USING NEW MOLECULAR TOOLS to EXPLORE HOW ORGANIC FARMING IMPACTS TOP-DOWN and BOTTOM-UP REGULATION of POTATO HERBIVORES by KARO

USING NEW MOLECULAR TOOLS TO EXPLORE HOW ORGANIC FARMING IMPACTS

TOP-DOWN AND BOTTOM-UP REGULATION OF POTATO HERBIVORES

KAROL LYNN KREY

A dissertation submitted in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

WASHINGTON STATE UNIVERSITY Department of Entomology

JULY 2017

To the Faculty of Washington State University:

The members of the Committee appointed to examine the dissertation of KAROL LYNN

KREY find it satisfactory and recommend that it be accepted.

William E. Snyder, Ph.D., Chair

David W. Crowder, Ph.D.

John P. Reganold, Ph.D.

Paul D. Nabity, Ph.D.

ii ACKNOWLEDGMENT

I would like to thank Bill Snyder, my advisor and committee chair, for the opportunity to be a part of the lab and for his help throughout my graduate career. I have made leaps and bounds in progress toward becoming a great researcher, writer, and mentor. I would also like to thank my committee members Dave Crowder, John Reganold, and Paul Nabity, for providing me guidance and constructive criticisms on my dissertation projects.

I am grateful for the many friends and comrades I have made in the entomology department and hope to always stay in touch. I am lucky to have had the support from the Snyder lab team, especially, Christine Lynch, Amanda Meadows, Carmen Castillo, Jake Asplund, Matt

Jones, Joseph Taylor, Olivia Smith, and the super amazing post-docs, Daisy Fu and Carmen

Blubaugh (your help and support was invaluable). Thanks to all the undergraduate workers that spent tireless hours helping me: Abbey Estep, Ashley Norberg, Trevor Snodgrass, Jen Madigan, and Samantha Beck.

Last, but most certainly not least, I would like to thank my family for their constant encouragement throughout my dissertation work. They were always there with positive words of advice to help me achieve my goals. I am especially indebted to my husband, Jesse Burke, whose unwavering love and support made everything possible.

iii USING NEW MOLECULAR TOOLS TO EXPLORE HOW ORGANIC FARMING IMPACTS

TOP-DOWN AND BOTTOM-UP REGULATION OF POTATO HERBIVORES

Abstract

by Karol Lynn Krey, Ph.D. Washington State University July 2017

Chair: William E. Snyder

Herbivorous agricultural pests are simultaneously threatened by predatory natural enemies and by toxic plant defenses. Both these “top down” and “bottom up” forces might be enhanced under organic farm management. For example, reductions in the use of broad-acting insecticides, typical of some organic systems, can lead to dramatically higher predator density and biodiversity on organic compared to conventional farms. Similarly, efforts by organic farmers to increase soil organic matter might lead to more robust plants better able to activate effective anti-herbivore defenses. We took advantage of modern molecular approaches to track predation and quantify the deployment of plant defenses for potatoes grown under organic or conventional practices in eastern Washington, USA. Chapter 2 reports significantly higher densities of predatory Geocoris and Nabis bugs in organic potato fields; because per-capita predation rates on spider mites were generally similar in the two farming systems, we would expect stronger top-down suppression of spider mites in organic than conventional potato fields.

Chapter 3 reports greater defense-gene activity for potato foliage on organic than conventional fields of the potato variety Norkotah, although no such effect was seen for the variety Alturas.

Chapter 4 reports heightened survivorship of Colorado potato beetles in the greenhouse on potato

iv plants grown in organic than conventional soils, but was unaffected by the presence of aphids. In contrast, aphids reached significantly higher densities when reared alone than when paired with

Colorado potato beetles, and were unaffected by soil type. Across the field and greenhouse studies reported in Chapters 3 and 4, we saw weakly heightened soil microbial activity in organic compared to conventional soils, but few differences in plant chemistry. Thus, further work is needed to explore the ecological basis of differences in plant defenses or herbivore survivorship mediated by soil management practices. Altogether, the work described in my dissertation suggests that the potential to alter and exploit organic versus conventional management practices to maximize plant deployment of natural defenses could form an exciting new approach to sustainable agriculture and host-plant resistance.

v TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ...... iii

ABSTRACT ...... iv

LIST OF TABLES ...... ix

LIST OF FIGURES ...... x

CHAPTER

CHAPTER ONE: INTRODUCTION ...... 1

Herbivore suppression from above and below ...... 1

Top-down suppression of herbivores in agricultural systems...... 3

Plant defense against herbivores – Evolution and types of defensive traits ...... 4

Plant defense costs and benefits ...... 5

Plant compounds related to defense ...... 7

Direct induced defense genes...... 8

Soil resources related to plant health and defense against herbivory ...... 9

Organic farming’s impact on top-down and bottom-up processes in potato crops 11

Dissertation overview ...... 13

References ...... 15

CHAPTER TWO: COULD GENERALIST PREDATORS DIFFUSE SPIDER MITE OUTBREAKS ON ORGANIC FARMS? ...... 37

ABSTRACT ...... 37

1. INTRODUCTION ...... 38

2. MATERIALS AND METHODS ...... 41

vi Primer design ...... 41

DNA detectability feeding trials ...... 42

Arthropod survey and collections in commercial potato fields ...... 43

Molecular gut-content analysis ...... 45

Statistical analyses ...... 45

3. RESULTS ...... 46

Two-spotted spider mite primers ...... 46

Gut retention times of spider mites in predator guts ...... 47

Predator and pest densities in commercial potato fields ...... 48

Detection of prey DNA in predators ...... 48

4. DISCUSSION ...... 49

5. REFERENCES ...... 54

CHAPTER THREE: CAN ORGANIC FARMING SHARPEN PLANT DEFENSES AGAINST HERBIVORY? ...... 72

ABSTRACT ...... 72

1. INTRODUCTION ...... 73

2. MATERIALS AND METHODS ...... 76

Study Area ...... 76

Quantifying Gene-Expression Patterns in Potato ...... 76

Arthropod Sampling ...... 78

Analysis of Soil Properties ...... 79

Analysis of Foliar Metabolites and Nutrients ...... 81

Statistical analyses ...... 82

vii 3. RESULTS ...... 85

Plant transcriptomics and differentially expressed genes ...... 86

Pest densities in potato fields ...... 87

Soil nutrients ...... 87

Soil microbial communities ...... 87

Foliar nutrient analysis and primary metabolites ...... 88

4. DISCUSSION ...... 89

5. REFERENCES ...... 95

CHAPTER FOUR: SURVIVORSHIP OF TWO POTATO PESTS ON PLANTS GROWN IN ORGANIC VERSUS CONVENTIONAL SOILS ...... 130

ABSTRACT ...... 130

1. INTRODUCTION ...... 131

2. MATERIALS AND METHODS ...... 133

Study Area ...... 133

Characterizing soil characteristics and microbial ecology at each farm ...... 134

Statistical analyses ...... 136

3. RESULTS ...... 137

Herbivore numbers and survivorship ...... 137

Soil nutrients ...... 138

Soil microbial communities ...... 138

4. DISCUSSION ...... 139

5. REFERENCES ...... 143

viii LIST OF TABLES

Page

CHAPTER TWO

Table 1: Model output from two binomial GLMS evaluating the effects of mite density, aphid density and likelihood of detecting aphid predation on the likelihood of detecting spider mite predation by (a) Nabis alternatus and (b) Geocoris bullatus predators collected at conventional and organic potato farms in 2011 and 2013 ...... 62

Appendix Table 1: For each potato field, sampling date, farming system, nearest city (all in Washington State), number of predators of each species collected for use in molecular gut content analysis, and number of predators of each species where two-spotted spider mite (Mite+) or aphid (Aphid+) DNA was detected ...... 68

CHAPTER THREE

Table 1: List of tests performed on the soil collected from 12 potato fields ...... 112

Table 2: Number of differentially expressed genes with threshold of adjusted P-value<0.1 . 113

Table 3: Specific metabolites from GC-MS run related to plant defense and health ...... 114

Table 4: Elemental analyses and stable isotope values for potato varieties under different management and by different damage by insect. There were no differences between damage and undamaged leaves (carbon: P= 0.1956; nitrogen: P= 0.3405; C:N ratio: P= 0.1915) and combined damage and undamaged for further analysis...... 115

Table 5: Microbial richness and evenness of bacteria families sequenced in soil collected in central Washington potato fields ...... 116

Table 6: Number and direction of differentially expressed genes (DEG) per gene ontology (GO) at Padj<0.1 ...... 117

Table 7: Soil microbial taxa (Padj<0.1, log2FoldChange > 4) more abundant in organic compared to conventional field collected soil...... 118

CHAPTER FOUR

Table 1: List of tests performed on the soil collected from 10 potato fields ...... 153

Table 2: Output from the greenhouse pest trials with treatment (GPA only, GPA with CPB, CPB only, and CPB with GPA) and production management (organic and conventional soils) .... 155

ix LIST OF FIGURES

Page

CHAPTER TWO

Fig. 1: Locations in Washington State, U.S.A., of the organic (star) and conventional (circle) potato fields that we sampled in the (a) 2011 and (b) 2013 growing seasons ...... 64

Fig. 2: Mean number (per 50 plants) of the predators (a) Nabis alternatus and (b) Geocoris bullatus, and (c) of the herbivorous pest Tetranychus urticae, collected from either organic (black) or conventional (white) potato fields during the 2011 and 2013 growing seasons ...... 65

Fig. 3: Mean proportions of (a) Nabis alternatus and (b) Geocoris bullatus predators where spider mite DNA was detected, for predators collected from organic (black) or conventional (white) potato fields during the 2011 and 2013 growing seasons...... 66

Fig. 4: Mean proportion of Nabis alternatus (a-c) or Geocoris bullatus (d-f) predators where spider mite DNA was detected, regressed versus (a, d) mite or (b, e) aphid densities, and (c, f) with the proportion of predators of that same species where aphid DNA was detected. Predators were collected from organic (black) or conventional (white) potato fields...... 67

Appendix Fig. 1: Mean proportion of predator guts where spider mite DNA was detected, for Nabis alternatus and Geocoris bullatus predators, within increasing time from predation of two- spotted spider mite, Tetranychus urticae ...... 70

Appendix Fig. 2: (a) Aphid densities collected in vacuum samples of 50 potato plants, and frequencies of detection of aphid DNA in predatory (b) Nabis alternatus and (c) Geocoris bullatus, for collections from organic (black) or conventional (white) potato fields during the 2011 and 2013 growing seasons. Aphid densities (a) were compared using a negative binomial glm, and did not differ between growing regimes (z = -1.449, P = 0.147), but were higher in 2011 than in 2013 (z = -4.253, P <0.0001). Frequencies of detection of aphid predation (b and c) were compared using a binomial glm, and did not differ across growing regimes (t = -1.652, P = 0.107), nor predator species (t = 0.492, P = 0.626), nor years (t = 0.172, P = 0.865) ...... 71

CHAPTER THREE

Fig. 1: Locations in Washington, U.S.A., of the 5 organic (star) and 7 conventional (circle) potato fields that were sampled in the 2015 growing season...... 121

Fig. 2: Graphical representation of the transcripts up (red) - and down (blue)-regulated in Norkotah damaged leaves when compared to undamaged leaves in biotic defense signaling. Green boxes represent metabolites and enzymes, whereas pink boxes represent processes. . 122

x Fig. 3: Graphical representation of the transcripts up (red) - and down (blue)-regulated in Alturas damaged leaves when compared to undamaged leaves in biotic defense signaling. Green boxes represent metabolites and enzymes, whereas pink boxes represent processes...... 123

Fig. 4: Graphical representation of the transcripts up (red) - and down (blue)-regulated in Norkotah Organic damaged leaves when compared to organic undamaged leaves in biotic defense signaling. Green boxes represent metabolites and enzymes, whereas pink boxes represent processes...... 124

Fig. 5: Graphical representation of the transcripts up- and down-regulated in (a) Norkotah damaged leaves when compared to undamaged leaves, (b) Alturas damaged leaves when compared to undamaged leaves and (c) Norkotah organic damaged leaves when compared to organic undamaged leaves in photosynthetic signaling...... 125

Fig. 6: Elemental analysis of (a) Carbon %, (b) Nitrogen %, and (c) C:N ratio with combined damaged and undamaged leaves...... 126

Fig. 7: PCA plot of primary metabolites found in potato leaves separated by variety, management, and damage. Alturas variety is represented by A and Norkotah variety by N 127

Fig. 8: Average (a) potato pest and (b) predator densities collected in vacuum samples of 50 potato plants per variety and management type ...... 128

Fig. 9: Concentrations of nutrients in soil collected from organic and conventional potato fields at mid-season. (a) Nitrate, (b) Phosphorus, (c) Potassium, (d) Sulfate, (e) Microbial biomass, and (f) Organic Matter. Gray bars indicate conventional soil and white bars organic ...... 129

CHAPTER FOUR

Fig. 1: Locations in Washington, U.S.A., of the organic (star) and conventional (circle) potato fields that were sampled in the 2016 growing season...... 157

Fig. 2: Total aphid counts per plant in greenhouse bioassay comparing conventional and organic soils, as well as co-occurrence of Colorado potato beetle ...... 158

Fig. 3: Total beetle (CPB) count surviving (out of 5) in greenhouse bioassay comparing conventional and organic soils, as well as co-occurrence of green peach aphids ...... 159

Fig. 4: Concentrations of nutrients in soil collected from organic and conventional potato fields at the beginning of the season. (a) Nitrate, (b) Phosphorus, (c) Potassium, (d) Sulfate, (e) Cation Exchange capacity, and (f) Organic Matter. Gray bars are conventional and white bars are organic fields ...... 160

xi Fig. 5: Microbial (a) richness and (b) evenness of bacteria families sequenced in soil collected in central Washington potato fields ...... 161

xii

Dedication

Husband Jesse Burke

My dogs Sookie and Chili

Best pals forever

xiii

CHAPTER ONE: INTRODUCTION

My dissertation examines how natural regulation of herbivores differs between organic and

conventional potato (Solanum tuberosum) crops. Herbivores in these fields face attack by

predatory insects and defensive responses from the potato plants themselves. In my literature

review I first briefly summarize views from the basic ecology literature of both “top down”

regulation of herbivores by their predators and “bottom up” regulation of herbivores by their host

plants. I next provide additional detail on the relationship between predator diversity and the

strength of top-down suppression, and on how plants defend themselves against herbivores.

Organic and conventional farming systems deploy very different soil-fertility-management

practices (e.g., Watson et al., 2002; Pimentel et al., 2005), which in turn leads to differing

microbial communities (e.g., Ling et al., 2016) and differing patterns of nutrient availability to

plants (e.g., Fernandez et al., 2016). Both soil communities and soil quality impact plant defenses

(Leeman et al., 1995; Liu et al., 1995; Bardgett et al., 1998; Bardgett and Wardle, 2003; Wardle

et al., 2004; Bezemer and van Dam, 2005), and I next summarize this literature. Finally, I close

with a brief overview of my study system while reviewing earlier studies examining top-down and bottom-up effects in organic versus conventional potato agroecosystems.

Herbivore suppression from above and below

Increasing predator abundance can increase net consumption of herbivorous prey, in turn allowing plants to grow more vigorously (Hairston et al., 1960). Whenever species at one trophic level directly reduce densities of the species they feed upon, indirectly releasing species two trophic level removed from top-down control, this is known as a “trophic cascade” (Paine, 1980).

Oksanen et al. (1981) suggested that food chain length varied with net primary productivity;

systems with low production cannot support predator trophic levels and can lead to herbivores

going unregulated, thus overwhelming plants. In contrast, highly-productive systems might

support four or more trophic levels, with high densities of top predators suppressing meso-

predators, releasing herbivores from top-down control to decimate plants (Oksanen et al., 1981).

Other ecologists, however, have suggested that such linear food chains with distinctly-separate trophic levels rarely exist in nature (e.g., Polis, 1991; Strong, 1992; Polis and Strong, 1996). This assertion is based on the common observation of surprisingly complex and reticulate webs of feeding relationships in real-world ecological communities. For example, in California cotton

(Gossypium hirsutum) fields the predatory bug Nabis americoferus feeds upon other predators,

herbivores, and the plants themselves; therefore, Nabis simultaneously occupies the apex predator, meso-predator, and herbivore trophic levels (Rosenheim et al., 1993). Of course, if

separate trophic levels do not exist then trophic cascades cannot occur (Snyder and Tylianakis,

2012; Heath et al., 2014). Meta-analyses of predator removal studies suggest that real-world communities capture aspects of both world views, with predators triggering strong trophic cascades in some communities while trophic complexity diffuses cascades in others (Sih et al.,

1998; Borer et al., 2005; Ives et al., 2005; Vance-Chalcraft et al., 2007; Straub et al., 2008;

Bruno and Cardinale, 2008; Finke and Snyder, 2010).

