EXPLORING SOIL ARTHROPOD ECOLOGY AND MANAGEMENT TACTICS IN PERENNIAL FRUIT CROP SYSTEMS DURING THE WINTER By Jason Matlock A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Entomology - Doctor of Philosophy 2018 EXPLORING SOIL ARTHROPOD ECOLOGY AND MANAGEMENT TACTICS IN PERENNIAL FRUIT CROP SYSTEMS DURING THE WINTER ABSTRACT By Jason Matlock Paralobesia viteana (the grape berry moth) and Venturia inaequalis (causal agent of apple scab) are two key pests of perennial fruit crops in Michigan and the Northeastern US. Both of these pests provide examples of the feedback potential of management decisions: they both overwinter within the cropping system and have multiple reproductive generations per growing season. This allows their populations to carry over from season to season, exacerbating any failures in management from previous years. Both pests overwinter on the ground in leaf litter. During that time, these organisms are affected by physical changes of the ground habitat and interactions with other ground-dwelling organisms. Manipulation of that habitat to alter the physical properties and community dynamics to decrease overwintering survivorship of these pests may provide growers with additional management tactics. Indeed, there is historical precedent for such tactics originating in the early 1900s. Publications from that time mention grape growers in Northeastern, PA throwing furrows over the leaf litter beneath vine canopies in late fall or early spring and observing reduced grape berry moth emergence. During this same time period, it was discovered that spraying a urea solution onto fallen leaves in apple orchards decreased the spore density released by V. inaequalis in the following season. Unfortunately, our understanding of the mechanisms underlying these techniques are incomplete and their adoption by growers remains low. Furthermore, our general understanding of the activities of floor dwelling organisms during the overwintering period is also limited. Increasing knowledge both of how these specific tactics affect their associated target pests, and of how overwintering populations are structured are essential steps in the development and improvement of winter management tactics. With regard to P. viteana and grape vineyards, I explored the effects of physical damage and burial resulting from a rotary cultivator used at the end of the season. Survivorship of pupae recovered from the vineyard immediately after tillage and held until emergence was not significantly different from those recovered from an untilled control area, indicating little effect of mechanical damage on this pest. However, a single pass of the tillage implement buried three quarters of pupae under at least 1 cm of soil. A laboratory experiment to recreate these conditions resulted in significant increase in mortality when pupae were buried in more than 1 cm of sand. I conclude that interference with adult emergence of diapausing pupae via burial is the primary mechanism by which tillage controls grape berry moth. With regards to apple V. inaequalis and apple orchards, I observed the response of overwintering, ground-dwelling arthropods to 1) the application of urea to fallen leaves; and 2) organic versus conventional management strategies. In addition, I screened the gut contents of collected arthropods for the presence of V. inaequalis to identify potential natural enemy taxa. My primary finding was that orchards host a diverse, winter active arthropod community. Management strategy did not affect family richness or intra-community complexity (alpha diversity). There was also considerable overlap in the dominant families detected under both management strategies. However, the relative abundances of those families did respond to management strategy. These changes were associated with differences in the secondary and tertiary decomposer sub guilds. There was also evidence that organic management supported a greater arthropod population. Urea application caused an up-regulation of tertiary decomposers and a down-regulation of primary decomposers during the first month following application. I also found evidence that the absolute decomposer populations were greater in the urea treatments. I propose that urea application caused a trophic cascade in which increased microbial growth leads to a recruitment of fungal feeding arthropods into leaf litter from surrounding areas of the orchard. ACKNOWLEDGMENTS Thanks to Mark Gregory of Gregory Farms (Lawton, MI) for providing a research site and assisting with field operations for the Grape Berry Moth research. Funding for that project was provided through the USDA-NIFA Pest Management Alternatives Program, grant 2013-34381-21204 to RI and MG. Funding for orchard ecology and urea application research was provided by the Michigan Apple Committee and The CERES Trust. I thank Steve Tennes, my grower collaborator, for providing access to his orchard, assisting with treatment applications, and providing insight during experimental design. Olivia Simaz, Mirijam Garske, Paul Owen-Smith, Rebecca Reneker, and Mike Mueller aided with fieldwork and sample processing. I also thank Dr. Jen Pechal for providing consultation on metabarcoding approaches and my committee Dr.’s Matthew Grieshop, Eric Benbow, George Sundin, and Zsofia Szendrei for their oversight. Special acknowledge is given to my friends, family and partner Natalie for their ongoing support during this challenging undertaking. iv TABLE OF CONTENTS LIST OF TABLES .................................................................................................................... viii LIST OF FIGURES ....................................................................................................................ix CHAPTER 1. OVERVIEW ......................................................................................................... 1 GRAPE BERRY MOTH IN VINEYARD SYSTEMS ................................................................. 3 Life Cycle ............................................................................................................................ 3 Economic Impact ................................................................................................................ 4 Conventional Management ................................................................................................. 4 Overwintering Vulnerability ................................................................................................. 5 APPLE SCAB IN ORCHARD SYSTEMS ................................................................................ 5 The Disease ....................................................................................................................... 5 Overwintering Vulnerability ................................................................................................. 6 Issues in Floor Sanitation .................................................................................................... 8 Identification of Primary Drivers .......................................................................................... 8 Regulation of Early Sexual Differentiation by Nitrogen..................................................... 8 Interactions with Orchard Floor Biology ........................................................................... 9 Incorporating Invertebrates ................................................................................................11 Arthropod Feeding ..........................................................................................................11 Arthropods in Winter .......................................................................................................12 Paradigms in Orchard Management ..................................................................................13 Influence of Management on Soil Communities ..............................................................13 Challenges & Technological Overview ...............................................................................15 Taxonomic Assignment ..................................................................................................15 Metabarcoding................................................................................................................15 Identification of Gut Contents .........................................................................................16 AIMS AND OBJECTIVES ......................................................................................................16 LITERATURE CITED ............................................................................................................18 CHAPTER 2. TILLAGE REDUCES SURVIVAL OF GRAPE BERRY MOTH, PARALOBESIA VITEANA (LEPIDOPTERA:TORTRICIDAE), VIA BURIAL RATHER THAN MECHANICAL INJURY.....................................................................................................................................27 ABSTRACT ...........................................................................................................................27 INTRODUCTION ...................................................................................................................28 MATERIALS AND METHODS ...............................................................................................30 Insect Collection and Rearing ............................................................................................30 Tillage Depth......................................................................................................................30 Tillage Damage & Survival .................................................................................................31 Burial Depth & Survival ......................................................................................................32 RESULTS AND DISCUSSION ..............................................................................................33 Mechanical Effects on Pupae .............................................................................................33 Burial Effects on Pupae .....................................................................................................33 Incorporating Tillage into Vineyard Management ...............................................................34 Application of Tillage in Conventional Vineyards ............................................................34 Application of Tillage in Organic Vineyards .....................................................................36 CONCLUSIONS ....................................................................................................................37 v APPENDIX ............................................................................................................................38 LITERATURE CITED ............................................................................................................44 CHAPTER 3. Overwintering soil arthropod communities in an orchard under organic and conventional management .....................................................................................................48 ABSTRACT ...........................................................................................................................48 INTRODUCTION ...................................................................................................................49 MATERIALS AND METHODS ...............................................................................................53 Experimental Design ..........................................................................................................53 Field Sample Collection and Processing ............................................................................53 DNA Extraction ..................................................................................................................54 Endpoint PCR ....................................................................................................................54 Amplicon Sequencing ........................................................................................................55 Sequence Data Processing ................................................................................................55 Internal Community Standard ............................................................................................56 Data Normalization ............................................................................................................57 Data Analysis .....................................................................................................................57 RESULTS ..............................................................................................................................58 Metagenomic Internal Community Standard ......................................................................58 Taxa Present in the Winter.................................................................................................59 Impacts of Management ....................................................................................................59 Feeding Guilds ...................................................................................................................60 DISCUSSION ........................................................................................................................60 Taxa Present in the Winter.................................................................................................60 Araneae .........................................................................................................................60 Astigmata .......................................................................................................................61 Mesostigmata .................................................................................................................62 Oribatida.........................................................................................................................63 Trombidiformes ..............................................................................................................63 Collembola .....................................................................................................................63 Diplopoda .......................................................................................................................64 Coleoptera ......................................................................................................................65 Diptera ...........................................................................................................................67 Impacts of Management ....................................................................................................68 Richness and Relative Abundance .................................................................................68 Population Size ..............................................................................................................70 Feeding Guilds ...............................................................................................................71 CONCLUSIONS ....................................................................................................................71 APPENDICES .......................................................................................................................73 LITERATURE CITED .......................................................................................................... 100 CHAPTER 4. Overwintering soil arthropod community responds to post-harvest application of urea to leaf litter in a conventionally managed orchard ............................. 110 ABSTRACT ......................................................................................................................... 110 INTRODUCTION ................................................................................................................. 111 MATERIALS AND METHODS ............................................................................................. 113 Experimental Design ........................................................................................................ 113 Field Sample Collection and Processing .......................................................................... 114 DNA Extraction ................................................................................................................ 114 Endpoint PCR .................................................................................................................. 115 Amplicon Sequencing ...................................................................................................... 115 vi Sequence Data Processing .............................................................................................. 115 Data Normalization .......................................................................................................... 117 Data Analysis ................................................................................................................... 117 RESULTS AND DISCUSSION ............................................................................................ 118 Trophic Succession in Control Plots ................................................................................. 118 Effect of Urea Application on Decomposer Relative Abundance ...................................... 119 Effect of Urea on Decomposer Population Size ............................................................... 121 Responsive Decomposer Taxa ........................................................................................ 122 Relating to Urea-based Floor Sanitation .......................................................................... 122 CONCLUSIONS .................................................................................................................. 123 APPENDICES ..................................................................................................................... 125 LITERATURE CITED .......................................................................................................... 148 CHAPTER 5. CONSUMPTION OF APPLE LEAF LITTER INFECTED WITH VENTURIA INAEQUALIS BY SOIL ARTHROPODS DURING THE WINTER ........................................... 154 ABSTRACT ......................................................................................................................... 154 INTRODUCTION ................................................................................................................. 155 MATERIALS AND METHODS ............................................................................................. 157 Experimental Design ........................................................................................................ 157 Field Sample Collection and Processing .......................................................................... 157 DNA Extraction ................................................................................................................ 158 Surface Decontamination ................................................................................................. 159 Real-Time PCR Screening ............................................................................................... 159 Determining Cycle Threshold (CT) Cutoffs ....................................................................... 160 Assay Validation in an Insect Model ................................................................................. 161 RESULTS AND DISCUSSION ............................................................................................ 162 Cycle Threshold (CT) Cutoffs ........................................................................................... 162 Surface Decontamination ................................................................................................. 162 Insect Gut Model Validation ............................................................................................. 162 Field Experiment .............................................................................................................. 164 Collembola & Oribatida ................................................................................................. 164 Julidae .......................................................................................................................... 165 Coleopteran Predators ................................................................................................. 165 Temporal Activity of Winter Arthropods ........................................................................ 165 Influencing Feeding Habits ........................................................................................... 166 Caveats & Challenges .................................................................................................. 167 CONCLUSIONS .................................................................................................................. 168 APPENDIX .......................................................................................................................... 169 LITERATURE CITED .......................................................................................................... 178 CHAPTER 6: SYNTHESIS ..................................................................................................... 183 vii LIST OF TABLES Table 2.1. Criteria for Qualitative Damage Assessment Scores of Grape Berry Moth Pupae that were Subjected to Tillage with a Rotary Cultivator Table 2.2. Comparing the Occurrence of Various Damage Types in Grape Berry Moth Pupae Subjected to a Tillage Pass with a Rotary Cultivator to an Uncultivated Control Group Table 3.1. Frequently Detected Taxa Table 3.2. PERMANOVA and Multivariate Dispersion Analysis Table 3.3. Unique Detection Events of Shared Taxa Table 3.4. Relative Abundance of Soil Trophic Guilds under Organic and Conventional Management Table 4.1. Significance Values of Differences in Feeding Guild Relative Abundance Table 4.2. Unique Detection Events in Decomposers Table 5.1. Efficacy of the Surface Decontamination Protocol Table 5.2. Field Sample Gut Content Screening Results Table 5.3. Detection of Target Analytes in an Insect Gut Model 40 41 77 79 81 82 129 130 173 175 176 viii LIST OF FIGURES Figure 2.1. Distribution of Grape Berry Moth Pupae within the Soil Profile after a Tillage Pass with a Rotary Cultivator Figure 2.2. Proportion of Diapausing Grape Berry Moth Pupae Emerging as Adults after Burial under Different Depths of Sand Figure 3.1. PCR Bias and Assignment Error in Arthropod Community Standard Data Figure 3.2. Distribution of Taxon Relative Abundance Figure 3.3. Difference in Alpha Diversity Resulting from Management Style Figure 4.1. Relative Abundance within the Decomposer Guild under Standard Management Figure 4.2. Fold Change in Decomposer Relative Abundance in response to Urea Application Figure 4.3. Decomposers Affected by Urea Application Figure 5.1. Determination of the M. domestica CT Value Cutoff Figure 5.2. Determination of the V. inaequalis CT Value Cutoff Figure 5.3. Detection of Target Analytes in an Insect Gut Model Figure 5.4. Change in Air Temperature During Study Period 42 43 75 76 80 127 128 131 171 172 174 177 ix CHAPTER 1. OVERVIEW Agricultural operations are managed ecosystems that derive and provide services from and to the organisms residing within them (Swinton et al. 2007; Zhang et al. 2007). The economic crop is one part of a complex and interconnected web of organisms spanning spatial scales, trophic levels, and organismal domains. Management of an agricultural ecosystem is, at its core, about manipulating that complex web to maximize net productivity of the economic crop through the realization of ecosystem services and the disruption of detractive processes. Some common examples of ecosystem services derived from agroecosystem members include mineralization of soil nutrients by soil microbiota, consumption of pest species by natural enemies, and crop pollination by insects. Detractive processes include competition for resources by weed species, infection of crop tissues by plant pathogens, and feeding on crop tissues by herbivores. Oftentimes management strategies must deal with the fact that tactics can have competing outcomes. For example, using burndown herbicides to kill off ground cover can increase the amount of water and soil nutrients available to the crop and reduce competition for pollinators (Zhang et al 2007). However, decreased habitat complexity, as realized by eliminating the ground cover, has also been linked to decreases in natural enemy populations (Langellotto et al. 2004; Szendrei et al. 2007). Thus controlling weeds through broad deployment of herbicides might increase pest pressures by reducing the background level of control provided by natural enemies. As illustrated by this example, agricultural management strategies require balancing the effects of individual tactics. Considerable systems-level knowledge of these effects and their interactions is required to make informed management decisions. Such knowledge is particularly important in perennial systems. One of the challenges faced in perennial agriculture is managing feedback loops whose effects amplify ecosystem services or disservices from season to season (Berryman 1987; Lewis et al. 1997). Pest populations are prime examples of this issue: events that generate a slight increase in survivorship in one season can predispose the next season to greater pest pressures, 1 which could be sufficient to prevent effective suppression. Each breakdown in control further complicates pest suppression downstream. Conversely, incremental increases in control can have the opposite effect. Unfortunately, the factors that must be considered and accounted for are substantial in number. At a conceptual level, these factors are described by the “disease triangle”: the pest organism needs to be present, the environment needs to be suitable for its survival, and a food source/host must be both available and susceptible to feeding/infection (James et al. 2015; Battisti and Larsson 2015). Many of Michigan’s key crops are perennial: apples, asparagus, blueberries, cherries, and grapes being notable examples (MDARD 2016). Michigan has a temperate climate with four distinct seasons. Winters run from December through March below the 45th parallel, with an average daily temperature over the last 18 years of -4℃ during that period (NOAA 2018). Low temperatures between January and February average -10.5℃, and extremes below -15℃ can be reached (NOAA 2018). Clearly, host plants are not available during this period, forcing pest organisms to adopt some strategy for overwintering until the following growing season. Typical strategies employed by Michigan pests include migrating to warmer climates with food resources (Schuh 2016); finding shelter within or on a host plant (Kennelly et al. 2007; Spencer 1973); and taking advantage of the insulatory effect of the soil and surface litter (Wilcox 1993; Bowen et al. 2011; Racette et al. 1992; Isaacs et al. 2012). Integrated Pest Management (IPM) can be defined and realized in a number of ways. A commonality among presentations of IPM is the use of multiple, complementary tactics to selectively disadvantage a pest wherever and whenever possible (Barzman et al. 2015; Guedes et al. 2016; Hokkanen 2015; Stenberg 2017). Thus, at its core, IPM is based on maximizing ecosystem pest management services to apply ongoing preventative pressures against a pest which can be supplemented with strategic reactionary measures. A logical branch of tactics that emerges from such an approach is seeking to understand the life cycle and physiology of a pest in order to exploit points of vulnerability. 2 In Michigan and other temperate climates, the overwintering life stage of a pest could be such a point of vulnerability, particularly for organisms residing in the leaf litter. The location of these organisms is known, their mobility is generally restricted, they are constantly at risk of lethal cold damage (Block 1990; Robinson 2001; Walker et al. 2006), and food/energy resources are limited. Furthermore, at the end of winter, all pests must somehow transition from their overwintering state back to the host plant. Thus, an understanding of a pest organism’s overwintering strategy could provide opportunities to increase their overwintering mortality and interfere with host plant re-infestation in the following season. Two key pests of perennial fruit crops in Michigan are Paralobesia viteana (the grape berry moth) in grape vineyards and Venturia inaequalis (apple scab) in apple orchards. These pests are prime examples of the feedback potential of management decisions: they both overwinter within the cropping system and have multiple reproductive generations per growing season. This allows their populations to carry over from season to season, exacerbating any failures in management from previous years. Both pests overwinter on the ground in leaf litter, which means that they share a potential life cycle vulnerability. GRAPE BERRY MOTH IN VINEYARD SYSTEMS Life Cycle Paralobesia viteana (Clemens), the grape berry moth, is a direct pest of wild and cultivated grapes. It is multi-voltine, with potential for up to four generations per year in Michigan (Tobin et al. 2008). The moth oviposits almost exclusively on grape hosts (Isaacs et al. 2012), with egglaying on foliage and blossoms in the first generation, and on maturing fruit during subsequent generations. A single larva can feed on multiple grape berries within a cluster, and their infestation later in the season can increase the risk of disease infections of clusters (Fermaud and Le Menn 1992). Mature larvae exit clusters to form cocoons from grape leaf flaps folded over with webbing, and eggs laid after a critical photoperiod of between 14 and 15 hours will develop into diapausing pupae that remain in cocoons until the start of the next growing season (Nagarkatti 3 et al. 2001). The diapausing pupae overwinter on the ground because leaves containing the cocoons fall from the canopy and mix with the floor litter. Economic Impact Michigan is home to the second largest juice grape processing plant owned by Welch’s Foods Inc., and in 2015, juice grapes accounted for approximately 4850 harvested hectares that produced an estimated 54,500 metric tonnes of Concord and Niagara grapes valued at $19.3 million (USDA NASS 2016). High infestation rates of berries with grape berry moth will lead to rejection of entire truckloads, which have no other viable purchaser. Because the machine harvesting leaves little room for quality sorting, growers are may be faced with leaving areas with high infestation unharvested in order to avoid the risk of rejection and the associated loss. Michigan is also home to a burgeoning wine industry, contributing $300 million annually to the state economy through wine sales and related agrotourism (MGWIC 2015). Climate change predictions suggest that the Lake Michigan coastline will experience milder winters and warmer spring temperatures (Wang et al. 2011), which is expected to increase the number of grape berry moth generations reaching adulthood in the growing season (Tobin et al. 2008), and may increase grape berry moth pressure above economic thresholds in northern winegrape growing regions. Conventional Management Control of grape berry moth relies predominantly on rotating applications of broad-spectrum insecticides (Isaacs et al. 2012). This style of insecticide- dominated management has come under public scrutiny in recent years as a result of human health risks, deleterious impacts on the environment, and increasing levels of resistance development in pest populations (Pimentel 2005). Indeed, grape berry moth populations resistant to the broad spectrum carbamate insecticide, carbaryl, have been discovered along Lake Erie (Nagarkatti et al. 2002). Even modern insecticides with relatively little history of field use are encountering resistant insect populations. Recently, it was discovered that Lobesia botrana (Denis & Schiffermüller), a totricid pest in Europe similar to the grape berry moth, is developing resistance in parts of Italy to indoxacarb, a next-generation reduced-risk insecticide registered in 4 2000 (USEPA 2000, Civolani et al. 2014). Rising awareness of insecticide resistance along with increased restrictions on the use of broad spectrum insecticides has created a need to decrease reliance on chemical tactics and create more diversified and sustainable control systems. Overwintering Vulnerability Tillage has a long history of use for managing weeds, and its reintegration into modern weed management programs has been recommended as a means of disrupting herbicide resistance development and decreasing dependency on chemical tactics (Norsworthy et al. 2012, Vencill et al. 2012). Tillage may be similarly integrated into pest management programs for control of insect pests that spend some portion of their life at or below the soil surface (Johnson et al. 1984, Seal et al. 1992, Chu et al. 1996, Baughman et al. 2015). Extension publications from the early 1900’s mention that growers in North East, PA were throwing furrows over the leaf litter beneath vine canopies in late fall or early spring to reduce grape berry moth emergence (Isley 1917). Rudimentary experiments reported in the same publication supported burial as an effective means of control. Recent work by Baughman et al. (2015) showed that burial of Cydia pomonella, a totricid pest of apples, under just 1 cm of sand completely inhibited emergence after diapause. The approach presented in Baughman et al. (2015) made use of the “Swiss Sandwich” technique, in which tillage is applied to narrow strips on either side of the tree row. This style of tillage limits soil disruption and has been shown to have minimal impact on soil health (Zopollo et al. 2011). Thus, the targeted use of tillage may provide a way for grape growers to manage grape berry moth populations while introducing diversity into vineyard pest management programs. APPLE SCAB IN ORCHARD SYSTEMS The Disease Apple scab, caused by the ascomycetous fungi Venturia inaequalis (Cooke) Wint., is the leading cause of economic loss in the global apple market (Bowen et al 2011). The principle source of loss results from lesions that make fruit cosmetically unsuitable for the fresh market. Infection can also decrease fruit set, reduce fruit size, and make fruit more susceptible to shrivelling during storage (MacHardy et al. 2001; Tomerlin and Jones 1983). The infection cycle 5 in apples begins in the early spring with the release of ascospores from infested leaf litter that form primary infections on young leaf tissue (Keitt and Jones 1926; MacHardy et al. 2001). Environmental conditions impact the success of infections and incubation periods; however, the time between spore germination and conidial release can be as little as 9 days, allowing for many generations over the growing season (Vaillancourt and Hartman 2000). The propagation of conidial spores is referred to as the secondary infection period. Secondary infections are difficult