The Pennsylvania State University

The Graduate School

Department of Entomology

ADVANCING ECOLOGICALLY BASED MANAGEMENT FOR ACALYMMA

VITTATUM, A KEY PEST OF CUCURBITS

A Thesis in

Entomology

by

Margaret Theresa Lewis

Submitted in Partial Fulfillment of the Requirements for the Degree of

Master of Science

May 2015

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The thesis of Margaret Theresa Lewis was reviewed and approved* by the following:

Shelby Fleischer Professor of Entomology Thesis Advisor

John Tooker Associate Professor of Entomology and Extension Specialist

Elsa Sanchez Associate Professor of Horticultural Systems Management

Gary Felton Professor of Entomology Head of the Department of Entomology

*Signatures are on file in the Graduate School

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ABSTRACT

Striped cucumber beetle, Acalymma vittatum (Coleoptera: Chrysomelidae), is a key pest of cucurbit crops in the northeastern United States. Systemic and foliar insecticides provide consistent control of the adult beetles and are the primary management tool available to growers. However, many of these chemicals are excluded from use in organic production systems. Additionally, there are concerns about the potential for development of insecticide resistant populations of A. vittatum and the non- target effects on beneficial insects within cucurbit cropping systems. In this thesis, I explore alternative management options for A. vittatum, considering both efficacy and the potential for integration into an ecologically based pest management system. I first take a systems based approach to A. vittatum management, considering how horticultural production practices shape pest and beneficial insect communities in cucurbit systems. In a two year field experiment, I measured how soil production systems and row cover use influence ground beetle (Coleoptera: Carabidae) activity density. The presence or absence of a row cover had no significant effect. However, soil production system did significantly influence the overall carabid community. Certain species, particularly Cicindela punctulata, had significantly higher activity density in a reduced tillage system relative to a conventional till/plasticulture system. Overall, a reduced tillage system also seemed to support greater species richness. These results suggest that horticultural production practices play an important role in shaping the natural enemy community within cucurbit cropping systems. I also monitored parasitism of A. vittatum at two field sites in central PA between June and October 2014. Two parasitoid species were found: a tachinid fly, Celatoria setosa, and a braconid wasp, Centistes diabroticae. Though their presence had been confirmed outside of Pennsylvania, this is the first record of parasitoid activity within the state. Parasitism rates were surprisingly high, reaching up to 56% for C. setosa and up to 17% for C. diabroticae. Based on the results of this initial survey, both species seem to be strong candidates for a conservation biocontrol program. iv

Finally, I integrated two plant and microbial metabolites into a biorational insecticide for A. vittatum: spinosad, a broad spectrum, oral insecticide derived from the soil bacterium Saccharopolyspora spinosa, and cucurbitacin, a secondary plant metabolite that induces a compulsive feeding response in A. vittatum. I attempted to increase spinosad’s efficacy as a control for A. vittatum by mixing in a cucurbitacin feeding stimulant, which presumable increases beetle ingestion of insecticide droplets. In laboratory bioassays, the addition of cucurbitacin significantly reduced the LC-50 of spinosad, though bioassay data also suggested that male beetles are more responsive to cucurbitacin compared to female beetles. Field trials in 2014 evaluated the efficacy of three different rates of spinosad/cucurbitacin. There was a dose dependent response to the insecticide, with higher rates of spinosad and cucurbitacin providing the best suppression of A. vittatum compared to an untreated control. These results suggest that integrating spinosad with cucurbitacin has the potential to suppress A. vittatum. In the final chapter, I summarize the results of this thesis and suggest future directions for this work. The management practices detailed in this thesis are not intended to be stand-alone tactics. However, with further refinement, each practice has the potential to play a key role in developing an ecologically based management program for A. vittatum.

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TABLE OF CONTENTS

List of Figures ...... vii

List of Tables ...... x

Acknowledgements ...... xii

Chapter 1 Introduction to Cucurbit Cropping Systems and Acalymma vittatum ...... 1

Thesis Objectives ...... 6 References ...... 9

Chapter 2 Cucurbit Production Practices and their Impact on Pest and Beneficial Arthropod Population Dynamics...... 14

Introduction ...... 14 Strip Tillage ...... 16 Row Covers ...... 16 Carabidae as Bioindicators ...... 17 Materials and Methods ...... 20 Study Site and Field Preparation ...... 20 Plot Design ...... 22 Pest Scouting and Management ...... 23 Above Ground Natural Enemy Scouting...... 24 Pitfall Trapping and Carabidae Activity Density ...... 25 Statistical Analysis ...... 26 Results ...... 28 Pest Population Dynamics ...... 28 Above Ground Natural Enemy Community ...... 29 Description of Epigeal Arthropod Community ...... 30 Carabidae Community...... 31 Carabidae Species Richness ...... 32 Univariate Analysis of Carbaidae Activity Density ...... 33 Production System and Crop Influence on Carabidae Community Composition .... 39 Discussion ...... 40 Carabidae Response to Tillage and Row Covers ...... 42 Conclusion ...... 45 Acknowledgements ...... 47 References ...... 48 Tables ...... 51 Figures ...... 59

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Chapter 3 Assessing the Potential for Conservation Biocontrol in Central Pennsylvania ...... 84

Introduction ...... 84 Materials and Methods ...... 88 Field Sites and Plot Management ...... 88 Field Collection of Adult SCB ...... 89 Parasitoid Rearing in Lab ...... 90 Statistical Analysis ...... 91 Results ...... 91 Discussion ...... 92 References ...... 97 Tables ...... 100 Figures ...... 101

Chapter 4 Integrating Plant and Microbial Metabolites into a Biorational Control Option for Acalymma vittatum ...... 104

Introduction ...... 104 Materials and Methods ...... 109 Materials ...... 109 Beetle Management for Laboratory Bioassays ...... 110 Leak Disk Bioassays ...... 110 Filter Paper Bioassays ...... 111 Whole Leaf Bioassay ...... 112 Field trials: Site Location and Plot Management ...... 113 Scouting ...... 114 Bacterial Wilt Assessment ...... 114 Statistical Analysis ...... 115 Results ...... 117 Cut Leaf Bioassays ...... 117 Filter Paper Bioassays ...... 117 Whole Leaf Dip Bioassays ...... 118 Logistic Regression for the Mortality Response ...... 119 Field Trials ...... 120 Discussion ...... 122 References ...... 127 Tables ...... 131 Figures ...... 136

Chapter 5 Conclusion: Shifting Towards and Ecologically Based Management Program ...... 142

References ...... 146

Appendix ...... 147

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LIST OF FIGURES

Figure 2-1. Mean number of pest beetles per plot across all scouting dates (2013), by cropping systems: ...... 59

Figure 2-2. Mean number of pest beetles per plant across all scouting dates (2014), by cropping system ...... 60

Figure 2-3. Mean numbers of A. vittatum across sampling all scouting dates in 2014, by subplot ...... 61

Figure 2-4. Seasonal activity of four natural enemy taxa from above-ground visual searches (2014 only)...... 62

Figure 2-5. Summary of epigeal arthropod community in 2013...... 62

Figure 2-6. Summary of epigeal arthropod community in 2014...... 63

Figure 2-7. Relative abundance of dominant carabid species in 2013 across all expriments...... 64

Figure 2-8. Relative abundance of dominant carabid species in 2014 across all experiments ...... 65

Figure 2-9. Seasonal curves for the four most abundant Carabid species in 2013 and 2014, pooled across all experiments and production systems ...... 66

Figure 2-10. Species rarefaction curves for Carabidae collected in 2013...... 67

Figure 2-11. Species rarefaction curves for Carabidae collected in 2014...... 68

Figure 2-12. Mean Harpalus pensylvanicus activity density (number beetles / trap / week) in the conventional melon in 2013 ...... 69

Figure 2-13. Mean Cicindela punctulata activity density (number beetles / trap / week) in the conventional melon in 2013...... 69

Figure 2-14. Mean Harpalus pensylvanicus activity density (number beetles / trap / week) in the organic melon in 2013...... 70

Figure 2-15. Mean Poecilus chalcites activity density (number beetles / trap / week) in the organic melon in 2013 ...... 70

Figure 2-16. Mean Harpalus pensylvanicus activity density (number beetles / trap / week) in the conventional squash in 2013...... 71

Figure 2-17. Mean Harpalus pennsylvnaicus activity density (number beetles / trap / week) in the organic squash in 2013 ...... 71 viii

Figure 2-18. Mean Poecilus chalcites activity density (number beetles / trap / week) in the organic squash in 2013 ...... 72

Figure 2-19. Mean Harpalus pensylvanicus activity density (number beetles / trap / week) in the organic melon in 2014...... 73

Figure 2-20. Mean Cicindela punctulata activity density (number beetles / trap / week) in the organic melon in 2014 ...... 73

Figure 2-21. Mean Poecilus chalcites activity density (number beetles / trap / week) in the organic melon in 2014...... 74

Figure 2-22. Mean Bembidion rapdium activity density (beetles / trap / week) in the organic melon in 2014 ...... 74

Figure 2-23. Mean of Bembidion quadrimaculatum oppositum activity density (beetles / trap / week) in the organic melon in 2014...... 75

Figure 2-24. Mean of Harpalus pensylvanicus activity density (beetles / trap / week) in the organic melon in 2014...... 75

Figure 2-26. Mean of Bembidion quadrimaculatum oppositum activity density (beetles / trap / week) in the organic squash in 2014...... 76

Figure 2-27. Mean of Poecilus chalcites activity density (beetles / trap / week) in the organic squash in 2014 ...... 77

Figure 2-28. Mean of Harpalus pensylvanicus activity density (beetles / trap / week) in the conventional melon in 2014...... 77

Figure 2-29. Mean of Poecilus chalcites activity density (beetles / trap / week) in the conventional melon in 2014 ...... 78

Figure 2-30. Mean of Bembidion quadrimaculatum oppositum activity density (beetles / trap / week) in the conventional melon in 2014 ...... 78

Figure 2-31. Mean of Bembidion rapidum activity density (beetles / trap / week) in the conventional melon in 2014 ...... 79

Figure 2-32. Mean of Harpalus pensylvanicus activity density (beetles / trap / week) in the conventional squash in 2014 ...... 79

Figure 2-33. Mean Poecilus chalcites activity density (beetles / trap / week) in the conventional squash in 2014 ...... 80

Figure 2-34. Mean Bembidion quadrimaculatum oppositum activity density (beetles / trap / week) in the conventional squash in 2014 ...... 80

Figure 2-35. Mean of Bembidion rapidum activity density (beetles / trap / week) in the conventional squash in 2014 ...... 81 ix

Figure 2-36. Multi-variate bi-plot demonstrating how crop, management, soil production system, and row cover shape key species within 2013 Carabidae community ...... 82

Figure 2-37. Multi-variate bi-plot demonstrating how crop, management, soil production system, and row cover shape key species within 2014 Carabidae community...... 83

Figure 3-1. Centistes diabroticae in a silken cocoon (Photo: Margaret Lewis)...... 101

Figure 3-2. Tachinid pupa that did not fully separate from A. vittatum abdomen (Photo: Margaret Lewis) ...... 101

Figure 3-3. Tachinid pupa that did not fully separate from A. vittatum abdomen (Photo: Margaret Lewis) ...... 101

Figure 3-4. Seasonal parasitism rates by C. setosa and C. diabroticae at the Landisville in 2014 ...... 102

Figure 3-5. Seasonal parasitism rates by C. setosa and C. diabroticae at Rock Springs in 2014 ...... 102

Figure 3-6. Total parasitism (C. diabroticae plus C. setosa) rates by week at Rock Springs and Landisville field sites...... 103

Figure 4-1. Mean corrected A. vittatum mortality ± SE across all concentration of spinosad for the filter paper bioassays at: (a) 24 hours (b) 48 hours and (c) 72 hours post exposure...... 136

Figure 4-2. Average beetle mortality with Abbots Correction across all treatments for the leap dip bioassay: ...... 137

Figure 4-3. Estimated LC-50 values for spinosad only and spinosad with cucurbitacin for the whole leaf dip bioassay...... 138

Figure 4-4. Logistic regression curves modeling the probability of male and female A. vittatum mortality at each dose...... 138

Figure 4-5. Proportion of male/female beetles that died at a given concentration in the whole leaf dip bioassays at the 72 hour timepoint ...... 139

Figure 4-6. Average number of A. vittatum per plant ± SE across all sampling dates for week 1 of the field trials (June 10th – June 17th)...... 139

Figure 4-7. Average number of A. vittatum per plant ± SE across all sampling dates for week 2 of the field trials (June 17th – June 24th)...... 140

Figure 4-8. Average number of A. vittatum ± SE per sampling unit across all sampling dates in week 3 (June 24th – July 1st)...... 140

Figure 4-9. Average number of A. vittatum ± SE per sampling unit across all sampling dates in week 4 (June 1st – July 4th)...... 141 x

LIST OF TABLES

Table 2-1. Summary of dates for cover crop management and planting dates in 2013 and 2014. Management practices not applicable for a particular system are indicated with a (-). Con=conventional ...... 21

Table 2-2. Summary of row cover removal dates in 2013 and 2014 ...... 23

Table 2-3. Output of mixed model ANOVA for treatment effects on populations of A . vittatum in 2014, across all cropping systems ...... 51

Table 2-4. Meanwise comparison of mean A. vittatum per plot, across all sampling dates, in the conventional melon. Numbers within a column that do not share a letter differ significantly (p=0.05)...... 51

Table 2-5. Summary of natural enemy scouting in 2014. Numbers are given as a total sum of natural enemies observed across all sampling dates and treatments within a given systems ...... 52

Table 2-6. List of Carabidae species found in 2013 and their relative abundance pooled across all four cropping systems ...... 52

Table 2-7. List of Carabidae species found in 2014and their relative abundance pooled across all four cropping systems ...... 53

Table 2-8. Mixed Model ANOVA output for key Carabid Species in conventional melon (2013) ...... 54

Table 2-9. Mixed Model ANOVA output for key Carabid Species in organic melon (2013) ...... 54

Table 2-10. Mixed Model ANOVA output for key Carabid Species in conventional squash (2013) ...... 54

Table 2-11. Mixed Model ANOVA output for key Carabid Species in organic squash (2013) ...... 55

Table 2-13. Mixed model ANOVA output for key Carabid species in organic squash (2014) ...... 56

Table 2-14. Mixed model ANOVA output for key Carabid species in conventional melon (2014) ...... 56

Table 2-15. Mixed model ANOVA output for key Carabid species in conventional squash (2014) ...... 57

Table 2-16. Summary statistics from CCA analysis for key 2013 Carabidae species ...... 58

Table 2-17. Summary statistics from CCA analysis for key 2014 Carabidae species ...... 58 xi

Table 3-1. Summary of parasitism rates for Celatoria setosa, Centistes diabroticae, and total combined parasitism rates at the Rock Springs (RS) and Landisville (LD) field sites...... 100

Table 4-1. Corrected A. vittatum mortality ± SE across different concentrations of spinosad 24 hours after exposure. Mortality was recorded 24 hours after exposure. N refers to the number of beetles tested at each concentration...... 131

Table 4-2. Chi-square statistics for binary logistic regression predicting A. vittatum mortality response to spinosad and spinosad with cucurbitacin. Regression was calculated from data collected in the whole leaf dip bioassays, 72 hours after exposure...... 131

Table 4-3. Repeated measures ANOVA output for the average number of beetles per plot by treatment. Date was included in the model as a repeated measure. All data collected in a given week was pooled for purposes of analysis...... 132

Table 4-4. One way ANOVA output for the average number of A. vittatum per plot on individual dates for the first week of sampling. Means within a column that have the same letter are not significantly different by Tukey’s HSD test (p ≤ 0.05) ...... 132

Table 4-5. One way ANOVA output for the average number of A. vittatum per plot on individual dates for the second week of sampling. .Means within a column that have the same letter are not significantly different by Tukey’s HSD test (p ≤ 0.05) ...... 133

Table 4-6. One way ANOVA output for the average number of A. vittatum per plot on individual dates for the third week of sampling. Means within a column that have the same letter are not significantly different by Tukey’s HSD test (p ≤ 0.05) ...... 133

Table 4-7. One way ANOVA output for the average number of A. vittatum per plot on individual dates for the third week of sampling. Means within a column that have the same letter are not significantly different by Tukey’s HSD test (p ≤ 0.05) ...... 134

Table A-1. Mean number of A. vittatum per subplot treatment by cropping system in 2013 and 2014. Data was pooled across all sampling dates...... 147

Table A-2. Total yields ± SE by subplot in the conventional summer squash (2013-2014) .... 148

Table A-3. Total yields ± SE by subplot in the organic summer squash (2013-2014) ...... 148

Table A-4. Total yields ± SE by subplot in the conventional muskmelon (2013-2014) ...... 148

Table A-5. Total yields ± SE by subplot in the organic muskmelon (2013-2014) ...... 148

Table A-6. Total number of insecticide sprays applied by subplot treatment in 2013 and 2014, for individual cropping systems. Data from (Lilley 2015) ...... 149

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ACKNOWLEDGEMENTS

Developing this thesis would not have been possible without the support of many individuals. In particular, I would like to thank my advisor, Shelby Fleischer, for his continued support, enthusiasm, and advice throughout the entire process. I also would like to thank my committee, John Tooker, and Elsa Sanchez, for their valuable input and advice. Your comments played a key role in shaping this thesis into its final product. Additionally, I would like to thank the staff at the Southeast Agricultural Research and Extension Center, particularly Tim Elkner and Alyssa Collins. The staff at the SEARC assumed primarily responsibility for plot maintenance, and a number of interns helped with data collection. I’d also like to thank Chris Mullin, who provided valuable advice in developing bioassays, and Stefanie Austin at the Statistical Consulting Center for her help in both developing and understanding the models I used to analyze data. The work conducted in Chapter 2 would not have been possible without support from many individuals. In particularly, Dana Roberts, Rachael Troyer, and Brianna Reed were responsible for collecting all of the data that I analyze in 2013, as well as some of the data in 2014. Jason Lilley also helped with data collection in 2014, and assumed responsibility for plot management and maintenance. I’d also like to thank Maggie Douglas for her assistance and input as I identified the Carabid beetles. I also would like to thank the rest of the Fleischer lab, including Kristal Watrous, Kevin Rice, Carley Miller, and William Mitchell. Thank you for your support, encouragement, advice, and for your patience when I filled the lab with beetles. I’d like to thank the Department of Entomology, including the graduate students, the faculty, and office staff for creating a supportive environment. I was fortunate enough to meet many wonderful individuals during my two years here and quickly found a strong support system within the department. To my friends and family, thank you for continued support and patience. At times, your confidence in me and my ability to finish this degree was the only thing that pushed me to keep moving forward. Joshua Prudent, thank you for being with me every step of the way, through the highs and lows of the processes, and for the unconditional love and support. Funding for the work detailed in this thesis came from the USDA Specialty Crops Research Initiative (SCRI) as well as the Pennsylvania Vegetable Growers Association. 1

Chapter 1

Introduction to Cucurbit Cropping Systems and Acalymma vittatum

The Cucurbitaceae is a widely distributed and diverse family of plants that encompasses approximately 130 genera and 960 species worldwide (Schaefer et al. 2009). Within Cucurbitaceae, a there are a number of species that produce edible fruits and are commonly cultivated for commercial markets. Common crops include pumpkins (Cucurbita pepo L.), cultivated squashes, (for example Cucurbita moschata Duchesne and Cucurbita maxima Duchesne), cucumbers (Cucumis sativus L.), and various types of melons, such as muskmelon, Cucumis melo L. (Nee, 1990, Hurd et al. 1971). Cucurbitacins are a group of oxygenated, tricyclic terpenes produced as a secondary plant metabolite within Cucurbitaceae (Metcalf et al. 1982). They are extremely bitter and toxic to many mammal and insect herbivore species. In some ancestral cucurbit species, such as the buffalo gourd, Cucurbita foetedissma Kunth, a high concentration of cucurbitacin in the fruiting body creates a bitter taste that renders the fruit inedible (Berry et al. 1975). Cucurbitacin is thought to have evolved as a plant defensive mechanism; it acts as a repellant against most insects and mammals (Ferguson 1985, Tallamy et al. 1989, Behle 2001). However, there are a number of specialist herbivores that have that have adapted to the cucurbitacins and achieved pest status in cultivated cropping systems. In particular, Diabroticite beetles (an informal section of beetles within the subtribe Diabroticina) have developed a strong association with cucurbitacins. Instead of acting as a repellant, cucurbitacin induces a compulsive feeding response in many Diabroticite species (Metcalfe et al. 1980, Metcalfe et al. 1982). It is hypothesized that an adult beetles can sequester cucurbitacin in its body tissue as a defensive strategy. The embittered tissues are thought to deter potential predators (Ferguson & Metcalfe 1985, Behle 2001). For example, under laboratory conditions, the Chinese praying mantis (Tenodera sinensis Saussure) was able to differentiate between Diabroticite beetles based 2 on their previous dietary history, preferentially consuming beetles reared on a cucurbitacin poor diet (Ferguson & Metcalfe 1985). Cucurbitacins may also play an important role in Diabroticite mating rituals. Male spotted cucumber beetles (Diabrotica undecimpunctata howardi Barber) deposit cucurbitacins into a spermataphore that is then presented to a female as ritual nuptial gift (Tallamy et al. 1993). The striped cucumber beetle, Acalymma vittatum, is a specialist herbivore and key pest of cucurbits in the Northeastern United States. Larvae are subterraneous and feed exclusively on cucurbit roots (Balduf 1925, Ellers-Kirk & Fleischer 2006). Adult beetles feed on foliage, flowers, and fruit. Past studies have demonstrated that cucumber beetle feeding can reduce yield in cultivated crops. In addition to reducing the total area of photosynthetic leaf tissue, direct foliar feeding by adults has been shown to significantly reduce both flower output and the number of pollen grains per staminate flower (Quesada et al. 1995, Sasu et al. 2012). Of greater concern, A. vittatum also acts as a competent vector for the bacterial pathogen Erwinia tracheiphila Smith, which causes a bacterial wilt disease in cucurbits. E. tracheiphila enters a plants xylem, where it replicates, and eventually obstructs water transport through the xylem vessels (Sasu et al. 2010, Saalau Rojas et al. in publication). Initial wilt symptoms include leaf necrosis and flaccidity; as the disease progresses, stems begin to wilt, and ultimately, the infection is fatal to the plant. Once a plant is infected with the bacterium, it is impossible to halt disease progression. Susceptibility to bacterial wilt varies by plant species. Among the cultivated cucurbits, cucumbers and muskmelons are considered most susceptible to wilt infection. Watermelons are considered very resistant to a wilt infection (Rand 1916). Wilt susceptibility also varies with crop age; as crops grow larger, their susceptibility decreases (Brust 1997). Erwinia tracheiphila is transmitted through A. vittatum frass (Garcia-Salazer et al. 2000). When A. vittatum feeds on an infected plant, E. tracheiphila remains in the beetle gut. It enters a host plant when beetle frass enters fresh leaf wounds or floral nectaries (Sasu et al. 2010, Salauu-Rojas et al 2015). Given the potential severity of a bacterial wilt outbreak, and the fact that there is currently no means of managing the disease, thresholds for A. vittatum are low, especially early in the season, when cucurbits are most susceptible to infection. The most recent 3 economic threshold for A. vittatum dictates that growers spray insecticides on young seedlings when there is one beetle per ten plants (Brust 1999). As the crop matures, the threshold increases to one beetle per one plant (Brust 1999). However, in practice, spraying typically happens more frequently than the threshold dictates; there can be between two and eight sprays per season, depending on beetle pressure (Brust et al. 1999, Cline et al. 2009). Seed treatments, including systemic neonicotinoids have also become more common. Synthetic insecticides, including pyrethroids, carbamates, and neonicotinoids, provide fairly consistent control of A. vittatum (Cline et al. 2009, Rice et al. 2001). However, in organic cucurbit production, these materials are excluded from use. Organic insecticide formulations, including pyrethrum, spinosad, neem oil, and kaolin clay, are available for beetle management. However, they tend to be expensive, ineffective, and typically are less persistent in the field compared to many synthetic formulations (Cline et al. 2009). There are a number of concerns associated with the current reliance on insecticides to manage A.vittatum. One concern is the potential for the development of insecticide resistance, especially given reports of resistance in closely related beetles. For example, populations of the Colorado potato beetle, Leptinotarsa decemlineata Say, are now resistant to imidacloprid. Imidacloprid is a systemic neonicotinoid often used to manage A. vittatum (Mota-Sanchez et al. 2006). There are also reports of cross resistance in L. decemlineata, with impadacloprid resistant beetles showing signs of tolerance to the systemic neonicotinoids thiamethoxam and clothianidin (Alyokhin et al. 2007). Beyond the potential for resistance, regular insecticide applications can be detrimental to beneficial insect populations, directly and indirectly affecting pollinators and natural enemies. The contribution of insecticides to recent declines in managed honeybee populations (Apis mellifera Linnaeus) is well documented (Iwasa et al. 2004, Pilling et al. 2013, Costa et al. 2014). Exposure to residues of imidacloprid has also been shown to cause dose-dependent reproductive declines in Bombus terrestris Linnaeus (Laycock et al. 2012). Soil applications of neonicotinoids also result in sub-lethal concentrations of insecticide residues within both the pollen and the nectar of squash seedlings (Stoner & 4 Eitzer 2012) .When neonicotinoids are applied as a foliar insecticide, another practice common in cucurbit production, again residues are found both in the pollen and nectar, although limiting use patterns to seed treatments resulted in concentration below detectable levels (Dively & Kamal 2012). Neonicotinoid seed treatments, fungicides, and fertilizer applications also place additional stress upon both native and managed pollinators. There is a clear need for a greater diversity of A. vittatum management strategies that can be incorporated into cucurbit production systems. Alternative management options do exist, though most are not commonly practiced in commercial production. Naturally occurring microbial metabolites can be formulated into insecticide products that are potentially less disruptive to the agroecosystem. Spinosad, for example, is an organic insecticide derived from the naturally occurring soil bacterium Saccaropolyspora spinosa Mertz & Yao (Thompson 2000). Spinosad is acutely toxic to pollinators when residues are wet; however, studies suggest that dry residues are less harmful. Additionally, spinosad possesses reduced toxicity towards mammals and some beneficial insects within cropping systems, including the minute pirate bug, lacewings, and lady beetles (Thompson et al. 2000). Perimeter trap cropping can be used to reduce the number of insecticide sprays applied. There are specific species within Cucurbitaceae, for example blue hubbard squash (Cucurbita maxima Duchesne), that are more attractive to A. vittatum. These crops are planted on the border of the main field, resulting in a heavy A. vittatum infestation within a smaller area, that is then sprayed. Reducing the number of sprays in the main field is thought to create a refuge for pollinators and other beneficial insects (Cavanagh et al. 2009, Cavanagh et al. 2010). Though effective, growers may be hesitant to employ trap cropping systems, particularly given the potential time, machinery, and labor that would be needed to plant and manage two separate crops. Additionally, trap crop products sometimes have a lower market value compared to the main crop (reviewed Hokkanen et al. 1991, Cavanagh et al. 2010). Floating row covers are another potential management strategy; large sheets of spunbonded fabric are draped over one or more rows of crop immediately after planting. This creates a barrier that separates seedlings from the outside environment. The row 5 cover is left on until shortly after flowering to allow for pollination. Traditionally, removal happens immediately after anthesis. However, Rojas et al. (2011) found that for muskmelon (Cucumis melo), delaying row cover removal by ten days after anthesis significantly enhanced bacterial wilt suppression and increased yield compared to row covers removal immediately after flowering. Additionally, efforts to develop permanent, season long row covers for cucurbits are ongoing, with pollination services provided by native, ground nesting pollinators that are manually placed under the covers (Logan & Bessin 2014). Row covers provide growers with several benefits. They increase yield, both by protecting seedlings from inclement weather and by creating a warm microclimate that promotes faster growth (Nair & Ngouajio 2010, Rojas et al. 2012). They also act as a pest exclusion barrier. There are a number of vegetable cropping systems in which row covers have successfully protected plants from herbivory and disease transmission, including cucurbits, bell peppers, and tomatoes (reviewed Hilje et al. 2000, Orozco-Santos et al. 1995). The pest exclusion aspect of row covers is especially relevant for managing A. vittatum; cucurbits are most susceptible to bacterial wilt infections as seedlings (Brust et al. 1999, Rojas et al. 2012). One major drawback of row covers is cost. Both their deployment and removal is an extremely labor intensive processes (Rojas et al. 2012). There are several known biocontrol agents of A. vittatum, though to the best of my knowledge, little has been done to study their incorporation in IPM programs. Two solitary endoparasitoids are known to attack the adult beetle: a tachinid fly, Celatoria setosa Coquillett, and a braconid wasp, Centistes diabroticae Shimer (Smyth & Hoffmann 2010, Fischer 1983). Little is known about the biology and life history of either species. Celatoria setosa was first described from Illiniois in 1871 by Shimer (Smythe & Hoffmann 2010) and is known to parasitize three species of Acalymma: A. vittatum, A. trivttatum, and A. blandula (Fischer 1983, Toepfer et al. 2009). C. setosa activity has been reported in a number of states, including New Hampshire, Ohio, Arkansas, New York, and Indiana, with parasitism rates ranging from 41% - 54% (Fischer 1983, Smyth & Hoffmann 2010, Phillips 2013). Centistes diabroticae was first reported and described in Ohio in 1922 by A.B. Gahan. This species has been poorly studied, and there are few subsequent reports of C. diabroticae activityThe most recent 6 record of C. diabroticae activity comes from New York, where reported parasitism rates ranged from 5% up to 54% (Smythe & Hoffmann 2010). Currently, mass rearing either parasitoid species as part of an augmentive biocontrol program for A. vittatum is not feasible. First, the beetle host itself is difficult to rear. There are small scale rearing protocols in place for A. vittatum; however, the process is very labor intensive. With current protocols, it is not possible to guarantee a continuous supply of the beetle host year round. The diapause biology of A. vittatum is poorly understood; in winter, egg laying rates and adult emergences drop significantly, even in a highly controlled environment that maintains a steady temperature, humidity, and photoperiod. Second, there are no small scale or mass rearing protocols in place for either parasitoid. Both species appear to have highly specific mating cues that are not well understood (Fischer 1983, Smyth & Hoffman 2010). Other potential biocontrol agents are entomopathogenic nematodes (EPNs). Several species of EPNs, including Steinernema riobrave Weiser and Heterorhabditis bacteriophora Poinar are known to attack larval A.vittatum. Additionally, EPNs are also known to attack other Diabroticite species, including the spotted cucumber beetle, Diabrotica undecimpunctata howardii Barber, and the western corn rootworm, Diabrotica virgifera virgifera LeConte (Barbercheck & Wang 1996, Toepfer et al. 2014). Nematodes can be purchased commercially and delivered through drip irrigation systems. In combination with black plastic mulch, the introduction of EPNS reduced adult A. vittatum emergence by as much as 50% (Ellers-Kirk et al. 2000). However, one factor that merits consideration is the tri-trophic interactions between the nematodes and cucurbitacins. Larval A. vittatum are known to sequester cucurbitacins in their tissue; it is likely that the cucurbitacin interacts with nematode efficiency as a control agent (Barbercheck & Wang 1996, Eben & Barbercheck 1997).

