Diptera: Dolichopodidae)

Home , Achradocera, Chrysotus, Condylostylus, Dactylomyia, Dolichopodidae, Dolichopus

Local Management and Landscape Effects on the Predator Guild in Vegetable Crops,

with a Focus on Long-legged Flies (Diptera: Dolichopodidae)

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Andrea Renee Kautz, B.S.

Graduate Program in Entomology

The Ohio State University

2015

Master's Examination Committee:

Mary M. Gardiner, Advisor

Norman F. Johnson

Celeste Welty

Andrea R. Kautz

2015

Abstract

Biological control is a vital ecosystem service provided by a diverse guild of predators in agroecosystems. Biodiversity is thought to be linked to ecosystem functioning through more efficient resource capture via niche partitioning. Understanding the factors that impact the diversity of predators in agroecosystems is therefore important to our understanding of how to enhance biological control services. Long-legged flies

(Diptera: Dolichopodidae) are a particularly ubiquitous yet understudied group of insect predators that are common in all habitats in Ohio, including agricultural systems.

Previous studies have shown that these flies are sensitive to environmental changes, at least in natural systems like grasslands and reed marshes. The goal of this study was to determine how local management and landscape-scale factors such as composition and heterogeneity influence the community assemblage of Dolichopodidae, and the predator community as a whole, found in agroecosystems. During the summer of 2013 and 2014, pan trapping was used to sample the long-legged fly community present in produce farms across northeast Ohio that represented a gradient of landscape complexity and management intensity. Communities found within sweet corn, summer squash, and unmanaged old fields were surveyed. Over 3,000 flies representing eleven dolichopodid genera and 33 species were found across both years. Dolichopodid abundance was actually higher in crop habitats than unmanaged habitats, but habitat preference varied by

ii genus. Landscape factors influencing the abundance of Dolichopodidae varied from year to year. Identifying which factors are driving the diversity of this family of flies will help us understand how to maximize the biological control services being provided.

During the same time, above-ground and ground dwelling predators were also sampled in sweet corn and summer squash using yellow sticky cards and pitfall traps in order to quantify the predator guild as a whole. Over 26,000 predatory arthropods were counted over the two summers. Predator abundance was generally higher in squash, but community composition varied between crops. Therefore, crop diversity in a farmscape may be important for supporting a diverse predator guild. In general, more agricultural habitat in the surrounding landscape had a negative effect on the predator guild, so diversification at the landscape scale may also be an important way to promote diverse predator guilds and biological control services.

iii

Dedication

This document is dedicated to my parents.

Acknowledgments

I would like to acknowledge my advisor Mary for her mentorship and encouragement throughout my graduate program, as well as the other members of my advisory committee, Norm and Celeste, for their guidance and wisdom. I would also like to thank

Dr. Dan Bickel, Dr. Scott Brooks, and Dr. Marc Pollet for their guidance and expertise regarding Dolichopodidae, which is a very fun yet challenging group. This work would not have been possible without the help of two great lab technicians, Chelsea Gordon and

Nicole Hoekstra, and all of the help provided by several undergraduate assistants and fellow members of the Gardiner Lab.

Vita

June 2008 ...... Diploma, Perkins High School

June 2012 ...... B.S. Zoology, The Ohio State University

August 2012 to present ...... Graduate Fellow and Graduate

Teaching/Research Associate, Department

of Entomology, The Ohio State University

Fields of Study

Major Field: Entomology

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments...... v

Vita ...... vi

List of Tables ...... viii

List of Figures ...... ix

Chapter 1: Characterizing the Dolichopodid Community in Agroecosystems and

Determining the Effect of Management and Landscape on Their Diversity and

Abundance ...... 1

Tables and Figures ...... 16

Chapter 2: Local Management and Landscape Effects on the Predator Guild in Vegetable

Crops ...... 29

Tables and Figures ...... 43

References ...... 60

vii

List of Tables

Chapter 1

Table 1. Sampling Schedule ...... 18

Table 2. Dolichopodid Species Summary ...... 19

Table 3. Landscape Analysis Summary ...... 28

Chapter 2

Table 4. Sampling Schedule ...... 45

Table 5. Predator Summary ...... 46

Table 6. Landscape Analysis Summary 2013 ...... 58

Table 7. Landscape Analysis Summary 2014 ...... 59

viii

List of Figures

Chapter 1

Figure 1. Field Sites ...... 16

Figure 2. Sampling Schematic ...... 17

Figure 3. 2013 Dolichopodidae Abundance ...... 22

Figure 4. 2014 Dolichopodidae Abundance ...... 24

Figure 5. Dolichopodidae Community Composition ...... 26

Chapter 2

Figure 6. Field Sites ...... 43

Figure 7. Sampling Shematic ...... 44

Figure 8. 2013 Predator Totals and Diversity ...... 47

Figure 9. 2013 Pitfall Predator Abundance...... 48

Figure 10. 2013 Sticky Card Predator Abundance ...... 50

Figure 11. 2014 Predator Totals and Diversity ...... 52

Figure 12. 2014 Pitfall Predator Abundance...... 53

Figure 13. 2014 Sticky Card Predator Abundance ...... 55

Chapter 1: Characterizing the dolichopodid community in agroecosystems and

determining the effect of management and landscape on their diversity and

abundance

INTRODUCTION

Sustainable agricultural practices aim to conserve biodiversity of beneficial arthropods to enhance the ecosystem services they support such as pollination and biological control (Losey and Vaughan 2006, Isaacs et al. 2012, Garibaldi et al. 2014).

The biodiversity ecosystem function (BEF) hypothesis states that increasing diversity has a positive influence on ecosystem function (Cardinale et al. 2006, Duffy et al. 2007). This has been demonstrated at many trophic levels in various systems (Tilman et al. 1996, van der Heijden et al. 1998, Downing and Leibold 2002, Balvanera et al. 2006, Lundholm

2015), and generally has been attributed to greater niche partitioning leading to more efficient resource capture (Finke and Snyder 2008). If the diversity of arthropod predators supported within an agroecosystem can influence the level of biological control services provided, then a better understanding of the predator guild contributing to biological control within the system is needed. A critical first step in reaching that level of understanding is recognizing all potentially-relevant members of the predator guild present in the system, and determining what local and landscape-scale factors influence their abundance and diversity.

Long-legged flies (Diptera: Dolichopodidae), although often overlooked as important members of a predator guild, are exceedingly common in a diversity of habitats including agroecosystems (Gardiner et al. 2010, Werling et al. 2011), urban areas

(Gardiner et al. 2014), forests (Pollet and Grootaert 1991), grasslands (Pollet 2001), and wetlands (Pollet 1992, Pollet 2001, Gelbič and Olejníček 2011). Research from disturbed habitats such as agroecosystems and urban areas have commonly quantified these predators at family-level resolution; Dolichopodidae diversity studies have thus far been limited to mainly natural systems such as woodlands (Pollet and Grootaert 1991) and wetlands (Pollet 2001, Gelbič and Olejníček 2011). Furthermore, this family is highly understudied in the United States and much of what is known about the ecology of

Dolichopodidae has come from studies conducted in western Europe (Pollet 2000). At the time of this investigation, only 56 species of Dolichopodidae had been documented in the state of Ohio, USA (Pollet et al. 2004). My goal was to characterize the dolichopodid community found within farmscapes in order to better understand this ubiquitous insect predator and help realize its value in biological control.

