5 0.4 0.8 19.8 8.5 0.44 Vitex agnus–castus 5 263 Jul–Aug non * 61.6 2.3 43.7 0 0.8 22.1 15.6 0.64 Abelia x grandiflora

5 275 Jul–Sep non *** 48.4 2.9 31.6 0 0 44 7.6 0.74

Heptacodium miconioides

5 265 Aug–Sep non **** 72.1 12.8 49.1 0 1.1 26.4 0.4 0.52

aBee–attractiveness ratings are based on quartiles of snapshot counts, with * = first quartile, ** = second quartile, *** = third quartile, and **** = fourth quartile bBee taxa distribution is presented as percentage of total bees collected for each plant species. Andr = Andrenidae, Coll = Colletidae, Hali = Halictidae, Mega = Megachilidae cDiversity is calculated as the inverse of Simpson's D, which generates a number between 0 and 1 with higher values indicating more genus–rich and even samples dNat = native; Non = non–native; Varies = both native and non–native species

27

Table 2.3. Summary of analysis of variance for effects of plant species, plant type (tree or shrub), provenance (native or nonnative), and Julian date number on bee genus diversity and snapshot counts.

Snapshot counts Bee genus diversity

Source df F Pr >F df F Pr >F

Plant species 70 18.40 <0.001 42 2.60 <0.001

Plant family 25 13.97 <0.001 20 2.60 <0.001

Plant type 1 20.37 <0.001 1 0.68 0.41

Provenance 1 1.28 0.26 1 1.11 0.29

Julian date number 1 10.63 0.001 1 3.60 0.06

28

Table 2.4. Characteristics and distribution of bee species and genera identified on 45 species of flowering woody plants.

Family Genus/Species Nesting Habit Nest Type

Percent of Family

Percent of Total

Apidae Apis mellifera social cavity 50.1 22.1 Bombus auricomus social above-ground < 0.1 < 0.1 Bombus bimaculatus social ground/cavity 4.3 1.9 Bombus citrinus solitary kleptoparasitic < 0.1 < 0.1 Bombus griseocolus social ground 7.7 3.4 Bombus impatiens social ground 15.8 6.9 Bombus pennsylvanicus social above-ground < 0.1 < 0.1 Bombus perplexus social ground < 0.1 < 0.1 Ceratina varies cavity 5.0 2.2 Melissodes solitary ground 0.1 < 0.1 Nomadinae solitary kleptoparasitic 0.8 0.4 Xylocopa varies cavity 16.0 7.1

Andrenidae Andrena solitary ground 100.0 21.4

Colletidae Colletes solitary ground 75.3 3.8 Hylaeus solitary cavity 24.7 1.2

Halictidae Agapostemon solitary ground 1.6 0.4 Augochlora solitary cavity 8.5 2.0 Augochlorella solitary ground 1.4 0.3 Augochloropsis solitary ground 1.2 0.3 Halictus solitary ground 3.8 0.9 Lasioglossum varies ground 83.0 19.6 Sphecodes solitary kleptoparasitic 0.5 0.1

Megachilidae Anthidium solitary cavity 0.1 < 0.1 Chelostoma solitary cavity 32.4 1.9 Coelioxys solitary kleptoparasitic 0.4 < 0.1 Heriades solitary cavity 8.0 0.5 Hoplitis solitary cavity 0.3 < 0.1 Megachile solitary cavity 29.8 1.8 Osmia solitary cavity 28.9 1.7

29

Table 2.5. Summary of analysis of variance for effects of plant species, plant type (tree or shrub), provenance (native or nonnative), site type (park–like or other), and Julian date number on bee taxa abundance.

Andrenidae Apidae Colletidae Halictidae Source df F Pr >F F Pr >F F Pr >F F Pr >F Plant family 20 5.74 <0.001 3.67 <0.001 1.98 0.01 2.27 0.002 Plant type 1 0.01 0.94 0.03 0.86 4.39 0.04 0.39 0.54 Provenance 1 3.39 0.07 0.01 0.91 4.58 0.03 3.29 0.07 Julian date number 1 2.29 0.13 0.06 0.8 19.65 <0.001 30.7 <0.001

Megachilidae

Apis mellifera

Bombus Source df F Pr >F

F Pr >F

F Pr >F

Plant family 20 2.37 0.001

4.11 <0.001

5.00 <0.001 Plant type 1 0.85 0.36

0.5 0.48

0.40 0.53

Provenance 1 1.13 0.29

0.91 0.34

0.00 0.95 Julian date

number 1 4.24 0.04 0.69 0.41 4.77 0.03

30

Fig 2.1. Comparison of snapshot counts on trees and shrubs and between native and nonnative plants. The bold line within the box indicates the median while the diamond indicates the mean. The lower whisker, lower box, upper box, and upper whisker indicate Q1, Q2, Q3, and Q4, respectively. Analysis of variance results are summarized in Table 2.3.

31

Fig 2.2. Comparison of bee genus diversity on trees and shrubs and between native and nonnative plants. The bold line within the box indicates the median while the diamond indicates the mean. The lower whisker, lower box, upper box, and upper whisker indicate Q1, Q2, Q3, and Q4, respectively. Analysis of variance results are summarized in Table 3.

32

CHAPTER THREE

Pollen nutritional quality of native and nonnative flowering woody landscape plants

Introduction

Green spaces in urban landscapes provide many environmental services besides

beautification and recreation, including flood mitigation, temperature moderation, air

quality improvement, and carbon sequestration (McPherson et al. 1997, Nowak and

Crane 2002, Xiao and McPherson 2002, Nowak et al. 2006, Rosenzweig et al. 2006).

Urban landscapes also act as important refuges for biodiversity, with plants playing a

crucial role by providing food and habitat for urban wildlife (Goddard et al. 2010, Potts et

al. 2016). Public interest in pollinator conservation has fueled demand for multi–purpose

urban landscapes that support pollinator populations, and a growing body of research

indicates that native bee communities can persist and even thrive in urban settings with

support from flowering plants in gardens, parks, and other properly managed green

spaces (Tommasi et al. 2004, Frankie et al. 2005, McFrederick and LeBuhn 2006,

Fetridge et al. 2008, Matteson et al. 2008, Hernandez et al. 2009, Tonietto et al. 2011,

Larson et al. 2014).

Urban landscapes are composed of a mix of native and nonnative plant species as

well as horticulturally modified versions of both (Collins et al. 2000, Dirr 2011,

Garbuzov and Ratnieks 2014b, Garbuzov et al. 2015, Harrison and Winfree 2015,

Salisbury et al. 2015, Mayer et al. 2017). Native plant species in urban landscapes are

often early successional “edge” species, horticulturally desirable species, or survivors in

natural area remnants, whereas nonnative plant species are usually horticulturally

33

desirable species and invasive species accidentally or intentionally imported. Although

plant biodiversity in urban landscapes is high and typically increases over time, it comes

at the cost of local extinctions of native plant species and rapid replacement with

nonnative species (McKinney 2005, Ellis et al. 2012).

It is unclear what repercussions bee populations may face in urban landscapes

increasingly dominated by nonnative plant species, as native plants have largely been the

focus for increasing urban biodiversity (Tallamy 2007, Pardee and Philpott 2014, Fukase

and Simmons 2016). Native plants have a long, shared evolutionary history with native

insects and often support more insect species and higher insect biomass than nonnative

plant species, which may have novel chemical defenses that native insects cannot

overcome (Tallamy 2007, Zuefle et al. 2008, Burghardt et al. 2010, Tallamy et al. 2010,

Ballard et al. 2013). Native plants are also key to supporting bee species that are pollen

specialists, which rely on one or a few closely related species for food during short

foraging periods synchronized to the flowering phenology of their host plant species

(Fowler & Droege 2016). However, since most bee species in urban landscapes are

polylectic (Fetridge et al. 2008, Matteson et al. 2008) and will readily forage on

nonnative plants (Garbuzov and Ratnieks 2014b, Harmon–Threatt and Kremen 2015),

there is a need to determine whether the numerous nonnative plants present in urban

landscapes are suitable, nutritious forage for bees.

In a previous study (Chapter 2, Mach and Potter 2018), I demonstrated that both

native and nonnative flowering woody plants can be similarly bee–attractive and support

equally diverse bee assemblages, but little is known about the nutritional quality of the

floral rewards these native and nonnative plants provide. Both nectar and pollen are

34

important food sources for bees, with sugar–rich nectar being the primary energy source

for adult bees and pollen providing key nutrients for larval development, including

protein (DeGroot 1953, Michener 2007). Adequate amounts of ten essential amino acids

(arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine,

tryptophan, valine) are particularly important for proper larval bee nutrition and

development (DeGroot 1953). The present study aims to investigate the nutritional

quality of nectar and pollen in bee–attractive native and nonnative flowering woody

plants to determine whether both are equally suitable for use in conservation plantings in

urban landscapes.