Thesis Objectives

With the exception of floating row covers, the alternative management practices described have yet to be widely adapted by farmers. Reasons behind this reluctance vary; 7 growers are influenced by economic concerns, labor requirements, or simply a lack of reliable information on a given tactic’s efficacy. Through the course of this thesis, my overall objective will be to assess and advance sustainable management practices for Acalymma vittatum. I plan to explore three potential management options for Acalymma vittatum control. In Chapter 2, I evaluate how strip tillage, plastic mulch, and row cover use impact the pest and beneficial insect population dynamics within four independent cucurbit cropping systems. By conducting this experiment across multiple systems, I hope to obtain a clearer understanding of the widespread applicability of each practice across different crops, and conventional versus organic systems. Strip tillage and the application of row covers are two management tactics that have been employed separately in a variety of vegetable cropping systems and are believed to be less disruptive to the agroecosystem. In this chapter, I integrate both practices, a novel approach to cucurbit production. Through their integration, I hope to provide additional management options for organic and conventional cucurbit growers and to begin shifting cucurbit production in the direction of a more conservation oriented approach. In Chapter 3, I assess the potential for conservation biocontrol of adult Acalymma vittatum within the state of Pennsylvania. There are two known parasitoids, Celatoria setosa and Centistes diabroticae. There are reports of parasitoid activity in neighboring states, though to the best of my knowledge, there are no parasitism records in Pennsylvania. In this chapter, I monitor parasitism rates at two field sites in Pennsylvania. Finally, in Chapter 4, I evaluate the potential for microbial metabolites as a biorational insecticidal option for Acalymma vittatum. Through laboratory bioassays and field trials, I attempted to increase the efficacy of the organic insecticide spinosad using a cucurbitacin based feeding stimulant. Spinosad is primarily an oral insecticide and currently not a reliable management options for A.vittatum. In this chapter, I explore the idea that its failure stems from limited ingestion of insecticide material and attempt to overcome this limitation with a feeding stimulant. Materials were tested through laboratory bioassays and field efficacy trials. Organic insecticide options for cucurbit growers are extremely limited; by integrating plant and microbial metabolites, I hope to provide organic growers with increased options for chemical control. 8 The approaches discussed in this thesis are not intended to be stand-alone management practices. I aim to increase our understanding of each tactic and generate knowledge that addresses grower concerns about the efficacy, implantation, and reliability of each method. This information can be used to incorporate each practice into a more ecologically based pest management program that conserves both natural enemy and native pollinator populations.

9 References

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Environmental Entomology, 39(6), 1953-1960. Cline, G. R., Sedlacek, J. D., Hillman, S. L., Parker, S. K., & Silvernail, A. F. (2008). Organic management of cucumber beetles in watermelon and muskmelon production. HortTechnology, 18(3), 436-444. 10 Costa, E. M., Araujo, E. L., Maia, A. V. P., Silva, F. E. L., Bezerra, C. E. S., & Silva, J. G. (2014). Toxicity of insecticides used in the Brazilian melon crop to the honey bee Apis mellifera under laboratory conditions. Apidologie, 45(1), 34-44. Dively, G., & Kamel, A. (2012). Insecticide residues in pollen and nectar of a cucurbit crop and their potential exposure to pollinators. Journal of agricultural and food chemistry, 60(18), 4449-4456. Eben, A., & Barbercheck, M. E. (1997). Host plant and substrate effects on mortality of southern corn rootworm from entomopathogenic nematodes. Biological Control, 8(2), 89-96. Ellers-Kirk, C. D., Fleischer, S. J., Snyder, R. H., & Lynch, J. P. (2000). Potential of entomopathogenic nematodes for biological control of Acalymma vittatum (Coleoptera: Chrysomelidae) in cucumbers grown in conventional and organic soil management systems. Journal of Economic Entomology, 93(3), 605-613. Ferguson, J. E., & Metcalf, R. L. (1985). Cucurbitacins plant derived defense compounds for Diabroticites (Coleoptera: Chrysomelidae). Journal of Chemical Ecology, 11(3), 311- 318. Fischer, D. C. (1983). Celatoria diabroticae Shimer and Celatoria setosa Coquillet: Tachinid parasiotids of the Diabroticite Coleoptera (Unpublished doctoral dissertation). University of Illinois, Urbana-Champagne, IL. Garcia-Salazar, C., Gildow, F. E., Fleischer, S. J., Cox-Foster, D., and Lukezic, F. L. 2000. ELISA versus immunolocalization to determine the association of Erwinia tracheiphila in Acalymma vittatum (Coleoptera: Chrysomelidae). Environmental Entomology. 29(1): 542-550. Hilje, L., Costa, H. S., & Stansly, P. A. (2001). 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Metcalf, R. L., Metcalf, R. A., & Rhodes, A. M. (1980). Cucurbitacins as kairomones for diabroticite beetles. Proceedings of the National Academy of Sciences, 77(7), 3769-3772 Metcalf, R. L., Rhodes, A. M., Metcalf, R. A., Ferguson, J., Metcalf, E. R., & Lu, P. Y. (1982). Cucurbitacin contents and Diabroticite Coleopetera Chrysomelidae feeding upon Cucurbita spp. Environmental Entomology, 11(4), 931-937. Minter, L. M., & Bessin, R. T. (2014). Evaluation of native bees as pollinators of cucurbit crops under floating row covers. Environmental Entomology, 43(5), 1354-1363. Mota-Sanchez, D., Hollingworth, R. M., Grafius, E. J., & Moyer, D. D. (2006). Resistance and cross-resistance to neonicotinoid insecticides and spinosad in the Colorado potato beetle, Leptinotarsa decemlineata (Say) (Coleoptera : Chrysomelidae). Pest Management Science, 62(1), 30-37. Nair, A., & Ngouajio, M. (2010). Transmission and control of bacterial wilt of cucurbits. Hort Science, 45(4), 566-577. Nee, M. (1990). The domestication of Cucurbita (Cucurbitaceae). Economic Botany,44(3), 56-68. Orozco-Santos, M., Perez-Zamora, O., & Lopez-Arriaga, O. (1995). Floating row cover and transparent mulch to reduce insect populations, virus diseases and increase yield in cantaloupe. Florida Entomologist, 78(3), 493-501. Pilling, E., Campbell, P., Coulson, M., Ruddle, N., & Tornier, I. (2013). A four-year field program investigating long-term effects of repeated exposure of honey bee colonies to flowering crops treated with Thiamethoxam. [Article]. Plos One, 8(10), 14. Phillips, B. (2013). The Ecological impacts of non-native annual and native perennial floral insectaries on beneficial insect activity density and arthropod-mediated ecosystem services within Ohio Pumpkin (Cucurbita pepo). (Unpublished Masters Thesis). Department of Entomology, The Ohio State University, Columbus, OH. Quesada, M., Bollman, K., & Stephenson, A. G. (1995). Leaf damage decreases pollen production and hinders pollen performance in Cucurbita texana. Ecology (Washington D C), 76(2), 437-443. 12 Rand, F. V., & Enlows, E. M. A. (1916). Transmission and control of bacterial wilt of cucurbits. Journal of Agricultural Research, 6, 417-434. Rehm, S., Enslin, P. R., Meeuse, A. D. J., & Wessels, J. H. (1957). Bitter principles of the cucurbitaceae. VII.—the distribution of bitter principles in this plant family. Journal of Science of Food and Agriculture, 8(12), 679-686. Rice, P. J., McConnell, L. L., Heighton, L. P., Sadeghi, A. M., Isensee, A. R., Teasdale, J. R., et al. (2001). Runoff loss of pesticides and soil: A comparison between vegetative mulch and plastic mulch in vegetable production systems. Journal of Environmental Quality, 30(5), 1808-1821. Rojas, E. S., & Gleason, M. L. (2012). Epiphytic survival of Erwinia tracheiphila on muskmelon (Cucumis melo L.). Plant Disease, 96(1), 62-66. Sasu, M. A., Seidl-Adams, I., Wall, K., Winsor, J. A., & Stephenson, A. G. (2010). Floral transmission of Erwinia tracheiphila by cucumber beetles in a wild Cucurbita pepo. Environmental Entomology, 39(1), 140-148. Saalau Rojas, E. Batzer, J. Beattie, G. Fleischer, S.J., Shapiro, L.R., Williams, M.A., Bessin, R., Bruton, B.D., Coucher, T.J., Jesse, L.C., Gleason, M.L. (2015). Bacterial wilt of cucurbits: resurrecting a classic pathosystem. Unpublished manuscript. Schaefer, H. (2009). "Gourd afloat: a dated phylogeny reveals an Asian origin of the gourd family (Cucurbitaceae) and numerous oversea dispersal events." Proceedings of the Royal Society B: Biological Sciences 276(1658): 843-851. Smyth, R. R., & Hoffmann, M. P. (2010). Seasonal incidence of two co-occurring adult parasitoids of Acalymma vittatum in New York State: Centistes (Syrrhizus) diabroticae and Celatoria setosa. BioControl, 55(2), 219-228. Stoner, K. A., & Eitzer, B. D. (2012). Movement of soil-applied imidacloprid and thiamethoxam into nectar and pollen of squash (Cucurbita pepo). Plos One, 7(6), 5. Tallamy, D. W., & Halaweish, F. T. (1993). Effects of age, reproductive activity, sex, and prior exposure on sensitivity to cucurbitacins in southern corn rootworm Coleopetera (Chrysomelidae). Environmental Entomology, 22(5), 925-932. Tallamy, D. W., & Krischik, V. A. (1989). Variation and function of cucurbitacins in Cucurbita: an examination of current hypotheses. American Naturalist, 133(6), 766-786. Thompson, G. D., Dutton, R., & Sparks, T. C. (2000). Spinosad: A case study: An example from a natural products discovery programm. Pest Management Science, 56(8), 696-702. 13 Toepfer, S., Knuth, P., Glas, M., & Kuhlmann, U. (2014). Successful application of entomopathogenic nematodes for the biological control of western corn rootworm larvae in Europe - a mini review. Tagungsband: Internationale F, 444, 59-66. Toepfer, S., Peters, A., Ehlers, R. U., & Kuhlmann, U. (2008). Comparative assessment of the efficacy of entomopathogenic nematode species at reducing western corn rootworm larvae and root damage in maize. Journal of Applied Entomology, 132(5), 337-348.

14 Chapter 2

Cucurbit Production Practices and their Impact on Pest and Beneficial Arthropod Population Dynamics

Introduction

The gourd family, Cucurbitaceae is a diverse group of plants that contains approximately 960 species worldwide (Schaefer et al. 2009). Within Cucurbitaceae, a number of species commonly cultivated as crops, including cucumbers, melons, various types of squashes, and pumpkins, are susceptible to attack from a diverse group of pests. Depending on geographic location, arthropod pests include the squash vine borer, Melitta curcurbitae Harris, the squash bug, Anasa tristis De Greer, the melon aphid, Aphis gossypii Glover, and Diabroticite beetles, most notably the striped cucumber, Acalymma vittatum Fabricus (Caldwell et al. 2014). In addition to arthropod pests, weeds can also greatly diminish cucurbit yield, particularly if weed pressure builds up early in the growing season (Macrae et al. 2008). In the northeastern United States, striped cucumber beetle is considered a key pest of cucurbits. Adult beetles feed on foliage, flowers, and fruit. Of greater concern, they also act as a competent vector for Erwinia tracheiphila¸ the causal agent of a bacterial wilt disease in cucurbits (Rojas et al. 2015). Once a plant is infected with this bacterium, it is impossible to halt disease progression, meaning that disease management happens primarily through the control of the beetle vector. Current management options for A. vittatum are limited. Synthetic insecticides, including neonicotinoids, pyrethroids, and carbamates, provide fairly consistent control of A. vittatum (Cline et al. 2009). The number of sprays per season varies depending on beetle density, but typically ranges between two and eight sprays per season (Brust et al. 1999, Cline et al. 2009). In organic systems, available organic insecticide formulations include spinosads, pyrethrum, kaolin clay, and neem oil. However, these materials tend to be expensive, less effective, and less persistent than many of their conventional counter parts (Cline et al. 2008, Cavanagh et al. 2011). 15 Black plastic (polyethylene) mulch is also commonly applied in both conventional and organic cucurbit production systems. In addition to suppressing weeds, the plastic mulch raises soil temperature and reduces water evaporation. That in turn increases both soil and plant productivity, ultimately increasing a grower’s expected yield (Lamont 1993, Necibi et al. 1992). Additionally, some theorize that the plastic mulch provides a certain level of control against A. vittatum by reducing egg and larval survivorship (Necibi et al. 1992). Reflective silver and silver-on-black plastic mulches are also thought to repel both striped and spotted cucumber beetles, potentially due to higher light reflectance causing insect disorientation (Andino & Motsenbocker 2004). Despite these advantages, there are a number of environmental issues associated with plastic mulch use. The question of mulch disposal has been well publicized in recent years, given that mulch is typically not degradable (Lamont 1993), though biodegradable technology is rapidly improving. Past studies have documented that in a plasticulture system, precipitation runoff contains increased levels of insecticide residues (Rice et al. 2001), compounding the negative environmental impact of insecticides. In addition, plastic mulch application requires conventional soil tillage, a practice in which the entire top layer of soil is turned over via primary tillage implements such as moldboard plows (Phillips et al. 1980). Conventional tillage is a very disruptive practice. It has been linked to loss of soil nutrients (Baker et al. 2007, Sainju et al. 2013), increased agricultural runoff, and a loss of topsoil (Rhoton et al. 2002). Conventional soil tillage is also considered very disruptive to the agroecosystem, particularly beneficial ground dwelling arthropods. The squash bee, Peponsapis pruinosa Say, is a solitary ground nesting bee that acts as specialist pollinator for cucurbit crops. Past studies suggest that under the right management conditions, P. pruinosa can make substantial contributions towards cucurbit pollination, particularly in the absence of managed honeybees (Cane et al. 2011, Winfree et al. 2007, Shuler et al. 2005). However, P. pruinosa tends to nest in the soil near its host plants, leaving itself very vulnerable to ground disturbances like conventional tillage (Shuler et al. 2005). 16 Strip Tillage

Strip till is a form of conservation or reduced tillage. A cover crop is generally planted in the fall before the growing season and is terminated prior to planting using a roller crimper implement (Walters & Kindhart 2002), by mowing and/or by the use of herbicides. Narrow strips of soil are cultivated within the cover crop residue where crops are planted. With strip tillage, it is hoped that some of the benefits associated with a plasticulture system, which includesconventional tillage, raised beds, drip tape, and plastic mulch, can still be obtained, particularly weed suppression (Walters & Kindhart 2002). Additionally, given the minimal disturbance to the field, this form of reduced tillage has the potential to support a stronger, more diverse beneficial insect community, allowing crops to obtain increased levels of biocontrol and pollinator services. Strip tillage is also thought to reduce agricultural runoff (Rapp et al. 2004) and preserve more soil organic matter, including surface residues, carbon, and nitrogen content (Sainju et al. 2013). With a strip tillage system, growers also avoid applying plastic mulch, avoiding issues of waste disposal.

Row Covers

Polypropylene row covers are typically installed immediately after planting. Large sheets of fabric are supported by wire hoops and draped over an entire row of crops, or are supported by plants and draped over multiple rows of crops, creating a complete barrier that separates seedlings and transplants from the outside environment. In addition to providing protection from inclement weather (Nair & Ngouajio 2010, Rojas et al. 2012), row covers also physically exclude arthropod pests. Row covers have been successfully deployed in a variety of vegetable cropping systems, including cucurbits, bell peppers, and tomatoes, and have prevented both herbivore damage and disease transmission (Hilje et al. 2000, Orozco-Santos et al. 1995). Though effective, a major drawback of row covers is that they significantly increase production costs. This cost can 17 be offset by both an increase in yields and earlier crop harvest. The row covers have to be removed soon after anthesis to allow for proper pollination and fruit set. Both their deployment and removal requires manual labor (Rojas et al. 2012) although mechanical methods are being investigated. Row covers are an especially promising tool for striped cucumber beetle and bacterial wilt management. Cucurbits are most vulnerable to bacterial wilt infection as young seedlings; as the season progresses and the plant grow larger, plant susceptibility decreases (Rojas et al. 2012). Consequentially, economic thresholds are lowest and the frequencies of insecticide sprays are highest in the period immediately following seedling transplant. By creating a physical barrier during this vulnerable period, growers may be able to significantly reduce the amount of insecticide material applied and the number of sprays applied throughout the course of a growing season.

Carabidae as Bioindicators

Understanding how these production practices influence beneficial insect populations will be a key step needed to successfully integrate these practices into IPM programs. Ground beetles (Coleoptera: Carabidae) are a group of generalist predators common to most agricultural systems. Though considered voracious predators, their role in providing biocontrol services is poorly understood. Some species of carabids are considered primarily predators of weed seeds, while others tend to favor invertebrate prey (Leslie et al. 2010). Several studies have attempted to answer this question of carabid biocontrol services. One found that increased carabid immigration significantly decreased the number of D. undecimpunctata howardi adults found within cucurbit plots, though no other changes in insect pest density or predatory activity were observed (Snyder & Wise 1999). Contrasting these results, another study found that artificially increasing carabid abundance in field corn plots significantly increased predation on sentinel prey (Menalled et al. 1999). 18 Independent of their role in biocontrol, carabid beetles are considered a very good bioindicator for environmental disturbance. Of practical concern, carabid assemblages are easily and inexpensively sampled using pitfall traps. Additionally, as a result of being extensively studied, their life history, taxonomy, and geographic distribution are well understood. These beetles have very specialized habitat preferences, which depend on abiotic factors (such as temperature or soil moisture content), prey availability, the presence of competitors, and suitability as a reproductive habitat. The larval and pupal stages are considered especially vulnerable to environmental disturbance and changes in microclimate (Ranio & Niemela 2003). A number of studies have attempted to characterize the response of carabid assemblages to different soil cultivation practices, particularly strip tillage, no-till, and conventional tillage, though results can be contradictory. Most comparisons of no-till and conventional till agriculture conclude that conventional till does not affect overall abundance, but found higher species diversity in no-till plots relative to conventionally managed fields (House & Stinner 1983, Trichard et al. 2013, Hatten et al. 2007). The response to soil tillage may be species specific, with only certain species of carabids sensitive to soil tillage. In particular, there may be a bias towards larger bodied species, for example, Pterostichus melanarius, in reduced till systems (Hatten et al. 2007, Kennedy et al. 2013). Pest management options within cucurbit cropping systems are limited. Both insecticide and plastic mulch applications are intensive practices that disrupt the agroecosystem and beneficial insect populations. However, they are also some of the only reliable pest management tools currently available to growers. This study attempts to expand the range of management options available by evaluating the impact of two alternative management practices, strip tillage and the use of row covers, on pest and beneficial insect populations. Both practices have been employed separately in vegetable cropping systems. However, their integration is a novel approach that will potentially increase management options for both organic and conventional growers. In this study we evaluated how strip tillage and plasticulture systems, and row cover use influence the pest and beneficial insect population dynamics within cucurbit cropping systems. This work is part of a larger, multi-state initiative in which field 19 experiments were replicated across four states: Iowa, Pennsylvania, Ohio, and Kentucky. However, the data detailed in this study was only collected in Pennsylvania and focuses on the arthropod population dynamics. Specific objectives include:

1. To monitor key cucurbit pests, including three Diabroticite beetles – A. vittatum, Diabrotica undecimpunctulata howardii,, and Diabrotica virgifera virgifera – and the squash bug, Anasa tristis, throughout the course of the field season. In addition to gathering information about their seasonal dynamics, we hope to better understand if any of these practices influence pest populations. 2. Monitor natural enemy populations throughout the course of the season using visual scouting. 3. Evaluate the impact of production systems and row cover use on the epigeal arthropod community, using carabid ground beetles (Coleoptera: Carabaidae) as a bioindicator for environmental disturbance I hypothesize that the presence of floating row covers will reduce pest pressure early in the growing season, consequentially reducing the number of insecticide sprays required compared to a system with no-row covers. For the duration of the growing season, I will be monitoring populations of three pest Diabroticite species, striped cucumber beetle, spotted cucumber beetle, and western corn rootworm. For all three species, I expect to see a gradual buildup in beetle numbers as the season progresses, though individual species will peak at different time points. I do not expect the presence or absence of row covers to have a significant impact on the epigeal arthropod community. I hypothesize that strip tillage will be less disruptive to epigeal arthropod assemblages when compared to a plasticulture system. Within Carabidae, I expect the response to soil production system to be species specific, with certain taxa demonstrating higher activity density in a specific production system. However, overall, I expect there to be greater species diversity and richness in the strip till plots.

20 Materials and Methods

Four production practices (strip tillage, plasticulture, row cover, and no row cover) were evaluated over the course of a two year field trial, conducted June – September in 2013 and 2014. Replicated experimental plots were established in four independent experiments – organic muskmelon [Cucumis melo cv. Athena (Syngenta)], organic yellow straightneck summer squash [Cucurbita pepo cv. Lioness (Harris Moran)], conventional muskmelon [Cucumis melo cv. Athena (Syngenta)], and conventional yellow straightneck summer squash [Cucurbita pepo cv. Lioness (Harris Moran)]. Experiments were not replicated and considered independent from one another for the purposes of data analysis. Unless specified otherwise, the following methods were identical for all systems in 2013 and 2014.

Study Site and Field Preparation

All field experiments were conducted at the Russell E. Larson Agricultural Research Center (Pennsylvania Furnace, PA). Fields were rotated between years, a practice very typical in cucurbit production; crops are usually rotated on a three year or greater cycle to prevent a buildup of pests and/or soil pathogens. The 2013 and 2014 field sites were close to one another (within 500m). The same cover crop, a mixture of 75% seed weight winter rye (Secale cereal), and 25% seed weight hairy vetch (Vicia villosa), was used in all four production systems. The cover crop was seeded at rate of ~40 kg (90 lbs) per acre. In the organic fields, the cover crop was managed using a roller crimper. Rolling the cover crop required two passes. The first pass happened at winter rye anthesis (Table 2-1). In 2013, it occurred May 20th, and in 2014, on May 28th. The second pass happened once the hairy vetch reached the two pod stage. In 2013, the second pass happened June 17th, and in 2014, the hairy vetch was rolled July 7th. A single pass was not sufficient to terminate the cover crop; once the hairy vetch reached the appropriate stage, the cover crop was first rolled, 21 then mowed down. In the conventional plots, the cover crop was managed with glyphosate sprays and mowing. Two rounds of glyphosphate sprays were needed. The first targeted the winter rye, and the second spray made sure the glyphosphate reached the hairy vetch once the winter rye was out of the way. In 2013, sprays occurred May 20th and June 1st. In 2014, glyphosphate was applied May 28th and June 4th. Seedlings at the three leaf stage were transplanted directly into field plots. Conventional squash and conventional melon seeds were treated Farmore F4100, a seed treatment coating that includes one systemic neonicotinoid, thiamethoxam, and three fungicides, azosystrobin, fludioxonil, and mefenoxam, (Syngenta Crop Protection LLC, Greensboro, NC.). Organic seeds were not treated prior to planting. All seeds were purchased from Seedway (Mifflinburg, PA). Planting dates were delayed due to slow cover crop maturation, particularly in 2014 (Table 2-1). In 2013, the conventional squash field was planted on June 18th. The conventional melons, the organic melons, and the organic squash were planted June 26th. In 2014, the conventional squash was planted on June 19th. The conventional melons were planted June 24th, and both the organic melon and organic squash were planted on July 10th.

Table 2-1. Summary of dates for cover crop management and planting dates in 2013 and 2014. Management practices not applicable for a particular system are indicated with a (-). Con=conventional Roller Roller Crimper Crimper Glyphosphate Glyphosphate - winter rye Planting hairy vetch Spray 1 Spray 2 anthesis two pods Organic Melon May 20th June 17th - - June 26th Organic Squash May 20th June 17th - - June 26th 2013 Con. Melon - - May 20th June 1st June 26th Con. Squash - - May 20th June 1st June 18th Organic Melon May 28th July 7th - - July 10th Organic Squash May 28th July 7th - - July 10th 2014 Con. Melon - - May 28th June 4th June 26th Con. Squash - - May 28th June 4th June 19th 22 Plot Design

Field experiments were set up in a randomized complete block split-plot design. Each cropping system contained four blocks, with a single block consisting of two main plots. Two subplots were nested within each main plot. In total, there were 16 subplots per field. Each main plot had an area of approximately 76.5 meters2 (840 square feet). One main plot consisted of four rows of a single crop, with a single row containing 15 plants, spaced 0.6 meters (2 feet) apart. The main plots received one of two production system treatments: strip tillage or plasticulture. In both the strip tillage and plasticulture plots, rows were placed on 2.1 meter (7 feet) centers. Strip till plots were prepared using a single row strip tiller (HinikerTM 6000, Mankato, Minnesota). Plasticulture plots were prepared by chisel board plowing and rototilling. Drip irrigation tape was applied on all rows. Crop rows were raised on a six inch bed; each row was 0.75 meters (2.5 feet) wide. A single line of drip irrigation tape (John Deere T-Tape model 508-12-450, Moline, IL) was applied on all rows. Black embossed plastic mulch (1.25 ml, Sigma Plastics Group, Allentown, PA) was applied over beds using a bed shaper and a plastic layer. The outer two rows of each main plot were considered untreated buffer rows. The inner two rows were each considered a subplot, and one of the two subplots was randomly assigned to receive a row cover; the other received no cover. Agribon spunbonded row covers (Agribon Inc., Mooresville, NC) were purchased in 2013 and re- used in 2014. In the organic melons, we installed an AG-19 cover (0.55oz / yd2); in all other experiments, organic squash, conventional squash, and conventional melon, we used AG-30 covers (0.9 oz / yd2), a heavier fabric. Row covers were installed immediately after planting and supported by spring steel hoops (SS6x1x6x11, Advancing Alternatives Inc., Schuylkill Haven, PA), spaced approximately 3-4 feet apart. In strip tillage plots, they were secured using six inch landscaping anchor pins (XX6x1x6x11, Advancing Alternatives Inc., Shuylkill Haven, PA) to avoid disturbing the cover crop residue between rows. In the plasticulture plots, the edges of the row covers were covered with field soil. Row covers were removed when 50% of plants in the strip tillage plots 23 had reach anthesis for the summer squash and ten days after anthesis for the muskmelon (Hernandez 2013) within an individual cropping system (Table 2-2).

Table 2-2. Summary of row cover removal dates in 2013 and 2014 2013 2014 Organic Squash July 22nd August 8th Organic Melon July 23rd August 13th Conventional Squash July 15th July 16th Conventional Melon July 23rd July 25th

Pest Scouting and Management

Pest scouting occurred weekly in 2013 and 2014. Scouting always happened early in the morning, starting around 9am, and if there was heavy rain, scouting was postponed until the following day. I was primarily interested counts of pest Diabroticite beetles, including A. vittatum, D. undecimpunctata howardii, and D. virgifera virgifera, though I also kept track of aphid and squash bug (Anasa tristis) numbers. For the Diabroticite beetles, counts of live and dead beetles were taken for each species. Each week, three randomly selected plants per subplot were scouted, excluding the first and last plant in a given row. Early in the season, when row covers were still on, counts were taken under the cover by carefully lifting the fabric and securing it after taking counts. When plants grew too large to distinguish from one another, due to an overlapping of foliage and vines, sampling units were switched from individual plants to one-meter square sections. We scouted for insects on flowers, foliage, stems, or on the ground under the plants and also monitored for any development of bacterial wilt symptoms. To determine if pest pressure was above threshold, the average number of beetles per plant in each subplot was calculated. Sprays were done separately for each subplot. In each cropping system, 12 plants were scouted (three plants per subplot) across all four blocks for a given treatment combination (e.g., one main plot effect and one subplot 24 effect). For the first 40 days after planting, spray thresholds were set to one pest beetle per ten plants. After forty days, the threshold then increased to one beetle per one plant. For squash bugs (Anasa tristis), the spray threshold was set at one egg mass per plant. Immediately after planting, the organic plots were treated with spinosad (Entrust SC; Dow Agrosciences LLC. Indianapolis, IN) at 6 oz/acre and buffalo gourd root powder (Cidetrak-D; Trece Inc. Adair, OK) at a rate of 3.1 oz/acre. Subsequent sprays ® consisted of a mixture of pyrethrins (Pyganic 5.0; MGK Minneapolis, MN) at 10 oz/acre, kaolin (Surround WP; NovaSource®, Phoenix, AZ) at 30lb/acre, and either neem (Trilogy; Certis USA, Columbia, MD) or a Reynoutria sachalinensis extract (Regalia; Marrone®, Davis, CA) at a rate of 1% of the total solution. In the conventional plots, seedlings were treated at transplant with a soil drench of imidacloprid (Admire Pro; Bayer CropScience LP, Research Triangle Park, NC) at a rate of 7 oz/acre. For the rest of season, pest beetle populations were managed with lambda-cyhalothrin (Warrior II; Syngenta Crop Protection LLC, Greensboro, NC) at a rate of 1.28 oz/acre if subplot means exceeded threshold. Fungicides were applied as needed to manage powdery mildew and downy mildew. Weeds were manually removed from crop rows when weed pressure was high. In 2014, the conventional melon plots were sprayed once for melon aphids (Aphis gossypii) due to a sever outbreak. Aphids were treated using a selective aphicide, pymetrozine (Fullfill 50WG; Syngenta Canada Inc. Guelph, ON). Squash bug populations never exceeded threshold.

Above Ground Natural Enemy Scouting

In addition to pests, I also scouted for above ground natural enemies twice a month; natural enemy scouting only occurred in 2014. One randomly selected plant per subplot was scouted for various families of predatory arthropods, including: Opiliones (Harvestmen), Linyphiidae (sheetweb spiders), Thomisidae (crab spiders), Araneidae (orb-web spiders) Lycosidae (wolf spiders), Salticidae (jumping spiders), Cocinellidae (lady beetles) larvae and adults, Carabidae (ground beetles), and Chyrsopidae (lacewings) larvae and adults. Similar to pest scouting, the entire plant, including all foliage, flowers, 25 stems, and the soil or plastic surrounding the base of the plant was searched. When individual plants were indistinguishable from one another, again, I switched to sampling one meter square sections.