The literature discussed above suggests that plant biomass is primarily determined by “top down” effects cascading from above (e.g., Hillebrand et al., 2007; Gruner et al., 2008; Mooney et al., 2010; Borer et al., 2014). We now know, however, that plants also exert “bottom up” effects

that impact the herbivores and predators at higher trophic levels (Stevens et al., 2004; Elser et al.,

2007). For example, herbivore densities often are strongly influenced by variation in plant

quality resulting from concentration or availability of key nutrients (e.g., Boege, 2005). Plants

also directly defend themselves (discussed in detail below). In turn, when plants directly reduce

herbivore numbers (e.g., Karban and Baldwin, 1997; Agrawal, 1999; Howe and Jander, 2008)

and/or the nutritional quality of herbivores (e.g., Bhonwong et al., 2009; Zhang et al., 2008), this

reduces food available to predators with the potential to indirectly limit herbivore numbers (e.g.,

Fürstenberg-Hägg et al., 2013). Today, most ecologists would acknowledge that herbivores often

find themselves “between a rock and hard place”, facing threats both from predators above, and

plant defenses below (Ives et al., 2005; Cardinale et al., 2006; Straub et al., 2008; Snyder, 2009;

Finke and Snyder, 2010; War et al., 2012; Fürstenberg-Hägg et al., 2013).

Top-down suppression of herbivores in agricultural systems

Biological control of herbivorous agricultural pests by their predators, parasitoids and

pathogens forms an important complement to chemical and cultural pest control (Stern et al.,

1959; Bale et al., 2008). Specialist natural enemies, like many parasitoid wasps, often are

purported to be more reliable biological control agents compared to polyphagous feeders

(Debach and Rosen, 1991). This is because natural enemies that are specialists on specific prey

species can tightly track, and perhaps reduce, increases in density among particular pest species

(Hassel and May, 1986; Murdoch, 1994; Symondson et al., 2002). At the same time, these highly-specialized feeders cannot exist in cropping fields in the absence of the prey species that serves as their sole food source (Tylianakis et al., 2004). These limitations of specialists suggest that narrower feeding relationships are not unquestionably beneficial for biological control

(Hassel and May, 1986; Stiling and Cornelissen, 2005).

Indeed, “generalist” predators with relatively broad feeding habits have been implicated in some of the best-documented examples of predator conservation to improve biological control

(Murdoch et al., 1985; Barbosa, 1998; Chang and Kareiva, 1999; Symondson et al., 2002).

Generalists that build their densities early in the season by feeding on one group of prey, can

later be available to defend plants against later-arriving pest species (Landis and van der Werf,

1997; Wiedenmann and Smith, 1997). The typical active hunting style of generalist predators

can contribute significantly to break up pest aggregations, cause herbivores to drop from plants

and thus render them more prone to desiccation (Mansour et al., 1981; Villemant and Ramzi,

1995; McClure, 1995; Osawa, 1996; Valenti et al., 1998). Generalists are also more versatile and

readily able to adjust to changing environmental conditions, and can take advantage of many

food resources (Wissinger, 1997; Symondson et al., 2002). Despite these strengths, generalist

predators also have some limitations as biological control agents. First, when generalist predators

have strong preferences for prey species other than the target pest, biological control of that pest

can be disrupted (e.g., Koss et al., 2004; Prasad and Snyder, 2006). Second, generalist predators

often feed upon one another (intraguild predation), and this has the potential to greatly limit

biological control (Polis et al., 1989; Rosenheim et al., 1993; Rosenheim et al., 1995; Harwood

et al., 2001; Sheppard et al., 2005; Juen and Traugott, 2005). Therefore, there are both positive

and negative aspects to generalists as biological control agents.

Plant defense against herbivores – Evolution and types of defensive traits

Plants are not capable of moving away from their herbivorous enemies, and herbivorous insects cannot survive without their host plants. Thus, plants and insects have become locked in an evolutionary “arms race”, with successive waves of plant defenses being overcome by herbivores before being refortified by the plants (Mitter et al., 1988; Futuyma and Keese, 1992;

Herrera and Pellmyr, 2002). This has resulted in the development of a sophisticated defense

system in plants that recognizes signals from damaged cells, or from insect mouthparts or saliva,

ultimately activating plant immune responses that harm or block herbivores (Ehrlich and Raven,

1964; Howe and Jander, 2008; Verhage et al., 2010; Hare, 2011). Plant defense mechanisms

include chemical and physical barriers such as the induction of volatiles that attract predators and

parasitoids that kill herbivores (Birkett et al., 2000), the production of secondary metabolites that

poison herbivores (Baldwin, 2001; Kliebenstein et al., 2001, 2005; Kessler and Baldwin, 2002),

and the development of physical barriers such as trichomes that make it more difficult for

herbivores to access nutritious plant tissues (Fordyce and Agrawal, 2001). At the same time, insects have developed strategies to overcome plant barriers such as detoxification of noxious compounds (Ivie et al., 1983; Scott and Wen, 2001; Burse et al., 2009), avoidance mechanisms when plants induce specific compounds (Zangerl, 1990; Gould, 1991; Mello and Silva-Filho,

2002), and sequestration of toxins (Nishida, 2002; Opitz and Müller, 2009; Winde and Wittstock,

2011). Therefore, the effectiveness of bottom-up herbivore regulation reflects both the defenses of the plants themselves, and the herbivores’ ability to overcome these defenses.

Plant defense costs and benefits

There are numerous plant defense theories explaining the evolution of inducible defenses against herbivory, but the prevailing hypothesis suggests that the variation of distribution in resources in defense over growth is in relation to the plants’ risk of attack, the value of the attacked tissue, and the production costs of the defenses (Feeny, 1976; McKey, 1979; Rhoades,

1979). Inducible defenses may spend less of the plant's resources by allowing it to invest in defense when necessary, and to avoid costly allocations to defense when herbivores are not present (Karban, 2011). More recently, induction has been examined from a more mixed

perspective, with concepts indicating that induction may have many types of benefits and costs

(Parker, 1992; Simms and Fritz, 1992; Takabayashi and Dicke, 1996; Karban et al., 1997;

Agrawal and Karban, 1999). For example, fitness benefit studies make clear that the presence of herbivores can counterbalance costs of induced responses seen in the absence of herbivores

(Agrawal 1998, 1999; Baldwin, 1998). Plants would likely evolve defenses in proportion to their risk from herbivores (‘plant apparency hypothesis’) and these defenses are costly because they divert resources from other essential plant needs such as growth (Stamp, 2003). Overall, loss to herbivores often exceeds the allocation to reproduction (Mooney, 1972; Bazzaz et al., 1987).

Nonetheless, an array of secondary metabolites (see section below) has evolved in plants, most

of which seem primarily involved in plant defense against herbivores (Berenbaum, 1995).

Overall, we now recognize that plant defense evolution is a very complex process that often

involves many complementary components (Agrawal, 2011).

The effects of differences in soil quality, in terms of nutrients available to plants, can be

detected as differences in productivity and/or quality of plants for herbivorous insects (Kagata

and Ohgushi, 2006). Resulting differences in host-plant characteristics may have varying effects

on an herbivore and its associated natural enemies (Teder and Tammaru, 2002; Harvey and Gols,

2011). Studies have shown that increased soil quality can sometimes benefit both herbivores and

predators (Barbosa et al., 1991; Zvereva and Rank, 2003; Kagata et al., 2005). This happens

when rich soils lead to more-nutritious (or less-well-defended) foliage that increases its quality

for the insect herbivores, with nutrient-rich herbivores serving as high-quality food for their

predators (Kagata and Ohgushi, 2006). Other studies have shown the opposite effects of plant

quality: well-fed plants can sometimes mount stronger defenses against herbivores, in turn

reducing herbivore numbers, reproduction and nutritious quality, which then can indirectly limit

predator densities and condition (Karowe and Schoonhoven, 1992; Holton et al., 2003; Klemola et al., 2007). Altogether, these studies suggest that nutrients in the soil sometimes act to improve the quality of plants as food for herbivores, indirectly benefiting predators, but other times can strengthen plant defenses to the detriment of all higher trophic levels.

Plant compounds related to defense

Metabolites are compounds created by plants for both necessary functions, such as growth and development (primary metabolites), and specific functions, such as defense against herbivory (secondary metabolites) (Geissman and Crout, 1969; Mann, 1978). Primary metabolites have functions that are essential to growth and development and are present in all plants (Geissman and Crout, 1969; Mann, 1978). In contrast, secondary metabolites are variously dispersed in plants, and their functions are specific to the plants in which they are found (Makkar et al., 2007). Secondary metabolites are often colored, fragrant, or flavorful compounds and research has focused on their role in plant defense (Makkar et al., 2007). These defensive metabolites can be constitutive, stored as inactive forms throughout the plant, or induced in response to an insect or microbe attack (War et al., 2012). Some of these compounds can even serve dual functions as plant growth regulators (e.g., for transport and storage of carbon and nitrogen molecules) and in defense against herbivores (Panda and Khush, 1995; Schultz et al.,

2013). Secondary plant compounds are involved in plant defense against insect herbivores by reducing the palatability of the plant tissues in which they are produced (Howe and Jander,

2008). Induced secondary metabolites include phenolics such as isoflavonoids, terpenoids, alkaloids, and several others, that influence the performance and survival of herbivores (Walling,

2000). Plant compounds can also be involved in plant water transport, photosynthetic efficiency,

and response to plant stressors (heat and drought), which are important in plant growth and reproduction (Wahid, 2007; Farooq et al., 2008, 2009). Mass spectrometry used for secondary metabolite profiling and gene expression analysis by high-throughput sequencing has made these metabolites easier to study in relation to herbivore defense (Frisvad et al., 2007; War et al.,

2012).

Direct induced defense genes

Inducible defenses play a major role in conferring disease resistance against plant pathogens

(Maleck and Dietrich, 1999), and their effects on herbivores can include increased toxicity, delay of larval development, or increased attack by insect parasitoids (Baldwin and Preston, 1999). A plant’s direct defense response to herbivory is characterized by specific shifts in gene expression, modifying plant traits to interfere with herbivore feeding, growth and development, and fecundity (Kant et al., 2004). Most plant-defense responses against insects are activated by signal-transduction pathways mediated by jasmonic acid (JA), salicylic acid (SA), and ethylene

(ET) (Reymond and Farmer, 1998; Arimura et al., 2008; Gill et al., 2010; Shivaji et al., 2010), in response to feeding. These pathways may act individually, synergistically or antagonistically, depending upon the attack type (e.g., chewing versus piercing-sucking herbivores; Thaler et al.,

2012). The signaling molecule SA is crucial for local hypersensitive responses and systemic acquired resistance against many plant pathogens (Maleck and Dietrich, 1999) and piercing/sucking herbivores (i.e. aphids; Thaler et al., 2012; War et al., 2012). Resistance against herbivorous insects and some fungal pathogens depends on wound-response signaling via JA and ethylene pathways (Maleck and Dietrich, 1999; War et al., 2012).

In 1972, Green and Ryan demonstrated that tomato and potato plants accumulate inhibitors

of trypsin and chymotrypsin-like serine proteinases throughout their tissues, as a direct

consequence of insect-mediated damage or mechanical wounding (Green and Ryan, 1972). Since

the initial observation of proteinase inhibitor accumulation in wounded tomato and potato plants,

inducibility by herbivory has been shown for a large number of other potential resistance factors

(Walling, 2000; Gatehouse, 2002). In light of recent studies analyzing induced responses at the

level of the entire transcriptome, the full scope and dynamic nature of plant-insect interactions is being revealed. Numerous studies have shown that herbivory causes large-scale changes in gene expression (Cheong et al., 2002; Delessert et al., 2004; Reymond et al., 2004; Smith et al., 2004;

Voelckel and Baldwin, 2004; Zhu-Salzman et al., 2004; De Vos et al., 2005; Schmidt et al.,

2005; Ralph et al., 2006; Thompson and Goggin, 2006; Broekgaarden et al., 2007). Linking gene expression to particular plant defensive traits is a key first step to exploiting these defenses to improve the control of herbivorous agricultural pests (Mitchell et al., 2016).

Soil resources related to plant health and defense against herbivory

Conventional and organic farming systems utilize very different soil fertility management practices (Stockdale et al., 2002; Watson et al., 2002; Pimentel et al., 2005). Modern conventional farming relies primarily on synthetic-chemical fertilizers applied relatively frequently throughout the growing season (e.g., Soffe, 2002; Stockdale et al., 2002; Klonsky,

2012). For example, on conventional potato farms in eastern Washington State, fertilizer input is based on synthetic chemicals per acre, with a typical pre-plant fertilizer application and in-season applications as needed throughout the growing season (Anonymous, pers. comm.). In contrast, organic farmers often utilize animal manures or other biotic fertilizers (Anonymous, pers.

comm.). Organic fertilizers contain nitrogen sources that are released over a longer time scale

than the nitrogen in synthetic fertilizers and less readily available to the host plant (Eigenbrode and Pimentel, 1988; Phelan et al., 1995, 1996; Hsu et al., 2009). Often, because of the relatively slow nutrient release patterns typical of these organic fertilizers, soil fertility is manipulated less

frequently in organic than conventional farming systems (Stockdale et al., 2002). For example, in

Washington State organic potato fields, organic fertility management is typically only

administered pre-planting and once more for the whole season (Anonymous, pers. comm.).

These differences in organic versus conventional fertility management schemes would be

expected to impact plants’ ability to defend themselves against herbivores, as I next describe.

When organic and conventional fertilizers differ in the patterns of nutrients they supply to plants, we would anticipate a direct effect of these differences on nutrient uptake by plants and thus anti-herbivore defenses (Coley et al., 1985). The use of organic soil amendments has been associated with desirable soil properties including higher plant available water holding capacity, increased cation exchange capacity and lower bulk density, and increased densities of beneficial soil microorganisms (Doran, 1995; Drinkwater et al., 1995). Numerous studies have also identified that practices typical of certified organic soil management replenish and maintain high soil organic matter (e.g., Jenny, 1980; Reganold et al., 1987; McGuiness, 1993; Altieri and

Nicholls, 2003). Studies have found that the application of animal manures may increase

microbial biomass as compared to the use of synthetic fertilizers (Gunapala and Scow, 1998;

Swezey et al., 1998; Altieri, 1999; Mäder et al, 2002). Potato plants defend themselves through

combinations of morphological variations, allelochemical defenses, and altered nutritional quality that can all be influenced by the soil (Tomlin and Sears, 1992a,b; Hlywka et al., 1994;

Bolter and Jongsma, 1995; Pelletier et al., 1999). For example, Davis et al. (2001), working in

commercial potato fields, found that the factors most closely related to soil integrity (i.e., organic matter, organic nitrogen and increased nutrient availability) were associated with reduced disease incidence and higher tuber yields. Therefore, replenishing farmland soil with organic amendments will influence belowground physical, chemical and biological processes, all of which can contribute to crop plants’ health and defense.

As noted above, fertility-management differences between organic and conventional farming

systems sometimes lead to dramatically different soil microbial communities (e.g., Gunapala and

Scow, 1998; Bloemberg and Lugtenberg, 2001; Hartman et al., 2015). Because soil microbes can

impact the deployment of plant defenses, soil fertility can have indirect effects on plant defense

(e.g., Davis et al., 2001; Bonanomi et al., 2010). Indeed, below-ground microorganisms have been shown to enhance plants’ stress tolerance, disease resistance, and nutrient uptake

(Bloemberg and Lugtenberg, 2001; Lugtenberg et al., 2002; Bais et al., 2004; Morrissey et al.,

2004; Goh et al., 2013). For instance, beneficial soil bacteria can confer immunity against a wide

range of foliar diseases by activating plant defenses, thereby reducing a plant's susceptibility to

pathogen or herbivore attack (van Loon et al., 1998). Because higher microbial activity in

organic soils sometimes improves plants’ ability to fight-off pests, these benefits might be

greater in organic than conventional farming systems (Altieri and Nicholls, 2003; Zehnder et al.,

2007).

Organic farming’s impact on top-down and bottom-up processes in potato crops

In Washington State, potato (Solanum tuberosum) crops face attack by a diverse community of herbivorous arthropods (Koss et al., 2005). This in turn leads most growers to rely upon regular applications of synthetic, broad-spectrum insecticides throughout the growing season

(Koss et al., 2005). Organic potato fields in Washington are often of similar sizes to conventional

potato crops, with fields under the two management systems spatially interspersed and often

managed by the same growers (Koss et al., 2005; Crowder et al., 2010). However, organic potato

fields receive fewer applications of broad-spectrum insecticides (Koss et al., 2005), which in turn

fosters dramatically larger (Koss et al., 2005) and more bio-diverse (Jabbour et al., 2011;

Crowder et al., 2010) populations of generalist natural enemies. Several generalist predators are

known to strongly suppress pests of potato, an effect that is disrupted when predators are at the

lower densities typical of conventional potato fields (Koss et al., 2005; Crowder et al., 2010). In general, then, we might expect stronger top-down control of herbivores in organic than conventional potato fields.

Bottom-up processes also might be more robust under organic farming practices. This could occur for at least two reasons. First, as described above, organic soils that support more-diverse microbial communities, higher organic matter, and other characteristics that promote stronger plant defenses might lead to “healthier”, more pest-resistant plants. For example, one study showed that the application of manure reduced densities of the Colorado potato beetle (CPB), a major pest in potato, apparently because of significant differences in soil nutrient concentrations, in particular Boron (Alyokhin et al., 2005). Second, relatively infrequent spraying of insecticides, and in particular broad-acting insecticides (Koss et al., 2005), might lead to earlier and more-persistent herbivore attacks in organic than conventional potato fields. It is well known that earlier-arriving potato pests can induce defenses that harm later-arriving herbivores

(e.g., Wise and Weinberg, 2002; Lynch et al., 2006; Kaplan et al., 2007); therefore, relatively- constant herbivore attack in organic compared to organic potato fields might lead to more- consistently-pest-resistant plants in organic fields. Resulting differences between organic and

conventional management in soil characteristics and pest pressure both might, then, alter potato

plants’ ability and need to deploy defenses against herbivores.