to control because spore number grows exponentially with each generation. Furthermore, each generation amplifies successfully adapted genotypes, making apple scab populations increasingly resilient as the season progresses. Therefore, it is critical to control apple scab during the primary infection period. Control is typically managed through disease resistant cultivars and repeated fungicide applications. Both conventional and certified-organic orchards in the North Central and North Eastern regions of the USA use aggressive fungicide programs that involve 10- 20 fungicide applications for apple scab. V. inaequalis is well adapted to selecting and distributing virulence and fungicide resistance genes. The asexual phase of apple scab reproduction ensures that favorable genes are amplified and rapidly spread throughout an orchard. Resistance genes are then shared during the sexual reproductive phase, which increases the probability of producing better-adapted genotypes than those of the previous year. Apple scab has become resistant to several fungicidal classes (dodine, methyl benzimidazole carbamates, demethylation inhibitors, and quinone outside inhibitors), and has overcome resistant apple cultivars (Guerrin et al. 2007; Chapman et al. 2011). Negative impacts of fungicides and rapid loss of management tools to resistance makes development of alternative management practices for apple scab an important goal. Overwintering Vulnerability The primary infection window, which is the focus of chemical management strategies, is a relatively small percentage of the apple scab life cycle. Apple scab spends most of its life as a saprophyte of leaves on the orchard floor (Machardy et al. 2001). This presents a target window for cultural management tactics, called floor sanitation, that reduce the 6 amount of viable inoculum prior to the primary infection period. As early as the 1880s, growers recognized that destroying leaf litter with fire or plowing had an effect on apple scab disease incidence (Trelease 1884; Scribner 1888). The mechanism of these techniques is quite straightforward: burning leaves destroys Venturia tissues; plowing leaves physically obstructs the release of any spores that develop over winter by covering pseudothecia with soil. Though simple, these techniques have limited applicability to modern orchard systems. Burning litter requires considerable amounts of labor to remove leaves from the orchard to a safe location for a burn pile. Repeated applications of either inversion tillage or burning would likely have a negative impact on soil structure and fertility: burning leaves diverts a key source of carbon from incorporation into the soil (Haynes and Goh 1980; Tagliavini et al. 2007); inversion tillage accelerates carbon loss as a result of upregulating oxidative microbial metabolism, destroys soil aggregates, and disrupts the networks of soil pores and fungal hyphae (Kabir 2005; Olson 2015). Modern floor sanitation techniques focus on managing infected leaf material in situ. Several techniques have been investigated, including covering leaves with organic mulches or plastic films (Holb 2006 and 2007), shredding leaves with flail mowers or vacuum choppers (Sutton et al. 2000; Vincent et al. 2004; Holb et al. 2006) , inoculating leaves with antagonistic microbial cultures (Vincent et al. 2004), and applying nitrogen (Burchill et al. 1965; Burchill 1968; Crosse et al. 1968; Sutton et al. 2000; Beresford et al. 2000; Vincent et al. 2004; Holb et al. 2006; Rudiger et al. 2012). Each method has been demonstrated to be effective in reducing the incidence of fruit infections in at least one study, though determining the relative efficacy of these techniques is complicated by a lack of standardized metrics and comparisons across studies. The two techniques that have seen any degree of regular use within commercial apple production are leaf shredding and the application of nitrogen. This is likely the result of the ease of performing these techniques with equipment/materials commonly available on a farm. Of the two, application of nitrogen is the simplest, requiring only a basic sprayer to reach leaves in trees, the drive row, and the tree row. Achieving the same degree of coverage with leaf shredding requires either 7 specialized vacuum equipment or additional passes with a rotary collection tool to bring leaves into the drive row for processing with a mower. Despite the simplicity of mowing or spraying leaves with nitrogen, little is truly understood about how these methods reduce scab infections. Issues in Floor Sanitation A key barrier to industry-wide adoption of floor sanitation is the unpredictable nature of the technique. Burchill et al. (1965) reported greater than 97% reduction in ascospore discharge during the single observed season, yet Burchill (1968) reported between 45 and 59% reduction over three years. Holb et al. (2006) reported a 24 to 65% reduction in primary scab infections at one site over three years, but detected significant differences in only one of those years (33%) at another site in that same time period. A collective view of reported data on floor sanitation reveals considerable variation in field-scale responses related to measurements of function and efficacy. This variability presents a major challenge to the adoption of these techniques; it is difficult to convince growers to invest time, money, and equipment into techniques when the data does not provide a consistent picture of expected outcomes. Identification of Primary Drivers The current body of work on floor sanitation is largely empirical and offers little to no mechanistic explanation about how these techniques impact the life cycle of V. inaequalis. This lack of mechanistic knowledge severely hinders our ability to identify sources of variability and adapt floor sanitation practices to produce consistent outcomes. There is sufficient data to show that floor sanitation can have a practical and economically viable impact on apple scab infection. If we can identity the primary driver(s) that determine the efficacy of floor sanitation, and then evaluate how conditional parameters that vary between orchards and seasons impact those drivers, then floor sanitation can be made into a highly applicable management strategy. The identification of these primary drivers is likely to be an involved and challenging process. The body of work on floor sanitation suggests that multiple modes of action are acting on V. inaequalis, potentially with overlapping timelines. Regulation of Early Sexual Differentiation by Nitrogen The use of nitrogen as an eradicant was first proposed by Ross (1961), who discovered that V. inaequalis exhibited 8 pronounced sensitivity to a variety of nitrogen salts in culture. In particular, Ross described a reduction in the number of pseudothecia formed in response to elevated concentrations of nitrate salts and certain amino acids. Ross also showed that the timing of nitrogen application in relation to the initiation of hyphal growth of mixed mating types has an impact on the efficacy of nitrogen in suppressing pseudothecial development. Ross was involved with two additional studies that expanded on the variety of tested nitrogenous compounds and further demonstrated a differential response by Venturia with regard to nitrogen source (Ross and Hamlin 1965; Ross and Bremner 1971). In his 1961 paper, Ross postulated that detection of falling nitrogen concentrations in leaf litter as a result of leaching and decay may be part of the mechanism used by Venturia to time its sexual reproduction. Ross’ hypothesis is supported by work done on a model Ascomycetous fungi with a mating pattern similar to V. inaequalis. Nelson and Metzenberg (1992) showed that expression of genes responsible for early sexual differentiation in Neurospora crassa responded strongly to nitrogen conditions; most being expressed only under starvation conditions. Venturia undergoes sexual differentiation shortly after leaf abscission (Keitt and Jones 1926), which corresponds with the timing work from Ross (1961) and the application window empirically demonstrated as most effective for foliar urea floor sanitation (Burchill 1968). Thus, it is likely that nitrogen applications have a direct impact on sexual development gene expression in Venturia. However, it is not known how Venturia detects ambient nitrogen concentrations or what genetic pathways exist between nitrogen detection and regulation of sexual gene expression. While direct interaction of nitrogen with Venturia is likely a part of nitrogen-based floor sanitation, the strength and duration of its effect in the field remains unexplored. Interactions with Orchard Floor Biology Leaf breakdown involves members of multiple trophic and spatial scales, including invertebrates, fungi, and bacteria, that form a complex community network in which the development of any particular member is dependent on the activities of several others (Hättenschwiler et al. 2005). During its saprophytic phase, V. inaequalis 9 becomes a member of this community and is subject to the pressures associated with it. There is ample evidence that the growth of many soil fungi is regulated by mesofaunal grazing, mycoparasitism, antibiosis, and nutrient competition (Crowther et al. 2012). Additionally, there is considerable evidence that nutrient pulses and physical disturbances invoke changes in the structure of the soil community network (Yang et al. 2006; Hobie et al. 2012; Strohm 2015). That microbial organisms with the capacity to antagonize Venturia are present in leaf litter is clear. There has been an abundance of research studies in which bacteria, fungi, and yeast were cultured from apple leaves or apple leaf litter and screened against Venturia using in vitro arenas (Carisse and Dewdney 2002). Observations of these organisms confirm that they are capable of employing multiples modes of action against V. inaequalis (Heye and Andrews 1983; Benyagoub et al. 1988; Steyaert et al. 2003; Kohl et al. 2015). Changes of leaf litter bacterial populations from gram-positive to gram-negative dominant have been associated both with the addition of nitrogenous compounds and the suppression of ascospore discharge (Crosse et al. 1968; Meszka and Bielenin 2001; Rudiger 2012). Therefore, it is probable that the decomposer community responds to floor sanitation treatments in a way that creates unfavorable conditions for Venturia. While such studies stand as evidence for the presence and potential of antagonists, they provide little insight into their actual interplay with V. inaequalis under real world conditions. An isolate from a lab culture can arise from a single organism; that something can be cultured has little to with whether it is functionally abundant in the field. Furthermore, only a small percentage of microorganisms taken from environmental samples can be successfully cultured in vivo, which leaves open the possibility of key antagonists going undetected. Finally, these screening studies represent single points in time and space. Variance in time and space are highly relevant to characterizing orchard ecosystems, where phenological events occur on a cycle and plantings are located throughout the globe (Bardgett et al 2005). Cullen and Andrews (1984) showed that antibiotic production of the known V. inaequalis antagonist, Chaetomium globosum, varied 10 significantly with respect to location. Such findings bring to question whether floor community composition and responses to floor sanitation practices differ from orchard to orchard. If such differences do exist, can they explain some of the variability observed in the efficacy of floor sanitation? Is there a core network of functional or taxonomic groups responsible for antagonistic activity? Identification of such a network and knowledge of its response to floor sanitation practices in situ would go a long way towards improving our ability to control apple scab. First, knowledge of taxa that are actively antagonistic towards V. inaequalis under field conditions could drastically improve the development of augmentative biological control agents. It is not uncommon to encounter disconnects between in vitro assays and field-scale efficacy during screenings for biocontrol agents (Heye and Andrews 1983). Such experiments often involve preliminary culturing of multiple isolates, most of which end up yielding no practical utility. Prior knowledge of conserved, functional members of the orchard floor associated with observed reductions in primary apple scab incidences would allow for targeted collections of taxa that are much more likely to function effectively under a variety of field conditions. Second, we may be able to use our knowledge of functional organisms to modify management, cultural, and orchard design decisions in such a way as to favor their upregulation and provide pressure against V. inaequalis. Incorporating Invertebrates Arthropod Feeding Arthropod taxa are represented within every functional group and trophic level associated with the soil community (Faber 1991). Their feeding behavior is intimately associated with agricultural ecosystem services such as water filtration, water and nutrient holding capacity and biological control of pests that spend some or all of their time in the soil. For example, mechanical fragmentation of bulk organic residues by arthropod primary detritivores or shredders is the first step in cycling carbon back into the soil (Seastedt and Crossley 1984; Reiners 1972). Likewise, arthropod herbivores feeding on weed seeds, arthropod predators feeding on plant herbivores, and arthropod fungal grazers feeding on plant pathogens can provide some level of 11 pest control (Landis et al. 2005; Friberg et al. 2005; Mathews et al. 2004). Throughout these processes, arthropod digestion converts bound nutrients into bioavailable forms. Even the movement of arthropods throughout the soil profile is important: arthropod burrowing loosens the soil, improving hydrology and aeration. However, arthropod feeding can also have negative impacts on agroecosystem functionality. Unchecked herbivore populations cause considerable economic loss through both direct and indirect feeding on crops. Excessive fungal and microbial grazing stifles nutrient and carbon cycling rates. Thus, changes in arthropod community structure influence the type and degree of services provided by soil ecosystems. Furthermore, arthropod feeding behavior and population structure have been found to change in response to the environmental availability of nitrogen (Denno and Fagan 2003; Klironomos et al. 1992; Wikings and Grandy 2013; White 1984). Thus, it is logical to suspect that changes in soil arthropod communities would follow applications of urea to leaf litter, and that those changes may be associated with decreased survival of a ground-dwelling fungal pathogen. However, the only research currently connecting invertebrates to apple scab management is limited to a few observations of increased earthworm activity correlated with floor sanitation treatments (Raw 1962; Holb et al. 2006; Werner 2006; Rudiger et al. 2012). Arthropods in Winter Research on arthropod communities in temperate agroecosystems has focused predominantly on above ground communities during the growing season. Consequently, we know very little about the structure and function of winter soil arthropod communities in agroecosystems. However, research into natural soil ecosystems has revealed that many soil arthropods are capable of opportunistic winter activity during warming events (Korenko and Pekar 2010, Schmidt and Lockwood 1992, Elbadry 1973,; Hågvar and Hågvar 2011). Temperate winters are marked by fluctuating temperatures and insulating snowpack, which create microhabitats with relatively elevated temperatures. Therefore, it is likely that cold- adapted soil arthropods in temperate agroecosystems are engaging in opportunistic feeding activity throughout the winter months. 12 Paradigms in Orchard Management There are major divisions in modern agricultural management styles and how these styles impact soil communities. The vast majority of of global food production relies on synthetic agrochemicals to control pest populations, eliminate weeds, and maintain soil fertility (Zhang 2018; Lassaletta et al. 2014). However, organic-certified operations, are prohibited from using the vast majority of synthetic pesticides and fertilizers relying instead on cultural, physical and biologically based management practices (USDA NOP 2017). The biologically-derived pesticides used in organic operations tend to have shorter environmental half-lives than their synthetic counterparts, and there are few chemical herbicide options for organic growers. Thus, weeds are typically managed with tillage, mulching, and cover crops. The differences between conventional and organic management styles expose soil organisms in those respective systems to very different environmental conditions (Reganold and Wachter 2016). Influence of Management on Soil Communities Ground cover, chemical exposure, and mechanical disturbance are primary drivers in shaping soil communities in agricultural systems. Plants exert influence over soil ecosystems in multiple ways. First, plant structures are a major part of the physical habitat that soil organisms occupy. Complex habitats formed by mixed ground cover types are associated with increased arthropod abundance and sometimes species richness, particularly of predators (Bengtsson et al. 2005; Simon et al. 2010). Second, plants are the base of the soil food web. During the growing season, soil-dwelling organisms feed both on plant tissues and on root exudates. The importance of plants as a food extends into the winter season as well. Leaf litter and plant detritus are the principal material and energy sources available during that time. Chemical exposure occurs as a result of incidental contact with pesticides sprayed on crops, direct contact with pesticides applied to control soil pests, herbicides being sprayed on weeds, and the application of fertilizers. While the specific responses of soil ecosystems to chemical inputs are varied and highly complex, meta-analyses have revealed a negative correlation between soil arthropod diversity and synthetic chemical use, in general (Bengtsson et al. 2005; Simon et al. 2010). Mechanical disturbance of the soil is most often 13 realized through tillage. Tillage is an established practice for controlling weeds, managing soil texture, and incorporating soil amendments. The area, frequency, depth, and aggressiveness of tillage applications are all factors that differ across management styles and cropping systems and determine the impact of tillage on arthropod communities. Large and burrowing arthropod populations are consistently smaller in areas with tillage compared to untilled equivalents (Kladivko 2001). Decisions about how to manage pests, pathogens, weeds, and fertility are fundamental parts of how a farm is designed and operated and determine its management, arguably the most significant within-farm factor in shaping soil arthropod communities. The effect of management style on soil organism habitat is readily apparent in temperate orchards, particularly in the understory. Conventional orchards typically use broad-spectrum, pre- emergent and burndown herbicides to keep the under-canopy bare, whereas organic growers rely on mowing and/or tillage to reduce, but not necessarily eliminate ground cover. Because of this, organic orchards tend to have greater and more diverse floor flora than conventionally managed orchards. Changes in habitat complexity and composition can have significant effects on the structure of, and ecosystem services provided by, soil organism communities (Simon et al. 2010; Bengtsson et al. 2005, Bianchi et al. 2006). Orchards have some of the longest recropping intervals among temperate crops, which means that the effects of even small changes in the agroecosystem can compound in magnitude over time. This makes them receptive to ecosystem engineering aimed at enhancing realized ecosystem services, but also makes them vulnerable to ecosystem disservices such as the accumulation of pest, pathogen and weed populations. Favorable manipulation of agroecosystems in orchards is already practiced during the growing season, using trap crops to reduce pest pressures, and strategic plantings to enhance pollination and natural enemy predation (Wan et al. 2016; Klein et al. 2012; Holzschuh et al. 2012; Berndt et al. 2005). Development of these practices required a thorough understanding of the population dynamics of the organisms living within orchards. Similar opportunities to optimize orchard ecosystem functionality may be achievable through manipulation overwintering soil 14 communities. Unfortunately, our ability to apply ecosystem engineering approaches to the inter- season period is limited by our lack of knowledge regarding the identity and activity of the organisms during that time. Challenges & Technological Overview Taxonomic Assignment Soil arthropods are a diverse collection of organisms with ancient evolutionary lineages that have diverged into high specialized ecological niches. Thus, associations between ecological function and taxonomy quickly degrade along the gradient from species to higher taxonomic assignments. Considerable expertise and time are required to identify a diverse assemblage of arthropods morphologically. The difficulty of this task is further increased by the fact that a large fraction of soil arthropods are immature and/or exceptionally small in size. The interaction between soil arthropod distribution and experimental design further compounds this challenge. Soil arthropods tend to be heterogeneously distributed within a given spatial area, particularly during cold seasons. Obtaining sufficient replication to adequately represent communities or to test treatment effects requires multiple samples. A field scale experiment testing treatment effects can quickly overwhelm a research group’s ability to process those samples in a timely or cost-efficient manner. The complexities of this “taxonomic dilemma” are discussed in detail by Behan-Pelletier and Newton (1999). Metabarcoding In order to address the challenges of field scale exploration of soil arthropods, I turned to metabarcoding, an approach that combines high-throughput sequencing (HTS), molecular markers, and curated sequence libraries to assign taxonomy to bulk sample assemblages. Molecular methods have become the standard for taxonomic assignment and diversity analysis in microbial community studies (Roumpeka et al. 2017; Martínez‐Porchas and Vargas‐Albores 2017). Considerable effort has been employed to apply these methods to the exploration of arthropod communities, and the first generation of field-scale studies have just recently appeared in the literature (Arribas et al. 2016; Pedro et al. 2017; Toju and Baba 2018). 15 The results of these studies show that molecular approaches are able to greatly expand the capacity of researchers to characterize diverse and cryptic arthropod communities. Identification of Gut Contents The most obvious method of determining diet is to visually inspect gut contents after forced regurgitation or gut excision, looking for characteristic body fragments or structural components. Indeed, this approach has been used to in arthropod feeding behavior research (Sunderland 1975). However, accurately associating gut fragments with sources of origin requires considerable skill and time, and will likely include identifiable specimens. Furthermore, the diets of many insects, including hemipterans and several ant species (Fisher and Cover 2007), are liquid and cannot be identified visually. Modern gut content analysis uses molecular markers to indicate the presence/absence of a target food source inside the gut of individual specimens. A common approach is to use PCR primers that amplify a short and unique (80-120 bp) nucleic acid sequence to screen DNA extracted from the gut for the target. Successful amplification indicates the presence of that target. Real-time PCR instruments offer a high level of sensitivity and rapid sample throughput and have become a standard platform for gut content analysis (González-Chang et al. 2016). Gut content can be obtained in several ways. Direct excision of the actual gut or forced regurgitation of gut contents from living specimens produce the highest quality samples, but such methods are less feasible for small organisms like mites and Collembola. Another option that is commonly employed is to decontaminate the surface of each specimen, and then perform a DNA extraction on a whole specimen homogenate (Greenstone et al. 2012). It is then assumed that any non-host DNA in the extract has originated from inside the specimen. Usually, extractions are performed on multiple specimens sharing the same taxonomic classification and the response variable is the number of positive detections. AIM 1: Determine the role of burial and tillage in managing the overwintering grape berry moth AIMS AND OBJECTIVES population in vineyards 16 Objective 1: Determine the distribution of depths at which pupa are buried after a tillage pass Objective 2: Characterize the type and severity of damage resulting from a tillage pass Objective 3: Determine the relationship between damage caused by tillage and adult emergence in the overwintering generation Objective 4: Determine the relationship between burial depth and adult emergence in the overwintering generation AIM 2: Observe changes in invertebrate detritivore community composition on apple orchard floors in response to management strategies and tactics Objective 1: Develop a metagenomic approach to identify invertebrates in mixed samples drawn from soil and decaying apple leaves Objective 2: Determine if management strategy (organic vs. conventional) influences measures of ecological diversity, including richness and relative abundance Objective 3: Determine if the late-season application of urea influences common measures of ecological diversity, including richness and relative abundance AIM 3: Measure soil arthropod consumption of infected apple leaves during the winter using real- time PCR. 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TILLAGE REDUCES SURVIVAL OF GRAPE BERRY MOTH, PARALOBESIA VITEANA (LEPIDOPTERA:TORTRICIDAE), VIA BURIAL RATHER THAN MECHANICAL INJURY This Chapter appears in: Matlock, J.M., Isaacs, R. and Grieshop, M., 2017. Tillage Reduces Survival of Grape Berry Moth (Lepidoptera: Tortricidae), via Burial Rather Than Mechanical Injury. Environmental entomology, 46(1), pp.100-106. ABSTRACT The grape berry moth, Paralobesia viteana (Clemens), is key pest of vineyards in Eastern North America that overwinters as pupae in leaf litter on the vineyard floor. This presents an opportunity for tillage to disturb and/or bury the pupae, providing a potential non-chemical approach to control of this pest. Using a Lilleston-style rotary cultivator, we determined the distribution of pupae within the soil profile after single tillage passes, measured the type and severity of damage inflicted on pupae, and investigated how these effects on pupae influenced their survival. Survivorship of pupae recovered from the vineyard immediately after tillage and held until emergence was not significantly different from those recovered from an untilled control area, indicating little effect of mechanical damage on this pest. However, a single pass of the tillage implement buried three quarters of pupae under at least 1 cm of soil. A laboratory experiment to recreate these conditions resulted in significant increase in mortality when pupae were buried in more than 1 cm of sand. I conclude that 1) interference with adult emergence of diapausing pupae via burial is the primary mechanism by which tillage controls grape berry moth, and 2) efforts to optimize the impact of tillage on grape berry moth populations should focus on maximizing the number of pupae buried.I discuss the potential integration of tillage into different vineyard management systems to enhance pest management. Keywords: Integrated Pest Management, Cultural Control, Organic Viticulture 27 INTRODUCTION Paralobesia viteana (Clemens), the grape berry moth, is a direct pest of wild and cultivated grapes. It is multi-voltine, with potential for up to four generations per year in Michigan (Tobin et al. 2008). The moth oviposits almost exclusively on grape hosts (Isaacs et al. 2012), with egglaying on blossoms in the first generation and on maturing fruit during subsequent generations. A single larva can feed on multiple grape berries within a cluster, and their infestation later in the season can increase the risk of disease infections of clusters (Fermaud and Le Menn 1992). Mature larvae exit clusters to form cocoons from grape leaf flaps folded over with webbing, and eggs laid after a critical photoperiod of between 14 and 15 hours will develop into diapausing pupae that remain in cocoons until the start of the next growing season (Nagarkatti et al. 2001). The diapausing pupae overwinter on the ground because leaves containing the cocoons fall from the canopy and mix with the floor litter. Michigan is home to the second largest juice grape processing plant owned by Welch’s Foods Inc., and in 2015, juice grapes accounted for approximately 4850 harvested hectares that produced an estimated 60,100 tons of Concord and Niagara grapes valued at $19.3 million (USDA NASS 2016). High infestation rates of berries with grape berry moth will lead to rejection of entire truckloads, which have no other viable purchaser. Because the machine harvesting leaves little room for quality sorting, growers are may be faced with leaving areas with high infestation unharvested in order to avoid the risk of rejection and the associated loss. Michigan is also home to a burgeoning wine industry, contributing $300 million annually to the state economy through wine sales and related agrotourism (MGWIC 2015). Climate change predictions suggest that the Lake Michigan coastline will experience milder winters and warmer spring temperatures (Wang et al. 2012), which is expected to increase the number of grape berry moth generations reaching adulthood in the growing season (Tobin et al. 2008), and may increase grape berry moth pressure above economic thresholds in northern winegrape growing regions. 28 Conventional management of grape berry moth relies predominantly on rotating applications of broad spectrum insecticides (Isaacs et al. 2012). This style of chemical-dominated management has come under public scrutiny in recent years as a result of human health risks, deleterious impacts on the environment, and increasing levels of resistance development in pest populations (Pimental and Peshin 2014). Indeed, grape berry moth populations resistant to the broad spectrum carbamate insecticide, carbaryl, have been discovered along Lake Erie (Nagarkatti et al. 2002). Even modern insecticides with relatively little history of field use are encountering resistant insect populations. Recently, it was discovered that Lobesia botrana (Denis & Schiffermüller), a totricid pest in Europe similar to the grape berry moth, is developing resistance in parts of Italy to indoxacarb, a next-generation reduced-risk insecticide registered in 2000 (USEPA 2000, Civolani et al. 2014). Rising awareness of insecticide resistance along with increased restrictions on the use of broad spectrum insecticides has created a need to decrease reliance on chemical tactics and create more diversified and sustainable control systems. Tillage has a long history of use for managing weeds, and its reintegration into modern weed management programs has been recommended as a means of disrupting herbicide resistance development and decreasing dependency on chemical tactics (Norsworthy et al. 2012, Vencill et al. 2012). Tillage may be similarly integrated into pest management programs for control of insect pests that spend some portion of their life at or below the soil surface (Johnson et al. 1984, Seal et al. 1992, Chu et al. 1996, Baughman et al. 2015). Extension publications from the early 1900’s mention that growers in North East, PA were throwing furrows over the leaf litter beneath vine canopies in late fall or early spring to reduce grape berry moth emergence (Isley 1917). Rudimentary experiments reported in the same publication supported burial as an effective means of control. Recent work by Baughman et al. (2015) showed that burial of Cydia pomonella, a totricid pest of apples, under just 1 cm of sand completely inhibited emergence after diapause. Thus, the targeted use of tillage may provide a way for grape growers to manage grape berry moth populations while introducing diversity into vineyard pest management programs. 