The Pennsylvania State University the Graduate School Intercollege

The Pennsylvania State University

The Graduate School

Intercollege Graduate Degree Program in Ecology

FLORAL RESOURCE PROVISIONING FOR BEES

IN PENNSYLVANIA AND THE MID-ATLANTIC REGION

A Thesis in

Ecology

Nelson Bernard DeBarros

 2010 Nelson Bernard DeBarros

Submitted in Partial Fulfillment of the Requirements for the Degree of

Master of Science

May 2010

The thesis of Nelson Bernard DeBarros was reviewed and approved* by the following:

David A. Mortensen Professor of Weed Ecology/Biology Thesis Advisor

Shelby J. Fleischer Professor of Entomology

James L. Frazier Professor of Entomology

Douglas A. Miller Associate Professor of Geography

David M. Eissenstat Professor of Woody Plant Physiology Head of the Intercollege Graduate Degree Program in Ecology

*Signatures are on file in the Graduate School

ABSTRACT

The abundance and diversity of bees, and the pollination services they provide, may be enhanced by a form of habitat management known as floral resource provisioning whereby flowering plants are established to provide food resources to a target organism or group of organisms. By increasing the availability of pollen and nectar, it may be possible to increase the size of bee populations or alter bee distribution and foraging activity within a landscape. First, to lay the groundwork for establishment of pollinator conservation plantings in Pennsylvania and the mid-Atlantic states, a group of twenty-five perennial plant species that are native to the region was selected based on criteria to ensure their suitability for widespread establishment on agricultural lands. Among other plant characteristics of interest was flowering phenology. To ensure a continuous supply of pollen and nectar from May through mid-October, the respective flowering phenologies of the candidate plant species were documented in 2008 and 2009 and were determined to provide a continuous sequence of blooms during this period. Second, to determine the relative attractiveness of the candidate plant species to members of the local bee community, bees were observed visiting the plants and were also collected from flowers to allow for species-level identifications. Significant differences in the number of bees existed among plants blooming during the early-, mid-, and late-season in both years (p<0.05 in all cases), with the exception of the early-season in 2008. Identifying plants species that are highly attractive to the entire bee community or to particular bee species will allow for the selection of the most appropriate plants for use in pollinator conservation plantings.

To test whether resource provisioning areas can enhance bee communities and crop pollination services, the number and diversity of bees associated with a pollinator-dependent crop were compared among fields in which a provisioning area was either present or absent. In

iii addition, pollination services provided within the fields were estimated through (1) timed counts of bee visits to crop flowers, (2) fruit weight, (3) number of seeds per fruit, and (4) number of fruits per plant. The number of seeds produced per fruit was significantly greater in fields that contained a provisioning area (F1,3=14.22, p=0.033), suggesting that these plantings can enhance pollination services. However, the other metrics did not yield consistent results. Developing a more refined understanding of the associations between plants and bee species will be important in developing pollinator conservation measures for agricultural systems in Pennsylvania and other regions of the United States.

TABLE OF CONTENTS

LIST OF FIGURES ...... viii LIST OF TABLES ...... ix ACKNOWLEDGEMENTS ...... xi

CHAPTER 1 INTRODUCTION TO POLLINATION AND BEE DECLINES ...... 1 POLLINATION ...... 1 POLLINATOR DECLINES ...... 3 NATIVE POLLINATORS ...... 7 HABITAT MANAGEMENT ...... 8 WORKS CITED ...... 11

CHAPTER 2 THE SELECTION AND FLOWERING PHENOLOGY OF CANDIDATE PERENNIAL PLANTS FOR POLLINATOR CONSERVATION IN PENNSYLVANIA ...... 16 INTRODUCTION ...... 16 MATERIALS AND METHODS ...... 20 PLANT SELECTION ...... 20 SITE CONFIGURATION ...... 21 FLOWER PHENOLOGY & CHARACTERISTICS ...... 23 ANALYSIS ...... 24 RESULTS ...... 27 DISCUSSION ...... 33 WORKS CITED ...... 35

CHAPTER 3 RELATIVE ATTRACTIVENESS OF TWENTY-FIVE NATIVE PERENNIAL PLANT SPECIES TO BEES IN CENTRAL PENNSYLVANIA ...... 37 INTRODUCTION ...... 37 VISUAL & OLFACTORY ATTRACTANTS ...... 38 FLORAL REWARDS ...... 41 EXTERNAL FACTORS INFLUENCING BEE VISITATION ...... 43

MATERIALS AND METHODS ...... 44 SITE DETAILS ...... 44 BEE OBSERVATIONS ...... 44 BEE COLLECTIONS ...... 45 BEE TAXONOMY ...... 46 STATISTICAL ANALYSIS ...... 47 RESULTS ...... 50 PLANT PREFERENCE ...... 51 FLOWER CHARACTERISTICS ...... 63 BEE DIVERSITY INDICES ...... 65 DISCUSSION ...... 70 WORKS CITED ...... 75

CHAPTER 4 INFLUENCE OF FLORAL RESOURCE PROVISIONING ON COMMUNITIES AND CROP POLLINATION RATES ...... 79 INTRODUCTION ...... 79 MATERIALS AND METHODS ...... 82 PLOT DESIGN ...... 83 POLLINATION RATE MEASURES ...... 85 BEE ABUNDANCE & DIVERSITY MEASURES ...... 85 STATISTICAL ANALYSIS ...... 86 RESULTS ...... 89 FRUIT PER PLANT, WEIGHT & SEED NUMBERS ...... 89 POLLINATORS COLLECTIONS ...... 91 POLLINATOR OBSERVATIONS ...... 95 SPECIES RICHNESS ...... 97 DISCUSSION ...... 98 WORKS CITED ...... 101

CHAPTER 5 EPILOGUE ...... 104 INTRODUCTION ...... 104 PLANT SELECTION ...... 104 PLANT-POLLINATOR ASSOCIATIONS ...... 107 MEASURING ECOSYSTEM SERVICES ...... 110 ESTABLISHING POLLINATOR CONSERVATION PLANTINGS ...... 111 CONCLUSION ...... 113

APPENDIX A: SOURCE AND MONTH OF TRANSPLANT FOR STUDIED PERENNIAL PLANTS ...... 115 APPENDIX B: R CODE FOR PRODUCING FLORAL AREA PHENOLOGY CHARTS ...... 118 APPENDIX C: PEARSON CORRELATION COEFFICIENTS FOR MEASURED FLOWER CHARACTERISTICS ...... 120

vii

LIST OF FIGURES

Figure 2.1 Experimental plot design...... 23

Figure 2.2 Floral area (cm2) of twenty-five native perennial plants (2008)...... 29

Figure 2.3 Floral area (cm2) of twenty-five native perennial plants (2009)...... 30

Figure 2.4 Average of log floral area for twenty-five native perennial plants...... 32

Figure 2.5 Average of log flower count for twenty-five native perennial plants ...... 32

Figure 3.1 Mean number of bees sampled from plant species during the early-season in 2008 and 2009 ...... 57

Figure 3.2 Mean number of bees observed visiting plant species during the early-season in 2008 and 2009 ...... 58

Figure 3.3 Mean number of bees sampled from plant species during the mid-season in 2008 and 2009 ...... 59

Figure 3.4 Mean number of bees observed visiting plant species during the mid-season in 2008 and 2009 ...... 60

Figure 3.5 Mean number of bees sampled from plant species during the late-season in 2008 and 2009...... 61

Figure 3.6 Mean number of bees observed visiting plant species during the late-season in 2008 and 2009 ...... 62

Figure 3.7 Ten most abundant bee species on study plants in 2008 and 2009 as determined through vacuum sampling...... 69

Figure 4.1 (a) Average number of seed from muskmelon fruits (b) Average fruit weight of muskmelon ...... 90

Figure 4.2 Number of bees collected from crop flowers during three collection periods...... 95

viii

LIST OF TABLES

Table 2.1 List of twenty-five native perennial candidate species by taxonomic family ...... 22

Table 2.2 Average planar area of individual blossoms by plant species in rank order...... 26

Table 3.1 Analysis of variance for the number of bees sampled from different plant species in the early-, mid-, and late-season in 2008 and 2009 ...... 52

Table 3.2 Analysis of variance for the number of bees observed visiting different plant species in the early-, mid-, and late-season in 2008 and 2009 ...... 52

Table 3.3 Pearson correlation coefficients for the flower characteristics of twenty-five native perennial plant species during weeks when destructive sampling of pollinators occurred...... 64

Table 3.4 Pearson correlation coefficients for the flower characteristics of twenty-five native perennial plant species during weeks when visual observations of pollinators occurred...... 64

Table 3.5 Bees collected from 25 native perennial plants species at Rock Springs, PA...... 67

Table 3.6 Bee diversity indices by plant species...... 70

Table 4.1 Thirteen annual native plant species in seed mix from Easyseedliving.com...... 83

Table 4.2 Analysis of variance and covariance for the number of seeds and the weight of harvested muskmelon fruit...... 90

Table 4.3 Distribution of twenty-nine bee species over experimental fields in Rock Springs and Manheim, PA...... 93

Table 4.4 Summary of ANCOVA testing effect of resource provisioning and distance on the number of collected bees in five bee groupings...... 94

Table 4.5 ANOVA summary for bee groupings as response variable and trt and block as factors with collection date...... 94

Table 4.6 ANCOVA summary testing the effect of provisioning areas and distance on count data for five categories of observed bees ...... 96

Table 4.7 ANOVA summary testing the effect of resource provisioning on count data for five categories of bees ...... 97

Table 4.8 ANOVA and ANCOVA summary for bee species richness in muskmelon crop...... 98

ACKNOWLEDGEMENTS

First, I would like to extend my sincere appreciation and deepest gratitude to David

Mortensen for his guidance and unfailing support throughout the research and writing process. I would also like to acknowledge Shelby Fleischer, James Frazier, and Douglas Miller for their invaluable advice and encouragement over the past few years.

For their assistance in the field and their ever-helpful manner, I thank David Sandy and

David Johnson. I am also grateful to Ashlin Mikolich, Ian Graham, Lindsey Ruhl, and Rene

Depierre for enduring long days in the field without complaint.

I greatly appreciate Leo Donovall‟s assistance in identifying hundreds of bee specimens, and I thank Ernst Conservation Seeds for donating native plants for use in this study.

I thank Matthew Ryan, Richard Smith, and Marvin Risius for fielding my questions about statistics, and I owe a great debt of gratitude to Eric Nord for his computer programming prowess and his willingness to help produce some of the handsome figures in this manuscript.

Thank you to Andrea Nord for her kind words, delicious baked goods, and shared interest in wild plants. I am also thankful to my friends and fellow graduate students for providing me with moral support.

Lastly, I would like to thank my family, especially my parents, without whose unconditional love and support, I would not be who I am today. I would also like to acknowledge

Christopher Malapit for his unyielding faith in my abilities and his uncanny ability to make me smile.

para todos

xii

CHAPTER 1 INTRODUCTION TO POLLINATION AND BEE DECLINES

POLLINATION

Pollination is the transfer of pollen from the stamens of a flower to the stigmatic surface of the same flower or a different flower. Some plants are self-pollinating, others rely on abiotic forces such as wind and water to disperse their male gametes, but more than three-fourths of the planet‟s angiosperms depend on a biotic mediator, usually an insect vector, to successfully complete this transfer (NRC, 2007). In many of these plant-insect interactions, bees serve as the genetic couriers, transferring pollen as they forage on blossoms for both pollen and nectar to provision their brood. These associations can be very specific, whereby an individual bee species will visit only a single plant species (monolecty) or a closely related group of plant species

(oligolecty), but more commonly, bees will visit a variety of plants (polylecty), perhaps developing a preference with time (Michener, McGinley, & Danforth, 1994). Many of these mutualisms are now threatened, however, as pollinator numbers decline. Without their insect consorts, many plants will fail to reproduce, jeopardizing floristic diversity and ecosystem stability in natural systems and crop yields in agricultural settings.

In natural ecosystems, bees and other pollinators play an important role in structuring and maintaining plant communities through their foraging activities. First, the rate of pollinator visitation to flowers can influence the reproductive success of plant populations (Campbell &

Motten, 1985; NRC, 2007). Although factors such as soil fertility, available moisture, and competition may limit fecundity, plant species which are preferentially visited by pollinators may experience higher reproductive output and increased local abundance. Bees may also help to

maintain plant populations by increasing the likelihood of out-crossing (NRC, 2007). This transfer of pollen between individual plants is necessary for self-incompatible species which require pollen from genetically distinct individuals for proper seed development, but out-crossing benefits other plant species by increasing genetic diversity thereby allowing populations to adapt to their environment. Associations with pollinators can thus prevent inbreeding depression among plants occurring at very low densities (NRC, 2007).

The foraging activities of bees and other pollinators are also crucial in agricultural systems. Without insect visits to crop flowers, the large-scale production of many crops would not be economically feasible. While major grain crops like corn, rice, soybean, and wheat do not require pollinators for production, a myriad of fruit, vegetable, and nut crops rely on insect visitors to produce adequate yields. Without a sufficient number of pollinator visits to such crops, the produce may be undersized or misshapen or may fail to yield at all (Klein et al., 2007).

In addition to directly assisting in the production of the human food supply, pollinators contribute to the production of fibers and forage crops. Bees pollinate important fiber crops like cotton and flax that are essential components of many textiles (Levin, 1983; NRC, 2007).

Through their feeding activities, bees also assist in the production of seed for forage crops such as red clover and alfalfa which are fed to dairy and beef cattle (Levin, 1983).

Attempts to place a monetary value on pollination services in the United States have produced varied estimates. These values range from $150 million, which agricultural producers already pay for honeybee hive rentals, to upwards of $18.9 billion which includes 10% of

American cattle and dairy production due to the role bees play in alfalfa seed production (Levin,

1983; NRC, 2007). The true value may be difficult to ascertain, but the sizes of these estimates attest to the important role that pollinators play in agricultural production.

Demand for pollination services has increased in recent years as larger areas have been devoted to pollinator-dependant crops. The acreage devoted to almond production, for example, increased nearly 110% from 1980 to 2008 (NASS, 2009). At the recommended minimum of five honeybee colonies per hectare (two hives per acre) for proper pollination, California‟s almond crop currently requires over 1.4 million hives to pollinate 680,000 acres of almond orchards

(representing 60% of current hives; NASS, 2009). Over the same period, domestic production of squash and cantaloupe, both pollinator-dependent crops, nearly doubled (NRC, 2007). These increases in crop acreage, coupled with pollinator declines, resulted in a shortage of colonies in

2005 that forced the United States to allow the first commercial importation of honeybee colonies from outside North America since 1922 (Quarles, 2008). If the current levels of crop production are to be maintained, it is essential that bee populations be protected.

POLLINATOR DECLINES

As the demand for pollination increases, the populations of several pollinator species are declining. The precise causes are not currently known, but some truly wild conjectures have appeared in the media; everything from cell phone towers to extraterrestrials has been reported to be killing bees. There appears to be a growing consensus among scientific researchers however, that the declines are likely being caused by a number of interacting stressors (Oldroyd, 2007).

These stressors include pathogens, parasites, pesticide exposure, and the loss of nesting habitat and food resources.

The first pollinator species to garner attention for its plummeting population numbers was the European honeybee (Apis mellifera). Although the number of honeybee colonies has been declining for decades, serious concerns were prompted when commercial beekeepers saw 50-

90% of their colonies fail in the winter of 2005-2006 (Cox-Foster et al., 2007). Unlike past

declines, affected colonies were peculiar in that there were no or few dead bees in or near the hive. The syndrome, now termed Colony Collapse Disorder (CCD), is characterized by this sudden loss of the adult population from honeybee colonies.

The severe losses in 2006 and again in 2007 prompted many researchers to speculate that a novel infectious agent was to blame. Studies of microbial genomics within the hive indicated that the presence of a relatively new virus, the Israeli acute paralysis virus (IAPV;

Dicistroviridae), was strongly correlated with CCD, but it was not present in all of the affected hives (Cox-Foster et al., 2007). Similarly, Nosema ceranae, a microsporidian pathogen of the

Asian honeybee (Apis cerana), was present in all of the infected hives, but in this case, it was also detected in seemingly healthy colonies (Cox-Foster et al., 2007). Several other infectious pathogens have been isolated from honeybee colonies, but none have been identified as the etiological cause of the decline.

Since the 1980s, honeybee declines have been attributed to the accidental introduction of two mite pests that led to the near eradication of feral colonies in some regions (Seeley, 2007).

The number of managed colonies also declined from 4.1 million in 1980 to 2.3 million in 2008

(NASS, 2009). The Varroa mite (Varroa destructor) continues to adversely affect honeybee colonies. V. destructor affects bees by attaching to their bodies and feeding on hemolymph, increasing their vulnerability to infection and also transmitting diseases like deformed wing virus

(DWV; Iflaviridae). The mites can be controlled in managed colonies through methods of integrated pest management, but most beekeepers have relied solely on the aide of miticides

(Collison, 2007). As with most singular-tactic controls, populations of the Varroa mite have developed resistance, forcing hive managers to use highly active alternative miticides which pose greater risks to hive health and applicator safety (Collison, 2007). These chemicals, which were

intended to protect the honeybee, may now be affecting pollinator health in conjunction with other pesticides.

There is speculation that insecticides may be largely responsible for the pollinator decline

(Mullin et al., 2010; Quarles, 2008; Thompson, 2001). Already, several member countries of the

European Union have banned or restricted the use of neonicotinoid insecticides over concerns for pollinator health. In 1999, France limited use of this class of chemistry when imidacloprid was banned as a sunflower seed treatment, a ban that continues to this day (Stokstad, 2007). In May

2008, Germany followed suit and banned the use of eight pesticide active ingredients after clothianidin, another neonicotinoid, was implicated in the loss of over 12,000 honeybee colonies

(Williams & Osborne, 2009). In this case, the insecticide seed treatment was improperly bonded to the crop seed, resulting in bee exposure to the compound.

Even when applied as directed, pesticides still pose a threat to pollinator health. Pollen samples from honeybee hives have tested positive for 54 pesticides, including fourteen systemic insecticides that translocate to the nectar and pollen of treated plants (Mullin et al., 2009). Even when these chemicals do not kill bees outright, the sublethal effects can be significant, resulting in impaired navigation and reduced foraging activity which can ultimately affect fecundity

(Aliouane et al., 2009; Thompson, 2001; Thompson & Maus, 2007). Developing larvae are also exposed to these compounds, as the pollen and nectar collected by adult female bees during the growing season is the only source of nutrition for developing brood.

There is also evidence that herbicides may be indirectly affecting pollinator populations by removing food resources from the landscape. Research in fields of genetically modified herbicide-tolerant crops (GMHT), for example, has shown decreased bee abundance, not due to toxicity of the GMHT, but because floral resources were so scarce (Bohan et al., 2005; Haughton et al., 2003). A similar reduction in the density of flowering plants may also be realized through

the application of synthetic fertilizer and frequent harvesting of hay or overgrazing of pasture and rangeland (Williams & Osborne, 2009).

The intensification of agriculture may be further constraining pollinator abundance through reductions in landscape diversity (Grixti, 2009). Increased field sizes in both Europe and

North America have led to the removal or degradation of hedgerows and field edges that once criss-crossed agricultural landscapes (Iverson, 1988; Robinson & Sutherland, 2002). Reductions in crop diversity and crop rotations have also homogenized the landscape temporally as large areas of mass-flowering plants now concentrate pollinator effort into short blocks of time

(Westphal, Steffan-Dewenter, & Tscharntke, 2003).

The loss of natural and semi-natural areas from the landscape may be especially detrimental to pollinator populations as these areas provide both food and nesting resources for bees. When crops are not in flower, wild plants provide important sources of pollen and nectar for pollinators. Remaining fragments of natural or semi-natural land thus serve as important refuges for native bees in agricultural landscapes. Indeed, studies have shown that the abundance and diversity of bees tends to decrease with increasing distance from natural areas (Kim,

Williams, & Kremen, 2006; Klein, Steffan-Dewenter, & Tscharntke, 2003a, 2003b; Kremen,

Williams, & Thorp, 2002; Steffan-Dewenter & Tscharntke, 1999). Not surprisingly, levels of pollination received on farms near natural areas are higher than those occurring within more homogenous farmscapes (Kremen, Williams et al., 2002). Preserving remnant habitats, establishing new natural areas, or supplementing existing fragments may therefore be effective ways of managing beneficial insects and the services they provide (Kim et al., 2006; Lagerlof,

Stark, & Svensson, 1992).

NATIVE POLLINATORS

The preservation of natural and semi-natural habitats may be especially important for unmanaged pollinator species that utilize these areas for nesting resources and nutrition. In addition to the familiar honeybee, there are approximately 4,000 described bee species in North

America (Cane & Tepedino, 2001). It has been suggested that these often over-looked species may be an important form of insurance against the honeybee decline (Winfree, Williams,

Dushoff, & Kremen, 2007). In addition to increasing the total number of available pollinators, wild bees can also pollinate some crops more efficiently (Banaszak, 1978). Certain morphological and behavioral traits increase pollen transfer, and many wild bees are capable of foraging at temperatures and under light conditions considered unsuitable for honeybees (Canto-

Aguilar & Parra-Tabla, 2000; Kremen, Nicola, Smith, Thorp, & Williams, 2002). In areas of

New Jersey and Pennsylvania, wild bees have already been shown to provide the majority of crop flower visits (Winfree, Williams, Gaines, Ascher, & Kremen, 2008). This is likely true in areas of considerable landscape diversity.

Bumble bees (Bombus spp.) are a group of wild bees that are of great value to agriculture.

It is estimated they contribute to 15% of total crop pollination, with their services valued at $3 billion (Vaughan & Black, 2008). Like the honeybee, bumblebees are polylectic species that will forage on many different plant species. Bumblebees, however, are considered superior pollinators of both ericaceous and solanaceous crops because unlike honeybees, they are able to sonicate pollen from anthers through „buzz pollination.‟ For this reason, they are preferred in glasshouse production of tomatoes and peppers and are already reared commercially for this purpose (NRC, 2007). Even field-grown tomatoes, which are commonly believed to be self- pollinating, will produce heavier fruits and greater total yields as a result of bee visitation

(Greenleaf & Kremen, 2006).

In addition to bumblebees, other bee species are now being utilized for crop pollination.

Already, fruit producers can purchase blue mason bees (Osmia lignaria), a North American native, for use in orchard production while alkali bees (Nomia melanderi) and alfalfa leafcutter bees (Megachile rotundata) are being employed for alfalfa seed production in Canada and the western United States (Bosch & Kemp, 2001; Johansen, Mayer, Stanford, & Kious, 1991; NRC,

2007). Currently, the Bee Research Group in Logan, Utah is studying the biology and habits of other native bees with the goal of managing them for use in agriculture.

Although there are a great number of bee species in North America, their populations are also sensitive to the same stressors affecting honeybees. Disease, mites, pesticides, and habitat loss can limit populations, but several common-sense tactics can be implemented to protect bee populations at different scales. First, restricting the movement of materials that could contain bee pathogens or pests would limit the spread of disease. Second, reducing or restricting pesticide- use (herbicides, fungicides, and insecticides), or using least harmful formulations at the appropriate time of day would limit pollinator exposure to pesticides. Third, maintaining or creating nesting and foraging habitat would provide resources with which pollinators could increase their populations. Given the loss of large areas of non-crop habitat and the increasing efficiency of weed control programs, remaining fragments of natural or semi-natural vegetation likely serve as increasingly important foraging grounds for bee species and other insects

(Backman & Tiainen, 2002; Ekroos, Piha, & Tiainen, 2008; Lagerlof et al., 1992).