Dissertation overview

My dissertation considers both top-down and bottom-up impacts on herbivores of potato, and how the strength of forces from both above and below might differ in organic versus conventional farming systems. In all cases, my work involved a combination of traditional field work and cutting-edge molecular techniques; these complementary perspectives are not often as fully integrated as in the work presented here.

Chapter 2 combines a large-scale survey of spider mites and their predators in Washington potatoes, with molecular gut-content analysis to compare predation patterns in organic versus conventional fields. This work revealed significantly higher densities of predatory Geocoris and

Nabis bugs in organic potato fields; because per-capita predation rates on spider mites were generally similar in the two farming systems, we would expect stronger top-down suppression of spider mites in organic than conventional potato fields.

Chapter 3 examines interrelations among soil characteristics, soil microbial communities, plant chemistry and defense-gene activity, and arthropod communities in organic versus conventional potato fields. Potatoes of both varieties exhibited consistently greater defense-gene

activity in foliage we scored as damaged compared to visibly undamaged foliage. However, soil

fertility and microbial biodiversity metrics did not clearly align with these differences. Our

results suggest that open-field examinations of gene expression patterns have the potential to

reveal important insights into how plants experience their environments.

Finally, Chapter 4 reports results from a greenhouse experiment, wherein we collected soil from the commercial organic and conventional potato farms and subjected potatoes grown in these soils to attack by green peach aphids and/or Colorado potato beetles. Survivorship of

Colorado potato beetles was significantly higher on plants grown in organic than conventional soils, but was unaffected by the presence of aphids. In contrast, aphids reached significantly higher densities when reared alone than when paired with Colorado potato beetles, but were unaffected by the soil type in which their host plants were grown. Unexpectedly, soil chemistry and microbial communities did not differ between organic and conventional soils. Altogether, the work described in my dissertation suggests that natural regulation of herbivores differs between organic and conventional potato crops and that the potential to alter and exploit these management practices to maximize plant deployment of natural defenses could form an exciting new approach to sustainable agriculture and host-plant resistance.

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CHAPTER TWO: COULD GENERALIST PREDATORS DEFUSE SPIDER MITE

OUTBREAKS ON ORGANIC FARMS?

ABSTRACT

Supported by other prey, resident populations of generalist predators might disrupt establishment of later-arriving pests. This can occur if predators consume prey at the low densities typical of initial pest establishment. We used molecular gut-content analysis to examine predation of two-spotted spider mites (Tetranychus urticae) by predatory Nabis and Geocoris bugs relatively early in the growing season, in Washington state potato (Solanum tuberosum) crops. Here, late-season spider mite outbreaks are common in potato fields under conventional management, but have never been recorded in certified organic fields. Consistent with a role for biological control in explaining this pattern, we found significantly greater numbers of Geocoris and Nabis predators in organic potato fields than in conventional fields at mid-growing season.

During this same period, before outbreaks are typically observed, spider mite densities did not significantly differ between the two farming systems. The likelihood of detecting spider mite

DNA in predators was not tightly correlated with spider mite densities. Rather, we often found spider mite DNA in predators collected from fields with relatively small spider mite populations.

Molecular evidence of predation of green peach aphids (Myzus persicae), common prey of these predators in potato fields, did not reduce the likelihood of detecting spider mite predation.

Altogether, our results support the idea that generalist predators could be defusing spider mite outbreaks, preying upon spider mites at low initial pest densities and despite the presence of

attractive alternative prey. Organic farming methods may be enhancing this form of biological control by encouraging robust predator communities.

Keywords:

Generalist predators; Tetranychus urticae; Myzus persicae; Molecular gut-content analysis;

Conservation biological control; Alternative prey; Solanum tuberosum

INTRODUCTION

Specialist natural enemies, like many parasitoid wasps, have often been suggested to be more reliable biological control agents than polyphagous feeders (Debach and Rosen, 1991). This is because natural enemies that feed on just one (or a few) prey species can tightly track, and perhaps suppress particular pest species (Hassel and May, 1986; Murdoch, 1994). Such density- dependent responses can occur either as specialist natural enemies increase their own reproductive rate in response to growing availability of their preferred prey (e.g., Hassel and

May, 1986; Turchin, 2003), or when specialist enemies aggregate at sites where that pest is becoming relatively abundant (e.g., Beddington et al., 1978; Walde and Murdoch, 1988). At the same time, highly-specialized feeders cannot exist in cropping fields in the absence of the prey species that serves as their sole food source (Tylianakis et al., 2004). This means that newly- arriving pests often can gain a foothold in the crop before their specialized natural enemies arrive, in turn allowing pests to reach injurious densities before biological control can occur

(Landis and van der Werf, 1997). These limitations of specialists suggest that narrower feeding relationships are not inevitably beneficial for biological control (Symondson et al., 2002).

Indeed, there is growing evidence that polyphagous feeding habits can sometimes facilitate,

rather than discourage, a natural enemy’s effectiveness in pest suppression (Symondson et al.,

2002; Costamagna et al., 2007). Generalists that build their densities early in the season by

feeding on one group of prey can be available as a first line of defense against later-arriving pest

species (Landis and van der Werf, 1997; Wiedenmann and Smith, 1997). For example, in

Indonesian rice crops, a diverse group of generalists that feeds on detritus-feeding insects at planting-time later switches to feeding on herbivorous planthoppers, key pests on these crops, once the herbivores arrive later in the growing season (Settle et al., 1996). Similarly, in a

European sugar beet field, predation of early-arriving aphids by resident generalist predators significantly reduced later infection of the plants by an aphid-transmitted virus (note, however, that this protective effect was not seen in all fields; Landis and van der Werf, 1997). For this to occur, generalists must attack newly-arriving pests at relatively low densities, often in spite of the presence of other prey at relatively high densities (Agustí et al., 2003; Harmon and Andow,

2004; Symondson et al., 2006). Indeed, when generalist predators have strong preferences for prey species other than the target pest, biological control of that pest can be disrupted (e.g., Koss et al., 2004; Prasad and Snyder, 2006).

In Washington State, potato (Solanum tuberosum) crops face attack by a diverse community of herbivorous arthropods (Koss et al., 2005). This in turn leads most growers to rely upon regular applications of synthetic insecticides throughout the growing season (Koss et al., 2005).

Under this management regime, two-spotted spider mites (Tetranychus urticae) commonly reach outbreak densities late in the growing season, necessitating costly chemical control (Penman and

Chapman, 1988; Woods et al., 2012). It is unclear whether these outbreaks reflect disruption of biological control as natural enemies succumb to pesticide applications earlier in the growing

season (e.g., Prischmann et al., 2005), increased spider mite reproduction following the

application of neonicotinoid insecticides (e.g., James and Price, 2002; Szczepaniec et al., 2013),

or some combination of the two. Intriguingly, we know of no instances of spider mites reaching

injurious densities in potatoes managed to meet USDA organic standards (A. S. Jensen, personal

observation). Organic potato fields in our region are often of similar sizes to conventional potato

fields, with crops under the two management systems spatially interspersed and often managed

by the same growers (Koss et al., 2005; Crowder et al., 2010). However, organic potato fields

receive fewer applications of broad-spectrum insecticides (Koss et al., 2005), which in turn

fosters dramatically larger (Koss et al., 2005) and more bio-diverse (Jabbour et al., 2011;

Crowder et al., 2010) populations of generalist natural enemies. Key among these are the

predatory bugs Nabis alternatus and Geocoris bullatus, which together often constitute > 50% of

all predatory arthropods in these fields (Koss et al., 2005). These predators are known to strongly

suppress aphid and beetle pests of potato, an effect that is reduced when predators are at the

lower densities typical of conventional fields (Koss et al., 2004; Koss et al., 2005; Crowder et al.,

2010). However, the impact of these predators on spider mites, if any, has not previously been

investigated in these organic or conventional farming systems.

Here, we use molecular gut-content analysis of N. alternatus and G. bullatus, collected from widely-dispersed (Fig. 1) commercial conventional and organic potato fields at time points before spider mite outbreaks typically occur, to (1) infer the relationship between spider mite density and spider mite predation by these predators; (2) determine whether spider mite predation occurs at low spider mite densities typical of initial colonization of the crop by these pests; and (3) examine whether predation on green peach aphids (Myzus persicae), which have been previously shown to reduce control of less-preferred prey (Koss and Snyder, 2005), limits

predation on spider mites. In turn, we use these findings to examine whether disrupted mid- season biological control by generalists could contribute to spider mites later reaching outbreak densities in conventional, but not organic, fields.

MATERIALS AND METHODS

Our project had several complementary components. First, we developed and verified a taxon-specific polymerase chain reaction (PCR) primer pair that allowed us to infer predation of two-spotted spider mites (considered the target pest in this study), while taking advantage of a previously-developed primer to detect predation of aphids (our focal alternative prey, relative to spider mites) (Chapman et al., 2010). Second, we surveyed densities of Nabis and Geocoris predators, and of herbivorous spider mites and aphids, in organic and conventional potato fields managed by cooperating commercial growers. During these surveys we also collected predators that we later subjected to molecular gut-content analysis.

Primer design

To design primers to test for T. urticae predation, all of the tetranychid COI sequences available on GenBank were downloaded with the search criterion ‘‘Tetranychidae and (coi or co1 or cox1)’’ which resulted in 268 hits (search conducted in September, 2011). Additional sequences (6) were obtained from a T. urticae colony maintained at Washington State University

(Pullman, WA, USA). After removal of duplicate sequences and sequences that would not align

(using MUSCLE; Edgar, 2004) with the barcode region (Hebert et al., 2003), we were left with

an alignment that included 68 operational taxonomic units (OTU). After using maximum likelihood (Garli 0.95, default settings; Zwickl, 2006) to build a tree from these terminals, OTUs were arranged in the data set in a similar fashion to the relationships shown in the ML tree. This facilitated easy searches for DNA sites that were different from the other species, and therefore potentially T. urticae-specific. Two pairs of primers were designed such that the 3’ base in both directions was unique to T. urticae using Primer3 (Rozen and Skaletsky, 1998). Initial testing showed that one primer pair worked better than the other, so we optimized them for amplification of T. urticae (see below). The primers we settled on were Turtic-181-F (5'-

AGGATTTGGAAATTGATTGA-3') and Turtic-396-R (5'-

AAAATTATTATTTCAATAGAGGAAGAC-3'). The numbers in the primer names reflect the position of the 5’ base relative to an alignment of the barcode region of COI (Hebert et al., 2003) amplified using the Folmer et al. (1994) COI primers.

DNA detectability feeding trials

Our next objective was to determine whether the spider mite primer allowed similar detection times for the two predator species that we considered. A total of 50 adult N. alternatus and 50 adult G. bullatus were collected with a suction sampler from commercial potato fields near

Othello, Washington on 16 June 2016, and kept in individual petri dishes (60-mm diameter x 15- mm height) with a wet cotton wick, in an environmental chamber (24°C, photoperiod 16:8 light: dark). For one week after collection, predators were maintained on green peach aphids to eliminate any two-spotted spider mite DNA remains in the gut (e.g., Harwood et al., 2007;

Weber and Lundgren, 2009). Next, predators were starved for 24 h, then offered a single adult

female T. urticae and observed for up to 2 hours to verify that predation occurred; thereafter any

remaining prey fragments were removed (if no feeding occurred within 2 h, that predator was not

used). After the observed feeding period, 8 N. alternatus and 8 G. bullatus were immediately

preserved in 95% ethanol, representing time zero. All remaining predators were provided with

green peach aphids as ‘chaser prey’. Chaser meals simulate normal feeding rates and eliminate

adverse effects of starvation on digestion rate and DNA detectability (Harwood et al., 2007;

Greenstone and Hunt, 1993; Chen et al., 2000). Eight additional individual predators of each

species were killed at 2, 4, 12, and 24h after feeding. These time intervals were selected based on

previous studies, which suggested reasonable windows of detection for arthropod predators and

prey of these sizes (Harwood et al., 2007; Schmidt et al., 2014). All samples were stored at -20°

C until DNA extraction, and PCRs were conducted using T. urticae DNA as positive controls

(see below).

Arthropod survey and collections in commercial potato fields

Our next objective was to estimate densities of the predators and focal prey in

commercial potato fields under either organic or conventional management regimes, and to

collect predators for gut-content analysis. We sampled from 6 organic and 4 conventional fields in the first year (2011), and 6 organic and 6 conventional fields in the second year (2013), with all fields managed by cooperating growers and located throughout the Columbia Basin of central

Washington (Fig. 1). Predators were collected in July–early August of each year, which is the approximate midpoint of the growing season and before spider mite outbreaks are typically observed (in late August–September; A. Jensen, personal observation). All predators were

collected using a D-vac suction-sampling device using previously-described methods (e.g., Koss

et al., 2005). Briefly, we haphazardly identified 5 groups of 10 potato plants per field, walking in

a zigzag pattern from the field edge towards the center of the field, for sampling. We held the

collecting cone over each plant, gently shaking the foliage for 20 seconds and changed collecting

bags between each group of 10 plants (Koss et al., 2005).

Arthropods in D-vac bags were immediately placed on dry ice. From the bags, up to 83

individuals of G. bullatus and N. alternatus (Appendix Table 1) were removed using forceps,

placed individually in 95% EtOH in 1.5-mL microcentrifuge tubes on ice for transport, and then

transferred to a -80 °C freezer to await DNA extraction; Chapman et al. (2010) found that this

methodology avoids contamination of predators with prey DNA. Following the removal of

predators for gut-content analysis, all other remaining arthropods from each D-vac bag were

retained from vacuum samples and stored in a -20oC freezer before being sorted to allow us to

estimate predator and aphid abundance (predators removed from samples for gut-content analysis were included in predator-density estimates for each field). D-vac bags were washed with a 10% bleach solution and air-dried before being re-used, in order to minimize the risk of cross-contamination of DNA from one sampling period to another (Chapman et al., 2010).

Spider-mite densities are poorly quantified in vacuum samples because of the herbivores’ small size and protective webbing. Therefore, we manually sampled T. urticae on 25 potato plants per field, haphazardly chosen as described in Koss et al. (2005) and above, by removing an entire leaf from the upper and lower portions of each plant; plants chosen for leaf collection were never the same plants that were used for suction-sampling. Leaves were placed in individual plastic bags, labeled, and stored in coolers (~15°C) for transport to the laboratory for processing later that day. We then passed each leaf through a mite brush machine (Leedom

Enterprises, Mi-Wuk Village, CA, USA) for five passes, capturing mites in a petri dish of

ethanol below (Henderson and McBurnie, 1943). Mites in ethanol were transferred and stored in

a freezer at -20oC before being counted and identified under a dissecting microscope (Leica

MZ6, Leica Micro systems Inc., Buffalo, NY).

Molecular gut-content analysis

Our final objective was to use molecular gut-content analysis to infer, for both predator

species, consumption of spider mites and how this was impacted by numbers of co-occurring

aphids. Total DNA was extracted from crushed field-collected predators using the QIAGEN

DNeasy Blood & Tissue Kit following the manufacturer’s animal tissue protocol (QIAGEN Inc.,

Chatsworth, CA, USA). PCRs (50uL) consisted of 1X Takara buffer (Takara Bio Inc., Shiga,

Japan), 0.2 mM of each dNTP, 0.25 mM of each primer, 0.625 U Takara Ex Taq TM (Takara

Bio Inc.), and template DNA (3uL of total DNA). PCRs were carried out in Bio-Rad PTC-200 and C1000 thermal cyclers (Bio-Rad Laboratories, Hercules, CA, USA). The 2011 samples were processed at the University of Kentucky, Lexington, KY, USA, and the 2013 samples were processed at Washington State University, Pullman, WA, USA, using the same protocol.

Electrophoresis was used to confirm amplification using 10 uL of PCR product in 1.5% SeaKem agarose (Lonza, Rockland, ME, USA) stained with GelRed (0.1 mg/uL; Phenix Research,

Chandler, NC, USA).

Statistical analyses

Statistics were conducted in R (version 3.2.1; R Core Team 2015). We examined the

probability of detection of prey DNA in guts of Nabis and Geocoris over time using generalized

linear models (GLMs) with a binomial distribution, with predator species and time points (2, 4,

12, and 24h) as fixed effects.

Densities of predators of both species were ln(x+1) transformed and compared using

MANOVA, with production regime (conventional and organic) and year (2011 and 2013) as

predictor variables. Abundances of mites were compared with GLMs assuming a negative

binomial distribution for overdispersed count data using the glm.nb function in the MASS

package of R. Predictor variables, again, were regime and year. Proportions of predator guts

testing positive for mite DNA were arcsine square root transformed and compared for Nabis and

Geocoris predators using MANOVA, with year and regime as predictors. Tukey pairwise

contrasts of regime effects within years were performed using the glht function in the multcomp

package of R. To explicitly examine density dependence and alternative prey availability in pest

consumption, we used separate logistic models of the proportions of Nabis and Geocoris positive

for mite DNA with the following predictor variables: ln(x +1) transformed aphid densities, ln(x

+1) transformed mite densities, and consumption frequencies of aphids (alternative prey), pooling across both years of the study.

