bee conservation in urban landscapes: assessing bee
TRANSCRIPT
University of Kentucky University of Kentucky
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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, [email protected] 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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The document mentioned above has been reviewed and accepted by the student’s advisor, on
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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.
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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
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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
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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
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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
74
(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).
75
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.
77
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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.