HABITAT MANAGEMENT

Habitat management may be an effective means of supporting both managed and wild bee populations. Ideally, managed areas should provide pollen and nectar sources along with nesting habitat while serving as refuges from agrichemical drift. Although conservation schemes

are generally tailored for agricultural landscapes, the same principles may be applied by any land manager, from large agricultural producers to individual homeowners. Actively altering landscapes, even on a small scale, could significantly contribute to the success of local bee populations.

Floral resource provisioning, a form of habitat management whereby flowering plants are established for the purpose of supporting a target organism or group of organisms, has already been adopted by some landholders (Daane, Mills, & Nelson, 2009). Plant species such as yarrow

(Achillea millefolium), buckwheat (Fagopyrum esculentum), and sweet alyssum (Lobularia maritima) have been intercropped or established along agricultural field edges to create flowering corridors that provide nectar and pollen resources for beneficial insects (Daane, Mills,

& Nelson, 2009; Pontin, Wade, Kehrli, & Wratten, 2006). In selecting plants for resource provisioning, consideration should be given to choosing species that are known to be attractive to the target organisms.

Work has been conducted to determine the floral preferences of beneficial insect groups in North America. In 2005, researchers at the Michigan State University surveyed the pollinators and natural enemies associated with forty plant species during anthesis (Landis, Fiedler, Isaacs,

& Tuell, 2007; Tuell, Fiedler, Landis, & Isaacs, 2008). In addition to reporting significant differences in the visitation rates of beneficial insects among the plant species, the attractiveness of the studied plants was also found to differ with insect taxa. Sweet alyssum, for example, was visited by a large numbers of natural enemies, but only attracted a moderate number of bees, and only the smallest of species which likely contribute little to crop pollination (Landis & Fiedler,

2006). As a result, plant species commonly used in companion plantings for pest predators and parasitoids may not be suitable for pollinator conservation.

Plants that are attractive to bees may still be unsuitable for use in pollinator conservation efforts due to their aggressive growth habits or potential for invasive spread. Unfortunate and unintended results have arisen from habitat management attempts where non-native species were widely established to provide food for wildlife (primarily game birds). For example, the incorporation of Japanese barberry (Berberis thunbergii), autumn olive (Elaeagnus umbellata), and honeysuckle (Lonicera spp.) into wildlife plantings has contributed to the invasive success of these non-native shrubs (Rhoads & Block, 2002a, 2002b). Although these species may supply a limited resource to the target organisms, they now threaten other species as they continue to spread (Miller & Gorchov, 2004).

If approached with care, floral resource provisioning may provide vital pollen and nectar resources for multiple groups of beneficial insects while minimizing the risk of introducing plant pests into the landscape. One objective of this study was to identify a group of native perennial plants as suitable candidates for use in a habitat management program. Perennials were initially selected because they were deemed to require less maintenance over the long term. Once established, plants would return each year while annuals would need to be re-sown. Native species were then chosen for three reasons: (1) native species are adapted to the climate of a region, (2) they have co-evolved with local pollinator communities, and (3) they have co-evolved with natural enemies that can limit population spread.

In addition to composing a mix of plants for use in pollinator conservation schemes, another objective of my research was to test the hypothesis that floral resource provisioning areas can enhance crop pollination by increasing the local abundance and diversity of pollinators.

Other studies have indicated that resource provisioning can contribute to higher rates of pest parasitism in crop fields (Hickman, 1996; Patt, Hamilton, & Lashomb, 1997) and even in golf fairways (Frank & Shrewsbury, 2004). However, in these instances, the floral resources that

were utilized by beneficial insects in the provisioning area differed greatly from the insect pests that were preyed upon in the field. Pollinators, in contrast, are in search of the same resources

(pollen and nectar) from both areas. In this way, plants in a floral resource provisioning area can compete with crop flowers for pollinator services. Consequently, increasing the local abundance of pollinators may not result in higher rates of crop pollination. My interest is to conduct this research to expand our understanding of how floristic diversity may be enhanced within our landscapes to benefit agricultural production. In doing so, I hope to extend my findings to those who manage the cultivated landscape (http://pubs.cas.psu.edu/FreePubs/pdfs/uf023.pdf).

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CHAPTER 2

THE SELECTION AND FLOWERING PHENOLOGY OF CANDIDATE PERENNIAL PLANTS FOR POLLINATOR CONSERVATION IN PENNSYLVANIA

INTRODUCTION

Loss of biodiversity from agricultural landscapes has led to increased interest in management strategies to enhance species richness and ecosystem function (Tilman, 2002). One form of habitat management that has begun to receive attention is the concept of floral resource provisioning, whereby flowering plants are established to provide a target organism or group of organisms with pollen and nectar resources. By establishing areas of flowering plants within a cropping system or along its perimeter, it may be possible to increase populations of beneficial insects (Landis, Wratten, & Gurr, 2000; Pontin, Wade, Kehrli, & Wratten, 2006). The pollen and nectar produced by these plants serve as alternate food sources for natural predators and contribute to the diet of wild and managed pollinators (Jervis, Kidd, Fitton, Huddleston, &

Dawah, 1993; Tuell, Fiedler, Landis, & Isaacs, 2008). Through supporting populations of natural predators, parasitoids, and pollinators, it may be possible to enhance crop production by reducing crop pest pressure and increasing pollination.

The concept of floral resource provisioning can be applied through a number of different practices. One simple method is to allow cover crops such as buckwheat (Fagopyrum esculentum) and hairy vetch (Vicia villosa) to flower in agricultural fields or home gardens. In this way, large volumes of pollen and nectar can be produced for pollinators and other beneficial insects, though usually for a short duration of time. Another method includes the establishment of flower strips through agricultural fields. Flowers such as sweet alyssum (Lobularia maritima)

are already utilized in California vegetable fields to support pest predator populations. By being intercropped, these floral „alleyways‟ increase the area of the crop field in which these beneficial insects will provide their valuable service. The method that will be investigated in this body of work consists of establishing native perennial plants in field boundaries and other uncultivated agricultural areas. This method may be of particular interest to agricultural producers since land does not necessarily need to be taken out of cultivation.

Floral resource provisioning can increase populations of desired arthropods and their associated ecosystem services. Experimental studies have documented both increased abundances of natural enemy species and enhanced rates of pest predation in field locations with areas of non-crop flowering plants (Begum, Gurr, Wratten, Hedberg, & Nicol, 2006; Frank &

Shrewsbury, 2004; Hickman, 1996; Patt, Hamilton, & Lashomb, 1997). Although some attempts at increasing ecosystem services have been successful, part of their success probably lies in the selection of appropriate companion plant species (Letourneau & Bothwell, 2008).

Special care should be taken in the selection of plants for use in the conservation of arthropods, since insect groups tend to exhibit innate preferences for different floral characteristics (Baker & Baker, 1983; Faegri & Pijl, 1979; Waser, Chittka, Price, Williams, &

Ollerton, 1996). These perceived preferences have given rise to the „pollination syndromes‟ described by Faegri and Pijl (1979), which generalize the traits of flowers commonly visited by different pollinator taxa. Although some pollinators have been shown to be very plastic in their preferences, factors such as flower color, floral morphology, pollen and nectar quantity and composition, and blooming period can influence the abundance and species of insect visitors

(Gumbert, 2000; Waser et al., 1996). In a study of several plant species commonly used for resource provisioning, honeybees and bumblebees showed a strong preference for buckwheat

(Fagopyrum esculentum) while hover flies exhibited no preference for buckwheat, phacelia

(Phacelia tanacetifolia), or other flowers included in the study (Pontin, et al., 2006).

Selected plants must also be amenable to establishment in agricultural and residential landscapes. As a result, aggressive species that can spread into crop fields and impede crop production or substantially increase the cost of property maintenance should be excluded from provisioning areas. In addition, plants not adapted to the stresses associated with agricultural landscapes should be given low ranking when composing plant mixes intended for widespread use.

Other considerations in plant selection may also enhance the effectiveness and appeal of floral resource provisioning. In past efforts to conserve beneficial insects, the established plant species have often been non-native annuals (Begum et al., 2006; Tuell et al., 2008). Selecting native plants is a more ecologically-sound option, since these species are presumably adapted to the climate and soil conditions of their home ranges and have likely coevolved with members of the local pollinator community (Fiedler, 2006). The risk of a native plant becoming weedy is also reduced, as herbivores and pathogens which can limit population growth are also likely present

(Blossey & Notzold, 1995). Selecting perennial species over annuals may especially appeal to property owners, as the plants persist for multiple seasons and thus do not require soil preparation and sowing each year. In this way, perennials represent a single-time investment with reduced maintenance costs.

Flowering phenology is an important attribute of a resource provisioning scheme. In supporting populations of social or semi-social bees, such as honeybees or bumblebees which forage for an extended period, it is essential that adequate food sources be available throughout the growing season. If a substantial gap or decrease in the availability of pollen and nectar

occurred during the season, pollinator numbers could decrease due to emigration, reduced survivorship, or reduced fecundity (Waser & Real, 1979).

A complement of plant species that flower sequentially can be thought of as mutualists.

Early blooming species support pollinators that persist through the season and subsequently pollinate late-flowering plants. A study of two sequentially flowering plant species in the western

U.S. illustrates this inadvertent mutualism (Waser & Real, 1979). In years when flowering of the early-blooming Delphinium nelsonii was poor, seed set of the late-blooming Ipomopsis aggregata was significantly reduced. A census of the hummingbirds that pollinate both of these plants found smaller populations when blooming of Delphinium nelsonii was sparse, resulting in reduced pollinator availability later in the season. While all pollinators may not have the same ability to relocate, the amount of food available at the beginning of the season may certainly have an effect on the number of bees available for pollination later in the season.

Maintaining a community of sequentially flowering species may be particularly advantageous in agricultural landscapes where natural habitat is lacking. Because many crops tend to flower in short pulses, pollinators whose foraging periods extend beyond the crop‟s flowering window must seek alternate sources of nutrition for survival and the provisioning of offspring (Tuell et al., 2008). As a result, crop flowers should be viewed as a single, incomplete component of a pollinator‟s diet. By establishing strategically placed communities of sequentially flowering plants, it may be possible to provide food resources when the crop is not in flower, thus supporting pollinators before and after crop anthesis.

The objective of the following study was to select a set of native perennial plants whose sequential blooming periods would provide food resources for pollinator species throughout the growing season. To accomplish this, a set of predetermined criteria were used to select a set of twenty-five plant species for potential use in pollinator conservation efforts. The flowering

period of each species was determined through the season along with phenological parameters such as onset of bloom, maximum bloom, and range of flowering. This process represents the first step in identifying an appropriate group of plant species for use in pollinator conservation efforts in Pennsylvania and the Mid-Atlantic region.

MATERIALS AND METHODS

PLANT SELECTION

Twenty-five plant species were selected for this study based on eight criteria. First, the selected plant species are native to Pennsylvania. Native plants are adapted to the soils and climate of a region or local area and also pose a lesser threat of invading natural habitats compared to exotic plant species. In addition, native plants have co-evolved with local pollinator communities. Candidate plant species were determined to be native or introduced to the region based on information from the online USDA PLANTS database (plants.usda.gov) and The

Manual of Vascular Flora of Northeastern United States and Adjacent Canada (Gleason &

Cronquist, 1991). Second, a perennial growth habit was preferred. Establishing perennials eliminates the need to reseed provisioning areas every growing season and should reduce maintenance requirements. Third, upland species tolerant of full sun or partial shade were selected as any plant species chosen for pollinator conservation in agricultural systems should be capable of thriving in soil moisture and light conditions typical to farm field interiors or edges.

Fourth, plant species known to exhibit aggressive growth habits were excluded. In an attempt to prevent selected plants from becoming weed problems within the crop field or along field edges, species listed on the USDA PLANTS database as weedy, invasive, or noxious for the northeastern United States were not considered. Fifth, the twenty-five selected species were chosen to ensure that at least one plant species would be in bloom between May and October.

Sixth, only plants whose seeds were commercially available were considered to ensure that selected species could be readily obtained for establishment. Seventh, only herbaceous species were considered because plants with woody growth might increase the cost or required frequency of maintenance. Finally, consideration was given to the taxonomic diversity of the study species. By increasing the number of plant families and genera included in the study, it was thought the diversity of flower morphology and chemical composition of floral resources would be enhanced, possibly resulting in an increase in the diversity of pollinators.

Based on the preceding criteria, the species listed in Table 2.1 were selected for use in this study. In total, ten families and twenty-three genera are represented in the group of twenty five species. Mature, blooming-size plants were obtained from one of seven sources (see

Appendix A).

SITE CONFIGURATION

In the summer of 2007, twenty of the twenty-five native perennial plant species were established in four randomized complete blocks at the Russell E. Larson Agricultural Research

Center in Pennsylvania Furnace, PA (coordinates; 40.712019,-77.934192). Individuals of the remaining five species were established in early May 2008 (see Appendix A for the origin and date of establishment for each plant). The soil at the site is a Murrill channery silt loam with an

8-15 percent slope (Web Soil Survey, NRCS). The study was arranged as a randomized complete block with each of the twenty-five species occurring in each of four blocks. Blocks were placed six meters apart and consist of individual plants arranged in a 12m x 12m grid with three meter spacing between plants (see Figure 2.1). To establish the blocks, a tractor mounted auger was used to excavate 60cm wide by 60cm deep holes that were then lined with 56cm diameter plastic collars to prevent encroachment from surrounding vegetation. The excavated holes were then filled with the native soil and blooming-sized plants were transplanted and watered with

approximately 2L (0.53 gallons) of water. No supplemental fertilizer was added. Due to signs of water stress in some plants, all specimens were watered again in August 2008. In 2008, rainfall during the growing season (April-October) was 191mm (7.8 inches) below the average of

Table 2.1 List of twenty-five native perennial candidate species by taxonomic family

SPECIES BINOMIAL COMMON NAME

Asclepidaceae Ascelpias tuberosa L. butterfly milkweed Asteraceae Conoclinium coelestinum (L.) DC. blue mistflower Coreopsis tripteris L. tall tickseed Echinacea purpurea (L.) Moench eastern purple coneflower Eupatorium perfoliatum L. common boneset Eupatorium purpureum L. sweetscented joe pye weed Eurybia macrophylla (L.) Cass. bigleaf aster Liatris pycnostachya Michx. prairie blazing star Solidago rugosa Mill. wrinkleleaf goldenrod Symphyotrichum novae-angliae (L.) G.L. Nesom New England aster Symphyotrichum novi-belgii (L.) G.L. Nesom New York aster Vernonia gigantea (Walter) Trel. giant ironweed Campanulaceae Campanula rotundifolia L. bluebell bellflower Commelinaceae Tradescantia ohiensis Raf. Ohio spiderwort Fabaceae Desmodium canadense (L.) DC. showy ticktrefoil Lespedeza capitata Michx. roundhead lespedeza Senna hebecarpa (Fernald) Irwin & Barneby American senna Lamiaceae Monarda fistulosa L. wild bergamot Pycnanthemum tenuifolium Schrad. narrowleaf mountainmint Polemoniacae Phlox divaricata L. wild blue phlox Primulaceae Lysimachia quadrifolia L. whorled yellow loosestrife Ranunculaceae Aquilegia canadensis L. red columbine Actaea racemosa L. black bugbane Scrophulariaceae Penstemon digitalis Nutt. ex Sims talus slope penstemon Veronicastrum virginicum (L.) Farw. Culver's root

Figure 2.1 Experimental plot design.

Perennial plant A1 Plant ID Block Deer fence

6m A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 B14 B15 B16 B17 B18 B19 B20

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 D15 D16 D17 D18 D19 D20

3m E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 E16 E17 E18 E19 E20

3m 6m

596mm (23.5 inches) for this area. A rainfall deficit of 125mm (5.1 inches) was also observed in

2009, but cooler temperatures, frequent cloud cover, and regular light rains appear to have prevented water stress among the plants. Grasses and forbs growing between the plastic collars

(here forth referred to as the „matrix‟) were mowed weekly to reduce the number of flowers occurring in this area.

FLOWER PHENOLOGY & CHARACTERISTICS

Phenological observations are important when assessing plant-pollinator associations.

Although a flower may serve as an appropriate food resource for a particular bee species, visitation cannot occur if the blooming period and the pollinator‟s foraging period are not in synchrony. Blooming phenology for each plant was documented to estimate the onset, length of the flowering window, and the average time of peak bloom for each study species.

Before flowering occurred in 2008, plant emergence was documented weekly from mid-

April until the beginning of May. Emergence was documented as the presence of new

aboveground biomass for that season. By comparing the emergence of plants across the blocks, this data was used to indicate the need to replace plants that failed to survive the winter.

Three pieces of information were collected from each study plant. First, the number of flowers or flower clusters on each plant was counted weekly from May until mid-October during the 2008 and 2009 field seasons. Second, the maximum flower height for each plant was estimated weekly by measuring the height of the ten tallest flowers to the nearest 0.5cm. Third, the average planar area of an individual blossom floral area was measured for each species by taking digital photographs of 10 representative blossoms or blossom clusters, keeping a metric ruler in each photographic frame for reference. The images were imported into Adobe Photoshop

CS4 Extended (Version 11.0, Adobe 2008) and cropped to represent a known area. A single blossom within each photograph was outlined using the „magnetic lasso‟ tool and removed from the image, leaving behind a transparent silhouette. The remaining background was then transformed to black by creating a new clipping layer and disabling the masking layer. The resulting images were saved as TIF files, imported into ImageJ 1.42q and converted to binary images. Using values from the histograms, the ratio of black to white pixels was used to estimate the planar area of individual blossoms. For the average areas of individual blossoms by species, see Table 2.2. The total floral area for individual plants was then estimated for each week by multiplying the number of observed flowers by the average blossom area for the species.

Phenology charts were constructed using the average floral areas of each plant species during each week. These two-dimensional figures, which express changes in floral area through time, allow for the visualization of (1) the time of the blooming period with respect to calendar date, (2) the range of the blooming period, (3) the distribution of flower production over time, and (3) the relative floral area for each species. The plots for each plant species were created with the R programming software (R version 2.10.1; see Appendix B for R code). Because we

were unable to render plots for all twenty-five species in a single pane, ten graphics that contained the phenology plots for 2-5 plant species were generated. Each graphic contained the phenology plot for Actaea racemosa as a reference, allowing each chart to be resized so that the individual plots would be on the same scale. The graphics were resized and assembled into a single graphic using Adobe Photoshop. The final figures, which included labels for the plant species, were created with Microsoft Office Publisher 2007.

ANALYSIS

The flower count, floral area, and average flower height data were analyzed to identify differences among the study plant species and between the seasons. These variables will also be investigated in the next chapter as potential explanatory variables for bee visitation rates.

Flower count & area

A paired T-test (MINITAB Release 14.1) was conducted to determine if the number of flowers or floral area produced by the plants differed (1) between seasons, (2) among the plant species, and (3) during three distinct periods during the growing season. An analysis was also conducted to determine if the number of flowers or floral area changed at different rates for the twenty-five plant species. All statistical tests were performed at the α =0.05 level. To test for differences in the number of flowers and floral area among the plant species, the weekly measurements were summed over the season for individual plants and subjected to an analysis of variance (ANOVA) with block and plant species as factors (JMP 8.0.2). The 2008 and 2009 flower count data were log transformed and measurements of floral area were square root transformed to satisfy the assumptions of the analysis. To rank the plant species, and to identify significant differences among the species, a Tukey‟s mean separation was performed.

Table 2.2 Average planar area of individual blossoms by plant species in rank order.

BLOSSOM STANDARD FAMILY PLANT SPECIES AREA (cm2) ERROR Asteraceae Solidago rugosa Mill. 0.07 ± 0.00 Scrophulariaceae Veronicastrum virginicum (L.) Farw. 0.08 ± 0.00 Fabaceae Lespedeza capitata Michx. 0.14 ± 0.01 Lamiaceae Pycnanthemum tenuifolium Schrad. 0.21 ± 0.02 Asteraceae Eupatorium perfoliatum L. 0.22 ± 0.01 Asteraceae Conoclinium coelestinum (L.) DC. 0.28 ± 0.02 Asteraceae Eupatorium purpureum L. 0.37 ± 0.05 Asclepidaceae Ascelpias tuberosa L. 0.52 ± 0.04 Ranunculaceae Actaea racemosa L. 0.72 ± 0.06 Primulaceae Lysimachia quadrifolia L. 0.76 ± 0.05 Asteraceae Liatris pycnostachya Michx. 0.98 ± 0.10 Scrophulariaceae Penstemon digitalis Nutt. ex Sims 1.24 ± 0.08 Fabaceae Desmodium canadense (L.) DC. 1.27 ± 0.14 Fabaceae Senna hebecarpa (Fernald) Irwin & Barneby 1.46 ± 0.07 Asteraceae Eurybia macrophylla (L.) Cass. 1.76 ± 0.11 Asteraceae Vernonia gigantea (Walter) Trel. 1.86 ± 0.20 Campanulaceae Campanula rotundifolia L. 3.69 ± 0.16 Lamiaceae Monarda fistulosa L. 3.78 ± 0.02 Ranunculaceae Aquilegia canadensis L. 4.00 ± 0.37 Polemoniacae Phlox divaricata L. 4.30 ± 0.25 Commelinaceae Tradescantia ohiensis Raf. 5.78 ± 0.38 Asteraceae Symphyotrichum novi-belgii (L.) G.L. Nesom 6.02 ± 0.25 Asteraceae Coreopsis tripteris L. 8.88 ± 0.47 Asteraceae Symphyotrichum novae-angliae (L.) G.L. Nesom 9.43 ± 0.49 Asteraceae Echinacea purpurea (L.) Moench 29.90 ± 2.32

The number of flowers and floral area produced by each plant in 2008 and 2009 were subjected to a multivariate analysis of variance (MANOVA) to determine if differences in the response variables could be explained by (1) the plant species, (2) year, and/or (3) the interaction of plant species and year (JMP 8.0.2). Species served as the „between subjects‟ factor while year and the interaction term were considered „within subject‟ factors.

To determine if there was a difference in the number of flowers and floral area within the season, the 2008 and 2009 seasons were divided into three periods; early season (May-June), mid

season (July-August 6, 2008 and July-August 11, 2009) and late season (August 7, 2008 –

October and August 12, 2009 – October). The number of flowers and floral area measured within each block was averaged by the number of data collection dates within each period and log (x+1) transformed to maintain homoscedacity of variance. The transformed data were subjected to an

ANOVA with block and time period as factors with a Tukey‟s mean separation.

Flower height

The heights of the ten tallest flowers were averaged for each plant on each date. The maximum average flower height for each plant was then subjected to an ANOVA to test for differences in mean heights among the plant species with block and species as factors. A Tukey‟s mean separation was used to rank the species and identify significant differences among them.

The tests were performed at the α =0.05 level. A MANOVA was also conducted to test the effect of year and the species and year interaction to determine if height changed significantly between

2008 and 2009, and whether these changes were also influenced by plant species.

RESULTS

All twenty-five plant species bloomed in 2008 and 2009, but some plants did not flower in each season. In 2008, six plants did not bloom, including two individuals of Actaea racemosa, and one individual each of Campanula rotundifolia, Desmodium canadense, Eupatorium purpureum, and Lysimachia quadrifolia. In 2009, five plants did not reemerge after the winter or died prior to flowering. These plants included single specimens of Actaea. racemosa, Aquilegia canadensis, Campanula rotundifolia, Phlox divaricata, and Vernonia gigantea. In addition, the specimen of Lysimachia quadrifolia in the fourth block was present during the 2009 growing season, but did not bloom.

The twenty-five species exhibited similar blooming sequences in 2008 and 2009 (Figures

2.2, 2.3). There were differences however, in the week of peak bloom and in the weeks of first and last bloom for a number of species. In 2009, eight plant species (Aquilegia canadensis,

Lespedeza capitata, Liatris pycnostachya, Lysimachia quadrifolia, Monarda fistulosa,

Penstemon digitalis, Pycnanthemum tenuifolium, and Veronicastrum virginicum) achieved peak bloom one to weeks later than in 2008. Four other species bloomed one to two weeks earlier in

2009 (Coreopsis tripteris, Eupatorium purpureum, Solidago rugosa, and Symphyotrichum novae-angliae).