Long-legged flies comprise the family Dolichopodidae of the superfamily

Empidoidea of the order Diptera. Found on every continent except Antarctica, dolichopodid flies are a highly speciose and ubiquitous group, containing over 7300 species in over 260 genera, with more species being described frequently (Pape 2011,

Naglis and Bartak 2015, Grootaert et al. 2015). The United States and Canada alone contain over 1200 species in 55 genera, and the number continues to grow (Pollet et al.

2004). This makes Dolichopodidae the fourth largest Dipteran family in terms of described species (Pape 2011).

Dolichopodids are described as slender flies with elongate legs, ranging in size from 0.8-9.0 mm, often metallic green, blue, or gold in color, but may also be dull yellow or brown (Robinson and Vockeroth 1981). Males are often used for identification because of the wide variety of sexual structures they possess which are involved in complex mating behaviors. The larvae are cylindrical and white, and are largely soil- dwelling, semi-aquatic, or bark-dwelling. The pupae are typically enclosed in a loose cocoon and be recognized by a pair of respiratory horns arising dorsally behind the eyes

(Robinson and Vockeroth 1981).

This family of flies is cold- and moisture-sensitive, and therefore adults tend to appear from late spring to the first frost in temperate regions, and during the rainy season in tropical regions (Robinson and Vockeroth 1981). They have also been implicated as a valuable bioindicators of habitat change (Pollet 2000, Gelbič and Olejníček 2011). With few exceptions of species with phytophagous larval forms (leaf miners in the genus

Thrypticus), most adults and larvae are predaceous, feeding on a particularly wide range of prey, as indicated by a thorough review of dolichopodid predation by Ulrich (2004).

The most frequently observed and documented prey items include other dipterans

(especially Chironomidae and Culicidae), aphids, collembolans, mites, and thrips.

Feeding behavior is characterized by epipharyngeal processes that slice an opening in the prey, through which the liquefied contents are ingested (Ulrich 2004).

Dolichopodids have been recognized as important biological control agents in a few systems. The genus Medetera is a well-known predator of scolytines in forest systems (Schmid 1971, Wermelinger 2002), and other dolichopodids have been shown to be predators of aphids in tomato (Miranda et al. 1998), aphids and mites in apple

(Rathman et al. 1988), grass grubs in wheat (Chynoweth et al. 2013), and whiteflies in cassava (Lundgren et al. 2014). With some of these more recent examples illustrating the importance of dolichopodids in biological control of agricultural pests, a better understanding of this group in agroecosystems and how they are impacted by local and landscape-scale factors is needed. Herein, I characterize the dolichopodid community found within Ohio vegetable farms and determine how management and surrounding land-use characteristics affect abundance and diversity within this family. I hypothesized that dolichopodid abundance and diversity would be negatively impacted by disturbance, that is, lower in crop fields compared to unmanaged old fields, and lower on farms with a greater proportion of agricultural and urban habitat surrounding them. Expanding our knowledge of the beneficial predator guild in agroecosystems is crucial as more focus is being put on biological control (and less on chemical control) in the push for more sustainable, safe, and economical production strategies.

METHODS

Field Sites Fourteen produce farms throughout north-central Ohio were chosen for sampling sites in 2013, and twelve farms were used in 2014 (Figure 1). The farms were selected

4 based both on the variety of management practices (field size, pesticide use, tillage, etc.) used and along a continuum of landscape complexity from simple (agriculturally dominated, little surrounding non-crop habitat) to highly complex (diverse landscapes including significant non-crop habitat).

Fields of sweet corn, Zea mays, and summer squash, Cucurbita pepo, were selected to sample within each farm. Because these two crops often experience significant damage caused by common pests like corn earworm, Helicoverpa zea

(Lepidoptera: Noctuidae), squash bug, Anasa tristis (Hemiptera: Coreidae), and striped cucumber beetle, Acalymma vittatum (Coleoptera: Chrysomelidae), typically some form of pest control must be implemented for these crops to be successful. Therefore there are expected to be key differences in the management of these two crops between organic and conventional produce farms.

Along with sampling the managed cropland within each farm, an unmanaged plot

(old field or grassland habitat) in the close vicinity was also surveyed in order to compare communities in semi-natural areas to those in agricultural systems. The area had to be unmanaged for a period of at least two years in order to be sampled.

Dolichopodid data collection

A transect was established through the center of each site and four equally-sized plots were established (Figure 2). Each plot was sampled at three different times throughout the growing season, once in June, July, and August (Table 1). However, due to prolonged periods of precipitation, the June 2013 sampling was not successful. If the crop had not germinated yet or had reached the end of harvest, the sampling was not done

5 for that month except in cases where a sequential sweet corn planting was available nearby to sample. The old field habitat was only sampled if a crop on the farm was also being sampled that month.

At each plot, a yellow pan trap was used for sampling dolichopodids (Pollet

1994). These traps consist of a plastic yellow bowl, 18 cm in diameter, filled with a soapy water solution (Dawn© dish soap, original scent) as a surfactant. The pans were placed on the ground, between plants within the row, and deployed for a 24-hour period. After the sampling period, the liquid was strained out and the contents of the trap were placed in

70% ethanol. All dolichopodid flies in the sample were dried by soaking in pure ethanol for at least three hours and submerging in ethyl acetate to prevent shriveling, and then pinned for identification. If precipitation or strong wind washed out any of the pans, they were deployed again during another 24-hour period within that same week.

Identification to genus was done for all specimens according to Bickel (2009), and to species level for males and some females when possible according to Robinson

(1964), since taxonomy of this group is largely based on male genitalia and secondary sexual characters. Because only few females were identifiable to species, only males were included in analyses at the species level. Males in the genus Chrysotus were identified to species when possible, but due to recent revision and lack of an up-to-date key, there was a group of male Chrysotus specimens that may contain more than one species, which was not identified further. These specimens were all morphologically similar, but distinct from the other identified species. This group is referred to in the analysis as “Chrysotus sp.”.

Farm management

Growers at each farm were given a survey and asked to track management of their fields throughout the growing season. Details on pesticides (including insecticides, herbicides, and fungicides), fertilizers, tillage, seed varieties, seed treatments, other pest control measures, and crop rotation were gathered. For pesticides used, the type, date applied, active ingredient, formulation, and rate were provided. For fertilization, the type, date applied, formulation, and rate were given, and for tillage, type (no-till, conservation till, conventional till) and date were provided. Any other management practices used were also recorded by the grower (hoeing, plastic, hand weeding, row covers, etc.)

Site assessments

When traps were deployed each month, soil moisture was measured as percent moisture content using a soil moisture meter. Weed density and diversity was assessed by counting the species and abundance of weeds within a 1 m2 quadrat in each of the four plots. When traps were retrieved each month, a plant assessment was conducted by randomly selecting 4 plants in each plot to evaluate. For each plant, stage (number of leaves, height, flowering, silk, fruit set, etc.), pest counts, and percent defoliation were recorded.

Landscape data

The landscape surrounding each farm in a 2 km radius was digitized using aerial imagery and ArcGIS. Land use changes between the date of the image and the present were recorded by ground-verification. Each patch of the landscape was ground-verified when accessible and categorized based on the specific land use. If viewing was not

7 possible due to lack of accessibility, the patch was not recorded and was excluded from the analysis.