29 The overall aim of my study was to evaluate the efficacy of tillage in reducing survivorship of diapausing grape berry moth pupae. We had four specific objectives: 1) Determine the distribution of pupae within the soil profile after tillage; 2) Characterize the type and severity of damage inflicted on pupae by a tillage implement; 3) Determine the relationship between the damage caused by tillage and survivorship; and 4) Determine how burial depth affects survivorship of this pest. MATERIALS AND METHODS Insect Collection and Rearing Rearing procedures were based on methodology presented by Taschenberg (1969). Plastic containers (28Wx40Lx16H cm) were equipped with false bottoms made from coarse (5mm) metal mesh. Infested grape clusters were collected from three vineyards located in Van Buren County, MI on September 3, 2014. The grapes were placed on top of the false bottom after lining the container with 2 cm wide strips cut from clear plastic storage bags. The strips were used to simulate leaf litter and to provide a substrate for pupation. The containers were placed in a temperature-controlled room maintained at 22ºC with a 16:8 hour light cycle. Rearing containers were inspected biweekly for the presence of pre-pupae, which dropped from clusters, passed through the metal screen, and formed easily identified pupal casings in the plastic litter. The plastic around each pupal casing was cut away and the pupae were stored at 7.2°C in 60 ml plastic portion cups along with a piece of moistened dental wicking to maintain humidity. Wicking was remoistened as needed. Pupae were collected over an 8 week period. Tillage Depth On November 6, 2014 pupae were placed on the ground beneath the canopy of grapevines in a vineyard row, and treated with a single pass of a Wonder Weeder double-gang rotary cultivator (Harris Manufacturing, Burbank WA). Pupae were then excavated from the soil profile and the depth was noted for each recovered pupa. Recovery of small insects from a disturbed soil profile can pose a significant challenge. To solve this, we first coated pupae with blue Luminous Powder (Bioquip, Rancho Dominguez 30 CA). The powder left streaks in the soil that stood out visually and aided in tracking the path and location of each pupa. A point frame with a 4 x 15 grid of 10 x 10 cm squares was used to aid in even placement of pupae and their relocation. Using this combination of techniques helped to ensure thorough excavation and provided a standardized point of reference for depth measurements. Four point frames were placed on one side of the grape row, one for each replicate, spaced 5m apart. Each pupa was placed in the center of a grid cell. After placement, the locations of the point frames were marked with flags and then the frames were removed. After the tillage pass, the point frames were returned to their original positions. Each cell was carefully excavated with small spade trowels to recover buried pupae. The depth of a recovered pupa relative to the center of the plane of the cell above it was noted along with the distance from each edge of the cell to the soil surface. Burial depth was recorded as the difference between the depth of the pupae and the average distance to the soil surface. Any pupa recovered from the surface was assigned a burial depth of zero. Some pupae were recovered outside of the original placement area, in which case the point frame was shifted to recover depth information. Each recovered pupa was placed in a separate container, returned to the lab, and stored at room temperature with a piece of moistened dental wicking. We recovered >90% of pupae from each replicate. A control treatment which did not receive a tillage pass was established in an identical fashion on the opposite side of the row. Forty pupae were assigned to each control replicate and 50 pupae to each tillage replicate. The additional pupae in the tillage treatment were added to account for the likelihood of incomplete recovery. Tillage Damage & Survival Every pupa recovered from the tillage depth experiment was inspected and assigned a score for damage from one of four categories: tears in the plastic wrapping, peeling of the plastic wrapping, punctures/lacerations of the silken cocoon, or crushing/laceration of the pupa body (see Table 2.1). A single investigator performed all of the scoring to ensure consistency. After assessing the damage resulting from tillage, pupae were 31 placed in a growth chamber to simulate overwintering, beginning on November 24, 2014, with a 12:12 F:D cycle and 80% RH. Temperature was initially set to 20°C, lowered weekly by 5°C, and then maintained at 5°C until February 3, 2015. The pupae were then warmed back to 20°C using the same schedule. Pupae were checked weekly for emergence after the start of the warming period, and until there were two consecutive weeks without emergence. Fisher’s Exact Tests were performed to determine whether there were differences in the severity of damage for each observed damage type, and also to determine whether survivorship was different between the tillage and control groups. All analyses were performed in SAS 9.4 using PROC FREQ (SAS Institute Inc, Cary NC). Burial Depth & Survival Pupae allocated to this experiment were combined into a single container and maintained at room temperature until November 24, 2014 at which time they were transferred to the same growth chamber as the pupae used in the experiment described above, and managed identically. Immediately after warming, pupae were assigned to one of five burial treatments: 0, 1, 3, 5, and 15 cm depth burial. Each treatment was replicated five times and each replicate utilized 18 pupae. Experimental arenas consisted of a Schedule 40 PVC tube with a 15.3 cm internal diameter mounted to a piece of plywood with 0.158 cm holes drilled into the capped bottom for drainage. A piece of landscape fabric lined the bottom and 8 cm of sand was placed on top of the fabric. The sand used in the experiment was play sand (Home Depot Inc.). The sand was rinsed with three times its volume of distilled water to remove any salts and allowed to air dry to aid in ease of handling. After drying, the sand was autoclaved for 8 hours, maintained at room temperature inside the autoclaved container, and then autoclaved again for an additional 8 hours to destroy any germinating spores. The pupae were evenly distributed on the surface of the sand and then covered with an additional layer of sand whose thickness corresponded to the particular burial treatment. The sand was then slowly wetted with distilled water until runoff was observed from the drainage holes. A fine, see-through mesh was placed over the top of the arenas and held in place with a rubber band. The arenas were allowed to drain and then transferred to a 32 growth chamber at 28 °C with 50% RH and a 16:8 L:D cycle. The arenas were checked daily for emerged adult grape berry moth, which were counted and then removed. Arenas were checked until no emergence was observed for two consecutive weeks. Survivorship was expressed in terms of the proportion of pupae in each experimental unit that emerged over the course of the experiment. Survivorship values were arcsine transformed to achieve a normal distribution and homogeneity of variance was confirmed using Levene’s Test at α=0.05. Treatment effects were tested using a one-way ANOVA and multiple comparisons were performed using Fisher’s Protected LSD at α=0.05. A contrast statement was used to test the overall significance of burial compared to the control group. All analyses were performed in SAS 9.4 using PROC Mixed (SAS Institute Inc, Cary NC). RESULTS AND DISCUSSION Mechanical Effects on Pupae We found examples of damage fitting each of the four categories and did not encounter any damage that was not classifiable within those categories. The vast majority ( >94%) of pupae in both treatments were undamaged with regard to tears, punctures, or lacerations. Approximately 40% of the pupae exhibited some degree of peeling of the plastic wrapping. However, damage to pupae was not significantly different between the tillage and control groups in any of the categories (Table 2.2), suggesting that most of this damage occurred as a result of handling. Furthermore, survivorship of pupae recovered from the field immediately after a tillage pass was not significantly different than those recovered from an untilled control group (Χ2 = 2.25; df = 1; P = 0.148). These results are consistent with other research on tillage of surface-dwelling pests where little mechanical damage was inflicted in spite of a considerable alteration in the distribution of the organisms within the soil profile (Stinner and House 1990, Baughman et al. 2015). Thus, a tillage implement is unlikely to impart levels of damage that will lead to reduced survival of this pest and reduced crop infestation. Burial Effects on Pupae A single pass of a rotary tillage implement buried three quarters of pupae under at least 1 cm of soil (Fig. 2.1). Less than 3% of pupae were recovered from a 33 depth greater than 6 cm, suggesting that this is the practical depth limit for this style of tillage. The laboratory burial study revealed that covering pupae by at least 1 cm of sand in otherwise idealized conditions resulted in a significant increase in mortality (F = 87.86; df = 1,20; P<0.0001) compared with the unburied control. No significant differences were found in mortality between the 1, 3, and 5 cm depth treatments (Fig. 2.2). From these results it can be concluded that interference with adult emergence of the diapausing cohort via burial is the primary mechanism by which tillage controls grape berry moth. We also conclude that burial depth is not a critical factor affecting adult emergence; thus we suggest that efforts to optimize the impact of tillage on grape berry moth populations should focus on maximizing the proportion of pupae buried, regardless of depth. Incorporating Tillage into Vineyard Management It is important to acknowledge that the identification of an effective mechanism for reducing a pest population is only one part of a viable pest control strategy. It is also necessary to understand how an effective technique can be implemented into existing cultural practices and adapted to the realities of a particular agroecosystem before it can be regarded as a practical solution to a pest problem. The efficacy of tillage is highly dependent on identifying exploitable windows of vulnerability within pest life cycles and behaviors, and determining if it is practical to apply tillage within a cropping system during those windows (Wilson and Eisley 1992, Seal et al. 1992, Chu et al. 1996, Baughman et al. 2015). The nature of these points and methods for their exploitation varies considerably between pest-crop complexes. For grape berry moth, this means considering the seasonal and spatial dynamics of vineyard infestation and how tillage, a resource intensive operation, can be applied in a cost-effective manner. Application of Tillage in Conventional Vineyards One efficient way of incorporating tillage into conventional systems is to optimize the timing of other tillage-based vineyard practices to coincide with the susceptibility window of the GBM overwintering population. Two overlapping periods of opportunity to use tillage occur in typical vineyard management: late fall and early 34 spring. In late fall, growers may use tillage to incorporate slow-release amendments into soil or form a berm under the vine to protect cold sensitive graft unions (Weigle and Carroll 2015). In the early spring, cultivation is recommended as a means of reducing vineyard pathogen loads by burying potentially inoculated debris, and controlling weeds as they begin to emerge. For many conventional growers, this may still involve a significant overhaul of other management strategies (weed and soil fertility management in particular) in order to justify the purchase of the required tillage equipment. However, as seen with insect pests and insecticides (Nagarkatti et al. 2002), continued applications of broad-spectrum herbicides has led to the development of resistance in major weed species across the majority of herbicide chemistries (Heap 2016). Therefore, it may be cost-effective over the long run for growers to diversify both their weed and pest management strategies by including tillage. Tillage could also be used to further advance recent efforts to focus GBM spray programs on protecting border rows instead of entire vineyards. Multiple trapping studies have shown that infestation of conventional vineyards by GBM is an annual cycle in which a small portion of a standing population present in surrounding wood lots migrates into the vineyard, moving from border rows into the vineyard interior as the season progresses (Johnson et al. 1988, Hoffman and Dennehy 1989, Biever and Hostetter 1989, Trimble et al. 1991, Botero-Garces and Isaacs 2004). Border-focused pest control works by knocking down the interior pest population early in the season with a single, vineyard-wide insecticide application and then preventing pest migration from woodlots by making repeated applications of targeted insecticides to border rows. This style of control reduces overall application volumes, making the use of high-specificity insecticides an affordable option and decreasing the likelihood of detecting insecticide residues in harvested fruit. Mason et al. (2016) conducted the first field-scale evaluation of border-focused GBM management. They found no significant differences in GBM damage within vineyard interiors between vineyards managed with the new IPM program and those sprayed across the entire 35 vineyard. Tillage could potentially replace the start of season, whole vineyard, pesticide application in a border-focused IPM program. Application of Tillage in Organic Vineyards Organically managed vineyards are well- suited to adopt tillage-based GBM management strategies. Herbicides compliant with the USDA National Organic Program are either prohibitively expensive or lack efficacy to reliably manage ground cover (Liebman and Davis 2009). Therefore, organic-certified growers rely heavily on tillage/cultivation when suppression of ground cover competition is required. This means that organic operations are more likely to possess the equipment and training necessary to cultivate within and between vine rows. It also means that organic growers are already making multiple passes through the vineyard each season. Organic viticulturalists are also limited in their ability to control pest outbreaks once they have exceeded economic thresholds because of the limitations placed on using synthetic compounds. The 2015 Cornell Production Guide for Organic Grapes specifically states that “pesticides should not be relied on as a primary method of pest control” (Weigle and Carroll 2015). Chemical methods of controlling GBM in organic vineyards are based largely on Bacillus thuringiensis toxins (Bt) and pheromone mating disruption (Martinson 1995, Teixeira et al. 2010, Weigle and Carroll 2015). As a gut toxin, Bt has a very short window of efficacy because it is applied to the surface of leaves and clusters and therefore must be consumed prior to a larvae entering a berry. Bt also has a short half-life, persisting only a few days on fruit and a few weeks in soil in the vineyard (Sanahuja et al. 2011, Hung et al. 2016). Mating disruption is not directly lethal and therefore has no persisting toxicological impact on GBM. The infestation cycle of GBM in organic vineyards is likely quite different than that of conventional vineyards. The nature of organic pest management is such that the vineyard is free of the top-down chemical pressures associated with continued use of synthetic insecticides and therefore may be suitable for sustaining a standing population of GBM. The dynamics of GBM populations and infestation cycles in organic vineyards has yet to be investigated. However, if organic vineyards do support 36 standing GBM populations, then vineyard-wide tillage to disrupt the overwintering GBM cohort could have a considerable impact on GBM pest pressure in these systems. CONCLUSIONS I have shown that burial under even minimal amounts of soil substrate interferes with the successful emergence of diapausing grape berry moth. I also demonstrated that tillage is not effective at directly imparting damage to pupae in the field, which is consistent with previous studies. Organic grape growers are well positioned for adoption of tillage for management of grape berry moth, requiring only small changes to existing cultural practices. Conventional growers are likely to encounter short-term barriers to adoption related to the purchase of specialized tillage equipment and adjustment of management strategies. However, conventional growers are also in a position to incorporate tillage into next-generation IPM programs based on border-applied, high-specificity insecticides. A more thorough understanding of the relationship between vineyard management styles and grape berry moth population dynamics will assist in the development of diversified and sustainable pest control strategies. 37 APPENDIX 38 APPENDIX 2.A. Figures and Tables 39 Table 2.1. Criteria for Qualitative Damage Assessment Scores of Grape Berry Moth Pupae that were Subjected to Tillage with a Rotary Cultivator SCORE Torn Wrapping Peeled Wrapping 0 1 2 3 Not Present Multiple pin-sized A single pin-sized punctures or a puncture single large puncture >50% torn Not Present <33% opened 33 – 66% opened >66% opened Cocoon Not Present Multiple pin-sized A single pin-sized punctures or a puncture single large puncture >50% torn Pupa Body Not Present Present - - 40 Table 2.2. Comparing the Occurrence of Various Damage Types in Grape Berry Moth Pupae Subjected to a Tillage Pass with a Rotary Cultivator to an Uncultivated Control Group. Test statistics are reported for Fischer’s Exact Test. Pupae Counted by Damage Class Torn Wrapping Peeled Wrapping Group Tillage Control Test Stats Group Tillage Control Test Stats 0 173 1 7 2 3 (94.5%) (3.8%) (1.6%) 152 5 (96.8%) (3.2%) Chi- Square df 0 3 0 0 0 122 1 23 2 12 3 26 (65.2%) (12.3%) (6.4%) (13.9%) 96 32 9 20 (61.1%) (20.4%) (5.7%) (12.7%) Chi- Square df Sample Size P value Sample Size P value 2.7179 2 340 0.2569 3.8189 3 340 0.2855 Puncture/Tear of Cocoon Pupae Crushing & Laceration 0 177 1 4 2 1 3 1 Not Present Present 181 2 (96.7%) (2.2%) (0.05%) (0.05%) (98.9%) (1.1%) 152 5 (96.8%) (3.2%) Chi- Square df 0 0 157 (100%) 0 Sample Size P value Chi- Square df Sample Size P value 2.0345 3 340 0.9239 1.717 1 340 0.5017 41 Figure 2.1.Distribution of Grape Berry Moth Pupae within the Soil Profile after a Tillage Pass with a Rotary Cultivator 42 Figure 2.2. 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Journal of Climate, 25(4), pp.1318-1329. Weigle, T. and Carroll, J. (eds), 2015. Production guide for organic grapes. new york state integrated pest management program. Ithaca, NY. 74 pages. Willson, H. R., and Eisley, J. B., 1992. Effects of tillage and prior crop on the incidence of 5 key pests on ohio corn. J. Econ. Entomol, 85, 853–859. 47 CHAPTER 3. Overwintering soil arthropod communities in an orchard under organic and conventional management ABSTRACT Within-orchard management strategies can result in pronounced differences in the structure of soil arthropod habitat and the exposure of those organisms to chemicals. The composition of soil arthropod communities, and the ecosystem services derived from those communities, have been shown to respond to differences in management strategies during the growing season. Little is known about how such management differences impact overwintering arthropods or the ecosystem services they may be providing during the overwintering period. In temperate climates, the period between growing seasons can be a considerable portion of the calendar year. I inventoried epigeic arthropods from leaf fall through green tip in an orchard with both organically and conventionally managed trees using a metabarcoding approach to assign family-level taxonomy and calculate relative abundances. The primary result of this study was data suggesting that both organic and conventional orchards host a diverse, winter active arthropod community. Analysis of the relative abundance data showed no differences in family richness or alpha diversity. There was also considerable overlap in the dominant families detected under both management strategies. However, relative abundance values within those families differed between management strategies. When I explored changes in relative abundance values with respect to feeding guilds, I found that they were concentrated in the secondary and tertiary decomposer sub guilds. My work provides one of the first holistic descriptions of a winter soil arthropod community in a temperate agroecosystem, and the impact of management strategies on that community. KEYWORDS: Detritivores, Molecular Ecology, Perennial Agroecosystem 48 INTRODUCTION Research on arthropod communities in temperate agroecosystems has focused predominantly on above ground communities during the growing season. Consequently, we know very little about the structure and function of winter soil arthropod communities. However, research into natural soil ecosystems has revealed that many soil arthropods are capable of opportunistic winter activity during warming events. Temperate winters are marked by fluctuating temperatures and insulating snowpack, which create microhabitats with relatively elevated temperatures. Therefore, it is likely that cold-adapted soil arthropods in temperate agroecosystems are engaging in opportunistic feeding activity throughout the winter months. Arthropod feeding behavior is intimately associated with agricultural ecosystem services such as water filtration, water and nutrient holding capacity and biological control of pests that spend some or all of their time in the soil. For example, mechanical fragmentation of bulk organic residues by arthropod primary detritivores or shredders is the first step in cycling carbon back into the soil (Seastedt and Crossley 1984; Reiners 1972). Likewise, arthropod herbivores feeding on weed seeds, arthropod predators feeding on plant herbivores, and arthropod fungal grazers feeding on plant pathogens can provide some level of pest control (Landis et al. 2005; Friberg et al. 2005; Mathews et al. 2004). Throughout these processes, arthropod digestion converts bound nutrients into bioavailable forms. Even the movement of arthropods throughout the soil profile is important: arthropod burrowing loosens the soil, improving hydrology and aeration. However, arthropod feeding can also have negative impacts on agroecosystem functionality. Unchecked herbivore populations cause considerable economic loss through both direct and indirect feeding on crops. Excessive fungal and microbial grazing stifles nutrient and carbon cycling rates. Thus, changes in arthropod community structure influence the type and degree of services provided by soil ecosystems. Ground cover, chemical exposure, and mechanical disturbance are primary drivers in shaping soil communities in agricultural systems. Plants exert influence over soil ecosystems in 49 multiple ways. First, plant structures are a major part of the physical habitat that soil organisms occupy. Complex habitats formed by mixed ground cover types are associated with increased arthropod abundance and sometimes species richness, particularly of predators (Bengtsson et al. 2005; Simon et al. 2010). Second, plants are the base of the soil food web. During the growing season, soil-dwelling organisms feed both on plant tissues and on root exudates. The importance of plants as a food extends into the winter season as well. Leaf litter and plant detritus are the principal material and energy sources available during that time. Chemical exposure occurs as a result of incidental contact with pesticides sprayed on crops, direct contact with pesticides applied to control soil pests, herbicides being sprayed on weeds, and the application of fertilizers. While the specific responses of soil ecosystems to chemical inputs are varied and highly complex, meta- analyses have revealed a negative correlation between soil arthropod diversity and synthetic chemical use, in general (Bengtsson et al. 2005; Simon et al. 2010). Mechanical disturbance of the soil is most often realized through tillage. Tillage is an established practice for controlling weeds, managing soil texture, and incorporating soil amendments. The area, frequency, depth, and aggressiveness of tillage applications are all factors that differ across management styles and cropping systems and determine the impact of tillage on arthropod communities. Large and burrowing arthropod populations are consistently smaller in areas with tillage compared to untilled equivalents (Kladivko 2001). Decisions about how to manage pests, pathogens, weeds, and fertility are fundamental parts of how a farm is designed and operated and determine its management, arguably the most significant within-farm factor in shaping soil arthropod communities. There are major divisions in modern agricultural management styles and how these styles impact soil communities. The vast majority of global food production relies on synthetic chemicals to control pest populations, eliminate weeds, and maintain soil fertility (Zhang 2018; Lassaletta et al. 2014). However, organic-certified operations, are prohibited from using the vast majority of synthetic pesticides and fertilizers relying instead on cultural, physical and biologically based 50 management practices (USDA NOP 2017). The biologically-derived pesticides used in organic operations tend to have shorter environmental half-lives than their synthetic counterparts, and there are few chemical herbicide options for organic growers. Thus, weeds are typically managed with tillage, mulching, and cover crops. The differences between conventional and organic management styles expose soil organisms in those respective systems to very different environmental conditions (Reganold and Wachter 2016). The effect of management style on soil organism habitat is readily apparent in temperate orchards, particularly in the understory. Conventional orchards typically use broad-spectrum, pre- emergent and burndown herbicides to keep the under-canopy bare, whereas organic growers rely on mowing and/or tillage to reduce, but not necessarily eliminate ground cover. Because of this, organic orchards tend to have greater and more diverse floor flora than conventionally managed orchards. Changes in habitat complexity and composition can have significant effects on the structure of, and ecosystem services provided by, soil organism communities (Simon et al. 2010; Bengtsson et al. 2005, Bianchi et al. 2006). Orchards have some of the longest recropping intervals among temperate crops, which means that the effects of even small changes in the agroecosystem can compound in magnitude over time. This makes them receptive to ecosystem engineering aimed at enhancing realized ecosystem services, but also makes them vulnerable to ecosystem disservices such as the accumulation of pest, pathogen and weed populations. Favorable manipulation of agroecosystems in orchards is already practiced during the growing season, using trap crops to reduce pest pressures, and strategic plantings to enhance pollination and natural enemy predation (Wan et al. 2016; Klein et al. 2012; Holzschuh et al. 2012; Berndt et al. 2005). Development of these practices required a thorough understanding of the population dynamics of the organisms living within orchards. Similar opportunities to optimize orchard ecosystem functionality may be achievable through manipulation overwintering soil communities. Unfortunately, our ability to apply ecosystem engineering approaches to the inter- 51 season period is limited by our lack of knowledge regarding the identity and activity of the organisms during that time. Explorations of soil communities have been hindered by a number of technical and logistic challenges. First among these difficulties is what Behan-Pelletier and Newton (1999) describe as the “taxonomic dilemma.” Soil arthropods are a diverse collection of organisms with ancient evolutionary lineages that have diverged into high specialized ecological niches. Thus associations between ecological function and taxonomy quickly degrade along the gradient from species to higher taxonomic assignments. Considerable expertise and time are required to identify a diverse assemblage of arthropods morphologically. The difficulty of this task is further increased by the fact that a large fraction of soil arthropods are immature and/or exceptionally small in size. The interaction between soil arthropod distribution and experimental design further compounds this challenge. Soil arthropods tend to be heterogeneously distributed within a given spatial area, particularly during cold seasons. Obtaining sufficient replication to adequately represent communities or to test treatment effects requires multiple samples. A field scale experiment testing treatment effects can quickly overwhelm a research group’s ability to process those samples in a timely or cost-efficient manner. In order to address the challenges of field scale exploration of soil arthropods, I turned to metabarcoding, an approach that combines high-throughput sequencing (HTS), molecular markers, and curated sequence libraries to assign taxonomy to bulk sample assemblages. Molecular methods have become the standard for taxonomic assignment and diversity analysis in microbial community studies (Roumpeka et al. 2017; Martínez‐Porchas and Vargas‐Albores 2017). Considerable effort has been employed to apply these methods to the exploration of arthropod communities, and the first generation of field-scale studies have just recently appeared in the literature (Arribas et al. 2016; Pedro et al. 2017; Toju and Baba 2018). The results of these studies show that molecular approaches are able to greatly expand the capacity of researchers to characterize diverse and cryptic arthropod communities. 52 The overall aim of my study was to inventory the major epigeic arthropod taxa present in an orchard during the interval between harvest and early spring, a group and time that has been relatively unexplored. Within this aim I had two applied objectives: 1. Evaluate the performance of my molecular approach using an internal arthropod community standard 2. Determine and compare the biodiversity of overwintering epigeic arthropods in orchard blocks under organic and conventional management regimes MATERIALS AND METHODS Experimental Design The experiment was conducted in a 16.2 ha apple orchard located near Pottersville, MI (42.6292° N, 84.7389° W) which contained a 4 ha block of organically managed trees. A total of twelve 0.12 ha plots were established for sampling: 6 within the organically managed trees and 6 within conventionally managed trees. Both organic and conventional treatments were managed with grower standard practices for those respective management strategies. The replicates were established in scab-susceptible varieties found in both management types: MacIntosh, Jonagold, Red Delicious, and Golden Delicious. Key differences between the two management styles included: 1) understory weed management with rotary cultivation in the organic and with burndown herbicides in the conventional; and 2) pest management in conventional using of synthetic pesticides applied according to label and best- management practices. Field Sample Collection and Processing Samples were collected on eight dates: 11.28.16, 12.09.16, 12.16.16, 12.29.16, 1.11.17, 2.22.17, 3.28.17, and 4.21.17. On each date, four 33x33 cm areas were randomly subsampled from the dripline of trees within the central rows of each experimental plot. Sample locations were marked to prevent resampling on future dates. A subsample consisted of all leaf litter, above-ground biomass, and the top 1 cm of soil. Subsamples from each experimental plot were homogenized by gentle mixing in a bucket before transferring two 1L aliquots to Berlese funnel extraction devices to isolate arthropods from the 53 substrate matrix. Each 1L aliquot was transferred to a separate Berlese funnel. The funnels were heated from the top with 40W incandescent bulbs placed 10cm from the sample surface. The arthropods were collected and preserved in pure propylene glycol over a 120 hour period. A dimmer switch was used to reduce bulb outputs to 10W for the first 24 hours, 20W for hours 24 through 48, and 40W for the remaining time. After collection, samples were stored at -20°C for the remainder of the study. DNA Extraction Samples were homogenized in a FastPrep 24 for 45 sec at 5.5 m/s after the addition of 1 uL of 2-mercaptoethanol and 5 ul of 10 mg/mL Proteinase K, and then incubated for 1 hour at 56°C. The samples were extracted with two 500 uL washes of 24:1 chloroform:isoamyl alcohol, transferring the aqueous layer to a new container each time after a 5 minute centrifuge spin at 15,000 rcf. DNA was precipitated with the addition of 500 uL chilled isopropanol. Following a 30 minute incubation at -20°C, the DNA was pelleted with a 10 minute centrifuge spin at 17,000 rcf. The supernatant was discarded, and the pellet was triple-rinsed with chilled 70% ethanol to remove salts. The pellet was dried at 45°C. and resuspended in 100 uL of PCR-grade water, The resuspended DNA processed with the OneStep PCR Inhibitor Removal Kit from Zymo Research to avoid downstream complications with DNA sequencing resulting from carryover of contaminants from soil particles. Endpoint PCR A 313 bp region of the Cytochrome Oxidase Subunit 1 gene (CO1) was amplified using the primer set developed by Leray et al. (2013). The primers were modified to include Fluidigm CS1 and CS2 oligos on their 5’ ends to facilitate multiplex sequencing. Amplification was performed in 50 uL reactions containing 1 ug/uL of dsDNA template, 0.4uM of forward and reverse primers, and 25 uL of PCRBIO HS Taq 2x Master Mix. Reaction conditions were: an initial 95°C activation step of 2 minutes, followed by 40 cycles of 15 s at 95°C, 15 s at 58°C, and 30 s at 72°C. Reactions were chilled to 4°C after a final elongation step of 2 min at 72°C. Amplification was confirmed by running 5 uL of PCR product on a 1.5% TBE agarose gel stained with GelRed (Biotium) for 90 minutes at 90V. 54 Amplicon Sequencing Sequencing was performed at the Michigan State University Research Technology Support Facility (RTSF). Samples were multiplexed at 3.7 - 5% of a lane per sample. The RTSF added dual indexed, Illumina compatible adapters via PCR with primers targeting the Fluidigm oligo ends of the primary amplicons. Each pool was loaded on an Illumina MiSeq v2 Standard flow cell, and sequencing was performed in a 2x250 bp paired end format using a MiSeq v2 500 cycle reagent cartridge. Sequence Data Processing Data from RTSF were received with the Fluidigm oligos and Illumina adapters removed from the amplicon sequences. Sequencing errors were corrected with Bayes Hammer (Nikolenko et al. 2013). Corrected reads were assembled and pre-clustered using IPED, a two-step denoising algorithm developed specifically for Illumina paired end reads. IPED uses the base call quality data in FastQ files to mark low-quality positions during contig assembly, which are then masked in a downstream clustering step (Mysara et al 2016). Low quality contigs were removed prior to the pre-clustering step using the Mothur pipeline (Schloss et al. 2009). Contigs were screened to remove any with ambiguous bases, more than 10 homopolymers, or a difference in length from the expected amplicon of more than 2 bases. Retained contigs were then aligned against a reference alignment of arthropod CO1 sequences. Sequences were obtained from the NCBI non-redundant nucleotide library, and were limited to the full-length Folmer region (Folmer et al. 1994). Contigs that aligned outside of the target region were removed. After denoising with IPED, chimeras were removed with the vsearch algorithm implemented in Mothur. The bayesian clustering algorithm CROP was used to generate OTUs with lower and upper thresholds of 2.5 and 3.5%, respectively (Hao et al. 2011). Taxonomy was assigned to OTUs by performing BLAST searches of representative sequences and retrieving classifications from the NCBI taxonomy database using the R package taxonomizr V 0.2.2 (Sherrill-Mix 2017). OTUs that were less than 0.01% relative abundance per sample prior to rarefaction, or missing family level assignments, were removed to reduce the impact of sequencing artifacts. Read counts for each family were combined by sample, with the 55 exception of ants (Hymenoptera: Formicidae). Read counts for ants were combined at the genus level because of the broad functional diversity that exists within the family. Each family (or genus in the case of ants) was assigned a feeding guild based on observed feeding activity reported in literature. Feeding guild classes were derived from research on trophic levels in soil arthropods using stable nitrogen and carbon isotope analysis (Schneider et al. 2004; Chahartaghi et al. 2005; Oelbermann and Scheu 2010; Lagerlöf 2017 et al. 2017; Melguizo-Ruiz et al. 2017) which suggests that decomposers can be divided into three general classes: primary, secondary, and tertiary. Additional categories were created for herbivores, predators, omnivores, and parasites. The parasite category encompassed any organism likley to have been occupying a host during the study period, including parasitoids. Feeding guild assignments for each family and the sources supporting those assignments are available in Appendix 3.B. Internal Community Standard I created a diverse, standardized mixture of DNA extracted from identified arthropods to assist in selecting parameters for the bioinformatics pipeline, and to evaluate the degree of PCR bias on invertebrate community reconstruction. The mixture was comprised of 41 specimens covering 33 genera, 25 families, 14 orders, 4 classes (Fig 3.1). The specimens were obtained from field collections, laboratory colonies, biocontrol products, and reptile feed companies. DNA was extracted using the protocol described above. Multiple individuals were used for each taxon. Specimens were rinsed for 90 seconds with 2.5% NaOCl to remove foreign nucleic acids and materials prior to DNA extraction. PCR amplification of individual DNA extracts under the reaction conditions described above was confirmed prior to pooling equimolar quantities of DNA to create the community standard. PCR product of the pooled community standard was included as a sample in each of the 2016-2017 sequencing runs. Mean read counts were compared against the expected count distribution at the ordinal, family, and genus levels. Particular attention was paid to the number of “unexpected“ taxonomic assignments (assignments made to organisms not actually present in the community standard). 