Both the number of flowers and floral area produced within each block were significantly greater in 2009 than in 2008 (T=-5.59, p=0.011; T=-9.38, p=0.003, respectively). In 2009, the average number of flowers produced per block was 83,038 ± 11064, a 168% increase from the 30,942 ±

2540 flowers counted in 2008. This increase in the number of flowers appears to have been greater among plant species with smaller flowers since there was only a 75% increase in floral area from 2008 (39,446 ± 3,176 cm2) to 2009 (68,998 ± 4,242cm2). As the plants mature and are able to store more carbohydrate reserves, it is expected that floral area will continue to increase for some time. The floral area may stabilize or decline however, as the plants approach their maximum size or as individuals senesce, since some perennial species will not bloom indefinitely. Climatic effects such as temperature and rainfall are also expected to influence plant growth and blooming.

In 2008 and 2009, there were also significant differences in the average number of flowers and floral areas among the plant species (2008: F24,69=13.13, p<0.0001; F24,67=19.22, p<0.0001; 2009: F24,69=9.39, p<0.0001; F24,67=10.83, p<0.0001, respectively). In 2008,

Eupatorium perfoliatum produced the greatest number of flowers while Lysimachia quadrifolia produced the fewest. One year later, Veronicastrum virginicum produced the greatest number of

Figure 2.2 Floral area (cm2) of twenty-five native perennial plants from May-October 2008.

Figure 2.3 Floral area (cm2) of twenty-five native perennial plants from May-October 2009.

flowers in 2009 while Lespedeza capitata produced the least. The values for average floral areas were more consistent between the two seasons. In both 2008 and 2009, Symphyotrichum novae- angliae and Symphyotrichum novi-belgii had the greatest average floral area over the season while Lysimachia quadrifolia and Lespedeza capitata had the smallest floral area. Although there were significant differences among the species, there was also much overlap, indicating that many species had similar flower counts and floral areas. It also indicates that the variation within some species is such that the means are not distinguishable from those of other species

(Figures 2.4, 2.5).

When considered in combination, both species and year were significant predictors of the number of flowers produced (F24,66=16.29, p<0.0001; F1,66=81.61, p<0.0001, respectively).

Species and year were also significant predictors of total floral area (F24,66=12.45, p<0.0001;

F1,66=43.21, p<0.0001, respectively). In addition, the interaction between species and year was significant for both the number of flowers (F24,66=2.94, p=0.0003) and total floral area

(F24,66=3.09, p=0.0002), indicating that the rate of change in flower number and area between seasons differed among the species.

In both 2008 and 2009, the average number of flowers and the floral area in the early- season was significantly lower than in the mid- or late-season (flower number: F2,6=240.66, p<0.0001; F2,6=66.15, p<0.0001 and floral area: F2,6=56.26, p<0.0001; F2,6=56.12, p<0.0001).

There was not a significant difference in the number of flowers or floral area between the mid- and late-season in either year.

The maximum heights of flowers differed among plant species in 2008 and 2009

(F24,66=25.85, p<0.0001; F24,69=11.41, p<0.0001, respectively). In both years, the flowers of

Coreopsis tripteris and Symphyotrichum novae-angliae were tallest while the flowers of

Campanula rotundifolia and Lysimachia quadrifolia were lowest to the ground. Significant

Figure 2.4 Average of log floral area for twenty-five native perennial plants in 2008 and 2009 with Tukey‟s mean separations at the α = 0.05 level.

5 2008 a a-c ab ab 4 a-c a-c a-d a-d a-e a-d a-e a-e a-d d-g b-e b-f ef c-f c-f 3 c-f d-f fg fg 2

g )

2 g 1

0 5 2009 a a-c ab a-c a-c a-d 4 a-d a-d a-d b-d a-d a-d a-d a-d a-d b-e a-d b-e log floral area (cmarea floral log c-e b-e b-e de c-e 3

ef 2 f 1

Figure 2.5 Average of log flower count for twenty-five native perennial plants in 2008 and 2009 with Tukey‟s mean separations at the α = 0.05 level.

5 2008 ab a 4 a-d a-c b-f e-h a-e a-e a-e a-e a-e a-e a-e a-e a-e a-e 3 d-g c-g e-g e-h e-h f-h 2 gh h h 1

number 0 5 a 2009 ab ab ab ab a-c flower 4 a-c b-e b-d b-e b-d b-e b-e log log b-e b-e b-e b-e b-e 3 c-f c-f c-f c-f d-f ef 2 f

differences existed among the twenty-five species in both years, but there was also much overlap in mean flower height. Maximum flower heights also increased significantly between seasons (F24,66=3.21, p<0.0001), though these changes in height varied with plant species

(F24,66=3.21, p<0.0001).

DISCUSSION

It is possible to assemble a mixture of native perennial plant species that meet the seven criteria discussed here. While selecting plants that are adapted to the northeastern United States and suitable for establishment within agricultural settings, we were also able to assemble a group of plant species whose sequential blooming periods ensure a continuous supply of pollen and nectar resources through the season. This unbroken sequence of flowering is of particular importance in supporting populations of long-lived pollinators whose foraging periods exceed the blooming periods of the crops they pollinate.

It was also possible to estimate key phenological parameters for the candidate plant species with the collected data. By identifying the week of first bloom and the week of last bloom, the range of the blooming period for each species was determined. In collecting this data, we also recorded the time of blooming with respect to the calendar year.

The flower count data also allowed for graphical depictions of the distribution of flowering for the species. The flower production of some plants was observed to be normally distributed over time, while others were strongly skewed. These visualizations are more useful than simple reports of the range of the blooming as they allow for identification of periods when plants may be in bloom, but during which few flowers are produced. In this way, it is possible to identify periods of time for which other plant species may be added to increase the number of flowers available.

The flower count data was also combined with the average area of individual blossoms to estimate the total floral area for the plant species at each date. This information is useful because pollinators respond not just to the number of flowers, but to the area of individual inflorescences and to the floral area of aggregations of plants (Dafni, Lehrer, & Kevan, 1997). Enlarged petals, sepals, and other reproductive tissues serve to advertise a flower‟s presence to prospective pollinators. As a result, plants or plant communities that produce larger floral areas tend to draw more pollinators because they are more easily perceived.

In both years, differences in the number of flowers and floral area changed through the season. Both flower number and floral area were significantly lower in the early-season compared to the mid- and late-season. The early-season is a particularly crucial period for

Bombus queens which are establishing their colonies and to existing Apis mellifera colonies which must replenish honey and pollen stocks after the winter. Consideration should thus be given to increasing the rate at which early-blooming plant species are seeded in plant mixes.

Alternatively, other species that bloom during this time may be added or substituted to increase the amount of available food for these important pollinator species.

The flowering phenologies of the plant species followed similar patterns in both years.

However, differences were observed in both the duration of blooming and in the week of peak bloom for many of the species. This variation between seasons is to be expected though. The flowering phenology of perennial plant species can change as plants mature and may be influenced by climatic factors such as temperature and rainfall (Fenner, 1998). As a result, flowering phenology is also expected to vary between locations with differing environmental conditions. Lastly, differences in blooming period may also be observed among different genotypes of a plant species since flowering time can be inherited (Pors & Werner, 1989).

The study presented here demonstrates that it is possible to a assemble a group of native perennial plant species whose overlapping blooming periods produce a continuous supply of pollen and nectar. If established in the landscape, these assemblages have the potential to support pollinator populations from May through October, thus providing food resources when crops are not in bloom. More work must be conducted however, to determine if these candidate species are suitable plant hosts for bees and other pollinators. If the flowers produced by these plants are not visited by pollinators, or if the pollen and nectar resources are not easily acquired, then establishment of these plants in the landscape will fail to achieve their intended purpose of supporting bee populations.

WORKS CITED

Baker, H. G., & Baker, I. (1983). Floral nectar sugar constituents in relation to pollinator type. In C. E. Jones & R. J. Little (Eds.), Handbook of experimental pollination biology (pp. 117- 141). New York: Scientific and Academic Editions.

Begum, M., Gurr, G. M., Wratten, S. D., Hedberg, P. R., & Nicol, H. I. (2006). Using selective food plants to maximize biological control of vineyard pests. Journal of Applied Ecology, 43(3), 547-554.

Blossey, B., & Notzold, R. (1995). Evolution of increased competitive ability in invasive nonindigenous plants - a hypothesis. Journal of Ecology, 83(5), 887-889.

Dafni, A., Lehrer, M., & Kevan, P. G. (1997). Spatial flower parameters and insect spatial vision. Biological Reviews of the Cambridge Philosophical Society, 72(2), 239-282.

Faegri, K., & Pijl, L. v. d. (1979). The principles of pollination ecology (3d rev. ed.). Oxford ; New York: Pergamon Press.

Fenner, M. (1998). The phenology of growth and reproduction in plants. Perspectives in Plant Ecology, Evolution and Systematics, 1(1), 78-91.

Fiedler, A. K. (2006). Evaluation of Michigan native plants to provide resources for natural enemy arthropods. Michigan State University.

Frank, S. D., & Shrewsbury, P. M. (2004). Effect of Conservation Strips on the Abundance and Distribution of Natural Enemies and Predation of Agrotis ipsilon (Lepidoptera: Noctuidae) on Golf Course Fairways (Vol. 33, pp. 1662-1672).

Gleason, H. A., & Cronquist, A. (Eds.). (1991). Manual of vascular plants of northeastern United States and adjacent Canada. New York: New York Botanical Garden.

Gumbert, A. (2000). Color choices by bumble bees (Bombus terrestris): innate preferences and generalization after learning. Behavioral Ecology and Sociobiology, 48(1), 36-43.

Hickman, J. (1996). Use of Phacelia tanacetifolia strips to enhance biological control of aphids by hoverfly larvae in cereal fields. Journal of Economic Entomology, 89(4), 832-840.

Jervis, M. A., Kidd, N. A. C., Fitton, M. G., Huddleston, T., & Dawah, H. A. (1993). Flower- Visiting by Hymenopteran Parasitoids. Journal of Natural History, 27(1), 67-105.

Landis, D. A., Wratten, S. D., & Gurr, G. M. (2000). Habitat management to conserve natural enemies of arthropod pests in agriculture. Annual Review of Entomology, 45, 175-201.

Letourneau, D. K., & Bothwell, S. G. (2008). Comparison of organic and conventional farms: challenging ecologists to make biodiversity functional. Frontiers in Ecology and the Environment, 6(8), 430-438.

Pors, B., & Werner, P. A. (1989). Individual Flowering Time in a Goldenrod (Solidago canadensis): Field Experiment Shows Genotype more Important than Environment. American Journal of Botany, 76(11), 1681-1688.

Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Available online at http://websoilsurvey.nrcs.usda.gov/ accessed [02/10/2009].

Tilman, D. (2002). Agricultural sustainability and intensive production practices. Nature, 418(6898), 671.

Waser, N. M., Chittka, L., Price, M. V., Williams, N. M., & Ollerton, J. (1996). Generalization in pollination systems, and why it matters. Ecology, 77(4), 1043-1060.

Waser, N. M., & Real, L. A. (1979). Effective mutualism between sequentially flowering plant- species. Nature, 281(5733), 670-672.

CHAPTER 3

RELATIVE ATTRACTIVENESS OF TWENTY-FIVE NATIVE PERENNIAL PLANT SPECIES TO BEES IN CENTRAL PENNSYLVANIA

INTRODUCTION

In the previous chapter, the blooming phenology was documented for twenty-five plant species being considered for use in pollinator conservation efforts. The primary objective was to ensure that the candidate plant species could produce a sequence of blooms from May through

October, thereby providing pollen and nectar resources throughout the growing season.

However, the mere presence of a floral resource at a particular point in time does not guarantee that it will be visited by bees. It is possible to speculate which pollinators will visit a flower based on established patterns, but as Faegri and Pijl state in The Principles of Pollination

Ecology (1979), „such conclusions can only be hypothesis requiring verification by observation of actual conditions.‟

Various sources have documented the interactions among many plant and bee species.

Among the most exhaustive studies conducted in North America is the life‟s work of Charles

Robertson who recorded over 15,000 insect visits to 441 flowering species in Illinois from 1885 to 1916 (Marlin & LaBerge, 2001). Robertson‟s observations provide valuable information regarding plant-insect interactions, but their lack of quantitative data concerning the abundance of the insect or plant species makes it difficult to compare the relative attractiveness of individual plant species. More recent studies, such as those by Anna Fiedler and Juliana Tuell (Fiedler,

2006; Tuell, Fiedler, Landis, & Isaacs, 2008), also from the Midwestern region of the United

States, have been more systematic in determining the strength of relationships among insect

species and plant hosts. Also, less is known about the degree to which those plant and insect associations are influenced by landscape context and regional influences on plant and insect species pools.

The work presented in this chapter summarizes the interactions of the selected plant species with a local pollinator community in central Pennsylvania. However, before proceeding further, it is important to have an understanding of the many factors that influence pollinator visitation. These include attractive qualities of the reproductive structures, properties of the reward, competitive effects among pollinators, and external conditions such as weather and seasonality.

VISUAL & OLFACTORY ATTRACTANTS

The reproductive structures of many angiosperms have evolved to advertise their presence to pollinators (Dafni, Lehrer, & Kevan, 1997). Visual attributes such as the pigmentation, shape, and size of individual flowers or aggregations of flowers can serve as both long- and short-range attractants while floral fragrances stimulate the olfactory receptors of pollinators (Kevan & Baker, 1999). These cues attract pollinators and allow them to recognize and discriminate among plant species (Dobson, 1987), increasing their ability to maximize reward acquisition and simultaneously increase the transfer of conspecific pollen among flowers.

Among the most easily perceived floral attractant for humans is pigmentation. The pigmentation of floral structures increases the visual perceptibility of flowers by increasing their contrast against the background, but insect color perception differs from that of humans. The visual spectrum for bees and many other insects is shifted toward shorter wavelengths, ranging from about 300nm in the ultraviolet to 650nm in yellow-orange, with peak receptivity occurring in the ultraviolet, blue, and yellow range (Kevan, 1983). In general, yellow and white flowers tend to attract a wide variety of insects while blue flowers attract bees more frequently than other

insect groups (Kevan, 1983). Furthermore, flowers that reflect blue and purple wavelengths tend to be bilaterally symmetric (zygomorphic); a characteristic of flowers pollinated primarily by bees (Kevan, 1983). Despite the importance placed on UV reflectance, bees are no more responsive to UV than to the other primary colors (Kevan, 1983; Kevan, Chittka, & Dyer, 2001).

Flowers that reflect ultraviolet light across their entire surface are also very rare, perhaps because the amount of UV radiation under ambient conditions is more variable than other wavelengths

(Kevan, 1983).

Bees also respond to the presence of nectar guides, which are patterns on blossoms that orient pollinators to the location of nectar rewards (Dafni et al., 1997; Kevan & Baker, 1999;

Waser & Price, 1985). These specialized patterns may occur in either the visible or UV spectra, or sometimes both (Kevan, 1983). Nectar guides are often present on flowers where food resources are hidden and may take on various forms, from converging lines to blotches of color or even bullseye patterns (Kevan, 1983). Flowers that attract butterflies have the highest incidence of nectar guides (83%), followed by zygomorphic and then capitulate flowers. About half of bowl-shaped flowers examined have nectar guides (Kevan & Baker, 1999).

The colors and patterns of flowers can also change with the state of the blossoms, influencing the rate of bee visitation (Weiss & Lamont, 1997). After a flower is pollinated, or when a flower is no longer receptive, a color change can signal to pollinators that the availability of rewards is reduced (Gori, 1983). For example, bumblebee visitation of Lupinus texanus decreases markedly after the yellow-white spot on the banner petal changes to purple. After this color change, nectar production is decreased and the viability of pollen is much reduced (Schaal

& Leverich, 1980). Petals may still be retained by flowers, however, to attract pollinators from greater distances which can focus their attention on younger blossoms upon approaching.

In addition to coloration and patterning, the surface area of individual flowers or aggregation of flowers can influence attractiveness of a particular „display unit.‟ (Dafni et al.,

1997). Flower size, for example is positively associated with bee visitation (Ashman & Stanton,

1991; Bell, 1985; Conner & Rush, 1996; Candace Galen & Newport, 1988; Hegland & Totland,

2005). Pollinators may preferentially visit larger flowers with the expectation that a greater amount of resources is available (Ashman & Stanton, 1991). Similarly, bee visitation is also associated with the size of inflorescences (Pleasants & Zimmerman, 1990), flower number

(Conner & Rush, 1996; Ohashi & Yahara, 1998) and with flower density (Hegland & Boeke,

2006). In these cases, an increase in the number of flowers per unit area allow for increased foraging efficiency. It is no surprise then, that larger plants producing more flowers also attract more bees (Jong & Klinkhamer, 1994; Schaffer & Schaffer, 1979) or that isolated conspecific plants receive fewer bee visits than counterparts occurring in close proximity to each other

(Silander & Primack, 1978).

The contour length and contour density of flowers may also play an important role in determining attractiveness to pollinator species. The contour length refers to the perimeter of a flower while the contour density of a flower is the ratio of its perimeter to its area (Dafni &

Kevan, 1997; Dafni et al., 1997). Flowers with high contour densities are more dissected and may thus have unique forms that allow for enhanced attraction and recognition by bees (Dafni &

Kevan, 1997; Dafni et al., 1997). Many bees appear to have preferences for objects with high contour densities, perhaps because the broken nature of the flower profile contrast it better against the background (Dafni & Kevan, 1997; Kevan & Baker, 1999).

Olfactory cues are also important in the attraction of pollinators to flowers. It has been suggested that floral odors act as a close-range attractant after pollinators have been lured from greater distances by the general colors and patterns of flowers, but aggregations of flowering

plants can attract bees over longer distances (Kevan & Baker, 1999). Floral fragrances are generally blends of chemicals of different classes, including terpenoids, simple aromatics, aminoid compounds, and hydrocarbons (Williams, 1983). The composition of floral fragrances can be important in attracting specific pollinators. For example, morphs of Polemonium viscosum in the Rocky Mountains may have either sweet- or skunky-scented flowers. Bumblebees avoid skunky-smelling flowers which are found at altitudes where flies dominate the pollinator community (Candace Galen & Newport, 1988), but even after visiting skunky-smelling flowers that have had their nectar levels artificially enhanced, bumblebees continue to prefer sweetscented morphs (C. Galen & Kevan, 1983).

FLORAL REWARDS

Pollinators locate flowers through visual and olfactory cues, but ultimately, bees visit flowers to acquire specific rewards. The primary rewards produced by flowers are nectar and pollen. These two food sources provide nutrition for both adult and developing bees. Although less common, flowers may also provide rewards in the form of oils, fragrances, shelter, warmth, and reproductive sites (Kevan & Baker, 1999). The availability and nature of the resources may determine a pollinator‟s continued visitation to a particular flower type.

Nectar is produced from the phloem of plants and is best described as a sugar-water solution, though other compounds commonly occur in low concentrations (Kevan & Baker,

1999). The sugars most commonly occurring in nectar are sucrose, glucose, and fructose (H. G.

Baker & Baker, 1983). Although some nectars contain a high proportion of one sugar, it is very rare for a nectar to contain a single sugar (Baker & Baker, 1983). Baker and Baker (1983) classify nectars as falling into four classes determined by their sucrose to hexose ratios. Nectars are either (1) hexose dominant, (2) hexose-rich, (3) sucrose-rich, or (4) sucrose dominant. The nectar produced by some plant families is relatively consistent. Sucrose rich or dominant nectars

are produced by members of the Lamiaceae and Ranunculaceae, while hexose rich or dominant nectars are common among plants in the Brassicaceae and Asteraceae (Baker & Baker, 1983).

Other plant families exhibit more variation in the sugar ratios found in nectar, sometimes differing widely among members of the same genus (Baker & Baker, 1983).

Generalizations can be made about the pollinators of a flower based on nectar characteristics. For example, the amount of sugar and nectar secreted generally increases as pollinator specialization increases (Kevan & Baker, 1999). Zygomorphic flowers, which tend to be favored by bees, also have more sugar than open bowl-shaped flowers which attract a broader range of insects (Kevan & Baker, 1999). Furthermore, flowers associated with long-tongued bees are usually sucrose-rich while those visited by short-tongued bees are generally hexose-rich or hexose-dominant (Baker & Baker, 1983).

Other components of nectar can also serve as attractants or deterrents. The presence of amino acids, for example, may attract pollinators that otherwise lack a regular protein source

(Baker & Baker, 1986; Kevan & Baker, 1999). In contrast, the presence of secondary defense compounds in nectar may act as deterrents to nectar robbers (Adler, 2000; Kevan & Baker,

1999). Other compounds may alter pollinator behavior. For example, honeybees exhibit preferences for sugar solutions containing low concentrations of nicotine and caffeine

(Singaravelan, Nee'man, Inbar, & Izhaki, 2005) while honeybees that are exposed to cocaine engage in waggle dances more frequently upon returning to the hive (Barron, Maleszka,

Helliwell, & Robinson, 2009). It is hypothesized that the presence of these neurotoxins in nectar causes pollinators to overestimate the sugar concentrations, resulting in increased fidelity and recruitment to the flower type (Barron et al., 2009).

Nectar is an important source of carbohydrates for adult bees, but the availability of suitable pollen sources influence bee visitation as well. In general, the pollen of bee-pollinated

plants is rich in protein, peptides, and amino acids; all of which are crucial for the proper development of larval bees (Kevan & Baker, 1999). In many cases, pollinators will forage for pollen from fewer plants than from which they will collect nectar (Free, 1970). It is hypothesized that secondary compounds contained within pollen may be toxic to certain species. Larvae of some oligolectic bee species, for example, will fail to develop into adults if they are transferred onto pollen masses of other plant species (Praz, Mueller, & Dorn, 2008).

EXTERNAL FACTORS INFLUENCING BEE VISITATION

Visual and olfactory cues may attract bees, but competitive effects among pollinator species and other external conditions can influence the rate of the visitation rate of plants. In some cases, flowers may be suitable hosts, but bees are not observed to visit. Conversely, bees may appear to have high fidelity to a couple of species of plants when, in fact, they are capable of feeding from a much wider range.

Competitive effects among pollinators may lead to resource partitioning, influencing the rate of specific bees to flowering plants. Competition with honeybees, for example, may force native pollinators to forage on less productive flower patches (Schaffer et al., 1983). Competition may can result in specialization on particular flower species. Although long-tongued bees may visit a wide range of plant hosts, competition with short tongued bees may result in higher rates of visitation to flowers with deeper corollas which preclude entrance by the shorter tongued species (Inouye, 1978).

External conditions may also influence pollinator visitation at a particular time. Weather conditions may be among the most important factors. Weather conditions may influence insect visitation of flowers in two ways; (1) by influencing the availability and condition of floral rewards, and (2) by directly influencing the activity of visitors (Corbet, 1990). Factors such as temperature and humidity can influence the rate of pollen release and nectar productivity

(Corbet, 1990). Cloud cover, temperature, and windspeed may similarly influence pollinator activity and ability to visit flowers. Naturally, the respective phenologies of plant and pollinator species will dictate the possibility of visitation. If the foraging period of the pollinator and the blooming period of the plant do not overlap, an interaction cannot occur.

With commercial bee populations declining, and an accelerated loss of natural areas eliminating floral resources, we set out to assess the attractiveness of twenty-five perennial plants to the native pollinator community. Observations and collections of bees were conducted to compare (1) bee abundance, (2) bee species diversity, and (3) species-specific interactions among the plants and members of the pollinator community. With this knowledge, pollinator conservation efforts in agricultural landscapes can be structured to include plant species which attract and support bee species which have been identified as crop pollinators.