Data Analysis

Data were analyzed using SAS© and R© statistical software. Differences in abundance and diversity of dolichopodids among crops and months were determined using a generalized linear mixed model (GLMM, using the GLIMMIX Procedure in

SAS©, version 9.3) for each sampling year, using a Poisson distribution for counts and a

Gaussian distribution for Simpson’s Diversity Index. Fixed factors were month and crop, and random factors were site and plot within site. If the interaction was not significant, then it was dropped from the model. A Tukey-Kramer adjustment was used to correct for multiple comparisons.

Landscape data were analyzed using Akaike’s Information Criterion (AIC, using the glmmADMB and bblme packages in RStudio©) to choose the best-fit model for predicting abundance and diversity of dolichopodids in the crop habitats. Possible models included in the analysis were percentage of agricultural, urban, natural (including forest, grassland, wetland, water, etc.), forest, and grassland habitat within 1000 meters and

2000 meters of the field site. Any models with a ΔAIC less than 2 were considered competing models. Landscape models that were significant (at α = 0.05) and did not compete with the null model were considered predictive.

An analysis of similarity (ANOSIM, using the adonis function in RStudio) was used to compare community composition between sites and habitats and non-metric

8 multidimensional scaling (NMDS, vegan package in RStudio) was used to visualize these data.

RESULTS

A total of 1,910 dolichopodids were collected in 2013 (1,283 in July and 627 in

August) and 1,111 were caught in 2014 (424 in June, 351 in July, and 336 in August). In both years the most abundant genus collected was Chrysotus (47% and 53% of the total catch in 2013 and 2104, respectively), followed by Condylostylus (38% and 34% of the total catch in 2013 and 2014, respectively). Eleven genera were identified, nine in 2013 and eight in 2014. There were three genera unique to 2013 collections (Plagioneurus,

Amblypsilopus, and Argyra) and two to 2014 collections (Dactylomyia and

Gymnopternus). Thirty-three species were identified overall, 26 in 2013 and 25 in 2014.

Twenty-one of the 33 species and four of the eleven genera were not documented for the state of Ohio (Pollet et al. 2004). A summary of the dolichopodids sampled each year by habitat, sex, genus, and species can be found in Table 2.

2013 Dolichopodid community

In 2013, the average number of dolichopodids per trap varied among the three different habitats in both sampling periods in the study (month*habitat: F2,216 = 35.29, P

< 0.0001, Figure 3A), with old fields yielding the lowest average per trap in July and sweet corn yielding the lowest in August, and no difference between the other two habitats. Also, while averages per trap varied across the months within the crop habitats, no differences were detected across months within the old field. Simpson’s Diversity of

9 genera did not differ among habitats in 2013 (habitat: F2,216 = 2.38, P = 0.095, Figure

3B). Chrysotus abundance was lowest in old fields in July and highest in summer squash in August, with no differences between the other two habitats (month*habitat: F2,216 =

12.48, P < 0.0001, Figure 3C). Condylostylus abundance was highest in old fields in July, followed by summer squash and sweet corn, and highest in old fields in August, followed by summer squash and sweet corn (month*habitat: F2,216 = 12.64, P < 0.0001, Figure

3D). Dolichopus abundance did not vary between habitats (month*habitat: F2,216 = 4.13,

P = 0.017, Figure 3E). Achradocera abundance was highest in sweet corn in July, followed by summer squash and old field. Achradocera abundance was highest in summer squash and lowest in old field in August, with sweet corn intermediate

(month*habitat: F2,216 = 4.78, P = 0.0093, Figure 3F). Models for all other genera did not converge due to low abundance in trap catches in 2013. Simpson’s Diversity of species

(males only) did not vary among habitats in 2013 (habitat: F2,216 = 0.32, P = 0.73).

2014 Dolichopodid community

In 2014, the average number of dolichopodids per trap varied between the three different habitats in all three sampling periods in the study (habitat: F2,307 = 43.73, P <

0.0001, Figure 4A), with old fields yielding the lowest average per trap across the season, and no difference in abundance between the other two habitats. Simpson’s Diversity of genera was higher in the crop habitats than in old fields in June, but did not vary in July or August (month*habitat: F4,303 = 6.72, P < 0.0001, Figure 4B). Chrysotus abundance was lowest in old fields across all months, with no difference in the crop habitats in June and July, and higher in summer squash than sweet corn in August (month*habitat: F4,303

= 4.50, P = 0.0015, Figure 4C). Condylostylus abundance was higher in the crop habitats than old field across the season (crop: F2,307 = 26.86, P < 0.0001, Figure 4D). Dolichopus abundance did not vary between habitats (habitat: F2,307 = 1.62, P = 0.20, Figure 4E).

Models for all other genera did not converge due to low abundance in trap catches in

2014. Simpson’s Diversity of species (males only) was greater in the crop habitats compared to old fields across the 2014 season (habitat: F2,295 = 14.69, P < 0.0001).

Community composition among habitats

An analysis of similarity using the five most abundant genera (Chrysotus,

Condylostylus, Dolichopus, Achradocera, and Pelastoneurus) showed a significant difference in genus-level community composition among the three habitats in both 2013

(P = 0.0424) and 2014 (P = 0.0005). Nonmetric Multi-dimensional Scaling (NMDS) analysis was used to visualize these data, as shown in Figure 5. In both years, the old field sites were represented by the smallest ellipse in the NMDS, indicating more similarity in the dolichopodid communities within this habitat compared to the crop habitats.

Landscape effects

Model selection using AIC indicated landscape factors driving the abundance and diversity of Dolichopodidae in crop habitats (Table 3). In 2013, the amount of grassland habitat within 1000 meters was negatively associated with overall dolichopodid abundance, Chrysotus abundance, and Condylostylus abundance, and positively associated with Dolichopus abundance. Achradocera abundance was positively associated with the amount of grassland habitat within 2000 meters (best-fit), and

11 negatively associated with the amount of forested habitat within 1000 meters. In 2014, overall dolichopodid abundance, Simpson’s D of dolichopodid genera, and Condylostylus abundance were all negatively associated with the amount of agricultural habitat within

1000 meters. Simpson’s D was also positively associated with the amount of natural habitat within 1000 meters, although the best-fit model was agricultural habitat within

1000 meters. Chrysotus abundance was negatively associated with agricultural habitat at

2000 meters (best-fit) and 1000 meters, and positively associated with natural habitat at

2000 meters and 1000 meters.

DISCUSSION

Dolichopodidae are not only present in these agroecosystems, but this habitat supports a diverse and abundant community. It is therefore increasingly important that we consider the impact they are having on the food webs in these systems as generalist predators. Their success in these habitats may be due to top-down control (Hairston et al.

1960), bottom-up control (White 1978), or a combination of both. That is, the flies may be experiencing less predation themselves due to a lower abundance of their natural enemies (spiders, for example) in these systems, or there may be a higher abundance of preferred prey items in these systems which can support a larger dolichopodid population. Further work should look into trophic interactions involving Dolichopodidae in agroecosystems to better understand their role in these food webs and maximize the biological control services they are providing.

Additionally, studies on the dietary niche breadth of these flies are also needed to determine their contributions to pest control. Recent studies showing the potential of

Dolichopodidae in biological control of whiteflies in cassava (Lundgren et al. 2014) and grass grubs in wheat (Chynoweth et al. 2013) have increased awareness about this relatively understudied group of insect predators and have helped highlight their importance as natural enemies in those systems, but their role in biological control within vegetable crops in the United States is unknown.