56 Data Normalization I normalized read counts within each replicate, stratified by date. They were equalized by resampling OTU read counts to a subsample size equal to the lowest original total read count within each date. Resampling was done using the rarefaction function in Vegan v.2.4.3 (Okansen et al. 2007). Data Analysis A Bray-Curtis distance matrix was generated using the normalized read counts for each collapsed taxon. PERMANOVA was performed on the matrix using the adonis function in Vegan v.2.4.3 to assess the statistical significance of management on community composition (Anderson 2001). Permutations were stratified by experimental unit to account for the within subject effect of repeated measurements. A blocking factor was included to account for the effects of location within the orchard and apple tree cultivar. Multivariate dispersion around the management centroids was also compared using the betadisp function in Vegan v.2.4.3. Alpha diversity values were calculated for each comparison with a taxa-neutral approach using Entropart v.1.4 (Marcon and Hérault, 2015) to explore the effect of management on within- community diversity. Alpha diversity was expressed as effective taxa numbers (Chao et al. 2014). Values were calculated for diversity orders across the range of q=(0,2) where q=0 corresponds to taxonomic richness, q=1 corresponds to Shannon’s diversity, and q=2 corresponds to the Inverse Simpson’s diversity. The influence of dominant species on diversity values increases with the value of q. A univariate linear model was fit to relative abundance values for each taxon and each feeding guild with date and management as fixed effects using the gls function in the nlme v.3.1 package. A logit transformation was applied to the relative abundance values to achieve normality assumptions (Warton and Hui 2011). The variance between dates did not meet homogeneity assumptions for a common variance model, so separate variance terms were included for each date. Relative abundance values for the parasite and herbivore guilds were removed from the data set prior to analysis of feeding guild data. Given the lack of food resources available for 57 herbivores during the winter period, the likelihood of herbivore dormancy during the winter, and the internal nature of parasites, changes in these guilds were not relevant to the topic of this study. Values for the remaining guilds were proportionally scaled after the removal of herbivore and parasite values so that relative abundance in each sample again summed to 1. The number of unique detection events that occurred on each date was compared between management types by performing a Wilcoxon Rank Sum Rest. A detection event refers to the presence of a taxon or guild on a given date within a given treatment. When a taxa or guild was detected in only one of the two treatments on a given date, that detection was considered “unique”. A difference in unique detection events was used as an indicator of a potential difference in community population size. All analyses were performed in R v.3.4.3 (R Core Team 2018). RESULTS Metagenomic Internal Community Standard Evaluation of the community standard read counts revealed PCR bias whose severity increased with taxonomic resolution (Fig 3.1). Six unexpected orders were assigned to OTUs in the community standard, however these represented less than 0.02% of reads. Thirty-two unexpected families were assigned OTUs within orders with known families, but represented only 0.9% of reads. At the genera level, there were 48 unexpected assignments, representing 8.3% of reads. In regards to taxa that were not detected, two families (Hymenoptera:Aphelinidae and Hymenoptera:Pteromalidae) and six genera (Coccinellidae: Delphastus, Cecidomyiidae: Aphidoletes, Aphelinidae: Encarsia, Pteromalidae: Muscidifurax, Gryllidae: Gryllodes, Tettigoniidae: Neoconocephalus) were missed. There were no orders that failed to be detected. An additional and unexpected observation was that pass filter read counts varied by 3.5 fold across replicates, despite equivalent pooling across sequencing lanes. The spread increased to 5 fold after bioinformatic processing and read filtering. Despite the variability in sequencing effort, variation in relative abundance was low at all observed taxonomic levels. 58 Because the fidelity of identification decreased substantially at the genera level, I opted to conduct the bulk of my analysis at the family level. I also realized that the observed amplification bias meant that relative abundance values were unlikely to be representative of field populations. However, differences in relative abundance between treatments within a given taxa could be used to quantify treatment effects because the within-taxa bias was consistent across replicates. Taxa Present in the Winter A total of 196 arthropod families were detected in the orchard throughout the course of the study that spanned 29 orders and 6 classes. The number of families detected on any given date ranged between 46 and 99, indicating that a considerable number of taxa were unique to one or two dates. Furthermore, exploring the distribution of mean relative abundance over the course of the study showed that 46% of detected families clustered below 0.05%, with the remaining taxa distributed up to 16% mean relative abundance (Fig 3.1). Given the ability of high throughput sequencing platforms to detect small amounts of DNA, it is likely the clustering resulted from a combination of rare taxa with low biomass, gut contents, and environmental DNA extracted from the surface of collected specimens. A complete list of detected taxa is provided in Appendix 3.C. To focus discussion around taxa likely to be active during the winter, a truncated list was generated of probable candidates that: 1) were present on at least 3 dates; 2) had a mean relative abundance over the study period greater than 0.05%; and 3) were classified as decomposers, omnivores, or predators (Table 3.1). Fifty-two taxa met these criteria. Impacts of Management PERMANOVA detected a significant main effect for management, but the explanatory power (R-squared) of this effect was very low (Table 3.2). Furthermore, no significant differences were detected in alpha diversity at any order of q on any of the sampling dates (Fig 3.3). However, analysis of multivariate dispersion did reveal a potential difference between the two management levels. The univariate linear models fit to each taxon’s relative abundance revealed significant main effects from management for 13 of the 52 taxa in Table 3.2 and 44 of the 196 taxa detected in the study (Appendix B). For taxa observed in both treatments, a greater number of unique detection events were observed in the organic treatment 59 on 7 of the 8 sampling dates (Table 3.3), and the difference was statistically significant (W=3.5, p=0.001). Feeding Guilds Primary decomposer, omnivore, and predator guild abundances were similar in both treatments, but significant differences were detected in the secondary and tertiary decomposers sub guilds (Table 2.4). DISCUSSION Taxa Present in the Winter I employed cylindrical Berlese funnels which forced collected taxa to move through a profile of litter and soil 8-10 inches tall. The active navigation required for collection made the detection of non-living or inactive specimens unlikely. We can assume then that the taxa in Table 3.1. represent organisms that were either 1) engaged in active movement during the sampling period; 2) capable of exiting a dormant state and becoming mobile within the period of Berlese separation; or 3) a major dietary component of other actively moving taxa. We cannot distinguish between these three possibilities, nor can we make definitive claims regarding the behavior of detected organisms. However, a pool of literature exists describing winter behavior in a variety of study systems for several of the taxa detected in the study orchard. The combination of behavioral observations with my detection data provides compelling evidence in support of a winter active arthropod community in orchard systems. I therefore focus this portion of my discussion on the supporting literature for the taxa presented and highlight areas where evidence is lacking. Araneae Several taxa of spiders have been shown to actively move and engage in predatory behavior during winter months in both natural and agricultural ecosystems. Korenko and Pekar (2010) observed more than 15 Araneae families in cardboard bark traps in an apple orchard during a time with average ambient temperatures between 2.8 and 3.4°C. They noted that the number and composition of spiders in the traps changed throughout the winter, suggesting that the spiders were regularly migrating to new sheltered locations. They also observed evidence of intraguild predation amongst spiders of different sizes. The four most 60 frequently observed taxa in my study: Linyphiidae, Lycosidae, Salticidae, and Thomisidae, were all observed in that study. Predation by spiders has also been observed at temperatures as low as -4°C, and below snow cover (Korenko et al. 2010; Schmidt and Lockwood 1992). Astigmata The astigmatid mites are highly diverse in their life cycles and feeding habits (Kantz and Walter 2009). The high degree of diversity extends in the family Acaridae, making generalized commentary at these taxonomic levels difficult. However, members the genera Rhizoglyphus (bulb mites) and Tyrophagus, which were detected on every sampling date and had the highest relative abundance among Acaridae, are common agricultural and stored food pests that have been studied in some detail. In a well-cited review of Rhizoglyphus biology, Diaz et al. (2000) state that “while bulb mite activity may be lower during the colder months, they do not undergo a true diapause, and all stages can be recovered throughout the year.” However, a review of the cited literature supporting this statement revealed that Gerson et al. (1983) had observed laboratory-reared mites under controlled conditions which did not include temperatures below 16°C. In fact, I could not find a single reported observation of Rhizoglyphus activity at near or below freezing temperatures in either laboratory or field conditions. The closest to such an observation was reported by Poe (1971), who collected Rhizoglyphus mites from Gladiolus corms during a window of time where “ambient temperatures fell to or below freezing on 2 occasions, but minimal temperatures were usually several degrees above freezing.” Given that soil temperatures are heavily buffered against small changes in air temperature, it is highly unlikely that the collected mites were exposed to the conditions seen in Michigan winters. Slightly more data exist on the behavior of Tyrophagus mites, as cold exposure has been explored as a means of controlling infestations in stored food goods. Supercooling temperatures for T. putrescentiae -22.5 and -28.7 °C were reported by Eaton and Kells (2011). However, practical cold tolerance and behavior has not been explored in a natural setting. The only data that exists regarding cold survival comes from sudden changes from room temperature to 61 simulate cold sanitation practices, which is quite different from the gradients associated with seasonal transition (Eaton and Kells 2011; Abbar et al. 2016) Thus my collection of Rhizoglyphus and Tyrophagus is the first report of these genera being sampled during the winter under field conditions, though the nature of their activity during that time requires further investigation. Mesostigmata Several mesostigmatid families have been sampled from soils when temperatures were near or below freezing. Schmidt and Lockwood (1992) used pitfall traps to collect actively moving specimens beneath snow cover and collected mites from the family Parasitidae throughout the winter period in both sage and meadow habitats. Elbadry (1973) tracked mesostigmatid mite populations in German coniferous forests over the course of a year. Overall population and taxa richness were lowest during the winter months when temperatures dropped below freezing, but Parasitidae were observed during that time. Thus, it is probable that Parasitidae, which were observed on every sampling date in my study, were actively predating throughout winter. Elbadry (1973) also observed 12 other mesostigmatid families during the winter period. Of those, Laelapidae, Rhodacaridae, Uropodidae, and Veigaiidae were also observed in my study. However, there is no direct evidence in the literature that these taxa are actively moving or engaging in feeding behaviors in during winter. Interestingly, Laelapidae, which were one of the more frequently collected and abundant mesostigmatid families in my study, were found by Elbadry (1973) only during the warmer months of the year. Čoja and Bruckner (2003) showed no pattern of mesostigmatid abundance or diversity with respect to microhabitat type in a coniferous forest. They postulated that heavily sclerotization and relatively quick movement allows mesostigmatids to move quickly between microhabitats to locate favorable conditions and prey. Similar results were obtained by Minor and Cianciolo (2007) for mesostigmatid mite communities within managed landscape types. It seems that the adaptability and physical resiliency of mesostigmatids makes them relatively insensitive to the degree of difference that exists between organic and conventional orchard habitats. Parasitidae, 62 the most abundant and frequently observed mesostigmatid family in my study, were observed on every sampling date and were similar in overall relative abundance and detection frequency. Their resiliency has been attested to by other research during the growing season: Parasitidae populations were unaffected by pesticide applications in two studies when other predatory mite population densities were reduced to less than half of those observed in control treatments (James 2000; Hurlbutt 1958). It seems that the adaptability and resilience of Parasitidae extends into the winter. Oribatida Observation of oribatids during the winter is not surprising. Oribatids have been observed actively foraging on fungal hyphae and spores in forest litter during the winter (Leinaas 1981; Hågvar and Hågvar 2011), and have highly conserved supercooling points between 10 and 30°C below freezing (Sømme 1981,1982; Schatz and Sømme 1981; Block 1982; Sjursen and Sømme 2000). Trombidiformes Trombidiidae and Tarsonemidae overwinter as diapausing adults (Zhang 1998; Lin and Zhang 2002; Lindquist 1986). Explorations of cold hardiness in Tarsonemidae show that exposure to temperatures only a few degrees below freezing is fatal to active adults (Smith and Goldsmith 1936; Wiesmann 1941; Jeppson et al. 1975; Denmark 1988; Luypaert et al. 2015). Based on our current understanding of these families, it seems unlikely that they were engaged in active behavior during the study. Observations of Trombidiidae indicate that they hibernate in moss and the top layer of soil during winter months, further supporting that their detection was the result of incidental sampling of mites capable of breaking diapause during Berlese separation (Zhang and Xin 1989; Zhang 1998). However, there are examples of winter active Trombidiformes: Slayter et al. (2017) collected Bdellidae specimens from the subnivean space of alpine habitats. Collembola Winter activity is arguably most well documented in Collembola amongst soil organisms. Collembola are well adapted to low temperatures, producing a range of thermal hysteresis proteins and trehalose sugars that prevent nucleation and allow supercooling of 63 hemolymph Block 1982; Block and Zettel 2003; Ohlsson and Verhoef 1988). Not only are Collembola winter active, but they have been demonstrated to both feed and reproduce during winter months. Several authors have confirmed through gut content analysis and direct observation that collembola feed on fungal hyphae and lichen that develops on tree bark, on leaf litter, and within the soil profile (Hågvar and Hågvar 2011; Zettel et al. 2002; Matsumo et al. 2018). Furthermore, their grazing behavior seems to be linked to their winter survivability: many of their fungal and lichen food sources contain thermal hysteresis proteins and trehalose sugars that are incorporated into collembolan tissues and hemolymph to augment their cold tolerance (Block and Zettel 2003; Matsumo et al. 2018). Some of the lowest cold survival temperatures among arthropods have been observed in collembolan species adapted for antarctic climates: Sinclair and Sjursen (2001) recorded a supercooling point of -38°C in Gomphiocephalus hodgsoni. Among collembolans from temperate climates, supercooling temperatures below -10°C have been observed in several species (Zettel et al. 2002; Vanin et al. 2008). In addition to supercooling, Collembola also manage cold by migrating through soil profiles to reach more insulated depths, and by regurgitating gut contents to avoid nucleation of food within the digestive tract. These behaviors occur both on seasonal and diurnal time frames, and differ across species (Van der Woude and Verhoef 1986). This means that collembolan activity in the winter is highly dynamic. The significance of this activity in terms of nutrient cycling and the general ecology of soil ecosystems remains fairly unexplored, although we do know that Collembola form the base diet of many winter predators (Eitzinger and Traugott 2011). The dominant collembolan families observed in my study (Entomobryidae, Isotomidae, Tomoceridae, Hypogastruridae, Neanuridae, and Onychiuridae) have also appeared as abundant families in other studies in both agricultural and forest ecosystems located in temperate climates (Hågvar 2000; Matsumoto et al. 2018; Zhang et al. 2014; Hågvar and Hågvar 2011; Matthews et al. 2002). Diplopoda Julidae was the only family detected from Diplopoda. There is little research into the overwintering strategies and behaviors of this group in temperate climates, though a few 64 reports of winter behavior in milder climates exist. Julidae species in the Austrian Alps were found to develop over several years, overwintering in multiple life stages, though these species were not subjected to freezing temperatures (Meyer 1985). Similarly, Cylindroiulus truncorum (Verhoeffidae), a non-native millipede found in the Czech Republic where winter temperatures occasionally drop below freezing, undergoes winter breeding (Kocourek 2003). A laboratory evaluation of the effect of temperature on feeding behavior in Sarmatiulus kessleri (Julidae), Striganova (1972) reported low levels of feeding activity between 1 and 7°C. The author also reported that all feeding activity ceased below 1°C, but that the specimens experienced no fatality from freezing temperatures. It is possible that Julidae do not enter a true diapause during the winter, but are instead quiescent below freezing and able to opportunistically feed during periods of warming. Minimum air temperatures were above freezing on all three of the sampling dates associated with positive detections in Julidae, which is consistent with the behavioral observations reported by the other authors. The abundance of these organisms is highly correlated with ground flora complexity, and the availability of moisture and organic residues (Bogyó et al. 2015; Crawford 1992). These associations support the findings of upregulation in organic treatments. The ground flora in the understory is composed of many species, is more abundant which leads to increased quantities of organic residues, and is capable of retaining higher levels of moisture than bare ground or flattened leaf litter even in winter months. However, a relatively recent study exploring the interaction of agricultural management and surrounding landscape type in Germany found that farming style had no significant effect on millipede richness, community complexity, or feeding activity (Diekötter et al. 2010). In my study, Julidae were observed more frequently and with greater relative abundance in the organic treatment. Coleoptera All of the coleopteran families presented in Table x. have been previously documented during cold months in other agricultural and natural habitats, many of which used pitfall trapping to sample actively moving specimens. Carabidae, Staphylinidae, and Cantharidae 65 larvae were consistently collected in these studies (Noordhuis et al. 2001; Larochelle 1974; Juen et al. 2003; Traugott 2006; Slayter et al. 2017). In addition, molecular gut content analysis has shown that carabid and cantharid larvae consume collembolan and oligochaete prey throughout the winter (Eitzinger and Traugott 2011). I observed collembolan taxa on every sampling date. Therefore, it is likely that the Carabidae, Staphylinidae, and Cantharidae detected in my study were engaging in predatory behavior in the orchard with Collembola providing the base of their food supply. Erotylidae do not appear frequently in cold weather literature on arthropod communities, but have been documented as winter active fungal feeders (Shepard 1976; Wêgrzynowicz 2002). Shepard (1976) described Derodontus maculatus, a fungi-associated erotylid that is only observed in the winter in Oklahoma and Maryland. The author reported signs of feeding within proximity of collected larvae. The family Scarabaeidae also contains taxa with frequent documentation of winter activity. While Scarabaeidae is a large and diverse family, it does contain many species that feed on a range of pre-digested matter including organic residues colonized by bacteria and fungi, animal feces, and humus (Cambefort 2014; Li 2004; Cartwright 1974; Verdu and Galante 1999; Bhawane et al. 2015). These include the two genera which comprised the majority of scarabid detection events within this study: Ataenius and Glycyphana. Blatchley (1896) reported observations of Ataenius adults foraging, copulating and even flying during the winter months of Indiana, which has a temperate climate similar to Michigan. This is the only documented case I could find of winter feeding in Scarabaeidae. I could not find any documentation of winter activity within the Glycophana. The few explorations of their natural biology revealed that breeding and overwintering take place in decayed wood; however, the authors were not able to ascertain whether feeding occurred during the overwintering period (Iijima 2007; Bhawane et al. 2015). It also seems that winter behaviour within Scarabaeidae is species specific: Waßmer (1994) observed that within Aphodius, a genera of scarabid coprophages, only a small subset of species 66 were active in a German pasture during the winter. This included A. paykulli, which was most active during the coldest months. Latridiidae have received little attention in agricultural research, typically appearing as incidentally sampled taxa or undiscussed members of a larger assemblage of interest. The few studies conducted specifically on the Latridiidae during warmer months has established them as fungal feeders living in soils and decaying wood (Bukejs and Rücker 2013; Leather et al. 2014; Olberg and Anderson 2000). Some species even form mutualistic associations with fungus- tending ant colonies (Lapeva-Gjonova and Rücker 2011). With regard to winter activity, Latridiidae were observed in subnivean pitfall traps by Slatyer et al. (2017), though details regarding their behavior during that time are absent. Thus, these remain a relatively unexplored taxa, particularly with respect to the winter. Nitidulidae are pests of fruit, stored food goods, and honeybees (Palmeri et al. 2015). They are also important members of the decompositional succession of corpses (Saloña et al. 2010). Consequently, the bulk of research related to this family has been focused around these topics, leaving gaps in our knowledge regarding their natural roles in soil systems.The best information on nitidulid cold-tolerance comes from a study in which C.hemipterus adults survived exposure to 0°C for a week and larvae for two weeks (Donahaye et al. 1991).In that same study, the survival of another species in the same genera was more than half that time. Both species survived for less than a day at -5°C, and for only a few hours at -10°C. The authors made no comment regarding the activity level of the organisms. The practical implications of their ability to survive brief exposures to freezing temperatures in natural settings remained completely unexplored. Diptera Diptera have been collected in the soil and leaf litter of some of the coldest terrestrial habitats on the planet. They are among the most abundant arthropod taxa in the Alaskan tundra and are some of the only animals found in the Antarctic polar regions (Danks 1994; Convey and Block 1996). Winter activity in Scandinavian and Canadian regions has been extensively documented in several dipteran families including Chironomidae, Cecidomyiidae, and 67 Mycetophilidae (Hågvar and Ostbye 1973; Nielsen et al. 1994; Plassman 1975; Aitchinson 1979; Soszyńska 2004). Interestingly, reports of dipteran winter activity from the temperate United States are extremely limited: Schmidt and Lockwood (1992) reported the collection of Chionea (Limoniidae), a wingless fly encountered on snow surfaces throughout the northern United States and Canada (Marchand 1917), from subnivean sampling in Wyoming. My detection of dipteran taxa throughout the winter that have repeatedly appeared in other cold-climate samplings suggests that these dipteran taxa are likely winter active in the Northern United States as well, and should be further investigated. Impacts of Management Richness and Relative Abundance The low explanatory power of management as a main effect in the PERMANOVA model (Table 3.2) combined with a lack of significance in alpha diversity across all dates and orders orders of diversity suggests that, at the family level, taxonomic richness and the distribution of abundance in each management group were similar. However, the difference in multivariate dispersion around their respective centroids points indicates that the relative abundance of some specific taxa likely differed between the organic and conventional treatments, which is supported by statistically significant differences in relative abundance produced by the univariate models fit to each taxon (Table 3.1). Thus, it seems that the winter arthropod communities in the organic and conventionally managed sections of this studied orchard are equally taxonomically diverse, have similar dominant taxa, differ in the relative abundance of a subset of those dominant taxa, and also differ in the presence of a subset of rare taxa. While these results appear contrary to the historical convention that organic management tends to increase richness, they are supported by recent meta-analyses and explorations of management systems with respect to landscape context (Bengtsson et al. 2005; Boutin et al. 2009; Winqvist et al. 2012; Gkisakis et al. 2015). Surrounding landscape has been shown to have a controlling effect on the biodiversity of farms and the impact that within-farm management 68 practices have on that diversity (Bengtsson et al. 2005; Boutin et al. 2009; Winqvist et al. 2012; Gkisakis et al. 2015). In particular, complex surrounding landscapes such as meadows and forests tend to decrease the sensitivity of communities to management practices (Bengtsson et al. 2005; Boutin et al. 2009; Winqvist et al. 2012; Gkisakis et al. 2015). My study was conducted on a single farm with adjacent plots of trees that shared a surrounding landscape composed of rural property, forest, and agricultural fields. The influence of landscape context could very well have been the dominating factor influencing orchard biodiversity. In addition, consider the potential of the winter season acting as a temporal landscape context. Pruning is the only active farming practice in effect and temperatures are much lower than the growing season. The factors exerting influence on communities during the growing season, such as pesticide applications and tillage, may not have bearing on winter active communities, or are overpowered by seasonally- specific factors like cold tolerance. Furthermore, these contextualized explorations of farm biodiversity have also shown that arthropod communities do not behave as a collective, but respond in a taxon-specific manner (Bengtsson et al. 2005; Letourneau and Bothwell 2008; Gkisakis et al. 2015). This pattern was observed in my study as well. As an example, consider the collembolan families Entomobryidae, Isotomidae, and Tomoceridae. All three taxa are in the order Entomobryomorpha, but responded differently to management: no difference was detected in Entomobryidae in either relative abundance or detection frequency; Isotomidae were detected nearly twice as frequently in the organic treatment; and Tomoceridae were detected with the same frequency but twice the relative abundance in the organic treatment. It is also possible that species-level richness during the winter, which I was unable to capture, may have differed without producing an observable signal at higher taxonomic resolutions. Indeed, Kuem et al. (2013) observed during the growing season equal family-level richness in South Korean organic and conventional orchards, but also observed that species-level 69 richness was greater in the organic orchard, and that the dominant species differed between the two management types. Population Size Another factor in describing biodiversity is the size of populations being compared. A community twice the population of another community with equivalent richness and relative abundance should be considered twice as diverse (Chao et al. 2014). A principal limitation to the methodology employed in this study is that only changes in relative abundance can be detected, not changes in absolute population size. This means that the difference between equally proportioned communities with differing total population sizes will not be detected. Therefore, it is possible that my treatments, which appeared similar in richness and proportion, differed considerably in overall population size. While direct quantification of populations was not possible, the number of unique detection events in my data could be used to indirectly infer whether a difference in population size exists. That the probability of detecting a given taxa is function of its abundance and the applied sampling effort is well-established in ecological research (Chao and Jost 2012; Gotelli and Colwell 2001). For a fixed sampling effort, detection of a taxon in one treatment and not another could signify a higher absolute abundance of that taxon in that treatment. Of course, such an observation could also result from that taxon truly being unique to that environment. However, if that taxon’s presence in both treatments were confirmed over the course of multiple sampling events, then a greater number of unique detection events would be indicative of a greater population size. Thus, a greater number of unique detection events among shared taxa in one treatment over another would be expected over the course of multiple sampling events if a true difference existed in community size. Detection frequencies were consistently greater on each date in the organic treatment, which indicates a greater population size (Table 3.3). There is precedence for higher soil fauna abundance in farming systems that have reduced pesticide use and higher levels of soil organic 70 matter, which is the case with the organically managed orchard plots (Andrén & Lagerlöf 1983; El Titi & Ipach 1989; Lebbink et al. 1994; Zwart et al. 1994). Feeding Guilds The differences in secondary and tertiary decomposer relative abundances could be related to the amount of organic residue available as a food source for the decomposer communities (Table 3.4). The organic treatment had greater amounts of biomass available in the understory because of the ground flora that developed during the growing season. The biomass in organic treatment was also more complex, being from a mixture of plant parts and species, whereas the litter of the conventional treatment was almost exclusively made of fallen apple leaves. Mixed species litter tends to decompose faster than litter composed of a single species (Harguindeguy et al. 2008; Kaneko and Salamanca 1999). They also have higher rates of biological respiration, which indicates accelerated colonization by bacteria and fungi (Salamanca et al. 1998). Upon first consideration, enhanced colonization of litter in the organic treatment should have resulted in greater abundance of tertiary decomposers, which feed directly on fungi. However, the amount of litter on the ground must also be considered: while the mixed species litter of the organic treatment would support accelerated colonization, the greater mass could also lead to an overall slower progression through the decomposition process. This would explain the greater abundance of secondary decomposers, which feed on colonized residues, in the organic treatment. CONCLUSIONS The principal finding of my research into orchard floor ecology was that the orchard under study supported a diverse, winter active community comprised of decomposers and predators. Previous explorations of agroecosystems during cold seasons limited the scope of investigation to specific taxonomic groupings or functional groups. I have provided one of the first holistic descriptions of a winter soil arthropod community in a temperate agroecosystem. My work suggests that winter active communities in agroecosystems are far more active than is traditionally believed. I also conducted one of the first evaluations of agricultural management 71 impacts on winter active communities. Interestingly, family richness and intra-community complexity (alpha diversity) did not differ between the organic and conventionally managed plots. There was also considerable overlap in the dominant families detected under both management strategies. However, I did observe changes in the relative abundances of those families in response to management strategy. The changes in relative abundance were largely associated with differences in the secondary and tertiary decomposer sub guilds, which I suspect is related to the differences in the composition and quantity of organic residues in the orchard understory. I also saw indirect evidence that the organic orchard plots were able to support a larger overall arthropod population. In all, my results suggest that winter active arthropods could be a factor in the provisioning of ecosystem services in agroecosystems, and that management style may affect the type, timing, and magnitude of those services. 