Materials and Methods

Plant species and study sites

A subset of 11 bee–attractive flowering trees and shrubs sampled in a previous

study (Chapter 2, Mach and Potter 2018) were sampled between March and September

2017 (Table 3.1). Plant species sampled included highly bee–attractive (Chapter 2, Mach

and Potter 2018) trees and shrubs, including both native and nonnative species. Pollen

sampling occurred between 9:00 to 16:00 EST during above–freezing temperatures (>10

ºC), and nectar sampling occurred between 9:00 to 12:00 EST. Samples were collected

from four sites per plant species, except for two of the species where only three sites

could be found (Table 3.1). All plants were located within the urban landscape of

Lexington, Kentucky, and sample sites included street–side plantings, urban arboreta and

cemeteries. Same–species sample sites were separated by a minimum of 3 m for separate

but adjacent plantings, with most same–species sample sites being at least 0.5 km apart.

35

Pollen collection and analysis

Pollen analysis was modeled after the methods used in Somme et al. 2010. Pure

pollen was collected by removing the anthers and drying them at 23ºC for 24–48 h.

Pollen was then brushed off the anthers into 1.5 mL microcentrifuge tubes, labeled, then

stored at –18ºC until analysis. Pollen analyses were done in the lab of Dr. K. Urschel

(Department of Animal and Food Sciences, University of KY).

For analysis, 3 mg dry pollen was weighed out into a 15 mL glass tube. To each

sample, 1 ml of hydrolysis solution (52.2 mg 500 µM L–norleucine, 0.1% phenol, 246

mL 6 N HCL, and 4 mL nanopure H2O) was added. Samples were inverted 20 times,

placed under nitrogen for 30 s, then inverted 20 times again before incubation. Samples

were incubated at 110ºC for 24 h, and each sample was inverted 20 times at 2 h, 6 h, and

22 h. The hydrolysate was filtered using 13 mm PFTE 0.45 µm syringe filters and

transferred to 1.5 mL microcentrifuge tubes, which were labeled and stored at –80ºC until

drying and derivatization.

After thawing frozen samples, 50 µL hydrolysate was transferred into 1 mL

disposable glass tubes and placed under a vacuum to dry for 90 min. After drying, 10 µL

re–dry solution (300 µL 1 M sodium acetate, 150 µL triethylamine, 300 µL methanol)

was added and each tube was vortexed for 15 s. Samples were then vacuum dried for an

additional 30 min. To each sample 20 µL derivatizing solution (125 µL triethylamine,

125 µL phenylisothiocyanate, 875 µL methanol, and 125 µL nanopure H2O) was added.

Samples were vortexed for 15 s and incubated at 23ºC for 20 min before being vacuum

dried for 45 min. After the final drying, 100 µL hydrolysate diluent at pH 7.4 (0.71 g

disodium hydrogen phosphate, 1 L nanopure H2O, and 5 mL acenitrile) was added and

samples were vortexed for 15 s. Samples were then loaded into a high–performance

36

liquid chromatography vial and amino acids were measured using a Waters 2695

Separations Module. I tallied amino acid content to create two measures: total amino

acid content (g 100 g–1), which included all amino acids measured, and essential amino

acid content (g 100 g–1),which included amino acids identified as essential for honey bee

development (arginine, isoleucine, leucine, lysine, phenylalanine, threoninethreonine,

valine, and histidine; DeGroot 1953). Methionine, another essential amino acid, could not

be quantified due to degradation during acid hydrolysis.

Nectar collection and analysis

I attempted to create a standard measure of nectar production by sampling nectar

from 50 flowers per site. Nectar was collected directly from the flowers by using 5, 10,

or 15 µL microcapillary tubes that were then drained into 1.5 mL microcentrifuge tubes,

labeled, and stored at –18ºC until analysis. Nectar analyses were conducted by Dr. D.

Archbold (Department of Horticulture, University of KY).

After thawing, 2–5 µL of nectar plus 2 µL of ribitol internal standard was dried

under a stream of nitrogen at 30°C. The dry residues were reconstituted with 100 µL

pyridine (containing 50 mg hydroxylamine hydrochloride mL–1). The samples were

heated for 30 min at 80°C. The cooled samples were silylated with 100 µL of N,O–bis

(trimethylsilyl) trifluoroacetamide, heated for 10 min at 80°C, and used for sugar

measurement. Glucose, fructose, and sucrose content were determined using a Hewlett–

Packard 5890 II gas chromatograph equipped with a 60 m x 0.32 mm DB–5 column with

a 1 µm film thickness (J & W Scientific, Folsom, CA) and a flame ionization detector.

The operating conditions were a gradient of 210 to 270°C at a temperature increase of

2.5°C per min, and 20 min at 270°C. Helium was used as the carrier gas at a flow rate of

37

30 cm s–1. Injector and detector temperatures were set at 300°C. Quantitative values were

derived from peak areas relative to the ribitol internal standard. Preliminary analyses of

nectar from each species indicated that none had detectable ribitol.

Statistical analysis

Total amino acid content and essential amino acid (g 100 g–1) content were

analyzed for main effects of plant species, plant family (as a proxy for plant species due

to limited degrees of freedom), provenance (native or nonnative), plant type (tree or

shrub), and Julian date number as well as average snapshot count and bee genus diversity

(species averages, from Chapter 2, Mach and Potter 2018) using General Linear Models

(SAS, Version 9.4; SAS Institute, Cary, NC, USA), with mean separation by Least

Square Means. Sampling date was standardized by converting to a Julian date number,

which assigns each calendar date a unique integer starting from 0 on January 1. I also

planned to analyze main effects of plant species, plant family, provenance, plant type,

and Julian date number type on nectar volume and sugar content but was unable to do so

because only 5 of 11 species produced enough nectar for analysis, which resulted in

uneven distribution of the data among class variable levels.

Results

Total amino acid content ranged from 5.8 to 46.9 g 100 g–1, with an average of

27.0 ± 1.6 g 100 g–1(mean ± SE). Plant species, family, type, and provenance all had

significant effects on total amino acid content (Table 3.2). Shrubs had slightly higher

total amino acid content than trees on average, and native plants had higher total amino

acid content than nonnative plants (Fig. 3.1). Essential amino acid content ranged from

38

2.5 to 21.7 g 100 g–1, with an average of 11.1 ± 0.7 g 100 g–1. Plant species, family, type,

and provenance also had significant effects on essential amino acid content, with shrubs

having slightly higher essential amino acid content than trees and native plants having

higher essential amino acid content than nonnative plants (Fig. 3.2). Neither Julian date

number, average snapshot count (Chapter 2, Mach and Potter 2018), nor bee genus

diversity (Chapter 2, Mach and Potter 2018) had significant effects on either total amino

acid content or essential amino acid content (Table 3.2).

Of the 11 plant species sampled, five produced measureable nectar (Table 3.1).

Of these five species, only one was native (Aesculus parviflora); the rest were nonnative

trees and shrubs (Aesculus × carnea, Lonicera fragrantissima, Prunus subhirtella

‘autumnalis’, Tetradium daniellii, Tilia cordata). Nectar volume per 50 flowers for these

five species ranged from 0 to 85 µL, with an average of 27 µL, and sugar content (ºBx)

ranged from 0.0 to 38.8 ºBx, with an average of 11.0 ºBx.

Discussion

My results indicate that pollen nutritional quality varies widely from species to

species. Two of the plant species sampled (Aesculus × carnea and Tilia cordata) have

been previously studied for pollen nutritional parameters (Somme et al. 2016). The

values I obtained for their average total amino acid content were similar to those

previously reported (32.9% versus 31.8% for Aesculus × carnea, and 22.8% versus

24.3% for Tilia cordata; Table 3.1 and Somme et al. 2016, respectively), indicating

pollen nutritional quality is relatively consistent within those species. Although I

observed significant main effects of plant provenance and type on total amino acids and

39

essential amino acids, the differences between those groups were small when compared

to the main effect for plant species. Overall, the best fit model was plant species, similar

to a previous study on bee–attractiveness and diversity on flowering woody plants (Mach

and Potter 2018).

All 11 plant species sampled contained the full spectrum of essential amino acids

required for larval bee development (DeGroot 1953). Furthermore, eight species (four

native, four nonnative) had high quality pollen with total amino acid content >20%,

which is associated with higher larval growth and overwintering survivorship in bumble

bees (Vanderplanck et al. 2014). This demonstrates that both native and nonnative

flowering woody plants can provide nutritious pollen for bees in urban landscapes,

although each should be evaluated on a case by case, species by species basis.

Nectar volume was unexpectedly scant in some cases considering that all species I

sampled are highly bee–attractive (Mach and Potter 2018). Nectar volume was highly

variable from plant to plant and from species to species and were within similar ranges to

previous studies (Table 3.1, Somme et al. 2015). Some of the plant species sampled have

been reported to have greater nectar volume than what I observed (Somme et al. 2010),

and this discrepancy could be due to differences in the health and size of individual plants

sampled as well as year to year variation in weather and precipitation affecting nectar

production. Sugar content was also highly variable between same–species sites, and I

observed lower sugar content in nectar of Aesculus × carnea and Tilia cordata than

previous studies reported (Somme et al. 2016).