Haussaman (1996) found that dolichopodid abundance was higher near weed strips in winter wheat fields, so this may be a viable conservation biological control strategy for this group. Although they were not found in higher numbers in the old fields in my study, abundance did not fluctuate throughout the season in this habitat like it did in the crop habitats, and communities were more similar among old fields than among crops at different farms, suggesting that these habitats are more stable. It is also likely that these old field habitats provide refugia and alternative prey resources for dolichopodids during periods of high disturbance like cultivation, pesticide application, and harvest.

There were different landscape factors influencing dolichopodid abundance during the two years of the study. Grassland habitat had an overall negative impact on abundance in 2013, which did not support my hypothesis. This could be due to habitat preference and the flies occupying the surrounding grassland habitat instead of the crop fields, but this was not supported by the lack of fly abundance found in the old fields.

Two genera, Dolichopus and Achradocera, did respond positively to grassland habitat in

13 the landscape, so this may point to differences within the family in habitat preference and possibly prey preference.

In 2014 however, agricultural habitat had an overall negative impact on abundance, which did support my hypothesis. More agricultural habitat within the surrounding landscape may indicate less source populations for dolichopodids coming into the crop fields. I also found in both years that dolichopodids are responding to landscape scales of both 1000 meters and 2000 meters, although the 1000 meter scale was more commonly the best predictor. This suggests that these flies are capable of moving into agroecosystems from large distances surrounding the field, and landscape- level agroecosystem management is important for this group of arthropod predators.

More research should look into dispersal capabilities of these flies, especially in comparison to their larval development sites to determine how important outside recruitment is for this group. At this point, we are unsure if dolichopodid larvae are also residing in the soil within these agricultural habitats, or if they must be supplied by the surrounding landscape.

Local factors are likely influencing dolichopodid communities as well. For example, Steinborn and Meyer (1994) found increased evenness of the empidoid community in organically managed cereal crops compared to conventional fields. I found a few differences in abundance between summer squash and sweet corn fields throughout this study, and this could be due to management differences or resource availability.

Further studies are needed to determine what local factors in particular are driving these differences.

My study has shown that vegetable agroecosystems in Ohio are capable of supporting a diverse community of Dolichopodidae, with 33 species in 11 genera identified over two seasons. I found that the abundance of this family was, for the most part, actually higher in crop habitats than nearby unmanaged old fields. Landscape-scale factors were associated with abundance of these flies, however the factors differed each year. All associations with the proportion of agricultural habitat in the landscape were negative, however, indicating that incorporation of non-crop habitat within the landscape may be a viable tool for promoting dolichopodid populations in agroecosystems. This study has provided a useful foundation for understanding dolichopodid communities in agroecosystems, but much research still needs to be done with this group, including diet, larval habits, and response to disturbance.

FIGURES AND TABLES

Tr Ba

Bi Ho Gr Bm

Bu Ma Ca Mu He No Mo

Sw Mv

Figure 1. Field sites in north-central Ohio where dolichopodid communities were sampled in 2013 and 2014. 14 farms were sampled in 2013 and 12 farms were sampled in 2014. Farms are designated by a two-letter abbreviation. Red indicates farms that were sampled in both 2013 and 2014, blue indicates farms only sampled in 2013, and yellow indicates farms only sampled in 2014.

Figure 2. Schematic of field sampling technique. A transect was established along the center of each sampling habitat (sweet corn field, summer squash field, or old field) on the longest side. Four plots were assigned along this transect that were equidistant from each other and the edge for plots 1 and 4. A yellow pan trap was placed at each of these plots for 24-hours to sample for Dolichopodidae during each sampling period.

Table 1. Sampling sites and dates for 2013 and 2014. Farms are designated by a two- letter abbreviation. A dash implies that either the farm was not sampled that year, the crops were not yet germinated at the time of the sampling, or the crops had been harvested at the time of the sampling. The habitats sampled during each period are noted as “C” for sweet corn, “S” for summer squash, and “O” for old field. No pan traps were successful in June 2013 due to excessive rainfall.

Site July 2013 August 2013 June 2014 July 2014 August 2014 Ba SO SO - - - Bi CSO - CSO CSO CSO Bu CSO CSO CSO CSO CO Bm - - CSO CO - Ca CSO CSO CSO CSO CSO Gr CSO CSO - - - He SO SO CSO CSO CSO Ho - - CSO CSO CSO Ka CSO CSO - - - Ma - - CSO CSO SO Mo SO SO - - - Mv CSO CSO Mu CSO CSO CSO CSO CSO No CSO CSO - - - Si CSO CO SO SO - St CSO CSO - CSO CSO Sw CSO SO CSO CSO SO Tr - - - CSO CSO

Table 2. Dolichopodid species caught in pan traps during the study each year. *For most females, only genus counts are given due to a lack of available keys for females.

Species not identified are labeled “sp.”

Table 2 2013 2014

Genus Species Total Male Female* Total Male Female*

Achradocera* barbata* 171 100 71 7 4 3 Amblypsilopus* sp.* 1 0 1 0 0 0 Argyra sp. 1 1 0 0 0 0 Chrysotus 889 709 180 578 481 97 costalis* 11 11 - 2 2 - leucostoma* 76 76 - 52 52 - picticornis* 11 11 - 4 4 - 1 1 - spectabilis* 4 4 - tarsalis* 14 14 - 25 25 - sp. 593 593 - 397 397 - Condylostylus 733 483 250 392 173 219 brimleyi* 1 1 - 0 0 - calcaratus* 75 75 - 87 87 - caudatus 137 137 - 27 27 - comatus* 3 3 - 0 0 - flavipes* 56 56 - 28 28 - nigrofemoratus* 204 204 - 29 29 - patibulatus* 12 4 8 12 0 12 sipho* 3 3 - 1 1 - viridicoxa* 0 0 - 1 1 - Dactylomyia* lateralis* 0 0 0 1 1 0 Dolichopus 70 34 36 127 68 59 acuminatus 0 0 - 1 1 - correus* 5 5 - 3 3 - cuprinis 11 11 - 28 28 - gratus 1 1 - 0 0 - Incisuralis* 0 0 - 1 1 - longipennis 1 1 - 0 0 - reflectus 9 9 - 21 21 - scapularis 3 3 - 1 1 - vigilans 1 1 - 3 3 - vittatus* 3 3 - 10 10 - Gymnopternus sp. 0 0 0 3 2 1 Medetera 4 3 1 2 2 0 vittata 3 3 - 2 2 0

(cont.) 20

Table 2 (cont.)

Pelastoneurus 40 18 22 1 1 0 vagans* 18 18 - 1 1 0

Plagioneurus* univittatus* 1 0 1 0 0 0

TOTAL 1910 1348 562 1111 732 379

Figure 3. 2013 dolichopodid pan trap counts at the family level (A), Simpson’s D of dolichopodid genera (B) and the four most abundant genera: C: Chrysotus, D:

Condylostylus, E: Dolichopus, and F: Achradocera. Statistics were performed on the poisson-transformed means for counts, actual means shown. Letters above the bars indicate a significant difference (at α = 0.05) between habitats within a given month.

No letters indicate no significant differences were found among the habitats for a given month.