Pitfall Trapping and Carabidae Activity Density

Epigeal arthropod activity density was monitored bimonthly in 2013 and 2014 using pitfall traps. Both years, one pitfall trap was placed in each subplot near the base of an arbitrarily selected plant; plastic cups were placed in the ground in such a way that the rim of the cup was flush with the soil surface. When traps were deployed, cups were filled with a small amount (less than one quarter the cup’s volume) of propylene glycol and water (approximately 60% propylene glycol). Propylene glycol is a food additive; in the pitfall traps, it acted as preservative and slowed specimen decay. Pitfall trap design did change between years. In 2013, traps were constructed using two nested 12 oz plastic cups. However, in 2014, I increased the size of the cups in an effort to standardize methods between states. The 2014 traps were constructed using two 32 oz cups (11cm diameter). In the outer cup, three holes were drilled into the bottom. A second cup was then nested inside. The holes in the bottom of the outer cup provided drainage during rainfall, minimizing the change that a pitfall trap was pushed out of the ground during a storm. Cups were installed using a post-hole digger. Pitfall traps were left open for seven days; after the period expired, all specimens were collected and brought back to lab. All trap material was strained using a metal strainer with 0.5mm mesh, and all specimens were transferred to 70% ethanol for storage until later processing. Predatory pitfall trap specimens were initially sorted into groups of varying taxonomic levels. Insects were identified to family, a list that included Carabidae (adult and larvae), Formicidae, Gryllidae, Acrididae, Cocinellidae (adult and larvae), and Staphylinidae. Other arthropod groups included Opiliones, Aranae, Chilopoda, and Diplopoda, and slugs. For the purposes of this experiment, I did not keep count of 26 Collembolan specimens. Initial groups were selected based on their relative abundance in the pitfall traps and their potential to provide ecosystem services within the cropping system; earlier work in Ohio had identified several key predators of D. undecimpunctata howardii eggs, a group that included Opiliones, Formicidae, and Gryllidae (Phillips 2013). I also identified Carabid beetles down to the lowest possible taxonomic level, usually species, with assistance from Margaret Douglas. Most beetles were typically pinned prior to identification. However, for certain common, distinctive species, I made the identifications while beetles were still in ethanol. Voucher specimens were deposited in Shelby Fleischer’s lab at the Pennsylvania State University in University Park (530 ASI Building). Identifications were made using Bosquet (2010), Dillon & Dillon (1961) and a collection of voucher specimens of Carabidae from Rock Springs, prepared in previous studies (Leslie et al. 2007), also housed in the Fleischer Lab.

Statistical Analysis

All statistical analysis were conducted individually between years, cropping system, and cucurbit species. This accounted for any confounding factors in the data that might have arisen from differences in planting dates, field locations, and any insecticide or fungicide sprays. Pest count data was recorded as the average number pests per plot. When conducting surveys, I scouted three individual plants or three 1-meter sections per plot; however, for the purpose of analysis, I pooled all subsamples within a given plot by taking their mean. A mixed model, split-plot ANOVA with repeated measures (Proc Mixed, SAS v 9.4, SAS Institute Inc. 2013) was used to analyze any treatments effects in pest numbers. Block and the interaction term Block*Soil (representing the interaction between block and the main plot) were incorporated as random effects. Date was incorporated as a repeated measure. A log normal transformation was used to meet assumptions of normality and homoscedasticity of the residuals. When the ANOVA was 27 significant, a subsequent mean wise comparison with Tukey’s adjustment was used to determine levels of significance between treatments. The pitfall traps provided a measure of the carabid activity density (number of beetles per trap per sampling week). To compare species richness between the strip till and plasticulture plots, species rarefaction curves were generated separately for each soil treatment (EstimateS 9.4, Colwell 2013). The rarefaction curve depicted the expected number of species for a given sample size and calculated a 95% confidence interval for each point on the curve, which I used to determine if there was a significant difference between treatments (Colwell 2013). The mean activity density of key Carabid species between treatments was compared using a split plot repeated measures ANOVA (Proc Mixed, Sas Institute v. 9.4, 2013). For each experiment, I ranked the entire list of carabid species in order of relative abundance, conducting analysis for the five species with the highest activity density. Block and the interaction between block and main plot effect were included in the model as random variables. Date was incorporated as a repeated measure. Because I had equally spaced observations within time, I also applied an autoregressive structure to the model, which accounted for correlations between dates with the assumption that as dates are further removed from one another, correlation decreases. A logarithmic transformation was applied to all data to help meet assumptions of normality and homoscedasticity in the residuals. When the ANOVA was significant, a subsequent mean wise comparison with Tukey’s adjustment was used to determine levels of significance between treatments. Multivariate analysis using constrained ordination methods were used to capture the treatment effect on the carabid community as a whole using Canaco 5 (Braak & Smilauer 2012). Rare species (less than 10 specimens captured across the entire field season and all crop systems) were excluded from the analysis. Analysis was conducted separately for 2013 and 2014, though data was pooled across all cropping systems. Data was analyzed using a canonical correspondence analysis (CCA). This type of analysis was most appropriate for my data, given estimated lengths of the longest ordination axis. Soil production system (strip till or plastic mulch), row cover, crop, and system management (organic versus conventional) were included as environmental variables. Additionally, replication was included as a covariate term. A Monte Carlo permutation 28 test using 499 permutations was used to test significance of both the first ordination axis and all ordination axes (Leps & Smilauer 2003, Braak & Smailauer 2012).

Results

Pest Population Dynamics

Pest pressure was fairly low in 2013. The highest beetle numbers were found in the organic squash (Figure 2-1b), where A. vittatum population peaked mid-July (averaging 1.2 A. vittatum per plant on July 22nd). In August, there was then an increase in D. virgifera virgifera numbers. In the conventional melon, there again was a slight peak in A. vittatum numbers on July 22nd. Then, on August 12th, both A. vittatum and D. virgifera virgifera (western corn rootworm) numbers peaked (Figure 2-1c). The same trend was observed in the conventional squash as well (Figure 2-1d). Beetle numbers were low (less than one beetle per plant) across all sampling dates in the organic melon (Figure 2-1a). Pest numbers were generally much higher in 2014 across all four cropping systems, with Acalymma vittatum the most prevalent pest beetle (Figure 2-2). In both the organic melon and the organic squash, striped cucumber beetle numbers were high early season, generally greater than one beetle per plant. D. undecimpunctata (spotted cucumber beetle) and D. virgifera virgifera were also present to a lesser degree, though populations of both pests tended to increase later in the growing season, starting sometime in August (Figure 2-2). For the majority of the field season, beetle numbers were well above threshold. I only ran analysis for pest counts in the 2014 data. When data was collected in 2013, no pest counts were taken in the row cover subplots early in the season, when row covers were still out, creating an unbalanced design to the data. Additionally, beetle counts were low. For most scouting dates in 2014, there was a trend of higher numbers of Acalymma vittatum in the plasticulture subplots relative to the strip till across all cropping systems (Figure 2-3). However, the difference was only significant in the conventional 29 melon (F=28.89, df=1,3, P=0.012) and nearly significant in the organic melon (F=9.78, df=1,3, P=0.0552) (Table 2-3, Table 2-4). Immediately after planting, there was a jump in A. vittatum numbers in the plastic mulch, no row cover (PM NRC) plots in the organic melon and organic squash fields. From planting until row cover removal (August 8th for organic squash and August 13th for organic melon), counts in the plastic mulch row cover (PM RC), strip till no row cover (ST NRC), and strip till row cover (ST RC) plots remained low. However, immediately after row cover removal, beetle numbers in the row cover treatments spiked (Figure 2-3). A. vittatum pest density was not significantly influenced by row covers in any cropping system (Table 2-3).

Above Ground Natural Enemy Community

Counts from natural enemy scouting were low, so I pooled data across all treatments and sampling dates within a given experiment (Table 2-5). Dominant groups of natural enemies observed included parasitoids of aphids (measured as aphid mummies), Linyphiidae (sheetweb spiders), Carabidae, Coccinellidae (adults and larvae), and Lycosidae (wolf spiders). In the conventional melon, I counted 426 natural enemies across the entire field season, 69% of which were parasitic wasps, quantified as aphid mummies. The majority of aphid mummy counts came from a single date, August 26th (Figure 2-4). The second most abundant predator, Linyphiidae, accounted for 26% of the natural enemies scouted in the conventional melon. In the other three experiments, organic melon, conventional melon, and organic squash, aphid mummy counts were very low. In the organic melon, I counted 2 mummies out of 234 total specimens counted. Sheet web spiders (Linyphiidae) were the most abundant predator counted (29% of the total above ground predator counts). In conventional squash, there were very low levels of aphid mummy activity. Sheet web spiders were the most abundant natural predator found in the conventional squash, accounting for 59% of all natural enemies counted (n=89). Finally, in the organic squash, Linyphiidae accounted for 29%, and Carabidae accounted for 24% of the total natural enemy count (n=215). The only Carabidae I 30 observed through above-ground natural enemy scouting were small bodied species, likely belonging to the genus Bembidion. Looking at the seasonal dynamics of the four most abundant natural enemies scouted, Linyphiidae, Lycosidae, Carabidae, and aphid mummies, Linyphiidae was the most common predator early season. However, in August, there was a rapid buildup and subsequent decline in the number aphid mummies present. Mummy counts peaked on August 26th, averaging 4.25 mummies per plant (Figure 2-4).

Description of Epigeal Arthropod Community

In 2013 and 2014, I initially sorted pitfall specimens into 12 unique epigeal taxonomic groups: Aranae, Carabidae, Staphylinidae, Formicidae, Opiliones, “other Coleoptera”, Coleoptera larvae, Diplopoda, Chilopoda, Acridiae, and Gryllidae. Additionally, in 2014, I also began to keep count of slugs, as their numbers were moderately high that year. Any non-epigeal arthropod specimens that did not fall within the above taxonomic groups were ignored (but are still stored in ethanol in the Fleischer Lab at Penn State). Overall, pitfall catches were significantly higher in 2014 compared to the previous year, 2013. This might be due to the increase in pitfall trap size. In 2013, across all cropping systems and sampling dates, 1,724 epigeal arthropods were collected. In contrast, in 2014, I collected 10,903 specimens of interest. There was a slight shift in dominance between these higher level taxa. In 2013, Carabidae was by far the most abundant specimen collected, accounting for approximately 45% of the total of specimens collected that year, across all experiments and collection dates (Figure 2-5). Other dominant groups in 2013 included Aranae (accounting for 16.9% of the total trap capture) and Diplopoda (accouting for 12.5% of the total specimens captured). In 2014, there were three dominant taxa captured, Aranae, Carabidae, and Staphylinidae, representing approximately 26.1%, 22.8%, and 21.2% of the total number of specimens captured respectively (Figure 2-6). Also prevalent were ants (Formicidae), accounting for 31 approximately 10% of the total number of arthropod specimens captured. The other taxonomic groups were present at rates less than 5%.

Carabidae Community

In 2013, across all sampling dates and cropping systems, I captured a total of 820 carabid specimens, representing 15 unique species (Table 2-6) In contrast, across all 2014 collecting dates and cropping systems, I captured 2,459 specimens that represented 37 unique species (Table 2-7). In 2013, there was a strong skew in the carabid community across all four experiments. In the conventional melon, I captured a total of 549 specimens, of which, 80% were Harpalus pensylvanicus (Figure 2-7). In contrast, the second most abundant species in the conventional melon plots was the tiger beetle, Cicindela punctulata, representing approximately 12% of all specimens captured in the conventional melon. A similar trend was observed in the organic melon and the conventional squash, with H. pensylvanicus dominating the systems. In the organic squash, there were two dominant species: Harpalus pensylvanicus and Poecilus chalcites. The majority of the 2013 carabid catches happened in the conventional melon plots. In the conventional melon, I captured 549 carabids across the entire field season; in contrast, catches in the organic melon, conventional squash, and organic squash totaled 118 beetles, 74 beetles, and 78 beetles respectively. In 2014, the carabid distribution was not as strongly skewed. In the organic melon (Figure 2-8), I captured 998 specimens over the course of the entire season. Harpalus pensylvanicus was still the most dominant species, accounting for 30% of all Carabidae in the organic melon. Bembidion quadriculatum oppositum and Bembidion rapidum were also key species, accounting for 21% and 12% of the organic melon community respectively. In the 2014 organic squash, I captured a total of 742 carabid specimens across the entire field season. H. pensylvanicus was the dominant species, though again, the distribution was not as skewed as what was observed in 2013. In the conventional melon and conventional squash, 418 and 301 carabid specimens were captured across the 32 entire field season. Both cropping systems had similar patterns of species distribution; four species dominated the system: P. chalcites, H. pensylvanicus, B. rapidum, and B. quadriculatum oppositum. The remaining species all accounted for less than 5% of the carabid community in each cropping system. I also charted the temporal changes across sampling dates for the four most commonly captured Carabidae species in 2013 and 2014, pooled across all sampling dates (Figure 2-9). Harpalus pensylvanicus numbers were highest in the latter half of the field season in 2013, with numbers reaching a maximum activity density of 6.9 beetles / trap / week on September 9th (across all treatments and cropping systems). In 2013, all other beetle catches were extremely low compared to H. pensylvanicus, making it difficult to make inferences about their seasonal dynamics. Cicindela punctulata, the second most abundant Carabid, reached maximum activity density on August 28th (average of 0.8125 beetles / trap / week across all treatments and cropping systems). In 2014, Harpalus pensylvanicus was again the most abundant Carabid. The population peak happened slightly earlier compared to the previous year; on August 19th, H. pensylvanicus averaged 3.5 beetles / trap / week on August 19th. Poecilus chalcites was most active early in the season; highest pitfall catches occurred on the first sampling date (3.4 beetles / trap /week on July 8th); numbers steadily decreased after than initial sampling date for the remainder of the season. Bembidion rapidum’s population peaked shortly after Poecilus chalcites; on July 23rd, the population averaged 1.28 beetles / trap / week; for the following two sampling dates, August 5th, and August 19th, numbers remained fairly steady before declining. Bembidion quadrimaculatum oppositum also reached maximum numbers during the same range of sample dates, July 23rd – August 19th.

Carabidae Species Richness

To allow for a comparison of Carbaidae species richness between soil management treatments, species rarefaction curves were generated separately for strip tillage and plasticulture plots in each experiment. Each curve modeled the estimated 33 number of unique species of Carabidae that would be found for a given sample size. In 2013, slopes reached asymptotes ranging from four species to ten species (Figure 2-10). There was overlap of the 95% confidence intervals for strip tillage and plasticulture rarefaction curves in all four cropping systems, indicating that there was no significant difference between production system treatments. However, in both the conventional melon and the conventional squash, there was clear differentiation of the curves, with strip tillage consistently having higher species richness. Species richness was higher in 2014 (Figure 2-11). In the conventional melon and conventional squash plots, asymptotes for strip till and plasticulture rarefaction curves ranged between five and ten species. In the organic melon and organic squash plots, they reached between 20 and 24 species. Though the 95% confidence intervals overlapped, indicating the difference was not statistically significant, I did observe a trend of high species richness in the strip till relative to the plasticulture in conventional melon and squash.

Univariate Analysis of Carbaidae Activity Density

A split plot, repeated measures ANOVA was used to compare activity density for key Carabidae species between subplots. The model included production system (abbreviated as soil in my model), presence or absence of a row cover (abbreviated as cover), and the soil*cover interaction. Block and block*soil were included as random effects, and date was considered a repeated measure.

Conventional Melon 2013

Harpalus pennsylvanicus activity density was not significantly influenced by soil(F=0.05, df=1,3, P=0.836), row covers (F=2.16, df=1,81, P=0.1452), or the interaction between soil and row covers (F=5.63, df=3,81, P=0.9075) (Table 2-8, Figure 2-9a). As expected, sampling date did significantly influence activity density (F=5.63, 34 df=3,81, P=0.0002); there was a steady increase in H. pensylvanicus population across all treatments, with peak beetle activity occurring in September for most treatments, except ST RC (Figure 2-12b). Cicindela punctulata activity density was marginally influenced by soil (F=7.99, df=1,3, P=0.064) (Table 2-8, Figure 2-13). This species was captured almost exclusively in the strip till plots. However, across the season, instances of capture were sporadic, with some traps catching up to 15 Cicindela punctulata on a given date, and many traps catching 0. This variability is reflected in the high variance and standard error for C. punctulata in the strip till (Figure 2-13a).

Organic Melon 2013

Again, Harpalus pensylvanicus activity density did not change significantly in response to soil (F=0.41, df=1,3, P=0.5682), cover (F=0.66, df=1,81, P=0.4192), or the soil* cover interaction (F=0.01, df=1,81, p=0.9049) (Table 2-9, Figure 2-14). Likewise, Poecilus chalcites activity density was not significantly influence by soil (F=0.64, df = 1,3, P=0.4829), row covers (F=0.16, df=1,81, P=0.6922), or the soil by cover interaction (F=0.32, df=1,81, P=0.5612) (Table 2-9, Figure 2-15).

Conventional squash 2013

Again, Harpalus pensylvanicus demonstrated no change in activity density in response to soil (F=1.7, df=1,3, P=0.293), cover (F=0.79, df=1,51, P=0.3781), or the interaction between soil and cover (F=0.35, df=1,51, P=0.557) (Table 2-10, Figure 2-16). As usual, date was significant in the model (F=10.44, df=3,51, P=0.0001), reflecting the steady increase in H. pensylvanicus numbers across the season in all treatments (Figure 2- 16b). 35 Organic Squash 2013

Soil (F=0.01, df=1,3, P=0.9196), cover (F=0.01, df=1,36, P=0.9237), and soil*cover were not significant predictors in the model for Harpalus pensylvanicus (Table 2-11, Figure 2-17). The mean activity density was consistent across all sampling dates, ranging from 0.5 beetles / trap / week (n=12, se=0.23) to 0.85 beetles / trap / week (n=12, se=0.44). Likewise, Poecilus chalcites activity density did not vary significantly in response to soil (F=2.85, df=1,3, P=0.1901), cover treatment (F=1.91, df=1,36, P=0.1753), or the soil-cover interaction (F=0.48, df=1,36, P=0.495) (Table 2-11, Figure 2-18). Across all sampling dates, P. chalcites trap captures ranged from an average of 0.083 beetles / trap /week (n=12, se=0.083) to 1.25 beetles / trap / week (n=12, se=0.73).

Organic Melon 2014

For the 2014 organic melon, I modeled the activity density of H. pensylvanicus, P. chalcites, B. rapidum, B. quadrimaculatum oppositum, and C. punctulata (Table 2-12). Cicindela punctulata was included as a fifth species because it was almost as equally abundant as B. quadrimaculatum. Additionally, data from the previous year, although not statistically significant, suggested that C. punctulata was extremely sensitive to production system, and I was interested in seeing if this was a consistent effect. H. pensylvanicus activity density did not significantly differ due to any treatment effects (Table 2-12, Figure 2-19). In contrast, there was a significant interaction between soil and row cover for Cicindela punctulata (F=10.49, df=1, 81, P=0.0017). There was significantly higher C. punctulata activity density in the strip-till, no-row-cover (ST NRC) subplots relative to the other three subplot combinations, strip till with row cover (ST RC), plastic mulch and no row cover (PM NRC), and plastic mulch with row cover (PM RC) (Figure 2-20a). The majority of C. punctulata catches happened early in the field season, on July 23rd and August 6th (Figure 2-20b). On all sampling dates, C. 36 punctulata catches were consistently higher in the ST NRC plots relative to the ST RC and the two plasticulture subplots. For Poecilus chalcites, activity density was not significantly influenced by soil (F=4.5, df=1,3, P=0.1242) or the soil by row cover interaction (F=0.2, df=1,81, P=0.6566). However, row cover was a significant predictor (F=4.45, df=1,81, P=0.038), with there being significantly more beetles in the no row cover subplots relative to the row cover subplots (Table 2-12, Figure 2-21). Beetle activity was highest at the start of the season, and in the first half of the season, from July 23rd – September 9th, was consistently higher in the PM NRC treatment (Figure 2-21). Bembidion rapidum was significantly affected by the cover*soil interaction (F=4.95, df=1,81, P=0.0289). Both ST RC and ST NRC had significantly higher B. rapidum activity density compared to PM RC (Table 2-12, Figure 2-22a, Figure 2-22b) per Tukey’s meanwise comparison. Bembidion quadrimaculatum oppositum activity density was also significantly affected by the soil*cover interaction (F=4.12, df=1,81, P=0.0455) (Table 2-12). There was significantly higher activity density in the PM NRC plots relative to all other plots (Figure 2-23a), a trend that was particularly evident early season (Figure 2-23b).

Organic Squash 2014

Harpalus pensylvanicus activity density did not significantly change in response to soil (F=3.18, df=1,3, p=0.1725), cover (F=0.38, df=1,66, 0.5414) or soil*cover (F=1.14, df=1,66, p=0.2894) (Table 2-13, Figure 2-24). Likewise, Bembidion rapidum was not significantly influence by soil (F=0.67, df=1,3, p=0.4716), cover treatment (F=2.56, df=1,66, p=0.1145), or the soil*cover interaction (F=0.05, df=1,66, p=0.8299) (Table 2-13, Figure 2-25). The difference was again most noticeable early in the season, on the first two pitfall sampling dates (Figure 2-25b). B. quadrimaculatum oppositum activity density did not change significantly in response to soil or row cover (Table 2-13). However, the interaction between production 37 system and cover was significant (F=5.09, df=1,66, p=0.0274). Activity density was significantly higher in the PM NRC compared to the ST RC subplots (Figure 2-26a). PM RC and ST NRC did not differ significantly from any subplot. The most noticeable difference between treatments occurred on August 5th; activity density peaked only in the PM NRC subplot (across all four replicates, there was an average of 9.2 beetles per pitfall trap (Figure 2-26b). P. chalcites activity density did not differ significantly by soil treatment (F=0.21, df=1,3, p=0.6797), cover (F=0.92, df=1,66, p=0.3409), or soil*cover (F=0.53, df=1,66, F=0.4702) (Table 2-13, Figure 2-27a). Between subplots, beetle numbers were similar all sampling dates (Figure 2-27b).

Conventional Melon 2014

Harpalus pensylvanicus activity density did not change significantly in response to soil (F=2.59, df=1,3, p=0.2058), cover (F=0.1, df=1,96, p=0.7541), or soil*cover (F=0.62, df=1,96, p=9.4325) (Table 2-14, Figure 2-28a). There was a lot of fluctuation in activity density for individual subplots between sampling dates (Figure 2-28b). Likewise, Poecilus chalcites (Table 2-14, Figure 2-29a) and Bembidion quadrimaculatum oppositum (Table 2-14, Figure 2-30a) did not demonstrate significant changes in activity density in response to soil, cover, or the soil by cover interaction. Activity density for P. chalcites was very consistent across subplots on all sampling dates (Figure 2-29b), though for B. quadrimaculatum oppositum, there was fluctuation in activity density between dates on the subplot level (Figure 2-30b). Bembidion rapdium activity density did shift significantly in response to soil (F=34.09, d=1,3 , p=0.01), though cover and the cover*soil interaction terms were not significant (Table 2-14). Both strip tillage subplots, ST RC and ST NRC, had significantly higher activity densities compared to the plastic mulch plots (Figure 2-31a). Activity density in the plasticulture plots remained fairly low across the entire field season, while in the strip till plots, there was a peak in activity density mid-August (Figure 2-31b). 38 Conventional Squash 2014

Harpalus pensylvanicus activity density was significantly influenced by the soil*cover interaction (F-5.27, df=1,51, p=0.0258) (Table 2-15). Activity density was significantly lower in the ST NRC plots compared to the PM NRC. PM RC and ST RC did not differ significantly from any treatment combination (Figure 2-32a). There was a lot of fluctuation in activity density between dates across all treatments; however, the PM NRC subplots consistently had higher activity density relative to the ST NRC (Figure 2- 32b). Poecilus chalcites did not demonstrate any significant changes in activity density in response to soil, cover, or the soil*cover interaction (Table 2-15, Figure 2-33). Beetle numbers between subplots were comparable on all sampling dates; there was only a slight differentiation in the activity density on July 8th, the first sampling date (Figure 2-33b).. Bembidion quadrimaculatum oppositum activity density was not significantly influenced by cover or the soil*cover interaction (Table 2-15). However, soil did have a significant impact on trap catches (F=12.28, df=1,3, p=0.0393), there were higher numbers of beetles in the plasticulture relative to the strip till (Figure 2-34a). Though there was fluctuation in the numbers between the row cover and no row cover plots, across all sampling dates, activity density in plastic mulch was equal to or greater than activity density in the strip till (Figure 2-34b). Bembidion rapidum activity density also was significantly influenced by soil (F=18.3, df=1,3, p=0.0235), though not cover or the soil by cover interaction (Table 2- 15). There was higher activity density in the strip till plots relative to the plasticulture (Figure 2-35a). Early in the season, there was fluctuation in activity density between subplots. The soil effect was especially evident later in the field season; on the August 5th and August 19th trap collection dates, B. rapidum was only captured in the strip till plots (Figure 2-35b). 39 Production System and Crop Influence on Carabidae Community Composition

In 2013, the explanatory variables crop (melon or squash), crop management (conventional or organic), soil production system (strip tillage or plasticulture), and cover (row cover or no row cover) accounted for 14.2% of the total variation observed. There was a strong differentiation between strip tillage and plasticulture; these variables influenced both the primary horizontal axis and the secondary vertical axis (Figure 2-36). The primary horizontal axis was also influenced by squash and organic crop management, and axis accounted for 67.94% of the total fitted variation (Table 2-16). Monte Carlo permutation tests confirmed that this first axis was significant (F=18.1, P=0.002). Combined the first and second axis explained 94.68% of the fitted variation cumulatively (Table 2-16). Cover treatment (row cover versus no row cover) did not strongly affect the Carabidae community. On the multivariate biplot, these two variables were located close to one another (Figure 2-36). In 2014, the same set of explanatory variables were used, and accounted for 7.2% of the total variation present across all experiments. Crop management (conventional versus organic) strongly influenced the primary horizontal axis (Figure 2-37), which accounted for 48.15% of the total fitted variation (Table 2-17). Monte Carlo permutation tests again indicated that this first axis was significant (F=11.1, P=0.002). There again was a strong differentiation between the production systems (strip tillage and plasticulture); those variables strongly influenced the second, vertical constrained axis (Figure 2-37). Cumulatively, the first and second constrained axis explained 89.98% of the fitted variation (Table 2-17). Presence or absence of a row cover did not appear to strongly shape the carabid community. In both 2013 and 2014, certain species of Carabidae were more strongly pulled to one production system over the other. In particular, both years, the tiger beetle, Cicindela punctulata, and a slug predator, Chlaenius tricolor, seemed to cluster near strip tillage. No species seemed to cluster near plasticulture on the multivariate biplot (Figures 2-36, 2-37). A number of carabid species, including Harpalus pensylvanicus, Harpalus affinis, Poecilus chalcites, and Poecilus lucublandis, were clustered near the center of the 40 multivariate biplot, indicating that they were not strongly pulled by any environmental gradient.

Discussion

In this chapter, my overall objective was to characterize the pest and beneficial insect community within a variety of cucurbit cropping systems and to analyze the impact of various horticultural practices, both novel and standard within the industry, on the population dynamics. There was a large discrepancy in pest pressure between years. While pest numbers were low in 2013, there was significant pressure throughout the entire season in 2014, particularly in the organic squash and organic melon. This may be in part due to the delayed planting date in the organic fields compared to the conventional fields; perhaps planting coincided with the emergence of the first field generation. The high numbers in the organic field may also reflect the higher pesticide efficacy in the conventional system. Early in the season, row covers successfully excluded A. vittatum from subplots, which reduced the number of insecticide sprays necessary compared to the no row cover plots. In the 2014 organic squash experiment, there was an early season buildup of A. vittatum in the plasticulture plots; we were able to avoid three insecticide sprays in the plasticulture plots with a row cover and two insecticide sprays in the strip tillage plots with a row cover (Lilley 2015 – see Appendix A for details). There was also a trend of higher numbers of A. vittatum in the strip till plots relative to the plasticulture. However, this was likely an effect of plant size; plants were larger in the plastic mulch plots compared to the strip tillage. This was particularly evident in the muskmelon; there was a significant decrease in the leaf count and the number of staminate and pistalate blossoms produced (Lilley 2015). I analyzed A. vittatum numbers between production systems for the 2014 data (numbers were too low in 2013 to run proper analysis). Apart from in the conventional melon experiment, none of the production practices had any influence on A. vittatum counts. Scale likely was a major confounding factor, given that individual subplots 41 consisted of one row (a single row was approximately 0.75 meters wide by 9 meters long), allowing for easy beetle movement between plots and subplots. Additionally, there were differences in the number of times and dates insecticides were applied to plots; each set of subplots was scouted on a weekly basis, and spray decisions were made on an individual treatment basis. I did not analyze the 2014 natural enemy scouting data; again, scale was a confounding issue and counts were fairly low. The most common predators were sheetweb spiders (Linyphiidae) and parasitoids of aphids (counted as aphid mummies). Linyphiidae numbers remained fairly consistent over the field season. However, in the conventional melon experiment, on August 26th, I observed a dramatic spike in the number of aphid mummies, most likely in response to an aphid outbreak. One week earlier, on August 19th, there was a major outbreak of melon aphids in the conventional melon. Though I did not count natural enemies that week, anecdotally, few mummies were present. Generalist epigeal arthropods are another important group of predators highly prevalent within the agroecosystem. Within cucurbits specifically, there are a number of predators that have the potential to provide important biocontrol services for striped cucumber beetle. It has already been established that generalist ground predators will predate upon Diabroticite eggs. Molecular gut analysis of over 1500 ground dwelling specimens captured in corn fields found 17 unique taxa that will consume eggs of the western corn rootworm (Lundgren et al. 2009). Major predators of western corn rootworm overlapped with many of the taxa found within my pitfall traps, including Opilliones, Aranae (particularly the wolf spiders, Lycosidae), Staphylinidae (rove beetles), and Carabidae (Lundgren et al. 2009). It seems likely that these groups of predators will consume any A. vittatum eggs they encounter, though given that they are generalist predators, quantifying specific biocontrol services would be difficult. 42 Carabidae Community and Seasonal Dynamics

Though carabid beetles are considered generalist predators and opportunistic feeders, they tend to preferentially feed on different guilds of prey. For example, Harpalus pensylvanicus is a well-known predator of weed seeds, including common species such as giant foxtail and redfoot pigweed (Brust & House 1988, Leslie et al. 2010). Other weed predating species captured in our traps included Anisodactylus sanctaecrucis, Harpalus erythropus, and Harpalus affinis (Leslie et al. 2010). Some of the Carabidae species I observed that are considered preferentially predators of other arthropods included: Poecilus chalcites, Bembidion quadrimaculatum oppositum, Bembidion rapidum, Chlaenius tricolor (primarily a predator of slugs), and Cicindela punctulata (Leslie et al. 2010). More specifically, molecular gut analysis techniques have demonstrated that B. rapidum, B. quadrimaculaturm, P. chalcites, and the larvae of S. quadriceps, will predate upon western corn rootworm eggs (Lundgren et al. 2009). I observed some temporal partitioning among the key carabid species in 2014 and in 2013 to a lesser extent. In 2013, peak H. pensylvanicus numbers occurred near the end of the field season, early September. C. punctulata, P. chalcites, and C. tricolor appeared to reach peak numbers late August, though their populations were low enough that certain trends in seasonality might have been missed. In 2014, P. chalcites population peaked early in the season, in late July. In early August, Bembidion rapdium and Bembidion quadrimaculatum oppositum reached maximum activity density. Finally, in late August, Harpalus pensylvanicus reached its peak in the population. These patterns are consistent with previously published work out of the Rock Springs farm. There is a distinct temporal partitioning of species activity within Carabidae, with Poecilus chalcites reaching peak activity density before Harpalus pensylvanicus, a trend consistent with our own data (Leslie et al. 2007). There was overlap in peak activity density for Bembidion quadrimaculatum and Bembidion rapidum. Other studies also report an overlap in peak emergences between these two species (Lundgren et al. 2009).This is somewhat surprising given that both species are preferentially predators (Leslie et al. 2010) and likely competing for similar food sources. However, there may be geographic partitioning happening; my data 43 suggests that Bembidion quadrimaculatum has higher activity density in the plastic mulch, while Bembidion rapidum has higher activity density in the strip till.