RESULTS

Two-spotted spider mite primers

Spider mite primers were tested for cross-reactivity against a variety of non-target arthropod species including: Hemiptera (24 spp.), including, species from Cicadellidae,

Pentatomidae, Aphididae and Nabidae; Coleoptera (20), including species from Coccinellidae,

Staphylinidae and Curculionidae; Diptera (14); Hymenoptera (10); Lepidoptera (5); Orthoptera

(1); Lithibiomorpha (1); Arachnida (5). All samples screened were negative for T. urticae DNA.

These results are not surprising, because both primers have multiple mismatches at the 3’ end when aligned to Geocoris and Nabis sequences (including the first 2 bases in each), so there is

essentially no chance of them amplifying predator DNA. While their sequences are identical to

the strain of T. urticae in Washington potato fields, the forward primer also has 2 mismatches to

European strains of T. urticae at the first two 3’ bases, as well as to other species of Tetranychus.

In this regard, the reverse primer has a C at the 3’ base, whereas all other mite sequences

downloaded have a G at this position, except for one of three sequences of T. turkestani, which

was the only sequence that matched its 3’ base. Given the number of mismatches in both primers

and the completely negative non-target test results above, we can be reasonably assured that our

primers are specific to the strain of T. urticae that occurs in Washington potatoes.

Gut retention times of spider mites in predator guts

The detection of T. urticae decreased significantly through time after the predators fed (time

main effect: z = -1.985, P = 0.0471; Appendix Fig.1) and did not differ significantly between N.

alternatus and G. bullatus (time*species interaction: z = 0.134, P = 0.8934; Appendix Fig. 1).

There was 100% detection of T. urticae at time zero and this decreased to ca. 50% detection 24 h

after feeding (Appendix Fig. 1).

Predator and pest densities in commercial potato fields

N. alternatus and G. bullatus were more than twice as abundant in organic, compared to conventional, potato fields across both years (F = 15.0176, df = 2,17, P < 0.001; Fig. 2a,b), with numbers of these predators differing marginally between years (F = 2.8709, df = 2,17, P = 0.084:

Fig. 2a,b). Spider mite numbers at mid-season were not significantly different between organic and conventional fields (z = -1.152, P = 0.249; Fig 2c), but were significantly greater overall in

2013 compared with 2011 (z = 3.390, P = 0.0007; Fig. 2c). Aphid densities did not differ between farming systems (z = -1.449, P =0.147; Appendix Fig. 2a,b), but were higher in 2011 than in 2013 (z = -4.253, P < 0.0001; Appendix Fig. 2a,b).

Detection of prey DNA in predators

A marginally significant farming regime by year interaction (F =3.298, df = 1,18, P = 0.061;

Fig. 3a,b) in both Nabis and Geocoris suggested spider mite DNA detection rates were equivalent in conventional and organic potato fields in the first year of our study, when mite densities were relatively low (Fig. 2c), but in the second year, spider mite predation was detected more frequently in organic fields compared with conventional (Fig 3a,b). Frequencies of detection of mite predation events were higher in 2013 than in 2011 (year: F = 9.77, df = 1,18, P

= 0.001; Fig. 3a,b), while farm management was not significant as a main effect (regime: F =

0.129, df = 1,18, P = 0.724; Fig. 3a,b). Detection of spider mite DNA within Nabis or Geocoris predators was not significantly correlated with spider mite numbers (Fig. 4a,d; Table 1), numbers

of aphid alternative prey (Fig. 4b,e; Table 1), nor detection of aphid DNA within predators (Fig.

4c,f; Table 1).

DISCUSSION

We developed a species-specific PCR primer that showed high fidelity for detecting DNA of

the two-spotted spider mite, T. urticae, and used this tool to infer predation of the mites in widely dispersed potato fields (Fig. 1). This allowed us to examine whether a disruption of biological control by generalist Nabis and Geocoris predators could contribute to frequent spider mite outbreaks that are commonly seen in conventional, but not organic, potato crops (A. S. Jensen, personal observation). After all, previous molecular gut-content studies have suggested consistently-strong biological control of pests other than spider mites in potato crops due to predation by generalist predators (e.g., Weber and Lundgren, 2009; Greenstone et al., 2010;

Szendrei et al., 2010). Our field sampling revealed > 50% greater numbers of predatory N. alternatus (Fig. 2a) and G. bullatus (Fig. 2b) bugs in organic compared to conventional fields

(see also Koss et al., 2004).

Farming system impacted the likelihood that we would detect spider mite DNA in predators of the two species, although the pattern was complex. In the first year of the study, when spider mites were nearly absent from organic fields (Fig. 2c), predators collected from conventional potato fields were equally likely to contain spider mite DNA as those from organic (Fig. 3). In the second year, when spider mite densities were similar in the two farming systems, predators collected from organic potato fields were more likely to be positive for spider mite DNA (Fig. 3).

It is unclear why the effect of the two farming systems differed between years. It may be that the

absence of pattern in the first year was primarily determined by generally-low spider mite

densities in that year (Fig. 2c, 2011). Of course, when spider mites are largely absent there are

few opportunities for individual predators to find and eat them. The pattern in the second year is

more intriguing. Increased rates of predation in organic systems could be due to differences in

prey-community structure (e.g., Crowder et al., 2010) or differences in predator vigor reflecting

differing sub-lethal effects of insecticides (e.g., Haynes, 1988; Desneux et al., 2007). This is

consistent with a study that reported predators eating a higher proportion of herbivorous prey

within organic than conventional grain fields (Birkhofer et al., 2011), although results in other

organic-conventional comparisons revealed no difference in per-capita attacks on herbivorous

pests (e.g., Melnychuk et al., 2003; Macfadyen et al., 2009a,b). Clearly, more work is needed to

resolve any difference in per-capita predation rates between organic and conventional farming systems, for these and other predator-pest complexes.

Despite the farming-system-level differences discussed above, when looking across

individual fields, without consideration of farming system, we found no significant relationship

between detection of spider mite DNA and spider mite densities (Fig. 4a). One possible

explanation for the lack of this relationship is our relatively-common detection of spider mite

DNA in the guts of predators collected in potato fields with relatively low spider mite densities

(Fig. 3). This suggests the possibility that predators feed on spider mites as the pests first colonize fields at low densities. Unexpectedly frequent detection of predation events at relatively low pest densities has been reported in several other systems where predatory arthropods attack herbivorous insects as prey (e.g., Harwood et al., 2007; Schmidt et al., 2012).

Our study generated data on densities of aphids in our study fields, and we also used a previously-developed PCR primer (Chapman et al., 2010) to screen each predator for aphid

DNA. Aphids are known to be desirable prey of both Geocoris and Nabis, with the potential to

disrupt predation of other pest species (e.g., Koss et al., 2004; Koss and Snyder, 2005).

Consistently, however, we found no relationship between detection of spider mite DNA and

aphid numbers (Fig. 4b) nor the detection of aphid DNA (Fig. 4b,c). This contrasts with an

earlier study that found that the presence of green peach aphids significantly suppressed Nabis

and Geocoris predation on eggs of the Colorado potato beetle (Leptinotarsa decemlineata) (Koss

and Snyder, 2005). We note that this earlier study was conducted in field and laboratory cage

arenas where prey diversity was constrained and aphids were present at densities up to 100-

times-higher than seen in our production potato fields (see Koss et al., 2005); these differences from ambient densities may explain the disruptive role of aphids in the earlier study with potato beetles, but not in the current study with spider mites. Regardless, we found no evidence that the

presence of one common and apparently-attractive alternative prey species, aphids, was

disruptive of biological control of spider mites by the two generalist predator species that we

considered.

A complication with many studies that use molecular gut-content analysis to infer patterns of

predation, including ours, is that direct predation is not the only means through which DNA of

other arthropods might enter a predator’s gut. Rather, the predator may have eaten a second

predator species, with that intraguild prey having been the primary predator of the pest (e.g.,

Harwood et al., 2001a; Sheppard et al., 2005; Juen and Traugott, 2005). Intraguild predation has

often been found to impact biological control of spider mites (e.g., Laing and Huffaker, 1969;

Venzon et al., 2001). For example, biological control of two-spotted spider mites in California

cotton (Gossypium hirsutum) fields was disrupted as Geocoris spp. fed on two smaller generalist

predators, Orius tristicolor and Galendromus occidentalis, that otherwise served as primary

direct predators of spider mites (Rosenheim, 2005). Secondary predation is a potential source of

error, but should have relatively little impact on our results because piercing/sucking predators

have shorter gut retention times compared to other predators (King et al., 2008). Indeed, several

studies have shown these biases to be minor with nearly all DNA detections reflecting predation

of live prey (e.g., Sheppard et al., 2005; Harwood et al., 2001b).

When broad-acting insecticides are applied, collateral damage to generalist predators is an

often-cited reason for secondary pest outbreaks (Hardin et al., 1995). For example, in the 1960s,

Washington-state apple (Malus pumila) growers found that heavy use of organophosphate

insecticides to control codling moth (Cydia pomonella) was followed by destructive spider mite

outbreaks (Hoyt, 1969). These outbreaks subsided only after the predatory mite Galandromus

occidentalis, which apparently had previously suppressed spider mites, developed resistance to

the insecticides and again became abundant (Martínez-Rocha et al., 2008). Likewise, spider

mites are controlled by natural enemies in abandoned vineyards (Vitis vinifera) where broad-

acting insecticides are no longer applied (Prischmann et al., 2005). While predatory mites have

received particular attention in spider-mite biological control, Geocoris spp. and other predatory

bugs are also known to play a role (e.g., Colfer et al., 2003; Xu et al., 2006; Oida and Kadona,

2011; Rim et al., 2015; Martínez-Garcia et al., 2016). There is less evidence for Nabis spp.

contributing to spider mite suppression, although our study suggests this could also be an

impactful predator of these pests. Altogether, these findings are consistent with our suggestion

that higher predator densities in organic potatoes contribute to suppressing spider mite outbreaks

in those fields. However, we cannot exclude the possibility that neonicotinoid insecticides caused

changes in plant chemistry that enhanced spider mite population growth (e.g., James and Price,

2002; Szczepaniec et al., 2013); neonicotinoids are commonly used in conventional potato fields, but are barred from use in organic fields (Koss et al., 2005).

Generalist predators are often considered to be less-reliable than specialists as biological control agents (Debach and Rosen, 1991). Among other factors, generalists sometimes are

“distracted” by the presence of preferred alternative prey, drawing their attacks away from target pests and allowing significant crop damage (e.g., Eubanks and Denno, 2000; Prasad and Snyder,

2006). On the other hand, by persisting in fields on alternative prey before target pests colonize, generalists can form a “first line of defense” that slows pest buildup (e.g., Landis and van der

Werf, 1997; Wiedenmann and Smith, 1997). Our data suggest that Nabis and Geocoris predators in potato fields have characteristics likely to make them effective in suppressing spider mites.

First, these predators’ consumption of spider mites seems relatively unaffected by the presence of aphids, which are relatively common (and seemingly attractive) other prey in potato fields

(Koss et al., 2005). Second, we detected spider mite DNA in guts of both predator species when spider mites were at relatively low densities. Both traits might make these predators effective at suppressing spider mite outbreaks, whenever predator populations are not disrupted by the broad-acting insecticides commonly used in some conventionally-managed potato fields. This is consistent with the observation that biological control may play a particularly important, and impactful, role in organic farming systems where insecticide options often are limited and relatively expensive (e.g., Crowder et al., 2010).

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Xu, X., Borgemeister, C., Poehling, H.M., 2006. Interactions in the biological control of western

flower thrips Frankliniella occidentalis (Pergande) and two-spotted spider mite Tetranychus

urticae Koch by the predatory bug Orius insidiosus Say on beans. Biological Control 36, 57-

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Zwickl, D.J., 2006. Genetic algorithm approaches for the phylogenetic analysis of large

biological sequence datasets under the maximum likelihood criterion. PhD dissertation: The

University of Texas at Austin. Available from: https://www.nescent.org/wg_garli/Main_Page

TABLES

Table 1. Model output from two binomial GLMS evaluating the effects of mite density, aphid density and likelihood of detecting aphid predation on the likelihood of detecting spider mite predation by (a) Nabis alternatus and (b) Geocoris bullatus predators collected at conventional and organic potato farms in 2011 and 2013. a. Proportion Nabis guts positive for mite DNA

Parameter Coefficient z P

(Intercept) -1.2122 -1.182 0.237

ln(mite abundance +1) 0.1588 0.588 0.557

ln(aphid abundance +1) -0.4621 -1.291 0.197 proportion positive for aphid DNA 1.1961 0.694 0.488 b. Proportion Geocoris guts positive for mite DNA

(Intercept) -1.90871 -1.643 0.1

ln(mite abundance +1) 0.189433 0.533 0.594

ln(aphid abundance +1) 0.004597 0.015 0.988 proportion positive for aphid DNA -0.367358 -0.155 0.877

FIGURE CAPTIONS

Fig. 1. Locations in Washington State, U.S.A., of the organic (star) and conventional (circle)

potato fields that we sampled in the (a) 2011 and (b) 2013 growing seasons.

Fig. 2. Mean number (per 50 plants) of the predators (a) Nabis alternatus and (b) Geocoris

bullatus, and (c) of the herbivorous pest Tetranychus urticae, collected from either organic

(black) or conventional (white) potato fields during the 2011 and 2013 growing seasons.

Fig. 3. Mean proportions of (a) Nabis alternatus and (b) Geocoris bullatus predators where

spider mite DNA was detected, for predators collected from organic (black) or conventional

(white) potato fields during the 2011 and 2013 growing seasons.

Fig. 4. Mean proportion of Nabis alternatus (a-c) or Geocoris bullatus (d-f) predators where spider mite DNA was detected, regressed versus (a, d) mite or (b, e) aphid densities, and (c, f) with the proportion of predators of that same species where aphid DNA was detected. Predators were collected from organic (black) or conventional (white) potato fields.

FIGURES

Fig.1

Fig. 2

250 N. alternatus Organic a. Conventional a. N. alternatus 200

150

100

0 300 b. G. bullatus

250

200

150

100 No.arthropods/50 plants 50

300 c. T. urticae

250

200

150

100

0 2011 2013

Fig.3

0.8 a. Organic Conventional 0.6

0.4 positive for mite DNA mite for positive

0.2 Nabis

Prop. Prop. 0.0 2011 2013

0.8 b.

0.6

0.4 positive for mite DNA mite for positive

0.2 Geocoris

Prop. Prop. 0.0 2011 2013

Fig. 4

1.0 a. b. c. Organic Conventional 0.8

0.6

0.4 positive for spider mite DNA mite spider for positive

0.2 Nabis

Prop. Prop. 0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 0.0 0.2 0.4 0.6 0.8 1.0

0.7 d. e. f.

0.6

0.5

0.4

0.3 positive for spider mite DNA mite spider for positive 0.2

Geocoris 0.1

Prop. Prop. 0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 0.0 0.2 0.4 0.6 0.8 1.0 Log10 mite abundance Log10 aphid abundance Prop. positive for aphid DNA

APPENDIX TABLE

Appendix Table 1. For each potato field, sampling date, farming system, nearest city (all in

Washington State), number of predators of each species collected for use in molecular gut

content analysis, and number of predators of each species where two-spotted spider mite (Mite+)

or aphid (Aphid+) DNA was detected.

Number of Nabis Number of Geocoris

Date System Farm code Nearest city Tested Mite+ Aphid+ Tested Mite+ Aphid+

7/6/2011 Org W6 Patterson 42 0 5 52 0 0

7/5/2011 Org W12 Patterson 50 0 4 53 0 3

7/18/2011 Conv J231 Othello 12 0 1 50 0 4

7/7/2011 Org W11O Patterson 22 2 2 8 1 1

7/17/2011 Conv W11C Patterson 23 2 3 54 35 7

7/15/2011 Org J214 Othello 50 2 7 26 1 4

7/13/2011 Org J213 Othello 5 0 1 6 0 1

7/19/2011 Conv J215 Othello 38 0 15 50 0 13

8/5/2011 Org HMLO Moses Lake 52 1 36 83 5 8

8/4/2011 Conv HMLC Moses Lake 39 2 21 54 9 34

7/13/2013 Conv F2 Ephrata 20 0 0 20 12 0

7/16/2013 Org W18 Patterson 20 18 2 20 3 0

7/10/2013 Conv FW Ephrata 20 12 1 20 2 3

7/16/2013 Conv W112 Patterson 18 0 2 20 3 2

7/26/2013 Org H3 Moses Lake 20 6 4 20 6 2

8/12/2013 Org J265 Othello 20 4 0 10 3 9

7/16/2013 Conv W07 Patterson 20 13 0 20 0 16

7/16/2013 Conv W13 Patterson 20 12 1 20 0 16

8/13/2013 Conv J234 Othello 20 0 19 17 0 0

7/29/2013 Org A1 Othello 20 13 7 17 4 12

7/26/2013 Org HN Moses Lake 20 10 5 20 4 19

7/26/2013 Org HH Moses Lake 20 13 10 20 9 16

7/6/2011 Org W6 Patterson 42 0 5 52 0 0

7/5/2011 Org W12 Patterson 50 0 4 53 0 3

7/18/2011 Conv J231 Othello 12 0 1 50 0 4

7/7/2011 Org W11O Patterson 22 2 2 8 1 1

APPENDIX FIGURES

Appendix Fig. 1. Mean proportion of predator guts where spider mite DNA was detected, for

Nabis alternatus and Geocoris bullatus predators, within increasing time from predation of two- spotted spider mite, Tetranychus urticae.