72 APPENDICES 73 APPENDIX 3.A. Figures and Tables 74 Figure 3.1. PCR Bias and Assignment Error in Arthropod Community Standard Data. Log2 Fold Changes in read counts between treatments were mapped to color hue and saturation. 75 Figure 3.2. Distribution of Taxon Relative Abundance. Grey fill (Conventional) White fill (Organic). 76 Table 3.1. Frequently Detected Taxa. Each taxon was present on at least 3 dates; had a mean relative abundance over the study period greater than 0.05%; and was classified as a decomposer, omnivores, or predator. P-value: Significance levels from contrasts of mean relative abundance between management types as estimated by a univariate model fit to a logit transformation of each taxon’s relative abundance values. Reps: the number of replicates within a management type in which a taxon was detected over the course of the study. Unique: The number of times a taxon was present in only one of the two treatments on a given date. Total Dates: The number of sampling dates (max 8) on which a taxon was detected in either treatment. Class Order Family Feeding Group p-value Rel. Abund. Rel. Abund. Conv. Organic Reps Conv. Reps Organic Unique Conv. Unique Organic Total Dates Arachnida Araneae Linyphiidae predator 0.563 2.19E-03 1.12E-03 Arachnida Araneae Lycosidae predator 0.389 8.21E-03 6.66E-04 Arachnida Araneae Salticidae predator 0.170 3.06E-04 1.86E-03 Arachnida Araneae Thomisidae predator 0.500 8.72E-04 3.00E-03 6 5 1 3 Arachnida Astigmata Acaridae omnivore 0.024 1.62E-03 2.78E-03 10 Arachnida Mesostigmata Laelapidae predator 0.364 8.85E-05 9.21E-04 Arachnida Mesostigmata Ologamasidae predator 0.982 6.90E-04 8.23E-04 2 5 Arachnida Mesostigmata Parasitidae predator 0.113 6.42E-02 2.81E-02 25 Arachnida Oribatida Brachychthoniidae omnivore 0.927 6.34E-04 2.07E-05 Arachnida Oribatida Eremaeidae 3° decomposer 0.146 2.43E-06 1.01E-03 Arachnida Oribatida Eremobelbidae 2° decomposer 0.347 7.90E-04 1.27E-04 Arachnida Oribatida Oppiidae omnivore 0.110 7.91E-04 6.26E-04 Arachnida Oribatida Oribatellidae 1° decomposer 0.841 2.08E-03 2.34E-03 Arachnida Oribatida Oribatulidae 2° decomposer 0.482 2.29E-03 2.30E-04 Arachnida Oribatida Phenopelopidae 1° decomposer 0.382 9.53E-04 7.07E-04 Arachnida Oribatida Scheloribatidae 2° decomposer 0.412 5.21E-02 4.21E-02 Arachnida Oribatida Tegoribatidae 1° decomposer 0.301 1.43E-03 6.00E-04 Arachnida Pseudoscorpiones Neobisiidae predator 0.182 2.20E-03 4.68E-03 Arachnida Trombidiformes Rhagidiidae 1° decomposer 0.968 1.62E-04 1.99E-03 Arachnida Trombidiformes Tarsonemidae 3° decomposer 0.362 1.78E-03 1.22E-03 Arachnida Trombidiformes Trombidiidae predator 0.226 1.45E-02 2.29E-02 Chilopoda Geophilomorpha Schendylidae predator 0.640 1.13E-02 3.30E-03 Collembola Entomobryomorpha Entomobryidae 1° decomposer 0.761 6.69E-02 9.73E-02 Collembola Entomobryomorpha Isotomidae 2° decomposer 0.002 1.37E-02 6.57E-03 3 1 8 8 10 5 12 26 7 7 3 17 11 3 28 14 10 4 4 5 19 6 3 25 2 3 5 4 11 6 6 31 9 14 7 13 10 8 29 25 1 4 1 1 1 0 2 1 1 0 0 1 1 1 2 0 1 0 0 0 1 0 0 1 3 1 3 3 1 2 1 0 1 2 2 1 2 1 0 0 4 4 3 0 1 1 0 1 7 6 4 6 8 4 5 8 3 3 5 4 7 5 7 8 7 6 5 6 7 3 8 8 77 Table 3.1. (cont’d) Collembola Entomobryomorpha Tomoceridae 1° decomposer 0.943 6.65E-02 9.04E-03 Collembola Poduromorpha Hypogastruridae 2° decomposer 0.206 3.47E-02 1.25E-02 Collembola Poduromorpha Neanuridae 2° decomposer 0.086 3.59E-06 5.55E-03 Collembola Poduromorpha Onychiuridae 2° decomposer 0.490 1.10E-02 3.78E-03 Diplopoda Julida Julidae 2° decomposer 0.083 1.72E-02 1.61E-01 17 16 2 16 12 5 12 3 2 6 5 3 21 21 5 8 0 2 5 9 1 4 8 7 5 5 2 19 14 5 12 18 5 20 1 2 3 6 7 9 29 11 14 2 6 2 2 7 3 3 13 9 7 3 1 0 0 0 1 1 2 1 2 1 3 4 1 0 0 1 1 5 0 1 2 0 0 1 0 0 0 2 2 1 1 4 0 1 3 0 1 1 0 0 2 3 0 0 4 1 4 1 0 2 2 1 5 2 2 2 1 8 6 5 6 8 6 8 3 3 5 8 6 6 8 8 8 7 4 3 3 7 3 3 8 4 5 5 3 Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Coleoptera Cantharidae predator 0.132 4.67E-05 6.59E-02 Coleoptera Carabidae predator 0.191 3.27E-02 3.87E-02 Coleoptera Corylophidae 3° decomposer 0.024 1.30E-03 8.70E-05 Coleoptera Cryptophagidae 3° decomposer 0.710 1.62E-03 3.92E-03 Coleoptera Erotylidae 3° decomposer 0.571 3.60E-03 3.42E-04 Coleoptera Latridiidae 3° decomposer 0.003 4.03E-03 3.30E-03 15 Coleoptera Nitidulidae 1° decomposer 0.702 1.62E-02 1.75E-03 Coleoptera Scarabaeidae 2° decomposer 0.655 9.79E-04 9.83E-03 Coleoptera Staphylinidae predator 0.174 1.22E-01 1.19E-01 Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Cecidomyiidae 3° decomposer 0.002 5.18E-02 9.11E-04 Chironomidae 2° decomposer 0.001 4.47E-03 4.15E-03 Dolichopodidae predator 0.541 3.48E-02 7.96E-06 Drosophilidae 3° decomposer 0.936 - 1.87E-03 Empididae predator 0.018 4.31E-03 1.30E-05 Muscidae predator 0.726 8.98E-04 7.90E-03 Mycetophilidae 3° decomposer 0.856 1.58E-02 1.64E-04 Stratiomyidae 1° decomposer 0.038 1.63E-06 2.69E-02 Insecta Hymenoptera F: Brachymyrmex omnivore 0.489 8.45E-04 4.34E-03 Insecta Hymenoptera F: Lasius omnivore 0.092 4.16E-02 1.60E-02 Insecta Hymenoptera F: Ponera predator 0.687 2.04E-03 2.85E-03 Insecta Hymenoptera F: Prenolepis omnivore 0.473 2.13E-02 3.81E-03 Insecta Insecta Neuroptera Hemerobiidae predator 0.004 1.88E-02 2.55E-03 Psocoptera Liposcelidae 2° decomposer 0.006 3.21E-03 3.32E-04 78 Table 3.2. PERMANOVA and Multivariate Dispersion Analysis. 79 Figure 3.3. Difference in Alpha Diversity Resulting from Management Style. Effective taxa number was calculated for the family level. Diversity order q=0 corresponds to richness, q=1 to the exponential of Shannon entropy, and q=2 to the inverse of Simpson entropy. The influence of dominant species increases with the order of q. Organic treatments were under organic-certified grower standard practices. Conventional treatments were under conventional grower standard practices. 80 Table 3.3. Unique Detection Events of Shared Taxa. When a taxon was detected in only one of the two treatments on a given date, that detection was considered “unique”. 81 Table 3.4. Relative Abundance of Soil Trophic Guilds under Organic and Conventional Management. Reported p-values were from contrasts of mean relative abundance between management types as estimated by a univariate model fit to a logit transformation of each taxon’s relative abundance values. 82 APPENDIX 3.B. Feeding Guild Assignments & Supporting Literature 83 APPENDIX 3.B. Feeding Guild Assignments & Supporting Literature Phylum Class Order Arthropoda Arachnida Araneae Family Araneidae Arthropoda Arachnida Araneae Barychelidae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Clubionidae Dysderidae Gnaphosidae Hypochilidae Linyphiidae Lycosidae Arthropoda Arachnida Araneae Mecysmaucheniidae Arthropoda Arachnida Araneae Philodromidae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Pholcidae Pisauridae Salticidae Sicariidae Arthropoda Arachnida Araneae Tetragnathidae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Theridiidae Thomisidae Feeding Guild predator predator predator predator predator predator predator predator predator predator predator predator predator predator predator predator predator Arthropoda Arachnida Astigmata Acaridae omnivore Arthropoda Arachnida Astigmata Proctophyllodidae Arthropoda Arachnida Astigmata Psoroptidae Arthropoda Arachnida Astigmata Pteronyssidae Arthropoda Arachnida Astigmata Pyroglyphidae Arthropoda Arachnida Astigmata Suidasiidae Arthropoda Arachnida Astigmata Trouessartiidae Arthropoda Arachnida Astigmata Xolalgidae parasite parasite parasite parasite parasite parasite parasite Arthropoda Arachnida Endeostigmata Nanorchestidae tertiary decomposer Arthropoda Arachnida Endeostigmata Terpnacaridae tertiary decomposer Arthropoda Arachnida Ixodida Ixodidae Arthropoda Arachnida Mesostigmata Ascidae Arthropoda Arachnida Mesostigmata Blattisociidae Arthropoda Arachnida Mesostigmata Digamasellidae Arthropoda Arachnida Mesostigmata Laelapidae Arthropoda Arachnida Mesostigmata Macrochelidae Arthropoda Arachnida Mesostigmata Ologamasidae Arthropoda Arachnida Mesostigmata Parasitidae Arthropoda Arachnida Mesostigmata Phytoseiidae Arthropoda Arachnida Mesostigmata Rhodacaridae Arthropoda Arachnida Mesostigmata Uropodidae Arthropoda Arachnida Mesostigmata Veigaiidae parasite omnivore omnivore omnivore predator parasite predator predator predator predator parasite predator Arthropoda Arachnida Opiliones Phalangiidae omnivore 84 APPENDIX 3.B. (cont’d) Arthropoda Arachnida Opiliones Phalangodidae omnivore Arthropoda Arachnida Oribatida Achipteriidae primary decomposer Arthropoda Arachnida Oribatida Brachychthoniidae omnivore Arthropoda Arachnida Oribatida Ceratoppiidae primary decomposer Arthropoda Arachnida Oribatida Ceratozetidae primary decomposer Arthropoda Arachnida Oribatida Cymbaeremaeidae tertiary decomposer Arthropoda Arachnida Oribatida Eremaeidae tertiary decomposer Arthropoda Arachnida Oribatida Eremobelbidae secondary decomposer Arthropoda Arachnida Oribatida Euphthiracaridae tertiary decomposer Arthropoda Arachnida Oribatida Galumnidae primary decomposer Arthropoda Arachnida Oribatida Arthropoda Arachnida Oribatida Arthropoda Arachnida Oribatida Arthropoda Arachnida Oribatida Nothridae Oppiidae Oribatellidae Oribatulidae primary decomposer omnivore primary decomposer secondary decomposer Arthropoda Arachnida Oribatida Oripodidae primary decomposer Arthropoda Arachnida Oribatida Phenopelopidae primary decomposer Arthropoda Arachnida Oribatida Scheloribatidae secondary decomposer Arthropoda Arachnida Oribatida Scutoverticidae tertiary decomposer Arthropoda Arachnida Oribatida Suctobelbidae primary decomposer Arthropoda Arachnida Oribatida Tectocepheidae primary decomposer Arthropoda Arachnida Oribatida Tegoribatidae primary decomposer Arthropoda Arachnida Pseudoscorpiones Neobisiidae Arthropoda Arachnida Scorpiones Buthidae Arthropoda Arachnida Scorpiones Vaejovidae Arthropoda Arachnida Solifugae Eremobatidae Arthropoda Arachnida Trombidiformes Anystidae Arthropoda Arachnida Trombidiformes Bdellidae Arthropoda Arachnida Trombidiformes Ereynetidae Arthropoda Arachnida Trombidiformes Eriophyidae Arthropoda Arachnida Trombidiformes Eupodidae predator predator predator predator predator predator parasite herbivore parasite Arthropoda Arachnida Trombidiformes Microdispidae tertiary decomposer Arthropoda Arachnida Trombidiformes Pygmephoridae tertiary decomposer Arthropoda Arachnida Trombidiformes Rhagidiidae primary decomposer Arthropoda Arachnida Trombidiformes Scutacaridae tertiary decomposer Arthropoda Arachnida Trombidiformes Siteroptidae tertiary decomposer Arthropoda Arachnida Trombidiformes Stigmaeidae predator Arthropoda Arachnida Trombidiformes Tarsonemidae tertiary decomposer Arthropoda Arachnida Trombidiformes Tetranychidae Arthropoda Arachnida Trombidiformes Trombidiidae Arthropoda Arachnida Trombidiformes Tydeidae Arthropoda Chilopoda Geophilomorpha Himantariidae Arthropoda Chilopoda Geophilomorpha Schendylidae herbivore predator omnivore predator predator 85 APPENDIX 3.B. (cont’d) Arthropoda Chilopoda Lithobiomorpha Lithobiidae Arthropoda Chilopoda Scolopendromorpha Cryptopidae Arthropoda Chilopoda Scolopendromorpha Scolopocryptopidae Arthropoda Chilopoda Scutigeromorpha Scutigeridae predator predator predator predator Arthropoda Collembola Entomobryomorpha Entomobryidae primary decomposer Arthropoda Collembola Entomobryomorpha Isotomidae secondary decomposer Arthropoda Collembola Entomobryomorpha Tomoceridae primary decomposer Arthropoda Collembola Poduromorpha Hypogastruridae secondary decomposer Arthropoda Collembola Poduromorpha Neanuridae secondary decomposer Arthropoda Collembola Poduromorpha Onychiuridae secondary decomposer Arthropoda Collembola Poduromorpha Tullbergiidae secondary decomposer Arthropoda Collembola Symphypleona Bourletiellidae secondary decomposer Arthropoda Collembola Symphypleona Katiannidae primary decomposer Arthropoda Collembola Symphypleona Sminthuridae primary decomposer Arthropoda Collembola Symphypleona Sminthurididae herbivore Arthropoda Diplopoda Julida Julidae secondary decomposer Arthropoda Diplura Diplura Campodeidae omnivore Arthropoda Diplura Diplura Arthropoda Insecta Blattodea Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Japygidae Termitidae Anthicidae Brentidae Byrrhidae Arthropoda Insecta Coleoptera Cantharidae Arthropoda Insecta Coleoptera Carabidae tertiary decomposer primary decomposer omnivore herbivore herbivore predator predator Arthropoda Insecta Coleoptera Cerambycidae primary decomposer Arthropoda Insecta Coleoptera Chrysomelidae Arthropoda Insecta Coleoptera Cleridae Arthropoda Insecta Coleoptera Coccinellidae herbivore predator predator Arthropoda Insecta Coleoptera Corylophidae tertiary decomposer Arthropoda Insecta Coleoptera Cryptophagidae tertiary decomposer Arthropoda Insecta Coleoptera Curculionidae Arthropoda Insecta Coleoptera Dytiscidae Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Elateridae Erotylidae Gyrinidae herbivore predator primary decomposer tertiary decomposer predator Arthropoda Insecta Coleoptera Heteroceridae tertiary decomposer Arthropoda Insecta Coleoptera Histeridae predator Arthropoda Insecta Coleoptera Hydraenidae tertiary decomposer Arthropoda Insecta Coleoptera Lampyridae predator Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Latridiidae Leiodidae Lucanidae 86 tertiary decomposer tertiary decomposer primary decomposer APPENDIX 3.B. (cont’d) Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Melyridae Nitidulidae predator primary decomposer Arthropoda Insecta Coleoptera Phalacridae tertiary decomposer Arthropoda Insecta Coleoptera Scarabaeidae secondary decomposer Arthropoda Insecta Coleoptera Staphylinidae Arthropoda Insecta Coleoptera Tenebrionidae predator omnivore Arthropoda Insecta Coleoptera Throscidae tertiary decomposer Arthropoda Insecta Diptera Anthomyiidae primary decomposer Arthropoda Insecta Diptera Asilidae Arthropoda Insecta Diptera Aulacigastridae predator herbivore Arthropoda Insecta Diptera Calliphoridae primary decomposer Arthropoda Insecta Diptera Cecidomyiidae tertiary decomposer Arthropoda Insecta Diptera Ceratopogonidae omnivore Arthropoda Insecta Diptera Chironomidae secondary decomposer Arthropoda Insecta Diptera Chloropidae Arthropoda Insecta Diptera Dolichopodidae herbivore predator Arthropoda Insecta Diptera Drosophilidae tertiary decomposer Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Empididae Ephydridae Hybotidae predator primary decomposer predator Arthropoda Insecta Diptera Keroplatidae tertiary decomposer Arthropoda Insecta Diptera Muscidae predator Arthropoda Insecta Diptera Mycetophilidae tertiary decomposer Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Pediciidae Phoridae Psychodidae Scatopsidae Arthropoda Insecta Diptera Sciaridae predator omnivore tertiary decomposer tertiary decomposer tertiary decomposer Arthropoda Insecta Diptera Sciomyzidae parasite Arthropoda Insecta Diptera Sphaeroceridae secondary decomposer Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Stratiomyidae Synneuridae Syrphidae Tachinidae Tephritidae Arthropoda Insecta Diptera Xylophagidae Arthropoda Insecta Hemiptera Anthocoridae Arthropoda Insecta Hemiptera Arthropoda Insecta Hemiptera Aphididae Berytidae Arthropoda Insecta Hemiptera Cercopidae Arthropoda Insecta Hemiptera Cicadellidae Arthropoda Insecta Hemiptera Clastopteridae 87 primary decomposer tertiary decomposer predator parasite herbivore predator predator herbivore herbivore herbivore herbivore herbivore APPENDIX 3.B. (cont’d) Arthropoda Insecta Hemiptera Cydnidae Arthropoda Insecta Hemiptera Delphacidae Arthropoda Insecta Hemiptera Enicocephalidae Arthropoda Insecta Hemiptera Greenideidae Arthropoda Insecta Hemiptera Lachnidae Arthropoda Insecta Hemiptera Arthropoda Insecta Hemiptera Miridae Nabidae Arthropoda Insecta Hemiptera Pemphigidae Arthropoda Insecta Hemiptera Pentatomidae Arthropoda Insecta Hemiptera Rhopalidae Arthropoda Insecta Hemiptera Rhyparochromidae Arthropoda Insecta Hemiptera Scutelleridae Arthropoda Insecta Hemiptera Arthropoda Insecta Hemiptera Tingidae Triozidae Arthropoda Insecta Hymenoptera Agaonidae Arthropoda Insecta Hymenoptera Aphelinidae Arthropoda Insecta Hymenoptera Apidae Arthropoda Insecta Hymenoptera Bethylidae Arthropoda Insecta Hymenoptera Braconidae Arthropoda Insecta Hymenoptera Diapriidae Arthropoda Insecta Hymenoptera Dryinidae Arthropoda Insecta Hymenoptera Eucharitidae Arthropoda Insecta Hymenoptera Figitidae herbivore herbivore predator herbivore herbivore herbivore predator herbivore omnivore herbivore herbivore herbivore herbivore herbivore herbivore parasite herbivore parasite parasite parasite parasite parasite parasite Arthropoda Insecta Hymenoptera Formicidae : Brachymyrmex omnivore Arthropoda Insecta Hymenoptera Formicidae : Lasius omnivore Arthropoda Insecta Hymenoptera Formicidae : Myrmecina predator Arthropoda Insecta Hymenoptera Formicidae : Nylanderia omnivore Arthropoda Insecta Hymenoptera Formicidae : Odontomachus predator Arthropoda Insecta Hymenoptera Formicidae : Ponera predator Arthropoda Insecta Hymenoptera Formicidae : Prenolepis omnivore Arthropoda Insecta Hymenoptera Formicidae : Pyramica predator Arthropoda Insecta Hymenoptera Formicidae : Solenopsis omnivore Arthropoda Insecta Hymenoptera Formicidae : Stenamma predator Arthropoda Insecta Hymenoptera Formicidae : Tapinoma omnivore Arthropoda Insecta Hymenoptera Formicidae : Temnothorax omnivore Arthropoda Insecta Hymenoptera Formicidae : Tetramorium omnivore Arthropoda Insecta Hymenoptera Ichneumonidae Arthropoda Insecta Hymenoptera Megaspilidae Arthropoda Insecta Hymenoptera Pamphiliidae Arthropoda Insecta Hymenoptera Proctotrupidae Arthropoda Insecta Lepidoptera Adelidae Arthropoda Insecta Lepidoptera Anthelidae parasite parasite herbivore parasite herbivore herbivore 88 APPENDIX 3.B. (cont’d) Arthropoda Insecta Lepidoptera Blastobasidae primary decomposer Arthropoda Insecta Lepidoptera Crambidae Arthropoda Insecta Lepidoptera Elachistidae Arthropoda Insecta Lepidoptera Erebidae Arthropoda Insecta Lepidoptera Gelechiidae Arthropoda Insecta Lepidoptera Geometridae Arthropoda Insecta Lepidoptera Gracillariidae Arthropoda Insecta Lepidoptera Immidae Arthropoda Insecta Lepidoptera Lasiocampidae Arthropoda Insecta Lepidoptera Lymantriidae Arthropoda Insecta Lepidoptera Nepticulidae Arthropoda Insecta Lepidoptera Noctuidae Arthropoda Insecta Lepidoptera Nolidae Arthropoda Insecta Lepidoptera Prodoxidae Arthropoda Insecta Lepidoptera Psychidae Arthropoda Insecta Lepidoptera Pyralidae Arthropoda Insecta Lepidoptera Riodinidae Arthropoda Insecta Lepidoptera Saturniidae Arthropoda Insecta Lepidoptera Sesiidae Arthropoda Insecta Lepidoptera Sphingidae Arthropoda Insecta Lepidoptera Tortricidae Arthropoda Insecta Lepidoptera Yponomeutidae Arthropoda Insecta Neuroptera Hemerobiidae Arthropoda Insecta Neuroptera Myrmeleontidae Arthropoda Insecta Neuroptera Osmylidae Arthropoda Insecta Odonata Coenagrionidae Arthropoda Insecta Odonata Libellulidae Arthropoda Insecta Odonata Protoneuridae Arthropoda Insecta Orthoptera Acrididae Arthropoda Insecta Orthoptera Stenopelmatidae Arthropoda Insecta Orthoptera Tettigoniidae herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore predator predator predator predator predator predator herbivore omnivore herbivore Arthropoda Insecta Psocoptera Liposcelidae secondary decomposer Arthropoda Insecta Psocoptera Psyllipsocidae secondary decomposer Arthropoda Insecta Thysanoptera Phlaeothripidae Arthropoda Insecta Thysanoptera Thripidae herbivore herbivore Adamski, D., Johnson, P.J., Boe, A.A., Bradshaw, J. and Pultyniewicz, A., 2010. 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Acta entomologica serbica, 4, pp.131-140. Wharton, R.A. and Reddick, K.L., 2014. Solifuges (Arachnida: Solifugae) as predators and prey. Transactions of the Royal Society of South Africa, 69(3), pp.213-216. 91 APPENDIX 3.C. Detected Taxa and their Relative Abundance in Organic and Conventional Treatments 92 APPENDIX 3.C. Detected Taxa and their Relative Abundance in Organic and Conventional Treatments. P-value: Significance levels from contrasts of mean relative abundance between management types as estimated by a univariate model fit to a logit transformation of each taxon’s relative abundance values. Reps: the number of replicates within a management type in which a taxon was detected over the course of the study. Unique: The number of times a taxon was present in only one of the two treatments on a given date. Total Dates: The number of sampling dates (max 8) on which a taxon was detected in either treatment. Class Order Family Feeding Group p-value Rel. Abund. Rel. Abund. Conv. Organic Reps Conv. Reps Organic Unique Conv. Unique Organic Total Dates Arachnida Araneae Araneidae predator 0.398 8.44E-06 1.03E-05 Arachnida Araneae Barychelidae predator 0.260 5.76E-05 Arachnida Araneae Clubionidae predator 0.241 2.02E-04 - - Arachnida Araneae Linyphiidae predator 0.887 2.19E-03 1.12E-03 Arachnida Araneae Lycosidae predator 0.713 8.21E-03 6.66E-04 Arachnida Araneae Philodromidae predator 0.416 - 4.38E-05 Arachnida Araneae Salticidae predator 0.932 3.06E-04 1.86E-03 Arachnida Araneae Sicariidae predator 0.764 1.89E-06 - Arachnida Araneae Theridiidae predator 0.002 1.96E-05 2.92E-03 Arachnida Araneae Thomisidae predator 0.254 8.72E-04 3.00E-03 1 1 1 6 5 0 1 1 1 3 1 0 0 10 4 1 4 0 1 5 Arachnida Astigmata Acaridae omnivore 0.007 1.62E-03 2.78E-03 10 19 Arachnida Astigmata Proctophyllodidae parasite 0.567 1.40E-04 9.29E-04 Arachnida Astigmata Psoroptidae parasite Arachnida Astigmata Pteronyssidae parasite 0.888 0.025 - - 1.57E-04 4.51E-06 Arachnida Astigmata Pyroglyphidae parasite 0.168 3.33E-05 Arachnida Astigmata Suidasiidae parasite 0.224 4.16E-06 - - Arachnida Astigmata Trouessartiidae parasite 0.009 9.91E-06 1.68E-05 Arachnida Astigmata Xolalgidae parasite 0.496 3.24E-05 4.24E-05 Arachnida Endeostigmata Nanorchestidae 3° decomposer 0.131 2.99E-06 - Arachnida Endeostigmata Terpnacaridae 3° decomposer 0.447 Arachnida Ixodida Arachnida Mesostigmata Ixodidae Ascidae parasite 0.026 omnivore 0.174 - - - 8.49E-06 5.24E-04 4.16E-06 Arachnida Mesostigmata Blattisociidae omnivore 0.003 5.12E-06 - Arachnida Mesostigmata Digamasellidae omnivore 0.261 8.31E-06 3.60E-05 6 0 0 1 1 1 1 1 0 0 0 1 1 8 4 1 0 0 2 4 0 1 1 1 0 2 0 1 1 1 4 0 1 1 0 1 1 0 0 0 1 1 1 1 1 0 0 0 1 0 0 0 0 3 1 1 3 0 0 3 1 2 3 1 0 0 2 3 0 1 1 1 0 1 1 1 1 7 6 1 4 1 1 6 8 6 3 1 1 1 3 4 1 1 1 1 1 2 93 APPENDIX 3.C. (cont’d) Arachnida Mesostigmata Laelapidae predator 0.017 8.85E-05 9.21E-04 Arachnida Mesostigmata Macrochelidae parasite 0.737 - 1.72E-04 Arachnida Mesostigmata Ologamasidae predator 0.587 6.90E-04 8.23E-04 2 0 5 6 1 3 Arachnida Mesostigmata Parasitidae predator 0.099 6.42E-02 2.81E-02 25 25 Arachnida Mesostigmata Phytoseiidae predator 0.159 8.36E-05 2.95E-04 Arachnida Mesostigmata Rhodacaridae predator 0.966 4.60E-05 3.75E-04 Arachnida Mesostigmata Uropodidae parasite 0.155 - 1.90E-04 Arachnida Mesostigmata Veigaiidae predator 0.723 9.61E-05 8.08E-05 Arachnida Opiliones Phalangiidae omnivore 0.286 1.80E-04 4.17E-04 Arachnida Oribatida Achipteriidae 1° decomposer 0.770 - 2.25E-05 Arachnida Oribatida Brachychthoniidae omnivore 0.215 6.34E-04 2.07E-05 Arachnida Oribatida Ceratoppiidae 1° decomposer 0.416 4.70E-05 4.96E-05 Arachnida Oribatida Ceratozetidae 1° decomposer 0.018 - 7.01E-05 Arachnida Oribatida Cymbaeremaeidae 3° decomposer 0.882 1.33E-04 - Arachnida Oribatida Eremaeidae 3° decomposer 0.049 2.43E-06 1.01E-03 Arachnida Oribatida Eremobelbidae 2° decomposer 0.816 7.90E-04 1.27E-04 Arachnida Oribatida Euphthiracaridae 3° decomposer 0.703 1.28E-05 7.70E-05 Arachnida Oribatida Galumnidae 1° decomposer 0.601 Arachnida Oribatida Nothridae 1° decomposer 0.918 - - 3.47E-06 4.58E-05 Arachnida Oribatida Oppiidae omnivore 0.130 7.91E-04 6.26E-04 4 2 0 1 2 0 3 2 0 1 1 8 1 0 0 8 6 3 8 1 1 1 2 3 2 0 3 5 1 1 1 4 Arachnida Oribatida Oribatellidae 1° decomposer 0.000 2.08E-03 2.34E-03 10 11 Arachnida Oribatida Oribatulidae 2° decomposer 0.007 2.29E-03 2.30E-04 Arachnida Oribatida Oripodidae 1° decomposer 0.661 7.28E-06 - Arachnida Oribatida Phenopelopidae 1° decomposer 0.179 9.53E-04 7.07E-04 Arachnida Oribatida Scheloribatidae 2° decomposer 0.349 5.21E-02 4.21E-02 Arachnida Oribatida Suctobelbidae 1° decomposer 0.066 3.12E-06 2.38E-05 Arachnida Oribatida Tectocepheidae 1° decomposer 0.125 1.76E-04 1.15E-04 Arachnida Oribatida Tegoribatidae 1° decomposer 0.627 1.43E-03 6.00E-04 Arachnida Pseudoscorpiones Neobisiidae predator 0.539 2.20E-03 4.68E-03 Arachnida Trombidiformes Anystidae predator 0.344 3.93E-06 - 5 1 12 26 1 9 7 7 1 6 0 6 31 2 7 9 14 0 94 0 0 2 1 1 1 0 1 0 0 1 0 0 1 0 0 0 0 0 1 1 1 1 2 0 0 1 1 0 1 2 1 1 0 2 2 4 1 0 1 1 1 2 0 2 2 0 1 1 1 2 1 0 0 0 1 1 4 4 0 4 1 5 8 6 4 4 2 1 1 3 3 2 1 3 5 1 1 1 4 7 5 1 7 8 2 6 7 6 1 APPENDIX 3.C. (cont’d) Arachnida Trombidiformes Bdellidae predator 0.859 2.33E-05 Arachnida Trombidiformes Ereynetidae parasite 0.825 3.47E-06 - - Arachnida Trombidiformes Eriophyidae herbivore 0.000 - 1.99E-05 2 1 0 0 0 1 Arachnida Trombidiformes Eupodidae parasite 0.643 7.30E-03 1.50E-03 21 16 Arachnida Trombidiformes Microdispidae 3° decomposer 0.900 1.27E-05 - Arachnida Trombidiformes Pygmephoridae 3° decomposer 0.286 5.45E-06 2.59E-05 Arachnida Trombidiformes Rhagidiidae 1° decomposer 0.159 1.62E-04 1.99E-03 Arachnida Trombidiformes Scutacaridae 3° decomposer 0.467 1.56E-04 1.67E-04 Arachnida Trombidiformes Siteroptidae 3° decomposer 0.920 1.94E-04 1.03E-04 Arachnida Trombidiformes Stigmaeidae predator 0.493 6.27E-05 5.90E-06 Arachnida Trombidiformes Tarsonemidae 3° decomposer 0.164 1.78E-03 1.22E-03 Arachnida Trombidiformes Tetranychidae herbivore 0.291 7.56E-07 - Arachnida Trombidiformes Trombidiidae predator 0.468 1.45E-02 2.29E-02 Arachnida Trombidiformes Tydeidae omnivore 0.415 4.35E-04 4.94E-05 Chilopoda Geophilomorpha Schendylidae predator 0.198 1.13E-02 3.30E-03 Chilopoda Scolopendromorpha Cryptopidae predator 0.009 2.54E-05 Chilopoda Scutigeromorpha Scutigeridae predator 0.044 3.97E-06 - - Collembola Entomobryomorpha Entomobryidae 1° decomposer 0.711 6.69E-02 9.73E-02 Collembola Entomobryomorpha Isotomidae 2° decomposer 0.001 1.37E-02 6.57E-03 Collembola Entomobryomorpha Tomoceridae 1° decomposer 0.834 6.65E-02 9.04E-03 Collembola Poduromorpha Hypogastruridae 2° decomposer 0.612 3.47E-02 1.25E-02 Collembola Poduromorpha Neanuridae 2° decomposer 0.412 3.59E-06 5.55E-03 Collembola Poduromorpha Onychiuridae 2° decomposer 0.012 1.10E-02 3.78E-03 Collembola Poduromorpha Tullbergiidae 2° decomposer 0.219 - 2.54E-05 Collembola Symphypleona Katiannidae 1° decomposer 0.683 1.55E-04 9.91E-05 Collembola Symphypleona Sminthuridae 1° decomposer 0.644 3.93E-06 4.39E-06 Collembola Symphypleona Sminthurididae herbivore 0.116 - 5.31E-05 2 1 3 11 11 2 17 1 11 1 3 2 1 28 14 17 16 2 16 0 5 1 0 0 2 7 10 10 2 13 0 10 2 8 0 0 29 25 19 14 5 12 1 6 1 3 Diplopoda Diplura Diplura Julida Diplura Diplura Julidae 2° decomposer 0.069 1.72E-02 1.61E-01 12 18 Campodeidae omnivore 0.192 1.08E-03 - Japygidae 3° decomposer 0.167 - 2.07E-05 2 0 0 1 95 1 1 0 0 2 0 0 1 3 1 0 1 1 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 2 0 0 0 1 0 0 1 3 0 0 0 0 0 1 2 1 0 0 0 1 1 1 4 0 1 2 0 3 1 0 1 1 1 1 6 2 2 5 6 6 2 6 1 7 3 3 1 1 8 8 8 6 5 6 1 5 1 3 8 2 1 APPENDIX 3.C. (cont’d) Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Coleoptera Anthicidae omnivore 0.627 - 1.72E-03 Coleoptera Brentidae herbivore 0.622 7.56E-07 - Coleoptera Byrrhidae herbivore 0.312 - 8.46E-04 Coleoptera Cantharidae predator 0.663 4.67E-05 6.59E-02 0 1 0 5 1 0 3 5 Coleoptera Carabidae predator 0.843 3.27E-02 3.87E-02 12 20 Coleoptera Cerambycidae 1° decomposer 0.018 5.25E-05 9.38E-06 Coleoptera Chrysomelidae herbivore 0.063 7.07E-04 3.47E-03 Coleoptera Cleridae predator 0.609 5.47E-04 1.33E-02 Coleoptera Corylophidae 3° decomposer 0.293 1.30E-03 8.70E-05 Coleoptera Cryptophagidae 3° decomposer 0.270 1.62E-03 3.92E-03 Coleoptera Curculionidae herbivore 0.372 5.32E-02 2.38E-02 Coleoptera Dytiscidae predator 0.947 - 5.38E-05 Coleoptera Elateridae 1° decomposer 0.926 9.73E-06 2.98E-02 Coleoptera Erotylidae 3° decomposer 0.119 3.60E-03 3.42E-04 Coleoptera Heteroceridae 3° decomposer 0.769 2.65E-06 - Coleoptera Histeridae predator Coleoptera Lampyridae predator 0.109 0.091 - - 4.01E-05 1.08E-02 3 7 1 3 2 9 0 1 6 1 0 0 Coleoptera Latridiidae 3° decomposer 0.006 4.03E-03 3.30E-03 15 Coleoptera Leiodidae 3° decomposer 0.033 - 8.81E-06 Coleoptera Lucanidae 1° decomposer 0.790 9.27E-05 Coleoptera Melyridae predator 0.749 4.64E-04 - - Coleoptera Nitidulidae 1° decomposer 0.855 1.62E-02 1.75E-03 Coleoptera Phalacridae 3° decomposer 0.974 6.78E-04 1.41E-04 Coleoptera Scarabaeidae 2° decomposer 0.284 9.79E-04 9.83E-03 0 1 2 5 1 3 1 8 1 1 2 13 1 1 3 0 2 1 6 2 0 0 7 1 9 Coleoptera Staphylinidae predator 0.175 1.22E-01 1.19E-01 21 29 Coleoptera Tenebrionidae omnivore 0.801 1.36E-06 6.24E-06 Coleoptera Throscidae 3° decomposer 0.562 - 2.45E-06 Diptera Diptera Diptera Anthomyiidae 1° decomposer 0.011 2.56E-06 - Asilidae predator 0.959 - 7.08E-06 Aulacigastridae herbivore 0.845 6.91E-05 - 1 0 1 0 1 1 1 0 1 0 96 0 1 0 2 1 3 1 0 2 1 1 0 1 3 1 0 0 4 0 1 2 1 1 0 0 1 0 1 0 1 1 0 2 3 0 1 2 0 1 1 1 1 1 0 0 2 1 0 2 0 0 2 1 3 0 1 1 0 1 0 1 1 2 6 8 4 7 1 3 3 8 1 2 5 1 2 1 8 2 1 2 6 2 6 8 2 1 1 1 1 APPENDIX 3.C. (cont’d) Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Calliphoridae 1° decomposer 0.094 - 1.21E-02 Cecidomyiidae 3° decomposer 0.001 5.18E-02 9.11E-04 Ceratopogonidae omnivore 0.247 4.15E-05 2.53E-04 Chironomidae 2° decomposer 0.000 4.47E-03 4.15E-03 Chloropidae herbivore 0.846 - 1.47E-03 Dolichopodidae predator 0.618 3.48E-02 7.96E-06 Drosophilidae 3° decomposer 0.004 - 1.87E-03 Empididae predator 0.293 4.31E-03 1.30E-05 Ephydridae 1° decomposer 0.890 7.68E-04 - Hybotidae predator 0.442 Keroplatidae 3° decomposer 0.021 - - 1.14E-03 1.09E-05 Muscidae predator 0.597 8.98E-04 7.90E-03 Mycetophilidae 3° decomposer 0.816 1.58E-02 1.64E-04 Psychodidae 3° decomposer 0.325 Scatopsidae 3° decomposer 0.669 - - 5.69E-05 5.12E-05 Sciaridae 3° decomposer 0.951 1.14E-04 2.37E-04 Sciomyzidae parasite 0.212 2.83E-06 1.96E-05 Sphaeroceridae 2° decomposer 0.024 - 3.31E-06 Stratiomyidae 1° decomposer 0.057 1.63E-06 2.69E-02 Syrphidae predator 0.377 7.78E-06 - Tachinidae parasite 0.033 9.71E-03 1.48E-04 Tephritidae herbivore 0.240 5.31E-06 4.84E-05 Xylophagidae predator 0.020 9.81E-06 - - Hemiptera Anthocoridae predator 0.011 2.81E-05 Hemiptera Hemiptera Aphididae herbivore 0.782 4.38E-03 2.58E-05 Berytidae herbivore 0.050 4.40E-03 3.25E-03 Hemiptera Cercopidae herbivore 0.662 - 1.70E-06 Hemiptera Cicadellidae herbivore 0.078 1.12E-01 6.56E-02 19 28 Hemiptera Cydnidae herbivore 0.235 - 1.98E-05 Hemiptera Delphacidae herbivore 0.924 2.07E-02 7.84E-03 0 5 1 5 97 0 21 2 5 0 8 0 2 1 0 0 5 9 0 0 7 1 0 1 1 5 1 1 1 1 1 0 1 11 11 14 3 2 6 2 0 1 1 2 7 3 1 5 2 1 3 0 3 2 0 0 2 1 1 0 1 0 1 0 5 0 1 1 0 0 2 0 0 0 1 0 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 5 4 3 1 4 1 0 1 1 0 2 3 1 0 0 1 2 0 2 1 0 0 2 0 1 0 1 2 1 8 7 8 3 7 4 3 1 1 1 3 7 3 1 5 1 1 3 1 4 2 1 1 3 1 1 8 1 4 Hemiptera Enicocephalidae predator 0.039 - 9.53E-04 Hemiptera Greenideidae herbivore 0.351 1.60E-05 Hemiptera Hemiptera Miridae Nabidae herbivore 0.740 1.02E-05 predator 0.539 - 1.95E-02 Hemiptera Pemphigidae herbivore 0.932 3.51E-05 5.04E-03 Hemiptera Pentatomidae omnivore 0.000 4.55E-03 3.68E-03 Hemiptera Rhopalidae herbivore 0.085 1.80E-05 - Hemiptera Rhyparochromidae herbivore 0.474 1.06E-02 6.51E-03 Hemiptera Scutelleridae herbivore 0.576 1.77E-04 - - - - APPENDIX 3.C. (cont’d) Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Hymenoptera Apidae herbivore 0.412 9.02E-06 Insecta Hymenoptera Braconidae parasite 0.695 3.21E-04 1.87E-05 Insecta Hymenoptera Dryinidae parasite 0.397 5.20E-05 7.36E-06 Insecta Hymenoptera Eucharitidae parasite 0.003 1.61E-05 1.20E-04 Insecta Hymenoptera Figitidae parasite 0.034 2.56E-05 - Insecta Hymenoptera F : Brachymyrmex omnivore 0.825 8.45E-04 4.34E-03 Insecta Hymenoptera F : Lasius omnivore 0.053 4.16E-02 1.60E-02 Insecta Hymenoptera F : Myrmecina predator 0.293 - 3.41E-03 Insecta Hymenoptera F : Ponera predator 0.352 2.04E-03 2.85E-03 Insecta Hymenoptera F : Prenolepis omnivore 0.898 2.13E-02 3.81E-03 Insecta Hymenoptera F : Pyramica predator 0.358 1.62E-05 4.37E-05 Insecta Hymenoptera F : Solenopsis omnivore 0.271 Insecta Hymenoptera F : Stenamma predator 0.220 Insecta Hymenoptera F : Tapinoma omnivore 0.671 - - - 5.69E-04 3.66E-03 4.52E-04 Insecta Hymenoptera F : Temnothorax omnivore 0.405 1.01E-03 - Insecta Hymenoptera F : Tetramorium omnivore 0.316 Insecta Hymenoptera Ichneumonidae parasite 0.579 - - 1.60E-05 5.88E-05 Insecta Hymenoptera Megaspilidae parasite 0.004 5.97E-06 Insecta Hymenoptera Pamphiliidae herbivore 0.870 1.36E-06 Insecta Hymenoptera Proctotrupidae parasite 0.766 7.50E-05 - - - Insecta Lepidoptera Anthelidae herbivore 0.708 - 3.41E-06 98 0 1 1 0 3 2 1 2 1 2 2 1 2 1 4 8 0 7 5 1 0 0 0 2 0 0 1 1 1 0 1 0 0 3 3 1 0 4 0 0 4 1 1 0 3 13 3 9 7 2 1 1 1 0 1 2 0 0 0 1 0 1 1 0 2 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 2 0 0 1 1 1 0 1 0 0 2 3 0 0 2 0 0 2 0 0 0 1 5 1 2 2 1 1 1 1 0 1 2 0 0 0 1 1 1 1 2 5 2 1 4 1 1 4 1 2 1 3 8 1 4 5 2 1 1 1 2 1 2 1 1 1 1 APPENDIX 3.C. (cont’d) Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Lepidoptera Crambidae herbivore 0.136 Lepidoptera Elachistidae herbivore 0.404 - - 1.67E-02 7.68E-03 Lepidoptera Erebidae herbivore 0.760 7.47E-06 2.40E-05 Lepidoptera Gelechiidae herbivore 0.950 - 6.17E-05 Lepidoptera Geometridae herbivore 0.126 1.67E-03 7.84E-04 Lepidoptera Gracillariidae herbivore 0.147 1.31E-05 5.50E-03 Lepidoptera Lymantriidae herbivore 0.515 3.47E-06 - Lepidoptera Noctuidae herbivore 0.451 Lepidoptera Nolidae herbivore 0.384 Lepidoptera Pyralidae herbivore 0.121 - - - Lepidoptera Riodinidae herbivore 0.772 5.82E-06 Lepidoptera Saturniidae herbivore 0.347 6.37E-06 1.01E-03 8.77E-05 3.03E-03 - - Lepidoptera Sesiidae herbivore 0.512 - 4.01E-03 Neuroptera Hemerobiidae predator 0.459 1.88E-02 2.55E-03 Neuroptera Myrmeleontidae predator 0.394 3.78E-07 Odonata Coenagrionidae predator 0.136 4.72E-06 - - Orthoptera Acrididae herbivore 0.444 9.25E-06 1.51E-04 Orthoptera Tettigoniidae herbivore 0.942 4.36E-06 - Psocoptera Liposcelidae 2° decomposer 0.022 3.21E-03 3.32E-04 Psocoptera Psyllipsocidae 2° decomposer 0.056 1.56E-04 1.85E-04 Insecta Thysanoptera Phlaeothripidae herbivore 0.001 1.36E-04 2.58E-04 Insecta Thysanoptera Thripidae herbivore 0.154 5.91E-05 7.96E-05 0 0 2 0 6 1 1 0 0 0 1 1 0 5 1 1 1 1 2 2 1 3 2 2 1 1 11 2 0 3 1 1 0 0 2 3 0 0 1 0 1 5 3 8 0 0 1 0 0 1 1 0 0 0 1 1 0 2 1 1 1 1 2 0 0 0 2 2 0 1 2 2 0 3 1 1 0 0 1 2 0 0 1 0 1 1 2 4 2 2 2 1 6 3 1 3 1 1 1 1 1 5 1 1 2 1 3 3 3 7 99 LITERATURE CITED 100 LITERATURE CITED Abbar, S., Schilling, M.W. and Phillips, T.W., 2016. 