Figure 3

Figure 4. 2014 dolichopodid pan trap counts at the family level (A), Simpson’s D of dolichopodid genera (B) and the four most abundant genera: C: Chrysotus, D:

Condylostylus, and E: Dolichopus. Statistics were performed on the poisson- transformed means for counts, actual means shown. Letters above the bars indicate a significant difference (at α = 0.05) between habitats within a given month. No letters indicate no significant differences were found among the habitats for a given month.

Figure 4

Figure 5. NMDS plots of the dolichopodid community composition using the five most abundant genera caught throughout the study (Chrysotus, Condylostylus,

Dolichopus, Achradocera, and Pelastoneurus) in 2013 (top) and 2014 (bottom). Sites with a more similar dolichopodid community are closer to one another and the ellipses indicate the 95% confidence interval for how communities cluster within each habitat. An analysis of similarity (ANOSIM) was done to look for significant differences in dolichopodid community composition between habitats. In both 2013 and 2014, old field sites supported a more similar dolichopodid community than the crop habitats.

Figure 5

Summer Squash

Sweet Corn

Old Field

Summer Squash

Sweet Corn

Old Field

Table 3. Summary of model selection using Akaike’s Information Criterion (AIC) for predicting dolichopodid abundance and diversity in 2013 and 2014. Only variables where the null model was not competing are included. Where there were competing models, best-fit models are shown in bold.

Response Year Landscape predictors z Weight P

Dolichopodidae 2013 % grassland at 1000 m (-) -3.66 0.98 <0.001 abundance

Chrysotus 2013 % grassland at 1000 m (-) -3.15 0.91 0.0016 abundance

Condylostylus 2013 % grassland at 1000 m (-) -3.33 0.95 <0.001 abundance

Dolichopus 2013 % grassland at 1000 m (+) 4.56 0.99 <0.001 abundance

Achradocera 2013 % grassland at 2000 m (+), 2.36 0.57 0.018 abundance % forest at 1000 m (-) - 2.59 0.29 0.0096

Dolichopodidae 2014 % agriculture at 1000 m (-) -5.16 0.67 <0.001 abundance

Simpson’s D 2014 % agriculture at 1000 m (-), -3.22 0.43 0.0013 (genera) % natural at 1000 m (+) 2.70 0.18 0.0068

Chrysotus 2014 % agriculture at 2000 m (-), -3.07 0.28 0.0022 abundance % natural at 2000 m (+), 2.84 0.19 0.0045 % agriculture at 1000 m (-), -3.04 0.18 0.0023 % natural at 1000 m (+) 2.66 0.11 0.0078

Condylostylus 2014 % agriculture at 1000 m (-) -4.73 0.61 <0.001 abundance

Chapter 2: Local Management and Landscape Effects on the Predator Guild in

Vegetable Crops

INTRODUCTION

In addition to concerns about pesticide resistance and food safety, a recent EPA proposal that would ban the spraying of pesticides during crop bloom to protect managed honeybees (USEPA 2015) has increased the reliance on natural enemies to help control pests in agricultural systems. This leads agricultural ecologists to question why certain farm types are capable of supporting a more diverse and abundant predator guild than others and what characteristics are favorable for the survival of the natural enemy community. A review by Gonthier et al. (2014) found that both local management and landscape composition appear to have impacts on biodiversity in agroecosystems, but this depends on the type and mobility of the organisms being studied. The predator guild in agroecosystems consists of a diversity of arthropods both on and above the ground that likely have varying responses to these factors. I took a multivariate approach to determine these responses by looking at a combination of management and landscape-scale factors and their effect on the beneficial predator guild on vegetable farms in Ohio.

Many studies have explored the link between biodiversity and ecosystem functioning (Tilman et al. 1996, van der Heijden et al. 1998, Downing and Leibold 2002,

Balvanera et al. 2006, Lundholm 2015), resulting in significant evidence that the two are

29 directly related due to greater niche partitioning and more efficient resource capture

(Cardinale et al. 2006, Duffy et al. 2007, Finke and Snyder 2008). In agriculture, this biodiversity ecosystem function (BEF) hypothesis is particularly relevant because we rely on important ecosystem services like biological control and pollination to maximize yields. Because of this, many efforts to enhance biological control services have been focused on increasing and maintaining diverse and abundant predator communities, but in order these efforts to be successful, we must have a better understanding of how the predator guild is impacted by local management, as well surrounding landscape composition.

Agroecosystems are, by their nature, highly disturbed and fragmented habitats, and therefore may not be suitable for supporting a diverse and abundant predator community. The surrounding landscape can be an important source for these beneficial arthropods into an agroecosystem, and habitat management is crucial for conservation biological control efforts (Landis et al. 2000). Providing refugia through non-crop patches or borders in close proximity to fields is a common method for promoting beneficial arthropod populations in these systems (Barbosa 1998), but habitat diversification at a larger landscape-scale has been shown to affect these communities as well (Weibull et al. 2003, Clough et al. 2005, Winqvist et al. 2011, Marja et al. 2014, Liu et al. 2014).

Farm management strategies, such as tillage and pesticide use, can have obvious implications for the predator guild, but understanding how the surrounding landscape amplifies or mitigates those impacts is essential for wide-scale implementation of

30 sustainable vegetable production methods. The goal of this study was to examine the effect of surrounding landscape composition and management on the beneficial predator guild in vegetable agroecosystems, which differ greatly from large-acreage field crop systems. I hypothesized that the abundance and diversity of the predator guild would be negatively associated with management intensity and the proportion of agricultural and urban habitat surrounding the farm. This was done by sampling ground-dwelling and above-ground predator guilds in two vegetable crops grown widely across the state of

Ohio, sweet corn (Zea mays) and summer squash (Cucurbita pepo).

METHODS

Field sites

Fifteen farms throughout north-central Ohio were chosen for sampling sites in

2013, and twelve farms were used in 2014 (Figure 6). The farms were selected based both on the variety of management practices (field size, pesticide use, tillage, etc.) used and along a continuum of landscape complexity from simple (agriculturally dominated, little surrounding non-crop habitat) to highly complex (diverse landscapes including significant non-crop habitat).

Fields of sweet corn and summer squash were selected to sample within each farm. Because these two crops often experience significant damage caused by common pests like corn earworm, Helicoverpa zea (Lepidoptera: Noctuidae), squash bug, Anasa tristis (Hemiptera: Coreidae), and striped cucumber beetle, Acalymma vittatum

(Coleoptera: Chrysomelidae), typically some form of pest control must be implemented

31 for these crops to be successful. Therefore there are expected to be key differences in the management of these two crops between organic and conventional produce farms.

Arthropod data collection

A transect was established through the center of each site and four equally-sized plots were established (Figure 7). Each plot was sampled at three different times throughout the growing season, once in June, July, and August (Table 4). If the crop had not germinated yet or had reached the end of harvest, the sampling was not done for that month, except in cases where a sequential sweet corn planting was available nearby to sample. Each plot was sampled using a yellow sticky card trap (YSCT) for sampling above-ground predator abundance, and a pitfall trap for sampling ground-dwelling predator activity density.

An unbaited yellow sticky card trap (22.9 x 27.9 cm, Pherocon©) was attached to the top of a plastic, 4-foot step-in fence post which was anchored into the ground. After a sampling period of seven days, the cards were placed in plastic bags and kept at 4⁰C until identification. Insect predators were identified to family level (Anthocoridae,

Cantharidae, Chrysopidae, Coccinellidae, Dolichopodidae, and Syrphidae) and arachnids were identified to order level (Araneae). Hemerobiidae, Nabidae, and Reduviidae were also counted on the sticky cards, but were found on less than five occasions each year and were therefore omitted from the analysis.