1.0 N. alternatus G. bullatus

0.8

0.6

0.4

0.2 Prop. positive for spider mite DNA mite spider for positive Prop.

0.0 0 4 8 12 16 20 24

Time (hours)

Appendix Fig. 2. (a) Aphid densities collected in vacuum samples of 50 potato plants, and frequencies of detection of aphid DNA in predatory (b) Nabis alternatus and (c) Geocoris bullatus, for collections from organic (black) or conventional (white) potato fields during the

2011 and 2013 growing seasons. Aphid densities (a) were compared using a negative binomial glm, and did not differ between growing regimes (z = -1.449, P = 0.147), but were higher in

2011 than in 2013 (z = -4.253, P <0.0001). Frequencies of detection of aphid predation (b and c) were compared using a binomial glm, and did not differ across growing regimes (t = -1.652, P =

0.107), nor predator species (t = 0.492, P = 0.626), nor years (t = 0.172, P = 0.865).

200 a. Organic 150 Conventional

100

50 Mean aphid counts/50 plants counts/50 aphid Mean 0 2011 2013 0.8 b.

0.6

0.4

0.2

0.0 Prop. Nabis positive for aphid DNA aphid for positive Nabis Prop. 2011 2013 0.8 c.

0.6

0.4

0.2

0.0 Prop. Geocoris positive for aphid DNA aphid for positive Geocoris Prop. 2011 2013

CHAPTER THREE: CAN ORGANIC FARMING SHARPEN PLANT DEFENSES AGAINST

HERBIVORY?

ABSTRACT

Organic farming methods seek to replace synthetic insecticides with natural pest controls.

This could happen, for example, if organic soil and pest management practices lead to plants

capable of mounting relatively robust anti-herbivore defenses. We examined this possibility through an open-field comparison of potato (Solanum tuberosum) plants of two varieties

(Norkotah or Alturas) grown using either organic or conventional management practices. Our

work was conducted in commercial potato fields of 5 organic and 7 conventional farms spanning

central and southern Washington State, allowing us to examine gene expression under real-world conditions. From each field, we collected leaves showing chewing damage from herbivorous insects or that appeared undamaged, and used RNA-sequencing to quantify expression of a suite of genes induced by herbivore feeding. Damage to Norkotah leaves in organic fields generally induced a greater heightened expression of genes related to plant defense compared to leaves in conventionally managed fields. However, this farming-system effect was not seen for Alturas.

Potatoes of both varieties exhibited consistently greater defense-gene activity in foliage we scored as damaged compared to visibly undamaged foliage. Norkotah fields hosted more herbivores than those seen on Alturas, perhaps explaining the stronger management effect for

Norkotah. However, soil fertility and microbial biodiversity metrics did not clearly align with these differences. Our results suggest that open-field examinations of gene expression patterns,

although rare in the literature, have the potential to reveal important insights into how plants

experience their environments. Furthermore, for crop plants this approach can suggest ways to

use farming systems to enhance plant defenses and thus natural pest control.

Keywords:

Organic farming; soil health; 16s rRNA; RNA-seq; signaling pathway; biotic stress; plant

defense; tolerance; plant gene expression; Solanum tuberosum

INTRODUCTION

Conventional farming methods often largely rely on chemical insecticides for the control of

herbivorous insects (e.g., Soffe, 2002; Stockdale et al., 2002; Klonsky, 2012). This approach can

inadvertently disrupt biological control and worsen pest outbreaks, when broad-acting chemicals

kill natural enemies (Penman and Chapman, 1988; Prischmann et al., 2005; Woods et al., 2012).

Perhaps less appreciated, however, are the ways that conventional farming methods might

disrupt “bottom up” suppression of herbivores by plants. Plants utilize a broad range of chemical

and physical defenses against their herbivores (Feeny, 1976; Price, 1991; Howe and Jander,

2008; Mithöfer and Boland, 2012), and induction and deployment of these defenses might be impacted by farming practices (e.g., Davis et al., 2001; Bonanomi et al., 2010). For example, synthetic-chemical fertilizers provide sudden nutrient pulses that can be physiologically stressful for plants, including disruption of pathways related to plant defense against herbivores (Altieri

and Nicholls, 2003; Bhardwaj et al., 2014). Likewise, several insecticides are known to alter

plant physiology to the unintended benefit of plant-feeding insects (Karthikeyan et al., 2009;

Ford, et al., 2010; Szczepaniec et al., 2013). For example, neonicotinoid insecticides can

suppress expression of important plant defense genes, alter levels of phytohormones involved in

plant defense, and decrease plant resistance to herbivores, such as spider mites (e.g., James and

Price, 2002; Szczepaniec et al., 2013).

The role of herbivores and their significance in causing large-scale changes in gene

expression has been widely studied (Cheong et al., 2002; Delessert et al., 2004; Reymond et al.,

2004; Smith et al., 2004; Voelckel and Baldwin, 2004; Zhu-Salzman et al., 2004; De Vos et al.,

2005; Schmidt et al., 2005; Ralph et al., 2006; Thompson and Goggin, 2006; Broekgaarden et al.,

2007). Therefore, conventional modern agriculture can impact both top-down herbivore

suppression, by harming natural enemies, and bottom-up herbivore suppression, by disrupting

plant defenses.

As an alternative approach, organic farming seeks to replace synthetic-chemical insecticides

with natural pest controls (Rigby and Cáceres, 2001; Pimentel et al., 2005). Indeed, reduced

insecticide sprays in these systems likely contribute to more abundant and biodiverse

communities of predatory insects (Crowder et al., 2010), spiders (Letourneau and Goldstein,

2001), and songbirds (Bengtsson et al., 2005; Hole et al., 2005; Beecher et al., 2002; Bouvier et

al., 2011) on organic compared to conventional farms, which has been shown to improve

biological control of pest insects (Crowder et al., 2010; Birkhofer et al., 2008; Bengtsson et al.,

2005).

It is also possible that organic farming could improve bottom-up pest regulation (Altieri and

Nicholls, 2003). For example, by eliminating synthetic-chemical insecticides, organic methods would be expected to eliminate any negative effects of these chemicals on plant defenses

(discussed above). Beyond this, organic farming replaces synthetic-chemical fertilizers with

composted animal manures, cover crops, and other biological sources of fertility (Stockdale et

al., 2002; Watson et al., 2002; Pimentel et al., 2005). These inputs tend to release nitrogen and

other nutrients relatively slowly compared to synthetic fertilizers (Eigenbrode and Pimentel,

1988; Phelan et al., 1995, 1996; Hsu et al., 2009), and steady nutrient release likely exerts

widespread impacts on plant physiology and perhaps also defense (Phelan et al., 1995;

McGuiness, 1993). These fertility-management approaches also increase organic matter in the

soil, an explicit goal of organic farming (e.g., Jenny, 1980; Reganold et al., 1987; McGuiness,

1993; Altieri and Nicholls, 2003), which can increase biomass and biodiversity of soil-dwelling

organisms (e.g., Gunapala and Scow, 1998; Swezey et al., 1998; Altieri, 1999; Mäder et al,

2002); soil microbes are known to induce or prime plant defenses that can sometimes provide

indirect protection against herbivorous insects (Davis et al., 2001; Bonanomi et al., 2010). While

studies in controlled settings have indeed suggested enhanced defenses for plants grown using

organic versus conventional soil amendments (e.g., Bloemberg and Lugtenberg, 2001;

Lugtenberg et al., 2002; Bais et al., 2004; Morrissey et al., 2004; Goh et al., 2013), it remains

unclear whether this would also be seen in the more-complex setting of real farms varying in a

wide diversity of farming practices.

To examine the numerous physiological mechanisms by which organic farming may improve

pest resistance, we used RNA sequencing (RNA-seq) and compared expression of defense genes

in the foliage of commercially grown potato (Solanum tuberosum) crops collected from 5

organic and 7 conventional fields. These farms were located across the potato-growing region of central and southern Washington State(Fig. 1), and we considered the two most common potato varieties grown in the region, Norkotah and Alturas. Our samples were timed to fall at mid- growing season when pest damage, and thus risks to crop production, are greatest (O. Johnson,

personal communication). To assess ecological factors beyond farming system that could

contribute to defense-gene expression, we also intensively sampled arthropod communities and

collected soils to document soil chemical and biological properties (including assessing soil-

microbial diversity using 16s rRNA sequencing), as we collected potato foliage. Our goal then

was not just to look for consistent defense-gene-expression differences between the two farming

systems, but also to initiate a search for specific factors differing between the two farming

systems that could ultimately underlie any differences in gene expression patterns.

MATERIALS AND METHODS

Study Area

From cooperating potato growers fields of Norkotah and Alturas potato varieties from

throughout central Washington (5 organic and 7 conventional, Fig. 1), we collected damaged and

undamaged potato foliage for RNAseq analysis; intensively sampled communities of predatory

and herbivorous arthropods; and sampled soils for chemical and microbial characterization.

Below, we provide detailed methodologies for each of these components.

Quantifying Gene-Expression Patterns in Potato

Within a 400-m2 area of each farm we haphazardly chose 10 insect damaged (visible

chewing or piercing sucking damage) leaves and 10 undamaged leaves from 20 individual plants

that were located at mid-plant level (top leaves are newer and prone to excess defense; Zangerl and Bazzaz 1992; Hartley and Jones, 1997). While wearing nitrile gloves, we used a sterile razor

blade to quickly cut each leaf at the stem, inserting each leaf into a 50-ml conical Falcon tube and immediately placing it into a container of liquid nitrogen. Although all plants had some damage, the undamaged leaves chosen were visibly free of damage. The tubes were then stored in coolers with dry ice during transport back to the laboratory where they were immediately stored at -80oC.

Total RNA, quality check, library preparation and sequencing. -- Total RNA from each

leaf collected as described above was extracted using the RNeasy Plant Mini kit (Qiagen Inc.,

Germany). RNase-free DNase I digestion was performed post extraction using the TURBO

DNA-free Kit (Invitrogen Thermo Scientific, Lithuania). The integrity and quality of the total

RNA was checked using a NanoDrop 1000 spectrophotometer and formaldehyde-agarose gel

electrophoresis. The integrity of total RNA was assessed using a Fragment Analyzer (Advanced

Analytical Technologies, Ankeny, IA) with the High Sensitivity RNA Analysis Kit. RNA

samples with RQNs ranging from 6 to 10 were used for RNA library preparation with the TruSeq

Stranded mRNA Library Prep Kit (Illumina, San Diego, CA). Briefly, mRNA was isolated from

1 µg of total RNA using poly-T oligo attached to magnetic beads and then subjected to

fragmentation, followed by cDNA synthesis, dA-tailing, adaptor ligation and PCR enrichment.

The sizes of RNA libraries were assessed by Fragment Analyzer with the High Sensitivity NGS

Fragment Analysis Kit. The concentrations of RNA libraries were measured by StepOnePlus

Real-Time PCR System (ThermoFisher Scientific, San Jose, CA) with the KAPA Library

Quantification Kit (Kapabiosystems, Wilmington, MA). The libraries were diluted to 2 nM with

RSB (10 mM Tris-HCl, pH 8.5) and denatured with 0.1 M NaOH. Eighteen pM libraries were clustered in a high-output flow cell using HiSeq Cluster Kit v4 on a cBot (Illumina). After

cluster generation, the flow cell was loaded onto HiSeq 2500 for sequencing using HiSeq SBS kit v4 (Illumina). RNA-seq libraries were sequenced from both ends (paired-end) with a read length of 100 bp. The raw bcl files were converted to fastq files using software program bcl2fastq2.17.1.14. After conversion, adaptors were trimmed from the fastq files.

Processing and Mapping of Illumina Reads. -- The RNA-Seq reads were aligned to the S. tuberosum genome using the aligner Tophat and Bowtie (Trapnell et al., 2009; Langmead et al.,

2009; Version12x; Phytozome Version 9, Joint Genome Institute). Default Tophat parameters, which allow up to two mismatches and report up to 40 alignments for reads mapping at multiple positions, were used. To generate counts of gene abundance, Python Package HTSeq (Anders et al., 2014) was used.

Arthropod Sampling

Our next objective was to estimate densities of predators and herbivores in each of the potato fields. All predators were collected using a D-vac suction-sampling device using previously described methods (e.g., Koss et al., 2005). Briefly, we haphazardly identified 10 potato plants per field for sampling, walking in a zigzag pattern from the field edge towards the center of the field. We held the collecting cone over each plant, gently shaking the foliage for 20 seconds, and changed collecting bags between each group of 10 plants (Koss et al., 2005). We also visually examined an additional set of 10 plants, haphazardly chosen where we counted any arthropods present for 30 seconds. Arthropods in D-vac bags were immediately placed in a cooler on dry ice

and transported back to the laboratory for further identification to the family level using the

dichotomous key in Triplehorn et al. (2005).

Analysis of Soil Properties

At each farm site, five soil sub-samples were collected haphazardly from each corner and

middle within the same 400-m2 area in the field as noted above. Each of the five samples

consisted of 20-cm deep soil that was taken with a soil corer. The soil replicates within each field

were mixed and packed in sterile whirlpak bags, and each bag was sealed and transported to the

laboratory at 4oC. A 100-g fraction of the mixed sample was stored at −20 °C for molecular

analysis, while approximately 200 g of the soil was immediately sent to SoilTest labs (Moses

Lake, WA) for nutrient, texture, and SOLVITA analyses (Table 1). DNA was extracted from a

50 g sample using the PowerSoil DNA isolation Kit (MOBIO Laboratories, Inc., Carlsbad, CA,

USA).

DNA Sequencing. -- Our soil DNA extractions were sent to Oregon State University’s

Center for Genome Research & Biocomputing. This facility follows the Illumina 16S

metagenomic sequencing library preparation protocol using MiSeq. The gene‐specific sequences used in this protocol target the 16S V3 and V4 region. They are selected from Klindworth et al.

(2013) as the most promising bacterial primer pair. Illumina adapter overhang nucleotide sequences are added to the gene‐specific sequences. The full length primer sequences, using standard IUPAC nucleotide nomenclature, to follow the protocol targeting this region are: 16S

Amplicon PCR Forward Primer = 5'

TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG

16S Amplicon PCR Reverse Primer = 5'

GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC

The overhang adapter sequence must be added to the locus‐specific primer for the region to be targeted. The Illumina overhang adapter sequences to be added to locus‐specific sequences are:

Forward overhang: 5’ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG‐[locus specific

sequence]. Reverse overhang: 5’ GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG‐

[locus specific sequence].

Processing MiSeq reads. -- Raw Illumina reads were quality trimmed with Trimmomatic

v0.36 (Bolger et al., 2014) (LEADING:3 TRAILING:3 HEADCROP:15

SLIDINGWINDOW:5:15 MINLEN:100) and merged with FLASH v1.2.11 (Magoč and

Salzberg, 2011.) (-r 250 -f 500 -s 125). Merged reads were then cut to the same length using

Trimmomatic v0.36 (CROP:410 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15

MINLEN:410). Merged reads were error corrected and classified to species in Dada2 (Callahan

et al., 2016) using rdp (Wang et al., 2007) (rdp_speccies_assignment_14). Reads classifying only

to bacterial Kingdom, to mitochondria, and chloroplasts were dropped from the analysis. Also,

one sample was dropped due to low read counts. The remaining samples all had even coverage

(between 10,000 and 100,000 assigned reads). Sequences with less than 5 reads or in fewer than

10% of the samples were dropped prior to normalization.

Analysis of Foliar Metabolites and Nutrients

Primary Metabolite Derivatization and Gas Chromatography Time-of-flight Mass

Spectrometry Analysis. -- Extraction was carried out using a slight modification of an established procedure (Lee and Fiehn, 2008). A defined amount of powdered freeze-dried potato leaves (ca.

20 mg) was suspended in 500 μL of extraction solvent containing methanol, 2-propanol, and

water at a 5:2:2 ratio. After adding 1.5 μg of the internal standard ribitol, the material was

extracted by shaking at room temperature for 10 min (Vortex) and sonication at room

temperature for 10 min (Branson 5510 sonication bath). The extracts were then centrifuged for

10 min at 21,000 g, and the supernatants transferred into new vial. The extracts were dried in

vacuum. Dry residues were suspended in 500 μL of 50% aqueous acetonitrile, and re-extracted

as above by sequential vortexing and sonication. The debris was again removed by

centrifugation, and the supernatants dried in vacuum. The dry residues were suspended in 10 μL

O-methoxylamine hydrochloride (30 mg mL-1 in pyridine, both from Sigma) and incubated for

90 min at 30°C and 1000 rpm (Eppendorf Thermomixer). Subsequently, samples were derivatized with 90 μL of MSTFA with 1% TMCS (Thermo-Pierce cat.-no.TS-48915) for 30

min at 37°C and 1000 rpm (Eppendorf Thermomixer). Gas chromatography-mass spectroscopy

analysis was performed using a Pegasus 4D time-of-flight mass spectrometer (LECO) equipped with a Gerstel MPS2 autosampler and an Agilent 7890A oven. The derivatization products were separated on a 30 m, 0.25 mm i.d., 0.25 μm df Rxi-5Sil® column (Restek) with an

IntegraGuard® pre-column using ultrapure He at a constant flow of 1 mL min-1 as carrier gas.