MATERIALS AND METHODS

SITE DETAILS

The study was conducted at the Russell E. Larson Agricultural Research Station in Rock

Springs, PA in 2008 and 2009. See the site description in Chapter 2 (page 33) for more information regarding site details and plot design.

To determine which plants were preferentially visited by bees, pollinators were both observed and physically sampled from the twenty-five perennial plant species from the beginning of May until mid-October in 2008 and 2009.

BEE OBSERVATIONS

Observations were conducted biweekly from May 24 through October 15, 2008 and May

6 through October 8, 2009. Plants were observed for bee visitation between 0900-1200 EST and again from 1300-1600 EST to account for possible changes in foraging activity through the day.

Observations were also made from two distances during each time interval. First, each flowering plant within a block was observed with binoculars for 90 seconds from a distance of 12m.

Observations were conducted from this distance to avoid inhibiting the visitation of more „timid‟ pollinator species. The same plants were then observed for 90 seconds at a distance of 1m

(without the use of optical aides) to more easily view the smaller species that might otherwise go unnoticed from greater distances. Visiting bees were counted and classified into three broad categories: (1) honeybee, (2) bumblebee, (3) other bee.

BEE COLLECTIONS

Destructive sampling of pollinators was conducted to allow for the identification of bees beyond the broad categories used in the visual assessment of visitation. Destructive samples were conducted biweekly from May 13 through October 8, 2008 and May 15 through October 1, 2009.

In 2008, all plants within a block were sampled in a randomized sequence from 0900-1200 EST and again from 1300-1600 EST, maintaining the same sampling sequence used earlier. In 2009, bees were sampled only from plant species in bloom after the previous season‟s samples indicated that bees were not strongly associated with plants that were not in flower.

Bees were collected using a leafblower (Craftsman, model #358794760) that was converted into a vacuum sampler by means of a mulching attachment connected to the air intake vent. For a single sample, a tulle netting bag with a mesh size of 1.5 mm was affixed at the end of the mulching attachment with a rubber band and a single plant was sampled for fifteen seconds, starting at the apex of the plant and traveling down to the base. Sampling net bags were removed, the open end closed with a rubber band and immediately stored in a portable ice chest with frozen icepacks to reduce insect activity. Within one hour from the end of the afternoon sampling period, the samples were relocated to a walk-in freezer and stored below 0o C for two days to increase insect mortality. To process the samples, the contents of a single bag were

emptied and all arthropods were separated from the contained plant litter. In 2008, all arthropods from a single sample were preserved in scintillation vials with 70% or 90% ethanol. In 2009, bee species were immediately pinned for identification while non-bee arthropods were stored in 70% ethanol.

In 2008, collected bees were preserved in 70% ethanol prior to being pinned and identified. Samples with Bombus spp. specimens, however, were stored in 90% ethanol to preserve their DNA for potential genetic assays. To prepare these specimens for identification, all bees were washed in warm water with a liquid dish detergent, rinsed, dipped in 90% ethanol and tossed on a dry paper towel. Specimens were then placed in a ½ quart glass mason jar and

„tumble-dried‟ by directing the air from an electric hair dryer into the jar‟s opening, which was covered with tulle mesh, for approximately 30 seconds. This process was conducted to dry and separate the hairs on specimens: an important step for the proper visual identification of bee specimens that have been stored in liquid. In 2009, bee specimens were not stored in ethanol prior to pinning, and as a result were not subjected to the washing or drying process.

BEE TAXONOMY

Bee specimens were identified to the lowest possible taxonomic level by the author and by Leo R. Donovall at the Pennsylvania Department of Agriculture using the guides available at discoverlife.org along with the taxonomic keys in Mitchell‟s Bees of the eastern United States

(1960, 1962). All specimens were identified to the species level except for 62 Lasioglossum specimens that could only be resolved to morphospecies. Voucher specimens were deposited in the entomology lab collections at the Pennsylvania Department of Agriculture in Harrisburg, PA.

STATISTICAL ANALYSIS

To account for temporal changes in pollinator populations and for the periodicity in flowering, the 2008 and 2009 seasons were divided into three periods; early-season (May-June), mid-season (July-August 6, 2008 and July-August 11, 2009) and late-season (August 7, 2008 –

October and August 12, 2009 – October). Plant species were included in the analysis for a particular time period if at least half of the individuals produced more than 10% of their total floral area for the season during the respective dates for the bee observation and collection datasets. Because the flowering phenology of some plants extended over several periods, some plant species were included in multiple analyses. Due to changes in the flowering phenology of some plants between seasons, the periods during which these species were included also changed in 2009. Lastly, the species analyzed in each period also differed between the collection and observation analyses. Because these datasets were collected on alternating weeks, the number of flowers produced differed between the datasets and in some instances, did not fulfill the criteria for inclusion.

Statistical analyses were conducted to identify significant differences in the number of collected bees and the number of observed bees in 2008 and 2009, and among the early-, mid-, and late-season in both years. To test for differences in the number of collected bees between years, the total number of bees collected from each block during each season was subjected to a paired-T test at the α=0.05 significance level. To identify differences among the number of collected bees in the early-, mid-, and late-season, the total number of bees collected from each block during these periods was subjected to an analysis of variance (ANOVA) with period, and block as factors. The same statistical approaches were used to compare the number of observed bee visits in the two seasons, and in the early-, mid-, and late-season periods.

Plant preference

Both physical samples and visual observations of bees were made to compare the relative attractiveness of the candidate plant species to members of the local bee community. In conducting the analyses for these datasets, four bee groupings were considered; (1) „All bees,‟ which included all specimens (2) Apis mellifera L., (3) Bombus spp., and (4) „Other bees,‟ which included non-Apis and non-Bombus bees. These categories were created to account for possible differences in preference and behavior among pollinator species. Apis mellifera and Bombus spp. were considered separately because these species are important to agricultural systems and because their unique biology and social structures may result in different foraging habits compared to solitary bee species.

To test for differences in the number of collected or observed bees among the plant species, the number of specimens in each of the four bee groupings was summed for each plant over all dates within a time period. The 2008 and 2009 data for the early-, mid-, and late-season were log (x+1) transformed to satisfy the assumptions for analysis and subjected to an analysis of variance (ANOVA) with plant species and block as factors. A Tukey-Kramer means separation was performed at the α=0.05 level to identify significant differences in the response variables among the plant species.

Floral characteristics

To assess whether certain characteristics of floral display influenced bee visitation rates, regressions were performed on the bee collection and observation data with three floral display variables; (1) the number of flowers, (2) floral area, and (3) the maximum height of flowers.

The number of bees collected or observed on each plant was summed over dates within each time period. The values for the corresponding explanatory variables were calculated by averaging the values for each plant over the number of dates considered in each analysis. The

2008 and 2009 data for the early-, mid-, and late-season were log (x+1) transformed and regressed against a single explanatory variable. Due to high levels of collinearity among the floral display variables, separate regressions were conducted for each time period and explanatory variable combination.

Bee diversity indices

The species richness of the bee assemblages collected from each plant was calculated by determining the total number of species collected from the respective plant over the entire season. To determine if bee species richness differed among the plant species, the data from

2008 and 2009 were each subjected to an ANOVA with plant species and block as factors

(PROC MIXED). A Tukey-Kramer means separation was also conducted to identify significant differences in the number of bee species associated with the candidate plant species.

To explore the heterogeneity of the bee species assemblages, the antilogarithm of the

Shannon-Wiener index (H‟) was calculated for the bee collections from each plant. This value represents the number of evenly common species that would result in the same heterogeneity or

H‟ that was calculated from the original sample (Peet, 1974). Comparisons can then be made between the observed species richness values and the expected number of evenly common species to determine if bee species were equally abundant. This approach is arguably more informative than simply reporting the proportions produced by Pielou‟s evenness index (Peet,

1974), as it requires the consideration of more than one species diversity index before drawing conclusions about the composition of a community. This method also allowed for the inclusion of plants that attracted only one bee species or no specimens at all. To test for differences in species richness and community heterogeneity, the 2008 and 2009 species richness and exp [H‟] values were then subjected to an ANOVA with plant species and block as factors. A Tukey-

Kramer means separation was also conducted at the α = 0.05 level.

RESULTS

In all, 1651 bee specimens were collected from the perennial plants in this study during

2008 and 2009. In 2008, 612 bees were collected with an average of 6.51 bees sampled per plant over the course of the season. The number of specimens collected in 2009 increased by 70% to

1039 specimens; a significant increase from the previous season (T=-3.17, p=0.025). The average number of bees collected in 2009 was 10.93 bees per plant.

Observations of the number of bee visits also indicated a significant increase in bee visitation over the two seasons (T =-11.66, p= 0.001). In 2008, a total of 1012 bee visits to the study plants were recorded. The following year, 3012 visits to the plants were observed; a 198% increase. The average number of bee visits observed per plant increased from 10.76 in 2008 to

31.71 in 2009.

In addition to increasing between seasons, the number of bee species collected increased through the season in 2008 and 2009. In both years, the number of bees sampled were significantly different among the early- mid-, and late- season (2008, F2,9=163.98, p<0.001;

2009, F2,9=10.68, p=0.0042). The average number of bees was significantly smaller in the early- season than in the mid- and late-season in 2008 and 2009 (p<0.0001, p<0.0001; p=0.0395, p=0.0035). Although the average number of bees captured in the late-season was greater than the number collected in the mid-season, the differences were not significant in 2008 or 2009

(p=0.0954, p=0.2888).

The number of observed bee visits also increased through the season in 2008 and 2009. In both years, the number of observed bees were significantly different among the early-, mid-, and late-season (2008, F2,9=61.24, p<0.001; 2009, F2,9=59.06, p<0.0001). The average number of bees was significantly less in the early-season than in the mid- and late-season in 2008 and 2009

(p<0.0001, p<0.0001; p<0.0001, p=0.0004). The average number of observed bees was also

greater in the late-season than in the mid-season. This difference in higher abundance later in the season was significant in 2008 but not in 2009 (p=0.0455, p=0.2724).

The number of Apis mellifera specimens collected from the study plants increased 526% from 27 in 2008 to 169 in 2009. Similarly, the number of observed honeybees also increased from 97 in 2008 to 448 in 2009; a 362% increase. The honeybees that were collected and observed in this study likely originated from experimental hives located 0.9 km from the research plots. Although a larger number of flowers produced in the second season probably contributed to the increased honeybee visitation, it is also possible that changes in the number or sizes of the managed colonies influenced the observed visitation rates in 2009.

PLANT PREFERENCE

The mean number of bees collected from the perennial plants differed significantly among the plant species in the mid- and late-season in 2008, and during the early-, mid-, and late-season in 2009 (Table 3.1) This indicates that bees preferentially visited some plant species over others.

The total number of bees observed visiting plants also differed significantly among the plant species during each period in 2008 and 2009 (Table 3.2). The ranks of the plant species by the number of observed bee visits were generally consistent with data from the destructive samples, but differences did occur. Inconsistencies between the two datasets are likely the result of conducting destructive samples and making visual observations on alternating weeks.

Early-season

In the early-season of 2008, the mean number of collected bees did not differ significantly among the plant species (Figure 3.1). Bee counts were low, and no Bombus specimens were captured during this period. Significant differences were recorded in 2009

Table 3.1 Analysis of variance for the number of bees (response variable) sampled from different plant species (explanatory variable) in the early-, mid-, and late-season in 2008 and 2009. The number of bees in each bee grouping were log10(x+1) for the respective analyses.

EARLY MID LATE Response Variable df F P df F P df F P 2008

Log10 (All bees) 5 2.36 0.0990 11 5.40 0.0001 * 11 12.17 <0.0001 *

Log10 (Apis mellifera ) 5 0.87 0.5254 11 1.00 0.4668 11 4.92 0.0002 *

Log10 (Bombus spp.) - - - 11 5.71 <0.0001 * 11 7.16 <0.0001 *

Log10 (Other bees) 5 1.77 0.1890 11 8.30 <0.0001 * 11 12.13 <0.0001 * 2009

Log10 (All bees) 5 3.53 0.0310 * 14 6.77 <0.0001 * 13 9.53 <0.0001 *

Log10 (Apis mellifera ) 5 1.62 0.2231 14 4.29 0.0001 * 13 3.92 0.0005 *

Log10 (Bombus spp.) 5 6.48 0.0031 * 14 6.20 <0.0001 * 13 10.14 <0.0001 *

Log10 (Other bees) 5 3.64 0.0280 * 14 5.04 <0.0001 * 13 8.95 <0.0001 *

Table 3.2 Analysis of variance for the number of bees (response variable) observed visiting different plant species (explanatory variable) in the early-, mid-, and late-season in 2008 and 2009. The number of bees in each bee grouping were log10(x+1) for the respective analyses.

EARLY MID LATE Response Variable df F P df F P df F P 2008

Log10 (All bees) 5 6.62 0.0029 * 12 5.19 <0.0001 * 14 7.40 <0.0001 *

Log10 (Apis mellifera ) 5 6.48 0.0031 * 12 2.74 0.0112 * 14 11.03 <0.0001 *

Log10 (Bombus spp.) 5 6.48 0.0031 * 12 6.61 <0.0001 * 14 8.08 <0.0001 *

Log10 (Other bees) 5 2.89 0.0571 12 5.66 <0.0001 * 14 6.76 <0.0001 * 2009

Log10 (All bees) 5 5.02 0.0147 * 18 8.45 <0.0001 * 9 27.40 <0.0001 *

Log10 (Apis mellifera ) 5 1.71 0.2195 18 10.42 <0.0001 * 9 13.01 <0.0001 *

Log10 (Bombus spp.) 5 5.48 0.0110 * 18 4.65 <0.0001 * 9 22.35 <0.0001 *

Log10 (Other bees) 5 7.11 0.0044 * 18 6.95 <0.0001 * 9 18.99 <0.0001 *

however (F5,13=3.53,p=0.031), with the greatest number of bees found on Penstemon digitalis

(6.25±2.06). This was the only plant species that Bombus specimens were collected from during

the early-season. Bees in the „other bees‟ group were also found most frequently on Penstemon digitalis (5.25±1.65). Collections of Apis mellifera did not indicate a strong preference for any of the plant species (F5,13=1.62,p=0.223). Collections from Phlox divaricata during the early-season were low in both years. In 2008, only one specimen was collected from this species; in 2009 none were captured.

The number of observed bee visits also differed significantly among the plant species in the early-season of 2008 and 2009 (Figure 3.2; F5,13=6.62, p=0.0029; F5,10=5.02, p=0.0147). In

2008, Tradescantia ohiensis received the greatest number of visits per plant (4.25±0.85), but in

2009, Aquilegia canadensis and Penstemon digitalis had higher visitation numbers (9.00±4.00,

7.50±2.99, respectively). In both years, no bees were observed visiting Lysimachia quadrifolia.

In 2008, Apis mellifera and Bombus individuals were only observed on Tradescantia ohiensis, resulting in a significant plant species effects (F5,13=6.48, p=0.0031; F5,13=6.48, p=0.0031, respectively). Other bees were also observed most frequently on Tradescantia ohiensis during this period (2.25±0.75), but the observed counts were not significantly different from those made on other plant species (F5,13=2.89, p=0.0571). In 2009, honeybees did not exhibit a flower preference in the early-season, but Bombus individuals were observed visiting Penstemon digitalis with increased frequency (5.50±2.50), followed by Tradescantia ohiensis (3.00±0.91) during this period. In 2009, the greatest number of „other bees‟ were observed on Aquilegia canadensis (9.00±4.00), and Penstemon digitalis (1.75±0.85). Differences in observed bee visitations among the early-blooming plant species were statistically significant for both Bombus spp. and other bees in 2009 (F5,10=5.48, p=0.011; F5,13=7.11, p=0.0044, respectively).

Mid-season

The number of bees collected in the mid-season differed significantly among plant species in both 2008 and 2009 (Figure 3.3; F11,30=5.40, p=0.0001; F14,40=6.77, p<0.0001). In

2008, Eupatorium perfoliatum attracted the greatest number of bees (22.5±5.87) while

Desmodium canadense and Senna hebecarpa had the fewest (0.33±0.33, 0.75±0.25, respectively). While bee visitation to Eupatorium perfoliatum was high again in 2009, the number of bees collected from Pycnanthemum. tenuifolium and Veronicastrum virginicum increased substantially from the previous season. The number of honeybees did not differ among plant species in 2008, but in 2009, there were significant differences, with greater numbers of

Apis mellifera collected from Pycnanthemum tenuifolium and Eupatorium perfoliatum

(9.25±4.33, 10.75±8.23, respectively), though there was overlap with other plant species. In

2008, Bombus specimens were collected most frequently from Monarda fistulosa (11.75±3.97), followed by Veronicastrum virginicum and Liatris pycnostachya (5.25±5.32, 3.00±0.91, respectively). While large numbers of bees were found on E. perfoliatum, not a single Bombus individual was found on this plant species. In 2009, Bombus individuals were collected most frequently from Pycnanthemum tenuifolium and Veronicastrum virginicum (8.25±2.25,

8.00±1.78, respectively), though there was overlap with other species. In 2008, the number of

„other bees‟ collected from Eupatorium perfoliatum was significantly greater than other plant species (22.25±5.63; F11,30=8.30,p<0.0001). In 2009, other bees were captured most frequently on Veronicastrum virginicum, and then on Eupatorium perfoliatum (8.25±2.50, 6.25±2.29, respectively).

The number of bee visits observed during the mid-season also differed significantly among plants species in both 2008 and 2009 (Figure 3.4; F12,32=5.19, p<0.0001; F15,51=8.45, p<0.0001). In 2008, Monarda fistulosa was visited most frequently (22.2±54.31). In 2009, the greatest number of bees was observed on Pycnanthemum tenuifolium and Veronicastrum virginicum (68.50±24.17; 51.00±63.36, respectively). Observations of bee visits were lowest for

Campanula rotundifolia and Desmodium canadense in both seasons (2008, 0.00±0.00,

1.67±0.67; 2009, 0.67±0.33, 2.75±1.25, respectively). In 2008, honeybees were observed visiting

Veronicastrum virginicum most frequently (3.00±1.78), which is consistent with data from the destructive samples made during this period; however, differences in the number of observed honeybees were statistically significant (F12,32=2.74, p=0.0112). In 2009, Apis mellifera was observed on Pycnanthemum tenuifolium and Eupatorium perfoliatum most often (21.75±6.75,

18.25±4.89). Observations of bumblebees in 2008 suggested that Monarda fistulosa was the most attractive plant, followed by Veronicastrum virginicum and Liatris pycnostachya. There was also much overlap with other plant species during this period, but this ranking was consistent with results from collections. In 2009, Veronicastrum virginicum was observed to host the greatest number of bumblebees (40.75±5.02), followed by Monarda fistulosa and

Pycnanthemum tenuifolium (20.75±4.97, 37.50±14.54). There was also much overlap with other species in this mid-season, but the rankings were less consistent with the collection data as fewer bees were collected from Monarda fistulosa during this period. In 2008, other bees were observed to be most abundant on Eupatorium perfoliatum (14.25±2.17). In 2009, other bees were observed in greater frequency on Coreopsis tripteris (19.00±6.82), which bloomed earlier in

2009, and on Echinacea purpurea (16.50±3.88).

Late-season

The number of bees collected during the late-season differed significantly among plants species in 2008 and 2009 (Figure 3.5; F11,32=12.17, p<0.0001; F13,37=9.53, p<0.0001). In 2008, the greatest number of bees was collected from Eupatorium perfoliatum. In 2009,

Symphyotrichum. novae-angliae attracted the greatest number of bees. In both years, Lespedeza capitata failed to attract many bees during this period. In 2008 and 2009, the honeybees were sampled most frequently from Eupatorium perfoliatum and Symphyotrichum novae-angliae. In

2008, the number of Bombus specimens sampled from Symphyotrichum novae-angliae was

significantly greater than for any other plant species. In 2009, Symphyotrichum novae-angliae and Eurybia macrophylla hosted greater numbers of bumblebees. Of the plants blooming in the late period, the greatest number of „other bees‟ was collected from Eupatorium perfoliatum

(F11,32=12.13,p<0.0001). In 2009, „other bees‟ were found most frequently on Conoclinium coelestinum and Symphyotrichum novae-angliae.

The number of bees observed during the late season also differed significantly among plant species in 2008 and 2009 (Figure 3.6; F14,38=7.40, p<0.0001; F9,25=27.40, p<0.0001). In both years, the greatest number of bees was observed on Symphyotrichum novae-angliae

(52.25±11.64, 152.00±15.67). Surprisingly, Solidago rugosa failed to attract an appreciable number of bees in 2008 or 2009 (8.50±6.86, 16.00±10.43, respectively). This was probably due to the poor performance of Solidago specimens in three of the four blocks. Casual observations of other Solidago plants co-occurring in the landscape would suggest that members of this genus are highly attractive to bees and other beneficial insects in the late-season. In 2008, honeybees were observed most frequently on Eupatorium perfoliatum and Symphyotrichum novae-angliae

(9.00±3.72, 4.50±1.04), consistent with the destructive sampling data. The number of honeybees observed on Symphyotrichum novae-angliae and Conoclinium coelestinum were greater in 2009

(21.25±3.42, 7.50±2.78). In 2008, Bombus individuals were observed to preferentially visit

Symphyotrichum novae-angliae (43.25±9.74), followed by Symphyotrichum novi-belgii

(8.75±1.31). In 2009, Bombus still frequented Symphyotrichum novae-angliae (95.25±16.01), but next most abundant on Eurybia macrophylla (27.50±9.17). In 2008, other bees were most frequently observed on Eupatorium perfoliatum and Vernonia gigantea (13.00±2.48, 6.00±1.47), but exhibited similar preferences as bumblebees in 2009, visiting Symphyotrichum novae- angliae, Symphyotrichum novi-belgii and Eurybia macrophylla most frequently (35.50±7.24,

23.75±2.78, 23.00±3.89, respectively).

Figure 3.1 Mean number of bees sampled from plant species during the early-season in 2008 and 2009. Tukey-Kramer means separations conducted at the α = 0.05 level for log(x+1) transformed data. Means of plant species that do not share the same letters are statistically different. 10 Apis mellifera 2008 Bombus spp. 8 Other bee

4 bees collected per plant per collected bees a 2 No. a a a a a 0

10 a 2009 8 ab

4 ab

ab bees collected per plant plant per collected bees 2

ab No. b 0

Figure 3.2 Mean number of bees observed visiting plant species during the early-season in 2008 and 2009. Tukey-Kramer means separations conducted at the α = 0.05 level for log(x+1) transformed data. Means of plant species that do not share the same letters are statistically different. 15 Apis mellifera 2008 Bombus spp. Other bees

a 5

bees observed per plant per observed bees ab No. b b b b 0

15 a 2009

ab 10

5 bees observed per plant per observed bees

No. ab ab b 0

Figure 3.3 Mean number of bees sampled from plant species during the mid-season in 2008 and 2009. Tukey-Kramer means separations conducted at the α = 0.05 level for log(x+1) transformed data. Means of plant species that do not share the same letters are statistically different. 40 Apis mellifera 2008 35 Bombus spp. Other bee a 30

20 ab 15 a-c a-c

bees collected perpkant bees collected 10 a-c bc bc No. 5 bc bc bc c c 0

40 a 2009 35 a 30

25 a

bees collected perplant beescollected 10 ab ab ab ab ab

No. 5 ab b b b b b b 0

Figure 3.4 Mean number of bees observed visiting plant species during the mid-season in 2008 and 2009. Tukey-Kramer means separations conducted at the α = 0.05 level for log(x+1) transformed data. Means of plant species that do not share the same letters are statistically different. 100 Apis mellifera 2008 Bombus spp. 80 Other bees

40 a

bees observed per plant per observed bees ab a-c ab 20 a-c

No. a-d a-d a-d a-d b-d cd b-d d 0

100 a 2009

ab a 60 ab a-c 40 a-c a-d a-c a-d a-d

bees observed per plant per observed bees a-d 20 a-d No. a-e a-e c-e b-e de e e 0

Figure 3.5 Mean number of bees sampled from plant species during the late-season in 2008 and 2009. Tukey-Kramer means separations conducted at the α = 0.05 level for log(x+1) transformed data. Means of plant species that do not share the same letters are statistically different. 50 Apis mellifera 2008 45 Bombus spp. 40 Other bee a 35

25 a

15 ab ab

No. bees collected perplant beescollected No. 10 bc bc bc bc bc bc 5 c c 0

50 a 45 2009

40 ab

30 ab 25 a-e 20 a-c b-f a-d 15 a-e 10 No. bees collected perplant beescollected No. c-f b-f 5 d-f ef f ef 0

Figure 3.6 Mean number of bees observed visiting plant species during the late-season in 2008 and 2009. Tukey-Kramer means separations conducted at the α = 0.05 level for log(x+1) transformed data. Means of plant species that do not share the same letters are statistically different. 180 Apis mellifera 2008 160 Bombus spp. Other bees 140

120

100

80 a

60 bees observed per plant plant per observed bees ab No. 40 b-e a-c 20 b-e b-e b-e a-d c-e c-e de c-e a-e e c-e 0

180 a 2009 160

140

120

100

80 ab bc bees observed per plant plant per observed bees 60 a-c No. 40 c-e b-d 20 d-f ef f f 0

FLOWER CHARACTERISTICS

The measured floral display factors were significant explanatory variables for the number of collected bees and the number of observed bees during at least one period in both years. The averages of (1) the number of flowers, (2) the total floral area, and (3) the maximum height of flowers produced by a plant accounted for variability in the visitation of bees, but, as indicated by their low R2 values, the variables were weak predictors in most of the analyses.