Native and nonnative plants each have their own distinct advantages and

disadvantages that should be taken into consideration when planning green spaces for

40

pollinator conservation in urban landscapes. Native plants are well–adapted to local

climates and have close co–evolutionary relationships with native insects, including

specialist native pollinators (Tallamy 2007). Native plants also generally support more

insect herbivores, particularly dietary specialists, and more insect biomass than nonnative

species (Ballard et al. 2013, Burghardt et al. 2010, Tallamy 2007, Tallamy et al. 2010,

Zuefle et al. 2008), which in turn supports more insectivorous wildlife (Tallamy and

Shropshire 2009, Burghardt et al. 2010, Clem and Held 2015). However, the presence of

insect herbivores is not always tolerated in urban landscapes, particularly in highly

ornamental plantings where damage to the plants is noticeable and undesirable. Such

feeding damage may prompt treatment with insecticides and creates a hazard for

pollinators. Less formal plantings and native–focused gardens where bee–friendly pest

management strategies are used may be appropriate sites for native plants that attract both

bees and insect herbivores that might be construed as pests. Conversely, nonnative plants

support fewer insect herbivores and are often less pest–prone, especially to invasive pests,

than native species (Rebek et al. 2008, Potter and Redmond 2013). This can result in

fewer insecticide treatments and less hazard for pollinators in urban landscapes.

Ultimately, landscapes containing a mixture of native and nonnative flowering woody

plants may be best for supporting both specialist and generalist bees throughout the

growing season. These data add to a growing body of research that demonstrates both

native and nonnative plants can provide valuable floral resources for bees in urban

landscapes (Comba et al. 1999, Frankie et al. 2005; Matteson and Langellotto 2011,

Garbuzov and Ratnieks 2014b, Larson et al. 2014, Harrison and Winfree 2015, Salisbury

et al. 2015, Hausmann et al. 2015, Somme et al. 2016, Mach and Potter 2018). The

41

information gained in the present study will help inform land managers, urban foresters,

and the public to select well–adapted plants that will provide high–quality floral rewards

and ecosystem services to pollinators and other urban wildlife.

42

Table 3.1. Plant, pollen, and nectar characteristics of 11 flowering trees and shrubs sampled in 2017. Total amino acid and essential amino acids (g 100 g–1), nectar volume (µL) per 50 flowers, and sugar concentration are presented as means (range). Concentrations of sugar are measured in degrees Brix (°Bx) where one °Bx is equal to 1 g sucrose in 100 g solution.

Species n Plant Family Plant Type

Bloom Period

Plant Provenance

Total Amino Acids g 100 g–1

Essential Amino Acids g 100 g–1 Nectar Sugar

Aesculus parviflora

3 Sapindaceae shrub Jun–Jul native 38.6 (37.1–41.2) 16.9 (16.0–18.3) 12 (5–24) 3.1 (1.6–3.9)

Aesculus x carnea

4 Sapindaceae tree Apr–May nonnative 32.9 (30.1–35.1) 14.1 (13.2–16.0) 56 (17–85) 9.4 (6.0–13.3)

Cercis canadensis

4 Fabaceae tree Apr–May native 37.4 (28.7–46.9) 15.5 (13.1–18.6) 0 0

Heptacodium miconioides

3 Caprifoliaceae tree Aug–Sep nonnative 18.1 (15.4–20.2) 7.4 (6.1–8.1) 0 0

Hypericum frondosum

4 Hypericaceae shrub Jun–Aug native 40.7 (34.5–46.6) 18.8 (16.0–21.7) 0 0

Itea virginica 4 Iteaceae shrub Jun–Jul native 21.7 (20.2–22.9) 8.7 (7.8–9.1) 0 0

Lonicera fragrantissima

4 Caprifoliaceae shrub Mar–Apr nonnative 33.5 (28.9–44.0) 12.9 (11.3–16.8) 18 (12–29) 24 (12.6–38.8)

Prunus subhirtella 'Autumnalis'

4 Rosaceae tree Mar–Apr nonnative 16.5 (11.9–20.9) 6.4 (5.4–7.8) 30 (25–34) 10.1 (6.1–17.9)

Rosa setigera 4 Rosaceae shrub Jun native 12.0 (5.8–30.0) 4.8 (2.5–11.7) 0 0

Tetradium daniellii

4 Rutaceae tree Jul–Aug nonnative 23.2 (20.2–27.3) 8.6 (7.8–9.6) 18 (0–24) 8.7 (5.6–10.7)

Tilia cordata 4 Tiliaceae tree Jun–Jul nonnative 22.8 (20.9–25.5) 8.8 (7.8–10.0) 0 0

43

Table 3.2. Summary of analysis of variance for effects of plant species, family, plant type (tree or shrub), plant provenance (native or nonnative), Julian date number, snapshot count and bee genus diversity (Mach and Potter 2018) on total amino acid content and essential amino acid content.

Total amino acid content

Essential amino acid content

Source df F Pr > F df F Pr > F Plant species 10 16.62 <0.001

10 18.67 <0.001

Plant family 7 14.67 <0.001

7 21.20 <0.001 Plant type 1 12.50 0.001

1 10.92 0.002

Provenance 1 8.33 0.007 1 6.33 0.020 Julian date number 1 0.20 0.656 1 0.02 0.882 Snapshot count 1 0.12 0.734 1 0.28 0.599 Bee genus diversity 1 0.01 0.932 1 0.19 0.667

44

Fig 3.1. Comparison of total amino acid content between trees and shrubs and native and nonnative plants. The bold line within the box indicates the median while the diamond indicates the mean. The lower whisker, lower box, upper box, and upper whisker indicate Q1, Q2, Q3, and Q4, respectively. Analysis of variance results are summarized in Table 2.

45

Fig 3.2. Comparison of total essential amino acid content between trees and shrubs and native and nonnative plants. The bold line within the box indicates the median while the diamond indicates the mean. The lower whisker, lower box, upper box, and upper whisker indicate Q1, Q2, Q3, and Q4, respectively. Analysis of variance results are summarized in Table 2.

46

CHAPTER FOUR

Uptake and Dissipation of Neonicotinoid Residues in Nectar and Foliage of Systemically–

Treated Woody Landscape Plants

Introduction

Neonicotinoid insecticides, particularly the nitroguanidine compounds imidacloprid and

dinotefuran, are widely used in urban arboriculture for managing insect pests of trees and shrubs.

Their targets include invasive species such as emerald ash borer (Agrilus planipennis fairmare),

Asian longhorned beetle (Anoplophora glabripennis motschulsky), hemlock wooly adelgid

(Adelges tsugae), and Japanese beetle (Popillia japonica) as well as aphids, psyllids, scale insects,

leaf miners, and other pests (Sclar and Cranshaw 1996, Gill et al. 1999, Cowles et al. 2006,

Hubbard and Potter 2006, Frank et al. 2007, Ugine et al. 2012, Smitley et al. 2015).

Neonicotinoid insecticides can be applied via foliar sprays, but they are more frequently used as

systemic treatments via trunk injection or infusion, basal bark sprays, or soil injections or

drenches. Systemic treatments are increasingly preferred for protecting woody landscape plants

because they can be done without specialized equipment and minimize spray drift, applicator and

bystander exposure, visual anxiety associated with spraying in public places, and direct exposure

of beneficial insects and other non–target organisms (Raupp et al. 2004, Mota–Sanchez et al.

2009). When neonicotinoids are applied to the soil, they are taken up by the roots and move

acropetally in xylem sap (Bonmatin et al. 2015) enabling them to reach pest feeding sites (e.g.,

phloem–xylem interface under bark; upper–canopy foliage of trees) that are impossible or

impractical to protect with non–systemic treatments.

47

Neonicotinoids may also be translocated into nectar or pollen, the principal food sources

for bees (Bonmatin et al. 2015). Both imidacloprid and dinotefuran are acutely toxic to bees

(Johnson 2015). Furthermore, imidacloprid breaks down into additional insecticidal metabolites,

including imidacloprid olefin and 5–hydroxy imidacloprid, the former of which can be more

acutely toxic to honeybees than imidacloprid itself (Nauen et al. 2001, Suchail et al. 2001).

Potential risk to bees is a major reason that neonicotinoids are under regulatory scrutiny (EFSA

2013, US EPA 2014). Neonicotinoids are currently facing pressure from environmental advocacy

groups calling for restrictions on their use (Hopwood et al. 2012).

Globally, bee populations face pressures from habitat loss and fragmentation, exotic pests

and pathogens, nutritional stress, pesticide exposures, loss of genetic diversity, and other stressors

(Potts et al. 2010, Goulson et al. 2015). In urban areas, bees provide pollination services to

community and residential gardens, to ornamental fruit–bearing trees and shrubs that feed birds

and other desirable wildlife, and to many wild plant species (Hernandez et al. 2009, Ollerton et al.

2011, Baldock et al. 2015, Lowenstein and Minor 2015, Smitley et al. 2016, Howell et al. 2017,

Senapathi et al. 2017). Urban bee communities are dominated by polylectic species (Fetridge et

al. 2008, Matteson et al. 2008, Hernandez et al. 2009, Tonietto et al. 2011, Baldock et al. 2015)

that collect nectar and pollen from diverse plants, including flowering trees and shrubs

(Hausmann et al. 2016, Somme et al. 2016, Mach and Potter 2017), so it is important that those

floral resources not contain harmful levels of pesticide residues.

Many laboratory and semi–field studies have demonstrated that exposure to food spiked

with sublethal concentrations of neonicotinoids can adversely affect bees’ learning, foraging

ability, homing ability, and reproduction (Blacquière et al. 2012, Goulson 2013, Pisa et al. 2015).