Pitfalls were dug into the soil between two plants within the row using a golf course cup cutter just deep enough to hold a 946 ml plastic cup with an 11 cm diameter flush with ground level. The cup was filled halfway with a soapy water solution (Dawn©

32 dish soap, original scent) as a surfactant. After a sampling period of seven days, the liquid was strained out and the contents of the trap were placed in 70% ethanol for identification. Insect predators were identified to family level (Carabidae, Formicidae, and Staphylinidae), arachnids were identified to order level (Araneae and Opiliones), and centipedes were identified to class level (Chilopoda). Predator diversity was calculated using Simpson’s Diversity Index.

Farm management

Site assessments

33 leaves, height, flowering, silk, fruit set, etc.), pest counts, and percent defoliation were recorded.

Landscape analysis

Data Analysis

Data were analyzed using SAS© and R© statistical software. Differences in abundance and diversity of the predator guild among crops and months were determined using a generalized linear mixed model (GLMM, using the GLIMMIX Procedure in

SAS©, version 9.3) for each sampling year, using a negative binomial or Poisson distribution for counts and a Gaussian distribution for Simpson’s Diversity Index. Fixed factors were month, crop, and the interaction, and random factors were site and plot within site. If the interaction was not significant, then it was dropped from the model. For analyzing differences in management practices, pesticide use (yes or no) and tillage type

(no-till, conservation till, or conventional till) were fixed factors, and site and plot within site were random factors. A Tukey-Kramer adjustment was used to correct for multiple comparisons.

Landscape data were analyzed using Akaike’s Information Criterion (AIC, using the glmmADMB and bblme packages in RStudio©) to choose the best-fit model for predicting abundance and diversity of the predator guild. Possible models included in the analysis were percentage of agricultural, urban, natural (including forest, grassland, wetland, water, etc.), forest, and grassland habitat within 1000 meters and 2000 meters of the field site. Any models with a ΔAIC less than 2 were considered competing models.

Landscape models that were significant (at α = 0.05) and did not compete with the null model were considered predictive.

RESULTS

A total of 26,164 predators were counted over the course of study from pitfall traps and yellow sticky card traps: 15,913 in summer squash and 10,251 in sweet corn.

Table 5 shows the number of each predator group caught from each crop during each year of the study.

Ground-dwelling Predators

In 2013, the total number of predators per pitfall was higher in summer squash compared to sweet corn across the season (month*crop: F2,177 = 31.30, P < 0.0001,

Figure 8A.). Predator diversity (Simpson’s D) did not vary between crop (crop: F1,179 =

0.27, P = 0.6037, Figure 8B). Ground spider activity was higher in summer squash compared to sweet corn across the season (crop: F2,179 = 114.60, P < 0.0001, Figure 9A).

Opiliones activity was higher in sweet corn than summer squash in July only

(month*crop: F2,177 = 6.77, P = 0.0015, Figure 9B), and the same was true for

Staphylinidae activity (month*crop: F2,177 = 3.07, P = 0.049, Figure 9C). Carabidae activity was higher in squash than sweet corn across the season (crop: F1,179 = 32.80, P <

0.0001, Figure 9D). Formicidae activity was higher in summer squash than sweet corn in

July only (month*crop: F2,177 = 103.54, P < 0.0001, Figure 9E).

In 2014, the total number of predators per pitfall was higher in summer squash compared to sweet corn across the season (month*crop: F2,174 = 19.42, P < 0.0001,

Figure 11A). Predator diversity (Simpson’s D) did not vary between crops (crop: F1,176 =

0.00, P = 0.9473, Figure 11B). Ground spider activity was higher in summer squash than sweet corn in July and August (month*crop: F2,174 = 27.50, P < 0.0001, Figure 12A).

Opiliones activity did not vary by crop (crop: F1,176 = 0.80, P = 0.3711, Figure 12B).

Staphylinidae activity was higher in summer squash in June and August (month*crop:

F2,174 = 21.59, P < 0.0001, Figure 12C). Carabidae activity was higher in sweet corn in

June and August and higher in summer squash in July (month*crop: F2,174 = 85.36, P <

0.0001, Figure 12D). Formicidae activity was higher in summer squash than sweet corn across the season (month*crop: F2,174 = 23.16, P < 0.0001, Figure 12E).

Above-ground Predators

In 2013, the total number of predators per sticky card was higher in corn than squash across the season (crop: F1,201 = 53.22, P < 0.0001, Figure 8C). Predator diversity

(Simpson’s D) did not vary between crop (crop: F1,201= 2.38, P = 0.1247, Figure 8D).

Coccinellidae abundance was higher in sweet corn than summer squash across the season

(crop: F1,201 = 11.88, P = 0.0007, Figure 10A). Syrphidae abundance was higher in sweet corn than summer squash in July and August (month*crop: F2,199 = 7.65, P = 0.0006,

Figure 10B). Dolichopodidae abundance was higher in summer squash than sweet corn across the season (month*crop: F2,199 = 11.92, P < 0.0001, Figure 10C). Above-ground spider abundance was higher in summer squash than sweet corn in August only

(month*crop: F2,199 = 19.69, P < 0.0001, Figure 10D). Cantharidae abundance was higher in sweet corn in July and higher in summer squash in August (month*crop: F2,199 =

18.00, P < 0.0001, Figure 10E). Anthocoridae abundance was higher in sweet corn than summer squash across the season (crop: F1, 201= 18.46, P < 0.0001, Figure 10F).

Chilopoda and Chrysopidae models did not converge, due to low abundances of these predators in trap catches in 2013.

In 2014, the total number of predators per sticky card was higher in summer squash than sweet corn in July only (month*crop: F2,179 = 14.88, P < 0.0001, Figure

11C). Predator diversity (Simpson’s D) did not vary between crops (crop: F1,181 = 0.24, P

= 0.6214, Figure 11D). Coccinellidae abundance was higher in sweet corn than summer squash in August and June (month*crop: F2,179 = 5.98, P = 0.0031, Figure 13A).

Syrphidae abundance was higher in sweet corn than summer squash across the season

(crop: F1,181 = 8.36, P = 0.0043, Figure 13B). Dolichopodidae abundance was greater in summer squash than sweet corn across the season (crop: F1,179 = 7.20, P = 0.0080, Figure

13C). The same was true for above-ground spider abundance (crop: F1,181 = 68.99, P <

0.0001, Figure 13D). Cantharidae abundance was higher in sweet corn in June and higher in summer squash in July (month*crop: F2,179 = 12.97, P < 0.0001, Figure 13E).

Anthocoridae abundance was higher in sweet corn than summer squash across the season

(crop: F1,181 = 64.89, P < 0.0001, Figure 13F). Chrysopidae abundance did not vary

37 between crops (crop: F1,181 = 2.22, P = .1379, Figure 13G). The Chilopoda model did not converge due to low abundance of this predator in trap catches in 2014.

Landscape and Management Impacts

Based on information gathered from the grower survey in 2013, 7 of the 15 farms used pesticides on one or both of the crops studied. In summer squash, field size ranged from 10 m2 to 1150 m2 and in sweet corn, field size ranged from 24 m2 to 175000 m2.