The linear thermal gradient started with a one-minute hold at 50°C, followed by a ramp to 330°C at 20°C min-1. The final temperature was held for 5 min prior to returning to initial conditions.

Mass spectra were collected at 17 spectra s-1. The injection port was held at 250°C, and 1 μL of the sample were injected at an appropriate split ratio.

Foliar nutrients. -- Samples for carbon and nitrogen isotopic analysis were converted to

N2 and CO2 with an elemental analyzer (ECS 4010, Costech Analytical, Valencia, CA); these

two gases are separated with a 3m GC column and analyzed with a continuous flow isotope ratio

mass spectrometer (Delta PlusXP, Thermofinnigan, Bremen) (Brenna et al., 1997; Qi et al.,

2003).

Statistical analyses

We wanted to examine if any correlative links between farm management (organic and

conventional) and potato variety (Norkotah and Alturas) occurred within this study. Statistics

were conducted in R (version 3.2.1; R Core Team 2015) unless otherwise noted.

Differential Expression. – The EdgeR (Robinson et al., 2010) package was used in R studio

(v.0.99.903) to trim mean of M-values (TMM) using the calcNormFactors function. The

normalization factors calculated here are used as a scaling factor for the library sizes. Genes with

< 2 read count were removed from calculation to ensure low gene counts did not account for

false positives. The limma (Ritchie et al., 2015) package within R (Version 2.6.9) was used to

detect significantly differentially expressed genes (DEG) between damaged and undamaged

leaves and organic and conventionally grown leaves. In our study, we examined which genes are

expressed at different levels when comparing leaves collected from plants grown under two

management systems (organic versus conventional), and when leaves had physical damage or

appeared to be undamaged leaves. To make these comparisons, we established a design matrix

with management and insect as ‘treatment’ (where field is the unit of replication) and made

contrasts for pairwise comparisons (See Table 2). The contrasts are set up in limma using the

makeContrasts function. We added field as a random effect to estimate correlations using

duplicateCorrelation by specifying a block argument for both this function and in the lmFit linear

modelling step. In limma, linear modelling is carried out on the log-CPM values which are assumed to be normally distributed and the mean-variance relationship is accommodated using precision weights calculated by the voom function. Linear modelling in limma is carried out using the lmFit and contrasts. Next, empirical Bayes moderation is carried out by borrowing information across all genes to obtain more precise estimates of gene-wise variability.

Significance is defined using an FDR adjusted p-value cutoff at 10% (P < 0.1).

Differential Gene Annotation. -- Using Blast2GO (v.4.1.3, Conesa et al., 2005) with Blast database set at ‘nr’ and E-value at 1.0E-3, and the MapMan visualization tool kit (Thimm et al.,

2004), we were able to assign gene ontology classes to our differentially expressed genes with

BLAST matches to known sequences. Blast2GO also assigns biological functions based on

BLAST sequence homologies and GO annotations (with respect to biological processes,

molecular functions, and cellular components); each contrast that had DEGs were assigned a biological function and categorized accordingly (Table 2; Conesa et al., 2005). GO enrichment

analysis was used to compute enrichment of GO terms found in MapMan in each of the contrast

groups. The p values are adjusted using the Benjamini and Hochberg FDR with a cutoff of < 0.1.

Enrichment analysis was done using Fisher’s Exact Test found in the R ‘stats’ package.

Insect pest abundance and soil nutrients

Model assumptions were checked using histograms, Shapiro-Wilk tests, and residual plots, as

necessary. Pooled densities of herbivores from visual and vacuum samples were compared with

ANOVA. Pooled densities of predators from visual and vacuum samples were compared using

generalized linear models (GLMs) with a Poisson distribution. Soil nutrient concentrations were

compared using MANOVA, with production regime (conventional and organic) and variety

(Norkotah and Alturas) as predictor variables.

Soil microbial communities. -- Phyloseq v1.18.1 (McMurdie and Holmes, 2013) was used to visualize data. The differential abundance of bacteria between organic and conventional soils

was assessed using Deseq2 (Love et al., 2014) within the phyloseq package (test = “Wald”, fitType = “local”) with FDR value < 0.1 and log2FoldChange> 4. Compositional diversity was assessed by applying the Shannon diversity index considering the number and abundance of species using the estimate_richness function in the “phyloseq” package (Table 5). From each field, we calculated bacterial community evenness at the family level using Shannon

(H)/ln(richness). Diversity and evenness indices were compared with ANOVA with production regime (conventional and organic) and variety (Norkotah and Alturas) as the predictor variables.

Abundances of the top 10 bacteria families were ln(x+1) transformed and compared using

ANOVA, with production regime (conventional and organic) and variety (Norkotah and Alturas)

as predictor variables.

Primary metabolites. -- Peak identification of foliar metabolites was conducted using Fiehn primary metabolite library (Kind et al., 2009). For identification, identity score cutoff of 700 was used. Peak alignment and spectrum comparisons were carried out using the Statistical Compare feature of the ChromaTOF® software (LECO). The internal standard ribitol and the initial tissue weight were used for normalization. Statistical analyses with selected metabolites were carried out using Metaboanalyst 3.0 (Xia and Wishart, 2016). Normalized data were uploaded and autoscaled for further uni- and multivariate analyses. Specific metabolites (see Table 3) related to plant defense and health were selected and their concentrations were compared using a permutation multivariate analysis of variance using distance matrices (permutations = 999) using the adonis functions in the vegan package of R, with production regime (conventional and organic) and variety (Norkotah and Alturas) as predictor variables.

Foliar nutrients. -- Percentages of carbon, nitrogen, and C:N ratio were measured in foliar tissue from 120 samples of herbivore-damaged and undamaged leaves from each sample site.

Herbivore damage did not alter foliar nutrition (Table 4) and this enabled us to combine replicates (damaged and undamaged leaves) to increase our power to detect soil-mediated differences in plant quality across sites. We compared C, N, and C:N ratios with 3 separate linear mixed-effects models using the lme function in the nlme package of R, with production regime (conventional and organic) and variety (Norkotah and Alturas) as predictor variables and field as a random effect.

RESULTS

Plant transcriptomics and differentially expressed genes

For Norkotah, insect herbivory led to higher enrichment of defense-related genes in transcripts of plants from organic systems compared to conventional (stress-related genes, P =

0.03, secondary metabolite-related gene, P < 0.001; Table 6; Fig.4), and had more upregulated genes related to plant stress (P = 0.01; Table 6, Fig.4). In Norkotah, we saw a strong damage- induced response in the ethylene signaling pathway in organic systems (Fig.4), while Alturas had fewer differentially expressed genes overall, and most were down-regulated (Table 2). In

Norkotah insect damaged leaves, genes associated with light harvesting and photosynthetic carbon assimilation strongly decreased (Fig. 5c), photosynthetic assimilation (PS) and nutrient transport related genes were more enriched in transcripts present in organic damaged leaves (PS,

P < 0.001; Table 6, transport, P = 0.01; Table 6) and more PS genes were downregulated (P <

0.001; Fig. 5c).

Overall, Norkotah and Alturas varieties differed in abundances of differentially expressed genes (DEG) (Table 2). Examination of the transcriptome revealed 6,985 differentially expressed

(DE) genes in Norkotah in damaged tissue (combining organic and conventional) relative to undamaged leaf tissue, while Alturas had 1,585 DE genes. Expression of genes associated with stress was enriched or more represented within the transcript for both varieties (Norkotah, P =

0.005; Alturas, P = 0.001; Table 6), while more stress-related genes were upregulated in

Norkotah (P < 0.001) and downregulated (P < 0.001) in Alturas (Table 6). In Norkotah damaged leaves, there was an increase in the number of upregulated genes in salicylic acid and ethylene signaling pathways relative to undamaged leaves (Fig.2), while Alturas saw more downregulated genes (Fig. 3). When we pooled samples from organic and conventional systems to improve our

power to examine herbivore effects, herbivory led to more downregulated genes related to

photosynthetic assimilation (PS) in Norkotah (P < 0.001; Table 6, Fig. 5a), while Alturas did not

(P = 0.9445; Fig. 5b).

Pest densities in potato fields

Herbivore densities did not differ between organic and conventional fields (F1,9 = 0.112, P =

0.7458; Fig. 8a) but were higher in Norkotah than Alturas (F1,9 = 5.456, P = 0.0443; Fig.8a).

Predators were twice as abundant in organic Norkotah fields compared to conventional, but did

not differ in Alturas fields (management*variety interaction: z = 2.367, P = 0.0179; Fig. 8b).

Soil nutrients

Nearly 4x as many nitrates were available in conventional Norkotah fields compared to organic, whereas in Alturas nitrate levels were 4x lower in conventional fields compared to organic (management*variety interaction: F1,7 = 15.338, P = 0.005; Fig. 9a). Phosphorus

concentrations approximately doubled in organic and conventional Alturas fields and differed

marginally between varieties (F1,7 = 4.162, P = 0.06; Fig. 9b). Other soil components, including microbial biomass and organic matter percent, did not differ between farming systems or varieties (Fig. 9c-f).

Soil microbial communities

There were neither significant differences in soil bacterial diversity between management

systems (F1,7 = 0.025, P = 0.879; Table 5) nor varieties (F1,7 = -0.05, P = 0.830; Table 5), and

community evenness similarly did not vary according to management system (F1,7 = 0.009, P =

0.926; Table 5) or variety (F1,7 = 0.005, P = 0.944; Table 5). Ten bacterial taxa were

differentially abundant within organic management when compared to conventional management

(Padj<0.1; Table 7). Bacteria from the family Bacilliaceae, Chitinophagaceae, and

Planctomycetaceae were more abundant in organic Norkotah fields compared to conventional,

but less abundant in organic Alturas fields compared to conventional (Bacilliaceae;

management*variety interaction: F = 5.768, P = 0.0173, Chitinophagaceae; management*variety

interaction: F = 3.921, P = 0.0478, Planctomycetaceae; management*variety interaction: F =

25.550, P < 0.001), while the other taxa were not significantly different by variety.

Foliar nutrient analysis and primary metabolites

Percent carbon in leaf tissue did not differ among treatments (t= -0.321, P = 0.7491; Fig. 6a)

or varieties (t= 0.436, P = 0.6637; Fig. 6a). Percent nitrogen was higher in Norkotah versus

Alturas (t= 2.667, P = 0.009; Fig. 6b), and the carbon:nitrogen ratio in conventional management

decreased marginally in the leaf tissue but only in Norkotah fields, whereas conventional

management increased the carbon:nitrogen ratio in Alturas fields (t= 0.1.963, P = 0.052; Fig. 6c).

The suite of primary metabolites (Table 3) in leaf tissue did not differ based on variety,

management or herbivore damage (R2= 0.126, df = 1, 11, P = 0.22; Fig. 7).

DISCUSSION

Our central goal was to compare plant gene expression patterns in conventional versus

organic farming systems. To do this we evaluated herbivore defense gene expression in plant

tissue collected from open fields on working farms and quantified as many potentially-associated environmental variables (soil nutrients, microbial communities, herbivore pressure, etc.). The most punctuated effect of organic farming we observed was a strong increase in activity of several herbivore defense-associated genes in insect-damaged leaf tissue, whereas these genes were not differentially expressed in plants experiencing herbivory in conventional fields.

Importantly, these responses were variety-specific: we saw this pattern of increased defense gene expression in Norkotah, which is typically marketed as a fresh product and more susceptible to stress (A.S. Jensen, personal communication), but not in Alturas, which is primarily processed for dehydrated products and bred to be more tolerant to stresses (Novy et al., 2003).

The primary genes of interest upregulated in Norkotah were related to stress tolerance and production of secondary metabolites (Table 2), which poison or deter herbivores (Baldwin, 2001;

Kliebenstein et al., 2001, 2005; Kessler and Baldwin, 2002). Peroxidases (Fig. 2-4) are one of

the first enzymes activated in response to insect herbivore attack (Usha Rani and Jyothsna, 2010;

Green and Ryan, 1972; Hildebrand et al., 1986; Felton et al., 1989, 1994a, 1994b; Stout et al.,

1999; Constabel et al., 2000; Zhang and Wen, 2008). Peroxidase enzymes, because of their

potential roles in plant signaling and synthesis of defense compounds, have been implicated in

plant resistance to insect herbivores. For example, the role of peroxidase in toxic secondary

metabolite production enables it to play an important role in integrated defense response of

plants to a variety of stresses (Allison and Schultz, 2004; Han et al., 2009; Moore et al., 2003) and we see a general upregulation of these in Norkotah (Fig. 2,4).

In Norkotah, herbivores induced a strong response of gene expression in the ethylene signaling pathway in organic fields (Fig.4). Rapid ethylene biosynthesis is one of the earliest detectable signs of plant-pathogen interactions (Ecker and Davis, 1987). This plant stress hormone may be a signal for plants to activate systemic resistance against invading pathogens and pests. In organic Norkotah plants, we also saw upregulation of lipoxygenase and 12-oxo-

PDA activity, both precursors to jasmonic acid, which is another signaling hormone typically activated by chewing herbivores (Fig.4; Stout et al., 1994).

In addition to up-regulated defense genes, we also saw an overrepresentation of gene activity associated with plant “health” in organic Norkotah plants. In a study by Hermsmeier et al.

(2001), they saw that transcripts involved in photosynthesis were strongly downregulated, whereas transcripts responding to stress, wounding, and pathogens were upregulated when consumed by herbivores. We saw a similar response to herbivore damage: photosynthetic assimilation (PS; Table 6, Fig. 5c) and nutrient transport related genes (Table 6) were significantly more enriched in Norkotah plants grown in organic fields, and had more downregulated genes, which is consistent with numerous other studies that show suppression of

PS when plants are attacked by herbivores (Zou et al., 2005; Jung et al., 2007; Denoux et al.,

2008; Ishiga et al., 2009; Nabity et al., 2009; Bilgin et al., 2010; Gohre et al., 2012). Again, no such effects were seen in the Alturas variety.

Differences between the two potato varieties (Table 2) in organic and conventional farming suggest that Alturas is generally less responsive to herbivore damage than Norkotah. This could be because Alturas is grown mostly for the dehydration industry (where tuber shape is not

managed), and was bred for tolerance of low nitrogen rates, resistance to Verticillium dahliae

(the cause of early dying), and other stresses (Novy et al., 2003). These traits may trade-off with herbivore resistance (e.g., Fineblum and Rausher, 1995; Rudgers et al., 2004). Also, due to their tolerance of suboptimal soil conditions, Alturas potatoes are typically planted in poor-quality fields (e.g., fields with low soil fertility, marginal lands of rocks, tight potato rotations without green manures) compared to Norkotah (A.S. Jensen, personal communication). For this reason,

Alturas plants may be poorer quality hosts for herbivores, and therefore suffer less herbivory

(e.g., Awmack and Leather, 2002). On the other hand, Norkotah plants are generally produced for fresh marketing, and are relatively susceptible to herbivory (A.S. Jensen, personal communication). Indeed, percent N in leaf tissue was significantly higher for Norkotah than

Alturas, making it a higher quality food source for herbivores (Mattson, 1980; Awmack and

Leather, 2002). Thus, chemical defense induction may be more important for Norkotah due to plant traits and relatively greater herbivore pressure (e.g. Wink, 1988).

We measured several environmental factors to identify ones that might explain stronger upregulation of plant defense genes in organic systems compared to conventional. First, we quantified soil nutrients and sequenced soil microbial communities at each site where we sampled plants. In conventional fields, we saw a greater concentration of nitrates where

Norkotah was grown, compared to Alturas (Fig. 9a). Nitrate fertilization can improve host plant quality for herbivores, and has been associated with increases in phloem-feeding insects (Strauss,

1987; Lou and Baldwin, 2004; Staley et al., 2010). Nitrogen augmentation can cause plants to reduce investments in chemical defenses (Glynn et al., 2003; Donaldson et al., 2006), particularly the quick-release N subsidies applied on conventional fields (Staley et al., 2010).

Therefore, excess N availability in conventional fields where Norkotah grew (Fig. 9a) may have reduced defense gene expression.

The use of organic soil amendments could also be associated with desirable soil properties, such as increased organic matter and densities of beneficial soil microorganisms (Doran, 1995;

Drinkwater et al., 1995). We saw an increase in the soil bacteria family Bacillaceae in the organic fields where Norkotah was planted compared to conventional, but we did not observe the same effects in soils where Alturas grew. Several studies have shown the potential of Bacillus spp. to inhibit the growth of the pathogens (Kim et al., 1997; Coombs et al., 2004). In addition to this, other beneficial below-ground microorganisms have been shown to enhance plant stress tolerance and nutrient uptake (Bloemberg and Lugtenberg, 2001; Lugtenberg et al., 2002; Bais et al., 2004; Morrissey et al., 2004; Goh et al., 2013). Many studies find increased microbial biomass (Gunapala and Scow, 1998; Swezey et al., 1998; Altieri, 1999; Mäder et al, 2002;

Reganold et al., 2010), and diversity (Ge et al., 2008; Reilly et al., 2013; Lupatini et al., 2016) in response to organic soil management, but this is not consistent across every system (Liu et al.,

2007), and potato crops may be one exception. Additionally, our fields were not paired side-by- side to match soil-forming factors that have been shown to influence organic versus conventional differences (Reganold et al., 1993; Reganold et al., 2010). When we compared the broad communities of bacteria in the soil, overall we found few differences between organic and conventional fields (Table 7).