Less clear, however, is whether bees’ exposure to neonicotinoids as they are normally used in the

48

field is sufficient to cause such effects (Blacquière et al. 2012, Godfray et al. 2014, Lundin et al.

2015).

Presently there are almost no published data concerning rates of uptake and dissipation of

neonicotinoid residues in nectar or pollen of woody landscape plants. Such information is needed

to assess the range of concentrations to which bees might be exposed in urban landscape settings.

Soil–applied imidacloprid provides multiple years of protection from some foliage–feeding pests

of trees and shrubs (Cowles et al. 2006, Frank et al. 2007, Szcepaniec and Raupp 2007, Benton et

al. 2015), indicating the relative stability of it and its metabolites once absorbed by the plant.

Dinotefuran is more water soluble than imidacloprid, 39.8 versus 0.61 g/L, respectively

(Bonmatin et al. 2015), and has lower sorption to soil organic matter. Therefore, it tends to show

more rapid uptake and translocation but also more rapid decline in plant tissues (Cowles and

Lagalante 2009, Byrne et al. 2012, Nix et al. 2013, Byrne et al. 2014). Manipulating application

timing and taking advantage of the differences in systemic mobility and residual activity of

imidacloprid and dinotefuran may be a way to minimize hazard to bees and still enable effective

pest control for flowering woody landscape plants.

My objectives for this study were to measure uptake and dissipation of residues of soil–

applied imidacloprid and dinotefuran in nectar and foliage of two species of established woody

landscape plants using autumn (post–bloom), spring (pre–bloom), and summer (post–bloom)

timings. The end goal is to support protocols for integrating pest and pollinator management for

urban landscapes.

49

Methods

Plant species and study sites

Two species of woody landscape plants served as models in independent trials: Foster

holly (Ilex × attenuata) [Aquifoliaceae], a dioecious broadleaf evergreen tree that is a natural

hybrid of Ilex opaca (American holly) and Ilex cassine, both of which share native territory in

the southeastern USA (Gilman and Watson 1993), and summersweet, also called sweet

pepperbush (Clethra alnifolia) [Clethraceae], a small native deciduous shrub. At the latitude of

Kentucky, USA, Foster holly (Ilex) produces greenish–white flowers (6–7 mm diameter, 3 mm

deep) and typically blooms in mid–May to mid–June. Summersweet (Clethra) produces white

flowers (8 mm diameter, 7 mm deep) in upright spikes and blooms in late July to early August.

Both species are highly attractive to bees (Mach and Potter 2017) and are representative of large

groups of common woody landscape plants (small, deciduous shrubs and evergreen trees).

The Ilex (about 2.13 m tall; planted in 2009) were in an established hedge (180 same–age,

uniform–sized trees) on the University of Kentucky campus (lat: 38.029151, long: –84.509283)

on a disturbed silt loam soil (pH = 7.7; 33.4% sand, 50.9% silt, and 15.7% clay; 4.15% organic

matter). I used 72 female plants, enough for three treatment timings, two neonicotinoids plus

untreated checks, and eight replicates per combination. Trees used in the trial were at least 2 m

apart and buffered from one another by untreated trees.

Clethra alnifolia var. ‘Sixteen Candles’ were obtained from a commercial nursery as 0.76

m tall shrubs in 18.9 liter containers. They were transplanted to a site with tilled Maury silt loam

soil (pH = 6.0; 12.7% sand, 71.5% silt, and 15.9% clay; 5.6% organic matter) at the University

of Kentucky’s Spindletop Research Farm (lat: 38.129548, long: –84.499315) on 19 September

2014. There were 54 total shrubs (three timings × three treatments × six replicates), four rows

50

each with nine shrubs, with 2 m spacing between plants. The site was mulched and the shrubs

were irrigated weekly until 17 October 2014 to aid establishment, after which no further

irrigation was applied until July 2016, when the shrubs were irrigated twice to alleviate effects of

drought stress.

Neonicotinoid soil applications

Plants within each trial were treated with imidacloprid (Merit 2F, Bayer, Research

Triangle Park, NC) or dinotefuran (Safari 20 SG, Valent U.S.A. LLC, Walnut Creek, CA), or left

untreated as controls. Merit 2F is a liquid formulation containing 21.4% active ingredient (AI)

with a label rate of 0.7–1.44 g AI per 0.305 m of plant height. Safari 20 SG is a water soluble

granule containing 20% AI with a label rate of 0.6–1.2 g AI per 0.305 m of plant height.

Dinotefuran solutions were made using 709 mL distilled water and 126 g Safari 20 SG, and

imidacloprid solutions were made using 603 mL distilled water and 106 mL Merit 2F, each

within the products’ range of label rates. The neonicotinoids were applied at equivalent dosages:

1.05 g AI (5.25 g Safari 20 SG, 87.5% of max label rate) for dinotefuran, and 1.06 g AI (0.15 fl

oz Merit 2F, 73.6% of max label rate) for imidacloprid, per 0.305m of plant height. The Ilex had

multiple trunks so I used label rates for shrubs which are based on plant height as opposed to

trunk diameter for single–trunk trees. The Ilex were treated with 210 mL of dinotefuran or

imidacloprid solution to accommodate 2.13 m plant height, and Clethra were treated with 75 mL

of dinotefuran or imidacloprid solution to accommodate 0.76 m plant height.

I originally intended to use a pressured soil injector of the type used by arborists to

deliver the systemic pesticides into the soil, but found that it failed to deliver consistent enough

dosages for research purposes. I instead simulated soil injection by using a narrow–bladed hand

trowel to open six holes (10.1 cm deep) in a circle approximately 15.1 cm from the base of each

51

plant and injected equal portions of dinotefuran or imidacloprid solutions into each hole with a

10 mL syringe. Treatments were not watered in, and there was no rainfall for at least 24 h after

application. Treatment timings (Table 4.1) were either autumn (post–bloom), spring (pre–

bloom), or summer (post–bloom).

Collecting nectar and foliage samples

Samples of nectar and foliage were collected from all plants during their respective

bloom times in 2015 and 2016. Due to plant–to–plant variation in bloom times, each sampling

period required several days to collect enough flowers to yield sufficient nectar for analysis.

Sampling dates for Ilex were 2–12 June 2015 and 9–16 May 2016. Clethra was sampled 22–31

July 2015 and 20 July to 3 Aug 2016 (Table 4.1). Twigs with flowers were cut in the early

morning, put in separate plastic bags for each plant, placed in coolers, and brought to the lab for

processing the same day to maximize nectar yield. Ilex nectar was too viscous to collect via

capillary tubes. To extract it, I collected twigs (2.5–5 cm) each bearing several flowers, removed

the leaves, suspended the trimmed twigs from clips inside 15mL centrifuge tubes, and spun them

in a centrifuge (4000 rpm for 5 min). The expelled nectar was pipetted into 1.5mL

microcentrifuge tubes. Clethra nectar was collected directly from the flowers by using 5 µL

microcapillary tubes that were then drained into microcentrifuge tubes as above. In 2015, only

the minimum amount of leaves required for analysis (40–50 leaves) were collected from each

plant during the same period as nectar extraction. This was done to prevent defoliating and

damaging plants needed for the next year’s residue analysis. In 2016, samples (50–100 g) of

current–year (new) foliage were collected from all portions of each plant’s canopy during the

same period as nectar extractions. For Ilex, the broad–leaf evergreen, samples of the previous

years’ (old) leaves were also collected. Nectar samples (in microcapillary tubes) and leaf

52

samples (in plastic bags) were frozen at –80°C, labeled with a random number code, and shipped

on dry ice to the Valent Technical Center, Dublin, CA for residue analyses. Blanks and samples

that had been spiked with known concentrations of imidacloprid or dinotefuran were also coded

and included, ensuring a non–biased double–blind analysis procedure. Further processing and

analysis of the samples was conducted by S. Bondarenko, T. Chen, and B. Kowalsky (Valent

U.S.A. LLC).

Chemicals and reagents

All industrial sources for chemicals, reagents, supplies, and equipment described in this

section and subsequent ones are USA–based unless indicated otherwise. The analytical

reference standards and deuterated compounds of imidacloprid (purity, 99.9%) and

imidacloprid–d4 (purity, 99.9%) were purchased from Sigma–Aldrich (St. Louis, MO);

imidacloprid olefin (purity, 97.9%), 5–hydroxy imidacloprid (purity, 96.7%), imidacloprid

olefin–13C3,15N, D (98.7%), and 5–hydroxy imidacloprid–13C3, 15N, D (90.7%) were obtained

from Bayer CropScience (Research Triangle Park, NC); dinotefuran (purity, 99.9%) was

provided by Valent U.S.A. LLC (Dublin, CA) and dinotefuran–d3 (purity, 98%) was obtained

from C/D/N Isotopes (Pointe–Claire, QC, Canada). Acetonitrile (LC/MS grade), methanol

(LC/MS grade) and water (HPLC grade) were purchased from VWR International (Radnor, PA).

Formic acid (LC/MS grade) was obtained from Fisher Chemical (Waltham, MA). Magnesium

sulfate and sodium chloride salts (each reagent grade) were purchased from Sigma Aldrich.

Peach blossom honey was purchased from Cooper Farms Country Store (Fairfield, TX).