Number of pesticide applications ranged from 0 to 8 in both crops and number of active ingredients applied ranged from 0 to 7 in both crops. In 2014, 8 of the 12 farms used pesticides on one or both of the crops studied. Field size ranged from 33 m2 to 57000 m2 in summer squash and from 253 m2 to 19601 m2 in sweet corn. Number of pesticide applications ranged from 0 to 6 in summer squash and 0 to 2 sweet corn, and number of active ingredients applied ranged from 0 to 11 in summer squash and 0 to 3 in sweet corn.

Conventional tillage was the most common type in both crops in 2013 and 2014.

In 2013, ground-dwelling predator abundance was higher in fields with conservation tillage (F1,164 = 21.55, P < 0.0001), but not impacted by pesticide use (F1,164

= 3.45, P = 0.065). Above-ground predator abundance was higher in fields with pesticide use (F1,186 = 118.26, P < 0.0001), but not impacted by tillage (F1,186 = 0.03, P = 0.85). In

2014, ground-dwelling predator abundance was higher in fields with pesticide use (F1,173

= 102.08, P < 0.0001) and conventional tillage (F1,173 = 67.13, P < 0.0001). Above- ground predator abundance was higher in fields with pesticide use (F1,178 = 30.96, P <

0.0001), but not different based on tillage (F1,173 = 0.41, P = 0.52). Predator diversity was

38 not impacted by pesticide use or tillage above-ground on the ground in either 2013 or

2014.

Landscape-scale factors influenced arthropod predator diversity and abundance in the study, as shown by model selection using AIC (Tables 6 and 7). In 2013, the amount of agricultural habitat within 1000 meters (best-fit) and 2000 meters was negatively associated with Formicidae activity in summer squash. The amount of agricultural habitat within 1000 meters was negatively associated with Cantharidae abundance in sweet corn and summer squash. The amount of forested habitat within 1000 meters was positively associated with Dolichopodidae abundance in sweet corn. The amount of grassland habitat within 2000 meters was positively associated with total pitfall predator activity,

Simpson’s diversity of pitfall predators, and ground-dwelling spider activity in sweet corn. The amount of grassland habitat within 2000 meters (best-fit) and 1000 meters was positively associated with Coccinellidae abundance in summer squash. The amount of natural habitat within 1000 meters was positively associated with sticky card predator abundance and Dolichopodidae abundance in summer squash. Ground-dwelling spider activity in summer squash was negatively associated with natural habitat within 2000 meters (best-fit) and 1000 meters, negatively associated with forested habitat within 1000 meters and 2000 meters, and positively associated with grassland habitat within 2000 meters. The amount of urban habitat within 1000 meters was negatively associated with

Opiliones activity in summer squash.

In 2014, the amount of agricultural habitat within 1000 meters was negatively associated with above-ground predator abundance, Cantharidae abundance, Coccinellidae

39 abundance, and Dolichopodidae abundance in summer squash. The amount of agricultural habitat within 2000 meters was negatively associated with Formicidae activity and Staphylinidae activity (best-fit) in summer squash. Staphylinidae activity in summer squash was also positively associated with natural habitat within 1000 meters and forested habitat within 1000 meters and 2000 meters. In sweet corn, agricultural habitat within 1000 meters was negatively associated with Formicidae activity and

Dolichopodidae abundance (best-fit). Dolichopodidae abundance in sweet corn was also negatively associated with agricultural habitat within 2000 meters. The amount of natural habitat within 1000 meters (best-fit) and 2000 meters was positively associated with

Cantharidae abundance in sweet corn. Natural habitat within 2000 meters was negatively associated with Anthocoridae abundance and Opiliones activity (best-fit) in sweet corn.

Opiliones activity was also negatively associated with forested habitat within 2000 meters.

DISCUSSION

Summer squash and sweet corn supported distinct predator guilds. Diversity measures and total predator counts were generally higher in summer squash, but certain predator groups, such as Anthocoridae, Syrphidae, and Coccinellidae, were generally more abundant in sweet corn, while other groups, such as Formicidae, Dolichopodidae, and Araneae, were generally more abundant in summer squash. This illustrates the importance of diversity in cropping systems. Large monocultures are not likely to support a predator guild as abundant and diverse as a cropping system with many types of crops

40 all planted within the same acreage, following from the general principle that increased plant diversity supports higher arthropod diversity (Siemman et al. 1998). These other crops can also provide refugia (during sprays or harvests), alternative prey, and supplemental food resources, such as pollen and nectar, to these predators.

All of the predator groups that had an association with the amount of agricultural or urban habitat within the landscape (such as total pitfall predators, total sticky card predators, Araneae, Coccinellidae, Cantharidae, Dolichopodidae, Formicidae, and

Opiliones) were negatively associated. This is likely due to the high disturbance and instability of agricultural and urban habitats, which may not be capable of supporting the abundance and diversity of the predator guild found in natural habitats. It then makes sense that, with the exception of ground-dwelling Araneae in 2013 and Anthocoridae and

Opiliones in 2014, all groups that had an association with the amount of natural habitat in the landscape were positively associated. These results align with a review by Bianchi et al. 2006, which also found that diversified landscapes containing more non-crop habitat were important for promoting natural enemy populations in agroecosystems.

Predator diversity was not shown to be influenced by management practices in this study, but predator abundance was, although the response to management was highly variable. Pesticide use and tillage intensity either had a negative, positive, or neutral effect on predator abundance in pitfalls and on sticky cards. This inconsistency suggests that other factors (such as landscape factors or other local factors) are confounding or are simply more influential in determining predator abundance than these local management factors. With vegetable crops that are grown on a much smaller scale compared to large

41 acreage field crops, it is probably much easier for predators to recolonize a crop after a period of high disturbance. If this is the case, then the surrounding landscape would play an even larger role in supporting the predator guild in vegetable crops than in other types of agroecosystems.

Although my results regarding local management impacts on the predator guild were inconclusive, my study revealed differing predator communities between sweet corn and summer squash in general. This suggests that farms with high crop diversity are capable of supporting a more diverse predator guild than large monocultures, which should increase the biological control services being provided on those farms. I also found evidence that the surrounding landscape is influencing predator abundance and activity at both 1000 and 2000 meters, with the proportion of agricultural and urban habitat surrounding the farm having a negative impact, and proportion of natural habitat having a generally positive effect. These findings supported my hypothesis, and I maintain that habitat management on a landscape scale is crucial for promoting biological control by arthropod predators in agroecosystems.

FIGURES AND TABLES

Tr Ba

Bi Ho Gr Bm

Bu Ma Ca Mu

He No Mo

Mv Sw

St Da

Figure 6. Field sites in north-central Ohio where dolichopodid communities were sampled in 2013 and 2014. 15 farms were sampled in 2013 and 12 farms were sampled in 2014. Farms are designated by a two-letter abbreviation. Red indicates farms that were sampled in both 2013 and 2014, blue indicates farms only sampled in 2013, and yellow indicates farms only sampled in 2014.

Figure 7. Schematic of field sampling technique. A transect was established along the center of each crop (sweet corn field, summer squash field) on the longest side.

Four plots were assigned along this transect that were equidistant from each other and the edge for plots 1 and 4. A yellow sticky card trap and pitfall trap were deployed at each of these plots for one week to sample for above-ground and ground-dwelling predators during each sampling period.

Table 4. Sampling sites and dates for 2013 and 2014. Farms are designated by a two- letter abbreviation. A dash implies that either the farm was not sampled that year, the crops were not yet germinated at the time of the sampling, or the crops had been harvested at the time of the sampling. The crops sampled during each period are noted as “C” for sweet corn and “S” for summer squash.