In both 2008 and 2009, three of the four flower characteristics were significantly correlated with each other (Table 3.3, Table 3.4). Maximum flower height was significantly correlated with the other flower characteristics, though the weakest correlation occurred between flower height and the number of flowers produced. There may have been a general trend for taller plants to produce more flowers, but many short-statured species such as Conoclinium coelestinum and Pycnanthemum tenuifolium also produced large numbers of flowers. Although the number of flowers produced by a plant was used to calculate the floral area, the two variables were not correlated in either year. This may have resulted from counting the number of „flower clusters‟ for some plant species instead of counting the total number of flowers, but in general, plants that produced fewer flowers also produced larger flowers. Indeed, a negative correlation was found between the area of individual blossoms and the number of flowers produced.

Floral area was not a significant predictor of the number of collected or observed bees in the early-season of 2008 or 2009. Plant species such as Phlox divaricata which had a larger average floral area failed to attract the same number of bees as species with smaller floral displays. However, floral area was a significant predictor of the number of bees collected during the late season in 2008 (R2=0.1571, p=0.0058), and in both the mid- and late-season in 2009

(R2=0.0948, p=0.0187; R2=0.3098, p<0.0001, respectively). The number of observed bee visits was also adequately explained by floral area in the mid- and late-season in both 2008 and 2009

Table 3.3 Pearson correlation coefficients for the flower characteristics of twenty-five native perennial plant species during weeks when destructive sampling of pollinators occurred.

2008 2009 VARIABLE 1 2 3 4 1 2 3 4 1. Number of flowers ― ― 2. Blossom -0.260 ** ― -0.192 * ― 3. Total floral area 0.014 0.356 *** ― 0.103 0.306 *** ― 4. Maximum floral height 0.254 ** 0.208 * 0.365 *** ― 0.180 * 0.410 *** 0.528 *** ― * p < 0.5, ** p < 0.01, *** p < 0.001

Table 3.4 Pearson correlation coefficients for the flower characteristics of twenty-five native perennial plant species during weeks when visual observations of pollinators occurred.

2008 2009 VARIABLE 1 2 3 4 1 2 3 4 1. Number of flowers ― ― 2. Blossom -0.211 * ― -0.201 * ― 3. Total floral area 0.025 0.426 *** ― 0.072 0.306 *** ― 4. Maximum floral height 0.087 0.210 * 0.389 *** ― 0.171 * 0.412 *** 0.521 *** ― * p < 0.5, ** p < 0.01, *** p < 0.001

(2008, R2=0.1482, p=0.0069; R2=0.3504, p< 0.0001; 2009, R=0.3691, p<0.0001; R2=0.4830, p<

0.0001, respectively).

The average number of flowers was a significant predictor of the number of collected bees and observed bees in some periods. In 2008, the number of flowers was a significant predictor of the number of collected bees and observed bees in both the mid- and late-season

(collections, R2=0.334,p=<0.001; R2=0.1603, p=0.0053; observations, R2=0.1924, p=0.0018;

R2=0.2191, p= 0.0003, respectively). In 2009, flower number was a significant predictor of bee collections and observations in the early- and mid-season, and narrowly significant in the late- season (collections R2=0.2714, p=0.0129; R2=0.4042, p< 0.0001; R2=0.1574, p< 0.0001; observations, R2=0.2467, p=0.0305; R2=0.2887, p<0.0001; R2=0.1043, p=0.0480).

The average maximum height was also a significant predictor of bee collections and observations. In 2008, the maximum height of the flowers was a significant predictor of the 64

number of bees collected in the early-, mid-, and late-season (R2=0.2903, p=0.0097; R2=0.1796, p=0.0037; R2=0.1456, p=0.0081). However, in 2009, the maximum height was only a significant explanatory variable for collection numbers in the late-season (R2=0.6128, p< 0.0001). In 2008 and 2009, the maximum height of the flowers was a significant predictor of the number of bees observed in the early-, mid-, and late-season (2008, R2=0.5098, p=0.0006; R2=0.2275, p=0.0006;

R2=0.3221, p<0.0001; 2009, R2=0.5921, p=0.0001; R2=0.2780, p<0.0001; R2=0.7488, p<0.0001).

BEE DIVERSITY INDICES

Overall, 64 bee species were identified from the sampled specimens in both seasons

(Table 3.5). The number of collected bee species increased from 38 in 2008 to 56 species in

2009. Of the 38 species observed in 2008, 30 were collected again in 2009. However, the true number of sampled bee species is likely higher since 62 Lasioglossum specimens could not be resolved to the species level. Approximately 117 species from this large bee genus are expected to occur in Pennsylvania, but specimens are notoriously difficult to identify and much of the accepted nomenclature is still in flux.

The ten most abundant bee species in the vacuum samples were somewhat consistent in both years, though with notable differences (Figure 3.7). In both years, Bombus impatiens was the most frequently sampled bee species, composing 25% and 42% of the specimens collected in

2008 and 2009, respectively. In 2008, one of the small carpenter bees (Ceratina calcarata) was the second most abundant but was ranked third in 2009 due to a large increase in the number of

Apis mellifera specimens collected. A closely related species, Ceratina dupla, was also among the ten most sampled species in 2008 and 2009, ranking seventh and fourth most abundant, respectively. Other species decreased in abundance between the two seasons. In 2008, two

Halictid species (Augochlora pura and Augochloropsis metallica) were the third and fifth most

encountered species. In 2009, the species ranked eleventh and twenty-eighth, respectively, after their numbers dropped by more than 75%.

Both measured and expected bee species richness differed significantly among the plant species in both seasons (Table 3.6). In 2008, Eupatorium perfoliatum hosted the greatest average number of bee species (10.25±1.03 species). Although the bee species occurred at different frequencies, Eupatorium perfoliatum also had the highest expected number of evenly common species in 2008 (6.88±0.56 species). In 2009, the greatest average number of bee species was collected from Veronicastrum virginicum (10 species) despite smaller collection numbers compared to other plants in the same season. As in 2008, the plant species with the greatest species richness also had the highest expected number of evenly common species (8.90±1.87 species).

The number of bee species and the expected number of evenly common species were similar, if not exactly the same, for a majority of the plant species in both seasons, indicating high bee species evenness. This appears to be a result of either low species counts, low specimen counts, or a combination of the two factors. The reported indices of species diversity did differ substantially for some plant species, however. Most notably, Symphyotrichum novae-angliae hosted an average of 7.00±0.58 species in 2009 while the expected number of evenly common species was 2.94±0.33 species. This considerable disconnect between the actual number of bees and the expected number indicates unequal frequencies of occurrence among the visiting bee species. Indeed, Bombus impatiens accounted for over 50% of the specimens from each

Symphyotrichum novae-angliae plant.

Table 3.5 Bees collected from twenty-five native perennial plants species at Rock Springs, PA.

2009

16 36

Total for all plants all for Total 111 364

2008

149

CONTINUED

2009

_ _

1 1 Veronicastrum virginicum virginicum Veronicastrum (L.) Farw. (L.) 10

2008

2009

3 1 Penstemon digitalis digitalis Penstemon Nutt. ex Sims ex Nutt.

2008

_ _

2009

_ _ Aquilegia canadensis canadensis Aquilegia L.

2008

2009

_ 1 Actaea racemosa racemosa Actaea L.

2008

_ _

2009

_ 1 Lysimachia quadrifolia quadrifolia Lysimachia

2008

_ _

2009

_ _ Phlox divaricata divaricata Phlox

2008

_ _

2009

1 7 Pycnanthemum tenuifolium Pycnanthemum Schrad. 62

2008

2009

1 5 Monarda fistulosa fistulosa Monarda L.

2008

4 41

2009

_ _ Lespedeza capitata Lespedeza Michx.

2008

_ _

2009

_ 2 Desmodium canadense canadense Desmodium

(L.) DC. (L.)

2008

_ 1

2009

Barneby

_ 3

Senna hebecarpa hebecarpa Senna (Fernald) Irwin & & Irwin (Fernald)

2008

_ 3

2009

1 1 Tradescantia ohiensis ohiensis Tradescantia

Raf.

2008

1 1

2009

_ 1 Campanula rotundifolia rotundifolia Campanula

2008

_ _

Plant Family Plant 2009

2 1 Vernonia gigantea gigantea Vernonia (Walter) Trel. (Walter)

2008

3 13

2009

G.L. Nesom G.L.

10 23

Symphyotrichum novi-belgii Symphyotrichum (L.) (L.)

2008

2 4

2009

five native perennial plant species at Rock Springs, PA. Springs, Rock at species plant perennial native five Nesom G.L.

- 116 Symphyotrichum novae-angliae novae-angliae Symphyotrichum (L.) (L.)

2008

1 68

2009

2 2 Solidago rugosa rugosa Solidago Mill. 20

2008

1 6

2009

7 6 Liatris pycnostachya pycnostachya Liatris Michx.

2008

6 6

2009

1 3 Eurybia macrophylla macrophylla Eurybia (L.) Cass. (L.) 39

2008

10 11

2009

1 1 Eupatorium purpureum purpureum Eupatorium L. 12

2008

4 7

2009

34 Eupatorium perfoliatum perfoliatum Eupatorium L.

2008

15 40

2009

1 2 Echinacea purpurea Echinacea (L.) Moench (L.) 11

2008

1 1

2009

9 1 Coreopsis tripteris tripteris Coreopsis L. 14

2008

4 3

Table 3.5 Bees collected from twenty from collected Bees 3.5 Table

2009

2 1 Conoclinium coelestinum coelestinum Conoclinium

(L.) DC. (L.) 41

34 15

2008

3 13

2009

9 1 Ascelpias tuberosa tuberosa Ascelpias

2008

Kirby

(Robertson)

(Say)

Cresson

(Linnaeus)

Robertson

Smith

Cresson

(Cresson)

(Morawitz)

Cresson

(Smith)

Smith

(DeGeer)

Cresson

(Linnaeus)

Smith

Say

(Kirby)

Cresson

(Franklin)

LaBerge

Kirby

(Smith)

(Cockerell)

Smith

(Smith)

Mitchell

Smith

Bee species Bee

sp.

Lovell & Cockerell

Say

H. modestus

H. mesillae

H. leptocephalus

H. annulatus

Hylaeus affinis

Xylocopa virginica

Triepeolus Triepeolus helianthi

Peponapis pruinosa

M. subillata

M. illata

M. druriella M. druriella

M. desponsa

M. denticulata

Melissodes agilis

Epeolus bifasciatusEpeolus

C. strenua

C. dupla

Ceratina calcarata

B. vagans

B. terricola

B. perplexus

B. insularis

B. impatiens

B. griseocollis

B. fernaldae

B. citrinus

B. bimaculatus

Bombus ashtoni

Anthophora bomboides

(Smith)

Pseudopanurgus andrenoides

Andrena

A. placata

Andrena nubecula

Colletidae

Apidae Andrenidae 67

2009

13 20

Total for all plants all for Total 870 169

2008

52 63

585

2009

1 2 Veronicastrum virginicum virginicum Veronicastrum (L.) Farw. (L.) 65

2008

2009

1 2 Penstemon digitalis digitalis Penstemon

Sims ex Nutt. 26

2008

1 1

2009

2 2 Aquilegia canadensis canadensis Aquilegia L.

2008

2009

2 1 Actaea racemosa racemosa Actaea L.

2008

1 1

2009

2 1 Lysimachia quadrifolia quadrifolia Lysimachia

2008

_ _

2009

_ _ Phlox divaricata divaricata Phlox

2008

1 1

2009

1 1 Pycnanthemum tenuifolium Pycnanthemum

Schrad. 43

108

2008

2009

1 1 Monarda fistulosa fistulosa Monarda L.

2008

2 52

2009

1 1 Lespedeza capitata Lespedeza Michx.

2008

_ _

2009

1 1 Desmodium canadense canadense Desmodium

(L.) DC. (L.)

2008

_ 1

2009

Barneby

5 2

Senna hebecarpa hebecarpa Senna (Fernald) Irwin & & Irwin (Fernald)

2008

_ 3

2009

2 1 Tradescantia ohiensis ohiensis Tradescantia

Raf.

2008

1 1

2009

1 1 Campanula rotundifolia rotundifolia Campanula

2008

_ _

Plant Family Plant 2009

1 1 Vernonia gigantea gigantea Vernonia (Walter) Trel. (Walter) 21

2008

1 34

2009

G.L. Nesom G.L.

Symphyotrichum novi-belgii Symphyotrichum (L.) (L.)

2008

2 10

2009

G.L. Nesom G.L.

151 Symphyotrichum novae-angliae novae-angliae Symphyotrichum (L.) (L.)

2008

1 72

2009

1 6 Solidago rugosa rugosa Solidago Mill. 37

2008

1 1

2009

2 1 Liatris pycnostachya pycnostachya Liatris Michx. 21

2008

1 17

2009

1 2 Eurybia macrophylla macrophylla Eurybia (L.) Cass. (L.) 71

2008

5 41

2009

2 1 Eupatorium purpureum purpureum Eupatorium L. 19

2008

2 15

2009

59 14 Eupatorium perfoliatum perfoliatum Eupatorium L.

2008

204

2009

3 2 Echinacea purpurea Echinacea (L.) Moench (L.) 27

2008

1 11

2009

1 6 Coreopsis tripteris tripteris Coreopsis L. 41

2008

1 14

2009

1 1 Conoclinium coelestinum coelestinum Conoclinium

(L.) DC. (L.) 97 11

2008

1 30

2009

1 2 Ascelpias tuberosa tuberosa Ascelpias

L. 25

2008

(Smith)

(Forster)

Lovell &

(Smith)

Cresson

Say

Cresson

(Cresson)

Smith

(Lepeletier)

Cresson

(Cresson)

(Say)

Say

(Schrank)

Cresson

(Knerer & Atwood) (Knerer

(Christ)

spp.

(Smith)

(Robertson)

(Smith)

(Fabricius)

(Robertson)

(Smith)

(Mitchell)

Say

Bee species Bee

Apis mellifera

Osmia pumila

Osmia cornifrons

Osmia bucephala

Osmia atriventris Osmia atriventris

Megachile relativa

Megachile pugnata

Megachile mendica

Megachile brevis

Hoplitis producta

Heriades variolosa

Heriades carinatus

Cockerell

Sphecodes prosphorus

L. tegulare

Lasioglossum

L. pilosum

L. perspicuum

L. leucozonium

L. imitatum

L. coriaceum

L. coeruleum

L. asteris McGinley

Lasioglossum acuminatum

H. rubicundus

H. ligatus

Halictus confusus

Augochloropsis metallica

Augochlorella aurata

Augochlora pura

A. virescens

Agapostemon sericeus

No. ofbees non-Apis

No. of

Megachilidae Halictidae 68

Figure 3.7 Ten most abundant bee species on study plants in 2008 and 2009 as determined through vacuum sampling.

160 140 2008 120 100 80 60

No. collected No. 40 20 0

400 350 2009 300 250 200 150 100

No. collected No. 50 0

Table 3.6 Bee diversity indices by plant species. (1) Number of bees collected per plant, (2) total number of collected bee species, (3) number of bee species collected per plant, and (4) the expected number of evenly common species (exp[H‟]) per plant for twenty-five perennial plant species in 2008 and 2009. 2008 2009 Bees Total Bees Total No. bee species exp [H'] No. bee species exp [H'] per species per species per plant per plant per plant per plant PLANT SPECIES plant richness plant richness Actaea racemosa 1.00 2 1.00 bc 1.00 bcd 0.67 2 0.67 cd 0.67 bcd Aquilegia canadensis 1.00 4 1.00 bc 1.00 bcd 2.00 4 1.67 cd 1.63 bcd Asclepias tuberosa 4.00 10 2.75 bc 2.47 bcd 9.75 13 5.00 abcd 4.27 bcd Campanula rotundifolia 0.00 0 0.00 c 0.00 d 1.33 4 1.33 cd 1.33 bcd Conoclinium coelestinum 7.50 8 3.75 bc 3.27 bcd 27.00 10 5.00 abcd 3.77 bcd Coreopsis tripteris 3.50 6 2.50 bc 2.33 bcd 12.00 12 6.00 abcd 5.18 ab Desmodium canadense 0.33 1 0.33 bc 0.33 bcd 1.00 3 1.00 cd 1.00 bcd Echinacea purpurea 3.50 10 3.00 bc 2.88 bcd 7.50 12 5.25 abcd 4.69 abc Eupatorium perfoliatum 52.75 15 10.25 a 6.88 a 31.75 9 6.25 abcd 3.97 bcd Eupatorium purpureum 5.00 5 3.00 bc 2.68 bcd 5.25 8 2.75 bcd 2.07 bcd Eurybia macrophylla 10.50 11 4.25 b 3.48 bc 18.50 12 5.50 abcd 3.86 bcd Lespedeza capitata 0.00 0 0.00 c 0.00 d 0.25 1 0.25 d 0.25 d Liatris pycnostachya 4.50 8 3.25 bc 2.91 bcd 6.25 9 3.75 bcd 3.38 bcd Lysimachia quadrifolia 0.00 0 0.00 c 0.00 d 0.50 2 0.50 d 0.50 cd Monarda fistulosa 13.00 7 3.00 bc 2.16 bcd 2.25 5 2.00 cd 1.97 bcd Penstemon digitalis 0.25 1 0.25 bc 0.25 cd 6.50 13 4.75 abcd 4.45 bcd Phlox divaricata 0.25 1 0.25 bc 0.25 cd 0.00 0 0.00 d 0.00 cd Pycnanthemum tenuifolium 4.00 5 1.50 bc 1.29 bcd 37.75 18 8.25 ab 4.65 abc Senna hebecarpa 0.75 1 0.75 bc 0.75 bcd 1.25 2 1.00 cd 1.00 bcd Solidago rugosa 2.25 4 1.00 bc 0.88 bcd 9.25 10 3.00 bcd 1.83 bcd Symphyotrichum novae-angliae 19.25 6 3.00 bc 1.75 bcd 40.75 12 7.00 abc 2.94 bcd Symphyotrichum novi-belgii 2.75 6 2.25 bc 2.16 bcd 14.00 12 6.25 abcd 4.57 abcd Tradescantia ohiensis 2.50 7 2.00 bc 1.94 bcd 1.75 6 1.75 cd 1.75 bcd Vernonia gigantea 8.50 9 4.00 bc 3.77 ab 7.00 9 4.00 abcd 3.73 bcd Veronicastrum virginicum 7.75 9 4.00 bc 3.32 bcd 18.25 22 10.00 a 8.90 a

DISCUSSION

With recent declines in the number of honeybee colonies, unmanaged pollinator species have been recognized as an important form of insurance for agricultural producers (Winfree,

Williams, Dushoff, & Kremen, 2007). However, unlike their managed counterparts, wild bees are not readily relocated and their foraging ranges are generally much shorter. As a result, wild bees may best be managed by conserving current populations and restructuring landscapes to enhance the distribution of their services.

One form of habitat management that can benefit bee populations is floral resource provisioning. Increasing the density and diversity of flowers within a landscape can enhance bee

numbers by increasing the availability of pollen and nectar resources. In addition to increasing the total amount of food that is available, plantings can be established to distribute these resources through time. This temporal distribution of pollen and nectar may be of particular importance for pollinator species in agricultural systems whose foraging periods extend beyond the crop bloom period. Such provisioning schemes could also benefit populations of wild plants that suffer from severe pollen limitation or inbreeding due to a shortage of pollinators.

In composing mixes of plants to support bee populations, consideration must be given to the innate preferences of the target organisms. Through the systematic collection and observation of bee visitors to twenty-five native perennial plant species over two years, we were able to identify plants that are highly attractive to bees at different times during the growing season. In the early-season, bees were attracted to Penstemon digitalis, Aquilegia canadensis, and

Tradescantia ohiensis, though the number of bees observed visiting these early-blooming plants was significantly lower than for later-blooming species. Other plant species, including early- blooming woody species, should be considered to increase the amount of nectar and pollen available at the beginning of the season. Of the plant species that bloomed in the mid-season,

Eupatorium perfoliatum, Monarda fistulosa, Pycnanthemum tenuifolium, and Veronicastrum virginicum hosted the greatest number of bees. In the late season, Symphyotrichum novae- angliae and Eupatorium perfoliatum were best. We also identified plants that hosted few or no bees, and which should probably be substituted with more attractive species in pollinator conservation plantings. Those species included Campanula rotundifolia, Desmodium canadense,

Lespedeza capitata, and Phlox divaricata.

In addition to assessing the relative attractiveness of plant species to the total bee community, we also documented differences in the associations between the study plants and three bee groupings; (1) Apis mellifera, (2) Bombus spp., and (3) „other bees‟. Bees in the three

groupings were often observed to be most abundant on different plant species, indicating differences in flower preference or ability to extract resources among the bees. In 2008, for example, honeybees and „other bees‟ were most strongly associated with Eupatorium perfoliatum, while no Bombus individuals were collected from this plant species. Instead, bumblebees were most strongly associated with Monarda fistulosa which did not attract any honeybees.

Species level identifications of the sampled bees also allowed us to examine the diversity of the bee assemblages associated with each plant species. We identified the plants which hosted the greatest number of bee species in each year, and we also calculated a surrogate for species evenness to compare the expected number of evenly common species with the observed species richness. In 2009, the greatest number of species was captured from Ascelpias tuberosa,

Penstemon digitalis, Pycnanthemum tenuifolium, and Veronicastrum virginicum. Plants that host a diversity of bees should be considered for establishment in agricultural settings, as pollinator diversity is sometimes the best predictor of resulting crop pollination (Greenleaf & Kremen,

2006; Klein, Steffan-Dewenter, & Tscharntke, 2003).

Assessing the relative attractiveness of plants to pollinators is an essential step in identifying the most appropriate plant species for use in resource provisioning schemes. The data obtained in this study supports other work indicating that insect species exhibit strong preferences for different plant species (Fiedler, 2006; Frank, Shrewsbury, & Esiekpe, 2008;

Pontin, Wade, Kehrli, & Wratten, 2006; Tuell et al., 2008). As a result, simply establishing flowering plants within a landscape will fail to enhance bee populations or pollination services unless the established plant species represent suitable food sources for the target bee species.