Carabidae Response to Tillage and Row Covers

Scale was a confounding factor in this experiment. Carabidae are highly mobile species that can migrate up to hundreds of meters (Coombes and Sotherton, 1986, Raino & Naimela 2003), and plots were small. In spite of this issue, I was still able to observe noticeable changes in the Carabidae community in response to soil production system. In both 2013 and 2014, there was strong differentiation between strip till and plasticulture on multivariate biplots, and both years, multivariate analysis suggested that certain species are more drawn to one soil production system over another. In both 2013 and 2014, Cicindela punctulata, the tiger beetle, and Chlaenius tricolor, a slug predator, were more likely to be captured in strip till plots relative to the plasticulture (Figure 2-36, Figure 2-37). In 2014, there were additional species clustered near the strip till on the multivariate biplot, including Dyschirius globulosa and Bembidion rapidum to a lesser degree. In addition to influencing the overall Carabidae community, univariate analysis found there to be species specific response to soil product system, suggesting the Carabid response to soil management is species specific. In 2013, activity density of the dominant Carabid species, Harpalus pensylvanicus, was unaffected by production system treatment or row cover in any cropping system experiment. Likewise, Poecilus chalcites activity density did not appear to change significantly between treatments. However, there was a definite trend in Cicindela punctulata activity density, with pitfall captures happening almost exclusively in the strip till plots (Figure 2-13). Despite the trend, variance was high, with a few pitfall traps capturing the majority of the tiger beetles, and I was not able to document a significant difference between strip till and plasticulture systems. 44 In 2014, again, Harpalus pensylvanicus did not respond to any treatment, except in the conventional squash, when the soil by date interaction term was significant (Table 2-14). Given the lack of variation in H. pensylvanicus numbers across all other experiments in 2013 and 2014, overall, this data suggests that H. pennsylvanicus species is unaffected by production system and row covers. Blubaugh and Kapland (2014) also found that H. pensylvanicus adult mobility was not affected by soil disturbance. Like Harpalus pensylvanicus, Poecilus chalcites does not appear to be significantly affected by any treatment. This is consistent with findings by Clark et al. (2006), in which they compared carabid response to conventional tillage and reduced tillage. The data for Bembidion quadrimaculatum oppositum and Bembidion rapidum was noisy. However, there were trends that suggest sensitivity to production system. In 2014, in three of the four cropping systems, organic melon, organic squash, and conventional squash, Bembidion quadrimaculatum typically had a higher activity density in the plasticulture plots compared to the strip till. There was an opposite trend for Bembidion rapidum: in three of the four cropping systems (organic melon, conventional melon, and conventional squash), activity density was significantly higher in strip till plots compared to the plasticulture. The results for Bembidion rapdium in particular are somewhat contradict to previously published studies. Small bodied carabid species, like B. rapidum, are thought to be more resilient to soil tillage compared to larger bodied species (Clark et al. 2006). In some cases, it has even been suggested that small bodied carabids, including B. rapidum, are more abundant in conventional tillage systems compared to reduced tillage systems, though this conclusion was obtained by pool data across all species of Bembidion. It is possible certain trends in individual species were missed (Kennedy et al. 2013). In 2013, Cicindela punctulata activity density was only high enough in numbers to be analyzed in the conventional melon, and in 2014, only in the organic melon. Both years, there was higher C. punctulata activity density in the strip till plots compared to the plasticulture plots. In 2014, the soil by cover interaction significantly affected activity density; almost all the Cicindela punctulata captured came from the ST NRC subplots. Looking at individual dates, C. punctulata populations were highest early in the season, 45 when row covers were deployed (Figure 2-13). The row cover in organic melon was removed August 13th, and the most dramatic differences between ST RC and ST NRC happened before that date. The presence of a row cover may have physically excluded C. punctulata from reaching the pitfall traps, accounting for the differences in my data. In general, Cicindela punctulata appears to prefer open, sandy habitats (Schultz 1989, Bess et al. 2002). Larve within the genus Cicindela are typically restricted to highly specific environments.Cicindela punctulata larvae in particular are most often encountered in loose sand (Schultz 1989).

Conclusion

Based on yield data (Lilley 2015), strip tillage with row covers is not the most appropriate production system for cucurbit agriculture when compared to plasticulture. In both the organic and conventional melon, and to a less extent, the organic and conventional summer squash, there were significant reductions in marketable yield with the strip till (see Appendix A for more details on yield, Lilley 2015). However, the data collected on the epigeal arthropod community emphasize the idea that agricultural production practices can strongly influence insect communities. Differences in the pest community were not evident, likely due to an issue of scale. Despite the scale, I observed some changes in the Carabidae community in response to production system treatment, with strip tillage seeming to support a richer community. In both 2013 and 2014, there was a trend of higher species richness in the strip till plots relative to the plasticulture ones. The difference was not significant, but given the small scale of this experiment, seeing a trend that was consistent across years and cropping systems was encouraging. Furthermore, there were several Carabid species, including Cicindela punctulata, which is a voracious predator, that were found in significantly higher numbers in the strip till. Some rarer species, including the slug predator, Chlaenius tricolor, were also captured almost exclusively in strip tillage. Multivariate analysis emphasized this idea that production practices can shape the 46 Carabidae community. Soil production system significantly influenced the Carabidae community as a whole in both 2013 and 2014. One limitation of pitfall trapping is that it provides a measure of activity density, which does not necessarily equate to a measure of abundance (Matalin and Kirill 2011). This makes interpretation difficult; high catches could reflect a carabid’s high abundance and preference for a particular habitat. Alternatively, they could reflect a particular specimen’s movement. In the case of Cicindela punctulata, the habitat in the strip till plots, at least early season, seems to better reflect previous descriptions of adult and larval preference, suggesting that the numbers in the strip till plots do reflect higher abundance. A key step towards understanding what factors promote higher numbers of Carabidae species will be to understand Carabidae larval ecology. Carabid larvae live in the soil, and consequentially, are more vulnerable to environmental disturbances like soil tillage. Additionally, they are softer bodied than the adults; thus, they are more sensitive to changes in the microclimate, such as a loss of soil moisture content (Kromp 1999, Ranio & Niemela 2003). This vulnerability is somewhat reflected in adult carabid activity, in terms of adult beetle selection of oviposition sites (Blubaugh & Kaplan 2014). Understanding what factors promote or limit beneficial insect populations within an agroecosystem will be a critical step towards implementing more sustainable pest production systems. With Carabidae, I demonstrated sensitivity to soil management. There are a number of important predators within this group that potentially provide important biocontrol services, that through conventional soil tillage, growers might be losing. Beyond Carabidae, there are a number of predatory taxa that present in the cropping system, including lady bugs, lacewings, parasitic wasps, and spiders. The scale of this experiment was too small to capture changes in their population; however, it is very likely that these taxa are also vulnerable to specific practices. If cucurbit agriculture is going to shift towards a more ecologically based direction, it will be important to keep these predators in mind, and to continue working towards understanding what practices and what farmscape factors specifically harm or promote their populations. 47 Acknowledgements

The work detailed in this chapter is part of a large, multi-state collaboration between four institutions: Pennsylvania State University, Ohio State University, Iowa State University, and University of Kentucky. This field experiment was replicated in four states; the experimental design was created collaboratively; many individuals were involved. Mary Gardiner (Ohio State), Celeste Welty (Ohio State), and Shelby Fleischer (Penn State) played a key role in developing the arthropod sampling protocols used in this thesis. The data presented in this chapter represents effort from many individuals. Jason Lilley (Graduate Student, PSU Department of Plant Science) assumed primary responsibility for establishing and maintaining plots both years. Additionally, in 2013, I was not involved in collecting pest scouting data; that work was primarily done by Rachael Troyer, Dana Roberts, and Brianna Reed. In 2014, Dana Roberts, Jason Lilley, and I were all equally involved in scouting plots.

48 References

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(2007). Native bees provide insurance against ongoing honey bee losses. Ecology Letters, 10(11), 1105-1113.

51 Tables

Table 2-3. Output of mixed model ANOVA for effects of production systems, row cover, and date on populations of A. vittatum in 2014, across all experiments. Soil production system (strip tillage or plasticulture) is referred to as ‘Soil’. Cover refers to row cover versus no row cover. Experiment Effect Num DF Den DF F Pr > F Soil 1 3 0.02 0.888 Cover 1 111 0.08 0.7794 Organic Squash Soil*Cover 1 111 0.28 0.5984 Date 7 111 68.14 0.0001 Soil 1 3 9.78 0.0522 Cover 1 171 0.96 0.3291 Organic Melon Soil*Cover 1 171 0.48 0.3921 Date 7 171 16.22 0.0001 Soil 1 3 0.02 0.888 Conventional Cover 1 111 0.08 0.7794 Squash Soil*Cover 1 111 0.28 0.5984 Date 7 111 68.14 0.0001 Soil 1 3 29.89 0.012 Conventional Cover 1 171 0.63 0.4297 Melon Soil*Cover 1 171 1.11 0.2944 Date 11 171 19.29 0.001

Table 2-4. Meanwise comparison of mean A. vittatum per plot (two 30-plant rows) across all sampling dates, in the conventional melon. Numbers within a column that do not share a letter differ significantly (p=0.05). Plasticulture is abbreviated as PM, and strip tillage is abbreviated as ST. Production System Mean No. A. vittatum / plot PM 1.7187 a ST 0.211 b

52 Table 2-5. Summary of natural enemy scouting in 2014. Numbers are given as a total sum of natural enemies observed across all sampling dates and treatments within a given experiment Conventional Organic Conventional Natural Enemy Melon Melon Squash Organic Squash Aphid mummies 279 2 1 6 Linyphiidae 86 83 53 63 Carabidae 22 53 10 52 Lycosidae 18 19 13 28 Coccinellidae larvae 1 33 0 22 Coccinellidae adult 7 28 1 17 Opiliones 7 6 0 4 Formicidae 0 3 5 8 Lacewing larvae 0 1 0 11 Syprhidae 2 3 2 1 Parasitic wasp 0 1 3 3 Salticidae 2 1 0 0 Thomisidae 1 1 0 0 Araneidae 0 0 1 0 Lacewing adult 1 0 0 0

Table 2-6. List of Carabidae species found in 2013 and their relative abundance pooled across all four experiments Relative Abundance Species (%) Harpalus pensylvanicus Dejean 72.68 Cicindela punctulata Olivier 9.39 Poecilus chalcites Say 8.17 Chlaenius tricolor Dejean 3.65 Pterostichus melanarius Illiger 1.46 Anisodactylus sanctaecrucis Fabricius 1.09 Harpalus affinis Schrank 1.09 Patrobus longicornis Say 0.609 Agonum placidum Say 0.609 Scarites subterraneus Fabricius 0.365 Harpalus erythropus Dejean 0.365 Bembidion quadrimaculatum oppositum 0.122 Say Paraclivina bipustulata Fabricius 0.122 Trechus quadristriatus obtusus Schrank 0.122 Agonum punctiforme Say 0.122

53 Table 2-7. List of Carabidae species found in 2014 and their relative abundance pooled across all four experiments Relative Species Abundance (%) Harpalus pensylvanicus Degreer 28.5 Bembidion quadrimaculatum oppositum Say 18.99 Bembidion rapidum LeConte 12.84 Poecilus chalcites Say 12.3 Cicindela punctulata Olivier 4.17 Anisodactylus sanctaecrucis Fabricius 3.72 Stenolophus comma Fabricius 2.51 Elaphropus anceps Leconte 2.33 Harpalus affinis Schrank 1.97 Chlaenius tricolor Dejean 1.57 Poecilus lucublandus Say 1.48 Agonum cupripenne Say 1.44 Agonum placidum Say 1.35 Scarites quadriceps Chaudoir 1.08 Bradycellus rupestris Say 0.89 Dyschirius globulosa Say 0.85 Colliuris pennsylvanicus Linnaeus 0.67 Patrobus longicornis Say 0.58 Harpalus erythropus Dejean 0.49 Stenolophus conjunctus Say 0.44 Paraclivina bipustulata Fabricius 0.44 Scarites subterraneus Fabricius 0.35 Agonum punctiforme Say 0.22 Harpalus rufipes De Geer 0.17 Pterostichus mutus Say 0.089 Harpalus faunus Say 0.044 Harpalus somnulentus Dejean 0.044 Chlaenius emarginatus Say 0.044 Stenolophus species 1 0.044 Platynus tenuicollis LeConte 0.044 Agonum muelleri Herbst 0.044 Agonum octopunctatus Fabricius 0.044 Agonum nutans Say 0.044 Agonum cupreum Dejean 0.044 Anisodactylus rusticus Say 0.044 Pterostichus melanarius Illiger 0.044

54 Table 2-8. Mixed Model ANOVA output for key Carabid Species in conventional melon (2013). Soil refers to production system (plasticulture versus strip tillage), and Cover refers to row cover versus no row cover Carabid Species Effect Num DF Den DF F Pr > F Soil 1 3 0.05 0.836 Harpalus Cover 1 81 2.16 0.1452 pensylvanicus Soil*Cover 1 81 0.01 0.9075 Date 3 81 5.63 0.0002 Soil 1 3 7.99 0.0664 Cicindela Cover 1 81 0.1 0.7563 punctulata Soil*Cover 1 81 0.1 0.7563 Date 3 81 1.47 0.209

Table 2-9. Mixed Model ANOVA output for key Carabid Species in organic melon (2013). Soil refers to production system (plasticulture versus strip tillage), and Cover refers to row cover versus no row cover Carabid Species Effect Num DF Den DF F Pr > F Soil 1 3 0.41 0.5682 Harpalus Cover 1 81 0.66 0.4192 pensylvanicus Soil*Cover 1 81 0.01 0.9049 Date 3 81 2.29 0.0534 Soil 1 3 0.64 0.4829 Poecilus chalcites Cover 1 81 0.16 0.6922 Soil*Cover 1 81 0.34 0.5612 Date 3 81 4.26 0.0017

Table 2-10. Mixed Model ANOVA output for key Carabid Species in conventional squash (2013) Soil refers to production system (plasticulture versus strip tillage), and Cover refers to row cover versus no row cover Carabid Species Effect Num DF Den DF F Pr > F Soil 1 3 1.7 0.2893 Cover 1 51 0.79 0.3781 Harpalus pensylvanicus Soil*Cover 1 51 0.35 0.557 Date 3 51 10.44 0.0001

55 Table 2-11. Mixed Model ANOVA output for key Carabid Species in organic squash (2013). Soil refers to production system (plasticulture versus strip tillage), and Cover refers to row cover versus no row cover Carabid Species Effect Num DF Den DF F Pr > F Soil 1 3 0.01 0.9196 Harpalus Cover 1 36 0.01 0.9237 pensylvanicus Soil*Cover 1 36 0.29 0.5921 Date 2 36 4.46 0.0186 Soil 1 3 2.85 0.1901 Cover 1 36 1.91 0.1753 P. chalcites* Soil*Cover 1 36 0.48 0.495 Date 2 36 8.03 0.0013 *Square root transformation was used on data instead of log10

Table 2-12. Mixed Model ANOVA output for key Carabid species in organic melon (2014). Soil refers to production system (plasticulture versus strip tillage), and Cover refers to row cover versus no row cover Carabid Species Effect Num DF Den DF F Pr > F Soil 1 3 0.1 0.7706 Cover 1 81 0.12 0.7258 Harpalus pensylvanicus Soil*Cover 1 81 1.11 0.2961 Date 5 81 9.06 0.001 Soil 1 3 4.5 0.1242 Cover 1 81 4.45 0.038 Poecilus chalcites Soil*Cover 1 81 0.2 0.6566 Date 5 81 7.24 0.0001 Soil 1 3 15.67 0.0288 Cover 1 81 2.11 0.1503 Bembidion rapidum Soil*Cover 1 81 4.95 0.0289 Date 5 81 5.7 0.0001 Soil 1 3 25.58 0.0128 Bembidion Cover 1 81 4.21 0.0435 quadrimaculatum Soil*Cover 1 81 4.12 0.0455 oppositum Date 5 81 3.15 0.012 Soil 1 3 16.57 0.0268 Cover 1 81 12.26 0.0008 Cindela punctulata Soil*Cover 1 81 10.49 0.0017 Date 5 81 2.77 0.0231

56 Table 2-13. Mixed model ANOVA output for key Carabid species in organic squash (2014). Soil refers to production system (plasticulture versus strip tillage), and Cover refers to row cover versus no row cover Carabid Species Effect Num DF Den DF F Pr > F Soil 1 3 3.18 0.1725 Harpalus Cover 1 66 0.38 0.5414 pensylvanicus Soil*Cover 1 66 1.14 0.2894 Date 4 66 7.82 0.001 Soil 1 3 9.66 0.0529 Bembidion Cover 1 66 0.1 0.747 quadrimaculatum Soil*Cover 1 66 5.09 0.0274 oppositum Date 4 66 17.21 0.0001 Soil 1 3 0.67 0.4716 Cover 1 66 2.56 0.1145 Bembidion rapidum Soil*Cover 1 66 0.05 0.8299 Date 4 66 8.12 0.001 Soil 1 3 0.21 0.6797 Cover 1 66 0.92 0.3409 Poecilus chalcites Soil*Cover 1 66 0.53 0.4702 Date 4 66 10.52 0.001

Table 2-14. Mixed model ANOVA output for key Carabid species in conventional melon (2014). Soil refers to production system (plasticulture versus strip tillage), and Cover refers to row cover versus no row cover Carabid Species Effect Num DF Den DF F Pr > F Soil 1 3 2.59 0.2058 Cover 1 96 0.1 0.7541 Harpalus pensylvanicus Soil*Cover 1 96 0.62 0.4325 Date 6 96 1.79 0.1095 Soil 1 3 0.34 0.6028 Cover 1 96 0.66 0.4188 Poecilus chalcites Soil*Cover 1 96 2.41 0.1235 Date 6 96 20.91 0.0001 Soil 1 3 2.5 0.2117 Bembidion Cover 1 96 0.54 0.463 quadrimaculatum Soil*Cover 1 96 0.26 0.6112 oppositum Date 6 96 5.27 0.0001 Soil 1 3 34.09 0.01 Cover 1 96 0.8 0.3719 Bembidion rapidum Soil*Cover 1 96 2.5 0.1168 Date 6 96 7.39 0.0001 57

Table 2-15. Mixed model ANOVA output for key Carabid species in conventional squash (2014). Soil refers to production system (plasticulture versus strip tillage), and Cover refers to row cover versus no row cover Carabid Species Effect Num DF Den DF F Pr > F Soil 1 3 4.97 0.1122 Cover 1 51 0.06 0.8132 Harpalus pensylvanicus Soil*Cover 1 51 5.27 0.0258 Date 3 51 3.22 0.0301 Soil 1 3 0.81 0.4332 Cover 1 51 0 0.9942 Poecilus chalcites Soil*Cover 1 51 0.04 0.843 Date 3 51 20.97 0.0001 Soil 1 3 12.28 0.0393 Bembidion Cover 1 51 2.93 0.093 quadrimaculatum oppositum Soil*Cover 1 51 0.05 0.8177 Date 3 51 0.69 0.5651 Soil 1 3 18.3 0.0235 Cover 1 51 1.01 0.3192 Bembidion rapidum Soil*Cover 1 51 0.04 0.8518 Date 3 51 1.07 0.3701

58 Table 2-16. Summary statistics from CCA analysis for key 2013 Carabidae species Statistic Axis 1 Axis 2 Axis 3 Axis 4 Eigenvalues 0.1717 0.0676 0.0093 0.0042 Explained variation 9.66 13.46 13.98 14.22 (cumulative) Pseudo-canonical 0.5435 0.3982 0.1523 0.1064 correlation Explained fitted 67.94 94.68 98.34 100.00 variation (cumulative)

Table 2-17. Summary statistics from CCA analysis for key 2014 Carabidae species Statistic Axis 1 Axis 2 Axis 3 Axis 4 Eigenvalues 0.1395 0.1212 0.0155 0.0135 Explained variation 3.47 6.49 6.87 7.21 (cumulative) Pseudo-canonical 0.6276 0.5688 0.2560 0.2244 correlation Explained fitted 48.15 89.98 95.33 100.00 variation (cumulative)

59 Figures

2.5 a A. vittatum 2 D. undecimpunctata D. virgifera

1.5

plot 1

0.5 MeanNo.Beetles per 0 7/3 7/8 7/15 7/22 7/29 8/8 8/12 8/19 8/28 9/3 2.5 b A. vittatum 2 D. undecimpunctata D. virgifera

1.5

plot 1

0.5 MeanNo.Beetles per 0 7/3 7/8 7/15 7/22 7/29 8/8 8/12 8/19 2.5 c A. vittatum 2 D. undecimpunctata D. virgifera

1.5

plot 1 0.5

MeanNo.Beetles per 0 7/3 7/10 7/17 7/24 7/31 8/7 8/14 8/21 8/28

2.5 d A. vittatum 2 D. undecimpunctata D. virgifera

1.5

plot 1

0.5 MeanNo.Beetles per 0 7/3 7/8 7/15 7/22 7/29 8/7 8/12 8/19 8/28 9/3

Figure 2-1. Mean number of pest beetles per plot (two 15-plant-rows) across all scouting dates (2013), by cropping systems: (a) organic melon (b) organic squash (c) conventional melon (d) conventional squash 60 8 a A. vittatum per 6 D. undecimpunctata D. virgifera

4 plot

No.Beetles 2

Mean 0 7/16 7/22 7/29 8/5 8/13 8/19 8/26 9/2 9/9 9/16 9/30 10/6

8

b A. vittatum per 6 D. undecimpunctata

D. virgifera

4

plot No.Beetles

2 Mean 0 7/16 7/22 7/29 8/5 8/13 8/19 8/26 9/2 9/9 8

c A. vittatum per 6 D. undecimpunctata

D. virgifera

eetles

B 4 plot

No.of 2

Mean 0 7/1 7/8 7/16 7/22 7/29 8/5 8/13 8/19 8/26 9/2 9/9 9/16 8 d A. vittatum 6 D. undecimpunctata

D. virgifera

4 Plot

No.Beetles of per 2

Mean 0 7/16 7/22 7/29 8/5 8/13 8/19 8/26 9/2 9/9 9/16 9/30 10/6

Figure 2-2. Mean number of pest beetles per plot (two 15-plant rows) across all scouting dates (2014), by cropping system:(a) organic melon (b) organic squash (c) conventional melon (d) conventional squash

61 14 PM NRC 12 PM RC a 10 ST NRC 8 ST RC 6 4 2 0 16-Jul 22-Jul 29-Jul 5-Aug 13-Aug 19-Aug 26-Aug 2-Sep 9-Sep 16-Sep 30-Sep 6-Oct

10 PM NRC b 8 PM RC

ST NRC Subplot 6 ST RC

4

2

0 16-Jul 22-Jul 29-Jul 5-Aug 13-Aug 19-Aug 26-Aug 2-Sep 9-Sep

Mean No. Beetles per per No. Beetles Mean 20 PM NRC c PM RC 15 ST NRC ST RC 10

5

0 1-Jul 8-Jul 16-Jul 22-Jul 29-Jul 5-Aug 13-Aug 19-Aug 26-Aug 2-Sep 9-Sep 16-Sep 3 PM NRC 2.5 PM RC d 2 ST NRC ST RC 1.5 1 0.5 0 1-Jul 8-Jul 16-Jul 22-Jul 29-Jul 5-Aug 13-Aug 19-Aug Figure 2-3. Mean numbers of A. vittatum across sampling all scouting dates in 2014, by subplot treatment in (a) organic melon (b) organic squash (c) conventional melon and (d) conventional squash. Row cover removal is indicated with an arrow. Subplots are abbreviated as PM NRC (Plastic Mulch, No row cover), PM RC (Plastic mulch, Row cover), ST NRC (Strip till, No row cover), ST RC( Strip till, row cover).

62 4.5

4 3.5 3 Linyphiidae 2.5 Carabidae 2 1.5 Lycosidae 1

MeanNo.Speciemsn / Plot Aphid 0.5 mummies 0 7/1 7/17 7/29 8/13 8/26 9/9 9/23

Figure 2-4. Seasonal activity of four natural enemy taxa from above-ground visual searches (2014 only). Data was pooled across all sampling dates and treatments

0.5

0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 Relative Relative Abundance(%) 0.05 0

Figure 2-5. Summary of epigeal arthropod community in 2013. Relative abundance is given as a percentage of the total epigeal specimens of interest collected (N=1724). Data is pooled across all four experiments and all sampling dates.

63 0.3

0.25

0.2

0.15

0.1

0.05 Relative Relative Abundance(%) 0

Figure 2-6. Summary of epigeal arthropod community in 2014. Relative abundance is given as a percentage of the total arthropod specimens of interest collected (N=10,903). Data is pooled across all cropping systems and dates.

64

Figure 2-7. Relative abundance of dominant carabid species in 2013 across all cropping systems. a) conventional melon (N=549) (b) organic melon (N=118) (c) organic squash (N=78) (d) conventional squash (N=74). Only species that accounted for greater than 1% of the total community were included

65

Figure 2-8. Relative abundance of dominant carabid species in 2014 across all cropping systems. (a) organic melon (N=998) (b) conventional melon (N=418) (c) organic squash (N=742) (d) conventional squash (N=301). Only species that accounted for greater than 1% of the total community were included

66

2013 2014 8 4 Harpalus pennsylvanicus 6 3

4 2

2 1 Harpalus pennsylvanicus 0 0

8 4 Poecilus Cicindela punctulata 6 3 chalcites

4 2

2 1

0 0

8 4 Poecilus chalcites Bembidion rapidum 6 3

4 2

2 1

0 0 Mean No. Beetles / Trap Week / Trap / Mean No. Beetles

8 4 Chlaenius tricolor Bembidion 6 3 quadrimaculatum 4 2 oppositum

2 1

0 0 7/1 7/15 7/29 8/12 8/28 9/9 9/16 7/8 7/23 8/5 8/19 9/2 9/16 9/30

Figure 2-9. Seasonal curves for the four most abundant Carabid species in 2013 and 2014, pooled across all cropping systems and treatments

67

8 7 a b 7 6 6 5 5 4 4 3 3 2 2 ST ST 1 1 PM PM 0 0 1 6 11 16 21 26 31 36 41 46 1 6 11 16 21

12 12 c d 10 10

Estimated No. Species No. Estimated 8 8 6 6 4 4 ST 2 2 ST PM PM 0 0 0 5 10 15 20 25 30 35 40 45 50 1 6 11 16 21 26 31

No. Samples

Figure 2-10. Species rarefaction curves for Carabidae collected in 2013. Individual curves were generated for the specimens collected in the strip till and plasticulture by experiment. Data for the subplots (row cover and no row cover) was pooled for this analysis. Curves were generated separately for each cropping system (a) Organic Melon (b) Organic Squash (c) Conventional Melon and (d) Organic Squash. 95% confidence intervals not shown 68

30 30 a b 25 25 20 20 15 15 10 10 5 ST 5 ST PM PM 0 0 1 6 11 16 21 26 31 36 41 46 1 6 11 16 21 26 31 36

12 12 c d 10 Estimated No. Species No. Estimated 10 8 8 6 6 4 4

2 ST 2 ST PM PM 0 0 0 5 10 15 20 25 30 35 40 45 50 1 6 11 16 21 26 31

No. Samples

Figure 2-11. Species rarefaction curves for Carabidae collected in 2014. Individual curves were generated for the specimens collected in the strip till and plasticulture by experiment. Data for the subplots (row cover and no row cover) was pooled for this analysis. Curves were generated separately for each cropping system (a) Organic Melon (b) Organic Squash (c) Conventional Melon and (d) Organic Squash. 95% confidence intervals not shown

69

8 a a a H. H. 6 a a 4

MeanNo. 2

pennsylvnaicus / trap/week (+SE) 0 PM NRC PM RC ST NRC ST RC 25 ST NRC 20 b

H. H. ST RC 15 PM NRC

10 PM RC trap/week

MeanNo. 5 pennsylvanicus pennsylvanicus / 0 7/15 7/29 8/12 8/28 9/9 9/16 Figure 2-12. Mean Harpalus pensylvanicus activity density (number beetles / trap / week) in the 2013 conventional melon Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

2.5 a a

2 C. C.

trap / / trap a 1.5

1 week (+SE) MeanNo. 0.5

punctulata / / punctulata a a 0 PM NRC PM RC ST NRC ST RC 5 ST NRC b

4 ST RC

C. C.

trap/

3 PM NRC PM RC week 2

MeanNo. 1 punctulata / 0 7/15 7/29 8/12 8/28 9/9 9/16 Figure 2-13. Mean Cicindela punctulata activity density (number beetles / trap / week) in the 2013 conventional melon Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover) 70 1.5 a a a H. H. a 1 a

0.5

MeanNo.

pennsylvanicus / trap/week (+SE) 0 PM NRC PM RC ST NRC ST RC 3 ST NRC b

2.5 ST RC

trap / / trap

H. H. 2 PM NRC

1.5 PM RC

week 1

MeanNo. 0.5

pennsylvanicus pennsylvanicus / 0 7/15 7/29 8/12 8/28 9/9 9/16

Figure 2-14. Mean Harpalus pensylvanicus activity density (number beetles / trap / week) in the organic melon in 2013. Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

0.4 a

a a a

P. P. 0.3 a / / / trap

0.2 week (+SE)

MeanNo. 0.1 chalcites 0 PM NRC PM RC ST NRC ST RC 1.5 ST NRC b

P. P. ST RC

1

PM NRC / / / trap PM RC

week 0.5

MeanNo. chalcites chalcites 0 7/15 7/29 8/12 8/28 9/9 9/16 Figure 2-15. Mean Poecilus chalcites activity density (number beetles / trap / week) in the organic melon in 2013Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover) 71 2 a 1.5 a

1 a a a

MeanNo. 0.5 trap/week (+SE) H.pennsylvnaicus H.pennsylvnaicus / 0 PM NRC PM RC ST NRC ST RC 4

ST NRC b trap

3 ST RC H. H. PM NRC 2 PM RC

/week 1 MeanNo.

pennsyvanicus/ 0 7/1 7/15 7/29 8/12 Figure 2-16. Mean Harpalus pensylvanicus activity density (number beetles / trap / week) in the 2013 conventional squash.Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