Site June 2013 July 2013 August 2013 June 2014 July 2014 August 2014 Ba S S S - - - Bi CS CS - CS CS CS Bu CS CS CS CS CS C Bm - - - CS C - Ca C CS CS CS CS CS Da C C - - - - Gr CS CS CS - - - He CS S S CS CS CS Ho - - - CS CS CS Ka - CS CS - - - Ma - - - CS CS S Mo S S S - - - Mv - CS CS Mu C CS CS CS CS CS No S CS CS - - - Si CS CS C S S - St C CS CS - CS CS Sw CS CS S CS CS S Tr - - - - CS CS

Table 5. Predator counts from pitfall traps and yellow sticky card traps summed by crop (summer squash, sweet corn) and sampling year (2013 and 2014).

Predator group 2013 Squash 2013 Corn 2014 Squash 2014 Corn Total Anthocoridae 165 209 93 236 703 Araneae 1060 403 3536 2059 7058 (ground) Araneae (above- 222 135 512 250 1119 ground) Carabidae 471 276 356 501 1604 Cantharidae 282 303 240 113 938 Chilopoda 12 9 5 3 29 Chrysopidae 25 14 18 8 65 Coccinellidae 183 222 85 190 680 Dolichopodidae 2502 1271 963 612 5348 Formicidae 1576 524 1231 480 3811 Opiliones 59 127 155 138 479 Staphylinidae 207 313 658 302 1480 Syrphidae 1082 1243 215 310 2850 Total 7846 5049 8067 5202 26164

Figure 8. Total predator counts (A and C) and Simpson’s D (B and D) from 2013 pitfalls (A and B) and sticky cards (C and D), throughout the summer from each study crop. Statistics were performed on the negative binomial-transformed means for counts, actual means shown. An asterisk (*) indicates a significant difference (at

α = 0.05) between crops within that month.

Figure 9. 2013 pitfall predator counts throughout the summer from each study crop.

A: Ground-dwelling Araneae, B: Opiliones, C: Staphylinidae, D: Carabidae, and E:

Formicidae. Statistics were performed on the poisson-transformed means, actual means shown. An asterisk (*) indicates a significant difference (at α = 0.05) between crops within that month.

Figure 9

Figure 10. 2013 yellow sticky card predator counts throughout the summer from each study crop. A: Coccinellidae, B: Syrphidae, C: Dolichopodidae, D: Above- ground Araneae, E: Cantharidae, and F: Anthocoridae. Statistics were performed on the poisson-transformed means, actual means shown. An asterisk (*) indicates a significant difference (at α = 0.05) between crops within that month.

Figure 10

Figure 11. Total predator count (A and C) and Simpson’s D (B and D) from 2014 pitfalls (A and B) and sticky cards (C and D), throughout the summer from each study crop. Statistics were performed on the negative binomial-transformed means for counts, actual means shown. An asterisk (*) indicates a significant difference (at

α = 0.05) between crops within that month.

Figure 12. 2014 pitfall predator counts throughout the summer from each study crop. A: Ground-dwelling Araneae, B: Opiliones, C: Staphylinidae, D: Carabidae, and E: Formicidae. Statistics were performed on the poisson-transformed means, actual means shown. An asterisk (*) indicates a significant difference (at α = 0.05) between crops within that month.

Figure 12

Figure 13. 2013 yellow sticky card predator counts throughout the summer from each study crop. A: Coccinellidae, B: Syrphidae, C: Dolichopodidae, D: Above- ground Araneae, E: Cantharidae, F: Anthocoridae, and G: Chrysopidae. Statistics were performed on the poisson-transformed means, actual means shown. An asterisk (*) indicates a significant difference (at α = 0.05) between crops within that month.

Figure 13

(cont.) 56

Figure 13 (cont.)

Table 6. Summary of model selection using Akaike’s Information Criterion (AIC) for predicting predator abundance and diversity in 2013. Only variables where the null model was not competing are included. Best-fit models shown in bold.

Response Crop Landscape predictors z Weight P

Ground-dwelling corn % grassland at 2 km (+) 2.84 0.55 0.0046 predator activity

Ground-dwelling corn % grassland at 2 km (+) 3.07 0.50 0.0022 predator diversity

Above-ground squash % natural at 1 km (+) 2.85 0.51 0.0043 predator abundance

Araneae (ground) corn % grassland at 2 km (+) 3.06 0.67 0.0022 activity

Araneae (ground) squash % natural at 2 km (-), -2.52 0.20 0.012 activity % forest at 2 km (-), - 2.41 0.18 0.016 % grassland at 2 km (+), 2.30 0.15 0.022 % natural at 1 km (-), -2.13 0.11 0.033 % forest at 1 km (-) -2.12 0.11 0.034

Cantharidae corn % agriculture at 1 km (-) -3.37 0.73 <0.001 abundance

Cantharidae squash % agriculture at 1 km (-) -4.06 0.89 <0.001 abundance

Coccinellidae squash % grassland at 2 km (+), 2.39 0.36 0.017 abundance % grassland at 1 km (+) 2.07 0.20 0.038

Dolichopodidae squash % natural at 1 km (+) 2.57 0.43 0.010 abundance

Dolichopodidae corn % forest at 1 km (+) 2.84 0.47 0.0046 abundance

Formicidae squash % agriculture at 1 km (-), -3.63 0.65 <0.001 activity % agriculture at 2 km (-) -3.25 0.25 0.0011

Opiliones activity squash % urban at 1 km (-) -2.11 0.45 0.035

Table 7. Summary of model selection using Akaike’s Information Criterion (AIC) for predicting predator abundance and diversity in 2014. Only variables where the null model was not competing are included. Best-fit models shown in bold.

Response Crop Landscape predictors z Weight P

Ground-dwelling squash % agriculture at 2 km (-) -2.13 0.22 0.033 predator activity

Above-ground squash % agriculture at 1 km (-) -2.95 0.44 0.0032 predator abundance

Anthocoridae corn % natural at 2 km (-) -3.57 0.64 <0.001

Araneae (ground) squash % agriculture at 2 km (-) -2.40 0.34 0.017 activity

Cantharidae corn % natural at 1 km (+), 4.39 0.47 <0.001 abundance % natural at 2 km (+) 3.87 0.23 <0.001

Cantharidae squash % agriculture at 1 km (-) -2.87 0.50 0.0041 abundance

Coccinellidae squash % agriculture at 1 km (-) -3.66 0.78 <0.001 abundance

Dolichopodidae squash % agriculture at 1 km (-) -4.54 0.81 <0.001 abundance

Dolichopodidae corn % agriculture at 1 km (-), -2.45 0.32 0.014 abundance % agriculture at 2 km (-) -1.99 0.16 0.047

Formicidae squash % natural at 2 km (+) 2.23 0.33 0.026 activity

Formicidae corn % agriculture at 1 km (-) -4.51 0.70 <0.001 activity

Opiliones activity corn % natural at 2 km (-), -3.31 0.56 <0.001 % forest at 2 km (-) -2.94 0.23 0.0033

Staphylinidae squash % natural at 2 km (+), 3.46 0.33 <0.001 activity % forest at 1 km (+), 3.14 0.19 0.0017 % natural at 1 km (+), 3.06 0.17 0.0022 % forest at 2 km (+) 3.05 0.16 0.0023

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