Having knowledge of species-specific preferences will aid in the selection of the most appropriate plants to optimize resource provisioning schemes.

It should be emphasized however, that the measured „attractiveness‟ of a plant species is relative and only has meaning when it is compared to the attractiveness of other plants in the surrounding landscape. Co-flowering plants essentially compete for visits from a limited pool of pollinators and those plants that attract the greatest number of bees are often considered the

„winners.‟ However, the success of a plant in attracting bees is heavily dependent on the composition of local pollinator and plant communities. As discussed, bees exhibit flower preferences that can vary with pollinator species, but a bee‟s ability to choose is limited by the availability of particular resources in the landscape. For example, when flowers that are preferred by bees are experimentally removed from a locale, bees will switch to another plant species if suitable alternatives are present (Heinrich, 1976). When the first plant species was present, bees visited it frequently, but in its absence, pollinators were forced to choose the next best alternative. In this respect, plants that have been observed to attract few bees may actually be suitable food resources that have just been outcompeted by another plant species.

In addition, perceived flower preference among bees may also be influenced by interactions with other insect species. Specialist bees, for example, may extract rewards more efficiently from a flower, thereby depleting the resources from a plant and reducing visitation by other species. In this study, Monarda fistulosa was rarely visited by Bombus impatiens, an important crop pollinator, but it was frequented by the longer tongued Bombus bimaculatus which was probably able to remove more nectar from the long-tubed corollas (Williams, Colla,

& Xie, 2009). In the absence of Bombus bimaculatus, Bombus impatiens may have foraged from this plant regularly. Territorial bees may also reduce pollinator visits by harassing or even killing other bees. Male bees in the genus Anthidium, for example, are known to establish and patrol territories for up to 23 days, allowing females of the same species to forage unhindered and attacking other visitors. Anthidium males were not captured from the study plots, but Melissodes

desponsa males were observed to „dive-bomb‟ other bees foraging on Echinacea purpurea in the four blocks. It is unknown whether this was a territorial behavior or whether these acts were simply failed attempts at copulation, but either way, it did serve to scare off the unassuming victims.

The success of floral resource provisioning schemes will certainly depend upon the plant species that are utilized and the pollinator species that are present, but the odds of success may be increased if other limiting factors are addressed. For example, limiting the use of pesticides, applying formulations which are least harmful to bees, or spraying when bees are not active, are simple but potentially effective ways to protect bee populations. Furthermore, providing nesting resources such as nesting blocks, old tree snags, or even patches of mud can contribute to increased population size.

Before resource provisioning schemes can be applied on a large or economical scale, work must also be conducted on the proper establishment and maintenance of areas for pollinator conservation efforts. Information such as the survival and germination rate of seeds in field settings should be assessed, as this will help dictate the rates at which certain species must be seeded to establish proper stands. The appropriate seeding method should also be researched since most standard equipment will not handle the small seeds produced by many of the plant species studied here. Indeed, difficulty in establishment may preclude the use of some plant species. Maintenance issues are also pertinent because competition with preexisting weedy and invasive plants is likely to be a major obstacle in establishing large areas of perennial forbs from seed.

After identifying plant species whose growth habits make them suitable candidates for establishment in agricultural settings, the work presented here assessed the relative attractiveness of these species to a local pollinator community. In the next chapter, I take the next step and

attempt to measure increases in crop pollination rate and bee abundance and diversity in crop fields with resource provisioning areas.

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CHAPTER 4

INFLUENCE OF FLORAL RESOURCE PROVISIONING AREAS ON POLLINATOR COMMUNITIES AND CROP POLLINATION RATES

INTRODUCTION

The work presented in the previous chapters adds to a small but growing body of knowledge that links insect species to their native plant hosts. Recently, such studies have largely been conducted with the goal of identifying plants for incorporation into insect conservation programs (Tuell, Fiedler, Landis, & Isaacs, 2008). In the work presented here, the concept of floral resource provisioning was applied in the field to test the hypothesis that provisioning areas can increase local bee abundance and diversity and thereby enhance pollination services within a cropping system. Such evaluations will be helpful in documenting the effects of resource provisioning schemes on target populations and desired ecosystem services, but they will also allow for the identification of ways in which this form of habitat management can be strengthened to optimize returns.

By increasing the quantity and diversity of food resources within a landscape, floral resource provisioning has potential to affect the density, distribution, and diversity of bee species. Supplementing agricultural landscapes with floral resources may increase the local abundance of bees in two ways. First, an increase in the availability of food resources through the growing season may contribute to an overall increase in bee fecundity. Multivoltine species such as Bombus or Ceratina spp., which have multiple broods or generations per season, might be expected to respond positively within a single season while population changes among other

species may not be perceptible until the following year. In addition to increasing population numbers, local bee abundance may also be enhanced without increasing the total number of bees, per se. By establishing dense areas of flowering plants, it might be possible to concentrate pollinator activity within an area, thereby altering the spatial distribution of foraging bees within a landscape.

The diversity of bee communities may similarly be enhanced by floral provisioning in two ways. First, increased availability of food resources may reduce interspecific competition, allowing for increased species richness and evenness. Bee diversity may also be increased by enhancing the floral diversity of a landscape (Calabuig, 2000; Potts, Vulliamy, Dafni, Ne'eman,

& Willmer, 2003). Increased floral diversity typically results in a greater variety of floral morphology and rewards which allows for resource partitioning by bees (Inouye, 1978).

Both increased bee abundance and diversity can lead to enhanced pollination (Greenleaf

& Kremen, 2006; Kremen, Williams, Bugg, Fay, & Thorp, 2004; Kremen, Williams, & Thorp,

2002; Morandin & Winston, 2005; Ricketts, Daily, Ehrlich, & Michener, 2004). Greater bee numbers may result in more frequent flower visits, but sometimes diversity is a better predictor of pollination (Klein, Steffan-Dewenter, & Tscharntke, 2003). Pollinator diversity may enhance pollination rates because more efficient pollinator species are present, or there may be interactive effects, where behavioral traits are altered in the presence of other competing species (Greenleaf

& Kremen, 2006). Maintaining bee diversity may be especially important where annual crops are rotated. The presence of many different bee species will increase the likelihood that efficient pollinators of a particular crop are present.

While pollinator abundance and diversity may be positively associated with pollination rates, the establishment of floral provisioning areas may fail to enhance the desired ecosystem service. Some studies have indicated that floral resource provisioning can increase levels of

insect pest control within crops by supporting populations of insect pest predators and parasitoids

(Frank & Shrewsbury, 2004; Hickman, 1996; Patt, Hamilton, & Lashomb, 1997). The conserved populations in these instances, however, utilize different resources from two different habitats.

The resource provisioning areas supply pollen and nectar while the crop field hosts the insect prey that are required for many natural enemy species to complete their life cycles. Bee species, on the other hand, forage for pollen and nectar in both areas. If the provisioned flowers and crop are co-flowering, then they will likely compete for pollinator visits which could lead to reduced rates of crop pollination.

Even if floral provisioning areas do increase bee abundance, diversity, or crop pollination rate, the effects may not be consistent across a field. Work conducted in grapefruit plantations has shown marked declines in bee activity and species diversity with increasing distance from forest edges (Chacoff & Aizen, 2006). Developing a better understanding of how the spatial distribution of pollinator activity may vary within a cropping system is needed for land managers to structure landscapes for the optimization of ecosystem services.

State and federal agencies in the United States have already begun to assist in funding the establishment of pollinator conservation areas. Through the Conservation Steward Program

(CSP) administered by the National Resource Conservation Service (NRCS), managers of crop, pasture, and forest lands are eligible to receive technical and financial assistance for adopting practices or implementing changes that are aimed at protecting, maintaining, and increasing natural resources within the landscape. Among the activities encouraged by CSP are plant enhancement plans that include the seeding of plants in non-cropped areas for the specific purpose of providing food sources for pollinators and other beneficial insects. However, before the planting of such provisioning areas is more widely promoted in agricultural settings, work should be conducted to assess the effectiveness of the proposed efforts. Evaluations of similar

„agri-environment schemes‟ funded by the European Union have reported inconsistent results

(Berendse, Chamberlain, Kleijn, & Schekkerman, 2004; Kleijn & Baldi, 2005; Kleijn et al.,

2006; Kleijn et al., 2004; Kleijn & Sutherland, 2003) and jeopardized future funding for such programs (Whitfield 2006). Documenting a positive effect of conservation schemes on bee communities and the pollination services provided to agriculture will likely result in greater buy- in among land managers and policy-makers alike.

In the experiment described here, we attempted to address whether a floral resource established on the edge of a crop field influenced (1) bee abundance, (2) bee diversity, and (3) crop pollination. We also investigated whether these variables were correlated with distance from the resource provisioning area.

MATERIALS AND METHODS

In 2008, muskmelon (Cucumis melo L.) „Aphrodite‟ was planted to test the hypothesis that floral resource provisioning and distance from the provisioning area can affect bee abundance, bee diversity, and crop pollination. The cultivar „Aphrodite‟ was selected because the plants are monoecious, whereby the male (staminate) and female (pistillate) flowers are spatially separated on the same plant. This differs from most cultivars of muskmelon which are andromonecious, producing male and perfect flowers. This spatial separation of sexual structures and the large size of cucurbit pollen (130.15 ±0.87 µm) ensure that pollination and subsequent fruit set are the result of one or more animal-mediated pollination events (Hladun and Adler

2008).

Non-crop floral resources were provided in the form of an annual flower mix acquired from Easyseedliving (easyseedliving.com). The mix was composed of thirteen annual species native to the United States (Table 4.1), but not necessarily to the study sites. The thirteen species

Table 4.1 Thirteen annual native plant species included in seed mix from Easyseedliving.com.

Family Scientific Name Common Name Asteraceae Centaurea americana (Nutt.) basket flower Asteraceae Coreopsis basalis (A. Dietr.) S.F. Blake golden wave Asteraceae Coreopsis tinctoria (Nutt.) plains coreopsis Asteraceae Dracopis amplexicaulis (Vahl) Cass. clasping coneflower Asteraceae Gaillardia pulchella Foug. firewheel Asteraceae Ratibida columnifera (Nutt.) Woot. & Standl. Mexican hat Asteraceae Rudbeckia hirta L. black-eyed Susan Capparaceae Cleome serrulata Pursh. Rocky Mountain beeplant Fabaceae Chamaecrista fasciculata (Michx.) Greene partridge pea Fabaceae Lupinus texensis Hook. Texas bluebonnet Lamiaceae Monarda citriodora Cerv. ex Lag. lemon horsemint Lamiaceae Salvia coccinea P.J. Buchoz ex Etlinger scarlet sage Polemoniaceae Phlox drummondii Hook. annual phlox represent five plant families and twelve genera. An annual mix was selected to ensure flowering the same season it was seeded.

PLOT DESIGN

In order to test the effects of distance from the provisioning area, transects consisting of

10 by 12 m patches of muskmelon were established at distances from the floral resource. Those patches were nested within a growing soybean crop. Two transects with a floral resource and two without were established at both the Russell E. Larson Agricultural Research Center in

Pennsylvania Furnace, PA and the Southeast Research and Extension Center in Landisville, PA where fields are dominated by silty loam soils. The transects were established within soybean fields separated by at least 300m. Soybean was selected as a matrix because it was deemed that it would provide the most constant surrounding over the course of the growing season. Compared to corn which increases greatly in height, or a cereal grain that would be harvested during the muskmelon blooming period, a crop of soybean represented a less drastic change from July through late August, when muskmelon flowers are in bloom.

To test the effect of distance from the resource areas on bee abundance, diversity, and crop pollination, the 12x110m transects were partitioned into five 10x12m patches of muskmelon (five distance classes); each separated by 10m. The soybean crops were planted May

29, 2009 in Rock Springs and on May 20, 2009 at the Landisville site. On June 12, 2009 in Rock

Springs and June 4, 2008 at Landisville, 10x12 patches along the transect were sprayed with a mixture of paraquat and ethalfluralin at rates of 0.56kg/ha and 0.63kg/ha (clomazone was also applied at the Rock Springs site at 0.20k/ha), respectively, to chemically remove the soybean and to provide weed control for the subsequent muskmelon transplants.

Immediately prior to transplanting the muskmelon, a granular 19-19-19 fertilizer was broadcast over the patches at rate of 560kg/ha. Muskmelon transplants, which had been seeded on May 17, were planted into the fields at Manheim on June 9 and at Rock Spring on June 16.

Forty muskmelon transplants were established in four rows within each patch with 1.8m between-row and 0.6m within-row spacing. At the Rock Springs site, a hail event damaged the muskmelon the same day they were transplanted. One week later, surviving muskmelon plants were relocated to populate the two center rows within each patch. Muskmelon starts were hand- watered with approximately 1 liter of water shortly after being transplanted. All patches, including the provisioning areas, were irrigated with a drip irrigation system approximately every 12 days or as needed depending on rainfall patterns. At the Rock Springs site, the irrigation systems were attached to a pressurized irrigation line while the systems in Manheim were gravity fed with a 1,324 liter polyethylene tank.

Following planting, the muskmelon transplants were treated with 0.02g of imidacloprid to control cucumber beetles. Although imidacloprid is highly toxic to honeybees (Suchail, Guez,

& Belzunces, 2000), the active ingredient was applied to approximate the pesticide programs used by commercial growers. Weekly fungicide applications were also made at the Landisville

site starting in July to control powdery and downy mildew (chlorothalonil at 1.68kg/ha), but were not required at Rock Springs.

In addition to the muskmelon patches, 10x12m areas of the annual wildflower mix were sown at the end of two of the four transects (also the edge of the field) in Manheim on April 2,

2008 and at Rock Springs on April 10, 2008. These areas were moldboard plowed in early April and subsequently seeded with a hand-held broadcast spreader at a rate of 40kg/ha. To help ensure even coverage, the seed mix was added to 8 parts of medium-grade sand prior to sowing. In control fields, this area was planted to soybean.

POLLINATION RATE MEASURES

Rates of successful pollination were measured using two metrics. First, the number of fruits per plant was assessed at a single harvest within each patch to estimate the number of female flowers that were successfully pollinated. Second, pollination was estimated by determining the average number of seeds per fruit within each patch. The number of seeds produced within each fruit is indicative of the number of successfully fertilized ovules and has been shown to be positively correlated with pollen load on the stigmatic surface (Hladun and

Adler, 2008). Five fruits from each patch were harvested, and the seeds removed from the seed cavity. Upon drying, the pulp and seeds were separated and the seeds were counted with the aid of an electronic seed counter (The Old Mills Company, model #850-2).

BEE ABUNDANCE & DIVERSITY MEASURES

Two methods were used to estimate bee abundance and diversity of pollinators within muskmelon patches. First, pollinator activity was assessed by observing the number of pollinator visits to muskmelon flowers in a 1m2 area centered in the muskmelon patch for a period of 45 seconds. These observations were repeated six times within each 10x12m patch of muskmelon.

Observed bee pollinators were classified into one of three categories: (1) honeybee, (2) bumblebee, or (3) other bee. The number and sex of the muskmelon flowers occurring within the estimated 1m2 area were then documented. Pollinator observations were conducted weekly, alternating between sites, from July 22 until early September 8 between 0800 and 1500 EST.

In addition to observations made at muskmelon flowers, three visual areas of 1m2 were also observed for 45 seconds at three random locations 0-10m from the field edge to compare pollinator activity in the soybean crops and the floral resource areas. In the resource areas, the number of flowers for each annual plant species occurring within the 1m2 areas was counted following the observations.

The second metric for measuring relative bee abundance and diversity involved sampling bees from muskmelon flowers using a modified handheld vacuum from Bio-Quip (item

#2820B). This method allowed for the identification of bees which might be contributing to muskmelon pollination when the flowers are open and receptive to pollen transfer. Sampling occurred in each muskmelon patch after pollinator observations were completed within a field.

Each patch (including the floral resource patches) was sampled for bees for three minutes each.

Specimens were stored with icepacks until later processing. All bee specimens were pinned and identified to the species-level with taxonomic keys from www.discoverlife.org and The Bees of the Eastern United States (Mitchell, 1960, 1962).

STATISTICAL ANALYSIS

For the purposes of the following analyses, the main treatment factor was the presence or absence of a floral resource provisioning area within a field and the covariate was distance from the field edge. Analyses of variance (ANOVA) and analyses of covariance (ANCOVA) were conducted using PROC MIXED in SAS version 9.1 with Tukey‟s mean separations on the main factor (SAS Corporation 2003). Regression analysis was applied using PROC REG, also in SAS

9.1 (SAS Corporation 2003). MINITAB 14 was used to conduct all 2-Sample T tests. All conclusions were made at the α =0.05 significance level.

Fruit per Plant, Weight & Seed Numbers

Analysis of variance was applied to determine whether the presence of a provisioning area had a significant effect on (1) the number of fruit per plant, (2) muskmelon weight or

(3) seed number. The data were averaged within each field and analyzed with block and the presence of a provisioning area as factors. To determine whether distance from the field edge had an effect on fruit, weight, or seed number, an analysis of covariance was also performed. For this analysis, the respective data were pooled within each patch. Distance was used as a covariate, with block and floral resource provisioning as factors.

To test the effect of seed number on muskmelon fruit weight, simple linear regressions were performed. Muskmelon fruit weight data were square root transformed to satisfy statistical assumptions regarding normality, but they were analyzed with outliers intact. A regression was also performed with two outliers removed from the dataset after Cook‟s distance measures indicated these two values had a greater influence on the regression fit than the rest of the data.

The weight data in this regression were also square root transformed.

Pollinator Collections

Pollinators were sampled three times at each site and grouped into one of three sample periods: early, mid, and late, with early represent samples taken from July 22-28, mid representing samples collected from August 7-17, and late representing sampled from August

22- September 8. Specimens collected from the floral resource provisioning area were excluded from the following analyses since the primary interest of this study was to determine how provisioning areas influence pollinator abundance and diversity within crop areas.

The data for each time period, which consisted of the number of bees collected from each patch during the time period, were square root transformed to satisfy statistical assumptions of normality. An ANCOVA was conducted for each time period, with block and presence of a provisioning area as factors, and distance as a covariate. A simple ANOVA was also performed to test the effect of provisioning areas on the average number of bees captured within each field.

These analyses were conducted for five categories of bees; (1) „all bees,‟ (2) Apis mellifera, and

(3) non-Apis bees, which was subdivided into (4) Bombus spp., and (5) „other bees.‟ Data for these response variables were similarly square root transformed within each time period to satisfy assumptions of normality and homogeneity of variance.

Pollinator Observations

The data for pollinator observations were analyzed in the same fashion as the data for pollinator collections. Observations were divided into three time periods and square root transformed to satisfy assumptions for statistical analysis. The data were analyzed using

ANCOVA with block and treatment as factors, and distance from the floral resource as a covariate. ANOVAs with block and treatment as factors were also conducted using pollinator observations averaged over individual fields. The analysis was performed on square root transformed bee data classified as follows: (1) all bees (2) Apis mellifera, (3) non-Apis bees, (4)

Bombus spp., and (5) other bees.

Species Richness

Pollinator species richness of each muskmelon cantaloupe patch and field was determined by summing the number of bee species collected within the respective areas over the three collection periods. Here, rather than classing bees in one of five groups, bees were identified to species level. To test for significant differences in the number of bees between

treatments, an ANOVA was conducted with block and the presence of provisioning areas as factors. To assess the effect of proximity to floral resource patch, ANCOVA was performed with distance as a covariate.

RESULTS

All of the muskmelon plants at the Manheim site survived the transplanting process and grew to yield some of the best-tasting muskmelons that the author has ever eaten. However, a climatic event at the Rock Springs site severely reduced the survivorship of the transplants there.

On June 16, 2008 a hailstorm resulted in mortality of nearly half of the muskmelon plants which had been transplanted only hours earlier. Many of the remaining plants were damaged, but two complete rows (20 plants total) were established at the center of each muskmelon patch. In addition, feeding by groundhogs at both sites resulted in localized damage of muskmelon plants and fruits despite considerable control efforts.

FRUIT PER PLANT, WEIGHT & SEED NUMBERS

The number of muskmelon fruits produced per plant did not differ significantly between the treatment levels (F1,3= 0.20, p= 0.688). An average of 2.60 ± 0.67 fruits per plant were observed in fields with provisioning areas while 2.42 ± 0.44 fruits per plant were observed in fields without provisioning areas. The number of muskmelon produced per plant also did not differ with distance (F1,30= 0.29, p= 0.595), nor was there a significant interaction between the treatment and distance classes (F1,30= 0.22, p=0.645).

Seed number per fruit was significantly greater in fields with provisioning areas (Figure

4.1a; F1,3=14.22, p=0.033). Muskmelon fruits from these fields contained an average of 606 ± 20 seeds; approximately 48 more seeds than the average of 558 ± 20 seeds found in melons from the control fields. Muskmelon weight however, did not differ significantly between the treatment

Figure 4.1 (a) Average seed count of muskmelon from the two treatment levels at both study sites. (b) Average fruit weight of muskmelon from the two treatment levels at both sites.

660 3500 a b 640 3000

620 2500 600 2000 580 1500 560

Fruit weight (g) weight Fruit 1000 No. seedsperfruit No. 540

520 500

500 0 Rock Springs Landisville Rock Springs Landisville

No Provisioning Area Provisioning Area Present

Table 4.2 Analysis of variance and covariance for the number of seeds and the weight of harvested muskmelon fruit.

SEEDCOUNT WEIGHT Num Den Num Den Variable F P F P df df df df ANOVA Provisioning 1 3 14.22 0.033 * 1 3 0.22 0.673 ANCOVA Provisioning 1 30 0.75 0.394 1 30 1.02 0.321 Distance 1 30 2.04 0.164 1 30 2.04 0.164 Provisioning*Dist 1 30 0.16 0.691 1 30 2.61 0.117

levels (Figure 4.1b; F1,3=0.22, p=0.673). Furthermore, neither seed count nor weight differed with distance from the field edge (Table 4.2).

Seed count was a significant predictor of muskmelon weight before outliers were removed from both variables (R2= 0.04, p= 0.006). Calculations of Cook‟s distance identified two outliers as having a greater influence on the regression fit than the rest of the data. Upon excluding these two outliers from the complete dataset of 183 observations, seed count was not a significant predictor of fruit weight (R2= 0.02, p= 0.064). When data from each site was analyzed independently, seed count was a significant predictor at the Manheim site (R2=0.09, p= 0.0034), but not at Rock Springs (R2= 0.03, p= 0.136). Fruit weight also differed significantly between the two sites (T180= -5.38, p= 0.000). The mean weight of fruit from Manheim, PA was 2.80 ±

0.07kg, over half a kilogram more than the melons from Rock Springs, PA (2.27 ± 0.07kg). This may indicate that environmental factors at the two sites influenced final fruit weight. These differences in weights may also be explained by a greater number of fruits set per plant at Rock

Springs (T30 = -5.53, p= 0.000). Muskmelon plants in Rock Springs produced an average of 3.25

± 0.01 melons while 1.71 ± 0.00 fruits per plant were observed in Manheim. This increased production of fruits per plant likely led to a reduction in the weight of individual fruits.

Interestingly, seed count was also greater at the Rock Springs site (T129= 2.52, p= 0.013). Fruit at this location had an average of 605 ± 12 seeds while those from Manheim contained 572 ± 6 seeds. The increased number of fruits per plant and seeds per fruit suggest that muskmelon plants at the Rock Springs site received more conspecific pollen than their counterparts in Manheim.

POLLINATORS COLLECTIONS

In total, 609 bees were collected from the provisioning areas and muskmelon patches during the study (Table 4.3). Of this total, 436 specimens were collected from the muskmelon

crop. The number of bees collected from the two sites was similar, with 301 bees captured at

Rock Springs and 308 specimens from Manheim.