2.5 a

2 H. H. 1.5 a 1 a a a

MeanNo. 0.5

pennsylvnaicus pennsylvnaicus / trap/week (+SE) 0 PM NRC PM RC ST NRC ST RC 2.5

ST NRC b trap

2 ST RC H. H. 1.5 PM NRC PM RC

1 /week

MeanNo. 0.5

pennsyvanicus/ 0 7/15 7/29 8/12 Figure 2-17. Mean Harpalus pensylvanicus activity density (number beetles / trap / week) in the organic squash in 2013.Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

72 2 a a 1.5

P. P. chaclites 1 a a

0.5 trap/week (+SE)

/ / a MeanNo. 0 PM NRC PM RC ST NRC ST RC

4 / ST NRC b

3 ST RC PM NRC 2 PM RC

trap/week 1

MeanNo. P. chalcites 0 7/15 7/29 8/12

Figure 2-18. Mean Poecilus chalcites activity density (number beetles / trap / week) in the organic squash in 2013. Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover) 73 6 a a

H. H. a 4 a a

2

MeanNo.

pennsylvanicus pennsylvanicus / trap/week SE) (+ 0 PM NRC PM RC ST NRC ST RC 15 ST NRC

/ / b

ST RC H. H. 10 PM NRC PM RC

5

trap/week

MeanNo. pennsylvanicus 0 7/23 8/5 8/19 9/2 9/16 9/30 Figure 2-19. Mean Harpalus pensylvanicus activity density (number beetles / trap / week) in the 2014 organic melon. Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

3

a a

C. C.

SE) 2

//plot

1 week (+ MeanNo. b b punctulata 0 PM NRC PM RC ST NRC ST RC 6 ST NRC 5 ST RC b

C. C. PM NRC

4 / / / trap PM RC

3 week

2 MeanNo.

1 punctulata 0 7/23 7/30 8/6 8/13 8/20 8/27 9/3 9/10 9/17 9/24

Figure 2-20. Mean Cicindela punctulata activity density (number beetles / trap / week) in the 2014 organic melonColumns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover) 74 2 a

a

P. P. 1.5

SE)

trap/

1

b week (+

MeanNo. 0.5 chaclites chaclites / 0 PM NRC ST NRC PM RC ST RC

2.5 ST NRC b

2 ST RC

P. P.

trap / / trap PM NRC 1.5 PM RC

week 1

MeanNo. 0.5 chalcites chalcites / 0 7/23 8/5 8/19 9/2 9/16 9/30 Figure 2-21. Mean Poecilus chalcites activity density (number beetles / trap / week) in the 2014 organic melon.Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

2.5

SE) a 2 b B. ab

1.5 a b / plot (+ plot / 1

MeanNo. 0.5

rapidum 0 PM NRC PM RC ST NRC ST RC 5 ST NRC b 4 ST RC PM NRC 3

B.rapidum PM RC 2

/weektrap / 1

MeanNo. 0 23-Jul 5-Aug 19-Aug 2-Sep 16-Sep 30-Sep Figure 2-22. Mean Bembidion rapdium activity density (beetles / trap / week) in the organic melon in 2014Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover) 75 5 / / a

4 a

SE)

3 b B.quad b

2 b plot (+

1 MeanNo. 0 PM NRC PM RC ST NRC ST RC

15 / /

ST NRC b

10 ST RC

B.quad PM NRC

5 trap/week

MeanNo. 0 23-Jul 5-Aug 19-Aug 2-Sep 16-Sep 30-Sep Figure 2-23. Mean Bembidion quadrimaculatum oppositum activity density (beetles / trap / week) in the 2014 organic melon. Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

a / / 6

SE) a

+ + H. H. 4 a a a

2

MeanNo. pennsylvanicus pennsylvanicus trap/week ( 0 PM NRC PM RC ST NRC ST RC 15 ST NRC / / b

ST RC H. H. 10 PM NRC PM RC

5

MeanNo.

pennsylvanicus plot / trapplot /week / 0 23-Jul 5-Aug 19-Aug 2-Sep 16-Sep Figure 2-24. Mean Harpalus pensylvanicus activity density (beetles / trap / week) in the organic squash in 2014. Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover) 76

2.5 a a

2

SE) 1.5 a

+ + a B.rapidum 1 a /( plot 0.5

MeanNo. 0 PM NRC PM RC ST NRC ST RC

4 / /

ST NRC b ST RC 3 PM NRC PM RC

B.rapidum 2

1

plot / trapplot /week / MeanNo. 0 23-Jul 5-Aug 19-Aug 2-Sep 16-Sep Figure 2-25. Mean Bembidion rapidum activity density (beetles / trap / week) in the 2014 organic squashColumns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

/ / a

4 a

SE)

B. + 3 ab 2 ab

MeanNo. 1 b

quadriculatum trap/week ( 0 PM NRC PM RC ST NRC ST RC

10

/ / 8 b

6 B.quad 4 ST NRC ST RC

2 PM NRC plot / trap / / trapweek / plot MeanNo. 0 23-Jul 5-Aug 19-Aug 2-Sep 16-Sep Figure 2-26. Mean Bembidion quadrimaculatum oppositum activity density (beetles / trap / week) in 2014 organic squash Columns that do not share a letter are significantly different by Tukeys comparisons (p < 0.05). Production systems abbreviations: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover) 77 2.5 a

2 P. P.

1.5 a / Trap / / / Trap a a

1 a MeanNo.

Week (+ SE) 0.5 chalcites chalcites 0 PM NRC PM RC ST NRC ST RC 4 ST NRC b ST RC

P. P. 3

PM NRC

/ Trap / / / Trap 2 PM RC week

MeanNo. 1 chalcites chalcites 0 23-Jul 5-Aug 19-Aug 2-Sep 16-Sep Figure 2-27. Mean Poecilus chalcites activity density (beetles / trap / week) in the 2014 organic squashColumns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

1.5

/ / a

H. H. a 1 a a a

0.5

MeanNo. pennsylvanicus pennsylvanicus TrapWeek / (+ SE) 0 PM NRC PM RC ST NRC ST RC 2.5 ST NRC b

2 ST RC

/ Trap / H. H.

PM NRC 1.5 PM RC

/week 1

MeanNo. 0.5

pennsylvanicus pennsylvanicus 0 7/8 7/23 8/5 8/19 9/2 9/16 9/30 Figure 2-28. Mean Harpalus pensylvanicus activity density (beetles / trap / week) in the 2014 conventional melon. Columns that do not share a letter are significantly different by Tukeys comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover) 78 2 a a P. P. 1.5

/Trap / a a

1 a Week (+ SE)

MeanNo. 0.5 chalcites chalcites 0 PM NRC PM RC ST NRC ST RC 8 ST NRC b

P. P. 6 ST RC

PM NRC /trap /

4 PM RC week

MeanNo. 2 chalcites 0 7/8 7/23 8/5 8/19 9/2 9/16 9/30 Figure 2-29. Mean Poecilus chalcites activity density (beetles / trap / week) in the 2014 conventional melonColumns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

1

/ / a 0.8 a 0.6 a B.quad a 0.4 a

0.2 TrapWeek / (+ SE) MeanNo. 0 PM NRC PM RC ST NRC ST RC 2 / / ST NRC b 1.5 ST RC PM NRC B.quad 1 PM RC

trap/week 0.5

MeanNo. 0 7/8 7/23 8/5 8/19 9/2 9/16 9/30 Figure 2-30. Mean Bembidion quadrimaculatum oppositum activity density (beetles / trap / week) in the 2014 conventional melonColumns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover) 79 1.5

a a

B. 1 /Trap / b

0.5

Week (+ (+ Week SE)

MeanNo. rapidum 0 PM NRC PM RC ST NRC ST RC

5 ST NRC b

4 ST RC B. 3 PM NRC PM RC

/trap /week 2

MeanNo. 1

rapidum 0 7/8 7/23 8/5 8/19 9/2 9/16 9/30 Figure 2-31. Mean of Bembidion rapidum activity density (beetles / trap / week) in the conventional melon in 2014Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

2.5

/ /

a 2

H. H. a 1.5 ab ab 1

MeanNo. 0.5 b

Pennsylvanicus trap/week SE) (+ 0 PM NRC PM RC ST NRC ST RC 3 ST NRC

/ / b

ST RC H. H. 2 PM NRC PM RC

1

trap/week

MeanNo. pennsyvlanicus pennsyvlanicus 0 8-Jul 23-Jul 5-Aug 19-Aug Figure 2-32. Mean Harpalus pensylvanicus activity density (beetles / trap / week) in the 2014 conventional squashColumns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover) 80 2 a a a

P. P. 1.5 a /trap /

1 a week SE) (+

MeanNo. 0.5 chalcites chalcites 0 PM NRC PM RC ST NRC ST RC

6 PM - NRC

5 b PM - RC 4 ST - NRC

P. P. chalcites 3 ST - RC

2 /weektrap / 1 MeanNo. 0 8-Jul 23-Jul 5-Aug 19-Aug Figure 2-33. Mean Poecilus chalcites activity density (beetles / trap / week) in the conventional squash in 2014Columns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

5

/ / a a 4 3 B.quad b 2

1 trap/week SE) (+ Mean No. Mean 0 PM NRC PM RC ST NRC ST RC 3 ST NRC / / b

2.5 ST RC

2 PM NRC

B.quad PM RC 1.5

1 trap / / trapweek

0.5 MeanNo. 0 8-Jul 23-Jul 5-Aug 19-Aug Figure 2-34. Mean Bembidion quadrimaculatum oppositum activity density (beetles / trap / week) in the 2014 conventional squash. Columns that do not share a letter are significantly different by Tukeys mean comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover) 81 2.5 / / a

2 a

SE)

±

1.5 B.rapidum 1 b

trap/week ( 0.5 MeanNo. 0 PM NRC PM RC ST NRC ST RC 4 / / b ST NRC

3 ST RC PM NRC PM RC B.rapidum 2

trap/week 1 MeanNo. 0 8-Jul 23-Jul 5-Aug 19-Aug Figure 2-35. Mean Bembidion rapidum activity density (beetles / trap / week) in the 2014 conventional squashColumns that do not share a letter are significantly different by Tukeys meanwise comparisons. (p < 0.05). Production systems abbreviated as: PM (Plasticulture), ST (Strip Till), RC (Row Cover), NRC (No Row Cover)

82 1.0 Squash

ChTr ST

Org

CiPu RC PoCh PtMe

HaPe NRC Con Melon

PM -1.0 -1.0 1.0

Figure 2-36. Multi-variate bi-plot demonstrating how crop, management, soil production system, and row cover shape key species within 2013 Carabidae community. Environmental variables are abbreviated as: ST(strip tillage), PM (plastic mulch), RC (row cover), NRC (no row cover), Con (Conventional) and Org (Organic). Species names are abbreviated by combining the first two letters of the genus and species level name: CiPu=Cicindela punctulata, ChTr=Chlaenius tricolor, HaPe=Harpalus pennsylvanicus, PoCh=Poecilus chalcites, PtMe=Pterostichus melanarius 83

CiPu

0.6 ST ChTr CoPe DyGl ScQu Con

BeRa

NRC AgPl PoCh AgCu BrRu

Melon Squash AnSa PoLu HaPe Org BeQu StCo HaAf ElAn Unk Sp RC

PM -0.6 -0.6 1.0

Figure 2-37. Multi-variate bi-plot demonstrating how crop, management, soil production system, and row cover shape key species within 2014 Carabidae community. Environmental variables are abbreviated as: ST(strip tillage), PM (plastic mulch), RC (row cover), NRC (no row cover), Con (Conventional) and Org (Organic).

Species names are abbreviated by combining the first two letters of the genus and species level name: AgCu=Agonum cupripenne, AgPl=Agonum placidum AnSa=Anisodactylus sanctaecrucis, BeQu=Bembidion quadrimaculagtur oppositum, BeRa=Bembidion rapidum, BrRu=Bradycellus rufipes, ElAn= Elaphropus anceps, CiPu=Cicindela punctulata, ChTr=Chlaenius tricolor, CoPe=Collarius pennsylvanicus, DyGl= Dyschirius globulosa, HaAf=Harpalus affinis, HaPe=Hapralus pennsylvanicus, PoCh=Poecilus chalcites, ScQu=Scarites quadriceps

84 Chapter 3

Assessing the Potential for Conservation Biocontrol in Central Pennsylvania

Introduction

Acalymma vittatum (Fabricus), commonly referred to as the striped cucumber beetle, is a key pest of cucurbits in the North Eastern United States. As larvae, A. vittatum lives in the soil and feeds exclusively on cucurbit roots; adults feed on cucurbit foliage, flowers, and fruit. High levels of adult feeding sometimes leads to a reduction yield (Brust 1999). In addition to reducing the total area of photosynthetic leaf tissue, direct foliar feeding by adult beetles significantly reduces both staminate flower output and the number of pollen grains per staminate flower (Sasu et al. 2012, Quesada et al. 1995). Of greater concern, A. vittatum also acts as a competent vector for the bacterial pathogen Erwinia trachaeophilia, the causal agent of a bacterial wilt disease in cucurbits. Beetle infection rates can exceed 70% (Fleischer et al. 1999). Once a plant is infected with bacterial wilt, it is impossible to halt disease progression. Because adult beetles are highly mobile, infection can rapidly spread and destroy entire fields (Mitchell & Hanks 2009). Consequently, bacterial wilt management happens primarily through control of the beetle vector, Acalymma vittatum. In conventional production systems, synthetic insecticides, including neonicotinoids, pyrethroids, and carbamates, provide consistent control of A. vittatum; the number of sprays per season varies depending on beetle density and typically ranges between 2 and 8 sprays per season (Brust et al. 1999, Cline et al. 2008). More recently, neonicotinoid seed treatments are also utilized to manage beetle. Beyond insecticide use, management options for A. vittatum are very limited. However, there are three known parasitoids of the adult, two of which are found in the northeast United States: a tachinid fly, Celatoria setosa (Coquillett), and a braconid wasp, Centistes diabroticae (Gahan). The third parasitoid, a braconid wasp native to South America, is Centistes gasseni (Shaw) (Fischer 1983, Smythe & Hoffmann 2009). 85 Neither North American parasitoid has been extensively studied, and little is known about key aspects of their life history, distribution, or seasonality, making it difficult to assess their potential as a biocontrol agent. The tachinid fly, Celatoria setosa (Coquillett) was first described from X by Coquillett in 1890 (Toefper et al. 2009). Since Coquillett’s initial report, there have been records of C. setosa emergence from A. vittatum in New Hampshire (1908; rates of parasitization as high as 50%), Ohio (1925; 41%), Arkansas (1927; 46%), Indiana (1944; 33%), and New York (2010, 54%) (Smythe & Hoffmann 2010). It is a solitary endoparasitoid that is known to attack three species within the genus Acalymma: Acalymma vittatum, Acalymma trivttatum (the western striped cucumber beetle – a minor pest of cucurbits), and Acalymma blandula. To the best of our knowledge, this fly does not parasitize any other species or genera within Diabrotica (Fischer 1983, Toefper et al. 2009). Gravid females deposit C. setosa larvae directly into the haemocoel of adult beetles (Fischer 1983); developing larvae feed on the fat bodies of their beetle host (Bussart 1937). At 25O C, larval development takes an average of 10 days, and when a larva is ready to pupate, it exits its host by wriggling its body back and forth, a movement that separates the host abdomen from the thorax. Within an hour of emergence, a puparium is formed; adult emergence occurs seven or eight days later, depending on the sex of the fly (Fischer 1983). C. setosa reproduction, including successful mating and oviposition, will only happen under very particular conditions. In the laboratory, the males will mate approximately 48 hours after emerging from their puparium. In contrast, females mate almost immediately after emergence. There is approximately a 3 hour window during which female flies are both attractive to males and receptive towards copulation attempts (Fischer 1983). Oviposition is not well understood and previous attempts to get gravid females to oviposit larvae in a laboratory were unsuccessful (Fischer 1983). C. setosa overwinters as first instar larvae in its beetle host (Fischer 1983, Smythe & Hoffmann 2009). Centistes gasseni (Shaw) was first described by Shaw in 1995; it was reared from an adult Diabrotica speciosa (Germar) collected in Brazil; this species was initially explored as a potential biocontrol candidate for the southern corn rootworm, Diabrotica 86 undecimpunctata howardi (Barber) (Shaw 1995). Under no-choice laboratory scenarios, researchers demonstrated that this parasitoid will attack A. vittatum (Schroder 2002, Toepfer et al. 2009). However, actual examples of C. gasseni parasitizing the genus Acalymma outside the laboratory are extremely rare; in a 2011 survey of South American parasitoids of Diabrotica, a single Acalymma bivittulata specimen was found to be parasitized by C. gasseni (Walsh et al. 2003). The other braconid parasitoid, Centistes diabroticae, was initially described by A.B. Gahan in 1922 in Ohio. Gahan measured C. diabroticae activity late in the summer and reported very low parasitoid populations, with rates averaging 1-2% (Gahan 1922). Beyond Gahan’s initial report, few efforts were made to study C. diabroticae activity until 2010, when Smythe and Hoffmann investigated parasitism in New York state. Over a 2 year period, they surveyed A. vittatum parasitism at six locations in central New York, reporting C. diabroticae parasitism rates that ranged from 5% to 54% (Smythe & Hoffmann 2010). Centistes diabroticae is a solitary endoparasitoid that overwinters in its host as a second instar larva. Female wasps will typically oviposit a single egg in a given host. However, superparasitization is possible under laboratory conditions, if a single beetle is exposed to multiple gravid female wasps. In such instances, the first larva to hatch continues to develop and growth is halted in all other eggs (Smythe & Hoffmann 2010). Under laboratory conditions, total developmental time averages between 24 and 27 days (Smythe & Hoffmann 2009). Larvae emerge from the tip of the host abdomen and pupate into a silken cocoon within a few hours of emergence (Smythe & Hoffmann 2009). Mass rearing and release of C. setosa or C. diabroticae is not currently a feasible biocontrol option. First, there is a problem with mass rearing the host, A. vittatum. Small scale rearing protocols do exist, though the process is very labor intensive. Additionally, the diapause biology of A. vittatum is not well understood. This is particularly problematic for rearing in winter; egg laying rates and adult emergence drops significantly during this time period, even when the colony is maintained in a controlled environment, at appropriate temperatures, humidity, and photoperiod. Even if we are able to develop methods to guarantee a steady supply of A. vittatum year round, there are no mass rearing protocols in place for either parasitoid. 87 Tachinid parasitoids are notoriously difficult to rear. In instances where rearing is successful, the process is highly labor intensive (Fischer 1983) and can only be done on a small scale due to low rates of larviposition (Zhang et al. 2003). Rearing is a highly species specific process, and factors such as parasitoid age, the larviposition interval, light intensity, and humidity must all be carefully controlled (Zhang et al. 2003). The braconid parasitoid, C. diabroticae, may be easier to rear than the tachinid; Smythe and Hoffmann successfully reared one generation C. diabroticae. Additionally, there are successful rearing protocols for braconids in other systems, including braconid parasitoids of fruit flies (Diptera: Tephritidae) (Joyce et al. 2010) and leafminers (Lepidoptera: Liriomyza) (Liu et al. 2012). However, to the best of my knowledge, no work has been done to investigate mass rearing of C. diabroticae; before any mass- rearing protocol can be developed for this wasp, a more comprehensive understanding of its life history and specific mating cues is needed. If either parasitoid is naturally present within Pennsylvania cropping systems, it may be possible to boost their populations with changes to management practices and the surrounding farmscape. Given the sporadic parasitoid record, it seems likely the relative contribution of parasitoids in suppressing A. vittatum populations is poorly understood and underestimated. To the best of my knowledge, there have been no reports of Acalymma vittatum parasitism in Pennsylvania. In this study, I aimed to assess the baseline prevalence of both parasitoid species within the state. My specific objectives in this study were to: 1. Establish if either parasitoid species is present in Pennsylvania at two geographically distinct field locations 2. Monitor parasitoid populations weekly for the duration of the 2014 growing season to gain a sense of the seasonal dynamics for both species I hypothesize that both species will be present in Pennsylvania, given that their presence has already been confirmed in states neighboring Pennsylvania. I also hypothesize that observed parasitism rates will be low early in the field season but that the parasitoid population will continue to build up as the season progresses, given that past studies typically report low overwintering populations (Bussart 1937, Smythe & Hoffmann 2010, Fischer 1983). Once this baseline knowledge about parasitoid 88 prevalence and seasonality has been generated, future studies can begin addressing larger, conservation biocontrol oriented questions.

Materials and Methods

I monitored parasitoid activity at two field sites for the duration of the 2014 growing season. Field collected adult Acalymma vittatum were brought back to laboratory, reared for a 20 day period, and monitored for parasitoid emergence. Collecting and rearing methods were adapted from Smythe & Hoffmann’s work in 2010.

Field Sites and Plot Management

I monitored parasitism rates at two geographically distinct field sites. The first field site was a pumpkin patch (Cucurbita pepo cv. Gladiator) at the Russell E. Larson Agricultural Research Center (Pennsylvania Furnace, PA), commonly referred to as Rock Springs. Planting was staggered through the month of May and pumpkins were planted directly from seed. Seeds were purchased as treated with Farmore D14000, (Syngenta Crop Protection LCC.; Greensboro, NC), a coating that includes three fungicides and thiamethoxin, a systemic neonicotinoid. Other than selecting insecticide treated seeds, no management actions were taken to manage insect or disease outbreaks. The pumpkin field was surrounded by sweet corn (Zea mays), soybeans (Glycine max), and an empty field. A floral provisioning strip was also planted next to the pumpkins for a separate pollination study. The provisioning strip contained buckwheat (Fagopyrum esculentum), cowpeas (Vigna unguiculata), sunn hemp (Crotolaria juncea), wild mustard (Sinapis alba), phacelia (Phacelia tanacetifolia), and sunflower (Helianthus annus). Our second site was the Southeastern Agricultural Research and Extension Center in Landisville, PA (Lancaster Co.; approximately 100 miles south of our first field site at Rock Springs). Beetles were collected from various horticultural trials. Because fields were quickly destroyed after harvest, to prevent a buildup of weed and arthropod pest 89 populations, I collected beetles from three fields that represented unique crops and management regimes. For the first 8 weeks (6/20/14 – 8/8/14), adult beetles were collected out of a cucumber field that was part of an insecticide trial. Various concentrations of spinosad (Entrust SC; Dow Agrosciences LLC. Indianapolis, IN), buffalo gourd root powder (Cidetrak-D; Trece Inc. Adair, OK), and lambda-cyhalothin Warrior II; Syngenta Crop Protection LLC, Greensboro, NC) were tested. However, A. vittatum was only collected from buffer rows between plots, where no insecticide material was applied. The insecticide trial lasted for 5 weeks, but I continued to collect beetles from that field for an additional 3 weeks. For weeks 9 and 10 (8/15/2014 and 8/22/2014), beetles were collected from a zucchini cultivar trial. During the cultivar trial, zucchinis were managed conventionally, and pesticide material was applied as needed. However, I began collecting A. vittatum after harvest; during this time, the field was not managed for any pests or pathogens. On the final week (8/29/2014), A. vittatum were collected from an ongoing pumpkin cultivar trial. Adult beetles proved very difficult to find; getting a sufficient number of beetles required searching under straw mulch, in soil cracks, and on weed patches adjacent to the field.

Field Collection of Adult SCB

Sixty adult A. vittatum were collected from each field sites weekly using an aspirator. I primarily searched for the beetles resting on crop foliage or inside flowers and on fruit. In instances when beetles were scarce, the search was expanded to include under leaf debris, in cracks in the soil, and on weeds within the field. 90 Parasitoid Rearing in Lab

Adult beetles were transferred into individual 44 ml (1.5 oz) plastic cups. Three days a week, each beetle received fresh cucumber leaves and/or flowers. Beetles were stored in an incubator that was kept at 26OC on a 12 hour light-dark cycle; each beetle was reared for 20 days, the maximum amount of time it takes C. diabroticae to complete larval development (Smythe & Hoffmann 2010). Beetles were checked three times for a week for any emerged parasitoids. Dead beetles were immediately dissected to check for the presence of unemerged parasitoid larvae. To dissect beetles, one pair of forceps was used to grab the beetle at its pronotum. A second pair gently peeled back elytral plates, revealing the presence or absence of a parasitoid. At the end of the 20 day period, any beetles still alive were stored in a freezer for later dissections to check for remaining parasitoid larvae. Any larvae that successfully pupated were left in individual containers and returned to the incubator until adult emergence. Larvae that died before successfully pupating were transferred to 70% ethanol. I distinguished between immature C. setosa and C. diabroticae based on descriptions of larval and pupal morphology in the literature. Braconid larvae pupate into a silken cocoon (Gahan 1922) (Figure 3-1). In contrast, the tachinid cocoon is brown and has a rough, spikey texture (Figure 3-2). Tachinid larvae are covered with stiff, rigid hairs that look similar to spines (Bussart 1937) (Figure 3-3); braconid larvae have a smooth appearance. Tachinid larvae also have hook-like mandibles that are apparent in later instars (Michalkova et al. 2009), something braconids lack. Adult and larval voucher specimens are housed in the laboratory of Dr. Shelby J. Fleischer at the Pennsylvania State University (University Park, PA). 91 Statistical Analysis

For each collection, I calculated the percentage of beetles parasitized by the braconid wasp, and the tachinid, and total combined parasitism. Standard error for each proportion was also calculated. Mean seasonal parasitism rates for each species were calculated by taking the average for all collection dates at a given field site. A t-test was used to compare parasitism rates between Rock Springs and Landisville by week.

Results

Both parasitoids, the tachinid, Celatoria setosa, and the braconid, Centistes diabroticae, were found in Landisville and Rock Springs. At Landisville, instances of C. diabroticae parasitism were very low, especially when compared to rates of C. setosa emergence. C. setosa parasitism rates ranged between 10% and 56%, with a season long average of 29.2% [n=11, SE=4.32%] (Table 3-1). There appeared to be two distinct peaks in the C. setosa. The first peak [33%, n=60, SE=6.07%] occurred on June 27th. The second peak happened on August 15th, when parasitism by C. setosa reached 60% [n=60, SE=6.41%]. However, for C. diabroticae, of 11 total sampling dates, I only observed braconid emergence from A. vittatum on two collection dates, August 15th and August 22nd; each time, a single beetle was parasitized by C. diabroticae, giving a maximum parasitism rate of 3% [n=60, SE=2.20] (Figure 3-5). Observed parasitism rates dropped dramatically at the end of summer. The last collection date in Landisville was September 5th. C. setosa parasitism rates were 10% [n=60, SE=3.87%] and no C. diabroticae were observed. At Rock Springs, both C. setosa and C. diabroticae were present in fairly high numbers. C. setosa parasitism rates ranged from 0% to 11.6%, with a mean parasitism rate of 6.51% [n=13, SE=1.03%] (Table 3-1) across all sampling dates. Maximum parasitism by C. setosa was observed on July 24th [11.6%, n=60, SE=4.13]. After reaching this maximum, C. setosa parasitism rates gradually declined for the remainder 92 of the field season (Figure 3-4). C. diabroticae parasitism was slightly higher and ranged from 0% to 16.75%, with a mean seasonal parasitism rate of 8.28% [n=13, SE=1.75%] (Table 3-1). I observed two peaks in parasitism by C. diabroticae. The first occurred July 10th [16.7%, n=60, SE=4.81%], and the second peak occurred August 21st [15.0%, n=60, SE=4.6%] (Figure 3-5). Parasitism rates for both species dropped at the end of the field season. At Rock Springs, our final collection date was September 18th. We observed 0% parasitism by C. setosa and 5% parasitism [n=60, SE=2.81%] by C. diabroticae. Total combined parasitism ranged from 5% to 28% at Rock Springs, with a season-long average of 15.17% [n=13, SE=2.17%]. The maximum combined parasitism rate was observed on July 17th [28.0%, n=60, SE=5.79%]. At Landisville, total parasitism ranged from 10% to 60%, with an average rate of 30.12% [n=11, SE=4.75%] across the entire season. Total combined parasitism rates were significantly higher at Landisville relative to Rock Springs on four separate weeks: week 2 (Z=-2.35, df=59, P=0.019), week 8 (Z= - 2.15, df=59, P=0.032), week 9 (Z=-2.49, df=59, P=0.013) and week 10 (Z=- 4.25, df= 59, P=0.00001) (Figure 3-6).