Norkotah had overall experienced higher herbivore pressure than Alturas and had a correspondingly stronger response in defense gene activity (Fig.8a). This pattern of herbivore pressure is one potential reason why induction of defense genes was variety-specific. Prior herbivore damage can prime induced defenses for stronger expression later in the season

(Conrath et al., 2006; Frost et al., 2008) and may also be responsible for varying patterns of induction in our system. Insecticide use is another potential mechanism explaining differences in defense activity we observed. In conventional potato crops, neonicotinoids are commonly applied, but are barred from use in organic fields (Koss et al., 2005). Previous work shows that neonicotinoids can attenuate defensive activity in plants (e.g., James and Price, 2002;

Szczepaniec et al., 2013) and may have suppressed defense gene expression in our conventionally-managed fields.

RNA sequencing is a powerful tool for transcriptional profiling to identify genes associated with plants, revealing networks of genes that function in a coordinated fashion to drive metabolic pathways or cellular processes. For example, farming systems have been shown to have an effect on gene-expression, where organic management affected biological processes and pathways related to plant quality (Lu et al., 2005; Reganold et al., 2010; Pacifico and Paris, 2016; Pacifico et al., 2017). However, almost all studies examining gene-expression and farming systems are performed in controlled conditions and may be limited in understanding plant responses to multiple simultaneous stresses, as seen in real world conditions. When stresses occur in sequence or together, certain genes are activated that may not be induced independently (Rizhsky et al.,

2002, 2004; Mittler, 2006).

Future studies should take into account all the factors plants on commercial farms experience in the environment. Our study allowed us to show that specific patterns of gene expression may have useful applications in defining the differences between organically and conventionally grown plants. Linking gene expression to particular plant defensive traits is a key first step to exploiting these defenses to improve the control of herbivorous agricultural pests (Mitchell et al.,

2016). For crop plants, this approach can suggest ways to use farming systems to enhance plant defenses and thus natural pest control.

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TABLES

Table 1. List of tests performed on the soil collected from 12 potato fields.

SoilTest Laboratory tests Nitrate-N (lbs/acre) Nitrate-N (mg/kg) Phosphorus (mg/kg) Potassium (mg/kg Sulfate (mg/kg) Microbial Biomass (mg/kg) Organic Matter W.B (%) Ammonium-N (lbs/acre) Ammonium-N(mg/kg) Base Saturation (percent) Boron (mg/kg) Calcium (meq/100g) Copper (mg/kg) Zinc (mg/kg) Iron (mg/kg) Magnesium (meq/100g) Manganese (mg/kg) E.C. (m.mhos/cm) Effervescence ESP (Percent) Est Sat Paste E.C.(m.mhos/cm) Cation Exchange (meq/100g) pH Sodium (meq/100g) SOLVITA 24-hr burst (mg/kg/day) Total Bases (meq/100g) Clay (%) Silt (%) Sand(%)

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Table 2. Number of differentially expressed genes with threshold of adjusted P-value <0.1.

Contrast Number of DE genes Up Down 1. Organic (damage and undamaged) –

Conventional (damage and undamaged) Norkotah 4 2 2 Alturas 0 0 0 2. Damage (Organic and Conventional) - Undamaged (Organic and Conventional)

Norkotah 6985 3097 3888 Alturas 1585 596 989 3. Organic Damage – Organic Undamaged

Norkotah 4918 2118 2800 Alturas 117 10 107 4. Conventional Damage – Conventional Undamaged Norkotah 2958 1292 1666 Alturas 0 0 0 5. Organic Damage – Conventional Undamaged Norkotah 1632 598 1034 Alturas 120 57 63 6. Organic Undamaged - Conventional Undamaged Norkotah 249 178 71 Alturas 0 0 0

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Table 3. Specific metabolites from GC-MS run related to plant defense and health. Metabolites 4-aminobutyric acid 1 5-Methoxytryptamine 2 8-Aminocaprylic acid aspartic acid 1 Chlorogenic Acid 2 Citramalic acid citric acid D-Glyceric acid fructose 1 fructose 2 fucose 1 Galactinol 1 glutamic acid glycine 2 Isoleucine lactic acid L-Malic acid mannose 1 mannose 2 myo-inositol phenylalanine 1 phenylalanine 2 phosphate putrescine 2 quinic acid raffinose serine 1 shikimic acid succinic acid sucrose Threonic acid threonine 2 xylose 2

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Table 4. Elemental analyses and stable isotope values for potato varieties under different management and by different damage by insect. There were no differences between damage and undamaged leaves (carbon: P = 0.1956; nitrogen: P = 0.3405; C:N ratio: P = 0.1915) and combined damage and undamaged for further analysis.

Avg. Avg. Avg. C:N Management Variety Damaged Carbon Nitrogen ratio Conventional Alturas No 42.286 4.472 9.546 Organic Alturas No 42.421 4.899 8.800 Conventional Norkotah No 43.010 5.865 7.355 Organic Norkotah No 42.596 5.097 8.671

Conventional Alturas Yes 42.105 4.631 9.241 Organic Alturas Yes 41.679 5.235 8.075 Conventional Norkotah Yes 41.827 5.780 7.302 Organic Norkotah Yes 42.789 5.124 8.775

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Table 5. Microbial richness and evenness of bacteria families sequenced in soil collected in central Washington potato fields.

Shannon Diversity Management Variety Evenness Index Conventional Norkotah 5.415208 0.8710874 Conventional Norkotah 5.977052 0.9092398 Conventional Norkotah 5.837033 0.9122367 Conventional Alturas 4.981194 0.8616875 Conventional Alturas 6.127509 0.9105006 Conventional Alturas 6.211341 0.9208485 Conventional Alturas 6.274007 0.9167499 Organic Alturas 6.072581 0.9145625 Organic Alturas 5.404036 0.8832871 Organic Norkotah 5.852567 0.9091011 Organic Norkotah 5.814105 0.9000295

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Table 6. Number and direction of differentially expressed genes (DEG) per gene ontology (GO) at Padj<0.1.

GO DEG up (down) [not category regulated] Contrast Enriched Variety

Stress 145 (170) [771] Dmg-Undmg yes Norkotah Stress 13 (76) [771] Dmg-Undmg no Alturas Stress 113 (109) [771] Org Dmg-Org Undmg yes Norkotah Conv Dmg - Conv Stress 55 (78) [771] Undmg no Norkotah

Secondary Org Dmg-Org Undmg Metabolites 42 (52) [268] yes Norkotah Secondary Conv Dmg - Conv Metabolites 21 (20) [268] Undmg no Norkotah PS 17 (47) [177] Org Dmg-Org Undmg yes Norkotah Conv Dmg - Conv PS 3 (8) [177] Undmg no Norkotah Transport 71 (131) [683] Org Dmg-Org Undmg yes Norkotah Conv Dmg - Conv Transport 33 (76) [109] Undmg yes Norkotah Stress 0 (5) [771] Org Dmg-Org Undmg no Alturas Secondary Org Dmg-Org Undmg Metabolites 0 (0) [268] no Alturas

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Table 7. Soil microbial taxa (Padj<0.1, log2FoldChange > 4) more abundant in organic compared to conventional field collected soil.

DA Bacteria Bacillaceae Chitinophagaceae Ectothiorhodospiraceae Gemmatimonadaceae Methylophilaceae Phyllobacteriaceae Planctomycetaceae Rhodocyclaceae Rhodospirillaceae Streptomycetaceae

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FIGURE CAPTIONS

Fig. 1. Locations in Washington, U.S.A., of the 5 organic (star) and 7 conventional (circle)

potato fields that were sampled in the 2015 growing season.

Fig. 2. Graphical representation of the transcripts up (red) - and down (blue)-regulated in

Norkotah damaged leaves when compared to undamaged leaves in biotic defense signaling.

Green boxes represent metabolites and enzymes, whereas pink boxes represent processes.

Fig. 3 Graphical representation of the transcripts up (red) - and down (blue)-regulated in Alturas damaged leaves when compared to undamaged leaves in biotic defense signaling. Green boxes represent metabolites and enzymes, whereas pink boxes represent processes..

Fig. 4. Graphical representation of the transcripts up (red) - and down (blue)-regulated in

Norkotah Organic damaged leaves when compared to organic undamaged leaves in biotic

defense signaling. Green boxes represent metabolites and enzymes, whereas pink boxes

represent processes.

Fig. 5. Graphical representation of the photosynthetic-related transcripts up- and down-regulated

in (a) Norkotah damaged leaves when compared to undamaged leaves, (b) Alturas damaged

leaves when compared to undamaged leaves, and (c) Norkotah organic damaged leaves when

compared to organic undamaged leaves.

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Fig. 6. Elemental analysis of (a) Carbon %, (b) Nitrogen %, and (c) C:N ratio with combined damaged and undamaged leaves.

Fig.7. PCA plot of primary metabolites found in potato leaves separated by variety, management, and damage. Alturas variety is represented by A and Norkotah variety by N.

Fig. 8. Average (a) herbivore pest and (b) predator densities collected in vacuum samples of 50 potato plants per variety and management type.

Fig. 9. Concentrations of nutrients in soil collected from organic and conventional potato fields at mid-season. (a) Nitrate, (b) Phosphorus, (c) Potassium, (d) Sulfate, (e) Microbial biomass, and

(f) Organic Matter. Gray bars indicate conventional soil and white bars organic.

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FIGURES

Fig.1.

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Fig. 2.

122

Fig.3

123

Fig. 4.

124

Fig. 5.

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Fig. 6.

50 Conventional a. Organic

20 C% inleaves C%

7 b. Variety: P = 0.009 6

3 N% inleaves N% 2

0 12 c. Management*variety: P = 0.052 10

4 C:N ratio in leaves in ratio C:N

0 Norkotah Alturas

Fig.7.

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Fig. 8.

127

180 a. Variety: P = 0.0443 Conventional Organic 160

140

120

100

Average insect herbivore abundance herbivore insect Average 20

100 b. Management*Variety: P = 0.0179

20 Average predator abundance predator Average

0 Norkotah Alturas

Fig. 9.

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100 a. Management*variety: P = 0.005 80 d. Conventional Organic 80 60

60 40

40 Nitratemg/kg Sulfatemg/kg

20 20

0 0 80 b. Variety: P = 0.06 800 e.

60 600

40 400

Phosphorusmg/kg 20 MicrobialBiomass mg/kg 200

0 0 600 c. 2.5 f.

500 2.0

400 1.5

300

1.0 Potassium mg/kg Potassium 200 % OrganicMatter

0.5 100

0 0.0 Norkotah Alturas Norkotah Alturas

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CHAPTER FOUR: SURVIVORSHIP OF TWO POTATO PESTS ON PLANTS GROWN IN

ORGANIC VERSUS CONVENTIONAL SOILS

ABSTRACT

Soil chemistry and microbial diversity can impact the ability of plants to deploy anti-

herbivore defenses. Both characteristics may differ on organic versus conventional farms,

reflecting the many differences in soil-management practices under these two agroecosystems.

We examined this possibility by growing potato (Solanum tuberosum) plants in soils collected from organic or conventional commercial fields and then exposing these plants to herbivory by green peach aphids (Myzus persicae) and/or Colorado potato beetles (Leptinotarsa decemlineata). Responses of the two potato pests varied dramatically. Survivorship of Colorado potato beetles was significantly higher on plants grown in organic than conventional soils, but was unaffected by the presence of aphids. In contrast, aphids reached significantly higher densities when reared alone than when paired with Colorado potato beetles, but were unaffected by the soil type in which their host plants were grown. Unexpectedly, soil chemistry and microbial communities did not obviously differ between organic and conventional soils. While it appeared that the two herbivore species differed in their susceptibility to soil- versus competitor- mediated effects, the underlying drivers of these differences were unclear. Therefore, further work is needed to provide the mechanistic underpinnings of the interesting patterns reported here.

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Keywords:

Organic farming; soil health; 16s rRNA; Myzus persicae; Leptinotarsa decemlineata; Solanum tuberosum

INTRODUCTION

Soil chemistry and microbial diversity can impact the ability of plants to deploy anti- herbivore defenses (Meyer, 2000; Berendsen et al., 2012). Simultaneously, soil nutrient pools can alter the nutritional value of plants for herbivores (Kagata and Ohgushi, 2006).

Consequently, soil-mediated effects on plant nutrition and toxicity can lead to either increased herbivore growth (Klostermeyer, 1950; Adkisson, 1958; Staley et al., 2010; Zehnder and Hunter,

2008) or decreased herbivore growth (Staley et al., 2011), respectively. Some studies have shown that increased soil fertilization can benefit herbivores (e.g., Huberty and Denno, 2006;

Gruner et al., 2008; Staley et al., 2010). This happens when nitrogen augmentation leads to more nutritious (Throop and Lerdau, 2004) and less defended (Tavernier et al., 2007) foliage. Other studies have shown that fertilizing soil with compost or green manures can actually reduce herbivore populations (Culliney and Pimentel, 1986; Alyokhin et al., 2005; Ponti et al., 2007).

These contrasting effects may be mediated by soil microbes, which can prime or induce plant defenses (e.g., Berendsen et al., 2012; Lucas et al., 2015). Additionally, below-ground microorganisms can enhance plant stress tolerance, disease resistance, and nutrient uptake

(Hoitink and Boehm, 1999; Bloemberg and Lugtenberg, 2001; Lugtenberg et al., 2002;

Morrissey et al., 2004; Bonanomi et al., 2010), increasing plant vigor. Altogether, these studies

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suggest that nutrients and microbes in the soil sometimes act to improve the quality of plants as food for herbivores, but other times can strengthen plant defenses, to their detriment.

Soil characteristics and microbial communities frequently differ between organic and conventional farms (e.g., Mäder et al., 2002; Bengsston et al., 2005; Jack et al., 2011; Lucas et al., 2015; Ling et al., 2016; Fernandez et al., 2016), reflecting the many differences in soil management practices under these two agroecosystems. Practices typical of certified organic soil management often replenish and maintain high soil organic matter (e.g., Jenny, 1980; Reganold et al., 1990; McGuiness, 1993), which supports microbial activity (Bulluck et al., 2002).

Additionally, the use of organic soil amendments has been associated with other desirable soil properties including higher water holding capacity, increased cation exchange capacity, lower bulk density, and increased densities of beneficial soil microorganisms (Doran, 1995; Drinkwater et al., 1995), which improve plant health. Application of organic animal manures increases microbial biomass compared to synthetic fertilizers (Gunapala and Scow, 1998; Swezey et al.,

1998; Altieri, 1999; Mäder et al., 2002). Soil microbial communities and soil nutritional quality can both impact plant defenses (Leeman et al., 1995; Liu et al., 1995; Bardgett et al., 1998;

Bardgett and Wardle, 2003; Wardle et al., 2004; Bezemer and van Dam, 2005), and because higher microbial activity sometimes improves the plant’s ability to fight off pests (Hoitink and

Boehm, 1999; Reilly et al., 2013), it is frequently suggested that these benefits might be greater in organic than conventional farming systems (Altieri and Nicholls, 2003; Zehnder et al., 2007).

However, other characteristics of organically managed soils that promote plant health may also support herbivores (sensu Price, 1991).

To examine soil-mediated effects of conventional and organic agriculture on herbivore growth, we grew potato (Solanum tuberosum) plants in a common garden with soils collected

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from organic and conventional commercial fields in central Washington (Fig.1). We exposed these plants to herbivory by the two most common pests of potatoes, green peach aphids (Myzus persicae) and/or Colorado potato beetles (Leptinotarsa decemlineata), and tracked insect survivorship. We compared soil chemical and biological properites between organic and conventional soils, using traditional soil testing methods and 16s rRNA sequencing. Our goal was to determine whether soil characteristics typical of a farming system impacted the plant’s ability to defend itself against two common potato herbivores. Secondarily, we also examined whether herbivore survivorship was impacted when a second herbivore species with a different feeding mode (chewer versus sucker) co-occurred on plants.

MATERIALS AND METHODS

Study area

Our objective was to compare herbivore survivorship on plants grown in soil from commercial potato fields under either organic versus conventional management regimes, and in the presence or absence of a second herbivore species. To gather soils from farms of each type, on April 25, 2016, we collected soil from 5 organic and 5 conventional commercial potato fields throughout the Columbia Basin of central Washington (Fig. 1). At each farm site, 5 soil subsamples were collected haphazardly from the 0-20 cm profile with a soil corer (7.5 cm diameter) from each corner and middle within a 400-m2 area in the field and placed in a 19-liter bucket while maintaining soil structure within two weeks of the growers having planted potatoes at each site. Soil was transported to our greenhouse research facility at Washington State University and stored in a 4°C incubator. The next morning (26 April 2016), soil from each field was used to fill

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each of 3, 1-L planting pots. A Norkotah-variety seed potato was planted into each pot, and each pot was moved into a separate insect-proof cage (13.5 x 13.5 x 24“), where plants grew for 4 weeks in a greenhouse (26oC, 70% humidity, 16/8 hour light/dark cycles).