Preparation of standard solutions

Individual pesticide standard solutions (1 mg/mL or 2 mg/mL for neat and deuterated)

were prepared in acetonitrile. Imidacloprid, 5–hydroxy imidacloprid, imidacloprid olefin, and

53

dinotefuran standard stock solutions were further diluted with acetonitrile to prepare fortification

standard solutions at concentration levels of 1000 ng/mL, 100 ng/mL, and 10 ng/mL. An internal

standard mixture of imidacloprid–d4, imidacloprid olefin–13C3, 15N, D; 5–hydroxy imidacloprid–

13C3, 15N, D, and dinotefuran–d3 (2 ng/mL) was prepared in water/methanol (90/10, v/v).

Calibration standards ranging from 500 ng/mL to 0.05 ng/mL and containing 2 ng/mL of

deuterated compounds were prepared in water/methanol (90/10, v/v) daily. Stock and

fortification standard solutions were stored at –18 °C and 4 °C, respectively.

Nectar extraction

Received nectar samples were brought to the ambient temperature and were inspected for

their quality. The nectar sample was briefly vortexed before extraction, and a 0.100 g subsample

was weighed into a 1.8 mL autosampler vial. One mL of the internal standard solution (2

ng/mLin water/methanol (90/10, v/v)) was added to the sample. Further, the sample was

vortexed and filtered through a Whatman 0.2 µm GD/X nylon filter disk (GE Healthcare Life

Sciences, Pittsburgh, PA) to remove any particulate materials. For nectar samples with weight

less than 0.1 g required for residue analysis, the final volume of a sample was less than one mL,

and the sample was centrifuged at 13,000 rpm for 5 min using a Sorvall Biofuge Pico centrifuge

(Thermo Fisher Scientific, Waltham, MA) to remove particulates. The two used techniques to

separate particulate materials from the samples have not affected extraction efficiency of the

studied compounds. In addition, sugar content in nectar samples was measured using Eclipse

Hand Held Refractometer model 45–81 and 45–82 (Bellingham & Stanley, Suwanee, GA). For

that, a drop (1–5 μL) of nectar sample was placed on the refractometer and the reading was

rapidly performed to avoid alterations due to evaporation. Sugar content was measured in °Bx

where 1ºBx is equal to 1 g sucrose in 100 g solution.

54

Leaf tissue extraction

In 2015, a subsample (2 g) of leaf tissues (or the whole sample if < 2 g) was weighed

directly into 50–mL polypropylene tubes pre–filled with 2.8 mm ceramic beads (Omni,

Kennesaw, GA). Ten milliliters of water acidified with 0.05% formic acid were added; then the

samples were shaken vigorously using an Omni Bead Ruptor 24 (Omni) at 3.7 motions per

second for 60 sec. The sample was then extracted with 10 mL of acetonitrile using the same

procedure as for the water extraction. The extraction was followed by addition of 2.0 g of

sodium chloride and 4.0 g of anhydrous magnesium sulfate salts. The samples were then

centrifuged at 4000 rpm for 5 min using a Thermo Scientific Sorvall Evolution™ RC centrifuge

(Thermo Fisher Scientific) to separate aqueous and organic phases. One milliliter of the organic

supernatant was passed through a Strata C18–E cartridge (50 mg, Phenomenex, Torrance, CA)

preconditioned with 1.0 mL of acetonitrile and rinsed with 0.5 mL of acetonitrile. The

acetonitrile eluent was evaporated to dryness using a rotary vacuum evaporator and then

reconstituted in 1.0 mL of internal standard solution (2 ng/mL). The sample was then filtered

through a Whatman 0.2 µm GD/X nylon filter disk into an autosampler vial.

In 2016, the leaf tissues were homogenized in presence of dry ice using a KitchenAid

coffee grinder (KitchenAid, Benton Harbor, MI). Two grams of homogenized leaf sample were

weighed into a 50–mL polypropylene centrifuge tube and extracted with 10 mL of water

acidified with 0.05% formic acid and 10 mL of acetonitrile followed by addition of 2.0 g of

sodium chloride and 4.0 g of anhydrous magnesium sulfate salts, with rest of the extraction

procedure as described for the 2015 samples.

55

LC–MS/MS analyses

LC–MS/MS analyses were carried out using a SCIEX API 4000 QTRAP triple

quadrupole/linear ion trap mass spectrometer operating in positive electron spray ionization

mode (Applied Biosystems, Foster City, CA) coupled with an Agilent 1260 high performance

liquid chromatograph (HPLC) (Agilent Technologies, Palo Alto, CA) or a SCIEX 6500+ triple–

quadrupole mass spectrometer (AB Sciex, Framingham, MA) equipped with an Agilent 1290

UPLC. Samples were separated using a reverse phase Kinetex Biphenyl column (100 Å, 2.6 µm

particle size, 100 × 2.1 mm, Phenomenex, Torrance, CA) coupled with a C18 security guard

column (4.0 × 3.0 mm ID and 100 x 2.1 mm; Phenomenex, Torrance, CA) maintained at 30 °C.

The mobile phase solvents were water acidified with 0.05% formic acid (A) and methanol

acidified with 0.05% formic acid (B). The initial mobile phase composition was 90% A and 10%

B at flow rate of 0.3 mL/min. The initial conditions were held for 0.5 min, followed increase to

50% B by 5.5 min, then increase to 95% B by 10 min and held for 4 min at 95% B. The

analytical column was then brought back to initial conditions in 1 min and equilibrated for 5 min.

The total run time was 20 min and the injection volume was 30 µL for nectar samples and 10 or

30 µL for leaf samples. The mass spectrometer was operated using electrospray ionization (ESI)

in the positive ion mode. The mass spectrometer source was set at 400 °C; with nebulizer,

curtain and auxiliary gas at 50 (55 for 5–hydroxy–imidacloprid, imidacloprid olefin and their

counterparts), 30 and 40 units, respectively. The collision gas (N2) was set at 10 units. High–

purity nitrogen was used for all gases and air was used as an auxiliary gas. The ion spray voltage

was set at 5000 V (5500 V for 5–hydroxy–imidacloprid, imidacloprid olefin and their

counterparts). Entrance potential was kept at 10 V (5 V for 5–hydroxy–imidacloprid). Two

transitions, precursor and product ions, for each analyte were monitored. The monitored

56

transitions and their mass spectrometer specific parameters are indicated in Table 4.2. SCIEX

Analyst Software 1.5 and 1.6.2 was used for data acquisition and quantitation.

Quality control

Methods for extracting nectar and leaf tissues were validated before sample analysis

using diluted honey (25%, w/w) and cotton untreated control leaves. For nectar, the limit of

detection (LOD) for all analytes was 0.5 ng/g and the limit of quantitation (LOQ) was 1.0 ng/g.

For leaf tissues, the LOQ of quantitation for all analytes was 5.0 ng/g and the LOD was 2.5 ng/g.

Average recoveries and standard deviations for all analytes in nectar and leaf tissues are

presented in Table 4.3.

All samples were analyzed in sets that included at least one untreated control (UTC)

sample and two control matrix samples fortified at the LOQ and 10 times the LOQ levels.

Fortifications ranged from 1.0–5,000 ng/g for nectar samples and from 5.0–10,000 ng/g for leaf

samples. Average recoveries and standard deviations from concurrently analyzed fortified

control samples of nectar and leaf samples are presented in Table 4.4.

Statistical analysis

Residue data were analyzed for main effects and interaction of treatment date, sample

year, and (for Ilex) leaf age class using General linear models procedures (SAS, Version 9.4;

SAS Institute, Cary, NC, USA), with mean separation by Least Square Means. Pearson

correlation analysis was used to test for strength of correlation between imidacloprid and its

metabolites in nectar and leaves. Data were analyzed separately by plant species and chemical.

Data points below the LOD were entered as a zero, which is a conservative approach to handling

data below detectable concentrations (Benton et al. 2016). Data are presented as original means

± standard error (SE).

57

Results

Imidacloprid, imidacloprid olefin, and 5–hydroxy–imidacloprid residues in Ilex nectar and

leaves

Soil application of imidacloprid in autumn (post–bloom, November 2014) or spring (pre–

bloom, March 2015) resulted in mean concentrations of 276 and 166 ng/g imidacloprid,

respectively, in Ilex nectar when the trees bloomed in spring 2015 (Table 4.5), with no

significant difference between the treatment dates (Table 4.6, Fig. 4.1a). Those residue levels

declined by about 88 and 79%, respectively, by the time the trees bloomed again in spring 2016.

Trees treated in summer (post– bloom, June 2015) had only about 8 ng/g imidacloprid in their

2016 nectar, levels comparable or lower than those present in the second year of bloom for the

other two timings (Fig. 4.1a). Residues of the metabolites imidacloprid olefin and 5–hydroxy

imidacloprid in Ilex nectar (Table 4.5) were highly correlated with those of imidacloprid (r =

0.98, 0.96 for 2015 and 2016, respectively; P < 0.0001; n = 54). Sugar concentrations in Ilex

nectar (Table 4.5) ranged from 42–78 ºBx in 2015 and 13–66 ºBx in 2016.