Discussion

To the best of my knowledge, this is the first record of Celatoria setosa and Centistes diabroticae parasitism of Acalymma vittatum in Pennsylvania. Observed parasitism rates were surprisingly high. At Rock Springs, rates of parasitism by C. setosa and C. diabroticae reached 11.6% and 16.7% respectively. Parasitoid activity seemed even higher at Landisville, with C. setosa parasitism rates as high as 56%. Despite these relatively high rates of parasitoid activity, vittatum remains a key pest of cucurbits in Pennsylvania and the rest of the Northeastern United States, suggesting that naturally occurring parasitoid populations within cucurbit cropping systems cannot provide sufficient suppression of A. vittatum within agricultural systems. A more comprehensive understanding of parasitoid biology, seasonality, and distribution will be key in making these parasitoids a more viable biocontrol option. 93 C. setosa was fairly ubiquitous at both field sites. In contrast, C. diabroticae was only found in strong numbers at the Rock Springs field site. In Landisville, over the course of the entire field season, two beetles parasitized with the braconid were collected (one on August 15th and one on August 22nd). Each time, they accounted for approximately 3% of the total parasitism observed. With the limited scope and lack of replication inherent within this study, I cannot make any definitive conclusions about C. diabroticae distribution. However, given my results, and the fact that records of C. diabroticae in the literature are extremely rare (Smythe & Hoffmann 2010), the question of a limited geographic range for this particular wasp merits further study. Beyond distribution, it also will be important to understand how specific conditions or management practices impact each parasitoid species. It is possible that some of the observed differences in C. diabroticae prevalence between Rock Springs and Landisville can be attributed to the farm landscape or specific management decisions. A 2012 study found that intensive agricultural practices, particularly soil ploughing and heavy pesticide use, had a strong negative impact on parasitoids of the peach and cabbage aphids (Jonsson et al. 2012); management practices in the fields used for this study might have similarly affected C. diabroticae. In Landisville, all the fields I used to collect beetles were intensively managed before or during the time of collection. The first field I collected from within the farm, a cucumber field, received weekly sprays of spinosads (Entrust SC; Dow Agrosciences LLC., Indianapolis, IN), in concentrations up to four times the maximum labeled rate. Both the zucchini and pumpkin fields were part of conventionally managed cultivar trials. Additionally, it is theorized that heterogeneous landscapes support larger, more diverse parasitoid populations; floral resource strips can provide important food resources and refuges for a variety of beneficial insects, including parasitoids. For many species of parasitoids, such as Microplitis mediator, a braconid parasitoid of the cabbage moth, floral nectar resources increase parasitoid lifespan (Geneau et al. 2011). Floral provisioning has also been used to successfully increase parasitism rates of A. vittatum in pumpkin fields (Phillips 2013) suggesting that floral provisioning could be used to bolster C. setosa and C. diabroticae populations. There is a record of C. setosa visiting Cornus racemosa (Lamarck), a dogwood shrub within the family Cornaceae (Tooker et al. 2006), 94 suggesting one potential plant species that could be incorporated into a floral provisioning strip targeted towards parasitoids of A. vittatum. However, beyond this record, parasitoid host-plant associations are unknown. To the best of my knowledge, there are no records of C. diabroticae associations with any plant species. Further study would be needed to develop a flowering mix specific to both the tachinid and braconid parasitoids. At Landisville, parasitism rates peaked suddenly for a two week period, August 15th [60%, n=60, se=6.41] through August 22nd [55%, n=60, SE=6.40]. However, by September 4th, combined parasitism rates had dropped to 10% [N=60, SE=3.87%] (Figure 3-6). These extreme fluctuations are not consistent with data collected at Rock Springs, where we observed minimal variation in C. setosa prevalence across time. Additionally, when discussing seasonal variation of parasitism rates in New York, Smythe and Hoffamnn (2010) attributed most of their observed seasonal dynamics to changes in the number of C. diabroticae; C. setosa numbers did not vary significantly across collection dates. While some of the fluctuation in my data can be attributed to seasonality, these results were likely confounded by my switching fields at the Landisville farm. August 22nd and September 4th were the only dates I collected adult A. vittatum from zucchini. The following collection date, when I moved into a pumpkin field, combined parasitism dropped to 10%. Though collecting sites at Landisville were relatively close, within 500m of each other, they represented different host crops. A study of parasitoids of Diabrotica in Florida noted that host plant influenced behavior of adult Celatoria compressa, a tachinid fly. Parasitism by C. compressa was only observed in beetles collected from the cultivated gourd Cucurbita moscheta. No parasitoids were found on beetles collected from a bitter, wild gourd, Cucurbita okeechobeensis ssp. martinezii (Gamez-Virues and Eben, 2005). Understanding parasitoid seasonal dynamics will also be an important component in making C. setosa and C. diabroticae feasible biocontrol agents. For the purpose of striped cucumber beetle management, it is most important to suppress beetle populations early in the growing season, when plants are most susceptible to bacterial wilt infections (Brust 1997). Additionally, reducing the number of overwintering A. vittatum early spring limits that generation’s ability to reproduce, which consequently reduces the number of 95 second generation beetles that emerge later in summer. Thus, it would be most effective to cultivate a large parasitoid population early in the spring, something that could be achieved if a high proportion of overwintering beetles were parasitized. In both field sites, I observed a decline in C. setosa and C. diabroticae parasitism rates at the end of the field season. This could reflect low overwintering parasitoid populations. Alternatively, the data may reflect an effect of parasitism on our ability to measure the rate, if, for example, being parasitized changes adult beetle behavior and triggers earlier diapause induction. In a 2010 parasitism survey in New York, six of nine field sites also yielded low parasitoid numbers late in October (Smythe & Hofmann 2010). However, there were three field sites that, for their final collection, yielded combined parasitism rates greater than 40%, suggesting that with the right practices, it may be possible to ensure high numbers of overwintering parasitoids (Smythe & Hoffmann 2010). That being said, high parasitoid numbers alone will not guarantee increased parasitism in early spring. Many parasitoids rely on plant volatiles to locate their host; the blend of compounds released by a given plant is very specific and often varies between plant species and family (De Moraes et al. 1998). In some cases, plants actually increase the rate of volatile emissions in response to herbivory (Des Moraes et al. 1998). In other instances, pheromones produced by the host, such as mating pheromones or aggregation pheromones play a key role in parasitoid host location (Higaki 2014, Jumean et al. 2009). Depending on the cues employed by C. setosa and C. diabroticae, host location prior to seedling transplant may be difficult (Smythe & Hoffmann 2010), regardless of parasitoid numbers. Trap flats, large trays of cucurbit seedlings grown in lab, that are set out in field prior to planting may be a potential solution to this hurdle. In past studies, they have been used to assess early season phenology of A. vittatum (Ellers-Kirk et al. 2006); they provide a food source and a place for beetles to aggregate. Parasitoids may be able to use the volatile cues from these trap flats to locate their host. I established that both parasitoid species are present in Pennsylvania. However, there are many questions about their biology, distribution, and seasonality that remain. The scope of this study was small, limiting what questions could be answered. This was partly an issue of methodology; the methods employed in this chapter were extremely 96 labor intensive and not practical for large scale studies. However, until laboratory rearing methods are developed, field studies are the only option available for studying these parasitoids. A simpler method for assessing parasitism is needed. Currently, I am looking into the idea of developing a molecular probe that can detect the presence of parasitoid larvae inside an adult A. vittatum, distinguishing between the braconid and tachinid. C. setosa and C. diabroticae are not the only known natural enemies of A. vittatum. Previous studies have identified natural enemies of both the egg and larval stages. Various guilds of generalist predators, including ants (Hymenoptera: Formicidae), harvestmen (Aranae: Opilliones), and crickets (Orthoptera: Gryllidae) attack A. vittatum eggs (Philips 2013). Additionally, entomopathogenic nematodes target A. vittatum larvae; when introduced into cucurbit fields via drip irrigation systems, entomopathogenic nematodes have significantly reduced adult A. vittatum emergence (Ellers-Kirk et al 2000). By developing methods that bolster parasitoid populations, it may be possible to develop an integrated biocontrol program that, by incorporating biological controls of each life stage, provides highly efficient suppression of A. vittatum with minimal chemical inputs.

97 References

Brust, G. E., & Foster, R. E. (1999). New economic threshold for striped cucumber beetle (Coleoptera: Chrysomelidae) in cantaloupe in the Midwest. Journal of Economic Entomology, 92(4), 936-940. Bussart, J. E. (1937). The bionomics of Chaetophleps setosa Coquillett (Diptera: Tachinidae). Ann Ent Soc America, 30(2), 285-292. Walsh, G. C., Athanas, M. M., Salles, L. A. B., & Schroder, R. F. W. (2003). Distribution, host range, and climatic constraints on Centistes gasseni (Hymenoptera: Braconidae), a South American parasitoid of cucumber beetles, Diabrotica spp. (Coleoptera: Chrysomelidae). Bulletin of Entomological Research, 93(6), 561-567. Cline, G. R., Sedlacek, J. D., Hillman, S. L., Parker, S. K., & Silvernail, A. F. (2008). Organic management of cucumber beetles in watermelon and muskmelon production. HortTechnology, 18(3), 436-444. De Moraes, C. M., Lewis, W. J., Pare, P. W., Alborn, H. T., & Tumlinson, J. H. (1998). Herbivore-infested plants selectively attract parasitoids. Nature (London), 393(6685), 570-573. Ellers-Kirk, C., & Fleischer, S. J. (2006). Development and life table of Acalymma vittatum (Coleoptera : Chrysomelidae), a vector of Erwinia tracheiphila in cucurbits. Environmental Entomology, 35(4), 875-880 Ellers-Kirk, C. D., Fleischer, S. J., Snyder, R. H., & Lynch, J. P. (2000). Potential of entomopathogenic nematodes for biological control of Acalymma vittatum (Coleoptera : Chrysomelidae) in cucumbers grown in conventional and organic soil management systems. Journal of Economic Entomology, 93(3), 605-612 Fischer, D. C. (1983). Celatoria diabroticae Shimer and Celatoria setosa Coquillet: Tachinid parasiotids of the Diabroticite Coleoptera (Unpublished doctoral dissertation). University of Illinois, Urbana-Champagne, IL. Fleischer, S.J., de Mackiewicz, D., Gildow, F.E., and Lukezie, F.L. (1999). Serological Estimates of the seasonal dynamics of Erwinia tracheiphila in Acalymma vittatum (Coleoptera: Chrysomelidae). Environmental Entomology, 28(3) 470 – 476. Gahan, A. B. (1922). A new hymenopterous parasite upon adult beetles. Ohio Journal of Science, 22(5). 98 Gamez-Virues, S., & Eben, A. (2005). Comparison of beetle diversity and incidence of parasitism in Diabroticina (Coleoptera : Chrysomelidae) species collected on cucurbits. Florida Entomologist, 88(1), 72-76. Geneau, C. E., Wackers, F. L., Luka, H., Daniel, C., & Balmer, O. (2012). Selective flowers to enhance biological control of cabbage pests by parasitoids. Basic and Applied Ecology, 13(1), 85-93. Higaki, M., & Adachi, I. (2011). Response of a parasitoid fly, Gymnosoma rotundatum (Linnaeus) (Diptera: Tachinidae) to the aggregation pheromone of Plautia stali Scott (Hemiptera: Pentatomidae) and its parasitism of hosts under field conditions. Biological Control, 58(3), 215-221. Jonsson, M., Buckley, H. L., Case, B. S., Wratten, S. D., Hale, R. J., & Didham, R. K. (2012). Agricultural intensification drives landscape-context effects on host-parasitoid interactions in agroecosystems. Journal of Applied Ecology, 49(3), 706-714. Joyce, A. L., Aluja, M., Sivinski, J., Vinson, S. B., Ramirez-Romero, R., Bernal, J. S., & Guillen, L. (2010). Effect of continuous rearing on courtship acoustics of five braconid parasitoids, candidates for augmentative biological control of Anastrepha species. Biocontrol, 55(5), 573-582. Jumean, Z., Jones, E., & Gries, G. (2009). Does aggregation behavior of codling moth larvae, Cydia pomonella, increase the risk of parasitism by Mastrus ridibundus? Biological Control, 49(3), 254-258. Liu, T., Kang, L., Lei, Z., & Hernandez, R. (2012). Hymenopteran parasitoids and their role in biological control of vegetable Liriomyza leafminers. Recent Advances in Entomological Research, 376-403 Michalkova, V., Valigurova, A., Dindo, M. L., & Vanhara, J. (2009). Larval morphology and anatomy of the parasitoid Exorista larvarum (Diptera: Tachinidae) with an emphasis on ephalopharyngeal skeleton and digestive tract. Journal of Parasitology, 95(3), 544- 554. Mitchell, R. F., & Hanks, L. M. (2009). Insect frass as a pathway for transmission of bacterial wilt of cucurbits. Environmental Entomology, 38(2), 395-403. Phillips, B. (2013). The Ecological impacts of non-native annual and native perennial floral insectaries on beneficial insect activity density and arthropod-mediated ecosystem Services within Ohio Pumpkin (Cucurbita pepo). (Unpublished Masters 99 Thesis). Department of Entomology, The Ohio State University, Columbus, OH. Quesada, M., Bollman, K., & Stephenson, A. (1995). Leaf damage decreases pollen production and hinders pollen performance in Cucurbita texana. Ecology, 76(2), 437-443. Sasu, M. A., Seidl-Adams, I., Wall, K., Winsor, J. A., & Stephenson, A. G. (2010). Floral transmission of Erwinia tracheiphila by cucumber beetles in a Wild Cucurbita pepo. Environmental Entomology, 39(1), 140-148. Schroder, R. F. W., & Athanas, M. M. (2002). Biological observations of Centistes gasseni Shaw (Hymenoptera: Braconidae), a parasitoid of Diabrotica spp. (Coleoptera: Chrysomelidae). Proceedings of the Entomological Society of Washington, 104(3), 554-562. Shaw, S. (1995). A new species of Centistes from Brazil (Hymenoptera, Braconidae, Euphorinae) parasitizing adults of Diabrotica (Coleoptera, Chrysomelidae), with a key to new-world species. Proceedings of the Entomological Society of Washington, 97(1), 153-160. Smyth, R. R., & Hoffmann, M. P. (2010). Seasonal incidence of two co-occurring adult parasitoids of Acalymma vittatum in New York State: Centistes (Syrrhizus) diabroticae and Celatoria setosa. BioControl, 55(2), 219-228. Toepfer, S., Walsh, G. C., Eben, A., Alvarez-Zagoya, R., Haye, T., Zhang, F., & Kuhlmann, U. (2008). A critical evaluation of host ranges of parasitoids of the subtribe Diabroticina (Coleoptera : Chrysomelidae : Galerucinae : Luperini) using field and laboratory host records. Biocontrol Science and Technology, 18(5), 485-508. Tooker, J. F., Hauser, M., & Hanks, L. M. (2006). Floral host plants of Syrphidae and Tachinidae (Diptera) of central Illinois. Annals of the Ent. Society of America, 99(1), 96-112. Walsh, G. C., Athanas, M. M., Salles, L. A. B., & Schroder, R. F. W. (2003). Distribution, host range, and climatic constraints on Centistes gasseni (Hymenoptera: Braconidae), a South American parasitoid of cucumber beetles, Diabrotica spp. (Coleoptera: Chrysomelidae). Bulletin of Entomological Research, 93(6), 561-567. Zhang, F., Toepfer, S., & Kuhlmann, U. (2003). Basic biology and small-scale rearing of Celatoria compressa (Diptera : Tachinidae), a parasitoid of Diabrotica virgifera virgifera (Coleoptera : Chrysomelidae). Bulletin of Entomological Research, 93(6), 569-575.

100 Tables

Table 3-1. Summary of parasitism rates for adult Acalymma vittatum in 2014 by Celatoria setosa, Centistes diabroticae, and total combined parasitism (by both species) at the Rock Springs Research Farm (RS) in Centre Co., PA and Landisville Research Farm (LD) in Lancaster Co., PA. N refers to the number of collecting dates at each field site. Parasitism is summarized as an the average rate across all collecting dates and as the minimum and maximum percent parasitism observed.

Celatoria setosa Centistes diabroticae Total

Min Max Min Max Min Max Site N Mean (%) Mean (%) Mean (%) (%) (%) (%) (%) (%) (%)

RS 13 6.51 ± 1.03 0 11.6 8.28 ± 1.75 0 16.8 15.17 ± 2.17 5 18.3

LD 11 29.2 ± 4.32 10 56 0.64 ± 0.63 0 3 30.12 ± 4.75 10 60

101 Figures

Figure 3-1. Centistes diabroticae in a silken cocoon, shortly after pupation (Photo: Margaret Lewis).

Figure 3-2. Tachinid pupa that did not fully separate from A. vittatum abdomen (Photo: Margaret Lewis)

Figure 3-3. Tachinid pupa that did not fully separate from A. vittatum abdomen (Photo: Margaret Lewis) 102

Figure 3-4. Seasonal parasitism rates by C. setosa and C. diabroticae at the Southeastern Agricultural Research and Extension Center (Landisville, PA) in 2014.

Figure 3-5. Seasonal parasitism rates by C. setosa and C. diabroticae at Russell E. Larson Research Farm (Pennsylvania Furnace, PA) in 2014. 103

Figure 3-6. Total parasitism (C. diabroticae plus C. setosa) rates by week at Rock Springs (Pennsylvania Furnace, PA) and the Southeastern Agricultural Research and Extension Center (Landisville, PA) in 2014. Error bars represent standard error. Weeks denoted with an * indicate a significant difference in parasitism rates between Rock Springs and Landisville (α = 0.05)

104 Chapter 4

Integrating Plant and Microbial Metabolites into a Biorational Control Option for Acalymma vittatum

Introduction

The striped cucumber beetle, Acalymma vittatum Fabricus, is a specialist herbivore of cucurbits in the North Eastern United States. Adult beetles act as a competent vector for the bacterial pathogen Erwinia tracheiphila, the causal agent of a bacterial wilt disease in cucurbits. Once a plant is infected with bacterial wilt, it is impossible to stop disease progression within that plant. Thus, disease management happens primarily through control of the beetle vector, A. vittatum. Synthetic insecticides provide consistent control in conventional cucurbit production systems. Common groups of insecticides include neonicotinoids, for example, imidacloprid, carbamates, such as carbaryl, and pyrethroids, including lambda- cyhalothrin and esfenvalerate (Hazzaard et al. 2006, Brust 2009). Carbamates and pyrethroids are primarily applied as foliar sprays. Neonicotinoids are a class of systemic insecticides that can be applied via foliar sprays, seed treatments, or soil treatments (Fleischer et al. 1998, Hazzard 2006, Jasinksi et al. 2009, McLeod 2006). Though effective (Fleischer et al. 1998), there are a number of concerns associated with frequent insecticide application. Numerous studies have linked pesticide applications and pesticide residue buildup in bee products to major declines in pollinator health (Johnson et al. 2010), and recent studies have documented neonicotinoid residues in pollen and nectar even when applied at appropriate rates (Dively and Kamel 2012). Additional concerns include toxicity to other beneficial species in cucurbit production systems, including predatory beetles, parasitoids, and beneficial soil dwelling species. With frequent neonicotinoid use, there is also the potential of insecticide resistance developing in A. vittatum. Populations of the Colorado potato beetle, a closely related Chrysomelid, have developed resistance to imidacloprid, a systemic neonicotinoid that is commonly used in cucurbits (Morta-Sanchez et al. 2006). 105 Additionally, synthetic insecticides are precluded from use in organic cucurbit production systems. Organic insecticide options for A. vittatum are limited. Available formulations tend to be expensive and relatively ineffective. Many degrade rapidly under field conditions, consequentially requiring more frequent applications. Available organic insecticides for A. vittatum include pyrethrin formulations, like PyGanic EC 5.0, or kaolin clay, for example, Surround WP (Cavanagh et al. 2011). There are cultural and mechanical practices available to organic and conventional organic growers, including tactics such as crop rotation and delayed planting to avoid peak cucumber beetle emergence (Caldwell et al. 2013). Polypropylene row covers can be deployed as a physical barrier against insect pests; they also protect young seedlings from inclement weather (Nair & Ngouajio 2010, Rojas et al. 2012). Thought effective, row cover installation and removal is a highly labor intensive process that tends to drive up production costs (Rojas et al. 2012), although mechanical options are being explored. Perimeter trap cropping can also be used to reduce insecticide sprays in the main crop (Cavanagh et al. 2009). However, in organic systems, management of the beetles may be tricky within the trap crop due to the limited effective insecticide options (Caldwell et al. 2013). A number of microbial metabolites have been formulated into biorational insecticides, some of which are allowable in organic systems. For example, spinosad is derived from the naturally occurring soil bacterium Saccharopolyspora spinosa Mertz & Yao. The active ingredient of spinosad, spinosyn, is a family of secondary metabolites derived from aerobic fermentation of S. spinosa. There are many naturally occurring strains of spinosyn, though several formulated products, such as Entrust and Blackhawk are primarily a mixture spinosyn-A and spinosyn-D (Thompson et al. 2000). Spinosyns are marketed as broad-spectrum foliar insecticides that act both orally and through direct contact, though oral ingestion is considered a more reliable route of exposure (Elliot et al. 2007). Common commercial spinosad formulations include SpinTor, Radiant, and Entrust, the latter being a formulation permitted in certified- organic production systems. When consumed, spinosad will excite the insect’s central nervous system at a site in the nicotinic acetylcholine receptor that differs from the site of activity of neonicotinoids (Thompson et al. 2000, Morta-Sanchez 2006). This results in 106 prolonged neuron excitation, eventually causing neuromuscular fatigue and paralysis. All movement and feeding behavior is halted before death (Salgado 1998, Morta-Sanchez 2006). This is a novel mode of action, making spinosads a value tool for growers in terms of insecticide resistance management strategies. Spinosyns have the additional advantage of being relatively safe to handle. They have low toxicity towards mammals and moderate to low toxicity for most aquatic organisms (Thompson et al. 2000). In addition, spinosad is considered to be a relatively safe material for many beneficial insects in the agroecosystem. For both the lady beetle, Hippodamia convergens Guerin, and minute pirate bug, Orius insidiosus Say, the LC-50 of a wettable spinosad powder (Entrust SC) was greater than 200 mg L-1 (Thompson et al. 2000). That being said, without careful handling, spinosyns can have a detrimental effect on pollinator health. Spinosad laced honey has an LC-50 of 7.34 mg L-1 for the honeybee, Apis mellifera Linnaeus (Rabea et al. 2010). Furthermore, the EPA classified spinosad as being highly toxic against honey bees, with an acute toxicity of less than 1 microgram per bee (Rabea et al. 2010). Spinosyns are also acutely toxic to other bee genera, including species within the genus Bombus. Several species of Bombus are important pollinators of cucurbits (Scott-Dupree et al. 2009). Despite high acute toxicity under laboratory conditions, field tests indicate that dry spinosad residues are fairly innocuous. If growers ensure that there is a period of several hours between spinosad application and pollinator entry into a field, this material should have minimal impacts on their pollinators (Miles 2003, Rabea et al. 2010). Though primarily marketed for control of lepidopteron and dipteran pests, previous studies have established that spinosyns can provide effective control for certain Chrysomelid beetles, including the Colorado potato beetle, Leptinotarsa decemlineata Say (Kowalska 2001). However, attempts to control field populations of A. vittatum with spinosads have been unsuccessful to date (Cavanagh et al. 2011). But, laboratory bioassays conducted at UC Davis established that spinosads can be lethal to the Western Striped Cucumber Beetle, Acalymma trivittatum Mannerheim (Pedersen & Godfrey 2011), which is closely related to A. vittatum. Given that spinosads are considered a broad spectrum insecticide and very effective against other Chrysomelid beetles, it is 107 possible their inactivity against A. vittatum stems from insufficient ingestion of insecticide droplets, not reduced spinosad toxicity. The Colorado potato beetle, for example, feeds only on foliage during both its larval and adult stages. In contrast, A. vittatum larvae mature in the soil and feed on the roots of cucurbits. As adults, they consume cucurbit foliage, flowers, and fruit, presumably ingesting less spinosad droplets as a consequence of reduce foliage feeding compared to Colorado potato beetle. It may be possible to increase A. vittatum ingestion of spinosad droplets through the addition of a feeding stimulant. Cucurbitacin is a naturally occurring metabolite produced by plants within the family Cucurbitacae. It induces a compulsive feeding behavior in Diabroticite beetles (Ferguson 1985). This is a highly specialized response; most mammals and arthropods are repelled by cucurbitacin, given that it is an extremely bitter compound (Ferguson 1985, Behle 2001). Adults are thought to sequester cucurbitacins in their body tissues as a defensive strategy to make themselves them less appetizing to predators (Ferguson 1985, Behle 2001). Alternatively, as a mating strategy, males might provide cucurbitacins as nuptial gifts to females (Tallamy et al. 1993) There are many naturally occurring forms of cucurbitacin. Most Diabroticites, including A. vittatum, are primarily responsive to Cucurbitacin-B and Cucurbitacin E- glycoside (Metcalf et al. 1980). In laboratory feeding assays, A. vittatum required large amounts of cucurbitacin to induce a compulsive feeding response relative to other species, indicating a reduced sensitivity. For example, only 0.001 microgram of cucurbitacin was needed to see a response in the spotted cucumber beetle, Diabrotica undecimpunctata Barber. In contrast, 0.3 micrograms were needed for A. vittatum (Metcalf et al. 1980). The company Trece (Adair, OK) now sells the botanical extract Cidetrak-D, a wettable powder that is derived from the buffalo gourd, Cucurbita foetedissma, and listed for use in organic production systems by the Organic Materials Review Institute (OMRI). The active ingredient in Cidetrak-D is cucurbitacin. Cidetrak is marketed as a gustatory stimulant, not an insecticide; cucurbitacin possesses no insecticidal properties towards Diabroticite beetles. However, when mixed with an oral insecticide, this product is designed to induce compulsive beetle ingestion of the poison. Feeding stimulants like cucurbitacins potentially could improve the efficacy of spinosad insecticides by inducing 108 increased ingestion of insecticide droplets. Earlier formulations suggested potential application of cucurbitacins in kairomonal bates, although ingestion varied among Diabroticite species and genders (Fleischer 1994). In a preliminary field study conducted June through July 2013 at the Southeast Agricultural Research and Extension Center (Landisville, PA), Dr. Tim Elkner evaluated the efficacy of an spinosad-cucurbitacin formulation for management of A. vittatum. He observed that spinosad with cucurbitacin did not significantly decrease A. vittatum numbers relative to an untreated control. However, there were several correctable issues that arose in the field trials, which might account for spinosad/cucurbitacin’s failure. The cucurbitacin label warns against applications 24 hours prior to rain. However, due to pre- established pesticide spray schedules, an unusually rainy summer, and worker availability, there were several dates where cucurbitacin was applied in spite of forecasted rainfall. Also, in these preliminary field trials, spinosad was applied at the minimum labeled rate of 1.25 oz Entrust/acre. If spinosad’s failure in controlling Acalymma vittatum stems from insufficient ingestion on the part of adult cucumber beetles, it is likely that higher rates of these microbial metabolites are needed to observe a significant effect. Additionally, scale was a confounding factor in this trial; a single plot consisted of one 30 foot row of cucumbers, which would have allowed for considerable beetle movement between plots. In this chapter, I explore the potential of integrating two microbial metabolites, Entrust SC Naturalyte, an organic spinosad insecticide, and Cidetrak-D, for management of A. vittatum. Specific objectives were to: 1. Determine if incorporating cucucurbitacin into a spinosad mixture increases efficacy, presumably through compulsive A. vittatum feeding on the insecticide. Data will be used to generate dose response curves for beetle mortality in response to spinosad and spinosad mixed with cucurbitacin, and to look for variation in the response between genders. 2. Conduct field trials to test various concentrations of spinosad mixed with cucurbitacin for control of A. vittatum. Testing higher concentrations will help us to determine if ingestion is a limiting factor in spinosad’s efficacy. 109 I hypothesize that ingestion is a limiting factor in spinosad efficacy as a control for A. vittatum. By including cucurbitacin as a feeding stimulant, I expect to see spinosad improve as a chemical control. In laboratory assays, if the addition of cucurbitacin will induce compulsive beetle feeding on spinosad droplets, I then expect to see a significant reduction of the LC-50. I also hypothesize that in field trials, spinosad with cucurbitacin will suppress A. vittatum populations relative to an untreated control. I expect the amount of beetle suppression to be dose dependent, with the best control achieved at higher application rates.

Materials and Methods

Materials

I used the same microbial metabolite formulations for both laboratory bioassays and the field trials: spinosad (Entrust SC Naturalyte)and buffalo gourd root powder (Cidetrak-D).Entrust SC Naturalyte is a liquid formulation of spinosad manufactured by Dow Agrosciences and approved for use in certified organic agriculture by OMRI. Its active ingredient is a mixture of spinosyn A and spinosyn D; proportions were not specified on the label, but together, the spinosyn mixture accounts for 22.5% of the total volume. Cidetrak-D is a wettable powder manufactured by Trece that is certified for use in organic production systems. Its active ingredient is buffalo gourd root powder (the root powder accounts for 43.38% of the total dry volume). Buffalo gourd root powder contains cucurbitacins, which induce a compulsive feeding response in Diabroticite beetles. Cidetrak is marketed as a feeding stimulant. Alone, it has no lethal effects towards A. vittatum. Instead, the Cidetrak is mixed with an insecticide of choice and sprayed as a bait treatment. Spray droplets should have a mean volume diameter size of 600 micrometers. Dasher II (Seminis) untreated cucumber seeds were used both for field trials and laboratory bioassays. For bioassays, cucumbers were grown in a laboratory growth chamber in soil containing Mycorrhizae nutrients (Premiere Horticulture Inc, 110 Quakertown, PA). They were watered daily with tap water and received no insecticide or fungicide treatments. Growth chambers were kept on a sixteen hour light and eight hour dark cycle. Temperatures ranged from 23OC during the light period to 21OC during the dark period.

Beetle Management for Laboratory Bioassays

Adult Acalymma vittatum were collected from an unmanaged pumpkin field (Cucurbita pepo cv Gladiator) at the Russell E. Larson Agricultural Research Center at Rock Springs (Pennsylvania Furnace, PA) in late September and early October of 2014. Beetles were hand collected from flowers and foliage using an aspirator and brought back to the laboratory. They were held in a large mesh cage for a minimum of seven days prior to being used in an experiment, during which time they received a steady supply of fresh cucumber leaves, flowers, and fruit. Each mesh cage also contained a container of moist soil, providing beetles a place to oviposit eggs. Eggs were removed weekly from the cage. Only active, apparently healthy and uninjured beetles were used in bioassays. 24 hours prior to use in an experiment, beetles were transferred to a clean mesh cage where they received no food or water. This ensured they would consume the cucumber leaf and insecticide material provided during the assay.

Leak Disk Bioassays

Preliminary tests of A. vittatum’s response to spinosad only were conducted to determine an appropriate range of concentrations for subsequent bioassays. I wanted to determine spinosad rates that encompassed both high and low beetle mortality. I initially tested the following concentrations (ml Entrust per 1 ml of water): 0.004167, 0.00625, 0.005, 0.00830, and 0.0125 (0.0125 is equivalent to the maximum labeled rate). Twenty A. vittatum were evaluated for lethal effects at each treatment level, and water was used as a control. Cucumber leaf disks were cut using a plastic aspirator vial; leaves were 111 taken from plants in the 3-4 leaf stage, excluding cotyledons. Leaf disks were dipped into a given spinosad treatment and left in the fume hood to dry for 45 minutes. They were then transferred into individual 45 ml (1.5 oz) clear plastic cups. One adult beetle was placed in each cup, and mortality was assessed 24 hours and 48 hours post exposure. A beetle was defined as “dead” only if it remained completely still, even in response to gentle probing with a pair of soft forceps. Any tiny twitches of the legs or the antennae counted as a movement.

Filter Paper Bioassays

Cidetrak proved to be a difficult material to work with; when it was mixed into an insecticide, the solution required constant agitation to keep granules evenly distributed. Also, spraying insecticide mixtures onto cucumber leaves was not an option. Because the buffalo gourd root powder did not fully dissolve, the insecticide solution would have clogged the small openings in the sprayers. For field applications, a special nozzle with larger opening is needed to avoid clogging spray openings. I lacked the specialized equipment necessary to accurately simulate field spraying conditions and equipment in the laboratory. I developed a dip bioassay as an alternative to spraying insecticide mixtures on leaves. I first attempted to deliver insecticides to A. vittatum using filter paper. Uniform pieces of filter paper, with an approximate area of 3.5 cm2 (Whatman Grade 1) were dipped into one of five concentrations of spinosad (ml Entrust per 1 ml water): 0.00156, 0.00325, 0.00625, 0.0125(the maximum labeled rate), and 0.0250. For the spinosad only treatments, I tested 30 A. vittatum at each concentration. For the spinosad/Cidetrak treatments, due to atypical results, I tested an extra ten beetles per treatment level, totaling 40 beetles per concentration. Cidetrak was added to each solution at a rate equivalent to 3.1 oz per acre with a two gallon spray volume. After adding the Cidetrak, the solution was mixed vigorously for 1 minute, which seemed to be enough time to ensure Cidetrak was evenly dispersed 112 throughout the mixture. While dipping, I continued to gently shake the beaker to prevent the Cidetrak from sinking to the bottom of the container. Filter paper was dipped rapidly using metal forceps; the paper was removed immediately after being submerged in solution. After drying in a fume hood for 30 minutes, filter paper squares were transferred to individual 45 ml plastic cups containing one A. vittatum. Beetles received no food or water while in the plastic cups, and mortality was assessed 24, 48, and 72 hours after initial exposure. The filter paper remained in the cup for the duration of the experiment.