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Overwintering soil arthropod community responds to post-harvest application of urea to leaf litter in a conventionally managed orchard ABSTRACT Application of urea to leaf litter is used as method of reducing Spring infection of apple by Venturia inaequalis, which overwinters on infected apple leaves. This technique has been associated with accelerated leaf decomposition after application and decreased ascospore release by V. inaequalis in the following spring. Soil arthropods have been shown to respond to changes in soil nutrient concentrations and are likely influenced by urea applications. Soil arthropods are also known to participate in leaf degradation and fungal grazing. Therefore, it is probable that they are involved in reduction of overwintering V. inaequalis. However, the response of soil arthropods to post-harvest urea applications has been unexplored. I applied a 10% urea solution to leaf litter just after leaf fall and tracked changes in soil arthropod relative abundance using CO1 metabarcoding to identify mixed samples to the family level and assign feeding guilds. My experiment was conducted as a field scale, replicated design using 0.30 acre plots with 8 sampling events over a 5 month period. I demonstrated that urea application caused an up-regulation of tertiary decomposers and a down-regulation of primary decomposers during the first month following application. I also observed that a greater number of decomposer taxa were detected in the urea treatment than in the control, suggesting that absolute decomposer populations were greater in the urea treatments. I propose that urea application caused a trophic cascade in which increased microbial growth leads to a recruitment of fungal feeding arthropods into leaf litter from surrounding areas of the orchard. KEYWORDS: Floor Sanitation, Pathogen Management, Detritivores, Molecular Ecology 110 INTRODUCTION Apple scab, caused by the ascomycetous fungi Venturia inaequalis (Cooke) Wint., is the leading cause of economic loss in the global apple market (Bowen et al 2011). The principle source of loss results from lesions that make fruit cosmetically unsuitable for the fresh market. Infection can also decrease fruit set, reduce fruit size, and make fruit more susceptible to shrivelling during storage (MacHardy et al. 2001;Tomerlin and Jones 1983). The infection cycle in apples begins in the early spring with the release of ascospores from infested leaf litter that form primary infections on young leaf tissue (Keitt and Jones 1926; MacHardy et al. 2001). Environmental conditions impact the success of infections and incubation periods; however, the time between spore germination and conidial release can be as little as 9 days, allowing for many generations over the growing season (Vaillancourt and Hartman 2000). The propagation of conidial spores is referred to as the secondary infection period. Secondary infections are difficult to control because spore number grows exponentially with each generation. Furthermore, each generation amplifies successfully adapted genotypes, making apple scab populations increasingly resilient as the season progresses. Therefore, it is critical to control apple scab during the primary infection period. Control is typically managed through disease resistant cultivars and repeated fungicide applications. Both conventional and certified-organic orchards in the North Central and North Eastern regions of the USA use aggressive fungicide programs that involve 10- 20 fungicide applications for apple scab. V. inaequalis is well adapted to selecting and distributing virulence and fungicide resistance genes. The asexual phase of apple scab reproduction ensures that favorable genes are amplified and rapidly spread throughout an orchard. Resistance genes are then shared during the sexual reproductive phase, which increases the probability of producing better-adapted genotypes than those of the previous year. Apple scab has become resistant to several fungicidal classes (dodine, methyl benzimidazole carbamates, demethylation inhibitors, and quinone outside inhibitors), and has overcome resistant apple cultivars (Guerrin et al. 2007; Chapman et 111 al. 2011). Negative impacts of fungicides and rapid loss of management tools to resistance makes development of alternative management practices for apple scab an important goal. Apple scab spends most of its life as a saprophyte of leaves on the orchard floor (MacHardy et al. 2001). This presents a target window for cultural management tactics, called floor sanitation, that reduce the amount of viable inoculum prior to the primary infection period. As early as the 1880s, growers recognized that destroying leaf litter influenced apple scab disease incidence (Trelease 1884; Scribner 1888). The most widely explored and practiced form of floor sanitation is a type of in situ composting in which urea is applied to fallen leaves to accelerate their decomposition. A key barrier to industry-wide adoption of floor sanitation is the unpredictable nature of the technique. A collective view of reported data on floor sanitation reveals considerable variation in field-scale responses. Reported reductions of ascospore discharge and primary scab infections range from 45 to 97%, and 24 to 65%, respectively (Burchill et al. 1965; Burchill 1968; Holb et al. 2006). This variability presents a major challenge to the adoption of this scab management technique: it is difficult to convince growers to invest time, money, and equipment into techniques when the data does not provide a consistent picture of expected outcomes. The current body of work on floor sanitation is largely empirical with minimal consideration of how these techniques impact the life cycle of V. inaequalis. The lack of mechanistic knowledge severely hinders our ability to identify sources of variability and adapt floor sanitation practices to produce consistent outcomes. In particular, the response of arthropods to nitrogen enriched leaf litter has not been explored in the context of apple orchard. This is surprising given that arthropod taxa are represented within every functional group and trophic level associated with the soil community (Faber 1991; Brussard 1998). Arthropods feeding is a key determinant in soil microbe development, simultaneously reducing the growth of some through direct grazing and assisting others through colonization and nutrient release (Ineson et al. 1982; Tueben 1991). Furthermore, arthropod feeding behavior and population structure have been found to change in response environmental availability of nitrogen (Denno and Fagan 2003; Klironomos et al. 1992; Wickings 112 and Grandy 2013; White 1984). Thus, it is logical to suspect that changes in soil arthropod communities would follow applications of urea to leaf litter, and that those changes may be associated with decreased survival of a ground-dwelling fungal pathogen. However, the only research currently connecting invertebrates to apple scab management is limited to a few observations of increased earthworm activity correlated with floor sanitation treatments (Raw 1962; Holb et al. 2006; Werner 2006; Rudiger et al. 2012). The goal of my study was to evaluate the potential role of epigeic arthropods in urea- based floor sanitation practices used to control V. inaequalis in conventionally-managed orchards. I made use of a metabarcoding approach in a replicated, field-scale experiment to test the hypothesis that urea application to leaf litter causes changes in arthropod community structure that result in an upregulation of functional groups associated with leaf decomposition and fungal grazing. Molecular approaches that combine high-throughput sequencing (HTS), molecular markers, and curated sequence libraries to assign taxonomy to bulk sample assemblages have been successfully employed in microbial community studies to assign taxonomy and evaluate diversity (Roumpeka et al. 2017; Martínez‐Porchas and Vargas‐Albores 2017). Great strides have been made in transferring these approaches to the evaluation of arthropod communities, enabling research groups to overcome, at least in part, the knowledge and time challenges associated with morphological identification of cryptic soil organisms. The first field-scale studies of arthropods using molecular approaches have just begun to emerge in the literature over the last few years (Arribas et al. 2016; Pedro et al. 2017; Toju and Baba 2018). The results of these studies have shown that molecular ecology affords researchers with a previously unachievable capacity to explore arthropod community dynamics, particularly over large spatial and temporal scales. MATERIALS AND METHODS Experimental Design The experiment was conducted in a 16.2 ha apple orchard located near Pottersville, MI (42.6292° N, 84.7389° W). Two treatments were applied to experimental plots, referred to as “Control” and “Urea”. Both treatments were managed according to the 113 grower’s standard practices. Plots assigned to the Urea treatment received a single application of a 10% (w/v) urea solution at a rate of 454 l/ha. The applications were made in the Fall once 75% of leaves had fallen. I applied the urea solution directly to the ground using a truck mounted, pump-driven herbicide sprayer. I focused spray on the under story where leaves aggregated. Iapplied urea on November 22, 2016 and took my last field samples on April 22, 2017. Plots were established in apple scab susceptible varieties: MacIntosh, Jonagold, Red Delicious, and Golden Delicious. Six 0.12 ha replicates of each treatment were established in a randomized complete block design, where apple cultivar was the blocking factor. Field Sample Collection and Processing Samples were collected on eight dates: 11.28.16, 12.09.16, 12.16.16, 12.29.16, 1.11.17, 2.22.17, 3.28.17, and 4.21.17. On each date, four 33x33 cm areas were randomly subsampled from the dripline of trees within the central rows of each experimental plot. Sample locations were marked to prevent resampling on future dates. A subsample consisted of all leaf litter, above-ground biomass, and the top 1 cm of soil. Subsamples from each experimental plot were homogenized by gentle mixing in a bucket before transferring two 1L aliquots to Berlese funnel extraction devices to isolate arthropods from the substrate matrix. Each 1L aliquot was transferred to a separate Berlese funnel. The other two funnels were designated for a gut content analysis workflow. The funnels were heated from the top with 40W incandescent bulbs placed 10cm from the sample surface. The arthropods were collected and preserved in pure propylene glycol over a 120 hour period. A dimmer switch was used to reduce bulb outputs to 10W for the first 24 hours, 20W for hours 24 through 48, and 40W for the remaining time. After collection, samples were stored at -20°C for the remainder of the study. DNA Extraction Samples were homogenized in a FastPrep 24 for 45 sec at 5.5 m/s after the addition of 1 uL of 2-mercaptoethanol and 5 ul of 10 mg/mL Proteinase K, and then incubated for 1 hour at 56°C. The samples were extracted with two 500 uL washes of 24:1 chloroform:isoamyl alcohol, transferring the aqueous layer to a new container each time after a 5 114 minute centrifuge spin at 15,000 rcf. DNA was precipitated with the addition of 500 uL chilled isopropanol. Following a 30-minute incubation at -20°C, the DNA was pelleted with a 10 minute centrifuge spin at 17,000 rcf. The supernatant was discarded, and the pellet was triple-rinsed with chilled 70% ethanol to remove salts. The pellet was dried at 45°C and resuspended in 100 uL of PCR-grade water. The resuspended DNA was processed with the OneStep PCR Inhibitor Removal Kit from Zymo Research to avoid downstream complications with DNA sequencing resulting from carryover of contaminants from soil particles. Endpoint PCR A 313 bp region of the Cytochrome Oxidase Subunit 1 gene (CO1) was amplified using the primer set developed by Leray et al. (2013). The primers were modified to include Fluidigm CS1 and CS2 oligos on their 5’ ends to facilitate multiplex sequencing. Amplification was performed in 50 uL reactions containing 1 ug/uL of dsDNA template, 0.4uM of forward and reverse primers, and 25 uL of PCRBIO HS Taq 2x Master Mix. Reaction conditions were: an initial 95°C activation step of 2 minutes, followed by 40 cycles of 15 s at 95°C, 15 s at 58°C, and 30 s at 72°C. Reactions were chilled to 4°C after a final elongation step of 2 min at 72°C. Amplification was confirmed by running 5 uL of PCR product on a 1.5% TBE agarose gel stained with GelRed (Biotium) for 90 minutes at 90V. Amplicon Sequencing Sequencing was performed at the Michigan State University Research Technology Support Facility (RTSF). Samples were multiplexed at 3.7 - 5% of a lane per sample. The RTSF added dual indexed, Illumina compatible adapters via PCR with primers targeting the Fluidigm oligo ends of the primary amplicons. Each pool was loaded on an Illumina MiSeq v2 Standard flow cell, and sequencing was performed in a 2x250 bp paired end format using a MiSeq v2 500 cycle reagent cartridge. Sequence Data Processing Data from RTSF were received with the Fluidigm oligos and Illumina adapters removed from the amplicon sequences. Sequencing errors were corrected with Bayes Hammer (Nikolenko et al. 2013). Corrected reads were assembled and pre-clustered using IPED, a two-step denoising algorithm developed specifically for Illumina paired end reads. IPED 115 uses the base call quality data in FastQ files to mark low-quality positions during contig assembly, which are then masked in a downstream clustering step (Mysara et al 2016). Low quality contigs were removed prior to the pre-clustering step using the Mothur pipeline (Schloss et al. 2009). Contigs were screened to remove any with ambiguous bases, more than 10 homopolymers, or a difference in length from the expected amplicon of more than 2 bases. Retained contigs were then aligned against a reference alignment of arthropod CO1 sequences. Sequences were obtained from the NCBI non-redundant nucleotide library, and were limited to the full-length Folmer region (Folmer et al. 1994). Contigs that aligned outside of the target region were removed. After denoising with IPED, chimeras were removed with the vsearch algorithm implemented in Mothur. The bayesian clustering algorithm CROP was used to generate OTUs with lower and upper thresholds of 2.5 and 3.5%, respectively (Hao et al. 2011). Taxonomy was assigned to OTUs by performing BLAST searches of representative sequences and retrieving classifications from the NCBI taxonomy database using the R package taxonomizr V 0.2.2 (Sherrill-Mix 2017). OTUs that were less than 0.01% relative abundance per sample prior to rarefaction, or missing family level assignments, were removed to reduce the impact of sequencing artifacts. Read counts for each family were combined by sample, with the exception of ants (Hymenoptera: Formicidae). Read counts for ants were combined at the genus level because of the broad functional diversity that exists within the family. Each family (or genus in the case of ants) was assigned a feeding guild based on observed feeding activity reported in literature. Feeding guild classes were derived from research on trophic levels in soil arthropods using stable nitrogen and carbon isotope analysis (Schneider et al. 2004; Chahartaghi et al. 2005; Oelbermann and Scheu 2010; Lagerlöf 2017 et al. 2017; Melguizo-Ruiz et al. 2017) which suggests that decomposers can be divided into three general classes: primary, secondary, and tertiary. Additional categories were created for herbivores, predators, omnivores, and parasites. The parasite category encompassed any organism likely to have been occupying a host during 116 the study period, including parasitoids. Feeding guild assignments for each family and the sources supporting those assignments are available in Appendix 4.B. Data Normalization I normalized read counts within each replicate, stratified by date. They were equalized by resampling OTU read counts to a subsample size equal to the lowest original total read count within each date. Resampling was done using the rarefaction function in Vegan v.2.4.3 (Okansen et al. 2007). Data Analysis A Bray-Curtis distance matrix was generated using the normalized read counts for each collapsed taxon. PERMANOVA was performed on the matrix using the adonis function in Vegan v.2.4.3 to assess the statistical significance of urea application on community composition (Anderson 2001). Permutations were stratified by experimental unit to account for the within subject effect of repeated measurements. A blocking factor was included to account for the effects of location within the orchard and apple tree cultivar. Multivariate dispersion around the treatment centroids was also compared using the betadisp function in Vegan v.2.4.3. Alpha diversity values were calculated for each treatment with a taxa-neutral approach using Entropart v.1.4 (Marcon and Hérault, 2015) to explore the effect urea application on within- community diversity. Alpha diversity was expressed as effective taxa numbers (Chao et al. 2014). Values were calculated for diversity orders across the range of q=(0,2) where q=0 corresponds to taxonomic richness, q=1 corresponds to Shannon’s diversity, and q=2 corresponds to the Inverse Simpson’s diversity. The influence of dominant species on diversity values increases with the value of q. A univariate linear model was fit to relative abundance values for each taxon and each feeding guild with date and management as fixed effects using the gls function in the nlme v.3.1 package. A logit transformation was applied to the relative abundance values to achieve normality assumptions (Warton and Hui 2011). The variance between dates did not meet homogeneity assumptions for a common variance model, so separate variance terms were included for each date. 117 Relative abundance values for the parasite and herbivore guilds were removed from the data set prior to analysis of feeding guild data. Given the lack of food resources available for herbivores during the winter period, the likelihood of herbivore dormancy during the winter, and the internal nature of parasites, changes in these guilds were not relevant to the topic of this study. Values for the remaining guilds were proportionally scaled after the removal of herbivore and parasite values so that relative abundance in each sample again summed to 1. The number of unique detection events that occurred on each date was compared between management types for each decomposer guild using Wilcox Rank Sum Tests. A detection event refers to the presence of a taxon or guild on a given date within a given treatment. When a taxa or guild was detected in only one of the two treatments on a given date, that detection was considered “unique”. A difference in unique detection events was used as an indicator of a potential difference in community population size. All analyses were performed in R v.3.4.3 (R Core Team 2018). RESULTS AND DISCUSSION Trophic Succession in Control Plots Observations of other litter systems have shown that litter decomposition is a successive process in which organisms can be organized into broad trophic guilds based on their feeding order and strategy (Brabcová et al. 2018; Berg et al 2004; Schneider et al. 2004; Chahartaghi et al. 2005; Oelbermann and Scheu 2010; Lagerlöf 2017 et al. 2017; Melguizo-Ruiz et al. 2017). Decomposer species, which include microbiota and invertebrates, differ in their ability to obtain and utilize food resources as a result of factors such as enzymatic production, morphology, nutritional requirements, and foraging strategy. Primary decomposers are capable of degrading complex polysaccharides and structural molecules like cellulose, and can therefore feed directly on litter material. Secondary decomposers feed on litter material that has been partially degraded by primary consumers, as well as their metabolic by- products. Tertiary decomposers feed primarily on microbial biofilms and fungi, which are generally primary and secondary decomposers. 118 While microbiota generally dominate as primary decomposers, invertebrates play a key role in the initial degradation of litter resources and establishment of microbial populations (Lussenhop 1992). Microbial growth in soils is often limited by access to soluble nutrients, particularly nitrogen and phosphorus (Scheu and Schaefer 1998; Brussard 1998). Invertebrate grazing liberates these resources from litter tissues in the form of faeces and extra-orally digested material for use by microbes (Ineson et al. 1982; Lussenhop 1992; Seastedt and Crossley 1984). The movement of invertebrates helps to colonize leaf litter with microbes, and the fragmentation of litter by invertebrates increases the surface area available for colonization (Anslan et al. 2018; Lussenhop 1992). Arthropod primary decomposers were 85% of the decomposer community on the first sampling date, compared to 4% and 11% for secondary and tertiary decomposers, respectively (Fig 4.1). Primary decomposers steadily decreased in relative abundance through the fourth sampling date (12.29.16), reaching their lowest relative abundance in the decomposer guild of 8%. Secondary decomposers increased in the second week (12.09.16) and were the most abundance decomposer sub guild in the third week (12.16.16). By the fourth sampling period, tertiary decomposers had become the dominant decomposer sub guild. The remaining sample periods, which spanned from 1.11.17 to 4.21.17, show a relatively stochastic balance maintained in the decomposer guild. The change in decomposer sub guild abundances over the first month in the Control plots suggests that the transition from invertebrate to microbial dominated primary decomposition took place in the span of five weeks after leaf fall. Effect of Urea Application on Decomposer Relative Abundance The distribution of decomposer sub guilds in the urea treated plots did not follow the same pattern as observed in the untreated plots. Mean relative abundance of tertiary decomposers was consistently greater in the urea plots (Fig 4.2), and the overall difference was statistically significant (Table 4.1). Primary decomposer relative abundance was not consistently different across all dates, but did show a trend during the first three sampling periods of decreased relative abundance in the urea 119 treatment. High levels of variability were observed in the difference estimates of secondary decomposers and no discernable pattern was observed. This study was conducted entirely within one orchard using a randomized block design during a time of year when insect migration from outside of the study area was unlikely. Any changes in community arising from urea application are extremely likely to have arise from either increased reproduction rates, or movement of taxa between treated and untreated space. Given the low ambient temperatures during the study, it is unlikely that increased reproduction was driving those observations. Thus, the observed changes in relative abundance were indicative of soil arthropod movement in response to urea application. Two plausible, and possibly complementary, explanations for the observed response to urea are: 1) recruitment of microbial grazers (secondary and tertiary decomposers) to nutrient enriched food sources; and 2) a change in the arthropod:microbe successional pattern. It has been demonstrated that free nitrogen is rapidly assimilated into microbial biomass, and that changes in environmental nitrogen concentrations are correlated with those of fungal tissue in those environments (Scheu and Schaefer 1998; Klironomos et al. 1992). This means that microbial biomass in areas with elevated nitrogen concentrations becomes a source of nitrogen-rich food for microbial grazers. While there is some debate regarding the degree of limitation within specific ecosystem types and functional groups, it is generally accepted that most detritivorous arthropods are nitrogen limited (Denno and Fagan 2003; Fagan and Denno 2004; Martinson et al. 2008). Observations of arthropods in both natural and agricultural ecosystems have shown that populations cluster around areas that have nitrogen-rich food sources or have received nitrogen fertilization (Hamstead et al. 1957; White 1984; Lu et al. 2007). Klironomos et al. (1992) showed that Folsomia candida (Collembola: Entomobryomorpha), selectively grazed on fungi with higher nutrient contents, even within the same species of fungi. Thus the elevated relative abundance of tertiary decomposers in the urea treatment could be reflective of a 120 difference in the nutrient quality of microbial tissues resulting from the increase in environmental nitrogen. In addition to recruiting of arthropod tertiary decomposers, urea application may also have affected the relationship between arthropod and microbial primary decomposers. It is plausible that the application of urea substituted for the mineralization services normally provided by arthropod primary decomposers. This could have allowed microbial primary decomposers to overcome their nitrogen limitation, bypassing the normal order of trophic succession from arthropod to microbial dominance. Short-term additions of nitrogen to leaf litter systems with high C:N ratios have been shown to increase microbial biomass, stimulate cellulolytic enzyme release, and accelerate leaf litter breakdown (Carreiro et al. 2000; Hobbie 2005; Meidute et al. 2008; Sinsabaugh et al. 2002). Acceleration of microbial development would create food resources for tertiary decomposers while creating localized competition for arthropod primary decomposers. This would be consistent with the decreased relative abundance of arthropod primary decomposers observed during the first three weeks of sampling following urea application. Effect of Urea on Decomposer Population Size Relative abundance analysis cannot detect changes in absolute population sizes, and therefore would be unable to directly detect an enrichment effect. Any alternative method for providing indirect evidence of a recruitment effect of urea can be found in the number of unique detection events observed in control versus urea plots. Ecologists have long recognized the relationship between sampling effort and species richness: the number of species detected tends to increase with sample size or area because rare taxa are more likely to be encountered (Chao and Jost 2012; Gotelli and Colwell 2001). A logical corollary is that for any fixed sampling effort, abundant taxa are more likely to be detected and a threshold population exists below which detection is improbable. Therefore, the detection of a taxon unique to a particular treatment during a sampling period can be interpreted as either 1) the taxon being truly unique to that treatment; or 2) being differentially abundant such that it is detectable in one treatment but not another. That this experiment was conducted within a single 121 orchard using randomized plots under nearly identical management makes the association of unique detections with differential abundance the more logical of the two choices. The number of unique detection events over the course of the study was significantly greater in the urea treatment for both secondary and tertiary decomposers, but was similar between the urea and control treatments for primary decomposers (Table 4.2). This suggests that secondary and tertiary decomposer population sizes were larger in the urea treatment, which is consistent with the hypothesis that urea application causes recruitment of microbial grazers from either adjacent drive rows (which lack leaf litter) or from within the soil profile beneath the leaf litter. It also suggests that the decreased relative abundance of the primary decomposers may reflect dilution of an unchanged population size by the influx of secondary and tertiary decomposers. Responsive Decomposer Taxa A total of 65 decomposer taxa were detected throughout the course of the study, comprised of 4 classes and 12 orders. A complete list of detected taxa is included in Appendix 4.C. Of those detected, 30 taxa across 9 orders were detected on at least three sampling dates with at least one of those dates producing a significant (p<0.20) difference in relative abundance between treatments (Fig 4.3). Over 68% of significant detection events occurred within the first month, with the lowest number of detection events occurring in the last two sampling periods. This is consistent with the observation of the greatest number and magnitude of differences in feeding guilds also occurring in that time window. Evaluation of changes in litter chemistry and ecology within the first few weeks following urea application will be critical in future research looking to elucidate the specific mechanisms underlying the responses observed in this study. It is also interesting that the magnitude and direction of change in the relative abundance of individual taxons did not directly correlate with that of their respective feeding guilds. Relating to Urea-based Floor Sanitation Arthropod grazing, both directly on fungal tissues and indirectly on the leaf material colonized by fungi, can be a limiting factor for fungal 122 growth (Crowther and A’Bear 2012; Anslan et al. 2018; James et al. 2018). Leaf fragmentation can disrupt hyphal networks and lead to cytoplasmic leakage, preventing hyphae from mating or recruiting sufficient nutrients to complete life cycles. Thus, it is probable that arthropod grazing can provide some degree of control of fungal pathogens. Indeed, macroinvertebrate grazing has been associated with down regulation of fungal pathogens in a variety of agricultural systems (Friberg et al. 2005). I found evidence that arthropod decomposers, particularly those associated with fungal grazing, are recruited to urea-treated leaf litter, increasing decomposer populations. I also observed that tertiary decomposers dominate urea-treated litter during the first month following application. I propose that recruitment of arthropod decomposers with the capacity to retard fungal development through direct grazing and hyphal disruption is a primary driver in the causal mechanism underlying urea-based floor sanitation for the control of apple scab in orchards. The change in primary and tertiary decomposers during the first month is of particular interest because it correlates with the observation that the timing of the urea application is a determining factor in the efficacy of the technique (Burchill 1968). CONCLUSIONS Late-season application of urea to orchard leaf litter produced an observable impact on epigeic arthropod decomposers. Tertiary decomposers were up-regulated in urea-treated litter as evidenced by differences in both mean relative abundances and the number of unique detection events. Changes were most pronounced in the first month following urea application. The data suggest that the addition of urea recruited arthropod decomposers into leaf litter from surrounding areas in the orchard and accelerated the trophic succession that occurs during leaf degradation. The upregulation of taxa associated with fungal grazing resulting from urea application creates a potential mechanism by which V. inaequalis, a pathogen overwintering in leaf litter, is controlled by urea-based floor sanitation techniques. This is the first study to provide evidence linking soil arthropod communities to urea-based floor sanitation practices. Further investigation into the 123 winter feeding habits of taxa identified in this study is necessary to confirm if they are actually grazing on V. inaequalis. 124 APPENDICES 125 APPENDIX 4.A. Figures and Tables 126 Figure 4.1. Relative Abundance within the Decomposer Guild under Standard Management. Detected families within each replicate were assigned to a feeding guild and their read counts combined after rarefying to the minimum sample size within each date. Treatments were under conventional grower standard practices and did not receive a urea application. 127 Figure 4.2. Fold Change in Decomposer Relative Abundance in response to Urea Application. Detected families within each replicate were assigned to a feeding guild and their read counts combined after rarefying to the minimum sample size within each date. The urea treatment received a single post- harvest application of urea to the leaf litter. The control treatment was under conventional grower standard fold change in mean relative abundance observed in the urea treatment practices. Bars represent the log2 with respect to the control treatment by date. Values above the x-axis are more abundant in the urea treatment. 128 Table 4.1. Significance Values of Differences in Feeding Guild Relative Abundance. Detected families within each replicate were assigned to a feeding guild and their read counts combined after rarefying to the minimum sample size within each date. Reported p-values were from contrasts of mean relative abundance between urea and control treatments as estimated by a univariate model fit to a logit transformation of each guild’s relative abundance values. The urea treatment received a single post-harvest application of urea to the leaf litter. The control treatment was under conventional grower standard practices. 129 Table 4.2. Unique Detection Events in Decomposers. When a taxon was detected in only one of the two treatments on a given date, that detection was considered “unique”. Detected families were assigned to a feeding guild and their unique detection counts summed for each date. Wilcoxon Rank Sum Tests were performed on the per date sums of unique detection events between urea and control treatments. The urea treatment received a single post-harvest application of urea to the leaf litter. The control treatment was under conventional grower standard practices. Date Control Feeding Group 1° Decomposer 11.28.16 12.09.16 12.16.16 12.29.16 01.11.17 02.22.17 03.28.17 04.21.17 2° Decomposer 11.28.16 12.09.16 12.16.16 12.29.16 01.11.17 02.22.17 03.28.17 04.21.17 3° Decomposer 11.28.16 12.09.16 12.16.16 12.29.16 01.11.17 02.22.17 03.28.17 04.21.17 All Decomposers - 11.28.16 12.09.16 12.16.16 12.29.16 01.11.17 02.22.17 03.28.17 04.21.17 Urea 25 2 2 3 4 3 4 2 5 21 4 6 2 2 3 3 0 1 29 2 5 4 4 3 3 3 5 75 8 13 9 10 9 10 5 11 W 34 42 46 p-value 0.435 0.153 0.071 46.5 0.069 24 1 2 4 2 4 4 2 5 13 2 1 3 3 3 0 1 0 20 1 2 3 0 4 3 3 4 57 4 5 10 5 11 7 6 9 130 Figure 4.3 Decomposers Affected by Urea Application. Detected families within each replicate were assigned to a feeding guild and their read counts combined after rarefying to the minimum sample size within each date. The urea treatment received a single post-harvest application of urea to the leaf litter. The fold control treatment was under conventional grower standard practices. Color was mapped to the log2 change in mean relative abundance observed in the urea treatment with respect to the control treatment by date. Only taxa detected on at least 3 sampling dates with at least one of those dates associated with a significance value ≤0.20 from a contrast of mean relative abundance between urea and control treatments as estimated by a univariate model fit to a logit transformation of each guild’s relative abundance values. 