The floral resource treatment did not affect the number of bees collected in muskmelon patches located at distances from the field and floral resource edge. Numbers of bees did not differ significantly with distance from the field edge for any bee group during any of the three time periods (Table 4.4). When including the covariate of distance, only the number of Bombus specimens collected during the first time period differed significantly between the two treatment levels (F1,30=9.37, p=0.005). Bombus numbers were greater in control fields (2.25 ± 0.08 bees) compared to fields with provisioning areas (0.69 ± 0.08 bees).

When bee collections were averaged across individual fields, the number of collected bees differed in three instances (Table 4.5). During the first collection period, the mean number of Bombus specimens collected from fields with provisioning areas (1.06 ± 0.06 bees) was significantly less than that caught in control fields (2.66 ± 0.06 bees; F1,3=10.88, p=0.046). This suggests that the plant species in the provisioning areas were „luring‟ Bombus individuals from the muskmelon crop. During the third collection period, the number of „all bees‟ and non-Apis bees collected were greater in fields with the provisioning areas (Figure 4.2; 1.11 ± 0.04 and 0.93

± 0.03 bees, respectively) than in control fields (0.02 ± 0.04 and 0.02 ± 0.03 bees; F1,3=69.94, p=

0.004; F1,3= 151.78, p= 0.001, respectively). These latter results should be interpreted with caution since the density of crop flowers was significantly greater in fields with provisioning area during the third collection period (T31= -3.34p=0.002). This is of particular importance because crop flower density was determined to be a significant predictor of the number of collected bees (R2=0.52, p=0.000).

Table 4.3 Distribution of twenty-nine bee species over experimental fields in Rock Springs and Manheim, PA.

Site

308

113

Total

Southeast Agricultural Research & Extension Center (Manheim, PA)

Site

301

193

Total

Russell E. Agricultural ResearchLarson Center Springs,(Rock PA)

(Knerer & (Knerer

(Linsley)

Smith

(Fabricius)

(Lepeletier)

(L.)

(Smith)

(Fabricius)

Cresson

(DeGeer)

(Christ)

(Say)

Cresson

(L.)

Robertson

Cresson

Smith

Distance Class

Field Field designation

Study Location

(Say)

Say

Smith

(Fabricius)

Say

sp.

The distribution of twenty-nine species across experimental fields in Rock Springs and Manheim, PA. Distance class (0) designates a resource resource a designates (0) class PA.Distance and Manheim, Springs Rock in fields experimental across species twenty-nine of distribution The

Anthidium manicatum

Megachile rotundata

Megachile mendica

Megachile brevis

Megachile addenda

Lasioglossum

Lasioglossum pilosum

Atwood)

Lasioglossum perspicuum

Halictus rubicundus

Halictus ligatus

Halictus confusus

Augochlorella aurata Augochlorella aurata

Augochlora pura

Agapostemon virescens

Xylocopa virginica

Apis mellifera

Triepeolus remigatus Triepeolus

Peponapis pruinosa

Melissodes bimaculata

Holcopasites calliopsidis

Epeolus bifasciatusEpeolus

Ceratina Ceratina strenua

Ceratina Ceratina dupla

Ceratina Ceratina calcarata

Bombus impatiens

Bombus griseocollis

Bombus fervidus

Bombus bimaculatus

Calliopsis andreniformis

provisioning area. Distance classes (1-5) designate muskmelon patches with distance ([X*10]+10 meters) from the field edge. field ([X*10]+10 fromthe distance meters) with muskmelon patches designate (1-5) classes Distance area. provisioning

Table 4.3 Table

Total

Megachilidae

Halictidae

Apidae

Andrenidae Bee species Bee 93

Table 4.4 Summary of ANCOVA testing effect of resource provisioning and distance on the number of collected bees in five bee groupings.

EARLY MID LATE Variables df F P df F P df F P All bees Provisioning 1 1.40 0.246 1 0.00 0.973 1 0.04 0.849 Distance 1 0.53 0.472 1 0.46 0.502 1 2.42 0.131 Provisioning*Distance 1 2.04 0.164 1 1.14 0.294 1 1.76 0.196 Apis mellifera Provisioning 1 0.47 0.497 1 0.00 0.948 1 0.31 0.585 Distance 1 0.13 0.720 1 1.15 0.292 1 1.43 0.242 Provisioning*Distance 1 0.01 0.931 1 1.73 0.199 1 1.43 0.242 non-Apis bees Provisioning 1 2.55 0.121 1 0.15 0.704 1 0.13 0.717 Distance 1 0.40 0.533 1 0.01 0.922 1 1.83 0.187 Provisioning*Distance 1 2.64 0.115 1 0.28 0.600 1 1.06 0.313 Bombus Provisioning 1 9.37 0.005 * 1 0.11 0.743 1 0.84 0.366 Distance 1 0.41 0.526 1 0.07 0.797 1 0.03 0.875 Provisioning*Distance 1 2.93 0.097 1 0.37 0.548 1 0.07 0.787 Other bees Provisioning 1 0.33 0.572 1 0.00 0.987 1 0.17 0.687 Distance 1 0.02 0.883 1 0.12 0.728 1 3.27 0.081 Provisioning*Distance 1 0.39 0.536 1 0.15 0.698 1 2.66 0.114

Table 4.5 ANOVA output for bee groupings (response variable) and presence/absence of provisioning areas (predictor variable) during the three collection periods.

EARLY MID LATE Response Variable df F P df F P df F P All bees 1 0.01 0.921 1 0.25 0.653 1 69.94 0.004 * Apis mellifera 1 0.76 0.447 1 5.54 0.100 1 2.45 0.215 non-Apis bees 1 0.35 0.597 1 0.04 0.846 1 151.78 0.001 * Bombus 1 10.88 0.046 * 1 0.04 0.857 1 3.03 0.180 other bees 1 1.35 0.330 1 0.32 0.611 1 2.83 0.191

Figure 4.2 Number of bees collected from muskmelon flowers during three collection periods.

250 No Provisioning Area Provisioning Area Present 200

150

100 Total no. collected bees collected no. Total 50

0 Early Mid Late Collection Period

POLLINATOR OBSERVATIONS

The number of observed bees did not differ significantly with distance from the field edge for any bee group during any of the three observation periods (Table 4.6). In addition, the number of observed bees was not significantly different between treatment levels for any combination of bee group and time period (Table 4.7). Statistical tests were not performed on

Apis mellifera during the third observation period because none were observed at either site. It should also be noted that bee species included in the „other bee‟ category were not observed at the Rock Springs site during this period. Although visitation rates did not differ with the presence of provisioning areas, crop flower density was a significant predictor of visitation rate

(R2=0.36, p<0.001).

Table 4.6 ANCOVA testing the effect of provisioning areas and the covariate of distance on the square root transformed count data for five groups of observed bees.

EARLY MID LATE Variables df F P df F P df F P All bees Provisioning 1 32.00 0.253 1 0.03 0.862 1 1.16 0.289 Distance 1 32.00 0.257 1 0.67 0.419 1 0.01 0.943 Provisioning*Distance 1 32.00 0.270 1 0.00 0.993 1 1.95 0.172 Apis mellifera Provisioning 1 32.00 0.225 1 0.00 0.965 N/A N/A Distance 1 32.00 0.776 1 1.39 0.247 N/A N/A Provisioning*Distance 1 32.00 0.456 1 0.50 0.485 N/A N/A non-Apis bees Provisioning 1 32.00 0.053 1 0.04 0.844 1 1.22 0.277 Distance 1 32.00 0.207 1 1.06 0.310 1 0.02 0.884 Provisioning*Distance 1 32.00 0.141 1 0.01 0.925 1 2.07 0.160 Bombus Provisioning 1 32.00 0.108 1 0.00 0.968 1 0.00 0.996 Distance 1 32.00 0.242 1 1.26 0.270 1 0.67 0.420 Provisioning*Distance 1 32.00 0.422 1 0.07 0.790 1 0.11 0.737 Other bees Provisioning 1 32.00 0.131 1 0.31 0.579 1 2.46 0.127 Distance 1 32.00 0.324 1 0.09 0.768 1 0.05 0.831 Provisioning*Distance 1 32.00 0.023 * 1 0.02 0.891 1 2.46 0.127

Table 4.7 ANOVA testing the effect of resource provisioning on the square root transformed count data for five categories of bees.

EARLY MID LATE Response Variable df F P df F P df F P All bees 1 0.08 0.791 1 0.28 0.619 1 0.12 0.744 Apis mellifera 1 1.84 0.233 1 3.90 0.105 N/A N/A non-Apis bees 1 0.62 0.466 1 0.01 0.934 1 0.29 0.612 Bombus 1 2.02 0.214 1 0.66 0.452 1 0.25 0.635 other bees 1 0.07 0.799 1 0.33 0.589 1 0.09 0.776

SPECIES RICHNESS

A total of twenty-nine bee species were collected during the course of this study (Table

4.3). Twenty-four species were sampled from the crop areas and fourteen were collected from the provisioning areas. Nineteen species were collected at the Rock Springs site and twenty-four were identified from specimens collected in Manheim.

Species richness did not differ with treatment or distance from the field edge (Table 4.8), nor did it differ between the two sites (T34 = 1.22, p= 0.230). As expected, the mean number of bees species occurring in provisioning areas, which have greater floristic diversity, was greater

(6.50 ± 0.65 species) than that occurring in the muskmelon crop (3.93 ± 0.45 species; F1,7=

25.94, p= 0.001), though the block effect was significant (p=0.027). The strong block effect in this analysis suggests that a field‟s location within the landscape may have a large influence on the available diversity of pollinators. However, the density of crop flowers averaged within each patch over the season was also a significant predictor of species richness (R2=0.19, p= 0.008) and significantly different among the blocks (F3,32= 5.35,p= 0.004). Instead, this would suggest that crop flower density was a better predictor of bee species diversity.

Table 4.8 ANOVA and ANCOVA for species richness of muskmelon crop.

SPECIES RICHNESS Num Den Variable F P df df Analysis of Variance Provisioning 1 3 2.45 0.215 Analysis of Covariance Provisioning 1 30 0.00 0.949 Distance 1 30 1.69 0.203 Provisioning*Dist 1 30 0.86 0.362

DISCUSSION

Some of the findings presented here support the hypothesis that floral resource provisioning areas can enhance crop pollination rates, though some metrics yielded inconclusive or contradictory data. Some of the results however, are useful in identifying pitfalls that may be encountered in using resource provisioning areas to support crop pollinators.

A difference in the number of seeds produced within muskmelons supports the idea that provisioning areas can indirectly increase the pollination of nearby crops. On average, muskmelon fruit from fields with provisioning areas had 9% more seed, suggesting an increased transfer of conspecific pollen to muskmelon stigmas in these areas. Although seed count was a significant predictor of fruit weight at the Manheim site, the increase in seed production did not result in heavier fruits within fields containing a provisioning area. This increase in seed count may not have been large enough to result in a significant difference in fruit weight, but much of the variability in the weight data can likely be attributed to stochastic events that affected plant growth and fruit development unequally. Occurrences such as the hailstorm at the Rock Springs site eliminated nearly half of the muskmelon crop and left surviving plants with considerable damage. In addition, persistent but localized feeding by groundhogs at both sites reduced the vigor and productivity of the affected plants.

The effect of provisioning areas on bee abundance and activity also yielded inconsistent results. Bee abundance, measured using timed pollinator collections, indicated that Bombus individuals were more numerous in fields without provisioning areas during the first collection period. This is in direct contradiction to the hypothesis that provisioning areas should increase pollinator abundance in adjacent crops. It appears that the plant species or higher flower density in the provisioning area were more appealing to Bombus individuals than the crop at this point in time. Differences were also observed among other bee groups during the third time period, with a greater number of pollinators captured in fields with a floral resource provisioning area. These results should be interpreted with caution however, as the density of muskmelon flowers was significantly higher in fields with provisioning areas during the third collection period.

Distance from the field edge also had little effect on the metrics explored here. Distance was not a strong predictor for the weight or seed count of the muskmelon fruit, nor did it influence the number of bees observed or captured in the muskmelon crop. Although we were unable to identify any significant trends, there is substantial data to suggest that distance from a provisioning area or a natural area may influence the abundance and activity of pollinators within a cropping system (Chacoff & Aizen, 2006; Greenleaf & Kremen, 2006; Kim, Williams, &

Kremen, 2006; Klein, Steffan-Dewenter, & Tscharntke, 2003; Kremen, Williams, & Thorp,

2002). Some of the distances considered in other works were in excess of 500m, which suggests that the distances considered in the present study may not have been large enough to detect differences in bee abundance or activity. It is also clear that “noise” introduced by crop production activities and the possible confounding effects of landscape position call for considerably higher replication of studies of this kind.

Despite inconsistencies in other datasets, the average number of bee species collected from the provisioning and crop areas supported our initial hypotheses. The provisioning areas,

which contain greater florisitic diversity, hosted an average of 6.5 ± 0.645 bee species while the muskmelon crop, which was dominated by a single plant species, attracted a smaller mean of

3.84 ± 0.281 species. Although the total number of bee species collected from the crop area was greater (24 vs. 14 species), it should be mentioned that of the six hours and nine minutes that were spent collecting pollinators over the course of the project, only 36 minutes were devoted to collecting bees from the provisioning areas since these patches account for only 10% of the total area sampled. As a result, the greater number of species collected from the crop was likely due to the increased sampling effort within those areas.

The species level data also indicates some very strong differences in flower preference among pollinator species. For example, some bee species like Bombus griseocollis were abundant within the provisioning area (n=50) but were not observed among the crop. In fact, of the four Bombus species that were collected, only Bombus impatiens was captured from muskmelon plants. Perhaps the most surprising result was the complete absence of Apis mellifera from the provisioning areas despite its presence in the adjacent crop fields and its reputation as a generalist pollinator.

These latter findings highlight the importance of estimating insect fidelity to prospective plant hosts. If the plants established in provisioning areas are not readily used by the very target organisms that land managers intend to support, then populations of the target organism cannot be expected to increase. Although some long term studies have contributed much to our knowledge of plant-insect interactions, we still have much to learn regarding the preferences of different beneficial insects. This knowledge will be necessary to create effective plantings for the enhancement of pollination and other ecosystem services.

Although many of the findings presented were inconclusive, the data collected here are valuable nonetheless. The insect collections alone provide a detailed account of the pollinator

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species associated with a profitable horticultural crop whose acreage has increased substantially in the past two decades (NRCS, 2007). In addition, the consideration of distance in this project should serve as a reminder that scale and landscape context are important factors. Lastly, the differences observed in the floral preferences of pollinators, even closely related pollinators, underscores the need for further characterization research to strengthen our basic understanding of plant-insect interactions. If resource provisioning schemes are to be an effective means of enhancing crop pollination services, the primary pollinator species of target crops must be identified along with their preferred non-crop plant hosts. With these datasets, it will be possible to tailor floral provisioning areas to preferentially support the most relevant bee species for a given agricultural system. However, gathering this information will require careful sampling of crop plants at anthesis along with the collection of bees from wild plants occurring in the landscape or from a replicated garden as described in Chapter 3. Naturally, conducting this research at multiple locations will make the studies more robust and account for regional differences in plant and pollinator communities, but the amount of data likely to be collected from such studies will require considerable taxonomic knowledge of both the flora and insect fauna to yield meaningful results.

WORKS CITED

Berendse, F., Chamberlain, D., Kleijn, D., & Schekkerman, H. (2004). Declining biodiversity in agricultural landscapes and the effectiveness of agri-environment schemes. Ambio, 33(8), 499-502.

Calabuig, I. (2000). Solitary Bees and Bumblebees in a Danish Agricultural Landscape. University of Copenhagen; Department of Population Ecology.

Chacoff, N. P., & Aizen, M. A. (2006). Edge effects on flower-visiting insects in grapefruit plantations bordering premontane subtropical forest. Journal of Applied Ecology, 43(1), 18-27.

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Frank, S. D., & Shrewsbury, P. M. (2004). effect of conservation strips on the abundance and distribution of natural enemies and predation of Agrotis ipsilon (Lepidoptera: Noctuidae) on Golf Course Fairways (Vol. 33, pp. 1662-1672).

Greenleaf, S. S., & Kremen, C. (2006). Wild bee species increase tomato production and respond differently to surrounding land use in Northern California. Biological Conservation, 133(1), 81-87.

Hladun, K. R., & Adler, L. S. (2008). Effects of perimeter trap crop pollen on reproduction in butternut squash (Cucurbita moschata). Hortscience, 43(1), 276-278.

Hickman, J. (1996). Use of Phacelia tanacetifolia strips to enhance biological control of aphids by hoverfly larvae in cereal fields. Journal of Economic Entomology, 89(4), 832-840.

Inouye, D. W. (1978). Resource partitioning in bumblebees - experimental studies of foraging behavior. Ecology, 59(4), 672-678.

Kleijn, D., & Baldi, A. (2005). Effects of set-aside land on farmland biodiversity: Comments on Van Buskirk and Willi. Conservation Biology, 19(3), 963-966.

Kleijn, D., Baquero, R. A., Clough, Y., Diaz, M., De Esteban, J., Fernandez, F., et al. (2006). Mixed biodiversity benefits of agri-environment schemes in five European countries. Ecology Letters, 9(3), 243-254.

Kleijn, D., Berendse, F., Smit, R., Gilissen, N., Smit, J., Brak, B., et al. (2004). Ecological effectiveness of agri-environment schemes in different agricultural landscapes in the Netherlands. Conservation Biology, 18(3), 775-786.

Kleijn, D., & Sutherland, W. J. (2003). How effective are European agri-environment schemes in conserving and promoting biodiversity? Journal of Applied Ecology, 40(6), 947-969.

Kremen, C., Williams, N. M., Bugg, R. L., Fay, J. P., & Thorp, R. W. (2004). The area requirements of an ecosystem service: crop pollination by native bee communities in California. Ecology Letters, 7(11), 1109-1119.

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Mitchell, T. B. (1962). Bees of the eastern United States. II. : Technical bulletin (North Carolina Agricultural Experiment Station), 152, 1-557. [Megachilidae, Anthophoridae, Apidae s.s.].

Morandin, L. A., & Winston, M. L. (2005). Wild bee abundance and seed production in conventional, organic, and genetically modified canola. Ecological Applications, 15(3), 871-881.

Potts, S. G., Vulliamy, B., Dafni, A., Ne'eman, G., & Willmer, P. (2003). Linking bees and flowers: How do floral communities structure pollinator communities? Ecology, 84(10), 2628-2642.

Ricketts, T. H., Daily, G. C., Ehrlich, P. R., & Michener, C. D. (2004). Economic value of tropical forest to coffee production. Proceedings of the National Academy of Sciences of the United States of America, 101(34), 12579-12582.

Suchail, S., Guez, D., & Belzunces, L. P. (2000). Characteristics of imidacloprid toxicity in two Apis mellifera subspecies. Environmental Toxicology and Chemistry, 19(7), 1901-1905.

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CHAPTER 5

EPILOGUE

INTRODUCTION

If implemented properly, floral resource provisioning schemes can help to conserve bee populations by providing a continuous supply of appropriate pollen and nectar resources through the growing season. The following discussion includes a summary of my research efforts to identify suitable plant species for use in pollinator conservation efforts along with my attempt to measure the impact of resource provisioning on an important ecosystem service. In the process, I identify other considerations that should be included in the plant selection process, and I introduce my vision for future research directions. Finally, I conclude with some practical suggestions for effective and cost efficient methods of perennial plant establishment for habitat management schemes.

PLANT SELECTION

In the second chapter, I selected twenty-five native perennial species as candidates for incorporation into pollinator conservation plantings. Plants were selected based on eight predetermined criteria deemed to make the species suitable for widespread establishment in agricultural settings. The selected plants were established in central Pennsylvania and tracked over two seasons to obtain detailed data on their flowering phenologies. With this data, it is possible to visualize the distribution of their blooming through time and select a group of plant species whose sequential blooming phenologies can produce a continuous supply of pollen and nectar resources from May through mid-October.

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Based on the measured floral phenologies of the studied plant species, few flowers were produced during the early period (May-July) compared to the later periods. To increase the number of flowers during this time, including additional plant species could be considered.

Possible candidate species include golden Alexanders (Zizia aurea), wild strawberry (Fragaria virginiana), round-leaved ragwort (Packera obovata), and tall anemone (Anemone virginiana).

Alternatively, the number of flowers could be enhanced by increasing the density at which early- blooming species are established.

When selecting plants for resource provisioning area, woody species should also be considered for some situations. Although woody plants were excluded from this study to minimize the anticipated costs of maintenance, flowering shrubs and trees may be included where there is a desire to (1) increase food resources for bees in the early-season (May-July),

(2) augment nesting resources for bees, (3) provide food and habitat for other wildlife, or to (4) stabilize slopes and riparian areas. Many trees and shrubs flower before their leaves develop and thus provide food resources for pollinators in early spring. In addition to attracting large numbers of bees, shrubs and small trees such as black elderberry (Sambucus nigra), black raspberry

(Rubus occidentalis), and staghorn sumac (Rhus typhina) provide nesting habitat for bees. The stems of elderberry are hollow, and the pith of raspberry and sumac plants can be excavated by bee species such as Ceratina calcarata and Ceratina dupla. These plants also offer food and habitat for upland gamebirds, migrating song birds, and other wildlife. The establishment of flowering shrubs and trees might be especially appropriate on slopes or along riparian buffers as their extensive root systems can help stabilize the substrate and prevent erosion. Buttonbush

(Cephalanthus occidentalis) and red chokeberry (Photinia pyrifolia) might be appropriate flowering shrubs for moister conditions.

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Even large trees, including shade trees, can be incorporated into pollinator conservation plans. Although many trees like pine (Pinus spp.), oak (Quercus spp.), and beech (Fagus spp.) are primarily wind-pollinated, the reproductive structures of others tree species, such as willows

(Salix spp.), cherries (Prunus spp.), and maples (Acer spp.), can attract large numbers of bees. In lieu of establishing new trees, preserving existing woodlots and fencerows can be of considerable conservation value. Indeed, these areas can be dominated by tree species such as pin cherry

(Prunus serotina) and red maple (Acer rubrum) that host large numbers of bees.

Although the selected plants were originally envisioned to border upland agricultural fields, the establishment of native perennials in drainage ditches, retention ponds, and other areas with wetter soils could help to conserve wild pollinators. Naturally, plants selected for these areas should be capable of thriving under moister conditions. Some species from the list of plants evaluated here could be retained for this purpose. New England aster (Symphyotrichum novae- angliae), for example, can tolerate wetter soils. In addition, other species have closely related analogs that can tolerate wetter soil conditions. Swamp milkweed (Ascelpias incarnata), for instance, could be substituted for butterfly milkweed (Ascelpias tuberosa) and probably attract the same pollinators. Other plants however, will probably need to be replaced by unrelated species.

In addition to identifying other plants that could be used in resource provisioning areas, care must be taken to identify plant species whose traits or associations with pests make them a nuisance or threat to human interests. For example, individual plants of Desmodium canadense and Lespedeza capitata hosted large numbers of Japanese beetles (Popillia japonica) which are important pests of both crop and turf systems. Lespedeza captitata also attracted few bees. Upon researching the species further, it was determined that Lespedeza capitata can produce a large proportion of apetalous cleistogomous flowers that are strictly self-pollinating and do not receive

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pollinator visits (Cole & Biesboer, 1992). Consequently, including this species in pollinator conservation plantings would probably serve to increase pest rather than bee populations.