Whole Leaf Bioassay

In addition to using a filter paper substrate, I also developed a bioassay in which I dipped whole cucumber leaves in varying concentrations of Entrust or Entrust with Cidetrak. Solutions were prepared using 0.5 fold serial dilutions to obtain the following concentrations of Entrust (ml Entrust per 1 ml water): 0.001563, 0.00325, 0.00625, 0.0125 (maximum labeled rate), and 0.025. For the Entrust/Cidetrak treatments, Cidetrak was again added at a rate equivalent to 3.1 oz of Cidetrak per acre with a two gallon spray volume. I also used two controls; leaves were dipped into either distilled water or a Cidetrak only treatment. Bioassays were carried out in three blocks, each block containing 10 beetles per treatment level. Cucumber leaves (untreated Dasher II) were taken from plants at the 4-5 leaf stage. To standardize size, I only used cucumber leaves between 6 and 10 mm in diameter at their widest point. I also discarded any damaged cucumber leaves. Entire cucumber leaves were dipped into a given solution. Prior to dipping, the base of the excised leaf was wrapped in a strip of wet paper towel to minimize desiccation. Leaves were rapidly dipped and removed as soon as the entire leaf, excluding the stem, was submerged. They were then left to dry in a fume hood for 45 minutes. After the leaves were completely dry, I punched out 2mm disks from the leaves using a large pipette tip. Each leaf disk was placed into a small plastic petri dish (Falcon, 60 x 15 mm, Fischer Scientific Inc.) that contained one sheet of wet filter paper and one adult A. vittatum. The filter paper was added to raise the humidity in the petri dishes. 113 Beetles were not provided with any additional leaf material, and mortality was checked 24, 48, and 72 hours after initial exposure. Again, mortality was defined as complete cessation of movement, even when gently probed with forceps. To avoid stress due to handling prior to the bioassay, beetle gender was determined either at death, or, if the beetle did not die during the course of the experiment, after the 72 hour check was completed. To determine gender, I looked at the distal end of the beetle’s abdomen. Male A. vittatum have an extra sclerite that gives the abdomen a rounded appearance from a side view. Females lack that extra sclerite, giving their abdomen a more pointed appearance (Krysan 1986).When determining gender, it was important to check immediately after death; if the beetle was frozen, or allowed to sit for a day or two, the abdomen would shrink, making accurate determinations difficult.

Field trials: Site Location and Plot Management

Field trials were conducted over a four week period at the Southeastern Agricultural and Research Extension Center (Landisville, PA). The experiment was designed as a replicated complete block design, with six blocks, each consisting of a single row of cucumbers, separated by an untreated buffer row. There were six plots in a single row; a plot consisted of 30 cucumber plants spaced 0.30 meters (1 foot) apart. 1.5 meters (5 feet) of empty plastic was also left as a buffer between each plot in a block. Untreated Dasher II cucumber seeds were planted directly into a plasticulture production system on May 23rd. Cucumber beetles first appeared in the plots two weeks later, on June 4th.We were unable to start the field trial immediately after discovering beetles. As a temporary measure to minimize beetle damage on untreated plants, one round of insecticide treatments (listed below) was applied on June 6th. These sprays were not immediately followed by scouting. Starting the following week, applied treatments were applied to all plots weekly for four weeks (ever Tuesday, June 10th – July 1st), which were followed by scouting. Treatments were:

114 1. Water 2. Lambda-cyhalothrin (Warrior) at 3.8 fluid oz / acre 3. Spinosad at 8 fluid oz / acre (maximum labeled rate) + buffalo gourd root powder (Cidetrak) at 3.1 oz / acre 4. Spinosad at 16 fluid oz / acre (two times the maximum labeled rate) + buffalo gourd root powder (Cidetrak) at 3.1 oz / acre 5. Spinosad at 32 fluid oz / acre (four times the maximum labeled rate) + buffalo gourd root powder (Cidetrak) at 3.1 oz / acre

Scouting

Plots were scouted 0, 1, 3, and 7 days post spray. For the 0 day / 7 day count, scouting happened early in the morning. Immediately after all plots were scouted, insecticide treatments were applied using a backpack sprayer. Insecticide sprays were typically applied by 10 am. Scouting records included counts of live or dead striped cucumber beetle, and live or dead spotted cucumber beetle. For the first three weeks (June 10th – June 24th), we scouted five plants per plot. Plants, excluding the first or last in a row, were randomly selected. We searched each plant thoroughly, counting all beetles on foliage, flowers, the stems, or the plastic surrounding the base of the plant. By the fourth week (June 24th – July 1st), the cucumber vines had begun to intermingle within the row. Because it was not possible to distinguish between individual plants without damaging them, we switched to scouting two randomly selected one-meter sections per plot. We searched all foliage within that section, including vines or foliage that had grown beyond the plastic.

Bacterial Wilt Assessment

On week four and week five (one week after the field trials ended), I also conducted a bacterial wilt assessment. In each plot, I rated bacterial wilt severity on a 115 scale of 0 (no wilt) to 5 (most severe) in five haphazardly selected 1 meter sections per plot. I developed the following scale by walking through the field and studying the varying degrees of bacterial wilt severity present:

Score Description 0 Plant is “healthy”, demonstrating no symptoms of bacterial wilt 1 Partial leaf wilt; about half the leaf is drooping on one leaf 2 Entire leaf wilt on one leaf 3 >75% of leaves on plant are showing wilt symptoms (either partial or full) 4 Stems are showing wilt symptoms; beginning to drop and unable to support foliage 5 Entire plant is showing bacterial wilt symptoms

Statistical Analysis

For the bioassays, A. vittatum mortality rates were corrected using Abbott’s formula to account for the mortality in the control beetles (Abbott 1925). For the whole leaf dip bioassays, I generated dose response curves for A. vittatum mortality 72 hours post exposure in response to spinosad and spinosad with cucurbitacins by conducting a probit analysis in SAS (PROC PROBIT, SAS 9.4, SAS Institute Inc. 2013). Using the probit regression curve, I was able to estimate the LC-50 for each insecticide treatment. Data from earlier (leaf disk and filter paper) bioassay attempts are reported, though I did not run a probit analysis on any earlier bioassays. To determine if gender significantly influenced mortality, I ran a binary logistic regression using Minitab (Minitab v. 17, Minitab Inc., State College, PA), with mortality as my response variable. For predictor variables, I included insecticide concentration as a continuous variable, gender as a categorical variable, and a treatment*gender interaction. 116 For data collected from the field trials, I initially analyzed the average numbers of live A.vittatum within each treatment by week, using a repeated measures ANOVA with a randomized complete block design. For the purposes of analysis, I defined one week as to include the zero, one, three, and seven day counts within a given spray cycle. There was overlap between the zero and seven day counts between weeks. Week one included the sampling dates June 10th–June 17th. Week 2 included the sampling dates June 17th–June 24th. Week 3 ran from June 24th–July 1st; however, for the purposes of a repeated measures ANOVA in week 3, I only included the zero, one, and three day counts (June 24th, June 25th, and June 27th), because sampling units switched on the seven day count. Week 4 included the dates from July 1st – July 4th. The study ended July 4th; there was no seven day count that week. I ran the repeated measures ANOVA in SAS (PROC MIXED, SAS 9.4, SAS Institute Inc. 2013) using an autoregressive structure to account for correlations between dates. Replication (or block) was included as a random term, and sampling date was included as a repeated measure in my model. Additionally, I ran one-way ANOVAs using a replicated block design to determine if there was a significant difference between treatments on an individual date (PROC MIXED, SAS 9.4, SAS Institute Inc. 2013). Replication was again included in the model as a random effect. If the ANOVA was significant, I followed with mean-wise comparisons between treatments using Tukey’s adjustment. Bacterial wilt severity was also analyzed by date, using a one-way ANOVA within a replicated blocked design. For all ANOVA analysis, residual plots were checked for assumptions of normality and homoscedasticity of variances; no data transformations were necessary. Means are reported as ± the standard error unless otherwise noted. 117 Results

Cut Leaf Bioassays

Acalymma vittatum mortality was unexpectedly high in the cut leaf disk bioassays (Table 4-1). Beetle mortality rates ranged from 50% [n=20, se=11%] at the lowest dose, 0.0042 ml Entrust per 1 ml of water, to 100%, at the highest dose, 0.0125 ml Entrust per 1 ml water (this is equivalent of the maximum labeled field rate). Mortality in the control was 0%.

Filter Paper Bioassays

For the filter paper bioassays, dose response curves for A. vittatum mortality were drawn at 24, 48, and 72 hours after exposure to insecticide treatments. Mortality was low in the control; 24 hours after exposure, 0% of the A. vittatum had died. At 48 hours, mortality rate had risen to 0.025%, and by 72 hours, mortality was up to 0.05%. For spinosad alone, the dose response curve that best bracketed the full dose range occurred at the 48 hour time point (Figure 4-1b). At 48 hours post exposure, mortality ranged from 0% ± 8.8% up to 80.0% ± 5.7%. At the 24 hour time point, beetle mortality was low (Figure 4-1a), ranging 0% to 40% ± 23%, and by 72 hours post exposure, mortality was very high, even at the lowest concentrations (Figure 4-1b), ranging between 44% ± 6.35% and 92% ± 3.67% (Figure 4-1c). Because 48 hours encompassed the widest range of responses, I planned to use that time point when determining LC-50 values. However, when cucurbitacin was added to the spinosad mixture, mortality decreased significantly across all time points (Figure 4-1). At 48 hours post exposure, at the maximum concentration of spinosad with cucurbitacin (0.025 ml Entrust per 1 ml water), beetle mortality was only 10% ± 7.07%. Due to the unexpected results, I decided to try a third bioassay method. 118 Whole Leaf Dip Bioassays

As a final bioassay, I conducted whole leaf dip bioassays. Beetle mortality was again measured 24, 48, and 72 hours after exposure. Mortality for the water control was 0% [n=30] at the 24, 48, and 72 hour time points. Likewise, mortality for beetles exposed to only Cidetrak (at a rate equivalent to 3.1 oz /acre with a 2 gallon spray volume – the same amount used in all spinosad-cucurbitacin formulations) was also 0% [n=10] at each time point. In the whole leaf bioassays, the most comprehensive coverage of mortality rates in response to dose happened at the 72 hour time point (Figure 4-2); mortality for spinosad only ranged from 26.7% at the lowest concentration to 70% at the highest concentration, 0.025 ml Entrust per 1 ml water (this is equivalent to the two times the maximum labeled rate). Spinosad with cucurbitacin ranged from 46.7% to 96.7% at the 72 hour time point. The addition of cucurbitacin significantly impacted the dose response curves (Figure 4-2c). For spinosad only, we obtained a probit regression curve of 푃푒푟푐푒푛푡 푀표푟푡푎푙푖푡푦 = 0.406 ( 퐶표푛푐푒푛푡푟푎푡푖표푛) + 1.987. For the spinosad with cucurbitacin assays, there was a dramatic increase in the Y intercept for the probit regression curve: 푃푒푟푐푒푛푡 푀표푟푡푎푙푖푡푦 = 0.0675 ( 퐶표푛푐푒푛푡푟푎푡푖표푛) + 4.256. Chi-square goodness of fit tests confirmed that both models were a good fit for our data [Spinosad only: 휒 = 0.0526, df = 3, p=0.997. spinosad with cucurbitacin: 휒 = 2.724, df = 3, p=0.436]. With the addition of cucurbitacin, we significantly lowered the LC-50 of spinosad at 72 hours post exposure (Figure 4-3). The LC-50 of spinosad alone was 0.0075198 ml Entrust / ml water, with a 95% confidence interval of [0.00434, 0.01475]. In contrast, the LC-50 of spinosad/cucurbitacin dropped to 0.0018287 ml Entrust / ml water, with a 95% confidence interval of [0.0008589, 0.0027346]. There was no overlap of the confidence intervals. 119 Logistic Regression for the Mortality Response

We estimated the effect of gender in on the whole leaf bioassays at the 72-hour time point. Chi squared tests indicated that a binary logistic regression model, with mortality as a categorical response variable, was a good fit for the data. In the spinosad bioassays, the Hosmer-Lemeshow goodness of fit test yielded a 휒 2 statistic of 5.48 (df=5, p = 0.360), indicating that observed probabilities for mortality matched the expected probabilities from the model. For spinosad with cucurbitacin, the Hosmer-Lemeshow goodness of fit test gave a 휒 2 statistic of 6.72 (df =5, p=0.100). In the spinosad bioassays, dose was a significant predictor for the A.vittatum mortality response (휒 2=192.73, p=0.007) (Table 4-2). Gender and the interaction between gender and dose were not significant predictors (휒 2 = 0.66, df = 1, 146, p= 0.416 and 휒 2=0.05, df=1,146, p=0.828 respectively). The probability of mortality was

′ 푒푌 modeled as 푃 = (Figure 4-4a). For female A. vittatum, 푌′ = −1.407 + 1+푒푌′ 71.28 ( 퐷표푠푒). For the male beetles, the model for mortality was similar: 푌′ = −0.6183 + 81.38 ( 퐷표푠푒). In contrast, for spinosad with cucurbitacin, both dose (휒 2=4.81, df = 1, 145, p=0.028) and gender (휒 2=6.72, df = 1, 145, p=0.010) were significant predictors of mortality. The interaction between treatment level and gender was again insignificant (휒 2=0.06, p=0.814) (Table 4-2). Probability of mortality was again modeled as 푃 =

′ 푒푌 . (Figure 4-4b) For female A. vittatum, 푌′ = −0.9620 + 161.3 (퐷표푠푒). For males, 1+푒푌′ 푌′ = 0.9432 + 192.3 (퐷표푠푒). The influence of gender on A. vittatum mortality for the spinosad-cucurbitacin treatments was most obvious at the lowest concentration of 0.0015625 ml Entrust / 1 ml water (Figure 4-5). Out of the 16 males evaluated at that particular concentration, 81.25% were classified as dead. In contrast, out of the 14 females tested, only one female died (7.14% of all females tested at that concentration). A similar trend of higher proportion mortality in males relative to females was apparent to lesser degrees in the other four concentrations of spinosad with cucurbitacin. 120 Field Trials

Beetle pressure was low in week one (June 10th-June 17th) of the field trials (Figure 4-6). Across all sampling dates, treatment had a significant effect on the average number of live A. vittatum per plant (F=3.21, df=4, 95, p=0.0162) (Table 4-3). However, there was no significant treatment effect for any individual sampling date (Table 4-4). In the control plots, beetle numbers averaged from 0.60 A. vittatum per plant to 1.16 A. vittatum per plant. By week 2, beetle pressure began to increase across all treatments, most notably in the control plots (Figure 4-7). Across all sampling dates for the second week, there was a significant treatment effect on mean A. vittatum counts (F=7.65, df = 4, 95, p=0.001). The treatment by date interaction was also significant (F=3.26, df=12,95, p=0.001) (Table 4-3). Looking at individual scouting dates, on zero and seven days post spray, treatment did not influence A. vittatum counts. One-day post spray, only the lambda- cyhalothrin had significantly reduced the number of beetles per plant (Table 4-5). There was no significant decrease in any of the spinosad concentrations relative to the control, though spinosad at one and four times the maximum also did not differ significantly from the lambda-cyhalothrin. Three days post spray, all insecticide treatments, including lambda-cyhalothrin and the three concentrations of spinosad, significantly reduced beetle numbers relative to the control. By seven days post spray, on June 24th, again there was no significant effect (F=1.59, df=4,20, p=0.2159). For the third week of the trial, June 24th – July 1st, beetle pressure remained high (Figure 4-8). A repeated measures ANOVA across the zero, one, and three day post spray counts indicated that treatment (F=12.80, df=4, 70, p=0.0001) and date (F=15.61, df= 2, 70, p=0.001) significantly influenced beetle numbers. The treatment by date interaction was not significant (Table 4-3). On the zero day (pre-spray) count, there was no difference between treatments (Table 4-6). One day post spray, June 25th, both lambda- cyhalothrin and spinosad at four times the maximum rate significantly reduced beetle numbers relative to the control. The two lower concentrations of spinosad were not significantly different from the control. However, they also did not differ significantly 121 from the lambda-cyhalothrin. On June 27th, three days post spray, the same trend was observed. By the seven day count, no insecticide treatment suppressed A. vittatum counts significantly. Across all sampling dates in week 4, treatment again significantly influenced A. vittatum numbers (F=4.02, df = 4,70, p=0.0054) (Table 4-3, Figure 4-9). Date and the treatment by date interaction were not significant variables in the repeated measures model. There was only a significant reduction in beetle numbers one day post spray, on July 1st (Table 4-7). Treatment effects were not significant on the zero or three day counts. The lambda-cyhalothrin, spinosad at two times and spinosad at four times the maximum rate significantly reduced A. vittatum numbers compared to the control. On July 4th, the one-way ANOVA was significant (F=2.90, df = 4, 20, p=0.0481); however, subsequent mean-wise comparison using Tukey’s method found no significant difference between means. By the end of the field trial, bacterial wilt incidence was fairly high (Table 4-8). I assessed bacterial wilt severity on two separate weeks. Each week, only the lambda- cyhalothrin significantly reduced the average bacterial wilt score compared to the control. On July 4th, the control plots had an average bacterial wilt score of 2.37. In contrast, in the lambda-cyhalothrin plots dropped to 1.06. On July 11th, water control plots had an average score of 3.60; in the lambda-cyhalothrin treatment, bacterial wilt score dropped to 2.50. At four times the maximum labeled rate for spinosad, average bacterial wilt scores did not differ significantly from the water only control. However, they also did not differ significantly from the lambda-cyhalothrin. Instances of dead A. vittatum were sporadic, resulting in low counts (Table 4-9) that I was unable to analyze. Across all sampling dates in which five randomly selected plants per plot were sampled (June 10th – June 27th), the average number of dead A. vittatum in the control plots was 0.00741 ± 0.00523 per plant. Lambda-cyhalothrin, which had the highest numbers of dead A. vittatum, only averaged 0.36667 ± 0.0479 beetles per plant. For the dates that sampling united switched to one-meter sections, numbers of dead A. vittatum were slightly higher, though instances of dead beetles were still sporadic. In the lambda-cyhalothrin plots, there was an average of 2.72± 0.0823 A. vittatum per meter. The control plots averaged 0.083 ± 0.0123 A. vittatum per meter. 122 Numbers for spotted cucumber beetles were even lower. I encountered 0 dead D. undecimpunctata across the entire field trial. Across all dates, a single live D. undecimpunctata was found in each spinosad-cucurbitacin treatment.

Discussion

My main objectives was to test the hypothesis that ingestion is a limiting factor in spinosad’s efficacy as a control for A. vittatum and that incorporation of a feeding stimulant could overcome this limitation. To test this, I integrated spinosad with buffalo gourd root powder, a cucurbitacin based feeding stimulant that specifically targets Diabroticite beetles, and evaluated the combination’s lethality under lab and field conditions. Past attempts to manage field populations of A. vittatum with spinosads have been unsuccessful (Cavanagh et al. 2011, Pederson & Godfrey 2010). However, my data suggests that spinosad is lethal to A. vittatum and that with feeding stimulants like cucurbitacin, it may be possible to increase its efficacy in the field. Laboratory bioassays were used to evaluate the efficacy of spinosad with and without cucurbitacin. Ultimately, this data demonstrated that cucurbitacin lowers the LD- 50 of spinosad (Figure 4-3). However, to obtain this result, I tested three different bioassays before finding a reliable method. The first attempt, dipping cut leaf disks into insecticide solutions, yielded unexpectedly high mortality rates (Table 4-1). I observed 50% mortality at the lowest concentration tested, 0.0042 ml Entrust / ml water; that concentration is orders of magnitude lower than the equivalent of the labeled field rate for cucurbits ( 0.0125 ml Entrust/ ml water). Part of the high mortality can be attributed to the nature of dip bioassay. Dipping a material into an insecticide, as opposed to spraying, the typical method of delivery for many foliar insecticides, deposits more insecticide residue on the leaf tissue and ensures a more even coating than what is seen in the field. Additionally, leaf disks were cut prior to dipping. It is also likely that some amount of insecticide entered the leaf tissue through the wound, which would further increase the concentration of insecticides on a given leaf disk. Injuries to plant tissue, like wounding, 123 are known to have a major impact on the plant chemistry, influencing volatile emissions and production of plant metabolites or proteins (Saltveit 2000, Matsui et al. 2012). In an attempt to circumvent the issue of insecticides entering and interacting with the leaf tissue, we then tried dipping filter paper into insecticide solutions. For the spinosad only treatments, I generated a dose response curve at the 48 hour time point in which mortality spanned 13% - 80% (Figure 4-1b). Unexpectedly, when cucurbitacin was added to the mixture, A. vittatum mortality plummeted. At 48 hours post exposure, at the highest concentration tested, 0.025 ml Entrust / water (two times the maximum labeled field rate) , mortality averaged 10% ± 1.76%. In contrast, at that same concentration for the spinosad only treatment, mortality averaged 80% ± 5.76%. These results were the exact opposite of what I expected to see. I suspect that the presence of the cucurbitacin interfered with the absorption of spinosad into the filter paper, possibly due to differences in material polarities. If that were the case, there would be a lower amount of spinosad material on the filter paper disk for the spinosad-cucurbitacin treatments, artificially lowering the mortality. Given the atypical results, I decided to try a third method, dipping whole cucumber leaves into the various treatments. By dipping a whole leaf, I hoped to circumvent the issue of insecticide absorption into the leaf wound. Again, I generated dose response curves for beetle mortality response to spinosad and spinosad with cucurbitacin (Figure 4-2). This time, the addition of cucurbitacin significantly lowered the LC-50, dropping it from 0.0075 ml Entrust / ml water to 0.0018 ml Entrust / ml water (Figure 4-4). Based on the results of the third bioassay, the addition of cucurbitacin appears to increase spinosad’s efficacy. On its own, cucurbitacin does not possess any insecticidal properties towards A. vittatum; in a cucurbitacin only control, mortality was 0%. This suggests that the increase in mortality was due to the cucurbitacin inducing a compulsive feeding behavior on insecticide materials, supporting our hypothesis that ingestion is a limiting factor. Before incorporating cucurbitacins into cucurbit production systems, it will be important to understand the intraspecific response to cucurbitacins within A. vittatum. In the bioassay data generated for the whole leaf dip, I found that gender was a significant 124 factor in the beetle mortality response for the cucurbitacin-spinosad assays (Table 4-2), but not for spinosad assays. This idea of intraspecific variation in Diabroticite response to cucurbitacin has been suggested previously. For example, male A. vittatum are thought to lose their sensitivity to cucurbitacins after mating (Tallamy et al. 1993). Cucurbitacins play an important role in mating rituals. Male spotted cucumber beetles, Diabrotica undecimpunctata howardi, sequester cucurbitacins in spermataphores that are then presented to the female during mating. If the female accepts the spermataphore, she allocates most of the cucurbitacins towards her eggs; some of the bitter compounds are sequestered in her tissue or discarded. Interestingly, after accepting and consuming a full spermataphore packet, the female beetles appear to lose receptivity to subsequent mating attempts (Tallamy et al. 2002). Fleischer et al. (1984) used rubidium to track the feeding intensity of four species of Diabroticites on muskmelon treated with a cucurbitacin-carbaryl kairomonal bait. For three of the four species (D. virgifera virgifera, D. undecimpunctata howardii, and D. barberi), female beetles consumed significantly more of the treated muskmelon relative to males, indicating increased sensitivity. But, in A. vittatum, there was no significant difference between genders. Gender can also significantly beetle feeding intensity on bitter cucumber (cv. Marketmore 76) cotelydens, with females generally a greater quantity of cucurbitacins than the males in no-choice tests (Smythe et al. 2002). In contrast, in South America, male Diabroticite beetles across a wide variety of species demonstrated a much strong response to cucurbitacin kairomonal bait relative to their female counterparts (Walsch et al. 2013). The data presented in this chapter also found that males were more sensitive to cucurbitacin than females. The contrast between my results and the literature record could reflect the different types of cucurbitacins tested. Buffalo gourd root powder contains cucurbitacins E and I, though A. vittatum are thought to be most sensitive to cucurbitacin B (Metcalf et al. 1980). However, in, cucurbitacin B extract was used to test beetle consumption. used Marketmore 76 cucumbers in their experiments, which only contain cucurbitacin C when cotyledons are fully expanded. It is possible that gender response varies among specific 125 cucurbitacins. Additionally, the beetles that were used in these assays were field collected near the end of the season (late September) and held in lab for two – three weeks. In contrast, the oldest beetles tested in Smythe et al. 2002 were 14-16 days old. It is possible that the advanced age of the beetles I used contributed to these atypical results. A more comprehensive understanding of intraspecific variation in A. vittatum response will be important for designing cucurbitacin laced insecticide baits. Perhaps this intraspecific variation accounts for some of the variability observed in past studies. Despite the issue of intraspecific variation, in field trials, there was a measurable level of control of A. vittatum achieved in 2014 by mixing spinosad with Cidetrak. This is especially promising, given that plot size was a confounding factor. A single plot consisted of one thirty foot row of cucumbers. This probably allowed for considerable beetle movement between plots after exposure to the various insecticide treatments, creating a lot of noise within the data. Beetle response was dose dependent. Most counts taken one and three days post spray indicated that there was no significant difference in average beetle numbers between the lambda-cyhalothrin and spinosad 4x treatments. There were consistently fewer beetles in plots treated with the highest concentration of Cidetrak and spinosad (spinosad at four times the maximum rate) compared to lower treatment levels. On week 3, only spinosyn at two and four times the maximum level significantly suppressed A. vittatum relative to the control one-day post spray (June 25th) and three-days post spray (June 27th). In the fourth week, only the one day post spray counts at the maximum spinosad rate, significantly reduced beetle numbers relative to the control. Additionally, though the differences were not always significant, across treatment dates, I noticed a trend of decreased numbers of live A. vittatum as the concentration of spinosad increased. The increase in mortality rates with an increase in dose is consistent with the hypothesis that ingestion may be a limiting factor in spinosad’s efficacy. By finding a way to increase ingestion, we may be able to make spinosad with Cidetrak a viable control option for organic and conventional systems. Bait formulations are a promising avenue for incorporating spinosads and cucurbitacins into cucurbit IPM programs. Past work has identified three floral volatile emissions from Cucurbita, indole, 1,2,4-trimethoxybenze, and trans-cinnamaldehyde, 126 that are attractive to A. vittatum (Lewis et al. 1990, Andersen & Metcalfe 1986). Alternatively, male aggregation pheromones have been shown to be attractive to both male and female A. vittatum (Smythe & Hoffmann 2003). Fleischer and Kirk 1994 deployed granular kariomonal baits that consisted of carbaryl, cucurbitacin (in the form of buffalo gourd root powder) and a blend of floral volatile attractants. In field cage experiments, they significantly reduced feeding by Diabrotica undecimpunctata howardii and A. vittatum on cantaloupe. Field tests of a dry carbaryl-cucurbitacin bait formulation provided up to seven days of control for A. vittatum, compared to carbaryl sprays, which only lasted four-five days (Foster and Brust 1995). Bitter extracts from the Hawkesbury watermelon and phloxine B, a xanthine dye, have also been successfully integrated into baits that target Diabroticite beetles (Schroder et al. 2001). With careful selection of volatile attractants and a cucurbitacin feeding stimulant, it would be possible to create effective bait formulations using spinosad, that are certified for use in organic production systems. There are number of matrixes available for bait delivery. Many have the additional benefit of providing protection UV radiation and microbial degradation, which would increase the half-life of spinosad residuals (Thompson et al. 2000), consequentially reducing the number of applications needed in a season. Both the field trials and laboratory bioassays indicate that spinosad-cucurbitacin formulations have promise as a control option for A. vittatum in both organic and conventional production systems, one that has not been previously explored. If we are able to develop these products into an effective, consistent formulation, it will fill a critical gap in organic cucurbit production by providing a good chemical control option that can be integrated into pest management programs for enhanced control of A. vittatum, and reduce selection pressure for resistance in all production systems.