Imidacloprid residues in Ilex leaves followed a similar pattern to those in the nectar, with

no significant difference between treatment dates but a significant decline between 2015 and

2016, especially in the newly–flushed (current year) leaves (Table 4.6, Fig. 4.1b). Within

treatment dates and years, imidacloprid concentrations in new leaves were 28–90 times higher

than those in nectar. Summer treatment resulted in the least amount of imidacloprid in the

following spring’s foliage (Fig. 4.1b). Imidacloprid olefin and 5–hydroxy– imidacloprid

residues were highly correlated with imidacloprid levels in foliage (r = 0.86, 0.88 for 2015 and

2016; P < 0.0001; n = 137 for combined new and 1–year old leaves).

58

Imidacloprid, imidacloprid olefin, and 5–hydroxy–imidacloprid residues in Clethra nectar and

leaves

Uptake and dissipation of imidacloprid residues in Clethra nectar followed a similar

pattern to residues in Ilex nectar (Table 4.6, Fig. 4.1c). There was no significant difference

between the autumn ( post–bloom, November 2014) and spring (pre–bloom, March 2015)

treatment dates, both of which resulted in high levels in summer 2015 nectar. Those levels

declined by 83–85% when the trees bloomed again in summer of 2016 (Fig. 4.1c). As with the

Ilex, autumn treatment resulted in relatively low residue levels in nectar the following year (Fig.

4.1c). Imidacloprid olefin and 5–hydroxy–imidacloprid residues (Table 4.5) were highly

correlated with imidacloprid residues in Clethra nectar (r = 0.82, 0.94 for 2015 and 2016,

respectively; P < 0.001; n = 65 in each year). Residues of imidacloprid in Clethra leaves

followed a similar pattern to those in Clethra nectar, with no differences between autumn and

spring treatment timings and 77–84% declines between 2015 and 2016 (Table 4.6, Fig. 4.dD).

Concentrations were about 50–fold higher in leaves than in nectar. Imidacloprid olefin and 5–

hydroxy–imidacloprid residues were highly correlated with imidacloprid residues in Clethra

leaves (r = 0.97, 0.97 for 2015 and 2016, respectively; P < 0.0001, n = 69 in each year). Sugar

concentrations in Clethra nectar (Table 4.5) ranged from 1–26 ºBx in 2015 and 10–18 ºBx in

2016.

Dinotefuran residues in Ilex nectar and leaves

Uptake and dissipation of dinotefuran residues in Ilex nectar (Fig. 4.2a) showed a

different pattern from imidacloprid. For dinotefuran, there were significant main effects for

treatment date and year (Table 4.6). Soil injection in autumn (post–bloom, November 2014) or

spring (pre–bloom, March 2015) resulted in high concentrations in the 2015 nectar (Fig. 4.2a),

59

especially from the spring application. However, those residues were nearly gone (≤ 3 ng/g) by

the time the trees bloomed again in 2016. The summer (post–bloom, June 2015) application

resulted in high dinotefuran concentrations in nectar the following spring (Fig. 4.2a). Mean

(range) sugar concentrations in Ilex nectar were 62 (59–64) ºBx and 68 (64–74) ºBx in 2015 for

autumn and spring applications, respectively. Sugar concentrations in 2016 were 48 (42–54) ºBx,

27 (5–66) ºBx, and 46 (33–57) ºBx for autumn, spring, and summer applications, respectively.

Dinotefuran residues in Ilex leaves also showed significant main effects for treatment

date and year sampled (Table 4.6, Fig. 4.2b). November 2014 and March 2015 applications

resulted in mean concentrations of 4751 and 6287 ng/g in 1–year old and new leaves,

respectively, sampled coincident with bloom in May 2015, but those levels had declined by

>99% by spring 2016 (Fig. 4.2b). In contrast, residues from the summer treatment timing were

still present at high amounts in foliage sampled in spring of the following year.

Dinotefuran residues in Clethra nectar and leaves

Patterns of uptake and dissipation of dinotefuran in Clethra nectar and leaves were

similar to Ilex (Figs. 4.2c, d). Main effects for treatment date and sample year both were

significant (Table 4.6). Compared to the spring treatment timing, application in autumn resulted

in lower dinotefuran residues in spring 2015 nectar and leaf tissue. Treating in summer 2015

resulted in high levels of dinotefuran in nectar and foliage the following summer (Figs. 4.2c, d).

Mean (range) sugar concentrations in Clethra nectar were 11 (2–26) ºBx and 18 (3–54) ºBx in

2015 for autumn and spring applications, respectively. Sugar concentrations in 2016 were 16

(15–17) ºBx, 15 (14–19) ºBx, and 14 (12–18) ºBx for autumn, spring, and summer applications,

respectively.

60

Discussion

This study shows that soil application of imidacloprid or dinotefuran at landscape label

rates can result in residues in nectar of woody landscape plants that exceed concentrations shown

in semi–field and laboratory studies to adversely affect individual and colony–level traits of bees

(Blacquière et al. 2012, Goulson 2013, Godfray et al. 2014, Lundin et al. 2015, Pisa et al. 2015).

The levels I observed also exceed the no and lowest observed adverse effect concentrations (25

and 50 ng/g, respectively) for honeybee colonies (US EPA 2016). Those levels, particularly in

the first spring after autumn or spring application, were also much higher than the 1–10 ng/g

typically found in nectar of field crops such as canola or sunflower grown from treated seed

(Goulson 2013, Bonmatin et al. 2015). Neonicotinoid label rates for soil application to woody

landscape plants are much higher on a per–plant basis than those used to protect field crops

(Krischik et al. 2015). Those rates are broad, due in part to the variety of pests and plant species

in urban landscapes. Lowering the label rates may reduce risk to pollinators, but it is unknown if

doing so would still provide control of key pests, especially since uptake of residues will vary

depending on plant species, size, and health, as well as soil type and environmental conditions.

Reported half–lives of neonicotinoids in soils vary greatly across soil types and

conditions (Bonmatin et al. 2015). Calculated half–lives for imidacloprid range from 107 to

1250 days, depending on soil conditions (Goulson 2013, Bonmatin et al. 2015). Once it is

absorbed by a tree or shrub, imidacloprid may be relatively stable. In hemlock (Tsuga

canadensis), an evergreen species, imidacloprid concentrations in foliage peak about 9–15

months after soil application, but concentrations of its metabolite imidacloprid olefin continue to

rise for as long as 3 years after treatment (Coots et al. 2013). The multi–year suppression of

hemlock wooly adelgid provided by such treatments suggests multiple years of mobilization of

61

imidacloprid and imidacloprid olefin to new growth (Cowles et al. 2006, Benton et al. 2015). In

contrast, most of the accumulation of imidacloprid into leaves of ash (Fraxinus spp.), which is

deciduous, occurs during the growing season immediately after treatment with little

remobilization the following year (Mota–Sanchez et al. 2009, Tanis et al. 2012).

Dinotefuran is about 80 times more soluble than imidacloprid, and its soil sorption

coefficient is > 10–fold lower (US EPA 2014). Its estimated half–life in soil under field

conditions is about 75 days (Bonmatin et al. 2015). Studies comparing the two compounds’

metabolism in ash (McCullough et al. 2011), hemlock (Cowles and Lagalante 2009), walnut (Nix

et al. 2013), and avocado (Byrne et al. 2012) suggest that dinotefuran tends to be translocated

more quickly than imidacloprid, but it may also undergo relatively more rapid degradation in

woody plant tissues.

I hypothesized that differences in systemic mobility and persistence between

imidacloprid and dinotefuran would allow one or the other of the compounds to be applied in

autumn or summer with minimal transference of residues into floral resources. Ideally that

would allow landscape managers to match product and timing to control pests with minimal

hazard to bees.

Summer 2015 (early post–bloom) applications of imidacloprid resulted in relatively low

concentrations in 2016 nectar of both plant species. If that pattern holds for other woody plant

species, treating with imidacloprid soon after bloom may allow for control of pests such as

aphids, leaf–feeding beetles, or scale insects with minimal hazard to pollinators. However,

summer application also gave relatively little transference into new foliage, which could limit

effectiveness against pests such as psyllids that feed on and distort expanding new leaves in

spring. Autumn and spring imidacloprid treatments resulted in high concentrations in foliage

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and nectar, including some remobilization into new growth and floral resources in the second

year. Since the plants were dormant when treated in November, there was likely little uptake

from the soil until bud–break the following spring. Thus, both treatments would have had

similar time for the uptake of the relatively slow–mobilizing imidacloprid once the plants

became metabolically active in spring.

Dinotefuran showed a different pattern of uptake and dissipation than imidacloprid. In

Ilex, autumn 2014 or spring 2015 applications were followed by high nectar residue levels when

the plants bloomed in 2015 but almost no deposition into nectar or foliage the following year.

Summer application, however, resulted in unexpectedly high deposition of residues in nectar and

in both new and 1–year old leaves the following spring. Relationships between application

timing and dinotefuran residues in Clethra were generally similar to the patterns in Ilex. Given

the relatively short estimated half–life of dinotefuran in field soil (Bonmatin et al. 2015) it seems

unlikely that enough of the compound would persist for 11 months in the soil to account for the

high 2016 residue levels. Instead, residues taken up by the plants during the 2015 growing

season were likely still present in the woody tissues or 1–year old leaves and remobilized to both

nectar and new leaves during leaf flush and flowering the following spring.