131 APPENDIX 4.B. Feeding Guild Assignments & Supporting Literature 132 APPENDIX 4.B. Feeding Guild Assignments & Supporting Literature Phylum Class Order Arthropoda Arachnida Araneae Family Araneidae Arthropoda Arachnida Araneae Barychelidae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Clubionidae Dysderidae Gnaphosidae Hypochilidae Linyphiidae Lycosidae Arthropoda Arachnida Araneae Mecysmaucheniidae Arthropoda Arachnida Araneae Philodromidae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Pholcidae Pisauridae Salticidae Sicariidae Arthropoda Arachnida Araneae Tetragnathidae Arthropoda Arachnida Araneae Arthropoda Arachnida Araneae Theridiidae Thomisidae Feeding Guild predator predator predator predator predator predator predator predator predator predator predator predator predator predator predator predator predator Arthropoda Arachnida Astigmata Acaridae omnivore Arthropoda Arachnida Astigmata Proctophyllodidae Arthropoda Arachnida Astigmata Psoroptidae Arthropoda Arachnida Astigmata Pteronyssidae Arthropoda Arachnida Astigmata Pyroglyphidae Arthropoda Arachnida Astigmata Suidasiidae Arthropoda Arachnida Astigmata Trouessartiidae Arthropoda Arachnida Astigmata Xolalgidae parasite parasite parasite parasite parasite parasite parasite Arthropoda Arachnida Endeostigmata Nanorchestidae tertiary decomposer Arthropoda Arachnida Endeostigmata Terpnacaridae tertiary decomposer Arthropoda Arachnida Ixodida Ixodidae Arthropoda Arachnida Mesostigmata Ascidae Arthropoda Arachnida Mesostigmata Blattisociidae Arthropoda Arachnida Mesostigmata Digamasellidae Arthropoda Arachnida Mesostigmata Laelapidae Arthropoda Arachnida Mesostigmata Macrochelidae Arthropoda Arachnida Mesostigmata Ologamasidae Arthropoda Arachnida Mesostigmata Parasitidae Arthropoda Arachnida Mesostigmata Phytoseiidae Arthropoda Arachnida Mesostigmata Rhodacaridae Arthropoda Arachnida Mesostigmata Uropodidae Arthropoda Arachnida Mesostigmata Veigaiidae parasite omnivore omnivore omnivore predator parasite predator predator predator predator parasite predator Arthropoda Arachnida Opiliones Phalangiidae omnivore 133 APPENDIX 4.B. (cont’d) Arthropoda Arachnida Opiliones Phalangodidae omnivore Arthropoda Arachnida Oribatida Achipteriidae primary decomposer Arthropoda Arachnida Oribatida Brachychthoniidae omnivore Arthropoda Arachnida Oribatida Ceratoppiidae primary decomposer Arthropoda Arachnida Oribatida Ceratozetidae primary decomposer Arthropoda Arachnida Oribatida Cymbaeremaeidae tertiary decomposer Arthropoda Arachnida Oribatida Eremaeidae tertiary decomposer Arthropoda Arachnida Oribatida Eremobelbidae secondary decomposer Arthropoda Arachnida Oribatida Euphthiracaridae tertiary decomposer Arthropoda Arachnida Oribatida Galumnidae primary decomposer Arthropoda Arachnida Oribatida Arthropoda Arachnida Oribatida Arthropoda Arachnida Oribatida Arthropoda Arachnida Oribatida Nothridae Oppiidae Oribatellidae Oribatulidae primary decomposer omnivore primary decomposer secondary decomposer Arthropoda Arachnida Oribatida Oripodidae primary decomposer Arthropoda Arachnida Oribatida Phenopelopidae primary decomposer Arthropoda Arachnida Oribatida Scheloribatidae secondary decomposer Arthropoda Arachnida Oribatida Scutoverticidae tertiary decomposer Arthropoda Arachnida Oribatida Suctobelbidae primary decomposer Arthropoda Arachnida Oribatida Tectocepheidae primary decomposer Arthropoda Arachnida Oribatida Tegoribatidae primary decomposer Arthropoda Arachnida Pseudoscorpiones Neobisiidae Arthropoda Arachnida Scorpiones Buthidae Arthropoda Arachnida Scorpiones Vaejovidae Arthropoda Arachnida Solifugae Eremobatidae Arthropoda Arachnida Trombidiformes Anystidae Arthropoda Arachnida Trombidiformes Bdellidae Arthropoda Arachnida Trombidiformes Ereynetidae Arthropoda Arachnida Trombidiformes Eriophyidae Arthropoda Arachnida Trombidiformes Eupodidae predator predator predator predator predator predator parasite herbivore parasite Arthropoda Arachnida Trombidiformes Microdispidae tertiary decomposer Arthropoda Arachnida Trombidiformes Pygmephoridae tertiary decomposer Arthropoda Arachnida Trombidiformes Rhagidiidae primary decomposer Arthropoda Arachnida Trombidiformes Scutacaridae tertiary decomposer Arthropoda Arachnida Trombidiformes Siteroptidae tertiary decomposer Arthropoda Arachnida Trombidiformes Stigmaeidae predator Arthropoda Arachnida Trombidiformes Tarsonemidae tertiary decomposer Arthropoda Arachnida Trombidiformes Tetranychidae Arthropoda Arachnida Trombidiformes Trombidiidae Arthropoda Arachnida Trombidiformes Tydeidae Arthropoda Chilopoda Geophilomorpha Himantariidae Arthropoda Chilopoda Geophilomorpha Schendylidae herbivore predator omnivore predator predator 134 APPENDIX 4.B. (cont’d) Arthropoda Chilopoda Lithobiomorpha Lithobiidae Arthropoda Chilopoda Scolopendromorpha Cryptopidae Arthropoda Chilopoda Scolopendromorpha Scolopocryptopidae Arthropoda Chilopoda Scutigeromorpha Scutigeridae predator predator predator predator Arthropoda Collembola Entomobryomorpha Entomobryidae primary decomposer Arthropoda Collembola Entomobryomorpha Isotomidae secondary decomposer Arthropoda Collembola Entomobryomorpha Tomoceridae primary decomposer Arthropoda Collembola Poduromorpha Hypogastruridae secondary decomposer Arthropoda Collembola Poduromorpha Neanuridae secondary decomposer Arthropoda Collembola Poduromorpha Onychiuridae secondary decomposer Arthropoda Collembola Poduromorpha Tullbergiidae secondary decomposer Arthropoda Collembola Symphypleona Bourletiellidae secondary decomposer Arthropoda Collembola Symphypleona Katiannidae primary decomposer Arthropoda Collembola Symphypleona Sminthuridae primary decomposer Arthropoda Collembola Symphypleona Sminthurididae herbivore Arthropoda Diplopoda Julida Julidae secondary decomposer Arthropoda Diplura Diplura Campodeidae omnivore Arthropoda Diplura Diplura Arthropoda Insecta Blattodea Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Japygidae Termitidae Anthicidae Brentidae Byrrhidae Arthropoda Insecta Coleoptera Cantharidae Arthropoda Insecta Coleoptera Carabidae tertiary decomposer primary decomposer omnivore herbivore herbivore predator predator Arthropoda Insecta Coleoptera Cerambycidae primary decomposer Arthropoda Insecta Coleoptera Chrysomelidae Arthropoda Insecta Coleoptera Cleridae Arthropoda Insecta Coleoptera Coccinellidae herbivore predator predator Arthropoda Insecta Coleoptera Corylophidae tertiary decomposer Arthropoda Insecta Coleoptera Cryptophagidae tertiary decomposer Arthropoda Insecta Coleoptera Curculionidae Arthropoda Insecta Coleoptera Dytiscidae Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Elateridae Erotylidae Gyrinidae herbivore predator primary decomposer tertiary decomposer predator Arthropoda Insecta Coleoptera Heteroceridae tertiary decomposer Arthropoda Insecta Coleoptera Histeridae predator Arthropoda Insecta Coleoptera Hydraenidae tertiary decomposer Arthropoda Insecta Coleoptera Lampyridae predator Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Latridiidae Leiodidae Lucanidae 135 tertiary decomposer tertiary decomposer primary decomposer APPENDIX 4.B. (cont’d) Arthropoda Insecta Coleoptera Arthropoda Insecta Coleoptera Melyridae Nitidulidae predator primary decomposer Arthropoda Insecta Coleoptera Phalacridae tertiary decomposer Arthropoda Insecta Coleoptera Scarabaeidae secondary decomposer Arthropoda Insecta Coleoptera Staphylinidae Arthropoda Insecta Coleoptera Tenebrionidae predator omnivore Arthropoda Insecta Coleoptera Throscidae tertiary decomposer Arthropoda Insecta Diptera Anthomyiidae primary decomposer Arthropoda Insecta Diptera Asilidae Arthropoda Insecta Diptera Aulacigastridae predator herbivore Arthropoda Insecta Diptera Calliphoridae primary decomposer Arthropoda Insecta Diptera Cecidomyiidae tertiary decomposer Arthropoda Insecta Diptera Ceratopogonidae omnivore Arthropoda Insecta Diptera Chironomidae secondary decomposer Arthropoda Insecta Diptera Chloropidae Arthropoda Insecta Diptera Dolichopodidae herbivore predator Arthropoda Insecta Diptera Drosophilidae tertiary decomposer Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Empididae Ephydridae Hybotidae predator primary decomposer predator Arthropoda Insecta Diptera Keroplatidae tertiary decomposer Arthropoda Insecta Diptera Muscidae predator Arthropoda Insecta Diptera Mycetophilidae tertiary decomposer Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Pediciidae Phoridae Psychodidae Scatopsidae Arthropoda Insecta Diptera Sciaridae predator omnivore tertiary decomposer tertiary decomposer tertiary decomposer Arthropoda Insecta Diptera Sciomyzidae parasite Arthropoda Insecta Diptera Sphaeroceridae secondary decomposer Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Arthropoda Insecta Diptera Stratiomyidae Synneuridae Syrphidae Tachinidae Tephritidae Arthropoda Insecta Diptera Xylophagidae Arthropoda Insecta Hemiptera Anthocoridae Arthropoda Insecta Hemiptera Arthropoda Insecta Hemiptera Aphididae Berytidae Arthropoda Insecta Hemiptera Cercopidae Arthropoda Insecta Hemiptera Cicadellidae APPENDIX 4.A. (cont’d) 136 primary decomposer tertiary decomposer predator parasite herbivore predator predator herbivore herbivore herbivore herbivore APPENDIX 4.B. (cont’d) Arthropoda Insecta Hemiptera Clastopteridae Arthropoda Insecta Hemiptera Cydnidae Arthropoda Insecta Hemiptera Delphacidae Arthropoda Insecta Hemiptera Enicocephalidae Arthropoda Insecta Hemiptera Greenideidae Arthropoda Insecta Hemiptera Lachnidae Arthropoda Insecta Hemiptera Arthropoda Insecta Hemiptera Miridae Nabidae Arthropoda Insecta Hemiptera Pemphigidae Arthropoda Insecta Hemiptera Pentatomidae Arthropoda Insecta Hemiptera Rhopalidae Arthropoda Insecta Hemiptera Rhyparochromidae Arthropoda Insecta Hemiptera Scutelleridae Arthropoda Insecta Hemiptera Arthropoda Insecta Hemiptera Tingidae Triozidae Arthropoda Insecta Hymenoptera Agaonidae Arthropoda Insecta Hymenoptera Aphelinidae Arthropoda Insecta Hymenoptera Apidae Arthropoda Insecta Hymenoptera Bethylidae Arthropoda Insecta Hymenoptera Braconidae Arthropoda Insecta Hymenoptera Diapriidae Arthropoda Insecta Hymenoptera Dryinidae Arthropoda Insecta Hymenoptera Eucharitidae Arthropoda Insecta Hymenoptera Figitidae herbivore herbivore herbivore predator herbivore herbivore herbivore predator herbivore omnivore herbivore herbivore herbivore herbivore herbivore herbivore parasite herbivore parasite parasite parasite parasite parasite parasite Arthropoda Insecta Hymenoptera Formicidae : Brachymyrmex omnivore Arthropoda Insecta Hymenoptera Formicidae : Lasius omnivore Arthropoda Insecta Hymenoptera Formicidae : Myrmecina predator Arthropoda Insecta Hymenoptera Formicidae : Nylanderia omnivore Arthropoda Insecta Hymenoptera Formicidae : Odontomachus predator Arthropoda Insecta Hymenoptera Formicidae : Ponera predator Arthropoda Insecta Hymenoptera Formicidae : Prenolepis omnivore Arthropoda Insecta Hymenoptera Formicidae : Pyramica predator Arthropoda Insecta Hymenoptera Formicidae : Solenopsis omnivore Arthropoda Insecta Hymenoptera Formicidae : Stenamma predator Arthropoda Insecta Hymenoptera Formicidae : Tapinoma omnivore Arthropoda Insecta Hymenoptera Formicidae : Temnothorax omnivore Arthropoda Insecta Hymenoptera Formicidae : Tetramorium omnivore Arthropoda Insecta Hymenoptera Ichneumonidae Arthropoda Insecta Hymenoptera Megaspilidae Arthropoda Insecta Hymenoptera Pamphiliidae Arthropoda Insecta Hymenoptera Proctotrupidae Arthropoda Insecta Lepidoptera Adelidae parasite parasite herbivore parasite herbivore 137 APPENDIX 4.B. (cont’d) Arthropoda Insecta Lepidoptera Anthelidae herbivore Arthropoda Insecta Lepidoptera Blastobasidae primary decomposer Arthropoda Insecta Lepidoptera Crambidae Arthropoda Insecta Lepidoptera Elachistidae Arthropoda Insecta Lepidoptera Erebidae Arthropoda Insecta Lepidoptera Gelechiidae Arthropoda Insecta Lepidoptera Geometridae Arthropoda Insecta Lepidoptera Gracillariidae Arthropoda Insecta Lepidoptera Immidae Arthropoda Insecta Lepidoptera Lasiocampidae Arthropoda Insecta Lepidoptera Lymantriidae Arthropoda Insecta Lepidoptera Nepticulidae Arthropoda Insecta Lepidoptera Noctuidae Arthropoda Insecta Lepidoptera Nolidae Arthropoda Insecta Lepidoptera Prodoxidae Arthropoda Insecta Lepidoptera Psychidae Arthropoda Insecta Lepidoptera Pyralidae Arthropoda Insecta Lepidoptera Riodinidae Arthropoda Insecta Lepidoptera Saturniidae Arthropoda Insecta Lepidoptera Sesiidae Arthropoda Insecta Lepidoptera Sphingidae Arthropoda Insecta Lepidoptera Tortricidae Arthropoda Insecta Lepidoptera Yponomeutidae Arthropoda Insecta Neuroptera Hemerobiidae Arthropoda Insecta Neuroptera Myrmeleontidae Arthropoda Insecta Neuroptera Osmylidae Arthropoda Insecta Odonata Coenagrionidae Arthropoda Insecta Odonata Libellulidae Arthropoda Insecta Odonata Protoneuridae Arthropoda Insecta Orthoptera Acrididae Arthropoda Insecta Orthoptera Stenopelmatidae Arthropoda Insecta Orthoptera Tettigoniidae herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore herbivore predator predator predator predator predator predator herbivore omnivore herbivore Arthropoda Insecta Psocoptera Liposcelidae secondary decomposer Arthropoda Insecta Psocoptera Psyllipsocidae secondary decomposer Arthropoda Insecta Thysanoptera Phlaeothripidae Arthropoda Insecta Thysanoptera Thripidae herbivore herbivore Adamski, D., Johnson, P.J., Boe, A.A., Bradshaw, J. and Pultyniewicz, A., 2010. 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Acta entomologica serbica, 4, pp.131-140. Wharton, R.A. and Reddick, K.L., 2014. Solifuges (Arachnida: Solifugae) as predators and prey. Transactions of the Royal Society of South Africa, 69(3), pp.213-216. 140 APPENDIX 4.C. Detected Taxa and their Relative Abundance in Urea and Control Treatments 141 APPENDIX 4.C. Detected Taxa and their Relative Abundance in Urea and Control Treatments. P-value: Significance levels from contrasts of mean relative abundance between urea and control treatments as estimated by a univariate model fit to a logit transformation of each taxon’s relative abundance values. Reps: the number of replicates within a treatment type in which a taxon was detected over the course of the study. Unique: The number of times a taxon was present in only one of the two treatments on a given date. Total Dates: The number of sampling dates (max 8) on which a taxon was detected in either treatment. Class Order Family Arachnida Araneae Araneidae Feeding Group predator p-value Rel. Abund. Rel. Abund. Reps Control Urea Control Reps Urea Unique Control Unique Urea Total Dates 0.518 7.01E-06 8.05E-05 Arachnida Mesostigmata Parasitidae predator 0.859 6.32E-02 7.64E-02 25 27 142 Arachnida Araneae Barychelidae predator 0.491 5.85E-05 Arachnida Araneae Clubionidae predator 0.456 2.01E-04 Arachnida Araneae Dysderidae Arachnida Araneae Gnaphosidae Arachnida Araneae Hypochilidae predator predator predator 0.330 0.291 0.213 - - - - - 3.03E-03 3.05E-06 7.71E-06 Arachnida Araneae Linyphiidae predator 0.681 2.23E-03 6.02E-04 Arachnida Araneae Lycosidae predator 0.333 8.22E-03 1.03E-03 Arachnida Araneae Mecysmaucheniidae predator Arachnida Araneae Arachnida Araneae Arachnida Araneae Arachnida Araneae Pholcidae Pisauridae Salticidae Sicariidae predator predator 0.286 0.298 0.250 - - - 7.71E-06 3.85E-05 1.58E-03 predator 0.064 3.09E-04 6.66E-03 predator 0.567 3.85E-06 1.23E-04 Arachnida Araneae Tetragnathidae predator 0.635 - 1.16E-05 Arachnida Araneae Theridiidae predator 0.459 1.82E-05 - Arachnida Araneae Thomisidae predator 0.192 1.10E-03 8.86E-03 Arachnida Astigmata Acaridae omnivore 0.000 1.64E-03 3.72E-04 10 Arachnida Endeostigmata Nanorchestidae 3° decomposer 0.372 3.51E-06 - Arachnida Endeostigmata Terpnacaridae 3° decomposer 0.040 - 4.36E-05 Arachnida Mesostigmata Blattisociidae omnivore 0.142 7.46E-06 - Arachnida Mesostigmata Digamasellidae omnivore 0.858 6.70E-06 6.72E-05 Arachnida Mesostigmata Laelapidae predator 0.254 9.21E-05 1.57E-05 Arachnida Mesostigmata Ologamasidae predator 0.099 6.53E-04 2.41E-03 1 0 1 1 2 5 1 1 1 0 0 0 6 4 0 0 0 1 1 0 1 3 2 0 0 2 1 1 5 1 1 1 2 3 1 1 0 5 8 0 1 0 3 2 7 1 1 1 0 0 0 2 4 0 0 0 1 0 0 1 2 1 1 0 1 0 2 1 0 2 0 0 1 1 1 2 1 1 1 1 2 0 1 0 3 0 0 1 0 2 1 2 0 3 1 1 1 1 1 6 5 1 1 1 3 1 1 1 6 7 1 1 1 3 3 6 8 APPENDIX 4.C. (cont’d) Arachnida Mesostigmata Phytoseiidae predator 0.001 8.77E-05 3.18E-03 Arachnida Mesostigmata Rhodacaridae predator 0.057 4.26E-05 - Arachnida Mesostigmata Veigaiidae predator 0.029 9.51E-05 5.29E-05 Arachnida Opiliones Phalangiidae omnivore 0.963 1.76E-04 - Arachnida Opiliones Phalangodidae omnivore 0.214 - 1.54E-05 Arachnida Oribatida Brachychthoniidae omnivore 0.214 6.27E-04 Arachnida Oribatida Ceratoppiidae 1° decomposer 0.701 4.93E-05 - - Arachnida Oribatida Ceratozetidae 1° decomposer 0.086 - 3.45E-05 Arachnida Oribatida Cymbaeremaeidae 3° decomposer 0.821 1.36E-04 - Arachnida Oribatida Eremaeidae 3° decomposer 0.488 2.53E-06 9.60E-04 Arachnida Oribatida Eremobelbidae 2° decomposer 0.733 7.78E-04 2.34E-03 Arachnida Oribatida Euphthiracaridae 3° decomposer 0.024 1.38E-05 2.91E-04 Arachnida Oribatida Oppiidae omnivore 0.367 7.99E-04 2.66E-04 4 2 1 2 0 3 2 0 1 1 8 1 8 Arachnida Oribatida Oribatellidae 1° decomposer 0.219 2.12E-03 2.81E-03 10 Arachnida Oribatida Oribatulidae 2° decomposer 0.008 2.27E-03 3.50E-04 Arachnida Oribatida Oripodidae 1° decomposer 0.673 7.59E-06 - Arachnida Oribatida Phenopelopidae 1° decomposer 0.024 9.43E-04 3.08E-04 Arachnida Oribatida Scheloribatidae 2° decomposer 0.025 4.15E-02 1.51E-02 Arachnida Oribatida Scutoverticidae 3° decomposer 0.652 - 3.31E-04 Arachnida Oribatida Suctobelbidae 1° decomposer 0.553 3.94E-06 2.37E-04 Arachnida Oribatida Tectocepheidae 1° decomposer 0.613 1.74E-04 8.46E-04 Arachnida Oribatida Tegoribatidae 1° decomposer 0.801 1.42E-03 1.02E-03 Arachnida Pseudoscorpiones Neobisiidae predator 0.438 2.21E-03 6.84E-04 Arachnida Scorpiones Buthidae Arachnida Scorpiones Vaejovidae Arachnida Solifugae Eremobatidae predator predator predator 0.090 0.305 0.329 - - - 5.39E-05 5.52E-06 1.97E-04 Arachnida Trombidiformes Anystidae predator 0.441 2.35E-06 - Arachnida Trombidiformes Bdellidae predator 0.987 2.47E-05 3.24E-04 Arachnida Trombidiformes Microdispidae 3° decomposer 0.713 1.71E-05 - Arachnida Trombidiformes Pygmephoridae 3° decomposer 0.780 5.45E-06 9.33E-05 5 1 12 25 0 1 9 7 7 0 0 0 1 2 2 1 3 0 1 0 1 0 0 1 0 1 6 1 7 7 6 0 3 19 1 7 11 5 5 2 1 2 0 3 0 2 2 2 1 1 0 2 2 0 1 1 1 0 2 3 2 1 5 0 0 0 0 1 0 0 0 0 1 0 2 0 143 1 0 1 0 1 0 0 1 0 1 1 0 3 3 2 0 0 0 1 4 0 1 0 1 1 1 0 1 0 1 5 2 2 1 1 2 2 1 1 2 4 1 6 8 6 1 7 8 1 5 5 4 2 1 1 1 1 2 2 2 APPENDIX 4.C. (cont’d) Arachnida Trombidiformes Rhagidiidae 1° decomposer 0.062 1.55E-04 2.03E-03 Arachnida Trombidiformes Scutacaridae 3° decomposer 0.742 1.66E-04 1.92E-04 Arachnida Trombidiformes Siteroptidae 3° decomposer 0.001 1.89E-04 4.95E-04 Arachnida Trombidiformes Stigmaeidae predator 0.579 6.34E-05 5.40E-06 Arachnida Trombidiformes Tarsonemidae 3° decomposer 0.264 1.80E-03 2.37E-02 Arachnida Trombidiformes Trombidiidae predator 0.487 1.48E-02 2.32E-02 Arachnida Trombidiformes Tydeidae omnivore 0.193 4.40E-04 - Chilopoda Geophilomorpha Himantariidae predator 0.004 - 4.87E-06 Chilopoda Geophilomorpha Schendylidae predator 0.969 1.13E-02 9.38E-03 Chilopoda Lithobiomorpha Lithobiidae predator 0.133 - 4.70E-06 Chilopoda Scolopendromorpha Cryptopidae predator 0.165 2.73E-05 - Chilopoda Scolopendromorpha Scolopocryptopidae predator 0.768 - 2.12E-04 Collembola Entomobryomorpha Entomobryidae 1° decomposer 0.100 7.59E-02 7.62E-02 Collembola Entomobryomorpha Isotomidae 2° decomposer 0.013 1.38E-02 4.37E-03 Collembola Entomobryomorpha Tomoceridae 1° decomposer 0.352 7.18E-02 1.71E-02 Collembola Poduromorpha Hypogastruridae 2° decomposer 0.485 3.68E-02 1.82E-02 Collembola Poduromorpha Neanuridae 2° decomposer 0.423 7.71E-06 3.74E-04 Collembola Poduromorpha Onychiuridae 2° decomposer 0.018 1.18E-02 2.13E-02 Collembola Poduromorpha Tullbergiidae 2° decomposer 0.403 Collembola Symphypleona Bourletiellidae 2° decomposer 0.950 - - 3.33E-05 1.16E-05 Collembola Symphypleona Katiannidae 1° decomposer 0.967 1.57E-04 6.10E-04 Collembola Symphypleona Sminthuridae 1° decomposer 0.092 3.52E-06 5.56E-06 3 11 11 2 17 12 1 0 3 0 2 0 28 14 17 16 1 16 0 0 5 1 7 8 15 1 16 9 0 1 3 1 0 1 31 10 14 17 1 18 1 1 4 1 Diplopoda Diplura Insecta Insecta Insecta Insecta Insecta Insecta Julida Diplura Julidae 2° decomposer 0.116 1.87E-02 4.90E-02 11 16 Campodeidae omnivore 0.009 1.06E-03 8.16E-06 Blattodea Termitidae 1° decomposer 0.360 Coleoptera Anthicidae omnivore 0.071 - - 9.63E-05 1.15E-03 Coleoptera Cantharidae predator 0.873 4.90E-05 1.04E-02 Coleoptera Carabidae predator 0.607 3.28E-02 3.34E-02 11 12 Coleoptera Cerambycidae 1° decomposer 0.828 5.21E-05 5.11E-05 Coleoptera Cleridae predator 0.610 7.76E-04 5.64E-06 3 1 2 1 144 2 0 0 5 1 1 1 5 0 2 1 1 1 3 1 0 0 0 1 0 0 2 1 1 0 0 0 0 1 1 0 1 0 0 3 1 2 1 2 0 1 0 1 1 0 1 0 1 0 1 0 0 1 2 0 2 1 1 1 1 2 0 1 1 5 1 1 1 4 6 7 2 7 7 1 1 2 1 1 1 8 7 8 7 1 7 1 1 4 2 8 2 1 1 8 8 4 2 APPENDIX 4.C. (cont’d) Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Coleoptera Coccinellidae predator 0.309 - 3.24E-07 Coleoptera Corylophidae 3° decomposer 0.227 1.66E-03 1.19E-03 Coleoptera Cryptophagidae 3° decomposer 0.258 2.98E-03 - Coleoptera Dytiscidae predator 0.212 - 1.65E-03 Coleoptera Coleoptera Coleoptera Elateridae 1° decomposer 0.394 1.03E-05 3.48E-04 Erotylidae 3° decomposer 0.000 3.68E-03 1.76E-02 Gyrinidae predator 0.710 - 9.20E-06 Coleoptera Heteroceridae 3° decomposer 0.861 1.99E-06 - Coleoptera Hydraenidae 3° decomposer 0.852 - 2.27E-04 0 2 2 0 1 6 0 1 0 Coleoptera Latridiidae 3° decomposer 0.058 4.30E-03 1.43E-02 16 Coleoptera Leiodidae 3° decomposer 0.001 - 3.29E-06 Coleoptera Lucanidae 1° decomposer 0.527 9.41E-05 - Coleoptera Melyridae predator 0.604 4.81E-04 3.24E-07 Coleoptera Nitidulidae 1° decomposer 0.289 1.62E-02 9.62E-03 Coleoptera Phalacridae 3° decomposer 0.114 6.49E-04 3.29E-05 Coleoptera Scarabaeidae 2° decomposer 0.425 9.78E-04 1.96E-03 0 1 2 5 1 3 1 3 0 2 3 4 1 0 1 8 1 0 1 2 1 3 Coleoptera Staphylinidae predator 0.135 1.17E-01 1.69E-01 20 25 Coleoptera Tenebrionidae omnivore 0.189 1.31E-06 4.82E-06 Coleoptera Throscidae 3° decomposer 0.906 - 1.42E-02 1 0 1 0 1 3 3 1 Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Anthomyiidae 1° decomposer 0.627 2.59E-06 1.87E-02 Calliphoridae 1° decomposer 0.957 - 3.76E-06 Cecidomyiidae 3° decomposer 0.016 5.00E-02 7.38E-02 21 27 Ceratopogonidae omnivore 0.058 3.72E-05 1.77E-03 Chironomidae 2° decomposer 0.138 4.44E-03 1.69E-02 Dolichopodidae predator 0.813 3.47E-02 1.43E-02 Drosophilidae 3° decomposer 0.315 - 1.35E-03 Empididae predator 0.143 3.60E-03 4.50E-06 Ephydridae 1° decomposer 0.029 7.42E-04 9.74E-04 Muscidae predator 0.536 9.05E-04 1.97E-06 Mycetophilidae 3° decomposer 0.146 1.09E-02 1.77E-03 2 5 8 0 2 1 5 8 9 9 5 3 1 1 1 14 145 0 1 2 0 1 3 0 1 0 2 0 1 1 2 1 3 0 0 0 1 0 0 0 1 3 0 1 1 2 0 1 2 0 1 2 2 1 0 1 0 1 0 0 0 1 3 0 0 2 3 1 0 4 2 0 3 0 1 0 3 1 4 2 1 3 7 1 1 1 8 1 1 2 4 2 6 8 1 2 4 1 8 6 6 6 3 2 2 3 8 APPENDIX 4.C. (cont’d) Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Diptera Pediciidae Phoridae predator omnivore 0.881 0.151 Psychodidae 3° decomposer 0.166 Scatopsidae 3° decomposer 0.368 - - - - 1.93E-05 1.54E-05 1.16E-04 1.48E-03 Sciaridae 3° decomposer 0.261 1.28E-04 1.87E-02 Sphaeroceridae 2° decomposer 0.887 - 1.46E-02 Stratiomyidae 1° decomposer 0.260 8.77E-07 3.24E-07 Synneuridae 3° decomposer 0.574 - 4.93E-06 Syrphidae predator 0.581 6.27E-06 Xylophagidae predator 0.399 1.05E-05 - - Hemiptera Anthocoridae predator 0.007 3.21E-05 6.49E-07 Hemiptera Nabidae predator 0.610 - 1.35E-02 Hemiptera Pentatomidae omnivore 0.644 4.57E-03 3.24E-07 Insecta Hymenoptera Aphelinidae known parasitoid 0.643 Insecta Hymenoptera Bethylidae known parasitoid 0.000 - - 5.29E-06 4.60E-06 Insecta Hymenoptera Braconidae known parasitoid 0.705 3.31E-04 5.06E-04 Insecta Hymenoptera Diapriidae known parasitoid 0.994 - 3.24E-05 Insecta Hymenoptera Dryinidae known parasitoid 0.805 5.38E-05 Insecta Hymenoptera Eucharitidae known parasitoid 0.982 2.58E-05 - - Insecta Hymenoptera Figitidae known parasitoid 0.024 2.19E-05 4.82E-05 Insecta Hymenoptera F : Brachymyrmex omnivore 0.356 8.59E-04 7.74E-04 Insecta Hymenoptera F : Lasius omnivore 0.134 4.16E-02 4.08E-02 Insecta Hymenoptera F : Myrmecina predator Insecta Hymenoptera F : Nylanderia omnivore Insecta Hymenoptera F : Odontomachus predator 0.661 0.364 0.953 - - - 6.85E-04 5.36E-04 7.71E-06 Insecta Hymenoptera F : Ponera predator 0.928 2.03E-03 9.56E-05 Insecta Hymenoptera F : Prenolepis omnivore 0.884 1.88E-02 1.43E-02 Insecta Hymenoptera F : Pyramica predator 0.353 1.64E-05 - Insecta Hymenoptera F : Stenamma predator Insecta Hymenoptera F : Tapinoma omnivore - - 3.70E-03 4.17E-04 0.494 0.955 146 0 0 0 0 7 0 1 0 1 1 1 0 2 0 0 2 0 1 3 1 4 8 0 0 0 7 5 1 0 0 1 1 3 2 7 3 1 1 0 0 1 6 1 1 1 4 1 0 0 1 3 8 1 1 1 1 6 0 1 1 0 0 0 0 1 0 1 0 1 1 0 0 2 0 0 2 0 1 2 0 1 1 0 0 0 1 0 1 0 0 1 1 3 2 2 2 1 1 0 0 0 3 1 1 1 3 1 0 0 0 1 2 1 1 1 0 1 0 1 1 1 1 3 2 7 2 2 1 1 1 1 3 3 1 1 5 1 1 2 1 3 5 1 1 1 2 4 1 1 1 2 1 1 0 5 0 0 1 0 0 0 2 2 1 0 0 1 1 2 1 0 1 1 3 2 3 1 1 1 0 2 0 0 1 0 0 0 2 1 0 0 0 1 0 2 1 0 1 1 2 2 1 2 1 1 1 3 2 1 1 1 1 2 4 3 Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Insecta Odonata Odonata Odonata APPENDIX 4.C. (cont’d) Insecta Hymenoptera F : Temnothorax omnivore 0.077 1.12E-03 2.59E-06 Insecta Hymenoptera Megaspilidae known parasitoid 0.000 2.92E-06 Insecta Hymenoptera Proctotrupidae known parasitoid 0.894 7.36E-05 - - Lepidoptera Blastobasidae 1° decomposer 0.134 - 6.44E-05 Neuroptera Hemerobiidae predator 0.927 1.91E-02 2.28E-06 Neuroptera Myrmeleontidae predator Neuroptera Osmylidae predator 0.674 0.891 - - 9.94E-06 1.16E-05 Coenagrionidae predator 0.821 3.85E-06 - Libellulidae Protoneuridae predator predator 0.230 0.731 0.910 - - - 1.16E-05 7.71E-06 2.00E-04 Orthoptera Stenopelmatidae omnivore Psocoptera Liposcelidae 2° decomposer 0.666 2.28E-03 3.24E-05 Psocoptera Psyllipsocidae 2° decomposer 0.471 1.56E-04 8.82E-05 147 LITERATURE CITED 148 LITERATURE CITED Anslan, S., Bahram, M. and Tedersoo, L., 2018. Seasonal and annual variation in fungal communities associated with epigeic springtails (Collembola spp.) in boreal forests. Soil Biology and Biochemistry, 116, pp.245-252. 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DOI: 10.1094/PHI-I-2000-1005-01. Updated 2005. Warton, D.I. and Hui, F.K., 2011. The arcsine is asinine: the analysis of proportions in ecology. Ecology, 92(1), pp.3-10. 152 Wickings, K. and Grandy, A.S., 2013. Management intensity interacts with litter chemistry and climate to drive temporal patterns in arthropod communities during decomposition. Pedobiologia, 56(2), pp.105-112. White, T.C.R., 1984. The abundance of invertebrate herbivores in relation to the availability of nitrogen in stressed food plants. Oecologia, 63(1), pp.90-105. 153 CHAPTER 5. CONSUMPTION OF APPLE LEAF LITTER INFECTED WITH VENTURIA INAEQUALIS BY SOIL ARTHROPODS DURING THE WINTER ABSTRACT Venturia inaequalis is the dominant fungal pathogen in temperate apple cropping systems, accounting for the majority of fungicidal applications each season. V. inaequalis overwinters in infected apple leaf litter, releasing spores from the ground to re-infect apple tree tissue in early spring. Reducing primary infection levels in the spring is critical to maintaining economic control of the pathogen. Arthropod grazing on fungal tissue during the winter has been demonstrated in natural systems and may provide a check against V. inaequalis development. I sought to identify natural enemies of V. inaequalis among winter active epigeic arthropods. Arthropods were sampled from the leaf litter and surface soil of a temperate apple orchard between leaf fall and the beginning of the following growing season. Collected specimens were screened for the presence of Malus domestica (apple) and Venturia inaequalis (apple scab) DNA markers in their gut contents using a duplex real-time PCR Taqman assay to determine if arthropods are actively consuming scab-infected litter between seasons. Overall detection rates in the experiment were low, with 0.5% and 1.2% of samples testing positive for M. domestica or V. inaequalis DNA respectively. Detections occurred in 8 of the 71 field collected taxa: the coleopteran families Cantharidae, Curculionidae, Scarabaeidae, and Staphylinidae, the collembolan orders Entomobryomorpha and Poduromorpha; the diplopodic family Julidae; and the acararian order Oribatida. Keywords: Biological Control, Integrated Pest Management, Floor Sanitation 154 INTRODUCTION Considerable effort and capital are expended to overcome fungal pathogenicity. Research in many crops has been focused largely on creating disease-tolerant cultivars and new fungicide classes (Boyd et al. 2013; Rommens and Kishore 2000; Hollomon 2015; Sparks and Nauen 2015). However, fungal pathogens continue to be a major management issue, overcoming disease tolerance and developing fungicide resistance. Therefore, it is necessary to continue exploring additional methods of reducing fungal disease incidence that can be integrated into management systems. Interactions with fungal pathogens are not limited to the host plant. Agricultural operations support a wide range of organisms that influence the development of fungal organisms, particularly those that spend some part of their life cycle in the soil (Friberg et al. 2005). There are three principal mechanisms by which soil fauna can affect fungal development: changing the location and conditions of microhabitats; grazing on fungal tissues, and dispersing propagules (Friberg et al. 2005). Microhabitat changes are accomplished by the larger “ecosystem engineers” such as earthworms and burrowing beetles whose movements redistribute soil particles, nutrients, and moisture (Jones et al. 1994; Lavelle et al. 1997). Smaller organisms, particularly Collembola and Acari, actively target fungal tissues as a food source (Friberg et al. 2005; Anslan et al. 2018; Meyer-Wolfarth et al. 2017). Invertebrate feeding activity is a key regulatory factor of fungal growth in soils, including plant pathogens in agroecosystems (Friberg et al. 2005; Anslan et al. 2018; Meyer-Wolfarth et al. 2017 a,b). The degree to which fungal pathogen populations are regulated by arthropod populations in agroecosystems is relatively unexplored, as is the potential for enhancing that effect through management decisions. The dominant fungal pathogen in temperate apple cropping systems is Venturia inaequalis, the causal agent of apple scab, whose management accounts for the majority of fungicidal applications each season (Beckerman and Sundin 2016). The organism propagates asexually during the growing season by formation and release of conidiospores from lesions on 155 the surface of leaves and fruit but overwinters as a leaf litter saprophyte (Keitt and Jones 1926; MacHardy et al. 2001). The time between infection and spore release during the growing season can be as short as 9 days (Vaillancourt and Hartman 2000). The exponential growth potential of this pathogen means that preventing the establishment of primary infections at the start of the growing season is crucial for effective management. Fungicides and resistant apple cultivars are the de facto standards for preventing infections; however, the adaptability of this organism means that disease breakthrough is always a possibility. Thus, any cost-effective tactic that can contribute to inoculum reduction at the start of season should be considered for inclusion into modern scab management programs. Upregulation of arthropod feeding on V. inaequalis during the overwintering period could be a way of decreasing early season disease pressure. In a related set of experiments that explored soil-dwelling arthropod communities in orchards during winter, I generated an inventory of potentially active taxa, and observed that management tactics and strategies altered the structure of soil-dwelling arthropod communities (Matlock 2018a,b). Modification of agroecosystems to enhance natural enemy activity is referred to as conservation biological control (Jonsson et al. 2008; Griffiths et al. 2008). The first steps in development of a conservation biological control strategy are identification of natural enemies of the target pest, and quantification of the relative levels of consumption of the target pest by those natural enemies (Greenstone et al. 2010). The complexity of agroecosystems makes affecting unilateral changes across broad organismal groups unrealistic. Thus, it is necessary to design management tactics that favor natural enemy taxa with the greatest impact potential. One way of identifying natural enemies and quantifying their impact potential is to screen the gut contents of identified specimens for the presence of target pests. Modern gut content analysis uses molecular markers to indicate the presence/absence of a target food source inside the gut of individual specimens. A common approach is to use PCR primers that amplify a short and unique (80-120 bp) nucleic acid sequence to screen DNA 156 extracted from the gut for the target. Successful amplification indicates the presence of that target. Real-time PCR instruments offer a high level of sensitivity and rapid sample throughput and have become a standard platform for gut content analysis (González-Chang et al. 2016). Gut content can be obtained in several ways. Direct excision of the actual gut or forced regurgitation of gut contents from living specimens produce the highest quality samples, but such methods are less feasible for small organisms like mites and Collembola. Another option that is commonly employed is to decontaminate the surface of each specimen, and then perform a DNA extraction on a whole specimen homogenate (Greenstone et al. 2012). It is then assumed that any non-host DNA in the extract has originated from inside the specimen. Usually, extractions are performed on multiple specimens sharing the same taxonomic classification and the response variable is the number of positive detections. I sought to identify natural enemies of V. inaequalis by screening the gut contents of soil arthropod specimens collected from a scab-susceptible orchard during the winter. First, I validated the use of a Taqman real-time PCR assay in an insect model. Then, I applied that assay to field collected specimens. MATERIALS AND METHODS Experimental Design The experiment was conducted in a 16.2 ha apple orchard located near Pottersville, MI (42.6292° N, 84.7389° W). A total of eighteen 0.12 ha plots were established in scab-susceptible varieties: MacIntosh, Jonagold, Red Delicious, and Golden Delicious. Field Sample Collection and Processing Sampling began 1 week after leaf fall. Samples were collected on eight dates: 11.28.16, 12.09.16, 12.16.16, 12.29.16, 1.11.17, 2.22.17, 3.28.17, and 4.21.17. On each date, four 33x33 cm areas were randomly subsampled from the dripline of trees within the central rows of each experimental plot. Sample locations were marked to prevent resampling on future dates. A subsample consisted of all leaf litter, above-ground biomass, and the top 1 cm of soil. Subsamples from each experimental plot were homogenized by gentle mixing in a bucket before transferring two 1L aliquots to Berlese funnel extraction 157 devices to isolate arthropods from the substrate. Each 1L aliquot was transferred to a separate Berlese funnel. The funnels were heated from the top with 40W incandescent bulbs placed 10cm from the sample surface. The arthropods were collected and preserved in pure propylene glycol over a 120 hour period. Samples were collected every 24 hours to prevent degradation of DNA in the gut. A dimmer switch was used to reduce bulb outputs to 10W for the first 24 hours, 20W for hours 24 through 48, and 40W for the remaining time. After collection, samples were stored at -20°C for the remainder of the study. Taxonomic identifications were assigned to each specimen using morphological characteristics. Collembola, Oribatida, Thysanoptera were identified to order; Coleoptera, Diploploda, Diptera, and Hemiptera to family; and Formicidae to genus. Specimens were kept on ice when removed from -20°C storage, and were manipulated under the microscope using a watch glass placed in a bed of ice. The identified specimens were subjected to surface decontamination, DNA extraction, and screening for M. domestica and V. inaequalis DNA with a duplex real-time PCR assay. Samples from Collembola, Oribatida, and Thysanoptera were composed of 5 individuals grouped together to account for their minute size. DNA Extraction Samples were homogenized in a FastPrep 24 for 45 sec at 5.5 m/s after the addition of 1 uL of 2-mercaptoethanol and 5 ul of 10 mg/mL Proteinase K, and then incubated for 1 hour at 56°C. The samples were extracted with two 500 uL washes of 24:1 chloroform:isoamyl alcohol, transferring the aqueous layer to a new container each time after a 5 minute centrifuge spin at 15,000 rcf. DNA was precipitated with the addition of 500 uL chilled isopropanol. 