127 References

Abbot, W.S. (1925). A method of computing the effectiveness of an insecticide. Journal of Economic Entomology, 18(1), 265-267. Andersen, J. F., & Metcalf, R. L. (1986). Identification of a volatile attractant for Diabrotica and Acalymma species from blossoms of Cucurbita maxima. Journal of Chemical Ecology, 12(3), 687-700. Behle, R. W. (2001). Consumption of residue containing cucurbitacin feeding stimulant and reduced rates of carbaryl insecticide by western corn rootworm (Coleoptera: Chrysomelidae). Journal of Economic Entomology, 94(6), 1428-1433. Brust, G. (2009). Cucurbit Pest Management. University of Maryland Extension, Retrieved from http://extension.umd.edu/CucurbitPestManagement Caldwell, B. Sideman, E., Seaman, A., Shelton, A., Smart, C. (2013). Resource Guide for Organic Insect and Disease Management. New York State Agricultural Experiment Station. Geneva, New York Cavanagh, A., Hazzard, R., Brown, A. (2011) Efficacy of three OMRI listed materials, alone and in combination, for control of striped cucumber beetle in cucumber. Arthropod Management Tests. 36(E28), 1-2 Cavanagh, A., Hazzard, R., Adler, L. S., & Boucher, J. (2009). Using Trap Crops for Control of Acalymma vittatum (Coleoptera: Chrysomelidae) Reduces Insecticide Use in Butternut Squash. Journal of Economic Entomology, 102(3), 1101-1107. Dow Agroscienes (2014). Entrust SC Naturalyte [Insecticide], Indianapolis, Indiana. Dively, G. P., & Kamel, A. (2012). Insecticide residues in pollen and nectar of a cucurbit crop and their potential exposure to pollinators. Journal of Agricultural and Food Chemistry, 60(18), 4449-4456. Fleischer, S.J. Kirk, D. (1994). Kairomonal baits: effect on acquisition of a feeding indicator by Diabroticite vectors in cucurbits. Environmental Entomology, 25(3). 1138-1149. Elliot, R. H., Benjamin, M. C., & Gillott, C. (2007). Laboratory studies of the toxicity of spinosad and deltamethrin to Phyllotreta cruciferae (Coleoptera : Chrysomelidae). Canadian Entomologist, 139(4), 534-544. Ferguson, J. E., & Metcalf, R. L. (1985). Cucurbitacins – plant derived defense compounds for Diabroticites (Coleoptera: Chrysomelidae). Journal of Chemical Ecology, 11(3), 311- 318. 128 Foster, R. E., & Brust, G. E. (1995). Effects of insecticides applied to control cucumber beetles (Coleoptera: Chrysomelidae) on watermelon yields. Crop Protection, 14(8), 619-624. Hazzaard, R., Cavanagh, A., Duphily, A. (2006). Efficacy of furrow and foliar materials to control striped cucumber beetle in cucumber. Arthropod Management Tests,31(E24), 1-2. Jasinski, J., Darr, M., Ozkan, E., & Precheur, R. (2009). Applying imidacloprid via a precision banding system to control striped cucumber beetle (Coleoptera: Chrysomelidae) in cucurbits. Journal of Economic Entomology, 102(6), 2255-2264. Kryson, J.L. (1986). Biology, distribution, and identification of pest Diabrotica. In Krysan, J.L, & Miller, T.A (1986). Methods for the Study of Pest Diabrotica (pp.1-25). New York, New York: Springer-Verlag Inc. Kowalska, J. (2010). Spinosad effectively controls Colorado potato beetle, Leptinotarsa decemlineata (Coleoptera: Chrysomelidae) in organic potato. Acta Agriculturae Scandinavica Section B Soil and Plant Science, 60(3), 283-286. Lewis, P. A., Lampman, R. L., & Metcalf, R. L. (1990). Kairomonal attractants for Acalymma vittatum (Coleoptera: Chyrsomelidae). Environmental Entomology, 19(1), 8-14. Matsui, K., Sugimoto, K., Mano, J. i., Ozawa, R., & Takabayashi, J. (2012). Differential metabolisms of green leaf volatiles in injured and intact parts of a wounded leaf meet distinct ecophysiological requirements. Plos One, 7(4). Metcalf, R. L., Metcalf, R. A., & Rhodes, A. M. (1980). Cucurbitacins as kairomones for Diabroticite beetles. Proceedings of the National Academy of Sciences of the United States of America, 77(7), 3769-3772. McLeod, P. (2006). Use of neonicotinoid insecticides to manage cucumber beetles on seedling zucchini [Online] Plant Health Progress. Retrieved from: http://www.plantmanagementnetwork.org/pub/php/research/2006/zucchini/ Miles, M. (2003). The effects of spinosad, a naturally derived insect control agent to the honeybee. Bulletin of Insectology, 56(1), 119-124. Mota-Sanchez, D., Hollingworth, R. M., Grafius, E. J., & Moyer, D. D. (2006). Resistance and cross-resistance to neonicotinoid insecticides and spinosad in the Colorado potato beetle, Leptinotarsa decemlineata (Say) (Coleoptera : Chrysomelidae). Pest Management Science, 62(1), 30-37. Nair, A., & Ngouajio, M. (2010). Integrating rowcovers and soil amendments for organic cucumber production: implications on crop growth, yield, and microclimate. HortScience, 45(4), 566-574. 129 Pedersen, A. B., & Godfrey, L. D. (2011). Evaluation of cucurbitacin-based gustatory stimulant to facilitate cucumber beetle (Coleoptera: Chrysomelidae) management with foliar insecticides in melons. Journal of Economic Entomology, 104(4), 1294-1300. Rabea, E. I., Nasr, H. M., & Badawy, M. E. I. (2010). Toxic effect and biochemical study of chlorfluazuron, oxymatrine, and spinosad on honey bees (Apis mellifera). Archives of Environmental Contamination and Toxicology, 58(3), 722-732. Rojas, E. S., Gleason, M. L., Batzer, J. C., & Duffy, M. (2011). Feasibility of delaying removal of row covers to suppress bacterial wilt of muskmelon (Cucumis melo). Plant Disease, 95(6), 729-734. Salgado, V. L. (1998). Studies on the mode of action of spinosad: Insect symptoms and physiological correlates. Pesticide Biochemistry and Physiology, 60(2), 91-102. Saltveit, M. E. (2000). Wound induced changes in phenolic metabolism and tissue browning are altered by heat shock. Postharvest Biology and Technology, 21(1), 61-69. Schroder, R. F. W., Martin, P. A. W., & Athanas, M. M. (2001). Effect of a phloxine B- cucurbitacin bait on Diabroticite beetles (Coleoptera: Chrysomelidae). Journal of Economic Entomology, 94(4), 892-897. Scott-Dupree, C. D., Conroy, L., & Harris, C. R. (2009). Impact of currently used or potentially useful insecticides for canola agroecosystems on Bombus impatiens (Hymenoptera: Apidae), Megachile rotundata (Hymentoptera: Megachilidae), and Osmia lignaria (Hymenoptera: Megachilidae). Journal of Economic Entomology, 102(1), 177-182. Smyth, R.R., Tallamy, D.W., Renwick, A.A., Hoffmann, M.P. (2002). Effects of age,, sex, and dietary history on response to cucurbitacin in Acalymma vittatum. Entomologia Experimentalist et Applicata. 104: 69-78 Tallamy, D. W., & Halaweish, F. T. (1993). Effects of age, reproductive activity, sex, and prior exposure on sensitivity to cucurbitacins in southern corn rootworm (Coleoptera, Chrysomelidae). Environmental Entomology, 22(5), 925-932. Tallamy, D. W., Powell, B. E., & McClafferty, J. A. (2002). Male traits under cryptic female choice in the spotted cucumber beetle (Coleoptera: Chrysomelidae). Behavioral Ecology, 13(4), 511-518. Thompson, G. D., Dutton, R., & Sparks, T. C. (2000). Spinosad - a case study: an example from a natural products discovery programme. Pest Management Science, 56(8), 696-702. Trece (2013). Cucurbitacin-D [Gustatory Stimulant Label], Adair, Oklahoma.

130 Walsh, G., Weber, D.C., Mattioli, F., Heck, G. (2008) Qualitative and quantitative response of Diabrotica (Coleoptera: Chrysomelidae) to cucurbit extracts linked to species, sex, weather, and deployment method. Journal of Applied Entomology. 132(1), 205-215.

131 Tables

Table 4-1. Corrected A. vittatum mortality ± SE across different concentrations of spinosad 24 hours after exposure. Mortality was recorded 24 hours after exposure. N refers to the number of beetles tested at each concentration. Concentration - Corrected A. vittatum N ml Entrust (spinosad) per 1 ml water mortality 0.0042 20 0.50 ± 0.11 0.005 20 0.61 ± 0.11

0.00625 20 0.83 ± 0.0.08

0.0083 20 0.94 ± 0.05 0.0125 20 1 ± 0 0 (Control) 20 0

Table 4-2. Chi-square statistics for binary logistic regression predicting A. vittatum mortality response to spinosad and spinosad with cucurbitacin by beetle gender. Regression was calculated from data collected in the whole leaf dip bioassays, 72 hours after exposure. Treatment Effect DF Chi-Square pr > F Dose 1 7.17 0.007 Gender 1 0.66 0.416 Spinsoad Dose*Gender 1 0.05 0.828 Error 146

Total 149

Dose 1 4.81 0.028 Spinosad with Gender 1 6.72 0.010 Cucurbitacin Dose*Gender 1 0.06 0.814 Error 145

Total 148*

132 Table 4-3. Repeated measures ANOVA output for the average number of beetles per plot (one 30-plant row) by treatment. Date was included in the model as a repeated measure. All data collected in a given week was pooled for purposes of analysis. Week Sampling Dates Effect DF F p-value Treatment 4, 95 3.21 0.0162 1 June 10th – June 17th Date 3, 95 7.79 0.0001 Treatment*Date 12, 95 0.73 0.7208

Treatment 4, 95 7.65 ≤ 0.001 June 17th – June 24th Date 3, 95 15.51 ≤ 0.001 2 Treatment*Date 12, 95 3.26 ≤ 0.001

Treatment 4, 70 12.80 ≤ 0.001 June 24th – June 27th 3 Date 2, 70 15.61 ≤ 0.001 Treatment*Date 8, 70 1.53 0.1629

Treatment 4, 70 4.02 0.0054 4 July 1st – July 4th Date 2, 70 2.84 0.0650

Treatment*Date 8, 70 0.93 0.5012

Table 4-4. One way ANOVA output for the average number of A. vittatum per plot (one 30-plant row) on individual dates for the first week of sampling. Means within a column that have the same letter are not significantly different by Tukey’s HSD test (p ≤ 0.05) Average number A. vittatum per plot

6/10/14: 0 days 6/11/14: 1 day 6/13/14: 3 days 6/17/14: 7 days post spray post spray post spray: post spray Control (water) 0.60 a 0.87 a 1.16 a 0.93 a Warrior: 3.84 oz / acre 0.17 a 0.10 a 0.70 a 1.03 a Entrust 1X + Cidetrak 0.87 a 0.60 a 1.63 a 1.03 a Entrust 2X + Cidetrak 1.00 a 0.73 a 1.63 a 0.93 a Entrust 4X + Cidetrak 0.83 a 0.80 a 1.30 a 0.93 a F statistic 1.16 1.84 2.76 0.04 DF 4, 20 4, 20 4, 20 4, 20 Pr > F 0.356 0.1610 0.0562 0.9965

133 Table 4-5. One way ANOVA output for the average number of A. vittatum per plot (one 30-plant row) on individual dates for the second week of sampling. .Means within a column that have the same letter are not significantly different by Tukey’s HSD test (p ≤ 0.05)

Average number A. vittatum per plot 6/17/14: 0 6/18/14: 1 6/20/14: 3 6/24/14: 7 days post day post days post days post spray spray spray: spray Control (water) 0.93 a 1.73 a b 4.23 a 2.07 a Warrior: 3.84 oz / acre 1.03 a 0.63 b 1.07 b 1.43 a Entrust 1X + Cidetrak 1.03 a 1.63 a b 2.30 b 2.03 a Entrust 2X + Cidetrak 0.93 a 1.97 a 2.10 b 1.50 a Entrust 4X + Cidetrak 0.93 a 1.47 a b 1.90 b 1.20 a F statistic 0.04 3.14 9.33 1.59 DF 4, 20 4, 20 4, 20 4, 20 Pr > F 0.9965 0.0372 0.0002 0.2159

Table 4-6. One way ANOVA output for the average number of A. vittatum per plot (one 30-plant row) on individual dates for the third week of sampling. Means within a column that have the same letter are not significantly different by Tukey’s HSD test (p ≤ 0.05) Average number A. vittatum per plot 6/24/14: 0 6/25/14: 1 6/27/14: 3 7/1/14: 7 days days post day post days post post spray spray spray spray: Control (water) 2.07 a 3.67 a 4.5 a 4.08 a Warrior: 3.84 oz / acre 1.43 a 1.56 b 1.63 b 2.75 a Entrust 1X + Cidetrak 2.03 a 3.36 a b 3.00 a b 2.75 a Entrust 2X + Cidetrak 1.50 a 2.87 a b 3.13 a b 2.67 a Entrust 4X + Cidetrak 1.20 a 1.77 b 2.20 b 2.50 a F statistic 1.59 8.00 6.39 .066 DF 4, 20 4, 20 4, 20 4, 20 Pr > F 0.2159 0.0005 0.0018 0.6278

134 Table 4-7. One way ANOVA output for the average number of A. vittatum per plot (one 30-plant row) on individual dates for the third week of sampling. Means within a column that have the same letter are not significantly different by Tukey’s HSD test (p ≤ 0.05)

Average number A. vittatum per plot 7/1/14: 0 days 7/2/14: 1 day 7/4/14: 3 days

post spray post spray post spray: Control (water) 4.08 a 6.17 a 6.42 a Warrior: 3.84 oz / acre 2.75 a 2.50 c 1.75 a Entrust 1X + Cidetrak 2.75 a 5.50 a b 6.25 a Entrust 2X + Cidetrak 2.67 a 3.33 b c 4.41 a Entrust 4X + Cidetrak 2.50 a 2.50 c 3.83 a F statistic .066 7.14 2.90 DF 4, 20 4, 20 4, 20 Pr > F 0.6278 0.010 0.0481* * Though F was signfiicant, there was no significant difference in means with Tukey’s comparison

Table 4-8. Average bacterial wilt scores per plot (one 30-plant row) treatment on July 4th and July 11th. Means with a column that do not contain the same letter differ significantly (p < 0.05) by Tukey’s HSD.

Trt 7/4/2014 7/11/2014 Water (control) 2.37 a 3.60 a Warrior: 3.84 oz / acre 1.06 b 2.50 b Entrust 4 fluid oz/acre + Cidetrak 1.80 a 3.48 a Entrust 8 fluid oz/acre + Cidetrak 2.23 a 3.80 a Entrust 16 fluid oz/acre + Cidetrak 1.70 a b 3.24 a b

135

Table 4-9. Mean number of dead A. vittatum ± SE per plant by treatment. Averages are pooled across the sampling dates June 10th – June 27th (sampled 5 plants per plot) and July 1st – July 4th (sample one-meter sections). Mean no. dead A. Sampling Dates Treatment vittatum / plant Control (water) 0.00741± 0.00523 Warrior: 3.84 oz / acre 0.36667 ± 0.0479

June 10th -June 27th Entrust 4 fluid oz/acre + Cidetrak 0.04815 ± 0.0150 Entrust 8 fluid oz/acre + Cidetrak 0.11482 ± 0.0245 Entrust 16 fluid oz/acre + Cidetrak 0.23703 ± 0.0367

Control (water) 0.00741± 0.00523 Warrior: 3.84 oz / acre 0.36667 ± 0.0479 July 1st – July 4th Entrust 4 fluid oz/acre + Cidetrak 0.04815 ± 0.0150 Entrust 8 fluid oz/acre + Cidetrak 0.11482 ± 0.0245

Entrust 16 fluid oz/acre + Cidetrak 0.23703 ± 0.0367

136 Figures

1 a Entrust (Spinosad) 0.8

Entrust with Cidetrak SE)

± 0.6 A. A. vittatum

0.4 Mortality(

0.2 Corrected

0 0.00156 0.003125 0.00625 0.0125 0.025 Concentration (ml Entrust / 1 ml water) 1

b Entrust (Spinosad)

0.8 Entrust with Cidetrak

SE)

± 0.6 A. A. vittatum 0.4

Mortality( 0.2 Corrected

0 0.00156 0.003125 0.00625 0.0125 0.025 Concentration (ml Entrust/ 1 ml water) 1 0.9 c Entrust (Spinosad) 0.8 Entrust with Cidetrak

SE) 0.7 ± 0.6

A. A. vittatum 0.5 0.4 0.3 mortality ( 0.2 Corrected 0.1 0 0.00156 0.003125 0.00625 0.0125 0.025 Concentration (ml Entrust/ 1 ml water)

Figure 4-1. Mean corrected A. vittatum mortality ± SE across all concentrations of spinosad (Entrust) plus buffalo gourd root powder (Cidetrak) for the filter paper bioassays at: (a) 24 hours (b) 48 hours and (c) 72 hours post exposure.

137

1

a

SE)

± 0.8 Entrust (Spinosad) Spinosad with Cidetrak 0.6

0.4

0.2 CorrectedMortality ( 0 0.0015625 0.00325 0.00625 0.0125 0.025 Concentration (ml Entrust/ 1 ml water) 1

b Entrust (Spinosad)

SE)

± 0.8 Spinosad with Cidetrak

0.6

0.4

0.2 Correctedmortality ( 0 0.0015625 0.00325 0.00625 0.0125 0.025 Concentration (ml Entrust / 1 ml water)

1

c SE)

0.8 ±

0.6 mortality ( 0.4

0.2 Entrust (Spinosad) Corrected Spinosad with Cidetrak 0 0.0015625 0.00325 0.00625 0.0125 0.025 Concentration (ml Entrust/ 1 ml water)

Figure 4-2. Average beetle mortality with Abbots Correction across all treatments for the leap dip bioassay at: (a) 24 hours, (b) 48 hours, and (c) 72 hours post exposure

138

0.01 0.008

0.006

ml water) ml

/ 0.004

0.002 LC 50 50 LC SE + (ml

Entrust 0 Entrust Entrust with Cidetrak

Figure 4-3. Estimated LC-50 values for A. vittatum for spinosad only and spinosad with cucurbitacin for the whole leaf dip bioassay. Values were generated using the 72 hour dose response curve. There was a significant difference in the LC 50 values (p < 0.05)

1

a 0.8

0.6

0.4

0.2 Female

Probability Probability Mortality of Male 0 0.0015625 0.00325 0.00625 0.0125 0.025 Dose (ml Entrust / 1 ml water)

1 b 0.8

0.6

0.4

0.2 Female

Male Probability Probability Mortality of 0 0.0015625 0.00325 0.00625 0.0125 0.025 Dose (ml Entrust / 1 ml water with Cidetrak)

Figure 4-4. Logistic regression curves modeling the probability of male and female A. vittatum mortality at each dose. of (a) spinosad and (b) spinosad with cucurbitacin

139 1.00 a Female 0.80 Male

0.60

Female 0.40

0.20

ProportionDeadMale or 0.00 0.0015625 0.00325 0.00625 0.0125 0.025 Concentration (ml Entrust / 1 ml water) 1 b Female 0.8 Male

0.6

Female 0.4

0.2

ProportionDeadof Male or 0 0.0015625 0.00325 0.00625 0.0125 0.025 Concentration (ml Entrust / 1 ml water)

Figure 4-5. Proportion of male/female A. vittatum that died at a given concentration in the whole leaf dip bioassays at the 72 hour timepoint: (a) spinosad only and (b) spinosad with cucurbitacin

7 Water

6 Per Per Warrior 5 Cidetrak + Entrust 1X 4

Cidetrak + Entrust 2X

A. vittatumA. 3

Cidetrak+Entrust 4X Plant 2 1 0 Average num. num. Average 0 1 3 7 Days Post Spray

Figure 4-6. Average number of A. vittatum per plant ± SE across all sampling dates for week 1 of the field trials (June 10th – June 17th). The 0 day post spray counts were taken on June 10th, before insecticide treatments were applied. 140

7 Water 6 Warrior 5

Cidetrak + Entrust 1X 4 A. vittatumA. Cidetrak + Entrust 2X 3 Cidetrak+Entrust 4X Per Plant Per 2 1

Average num. num. Average 0 0 1 3 7 Days Post Spray Figure 4-7. Average number of A. vittatum per plant ± SE across all sampling dates for week 2 of the field trials (June 17th – June 24th). The 0 day post spray counts were taken on June 17th, before insecticide treatments were applied.

7 Water 6

Warrior 5

4 Cidetrak + Entrust 1X A. vittatumA. 3 Cidetrak + Entrust 2X

2 Cidetrak+Entrust 4X er Sampling Unit Sampling er

p 1

0 Average num. num. Average 0 1 3 7 Days Post Spray Figure 4-8. Average number of A. vittatum ± SE per sampling unit across all sampling dates in week 3 (June 24th – July 1st). For the 0, 1, and 3 day counts, we scouted five plants per plot. On the 7 day count (July 1st), sampling switched to two 1-meter sections per plot. The 0 day post spray counts were taken on June 24th, before insecticide treatments were applied.

141 7 Water

6 Warrior 5 Cidetrak + Entrust 1X

A. vittatumA. 4 Cidetrak + Entrust 2X 3 Cidetrak + Entrust 4X 2

1 Per 1 meter section meter 1 Per

Average num. num. Average 0 0 1 3 Days Post Spray Figure 4-9. Average number of A. vittatum ± SE per 1-meter section across all sampling dates in week 4 (June 1st – July 4th). No seven day counts were taken this final week. The 0 day post spray counts were taken on July 1st, before insecticide treatments were applied.

142 Chapter 5

Conclusion: Shifting Towards and Ecologically Based Management Program

The data collected and presented in this thesis details three very different management approaches to A. vittatum. In chapter 2, I discussed a systems based approach to cucurbit production, evaluating the impacts of various production practices on the pest and beneficial insect populations. Then in chapter 3, I evaluated baseline parasitism rates, creating, to the best of my knowledge, the first record of A. vittatum parasitism within Pennsylvania. Finally, I evaluated plant and microbial metabolites for chemical control of A. vittatum. None of these practices were intended to be stand-alone management tactics for A. vittatum. However, with tweaking, I believe they have the potential to shift cucurbit agriculture and management of A. vittatum towards a more ecologically based approach. The idea of ecologically based pest management (EBPM) was defined and reviewed in 1996 by the National Research Council. The basic principal and overarching goal is de-emphasize the use of insecticides, instead shifting towards a systems-based pest management approach that minimizes adverse effects to the agroecosystem (National Research Council 1996). One of the basis tenants of EBPM is the idea that under the right conditions, naturally occurring processes, namely natural enemies, can provide considerable control of insect pests. Agriculture creates an extremely artificial ecosystem and often disrupts these processes. However, through an in-depth understanding of the cropping system, particularly interactions between the crops, the insect pests, and the naturally occurring beneficial species, we can begin to develop management practices that encourage beneficial insect populations and high levels of biological control services (National Research Council 1996). Biological control is a key component of EBPM. Lewis et al. (1997) suggested that focus should be placed on the naturally occurring community of biocontrol agents (as opposed to augmenting populations through mass rearing and release). The work in both chapter 2 and 3 support this idea of conservation based biocontrol and developing practices that best support natural enemy populations. 143 In Chapter 2, one of my key interests was the impact of soil tillage on the epigeal arthropod community, particularly Carabidae. The family Carabidae is a diverse group of beetles common to many agricultural systems. They are a voracious group of generalist predators that provide biocontrol services by consuming weed seeds and arthropod prey (Leslie et al. 2010). The data collected suggests that the Carabid community is sensitive to soil disturbance, with a conservation tillage regime supporting a richer community. This study emphasizes this concept that agricultural practices can have significant impacts, both positive and detrimental, on the beneficial community. As any new practice is implemented into cucurbit production, it will be important to keep in mind the impacts on the beneficial community. In chapter 3, I established that there are parasitoids within central Pennsylvania that will attack adult A. vittatum, a tachinid fly, Celatoria setosa, and a braconid wasp, Centistes diabroticae. Though both species have been documented in states outside of Pennsylvania (Smythe & Hoffmann 2010), to the best of my knowledge, this is the first record of either parasitoid in Pennsylvania. Parasitism rates were surprisingly high, reaching 56% for C. setosa and 17% for C. diabroticae, suggesting that both species are strong biological control candidates for A. vittatum. However, very little is known about the biology or life history of either species. Key questions include questions about their geographic distribution, seasonality, and diapause. Of practical concern, we need to ascertain what farmscape factors will promote or harm their populations. This includes management practices, such as the timing and the frequency of insecticide sprays, the heterogeneity of the landscape, and availability of floral resources. Currently, we lack the knowledge and ability to rear either species in captivity, so addressing these questions will require large-scale studies in which parasitism rates are assessed in a wide diversity of landscapes. Current methods to assess parasitism rates are very time and labor intensive, as parasitoids must be reared back in the lab. Alternatively, adult beetles can be dissected to ascertain the presence or absence of parasitoid larvae, though this method is less reliable at early instars. Developing a tool to rapidly assess parasitism rates will be a key advancement in furthering our understanding of their biology. One potential avenue to 144 explore is to develop a molecular probe that can direct the presence of an unemerged parasitoid larva in the beetle. In the fourth chapter of this thesis, I evaluated plant and microbial metabolites as an alternative means of control. Cucurbitacin is a naturally occurring plant metabolite produced by the family Cucurbitaceae. It is a bitter substance that induces a compulsive beetle feeding response that, when mixed with oral insecticides is designed to induce compulsive beetle feeding on the insecticide droplets. In laboratory bioassays, I significantly reduced the LC-50 for spinosad, an organic insecticide derived from a naturally occurring soil bacterium, with the addition of a plant substance made from the roots of buffalo gourds. One potential future direction that this work can take is to incorporate both spinosad and cucurbitacin into bait formulations. Previous studies have identified male produced aggregation pheromones and floral volatile attractants for A. vittatum (Smythe & Hoffmann 2003, Anderson et al. 1986) that can be incorporated into bait matrixes. The company ISCA Technologies (Riverside, CA) markets a product, SPLAT, which is an attract-and-kill bait formulation. The bait can be impregnated with insecticides and volatiles attractants. There are already SPLAT products impregnated with spinosad, including one formulation designed to attract and kill fruit flies. By incorporating cucurbit flower volatiles, spinosads, and cucurbitacins, we may be able to develop a highly effective bait formulation that minimizes chemical inputs to the agroecosystem. Beyond the practices detailed in this chapter, there are a number of other sustainable management options that have been suggested. Entomopathgoenic nematodes are a potential biocontrol agent for the larval stages of Acalymma vittatum; several species are available commercially and nematodes can easily be introduced into the horticultural cropping systems through drip irrigation systems (Ellers-Kirk et al. 2000); the inclusion of protective mulches in these cropping systems may also help ensure the microenvironmental conditions conducive to survival of the nematodes. Additionally, perimeter trap cropping, a technique that has not been widely adopted, has been shown to significantly reduce insecticide inputs targeting A. vittatum. Trap cropping creates a refuge for beneficial insects within a cropping system, as only the perimeter trap crop receives regular insecticide sprays (Cavanagh et al. 2009, Cavanagh et al. 2010). Through 145 further development of the work in this thesis, and through integration with other alternative management practices, it may be possible to develop an integrated control program that provides excellent suppression of Acalymma vittatum with minimal chemical inputs or disturbances to the agroecosystem.

146 References

Andersen, J. F., & Metcalf, R. L. (1986). Identification of a volatile attractant for Diabrotica and Acalymma species from blossoms of Cucurbita maxima. Journal of Chemical Ecology, 12(3), 687-700. Cavanagh, A., Hazzard, R., Adler, L. S., & Boucher, J. (2009). Using Trap Crops for Control of Acalymma vittatum (Coleoptera: Chrysomelidae) Reduces Insecticide Use in Butternut Squash. Journal of Economic Entomology, 102(3), 1101-1107. Cavanagh, A. F., Adler, L. S., & Hazzard, R. V. (2010). Buttercup Squash Provides a Marketable Alternative to Blue Hubbard as a Trap Crop for Control of Striped Cucumber Beetles (Coleoptera: Chrysomelidae). Environmental Entomology, 39(6), 1953-1960. Ellers-Kirk, C. D., Fleischer, S. J., Snyder, R. H., & Lynch, J. P. (2000). Potential of entomopathogenic nematodes for biological control of Acalymma vittatum (Coleoptera : Chrysomelidae) in cucumbers grown in conventional and organic soil management systems. Journal of Economic Entomology, 93(3), 605-612. Leslie, T. W., Biddinger, D. J., Rohr, J. R., & Fleischer, S. J. (2010). Conventional and Seed- Based Insect Management Strategies Similarly Influence Nontarget Coleopteran Communities in Maize. Environmental Entomology, 39(6), 2045-2055. Lewis, W.J., Lentern, J.C., Phatak, S.C., Tumlinson, J.H. (1997) A total system approach to sustainable pest management. Proceedings of the National Academy of Sciences of the United States of America. 94(23): 12243-12248. National Research Council (1996). Ecologically Based Pest Management: New Solutions for a New Century. Washington, D.C: National Academy Press: 1-17, 42-49. Smyth, R. R., & Hoffmann, M. P. (2003). A male-produced aggregation pheromone facilitating Acalymma vittatum (F.) (Coleoptera: Chrysomelidae) early-season host plant colonization. Journal of Insect Behavior, 16(3), 347-359. Smyth, R. R., & Hoffmann, M. P. (2010). Seasonal incidence of two co-occurring adult parasitoids of Acalymma vittatum in New York State: Centistes (Syrrhizus) diabroticae and Celatoria setosa. BioControl (Dordrecht), 55(2), 219-228. 147 Appendix

Supplementary Data for Chapter 2

Table A-1. Mean number of A. vittatum per subplot treatment by cropping system in 2013 and 2014. Data was pooled across all sampling dates. Mean No. A. vittatum Year System Treatment SE per plot PM NRC 0.083333 0.045194 Conventional PM RC 0.111111 0.064263 Melon ST NRC 0.083333 0.030365 ST RC 0.111111 0.042217 PM NRC 0.064815 0.023801 Conventional PM RC 0.083333 0.043124 Squash ST NRC 0.12037 0.031457 ST RC 0.3 0.098864 2013 PM NRC 0.283333 0.05574 PM RC 0.236111 0.060943 Organic Melon ST NRC 0.1 0.0343 ST RC 0.111111 0.037297 PM NRC 0.572917 0.132242 PM RC 0.395833 0.102017 Organic Squash ST NRC 0.395833 0.118912 ST RC 0.6875 0.12687 PM NRC 2.076389 0.649927 Conventional PM RC 1.361111 0.3429 Melon ST NRC 0.194444 0.04546 ST RC 0.229167 0.048998 PM NRC 0.40625 0.120832 Conventional PM RC 0.385417 0.116951 Squash ST NRC 0.416667 0.118161 ST RC 0.375 0.102865 2014 PM NRC 2.979167 0.327227 PM RC 2.618056 0.3617 Organic Melon ST NRC 1.326389 0.394187 ST RC 1.590278 0.325134 PM NRC 2.481481 0.381622 PM RC 1.944444 0.277154 Organic Squash ST NRC 0.972222 0.184394 ST RC 1.287037 0.186072

148 Table A-2. Total yields ± SE in conventional summer squash for 2013 and 2014 by subplot (one 15-plant row). Yields are listed as average pounds per subplot. All data and statistical analysis from (Lilley 2015). Means within a row that do not share the same letter are statistically different (p<0.05) by Tukeys meanwise comparisons. No analysis was conducted between years. Plasticulture Strip Tillage Year Row Cover No Row Cover Row Cover No Row Cover 2013 97.0±7.5 a 88.6±9.5 a 55.4±7.2 b 53.0±6.8 b 2014 73.5±3.8 a 73.3±5.4 a 51.5±6.4 b 47.9±6.0 b

Table A-3. Total yields ± SE in organic summer squash for 2013 and 2014 by subplot (one 15- plant row). Yields are listed as average pounds per subplot. All data and statistical analysis from (Lilley 2015). Means within a row that do not share the same letter are statistically different (p<0.05) by Tukeys meanwise comparisons. No analysis was conducted between years. Plasticulture Strip Tillage Year Row Cover No Row Cover Row Cover No Row Cover 2013 104.7±3.3 b 117.0±3.8 a 63.7±2.8 c 43.7±8.8 d 2014 108.6±8.8 ab 113.7±6.0 a 91.7±7.6 b 51.0±10.6 c

Table A-4. Total yields ± SE in conventional muskmelon for 2013 and 2014 by subplot (one 15- plant row). Yields are listed as average pounds per subplot. All data and statistical analysis from (Lilley 2015). Means within a row that do not share the same letter are statistically different (p<0.05) by Tukeys meanwise comparisons. No analysis was conducted between years. Plasticulture Strip Tillage Year Row Cover No Row Cover Row Cover No Row Cover 2013 156.3±11.5 a 152.2±17.8 a 56.7±7.0 b 76.7±12.3 b 2014 171.2±18.1 a 146.0±13.6 b 80.7±8.9 c 23.1±6.1 d

Table A-5. Total yields ± SE in organic muskmelon for 2013 and 2014 by subplot (one 15-plant row). Yields are listed as average pounds per subplot. All data and statistical analysis from (Lilley 2015). Means within a row that do not share the same letter are statistically different (p<0.05) by Tukeys meanwise comparisons. No analysis was conducted between years Plasticulture Strip Tillage Year Row Cover No Row Cover Row Cover No Row Cover 2013 175.9±3.3 a 152.0±10.1 a 61.7±6.4 b 59.1±5.7 b 2014 98.3±9.4 a 81.7±8.8 a 16.4±3.1 b 0.3±0.3 b

149 Table A-6. Total number of insecticide sprays applied by subplot treatment in 2013 and 2014, for individual cropping systems. Data from (Lilley 2015) Organic Summer Organic Conventional Conventional Soil Squash Muskmelon Summer Squash Muskmelon 2013 2014 2013 2014 2013 2014 2013 2014

RC 2 3 2 6 1 2 0 2 PM NRC 3 6 3 7 2 3 1 5

RC 1 4 0 4 2 2 0 1 ST NRC 2 6 1 1 3 4 1 2