Neonicotinoids circulate mainly via xylem transport (Bonmatin et al. 2015) so their

uptake in plants is greatest during periods of active growth and transpiration. At the time of

sampling in spring 2016, plants treated in autumn 2014 and spring 2015 had undergone two

separate annual leaf flushes, whereas those treated in summer 2015 had undergone only one. I

hypothesize that residues in summer–treated plants experienced less dilution and degradation in

foliage over the course of one leaf flush versus two, leaving more available for remobilization

into nectar. It seems less likely that dinotefuran remained in the soil due to the steep drop–offs I

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observed in second–year residues. Compared to imidacloprid, much less is known about the fate

of dinotefuran in woody plant tissues. My data suggest that dinotefuran may be more persistent

in plants than is generally believed.

My data indicate that even if label directions to “make application prior to anticipated

pest infestation to achieve optimum levels of control” (Merit 2F and 75 WP labels), or “time

applications to coincide with when most vulnerable pest life stage is present on plants” (Safari 20

SG label) are followed, use of those products on bee–attractive woody landscape plants could

result in residue levels in floral resources higher than those known to adversely affect bees.

Likewise, instructions on the EPA Bee Advisory Box such as “Do not apply while bees are

foraging”, “Do not apply to plants that are flowering”, and “Only apply after all flower petals

have fallen off” would not necessarily alleviate potential risk on such plants.

Insecticide hazard to pollinators depends on toxicity of the pesticide, extent of exposure,

and the effects of that exposure on individual or colony fitness (Godfray et al. 2014).

Interpreting my results in the context of pest management and pollinator protection is complex

because no regulatory limit currently exists for either dinotefuran or imidacloprid residues in

nectar of woody landscape plants. Furthermore, little is known about bees’ exposure to treated

plants in landscape settings.

Most bee species in urban landscapes are polylectic, collecting pollen and nectar from a

variety of flowering weeds and other spontaneous plants, as well as from ornamental forbs,

shrubs, and trees (Larson and Potter 2014, Lowenstein and Minor 2015, Somme et al. 2016).

Such dietary diversity would likely dilute the effects of occasional sublethal exposure to

neonicotinoid– treated plants. Exposure will also be affected by the percentage of bee–attractive

plants in a given neighborhood that are treated with neonicotinoids, which is likely to be low,

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and the length of time that those plants are in bloom. Bee colonies in orchards or field crops may

be exposed to monocultures of treated plants for the duration of flowering which in some cases

(e.g., canola or oilseed rape) can last as long as 6 weeks (Cabrera et al. 2016). In contrast,

individual woody landscape plants tend to bloom and attract bees for shorter periods, often no

more than 1–2 weeks (author’s observations).

Systemic nitroguanidine neonicotinoids are versatile tools for managing insect pests,

including invasive species, of trees and shrubs, but guidelines are needed to help land care

professionals and homeowners use them without harming bees and other pollinators. My results

indicate that residues in nectar are likely to intoxicate individual pollinators foraging exclusively

on treated woody plants. Therefore, a recommendation for integrating pest and pollinator

management is to avoid their use on bee–attractive trees and shrubs unless there is no other way

to prevent significant pest damage to such plants. Future work is needed to define the percentage

of floral resources that systemically–treated plants represent in urban landscapes. Without such

data, it is difficult to draw conclusions about the impact of these treatments on pollinator health

at the landscape level.

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Table 4.1. Dates on which woody plants received one–time soil treatment with imidacloprid or dinotefuran and on which nectar and foliage were sampled for residue analyses 2015 Sampling 2016 Sampling Plant

Treatment date

Date sampled

Days since treatmenta

Date sampled

Days since treatmenta

Ilex 10 Nov 2014 2–12 June 204 9–16 May 546 27 Mar 2015 2–12 June 67 9–16 May 409 15 June 2015 – – 9–16 May 329 Clethra 11 Nov 2014 22–31 July 253 20 Jul–3 Aug 617 27 Mar 2015 22–31 July 117 20 Jul–3 Aug 481 3 Aug 2015 – – 20 Jul–3 Aug 352 aDays elapsed between systemic application and first day of sampling period. Each nectar harvest required several days to collect 100 µl samples per plant because of variation in flowering and the minute amounts of nectar per bloom.

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Table 4.2. Monitored transitions and their mass spectrometer specific parameters used in the residue analyses

Analyte Q1 Q3 CXP (V) CE (V) DP (V) Type

Dinotefuran 203.1 129.1 10 20 40 quantitation

203.1 157.0 10 20 40 confirmation

Dinotefuran–d3 206.2 132.2 10 20 40 quantitation 5–Hydroxy imidacloprid 272.2 191.2 5 30 50 quantitation

272.2 225.0 5 30 50 confirmation 5–hydroxy imidacloprid–13C3, 15N, D

277.0 196.1 15 30 20 quantitation

Imidacloprid olefin 254.0 171.1 12 25 40 quantitation

254.0 205.0 12 25 40 confirmation Imidacloprid olefin–13C3, 15N, D 258.9 176.1 15 30 40 quantitation

Imidacloprid 256.0 209.1 15 30 20 quantitation

256.0 175.0 15 30 20 confirmation

Imidacloprid–d4 260.0 213.1 15 30 20 quantitation CXP = Collision cell exit potential; CE = collision energy; DP = declustering potential; V = volt; Q1 = precursor ion; Q3 = quantification ion.

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Table 4.3. Average recoveries (and standard deviations) of analytes in nectar and leaf tissues

Analyte Nectar Leaf tissues

1.0 ng/g 10.0 ng/g All levels 5.0 ng/g 50.0 ng/g 10,000 ng/g

All levels

(n=13) (n=13) (n=26) (n=5) (n=5) (n=3) (n=13) Dinotefuran 93 (10) 102 (5.4) 97 (9.2) 95 (4.3) 96 (1.7) 89 (4.2) 94 (4.2)

5–hydroxy–imidacloprid

97 (16) 103 (7.4) 100 (13) 104 (2.0) 98 (2.3) 99 (6.2) 101 (4.1)

Imidacloprid olefin

106 (7.1) 101 (3.8) 104 (6.2) 94 (4.8) 95 (2.4) 101 (3.3) 96 (4.3)

Imidacloprid 102 (3.0) 105 (7.4) 103 (5.7) 100 (3.4) 99 (1.5) 116 (6.7) 103 (8.2)

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Table 4.4. Average recoveries (and standard deviations) of analytes from concurrently analyzed fortified control nectar and leaf samples Analyte Nectar Leaf tissues n All levels n All levels Dinotefuran 28 105 (10.6) 72 82.4 (7.5) 5–hydroxy–imidacloprid 28 109 (10.4) 64 100 (8.7) Imidacloprid olefin 28 112 (16.9) 69 101 (10.6) Imidacloprid 36 103 (14.9) 71 79.5 (29.0)

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Table 4.5. Mean (range) concentrations of sugar (°Bx)b, imidacloprid (ng/g), its metabolites imidacloprid olefin and 5–hydroxy–imidacloprid in nectar of Ilex (Foster holly) or Clethra (summersweet) following systemic treatment by soil injection Plant Species

Application Timing

Year Collected Sugar Imidacloprid

Imidacloprid olefin

5–hydroxy– imidacloprid

Ilex Nov 2014 2015 69 (66–74) 276 (122–560) 55 (22–123) 22 (7–54) 2016 33 (13–51) 32 (11–85) 4 (2–10) 3 (1–8) Mar 2015 2015 60 (42–78) 166 (1–459) 32 (0–81) 9 (0–23) 2016 36 (15–66) 52 (34–84) 10 (4–18) 5 (3–8) Jun 2015 2016 32 (14–55) 8 (6–15) 1 (1–2) 2 (0–2)

Clethra Nov 2014 2015 12 (3–24) 515 (213–1017)

55 (16–117) 69 (37–136)

2016 15 (15–17) 86 (16–192) 40 (13–65) 27 (8–51) Mar 2015 2015 11 (1–26) 381 (172–668) 40 (14–71) 46 (29–70) 2016 13 (11–17) 60 (25–107) 28 (19–35) 23 (17–30) Aug 2015 2016 13 (10–18) 31 (5–47) 16 (3–21) 12 (2–16)

aImidacloprid olefin and 5–hydroxy–imidacloprid residues were highly correlated with imidacloprid residues; Pearson correlation coefficient r = 0.98, 0.95, respectively, for Ilex; r = 0.82, 0.94 for Clethra, all P< 0.001. bOne ºBx is equal to 1 g sucrose in 100 g solution

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Table 4.6. Summary of analysis of variance for effects of treatment date (November 2014, March 2015, or post–bloom 2015), year sampled (2015 or 2016), and leaf age class (current or 1–year old leaves, Ilex only) on residue levels (ng/g) imidacloprid or dinotefuran in nectar or foliage of two species of woody landscape plants