10 ug of linear polyacrylamide (Invitrogen AM9520), an inert DNA coprecipitant, was added along with the chilled isopropanol to aid in recovery of dilute DNA and visualization of the DNA pellet. Following a 30 minute incubation at -20°C, the DNA was pelleted with a 10 minute centrifuge spin at 17,000 rcf. The supernatant was discarded, and the pellet was triple-rinsed with chilled 70% ethanol to remove salts. The pellet was dried at 45°C and resuspended in 20uL of nuclease free water to increase the concentration of dilute gut content DNA. 158 Surface Decontamination Contaminant DNA was removed from the surface of specimens prior to DNA extraction to ensure that isolated DNA came from either the host, gut, or symbionts. Each organism was subjected to a 90 second rinse with each of 2.5% bleach, ethanol, and distilled water. All fluids were chilled to 4°C. Rinsing was performed in a buchner funnel on top of a 30mm No. 1 Whatman filter paper disc. The buchner funnel and filter paper were rinsed for 90 seconds with each of 10% bleach and distilled water prior to the introduction of each specimen. The filter paper was changed in between specimens. Decontaminated specimens were transferred under a microscope with ultra-fine forceps into autoclaved 2 mL homogenization tubes filled with 500 uL of CTAB DNA extraction buffer (2% CTAB, 2% PVP-40, 3M NaCl,10 mM EDTA, 0.1M TRIS, pH 7.5) and 50 uL each of 0.1 and 1.0 mm silica grinding beads. The forceps were decontaminated in between specimens using DNase and distilled water. Samples were stored at -20°C until ready for DNA extraction. Evaluation of the decontamination procedure is described below. Real-Time PCR Screening Each sample was screened for the presence of Malus domestica and Venturia inaequalis DNA on an Applied Biosystems StepOnePlus Real-Time PCR System in a duplex Taqman assay using the primers and probes developed by Gusberti et al. (2012). The targets for M. domestica and V. inaequalis were ATP-binding cassette transporter 2 and elongation factor 1α, respectively. I ordered my probes from Thermo Fisher Scientific, which required the substitution of the reporter dye VIC for the original Yakima Yellow. The two dyes have nearly identical absorption and emission spectrums (Johannesen 2018). I used standard reaction conditions for Taqman Universal PCR Master Mix: 2 min at 50°C, 10 min at 95°C, followed by 50 cycles of 15 seconds at 95°C and 1 min at 60°C. Reactions were 20uL of total volume, composed of: 10 uL Master Mix, 5 uL of template gDNA, 900nM of each Forward and Reverse primer, and 250 nM of each Taqman hybridization probe. Two technical replicates were run for each biological replicate. Positive results from both technical replicates were required for the biological replicate to be considered positive. 159 Determining Cycle Threshold (CT) Cutoffs The exponential nature of real time PCR amplification means that the probability of a non-target artifact producing a detectable signal increases with each cycle. It is therefore necessary to determine a cycle threshold value beyond which a sample will be treated as a false positive. To do this, I observed the instrument responses to dilution series of genomic DNA extracted from pure tissues of the respective targets, M. domestica and V. inaequalis. Low copy number samples follow a Poisson distribution, with the probability of a sample containing no copies of the target DNA increasing with dilution (Applied Biosystems 2016). Thus, I would expect to see a sudden decrease in the number of replicates producing a detectable signal as the level of dilution approached a single copy concentration. I chose the highest cycle threshold value observed from dilution levels that still produced multiple positive signals. This was done to produce cutoff values associated with highly dilute target concentrations that were still likely to be true positives as evidenced by replication. Fruit was collected from apple scab infected apple trees in untreated plots at the Michigan State University Plant Pathology Orchard. Disease lesions were excised from infected apples. The disease organism was verified as V. inaequalis using wet smear mounts to inspect spore morphology under a microscope. Single spore isolates were prepared from verified disease lesions to obtain pure cultures. A cellulose membrane was placed over the growth media prior to placing the picked spores. This allowed mycelium to be collected without any accompanying agarose, which acts as a contaminant in DNA extraction. All cultures were grown on 3.9% (w/v) PDA (Difco) with 100 ppm Amoxicillin (Acros Organics) and 50 ppm Streptomycin (Acros Organics). Wet smear mounts were performed prior to sampling for DNA extraction to ensure that the cultures were free from contamination. Apple tissue for DNA extraction was obtained by aseptically excising small pieces of the fruit interior after rinsing the surface with a 10% bleach solution and double distilled water to remove contaminant material and DNA. Tissues were immediately freeze-dried after sampling. DNA was extracted using the CTAB procedure described in this section and quantified using a HS dsDNA Qubit Fluorometer assay. Three semi-log dilution 160 series were prepared ranging from 10 to 10-5 ng/uL gDNA. Two technical replicates were run for each sample, for a total of six replicates per dilution level. Assay Validation in an Insect Model Fruit and leaves were collected from apple scab infected apple trees in untreated plots at the Michigan State University Plant Pathology Orchard. Disease lesions were excised from infected tissue. The disease organism was verified as V. inaequalis using wet smear mounts to inspect spore morphology under a microscope. Collected tissue was freeze-dried and then powderized in a Vitamix blender. The blender was rinsed with 10% bleach and double-distilled water prior to blending. The powderized tissue was mixed 1:1 with an insect diet prepared from 100 g rodent feed, 60 g wheat bran, 10 g autolyzed yeast, and 240 mL water. Early instar (1-3 mm) Black Soldier Fly larvae, Hermetia illucens (L.) were obtained from the Phoenix Worm Store (Organic Value Recovery Solutions LLC, Georgia USA). I selected H. illucens because it is a soil-dwelling generalist detritivore with voracious feeding habits. The larvae were starved in their shipping containers at room temperature for 72 hours after delivery, and then transferred to 60 mm petri dishes in groups of 40. Approximately 3 cm3 of diet was added to each petri dish. The dishes were stored at 26℃ and 65% relative humidity under fluorescent lighting. Larvae were allowed to feed for 12 hours before being transferred to clean petri dishes. Each larva was briefly rinsed with distilled water and dried on filter paper to remove clinging food particles. Twenty live larvae were collected at 0, 6, 12, 24, 48, 72, 96, and 120 hours after removal from the food source. Sampled larvae were stored individually in autoclaved 1.5 mL eppendorf tubes filled with -20℃ ethanol. Unfed larvae and aliquots of the amended diet served as negative and positive controls, respectively. I also replicated the feeding trial with an unamended diet to generate additional controls for each time point. All samples were screened using the real-time PCR assay described above. Detectability curves were generated for Malus and V. inaequalis by plotting the proportion of positive testing larvae against the time since cessation of feeding. 161 The efficacy of the surface decontamination procedure was evaluated using unfed, early instar H. illucens larvae euthanized by freezing at -20°C. Three treatments were established: washed, unwashed, and control. Twenty larvae were placed in each treatment. The washed and unwashed treatments began by transferring larvae into a 60mm petri dish along with finely crumbled amended diet, and then shaking the petri dish for 1 minute to ensure thorough contact of the diet and the larvae. The larvae in the washed treatment were then subjected to the surface decontamination procedure. Those in the unwashed treatment were not decontaminated. The larvae in the control treatment were not exposed to the diet. All samples were screened using the real-time PCR assay described above. The feeding trial and surface decontamination evaluation were both run twice, each with a separate batch of H. illucens larvae. RESULTS AND DISCUSSION Cycle Threshold (CT) Cutoffs Both the M. domestica and V. inaequalis dilution series produced the expected sharp decrease in the number of technical replicates with detectable signals at higher levels of dilution (Figs 5.1, 5.2). The highest cycle threshold values observed from dilution levels that still produced multiple positive signals were 39.4 and 41.1 for M. domestica and V. inaequalis, respectively. Surface Decontamination The surface decontamination protocol was highly effective at preventing false positives in early instar larvae. Positive detection rates were greater than or equal to 87.5% in unwashed larvae exposed to the amended diet, but not a single larva tested positive in the washed group or the control group (Table 5.1). The use of duplicate agreement for positive detection was critical for elimination of false positives. When only one replicate was required for a positive result, the number of false positives increased to 5% in the washed group for V. inaequalis. This highlights the importance of technical replication in molecular experiments. Insect Gut Model Validation I was able to detect both M. domestica and V. inaequalis DNA in the gut contents of H. illucens larvae fed an amended diet (Fig 5.3). The probability of V. 162 inaequalis detection dropped rapidly in the first 12 hours after feeding from 92.5% to 40%, and then stabilized around 38% for the rest of the observation window. Detection rates of M. domestica started low, between 12.5% and 25%, and decreased in a linear fashion with time. The detection rates in M. domestica were unusual: positive signals from nearly all replicates is expected for the t=0 samples of a feeding trial. One explanation for the observed detection patterns lies in the “digestibility” of the diet. H. illucens feeds on primary waste materials like manures, carcasses, and rotting fruits, which means that it produces digestive enzymes suitable for degrading proteins, sugars, and celluloses (Li et al. 2011; Kim et al. 2011; Lee et al. 2014). It is not adapted to digesting recalcitrant structural molecules like chitin and lignin (Li et al. 2011). Fungal tissues, particularly spores, contain high concentrations of chitin in their cell walls (Bartnicki-Garcia 1968). Intact spores and mycelial threads may resist lysis in the digestive tract of H. illucens. The blending process in the making of the amended diet likely ruptured many fungal cells, releasing free DNA that could be detected in the early time points, and that was susceptible to rapid degradation in the gut thereafter. The remaining intact tissues may have protected DNA until the lysis step of the CTAB DNA extraction procedure. Broadleaf plants like apple trees use celluloses and pectins in their cell walls (Bret and Waldron 1996), which are readily degraded in the gut of primary decomposers like H. illucens. However, differential recalcitrance does not address the low detection rate of M. domestica immediately following the cessation of feeding, nor the high degree of variation between time points (Fig 5.3). In my design, I allowed larvae to feed ad libitum for 24 hours prior to the start of specimen collection. For any given larva, I could not be sure of when it ate, the size of its meal, or even whether it actually consumed diet. In a review of best practices for determination of detectability half lives, Greenstone et al. (2014) highlight the importance of ensuring synchronicity of feeding. In a preliminary run of this study, I attempted to meet this condition by housing larva in individual containers with standardized pellets of diet, and then inspecting the containers for signs of feeding 163 on regular intervals. The small size of the larvae and their natural tendency to walk through the pellet, dispersing it across the arena, made visual confirmation of feeding impractical. Consequently, each of the groups likely contained a distribution of specimens with different feeding times and quantities. Heidemann et al. (2011) also observed high variability across time points after screening the gut contents of oribatid mites fed nematodes ad libitum. An immediate question that comes from these results is: what happens with specimens that are naturally adapted for fungal diets? Field Experiment Overall detection rates in the experiment were low, with 0.5% and 1.2% of samples testing positive for M. domestica or V. inaequalis DNA respectively. Detections occurred in 8 of the 71 field collected taxa: the coleopteran families Cantharidae, Curculionidae, Scarabaeidae, and Staphylinidae, the collembolan orders Entomobryomorpha and Poduromorpha; the diplopodic family Julidae; and the acararian order Oribatida (Table 5.2). The number of sampling dates on which specimens were collected exceeded the number for which a positive detection event was observed for all positive testing taxa (Table 5.3). Most of the detection events were on the last three sampling dates, between 02.22.16 and 04.21.17 (Table 5.3). Weather records for that area show a shift towards above-freezing air temperatures that started in mid-March, and also that the 02.22.16 sampling occurred during a brief warm spell (Fig 5.4). Collembola & Oribatida Collembolan feeding on target species was detected throughout the winter, while oribatid feeding was detected only on the two warmest days at the end of the sampling period. This difference was surprising; I had anticipated that collembolan and oribatid specimens would both produce positive detections throughout the study period. Supercooling capability, winter activity, and fungal grazing have all been documented in these taxa (Block 1982; Block and Zettel 2003; Hågvar and Hågvar 2011; Leinaas 1981; Matsumo et al. 2018; Ohlsson and Verhoef 1988; Schatz and Sømme 1981; Sjursen and Sømme 2000; Sømme 1981; Zettel et al. 2002). It is possible that oribatids were not feeding on apple leaf litter during the winter, though 164 this seems unlikely given both the frequency of their detection and the documentation of winter feeding by other researchers. Julidae All positive detections in this family occurred during the last three sampling dates: 2.22.17 through 4.21.17. While March and April could be argued as falling within the start of a growing season, the February sampling date falls unarguably within the bounds of the winter season in Michigan. To date, evidence of winter activity in millipedes is very limited. Kocourek (2003) mentions a non-native millipede, Cylindroiulus truncorum (Verhoeffidae), with potential winter breeding found in the Czech Republic, where winter temperatures rarely drop below freezing. Similarly, in a laboratory evaluation of the effect of temperature on feeding behavior in Sarmatiulus kessleri (Julidae), Striganova (1972) reported low levels of feeding activity between 1 and 7°C. The author also reported that all activity ceased below 1°C. Minimum air temperatures were above freezing on all three of the sampling dates associated with positive detections in Julidae, which is consistent with the behavioral observations reported by the other authors. My study provides some of the first evidence of winter feeding by Julidae in a temperate climate, and also reinforces an emerging trend of millipede feeding behavior being limited by freezing temperatures. Coleopteran Predators Positive detection within Cantharidae and Staphylinidae is worth remark as species within these families are generally regarded predators and would not be expected to consume leaf litter (Traugott 2006; Birkhofer 2008). However, collembola, which are known leaf litter grazers and tested positive for V. inaequalis in this study, have been shown to form the base of predatory beetle winter diets (Eitzinger and Traugott 2011). Thus, it is possible that positive detection in the predatory beetles was a result of secondary predation: eating several collembola or other fungal grazers with recalcitrant V. inaequalis spores in their guts could produce sufficient target DNA in the extractions to test positive. Temporal Activity of Winter Arthropods Freezing temperatures present a number of challenges to overwintering arthropods (Cannon and Block 1987). Among these challenges is 165 that food in an arthropod gut can serve as a nucleation site for fatal ice crystal formation during cold snaps (Cannon and Block 1987; Sømme 1982). Many species regurgitate their gut contents prior to entering diapause or when temperatures drop below freezing, presumably to protect against gut nucleation (Cannon and Block 1987; Sømme 1982). I observed that organisms were collected throughout the winter while feeding observations were concentrated around warming periods (Table 5.3, Fig 5.4). Two possible explanations for the pattern in my data come from knowledge of the risk of freezing temperatures combined with a full gut. First, the energy and risk of freezing associated with foraging during low temperature periods may outweigh the benefit of obtaining food resources, limiting some species to opportunistic feeding during above-freezing periods. Alternatively, it is possible that organisms sampled during the morning may have regurgitated overnight the food consumed during the previous day. This hypothesis would imply that below-freezing temperatures can introduce false negatives into gut content analysis. In reality, it is likely that I encountered a combination of both possibilities. Future explorations of winter feeding in arthropods should address these potentialities. Influencing Feeding Habits This study was conducted in parallel with two others where I used a metabarcoding approach to survey soil arthropod taxa in the same orchard using the same sampling time points (see Chapters 2,3). The shared experimental design included long term management strategies (organic versus conventional) and short-term management tactics (urea-based floor sanitation for apple scab control) as experimental factors. The metabarcoding studies were able to observe changes in relative community abundances in response to the applied treatments. The original goal of this study was to determine if management practices were also influencing winter feeding activity. Unfortunately, the power of this experiment was limited by the small number of positive results and was insufficient to permit evaluation of treatment effects. While I have identified some winter-feeding natural enemies of V. inaequalis, I have no direct evidence to show that their feeding habits can be impacted by management. 166 However, the results of Chapters 2 and 3 did show that relative abundances of the taxa with positive gut content detections in this study were influenced by both short and long-term management practices. In particular, Julida, Oribatida, and Collembola taxa were upregulated by late season urea applications to leaf litter, a practice specifically intended to reduce V. inaequalis disease pressure in the following year (Burchill et al. 1965; Burchill 1868; Agnello et al. 2017). Together, these studies provide sufficient preliminary data to suggest that winter feeding by V. inaequalis natural enemies can be influenced by orchard ecosystem manipulation. Additional research aimed at robust reconstructions of winter food webs with increased taxonomic resolution, larger sample sizes, and greater instrumental sensitivities will be required to understand how agroecosystem manipulation can be used to achieve practical and reliable levels of control of V. inaequalis by winter feeding arthropods. Caveats & Challenges The size of the organisms under study, which were generally small (<1mm), may have been a confounding factor. The volume of their gut may have been so low that an individual specimen could not yield sufficient target DNA to generate a signal in my assay. Remén et al. (2010) were unable to detect DNA from a fungal mycelium diet in oribatid mites when extractions were performed on individuals. Pooling 5 mites together produced a true positive rate of 20%. Detection of all true positives only occurred when 10 mites were pooled together in each sample. In contrast, Heidemann et al. (2011) reported a 100% true positive detection rate in a similar experiment with a nematode diet where each specimen was processed individually. I pooled 5 specimens together in each sample belonging to Collembola or Acari as compromise between signal strength and replication. Another possible source of zero-inflation in my study was the taxonomic resolution used in my groupings. Family level resolution of insect taxa represented a practical limit with the number of samples, budget, and time frame associated with the project. However, explorations of trophic niche differentiation and fungal feeding habits of soil arthropods have revealed that dietary preferences are often highly specific, even amongst closely related organisms (Schneider et al. 167 2004; Schneider et al. 2005; Heidemann et al. 2011). The cited examples of feeding habits specifically highlight oribatid mites, which I was only able to identify to order. The taxonomic groupings may have been too coarse, at least in some taxa, to capture feeding habits for a specific fungal target. CONCLUSIONS I investigated the winter feeding habits of taxa identified in this study to determine if they were grazing on apple leaf litter infected with Venturia inaequalis. Slightly less than 2% of the screened specimens produced positive results for V. inaequalis and M. domestica DNA in their gut contents. While some of the large number of negatives are likely valid, confounding factors in the experimental design such as specimen size, the number of specimens collected, the sensitivity of the assay, and the taxonomic resolution used for grouping the specimens could have contributed to the low quantity of positive results. Consequently, it was not possible to compare grazing frequencies between management practices. However, I was able to identify four decomposer taxa as potential natural enemies of V. inaequalis: Entomobryomorpha, Poduromorpha, Scarabidae, and Oribatida. I also saw evidence of secondary predation in Cantharidae and Staphylinidae, suggesting that winter grazing activity on V. inaequalis may be limited by winter active generalist predators. 168 APPENDIX 169 APPENDIX 5.A. Figures and Tables 170 Figure 5.1. Determination of the M. domestica CT Value Cutoff. Three separate serial dilutions were prepared from genomic DNA extracted from V. inaequalis fruit. Two technical replicates were run for each sample. Plotted points represent mean CT values for each technical replicate. Bars depict the range of CT values. Shading depicts the standard error for the fitted regression line. Numbers in parentheses are the number of technical replicates that produced a positive signal at a given dilution level. The dashed line shows the cycle threshold value selected as the cutoff point for false positives. 171 Figure 5.2. Determination of the V. inaequalis CT Value Cutoff. Three separate serial dilutions were prepared from genomic DNA extracted from V. inaequalis fruit. Two technical replicates were run for each sample. Plotted points represent mean CT values for each technical replicate. Bars depict the range of CT values. Shading depicts the standard error for the fitted regression line. Numbers in parentheses are the number of technical replicates that produced a positive signal at a given dilution level. The dashed line shows the cycle threshold value selected as the cutoff point for false positives. 172 Table 5.1. Efficacy of the Surface Decontamination Protocol. Starved, early instar H. illucens larvae euthanized by freezing and then shaken in a petri dish with M. domestica and V. inaequalis tissues. Larvae in the Washed treatment were subjected to a 90 second rinse with each of 2.5% bleach, ethanol, and distilled water, and then screened for target DNA. Larvae in the Unwashed treatment were screened without any decontamination. Starved larvae without exposure to M. domestica and V. inaequalis tissues were used as a control. 173 Figure 5.3. Detection of Target Analytes in an Insect Gut Model. Early instar H. illucens larvae were starved for 72 hours, allowed to feed for 12 hours on a diet amended with M. domestica and V. inaequalis tissues, and then separated from the diet. Groups of 40 larvae were then euthanized at 0, 6, 12, 24, 48, 72, 96, and 120 hours after removal from the food source, surface decontaminated, and screened for the presence of target DNA. 174 Table 5.2. Field Sample Gut Content Screening Results. N represents the number of individual specimens screened, with the exception of taxa belonging to Collembola, Oribatida, and Thysanoptera. Samples belonging to these taxonomic groups were composed of 5 individual specimens pooled together. The number of positive testing samples is listed under the respective column heading for each taxon. 175 Table 5.3. Detection of Target Analytes in an Insect Gut Model. Numbers outside of parentheses represent the number of samples testing positive for V. inaequalis or M. domestica DNA. Numbers inside of parentheses represent the total number of samples screened. A dash indicates that no specimens were collected from the field on that date. 176 Figure 5.4. Change in Air Temperature During Study Period. Large, grey-filled dots represent sampling dates. 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Pedobiologia, 46(3-4), pp.404-413. 182 CHAPTER 6: SYNTHESIS Population growth and modernization following the Industrial Revolution have led to simultaneous increases in land use demands for food production and urban expansion. Resultant changes in agricultural production include extensive use of monoculture plantings, large cropping areas, and high planting densities. Furthermore, the development and global distribution of selectively bred cultivars has reduced on-farm genetic variability. A practical consequence of agricultural modernization is that modern farms have become idealized environments for the evolution of both insect pests and fungal pathogens. In response to this, farmers have widely adopted the use of top-down, chemical pest management strategies. These strategies excel at providing short term solutions to pest management problems but often fail over the long term due to the development of resistance in pest populations. Cultural control tacics on the other had seek to manage pests by altering agroecosystems in such a way that reduce pest habitat, reducing populations through a combination of “top down” and “bottom-up” pressures. In my dissertation I sought to evaluate the mechanisms of cultivation as a tool for managing overwintering grape berry moth and how floor management in apple orchards contributes to the management of overwintering Venturia inaequalis, the causal agent of apple scab. My first aim was to determine the role of burial and tillage in managing the overwintering grape berry moth population in vineyards. I was able to determine the distribution of depths at which pupa are buried after a tillage pass, characterize the type and severity of damage resulting from a tillage pass, determine the relationship between damage caused by tillage and adult emergence in the overwintering generation, and determine the relationship between burial depth and adult emergence in the overwintering generation. I showed that burial under even minimal amounts of soil substrate interferes with the successful emergence of diapausing grape berry moth. I also demonstrated that tillage is not effective at directly imparting damage to pupae in the field, which is consistent with previous studies. 183 My second aim was to observe changes in invertebrate detritivore community composition on apple orchard floors in response to management strategies and tactics. I developed a metagenomic approach to identify invertebrates in mixed samples drawn from soil and decaying apple leaves. I then determined if management strategy (organic vs. conventional) or late-season application of urea influence measures of ecological diversity in soil arthropod communities during the overwintering period between leaf fall and the start of the next season’s growth. The principal finding of my research into orchard floor ecology was that the orchard under study supported a diverse, winter active community comprised of decomposers and predators. Previous explorations of agroecosystems during cold seasons limited the scope of investigation to specific taxonomic groupings or functional groups. I provided one of the first holistic descriptions of a winter soil arthropod community in a temperate agroecosystem. My work suggests that winter active communities in agroecosystems are far more active than is traditionally believed. I also conducted one of the first evaluations of agricultural management impacts on winter active communities. Interestingly, family richness and intra-community complexity (alpha diversity) did not differ between the organic and conventionally managed plots. There was also considerable overlap in the dominant families detected under both management strategies. However, I did observe changes in the relative abundances of those families in response to management strategy. The changes in relative abundance were largely associated with differences in the secondary and tertiary decomposer sub guilds, which I suspect is related to the differences in the composition and quantity of organic residues in the orchard understory. I also saw indirect evidence that the organic orchard plots were able to support a larger overall arthropod population. In all, my results suggest that winter active arthropods could be a factor in the provisioning of ecosystem services in agroecosystems, and that management style may affect the type, timing, and magnitude of those services. 184 Late-season application of urea to orchard leaf litter produced an observable impact on epigeic arthropod decomposers. Tertiary decomposers were up-regulated in urea-treated litter as evidenced by differences in both mean relative abundances and the number of unique detection events. Changes were most pronounced in the first month following urea application. The data suggest that the addition of urea recruited arthropod decomposers into leaf litter from surrounding areas in the orchard and accelerated the trophic succession that occurs during leaf degradation. The upregulation of taxa associated with fungal grazing resulting from urea application creates a potential mechanism by which V. inaequalis, a pathogen overwintering in leaf litter, is controlled by urea-based floor sanitation techniques. This is the first study to provide evidence linking soil arthropod communities to urea-based floor sanitation practices. My final aim was to measure soil arthropod consumption of infected apple leaves during the winter using real-time PCR. My explorations of orchard floor ecology provided insight into what taxonomic groups responded to agroecosystem manipulation but could not confirm whether those groups were involved in controlling V. inaequalis. Linking responsive taxonomic groups to confirmed feeding on V. inaequalis would address that knowledge gap. I developed and validated a real-time PCR assay for screening invertebrate gut contents for apple scab and apple plant DNA and then used that assay to determine the presence of apple scab and apple plant DNA in the gut contents of sampled soil arthropods collected from the orchard floor. Slightly less than 2% of the screened specimens produced positive results for V. inaequalis and M. domestica DNA in their gut contents. While some of the large number of negatives are likely valid, confounding factors in the experimental design such as specimen size, the number of specimens collected, the sensitivity of the assay, and the taxonomic resolution used for grouping the specimens could have contributed to the low quantity of positive results. Consequently, it was not possible to compare grazing frequencies between management practices. However, I was able to identify four decomposer taxa as potential natural enemies of V. inaequalis: Entomobryomorpha, Poduromorpha, Scarabidae, and Oribatida. I also saw evidence of secondary predation in 185 Cantharidae and Staphylinidae, suggesting that winter grazing activity on V. inaequalis may be limited by winter active generalist predators. This dissertation highlights the relevance of the overwintering period to management strategies in temperate perennial agriculture. In the grape berry moth work, I was able to establish a direct linkage between a management tactic (burial by tillage) and reduced survivorship of a key pest. I was also able to propose how that management tactic could integrate into a broader, sustainable management strategy (border-applied pesticides). In the work on orchard floor ecology and feeding behavior, I showed that soil arthropods are active during the winter, that they respond to habitat manipulation, and that they are feeding on pathogen tissues. I proposed that habitat manipulation could be used invoke shifts in soil arthropod communities that reduce V. inaequalis, and potentially other pest, populations prior to the start of the next season. I also presented a potential linkage between one of the few existing V. inaequalis management tactics deployed in orchards after the growing season (application of late season urea) and the observed changes in overwintering soil arthropod communities. While the results of this dissertation point towards winter as a novel management window, a considerable amount of work remains in understanding the structure of overwintering communities, their activities, and their responses to a variety of management factors before practical measures can be developed. Integration of findings on natural ecosystems can inform the direction of future agricultural research. In many ways, winter soil ecology in natural systems has been explored more thoroughly than in agroecosystems. Future explorations must also continue and expand the systems-level approach taken in this dissertation. Responses of agroecosystems span across time and taxonomic domains and must be measured accordingly to understand their potential role in management. 186