Our experimental design manipulated herbivore diversity to establish three treatments: green peach aphids only, Colorado potato beetles only, or both herbivores. These herbivore treatments were established on plants grown in soils from each of the two farming systems (organic and conventional). With one replicate of each herbivore treatment in soil from each of the ten farms, this yielded 30 replicates total (3 herbivore treatments x 5 replicate farms per farm system x 2 farm systems = 30 total replicates).

Aphids and potato beetles used in our experiment came from a greenhouse colony, field collected from commercial Washington potato fields in June 2015 and reared on S. tuberosum under the greenhouse conditions described above. On May 25th, 2016, each plant slated to receive Colorado potato beetles was inoculated with 5 first-instar Colorado potato beetles, placed haphazardly on the middle leaves only, while each plant slated to receive aphids was infested with 20 mixed-age green peach aphids. Herbivores were then allowed to feed and, in the case of aphids, reproduce for 12 days until being counted on June 6th, 2016.

Characterizing soil characteristics and microbial ecology at each farm

Concurrent with soil collection at each farm for the herbivore survivorship study described above, 5 soil subsamples were collected haphazardly from the 0-20 cm profile with a soil corer

(7.5 cm diameter) from each corner and middle within a 400-m2 area in the field. The soil replicates within each field were mixed and packed in sterile whirlpak bags and kept on ice until

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they were transported to the laboratory that evening where they were stored at 4oC. A 100-g

fraction of the mixed sample was stored at −20°C for molecular analysis, while approximately

200 g of the soil was sent the following day to SoilTest labs (Moses Lake, WA) for nutrient,

texture, and SOLVITA analyses (Table 1). DNA was extracted from a 50-g sample using the

PowerSoil DNA isolation Kit (MOBIO Laboratories, Inc., Carlsbad, CA, USA).

Soil DNA extractions were sent to Oregon State University’s Center for Genome Research &

Biocomputing. This facility follows the Illumina 16S metagenomic sequencing library

preparation protocol using MiSeq. The gene‐specific sequences used in this protocol target the

16S V3 and V4 region. They are selected from Klindworth et al. (2013) as the most promising bacterial primer pair. Illumina adapter overhang nucleotide sequences are added to the gene‐ specific sequences. The full length primer sequences, using standard IUPAC nucleotide nomenclature, to follow the protocol targeting this region are: 16S Amplicon PCR Forward

Primer = 5'

TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG

16S Amplicon PCR Reverse Primer = 5'

GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC

The overhang adapter sequence must be added to the locus‐specific primer for the region to be targeted. The Illumina overhang adapter sequences to be added to locus‐specific sequences are:

Forward overhang: 5’ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG‐[locus specific

sequence]. Reverse overhang: 5’ GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG‐

[locus specific sequence].

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Following sequencing, raw Illumina reads were quality trimmed with Trimmomatic v0.36

(Bolger et al., 2014) (LEADING:3 TRAILING:3 HEADCROP:15 SLIDINGWINDOW:5:15

MINLEN:100) and merged with FLASH v1.2.11 (Magoč and Salzberg, 2011) (-r 250 -f 500 -s

125). Merged reads were then cut to the same length using Trimmomatic v0.36 (CROP:410

LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:410). Merged reads were error corrected and classified to species in Dada2 (Callahan et al., 2016) using rdp (Wang et al., 2007)

(rdp_speccies_assignment_14). Reads classifying only to bacterial Kingdom, to mitochondria, and chloroplasts were dropped from the analysis. Also, one sample was dropped due to low read counts. The remaining samples all had even enough coverage (between 10,000 and 100,000 assigned reads). Sequences with less than 5 reads or in fewer than 10% of the samples were dropped prior to normalization.

Statistical analyses

For herbivore numbers (aphids) or count survivorship (potato beetles), all statistics were conducted in R (version 3.2.1; R Core Team 2015), and assumptions of all models were checked using histograms, Shapiro-Wilk tests, and residual plots, as necessary. Aphid colony growth was compared with a generalized linear mixed model assuming a negative binomial distribution for overdispersed count data using the glmer.nb function in the MASS package of R with production regime (conventional or organic) and herbivore treatment (aphids only, or aphids present alongside beetles) as predictor variables and farm as a random effect. Counts of surviving

Colorado potato beetles were analyzed with a mixed-effect model assuming a Poisson

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distribution using the glmer function in the lme4 package of R. Predictor variables were

production regime and herbivore treatment, with farm included as a random effect.

Soil concentrations (mg/kg) of nitrates, phosphorus, potassium, sulfate, cation exchange

capacity (cmol/kg), and organic matter (%) were compared using separate ANOVA models, with

production regime (conventional and organic) as the predictor variable.

The differential abundance of bacteria between organic and inorganic soils was assessed

using Deseq2 (Love et al, 2014) within the phyloseq package of R (test = “Wald”, fitType =

“local”) with FDR p value < 0.1. Compositional diversity was assessed by applying the Shannon

diversity index considering the number and species richness using the estimate_richness function

in the “phyloseq” package (v1.18.1; McMurdie and Holmes, 2013). From each field we

calculated bacterial community evenness at the family level using Shannon (H)/ln(richness).

Diversity and evenness indices were compared with ANOVA with production regime

(conventional and organic) as the predictor variable.

RESULTS

Herbivore numbers and survivorship

Green peach aphids were more than twice as abundant in the aphid only treatment, compared to when aphids fed alongside potato beetles (z = -2.101, P =0.0356; Table 2, Fig. 2). However, there was no effect of production regime (z = 0.151, P = 0.8803; Table 2, Fig. 2) on final aphid numbers. Potato beetles were almost 3x more likely to survive 12 days of feeding when on

137

potato plants grown in soils from organic than conventional farms (z = 2.517, P = 0.0118; Table

2, Fig. 3). However, the presence of aphids had no effect on potato beetle survivorship (z = -

1.228, P = 0.2195; Table 2, Fig. 3).

Soil nutrients

Soil nutrients did not differ between the two production regimes (nitrate: F1,8 = 0.795, P =

0.398; Fig. 4a, phosphorus: F1,8 = 0.319, P = 0.588; Fig.4b, potassium: F1,8 = 2.603, P = 0.145;

Fig. 4c, sulfate: F1,8 = 1.315, P = 0.285; Fig. 4d, organic matter: F1,8 = 0.031, P = 0.865; Fig.

4f). Cation exchange capacity was marginally higher in the organic fields compared to the

conventional fields (F1,8 = 4.726, P = 0.0614; Fig. 4e).

Soil microbial communities

We found marginally higher bacterial community diversity in the organic than conventional

soils (P = 0.058; Fig. 5a), but no difference in evenness (P = 0.625; Fig. 5b). Bacterial sequences

from the family Opitutaceae were more abundant in organic than conventional soils (Padj < 0.1,

Fold Change > 4). End products made by bacteria in the family Opitutaceae are reduced sugars,

such as propionate and hydrogen; both of these products are important in the process of methane

production (Chin and Janssen, 2002). These bacteria also take part in the nitrogen cycle,

breaking down manure-based fertilizers, and reducing nitrates for other bacteria to further

convert (Chin et al., 2001). Bacterial sequences from the families Acidimicrobiaceae and

Rhodocyclaceae were differentially less abundant in organic soils (Padj < 0.1, Fold Change < 4).

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The bacteria families Acidimicrobiaceae and Rhodocyclaceae were found to be more abundant in

the conventional soils. Acidimicrobiaceae has been shown to thrive in acidic, mineral-rich

habitats and are capable of dissimilatory iron reduction (Itoh et al., 2011), a process that is

utilized by microbes to conserve energy through oxidizing electron donors and reducing a metal

(Lovley, 1991). Rhodocyclaceae is a newly discovered methylotrophic generalist (Smalley et al.,

2015), which is a group of microorganisms that utilize methane as the carbon source of their

growth (Anthony, 1982).

DISCUSSION

Our goals were to compare herbivore population growth (green peach aphids) or survivorship

(Colorado potato beetles) on plants grown in conventional and organic soils, and to identify

important soil characteristics that distinguished each soil type. To do this, we grew potato

(Solanum tuberosum) plants in soils collected from organic or conventional commercial fields and challenged them with common herbivores that attack potatoes: green peach aphids (Myzus

persicae), Colorado potato beetles (Leptinotarsa decemlineata), or the two herbivores combined.

Responses of the two potato pests varied; we saw that aphids reached higher densities when

reared alone than when paired with Colorado potato beetles, but were unaffected by the soil in

which their host plants were grown (Fig. 2). In contrast, survivorship of Colorado potato beetles

was higher on plants grown in organic than conventional soils, but was unaffected by the

presence of aphids (Fig. 3). Therefore, while both herbivores responded to our experimental

manipulations, the pattern was quite different for each, with the sucker impacted primarily by the

139

presence or absence of a chewing competitor and the chewer impacted primarily by the type of soil in which its host plant was grown.

Surprisingly, we found no differences in soil fertility between organic and conventional potato fields (Fig. 4), although we detected a marginal increase in cation exchange capacity (Fig.

4e) in organic soils. Cation exchange capacity (CEC) is a measure of the total negative charges within the soil that adsorb plant nutrient cations, such as calcium, magnesium, and potassium

(Brady and Weil, 2000). As such, CEC is a property of a soil that describes its capacity to supply nutrient cations to the plant. As CEC increases for a soil, it is able to retain more plant nutrients and reduces the potential for leaching (Brady and Weil, 2000). By maintaining nutrient availability, CEC may have increased the vigor of organically grown potato plants, improving host quality and survivorship for Colorado potato beetles.

We also found marginally higher diversity of bacteria in the organically managed soils (Fig.

5a). This increased diversity might have indirectly impacted our herbivores through at least 2 different channels. First, soil bacteria sometimes trigger plant defenses but are also active in repelling herbivore attack (e.g., Berendsen et al., 2012). Generally these bacteria activate defenses specific to sucking herbivores and pathogens, as plant defenses are common to both types of threat (Van Wees et al., 2008). In this case we might have expected slower aphid growth on potato plants grown in soils from organic potato fields, but no such effect was observed (Fig.

2). A second possible effect of heightened soil microbial diversity might be to facilitate key soil processes critical to plant health, including turnover of soil organic matter, formation of humus, nutrient cycling, and facilitating good soil structure (Kennedy and Papendick, 1995; Brussaard et al., 2007; Fernandez et al., 2016). Such effects might improve overall plant vigor and nutritional quality for our herbivores (e.g., Adkisson, 1958; Brodbeck et al., 2001; reviewed in Pineda et al.,

140

2010). Indeed, we saw heightened survivorship of Colorado potato beetles on plants grown in soils collected from organic fields (Fig. 3).

While it appeared that the two herbivore species differed in their susceptibility to soil versus competitor-mediated effects, the underlying drivers of these differences were unclear. However, aphid colony growth was clearly limited by co-infesting potato beetles; aphid growth was reduced by over 50% when beetles co-occurred compared to when aphids fed alone (Fig. 2). This suppressive effect of beetles on aphids may have been caused by plant defenses induced by beetles, consistent with numerous studies showing that chewing herbivores adversely affect piercing-sucking neighbors (Heidel and Baldwin, 2004; Cooper and Goggin, 2005; Mur et al.,

2006; Walling, 2008; reviewed in Ali and Agrawal 2014). Indeed, Chung et al. (2013) found that

Colorado potato beetle larvae have defense-suppressing bacteria that decrease jasmonic acid regulated anti-herbivore defenses, but increase salicylic acid-regulated defenses. The signaling molecule salicylic acid is crucial for systemic acquired resistance against many plant pathogens

(Maleck and Dietrich, 1999) and piercing/sucking herbivores (i.e., aphids; Thaler et al., 2012;

War et al., 2012). If our Colorado potato beetles contained similar bacterial symbionts, they may have suppressed plant defenses against the beetles while elevating production of metabolites that harmed the aphids.

The curious absence of differences between farming systems in soil nutrients or organic matter was in sharp contrast to numerous other comparisons of organic and conventional soils in other crops (Drinkwater et al., 1995; Gunapala and Scow, 1998; Hsu et al., 2009; Ling et al.,

2016). Herbivore densities often are strongly influenced by variation in plant quality resulting from concentration or availability of key nutrients (e.g., Boege, 2005), and this might explain why aphid colony growth was unaffected by farming system. This work is an important reminder

141

that well-established patterns rarely apply to all crop systems. Management of potatoes is unique

among other crops because soil health indicators typically associated with organic agriculture

(e.g., soil tilth, organic matter) are important for maintaining yields (underground) in both

conventional and organic potato systems. For this reason, some soil amendments are common to

both systems (G. Madison, pers. comm.).

Plant growth and defense tradeoffs are typically predicted along a nutrient gradient, where

plants reduce investments in defenses in the absence of resource limitations (Coley et al., 1985;

Simms and Rausher, 1987; Herms and Mattson, 1992). Due to the careful management of

fertility in agroecosystems, crop plants may invest less in defense and more in compensatory growth in response to herbivore attackers. Herbivore tolerance as a defense strategy is an under-

appreciated concept (Carrillo and Siemann, 2016) that also might provide some explanation for

why we observed no evidence of herbivore suppression mediated by soil quality and some

evidence of increased herbivore survival. Chemical defenses have physiological costs to plants

regardless of resource availability (Howe and Jander, 2008) and therefore may not provide as

effective pest suppression, as suggested by Raguso et al. (2015). Rather than organic

management leading to ‘herbivore suppressive soils’ (sensu Schroth and Hancock, 1982), our

results suggest neutral or positive effects on herbivores. Currently, we cannot determine whether

these are due to increased plant vigor (herbivore tolerance), increased host plant quality, or

compromised plant defenses. Further work quantifying relative investments in growth and

defense in conventional and organic soils is needed to provide the mechanistic underpinnings of

the interesting insect assay patterns reported here.

142

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152

TABLES

Table 1. List of tests performed on the soil collected from 10 potato fields.

SoilTest Laboratory tests Nitrate-N (lbs/acre) Nitrate-N (mg/kg) Phosphorus (mg/kg) Potassium (mg/kg Sulfate (mg/kg) Microbial Biomass (mg/kg) Organic Matte W.B (%) Ammonium-N (lbs/acre) Ammonium-N(mg/kg) Base Saturation (percent) Boron (mg/kg) Calcium (meq/100g) Copper (mg/kg) Zinc (mg/kg) Iron (mg/kg) Magnesium (meq/100g) Manganese (mg/kg) E.C. (m.mhos/cm) Effervescence ESP (Percent) Est Sat Paste E.C.(m.mhos/cm) Cation Exchange (meq/100g)

153

pH Sodium (meq/100g)

SOLVITA 24-hr burst (mg/kg/day) Total Bases (meq/100g) Clay (%) Silt (%) Sand(%)

154

Table 2. Output from the greenhouse pest trials with treatment (GPA only, GPA with CPB, CPB only, and CPB with GPA) and production management (organic and conventional soils).

GLM: Std. Estimate Error z value Pr(>|z|) (Intercept) 4.91182 0.32554 15.088 <2e-16 GPA treatment -0.45142 0.21485 -2.101 0.0356 Production Management 0.06961 0.46212 0.151 0.8803 Management*Treatment -0.4774 0.31055 -1.537 0.1242

(Intercept) 0.3365 0.378 0.89 0.3733 CPB treatment -0.8473 0.6901 -1.228 0.2195 Production Management 1.0986 0.4364 2.517 0.0118 Management*Treatment 0.5108 0.7684 0.665 0.5062

155

FIGURE CAPTIONS

Fig.1. Locations in Washington, U.S.A., of the organic (star) and conventional (circle) potato fields that were sampled in the 2016 growing season.

Fig. 2. Total aphid counts per plant in greenhouse bioassay comparing conventional and organic soils, as well as co-occurrence of Colorado potato beetle.

Fig.3. Total beetle (CPB) count surviving (out of 5) in greenhouse bioassay comparing conventional and organic soils, as well as co-occurrence of green peach aphids.

Fig. 4. Concentrations of nutrients in soil collected from organic and conventional potato fields at the beginning of the season. (a) Nitrate, (b) Phosphorus, (c) Potassium, (d) Sulfate, (e) Cation

Exchange capacity, and (f) Organic Matter. Gray bars are conventional and white bars are organic fields.

Fig. 5. Microbial (a) richness and (b) evenness of bacteria families sequenced in soil collected in central Washington potato fields.

156

FIGURES

Fig.1.

157

Fig. 2.

350 Treatment: P = 0.0356 Conventional Organic 300

250

200

150 Aphid counts/plant Aphid 100

0 Aphids Aphids + CPB

158

Fig.3.

Management: P = 0.0118 5 Conventional Organic

1 Colorado potato beetle count survive count beetle Coloradopotato

0 CPB CPB + GPA

159

Fig. 4.

Conventional 100 100 a. d. Organic

80 80

60 60

40 40 Nitratemg/kg Sulfatemg/kg

20 20

0 0

80 b. 12 e. Management: P = 0.0614

40 6

4 Phosphorusmg/kg 20

2 Cation Exchange Capacity meq/100g Capacity Exchange Cation

0 0

800 c. 1.6 f.

1.4

600 1.2

1.0

400 0.8

0.6 Potassium mg/kg Potassium Organic Matter % OrganicMatter

200 0.4

0.2

0 0.0

160

Fig. 5.

Microbial community diversity Microbial community evenness

Conventional Organic 5 a. Management: P = 0.058 1.0 b.

4 0.8

3 0.6

2 Evenness 0.4

Shannon Diversity Index (H) Index Diversity Shannon 1 0.2

0 0.0

161