Residues in Ilex Nectar

Residues in Clethra nectar

Imidacloprid

Dinotefuran

Imidacloprid

Dinotefuran

Source df F Pr > F

F Pr > F

F Pr > F

F

Pr > F

Trt date (T)a 2 0.69 0.51 8.24 0.002 1.21 0.003 6.58 0.005 Year (Y)b 1 14.7 <0.001 6.70 0.02 45.6 <0.001 5.29 0.03 T×Y 1 1.32 0.26 2.96 0.10 0.95 0.34 2.96 0.10 Residues in Ilex foliage Residues in Clethra foliage Imidacloprid Dinotefuran Imidacloprid Dinotefuran

T 2 1.47 0.24 12.0 <0.001 1.14 0.33 16.7 <0.001 Y 1 27.0 <0.001 4.38 0.04 45.7 <0.001 19.3 0.001 T×Y 1 2.1 0.15 2.14 0.15 1.82 0.19 9.1 0.005 Leaf age (A)c 1 0.48 0.49 0.35 0.56 – – – –

T×A 2 0.46 0.64 1.08 0.34 – – – –

Y×A 1 1.46 0.23 0.27 0.60 – – – –

T×Y×A 1 0.05 0.83 0.01 0.91 – – – – aTreatment dates were November 2014, pre–bloom (March 2015), or post–bloom (15 Jun or 3 Aug for Ilex or Clethra, respectively bNectar and foliage were sampled during bloom in 2015 or 2016 cFor Ilex, a broad–leaved evergreen, both current year and 1–year old leaves were sampled

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Figure 4.1. Mean (±SE) concentrations (ng/g) of imidacloprid in floral nectar of Ilex × attenuata (Foster holly) and Clethra alnifolia (summersweet) following systemic soil treatment in autumn (post–bloom, November 2014), spring (pre–bloom, March 2015), or summer (post–bloom, June or August 2015 for Ilex and Clethra, respectively). Data are means (±SE). N≥3 for all Ilex treatments. N≥5 for all Clethra treatments. Bars not topped by the same letter differ significantly (Least squares means, P < 0.05). Analysis of variance results are summarized in Table 6.

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Figure 4.2. Mean (±SE) concentrations (ng/g) of dinotefuran in leaves of Ilex × attenuata (Foster holly) and Clethra alnifolia (summersweet) following systemic soil treatment in autumn (post–bloom, November 2014), spring, (pre–bloom , March 2015), or summer (post–bloom , June or August 2015 for Ilex and Clethra, respectively). Data are means (±SE). Bars not topped by the same letter differ significantly (Least squares means, P < 0.05). Analysis of variance results are summarized in Table 6.

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CHAPTER FIVE

Conclusions and applications

The goals of Chapters 2 and 3 were to document the bee–attractiveness and bee

assemblages of flowering woody plants and to investigate the quality of their floral

rewards. Interest in creating bee–friendly urban landscapes is high, and many

conservation organizations have created lists of “bee–friendly” plants (Pollinator

Partnership 2017, Royal Horticultural Society 2017, Xerces Society 2017). However,

many such lists fail to cite published data (Garbuzov and Ratnieks 2014a) and only

characterize pollinator visitation broadly (e.g. “bee” and “butterfly”). Previous scientific

studies have focused on either native or herbaceous plants in urban landscapes (Tuell et al.

2008, Frankie et al. 2005, 2009; Garbuzov and Ratnieks 2014a, Garbuzov et al. 2015,

2017), leaving the bee conservation potential of nonnative flowering woody plants

largely unexplored. To my knowledge, this is the first study to quantify the bee–

attractiveness and bee assemblages of both native and nonnative flowering woody plants

in urban landscapes, and one of only a few to further examine the nutritional quality of

the floral rewards woody plants provide.

One of the major goals for Chapters 2 and 3 was to address the concern that

nonnative plants might not support bees in urban landscapes, either by failing to attract

bees or by providing low–quality floral resources. My data clearly indicate that both

native and nonnative woody plants can be highly bee–attractive and support equally

diverse assemblages of both native and nonnative bee species, in line with previous

research that shows bees forage readily on both native and nonnative plant species

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(Harmon–Threatt and Kremen 2015). In addition, many factors that I hypothesized would

be important to determining bee–attractiveness and bee assemblages, such as plant type,

bloom time, and site type had no significant impact.

Although nutritional analysis of pollen from native and nonnative plants revealed

statistically significant differences between total amino acids and essential amino acids,

the differences were small and likely a result of the small number of species sampled.

Both native and nonnative species provided pollen with high amino acid content and the

full spectrum of essential amino acids, similar to previous work on urban trees (Somme et

al. 2010). Plant type also had statistically significant but minor impacts on pollen quality.

Overall patterns in pollen quality and amino acid content were attributable to plant

species, which echoed my findings from Chapter 2. The results from Chapters 2 and 3

clearly demonstrate that flowering woody ornamentals need to be evaluated on a case by

case, species by species basis and that planting a diversity of woody plants is key to

supplying bees with season–long, high–quality forage.

The major goal of Chapter 4 was to investigate ways in which systemic

neonicotinoid hazard can be reduced when treating flowering woody plants for pests.

Nectar from only one treatment (imidacloprid post–bloom) had neonicotinoid residues at

or below the lowest observed adverse effects concentration of 50 ng/g during the first

year after treatment (US EPA 2016). None of the first–year dinotefuran treatments fell

below this threshold. Both neonicotinoids behaved very differently, which was expected,

but dinotefuran showed much longer persistence than previous studies in non-ornamental

woody plants suggest (Cowles and Lagalante 2009, Byrne et al. 2012, 2014; Nix et al.

2013, Bonmatin et al. 2015).

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This study is the first to address the uptake and dissipation of multiple

neonicotinoids in bee–attractive woody plants, and my results highlight the need for more

research into neonicotinoids other than imidacloprid as there is a lack of information on

how they behave in woody plants in non–agricultural settings. It also clearly illustrates

the need for guidance specific to each neonicotinoid compound rather than a “one size

fits all” approach to neonicotinoid regulation since compounds such as dinotefuran could

still result in hazard to bees even when following label instructions. In light of this, a

conservative best management practice is to avoid treating bee–attractive flowering

woody plants if possible, or to treat plants soon after flowers drop if application of a

systemic neonicotinoid is deemed necessary to protect the health of the plant. It is still

unknown what landscape–level effects neonicotinoids have on bees in urban landscapes

due to the irregular mix of treated and untreated plants as well as each bee species’

foraging preferences. However, current public opinion of neonicotinoids, and a growing

body of research that indicates their detrimental effects on bees, clearly shows the need

for more bee–friendly alternatives for treating pests in urban landscapes.

Each of these projects address important gaps in the scientific literature as there

have been few studies that focus on flowering woody plants in urban landscapes, and

each has unique applications for urban bee conservation. The data from Chapter 2 have

been adapted into a handout that is currently available at GrowWise.org in order to give

urban foresters, horticulturalists, and the general public access to data–based, bee–

friendly plant recommendations (Mach and Potter 2017). The full results and analyses are

expected to be published in 2018. The results from Chapter 3 further justify the use of

both native and nonnative plants for bee conservation in urban landscapes and will be

76

published in 2018–2019 and incorporated into extension presentations given by myself

and the Potter Lab. Finally, the results from Chapter 4 have been published in

Environmental Toxicology and Chemistry (Mach et al. 2017) and will be used by the US

EPA in the current review of neonicotinoids. Most importantly, this research

demonstrates that flowering woody plants have great potential for bee conservation.

These data show that planting a diverse range of bee–attractive, pest–free woody

ornamentals, as well as carefully considering if and how to treat them for pests, can help

create more bee–friendly urban landscapes.

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VITA

Education Bachelor of Science: Organismal Biology, Minor: Geology, The University of Tennessee

at Chattanooga, Chattanooga TN, May 2013

Professional experience Graduate research assistant, University of Kentucky, Lexington KY, Aug 2013—present Teaching assistant (ENT 320), University of Kentucky, Lexington KY, Fall Semester

2015 and 2016 Seasonal interpretive ranger, Harrison Bay State Park, Chattanooga TN, May 2014—Jul 2014 Seasonal interpretive ranger, AmeriCorps Appalachia CARES and Tennessee State Parks,

Nashville TN, Nov 2013—Apr 2014 Seasonal interpretive ranger, Bledsoe Creek State Park, Gallatin TN, May 2013—Oct

2013 Intern, United States Fish and Wildlife Service, Frankfort KY, Jun 2011—Aug 2011

Publications

Peer Reviewed Research Publications: Mach, B.M. and D.A. Potter. 2017. “Uptake and Dissipation of Neonicotinoid Residues in Nectar and Foliage of Systemically Treated Woody Landscape”. Environmental Toxicology and Chemistry. DOI:10.1002/etc.4021.

Mach, B.M. and D.A. Potter. (submitted). “Quantifying bee assemblages and attractiveness of flowering woody landscape plants for urban pollinator conservation”.

Mach, B.M. and D.A. Potter. (in prep). “Patterns in Nutritional Quality of Bee–attractive Woody Ornamentals”.

Other Publications: Mach, B.M. and D.A. Potter. “Plants bees like best”. Growwise.org. Horticultural Research Institute. February 2017.

Mach, B.M. and D.A. Potter. “Woody Ornamentals for Bee–friendly Landscapes (Ohio Valley Region”. The University of Kentucky. February 2017.

Mach, B.M. and T.D. McNamara. “Creating Pollinator Friendly Landscapes.” Greenhouse Production News. January 2017.