Plants that consistently attracted bees also attracted insect pests. Eupatorium perfoliatum, for example, had many lygus bugs (Lygus spp.) on its inflorescences while the flowers of

Solidago canadensis provided nutrition to adult northern and western corn rootworms

(Diabrotica barberi, Diabrotica virgifera virgifera, respectively). In addition, Phlox divaricata was observed to attract a number of great bee flies (Bombylius major) whose larvae are parasitic on solitary bees. When selecting plants for resource provisioning areas, some cost-benefit analysis should be performed to determine if a plant‟s attractiveness to bees and beneficial insects outweighs its attractiveness to pests.

Other traits can also make a plant species unsuitable for establishment in habitat management programs. For example, plants that produce appealing but toxic fruits (ex:

Sambucus racemosa) should be avoided to prevent accidental poisonings. Furthermore, plants whose tissues or pollen commonly illicit severe allergic reactions among humans should also be avoided. Although the seeds of ragweed (Ambrosia spp.) are often cited as excellent food sources for birds, the problems these plants pose to agriculture and human health should be (but are not always) sufficient to preclude their use in conservation efforts. Other plants may not pose a threat, but certain traits can make them a nuisance. Showy ticktrefoil (Desmodium canadense) for instance, produces seed pods that have the annoying ability to adhere tightly to clothes and fur, making it a less than ideal plant for areas where people and pets might venture.

PLANT-POLLINATOR ASSOCIATIONS

Upon selecting and establishing the candidate plant species, the relative attractiveness of the plants were compared by observing and sampling the bee visitors to each specimen. The

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resulting dataset allowed for the identification of the plant species that hosted the greatest number of bees at different points during the summer. It also allowed us to identify plants that attracted few or no bees and that could be replaced with a more attractive plant species.

In addition to collecting data on the total number of bees on each plant, the bee visitors were also classified into several categories or identified to species. Accurately identifying bees that are in flight can be extremely difficult, but it is relatively easy to classify them into categories or groups. Consequently, bee visitors documented in the „observation‟ dataset were classified as (1) Apis mellifera, (2) Bombus spp., and (3) other bees. Even with this crude classification system, we were able to document differences in plant preference among these bee groupings. The destructive sampling that was also conducted was especially helpful because it allowed for species level identifications of pollinators associated with the plants. This is of particular importance because plant preference can differ widely among pollinator species, even closely related species.

Having knowledge of the interactions among plant and pollinator species is of practical importance when establishing provisioning areas for bees because it allows for the selection of plants that attract the chief pollinators for a particular agricultural system. For example, it may be wise to establish higher densities of Penstemon digitalis in orchard settings since it is was the only studied plant species to be visited regularly by bees in the genus Osmia which are valued for their pollination of apple, almond, and other orchard crops. Although plantings of P. digitalis might be thought to compete with the main crop for these important pollinators, they could help support bee populations in years when crop buds are damaged by untimely frosts, helping to ensure the presence of these bees in the following season.

Knowing the associations of specific plants and bees might also provide sufficient reason to exclude a plant species from conservation plantings. For example, Vernonia gigantea ranked

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high in appeal to solitary bees, but most of its visitors were specialists on Vernonia species or on members of the Asteraceae. Conservation of these bee species may be of limited value in agricultural systems, except perhaps where sunflowers are cultivated for seed production.

Limiting numbers of certain pollinator species might be recommended if there is reason to believe they might compete for nesting resources with more desirable bees that pollinate crops.

When selecting plant species for provisioning areas, it is important to look beyond the number of observed pollinators and consider whether the plants are having a positive effect on the populations of their insect pollinators. This requires knowledge of the pollinator‟s biology and life history. For example, the late-blooming Symphyotrichum novae-angliae hosted large numbers of bees, but it does not necessarily contribute to their reproductive success. Of the 185

Bombus impatiens specimens captured on Symphyotrichum novae-angliae in both seasons, only

15 were female. Although male drones are necessary to mate with new queens that will establish colonies in the following year, few if any of the sampled males would have had an opportunity to mate at the time they were collected. In addition, any female worker bees were likely from disbanded colonies and only foraging for themselves. Indeed, many of the late blooming plants hosted a greater percentage of males, including Symphyotrichum novi-belgii (93%) and Solidago rugosa (92%). Although such plants can provide pollen and nectar for honeybee colonies that will brave the winter, it is not clear if they are contributing to the success of B. impatiens so late in the season.

Much data was collected from this study, but it would also have been informative to identify the plant species in the vicinity around the research plots, along with their abundance, and their associations with members of the bee community. This would have been particularly insightful since these plants may have competed with the research plants for pollinators. This appears to have occurred during the early season when bees were casually observed to visit

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patches of garlic mustard (Alliaria petiolata) and yellowrocket (Barbarea vulgaris) with much higher frequencies than were observed in the study plots. Although information that would arise from studying the plant/insect associations in the vicinity of the perennial garden would be useful, it would be very difficult to collect data from these plant communities at the same level of detail as that of the study plants. Characterization of the surrounding vegetation will require some rapid assessment method to quickly estimate the floral area or number of flowers for large, interrupted, and intermingled patches of species. A similar sampling scheme for pollinators occurring in these areas would also have to be devised.

MEASURING ECOSYSTEM SERVICES

In addition to evaluating the appropriateness of plant species for use in pollinator conservation plantings, I also tested the hypothesis that floral resource provisioning can increase crop pollination rates by enhancing the local abundance and diversity of pollinators. Despite some trouble with crop plant establishment at one site, the number of seeds produced per fruit were significantly greater in fields where a floral resource provisioning area was present, suggesting that this form of habitat management has potential to increase the level of ecosystem services provided to a crop. However, other measured metrics did not support this hypothesis.

The weights of the fruits did not differ between the treatments, and the numbers of bees that were collected and observed failed to provide consistent results. Furthermore, an attempt to measure changes in crop pollination rate and pollinator diversity and abundance with distance from a floral resource also failed to show a difference, though the scale used here was probably much too small.

The results from this study may have been inconclusive, but there is a need to document that habitat management schemes can increase the level or distribution of ecosystem services

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within a landscape. Having this data will be important to justify the expenditure of public funds on establishment of provisioning areas or other management practices on privately-owned land.

To best document changes in the level of ecosystem services, it might be practical to establish baseline datasets before habitat management schemes are implemented. Indeed, lack of baseline data has made it difficult to demonstrate increases in biodiversity or ecosystem services brought about by European agri-environment schemes (Kleijn & Sutherland, 2003). To accomplish this, landowners who have already expressed interest in establishing provisioning areas, or who might benefit from such plantings, should first be identified. An attempt should then be made to postpone establishment of pollinator conservation plantings for at least one season to allow for a baseline estimation of the abundance and diversity of bees on a property along with the pollination services that are provided. Because pollination services are often difficult to measure, collecting data on pollinator abundance and diversity should be a priority.

ESTABLISHING POLLINATOR CONSERVATION PLANTINGS

After identifying which species to include in floral resource provisioning areas, the next step will be determining how to establish them. Originally, the plants included in this study were envisioned to be established along agricultural field edges. However, these species could also be established as intercropped strips running through fields or in large blocks in the middle of orchards. These plants may also be seeded in otherwise uncultivated areas, such as where rocky outcrops prevent cultivation. Lastly, pollinator conservation plantings can also be worked into riparian buffers or established within drainage ditches.

In developing plans for the establishment and maintenance of pollinator plantings, it will be necessary to consider both the cost and effectiveness of available options. Currently, it appears the most economical method of establishing large areas of perennial plants is through

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seeding. However, the small seeds produced by many native perennials are ill-suited for use in standard farm machinery. Even when mixed with bulk materials like sand or sawdust, the distribution of some species will be uneven when the seeds are broadcast. Consequently, the most effective means of establishing pollinator plantings may be through hydroseeding which is most commonly used in the establishment of new lawns. The slurries used in hydroseeding operations typically consist of a mixture of seeds, paper mulch, and a tackifying agent that helps to ensure adhesion to the substrate and to protect the seeds from runoff and wind. The paper mulch in these mixes may also to suppress the existing weed seedbank, retain moisture, and protect the flower seeds from herbivores. However, unlike current formulations used for turfgrasses, it might be wise to exclude fertilizers, as increased nutrient concentrations may promote weed growth. Indeed, producers of native seed suggest reducing nutrient concentrations in the soil before establishing areas of native plants.

Competition with existing plants growing in the conservation planting site may prove a significant hurdle for the establishment and maintenance of pollinator plantings. Those plants could arise from a previously established seedbank or from perennial propagules. Because of the expected plant diversity within a conservation mix, it is unlikely that chemical control of weeds will be a feasible option in most situations. However, if grasses or monocots such as

Tradescantia ohiensis are not included in the planting, it may be possible to use sethoxydim or another grass-selective herbicide for the control of foxtails (Setaria spp.), perennial quackgrass

(Elymus repens), and other weedy grasses. Mowing seeded areas may be helpful in reducing the height and competitiveness of some weed species while controlled burns of these areas every 3 to 4 years may also be appropriate, depending on local ordinances. These methods of weed management may also contribute to maintenance of the plant diversity in plant mixes by preventing a few species from becoming dominant.

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Although mixes can be highly tailored to meet the needs of individual land managers, the overall acceptance and success of resource provisioning schemes may be increased if a small number of „standard mixes‟ are made available. First, the availability of such mixes should reduce apprehension among property owners who feel uneasy about composing their own plant mixes. Second, pre-assembled plant mixes may increase rates of successful establishment, encouraging land managers to maintain the plantings. At the current time, it seems appropriate to have a (1) dry, (2) wet, and (3) orchard mix. The „dry‟ mix would consist of upland plant species for establishment in drier agricultural settings while the „wet‟ mix would be more appropriate in drainage ditches, retention ponds, and along riparian buffers. Lastly, the orchard mix would be established among orchards of apples, cherries, peaches, plums, and other similar crops.

Alternatively, mixes could be composed for the different eco-regions of a state.

CONCLUSION

The conducted research I‟ve reported on in my thesis provides a wealth of knowledge that can be used to design habitat management plans for conserving pollinators in Pennsylvania and in the surrounding region. Rather than relying on popular assumptions about which flower colors or flower shapes attract bees, land managers and consultants can utilize the information gathered here to select plant species that attract the right pollinators for their agricultural systems. Mixes of plant species can also be composed using the phenology data to assemble plant communities that provide a crucial sequence of blooms throughout the growing season.

Although some questions, such as the most appropriate establishment methods, remain unanswered, this body of work provides a solid groundwork for the proper development of successful habitat management plans.

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WORKS CITED

Alarcon, R., Waser, N. M., & Ollerton, J. (2008). Year-to-year variation in the topology of a plant-pollinator interaction network. Oikos, 117(12), 1796-1807.

Carroll, A. B., Pallardy, S. G., & Galen, C. (2001). Drought stress, plant water status, and floral trait expression in fireweed, Epilobium angustifolium (Onagraceae). American Journal of Botany, 88(3), 438-446.

Cole, C. T., & Biesboer, D. D. (1992). Monomorphism, Reduced Gene Flow, and Cleistogamy in Rare and Common Species of Lespedeza (Fabaceae). American Journal of Botany, 79(5), 567-575.

Kleijn, D., & Sutherland, W. J. (2003). How effective are European agri-environment schemes in conserving and promoting biodiversity? Journal of Applied Ecology, 40(6), 947-969.

Pleasants, J. M. (1983). Nectar Production Patterns in Ipomopsis aggregata (Polemoniaceae). American Journal of Botany, 70(10), 1468-1475.

Wyatt, R., Broyles, S. B., & Derda, G. S. (1992). Environmental Influences on Nectar Production in Milkweeds (Asclepias syriaca and A. exaltata). American Journal of Botany, 79(6), 636-642.

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APPENDIX A

SOURCE AND MONTH OF TRANSPLANT FOR STUDIED PERENNIAL PLANTS

Location of seven sources from which all study plants were obtained followed by a list of the 100 plants, their location in the study plots (see Fig. 2.1), their source, and the month and year when they were transplanted into the plots.

SOURCE CODE PLANT SOURCE LOCATION ELKR Elk Ridge Natureworks Grantsville, MD ERNS Ernst Conservation Seeds Meadville, PA MEAD Meadowood Nursery Hummelstown, PA PRAI Prairie Moon Nursery Winona, MN RUSS Russell E. Larson Agricultural Research Center Rock Springs, PA TAIT Tait Farms Centre Hall, PA YELL Yellow Springs Nursery Chester Springs, PA

LOCATION SOURCE MONTH OF YEAR OF NO. PLANT SPECIES ID CODE TRANSPLANT TRANSPLANT 1 A1 Phlox divaricata PRAI October 2007 2 A2 Senna hebecarpa PRAI October 2007 3 A3 Lysimachia quadrifolia ELKR October 2007 4 A4 Eupatorium purpureum PRAI October 2007 5 A5 Actaea racemosa MEAD May 2008 6 A6 Coreopsis tripteris YELL May 2008 7 A7 Aquilegia canadensis ERNS July 2007 8 A8 Echinacea purpurea ERNS July 2007 9 A9 Asclepias tuberosa TAIT August 2007 10 A10 Veronicastrum virginicum PRAI October 2007 11 A11 Vernonia gigantea ERNS July 2007 12 A12 Eupatorium perfoliatum ERNS July 2007 13 A13 Eupatorium purpureum PRAI October 2007 14 A14 Pycnanthemum tenuifolium RUSS October 2007 15 A15 Liatris pycnostachya PRAI October 2007 16 A16 Echinacea purpurea ERNS July 2007 17 A17 Conoclinium coelestinum TAIT May 2008 18 A18 Pycnanthemum tenuifolium RUSS October 2007 19 A19 Asclepias tuberosa TAIT August 2007 20 A20 Coreopsis tripteris YELL May 2008 21 B1 Campanula rotundifolia MEAD May 2008 22 B2 Asclepias tuberosa TAIT August 2007 23 B3 Echinacea purpurea ERNS July 2007 24 B4 Symphyotrichum novae-angliae ELKR May 2008

CONTINUED… 115

LOCATION MONTH OF YEAR OF NO. SPECIES SOURCE ID TRANSPLANT TRANSPLANT 25 B5 Aquilegia canadensis ERNS July 2007 26 B6 Symphyotrichum novi-belgii ERNS July 2007 27 B7 Senna hebecarpa PRAI October 2007 28 B8 Lespedeza capitata ERNS July 2007 29 B9 Eurybia macrophylla ERNS July 2007 30 B10 Actaea racemosa MEAD May 2008 31 B11 Monarda fistulosa ERNS July 2007 32 B12 Asclepias tuberosa TAIT August 2007 33 B13 Lysimachia quadrifolia ELKR October 2007 34 B14 Tradescantia ohiensis ERNS July 2007 35 B15 Phlox divaricata PRAI October 2007 36 B16 Monarda fistulosa ERNS July 2007 37 B17 Liatris pycnostachya PRAI October 2007 38 B18 Lysimachia quadrifolia ELKR October 2007 39 B19 Solidago rugosa ERNS July 2007 40 B20 Penstemon digitalis ELKR October 2007 41 C1 Conoclinium coelestinum TAIT May 2008 42 C2 Monarda fistulosa ERNS July 2007 43 C3 Vernonia gigantea ERNS July 2007 44 C4 Coreopsis tripteris YELL May 2008 45 C5 Eurybia macrophylla ERNS July 2007 46 C6 Eupatorium perfoliatum ERNS July 2007 47 C7 Eupatorium purpureum PRAI October 2007 48 C8 Campanula rotundifolia MEAD May 2008 49 C9 Monarda fistulosa ERNS July 2007 50 C10 Tradescantia ohiensis ERNS July 2007 51 C11 Desmodium canadense ERNS July 2007 52 C12 Symphyotrichum novi-belgii ERNS July 2007 53 C13 Aquilegia canadensis ERNS July 2007 54 C14 Symphyotrichum novae-angliae ERNS July 2007 55 C15 Senna hebecarpa PRAI October 2007 56 C16 Lespedeza capitata ERNS July 2007 57 C17 Eurybia macrophylla ERNS July 2007 58 C18 Desmodium canadense ERNS July 2007 59 C19 Symphyotrichum novi-belgii ERNS July 2007 60 C20 Vernonia gigantea ERNS July 2007 61 D1 Solidago rugosa ERNS July 2007 62 D2 Penstemon digitalis ELKR October 2007 63 D3 Veronicastrum virginicum PRAI October 2007 64 D4 Tradescantia ohiensis ERNS July 2007 65 D5 Lespedeza capitata ERNS July 2007 CONTINUED… 116

LOCATION MONTH OF YEAR OF NO. SPECIES SOURCE ID TRANSPLANT TRANSPLANT 66 D6 Pycnanthemum tenuifolium RUSS October 2007 67 D7 Symphyotrichum novae-angliae RUSS September 2007 68 D8 Solidago rugosa ERNS July 2007 69 D9 Liatris pycnostachya PRAI October 2007 70 D10 Phlox divaricata PRAI October 2007 71 D11 Veronicastrum virginicum PRAI October 2007 72 D12 Eurybia macrophylla ERNS July 2007 73 D13 Penstemon digitalis ELKR October 2007 74 D14 Echinacea purpurea ERNS July 2007 75 D15 Conoclinium coelestinum TAIT May 2008 76 D16 Phlox divaricata PRAI October 2007 77 D17 Actaea racemosa MEAD May 2008 78 D18 Eupatorium perfoliatum ERNS July 2007 79 D19 Tradescantia ohiensis ERNS July 2007 80 D20 Aquilegia canadensis ERNS July 2007 81 E1 Symphyotrichum novi-belgii ERNS July 2007 82 E2 Pycnanthemum tenuifolium RUSS October 2007 83 E3 Eupatorium perfoliatum ERNS July 2007 84 E4 Desmodium canadense ERNS July 2007 85 E5 Liatris pycnostachya PRAI October 2007 86 E6 Penstemon digitalis ELKR October 2007 87 E7 Lysimachia quadrifolia ELKR October 2007 88 E8 Conoclinium coelestinum TAIT May 2008 89 E9 Desmodium canadense ERNS July 2007 90 E10 Vernonia gigantea ERNS July 2007 91 E11 Campanula rotundifolia MEAD May 2008 92 E12 Solidago rugosa ERNS July 2007 93 E13 Lespedeza capitata ERNS July 2007 94 E14 Coreopsis tripteris YELL May 2008 95 E15 Actaea racemosa MEAD May 2008 96 E16 Symphyotrichum novae-angliae ELKR May 2008 97 E17 Eupatorium purpureum PRAI October 2007 98 E18 Campanula rotundifolia MEAD May 2008 99 E19 Veronicastrum virginicum PRAI October 2007 100 E20 Senna hebecarpa PRAI October 2007

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APPENDIX B

R CODE FOR PRODUCING FLORAL AREAPHENOLOGY CHARTS (see figures 2.2 and 2.3)

The code included below limits the number of individual plots that can be generated in a single graphic. The code will be revised to resolve this limitation and further developed into a separate R package.

Code written by Eric Nord

### VIOLIN-LIKE AREA PLOTS ###

# Object: make violin plot like plots showing floral area over time.

# Notes: This is a work in progress. Currently this function seems prone to errors related to vertical spacing, particularly as settings for cex and mar interact. This is a byproduct of using layout() for paneling the individual plots - future versions will correct this. If errors show up (eg, x axis fails to print, or y axis label prints in the wrong place, try increasing the x.ax.ht or lab.area values, or reducing the cex value, or increasing the size of the graphics device. It may help to specify a larger than default graphic window (using the platform specific graphic window function). The zero.rm=T option can cause problems with some data - if this causes errors, try zero.rm=F. This function has been tested on Mac and Linux, but not Windows. areaplot=function(x,y,group,fill="grey",zero.rm=TRUE,x.lab="x", y.lab="y", cx=1.1,lab.area=0.2,x.ax.ht=0.10) # x and y are the x and y axis variables, group is the grouping variable. { x<-as.numeric(x) # make x numeric y<-as.numeric(y) # make y numeric group<-as.factor(as.character(group)) # make group a factor grp.num<-length(levels(group)) y.max<-tapply(y,group,max) xlims<-range(x) grp.labs<-sapply(lapply(names(y.max), strwrap, 15), paste, collapse = "\n") # setup layout plot.nums<-1:grp.num; lab.nums<-(grp.num+1):(2*grp.num) if(exists("plot.mat")==T) {rm(plot.mat)} for(i in 1:grp.num){ temp<-c(lab.nums[i],plot.nums[i]) plot.mat<-if(exists("plot.mat")==T){c(plot.mat,temp)}else{temp} } hts<-y.max/min(y.max) plot.mat<-matrix(plot.mat,nrow=grp.num,ncol=2,byrow=T) plot.mat<-cbind(plot.mat, plot.mat[,2]); plot.mat[,2]<-2*grp.num+2 plot.mat<-rbind(plot.mat, c(0,0,2*grp.num+1))

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layout(mat=plot.mat,widths=c(lab.area*0.8, lab.area*0.2, (1- lab.area)),heights=c(hts,sum(hts)*x.ax.ht)) # plot data shapes for (i in 1:grp.num){ # create data vectors x1<-x[which(group==names(y.max)[i])] y1<-y[which(group==names(y.max)[i])]/2 # remove lead/trail zeros if zero.rm=TRUE if (zero.rm == TRUE){ nas<-0; for(j in (1:length(y1))){ # this loop builds a vector of values to remove if (j==1){if (y1[j]==0 & y1[j+1]==0) {nas<-c(nas,j)}} if(j>1 & j

# y axis label par (mar=c(0.2,2,0.2,0.2)); plot.new() mtext(y.lab, side = 2, cex=cx, padj=0) }

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APPENDIX C

PEARSON CORRELATION COEFFICIENTS FOR MEASURED FLOWER CHARACTERISTICS

Pearson correlation coefficients for the flower characteristics of twenty-five native perennial plant species during weeks when collections of pollinators were taken.

2008 2009 VARIABLE 1 2 3 4 1 2 3 4 EARLY-SEASON 1. Number of flowers ― ― 2. Blossom 0.654 ** ― 0.001 ― 3. Total floral area 0.979 *** 0.741 *** ― 0.432 * 0.819 *** ― 4. Maximum floral height 0.309 0.596 ** 0.377 ― 0.131 0.597 ** 0.388 ― MID-SEASON 1. Number of flowers ― ― 2. Blossom -0.272 ― -0.228 ― 3. Total floral area -0.116 0.659 *** ― 0.166 0.434 ** ― 4. Maximum floral height 0.014 0.489 ** 0.644 *** ― 0.040 0.533 *** 0.634 *** ― LATE-SEASON 1. Number of flowers ― ― 2. Blossom -0.325 * ― -0.209 ― 3. Total floral area -0.011 0.132 ― 0.029 0.269 * ― 4. Maximum floral height 0.209 -0.007 0.583 *** ― 0.274 * 0.272 * 0.564 *** ―

* p < 0.5, ** p < 0.01, *** p < 0.001

Pearson correlation coefficients for the flower characteristics of twenty-five native perennial plant species during weeks when observations of pollinators occurred.

2008 2009 VARIABLE 1 2 3 4 1 2 3 4 EARLY-SEASON 1. Number of flowers ― ― 2. Blossom 0.501 * ― 0.001 ― 3. Total floral area 0.989 *** 0.588 ** ― 0.432 * 0.819 *** ― 4. Maximum floral height 0.288 0.380 0.380 ― 0.131 0.597 ** 0.388 ― MID-SEASON 1. Number of flowers ― ― 2. Blossom -0.216 ― -0.228 ― 3. Total floral area -0.067 0.626 *** ― 0.166 0.434 ** ― 4. Maximum floral height 0.088 0.281 0.380 ** ― 0.040 0.533 *** 0.634 *** ― LATE-SEASON 1. Number of flowers ― ― 2. Blossom -0.242 ― -0.209 ― 3. Total floral area 0.036 0.312 * ― 0.029 0.269 * ― 4. Maximum floral height 0.179 0.208 0.632 *** ― 0.274 * 0.272 * 0.564 *** ―

* p < 0.5, ** p < 0.01, *** p < 0.001

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