I ECOLOGY and EPIDEMIOLOGY of PODOSPHAERA MACULARIS

ECOLOGY AND EPIDEMIOLOGY OF PODOSPHAERA MACULARIS, THE CAUSAL AGENT OF HOP POWDERY MILDEW

A Dissertation Presented to the Faculty of the Graduate School Of Cornell University In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

by William Weldon August 2020

ECOLOGY AND EPIDEMIOLOGY OF PODOSPHAERA MACULARIS, THE CAUSAL AGENT OF HOP POWDERY MILDEW

William Weldon, Ph. D. Cornell University 2020

Over the past twenty years, hop production has expanded in parallel with the craft brewing industry, resulting in a high-value crop with low tolerance for defect in harvested hop cones. Podosphaera macularis is an ascomycete fungus that causes powdery mildew of hop, which is arguably the most destructive disease with respect to its potential for diminishing yield and cone quality. Historically, research on P. macularis has focused largely on the asexual growth forms of the pathogen, as that is the only phase currently observed in the Pacific Northwest (PNW) US region, where over 96% of US hop production resides. As such, the epidemiology and ecology of the disease with respect to the P. macularis ascigerous stage (chasmothecia) is not well understood, even though this growth form has been reported in most hop growing regions east of the Rocky Mountain range. Furthermore, due to the difficult- to-culture obligately biotrophic nature of the pathogen, there are relatively few molecular tools available to track P. macularis population structure and movement. As such, we developed a library of 54 high-throughput, cost effective amplicon sequencing (AmpSeq) molecular markers by re-purposing an existing transcriptome dataset as the source of genetic variation. While this marker design pipeline serves as a valuable template for generating similar marker libraries for other obligately-biotrophic or otherwise difficult to culture pathogens, the project also provided valuable insight into the current population structure of P. macularis throughout the US. Genotyping results indicate that the strains of P. macularis recently introduced into commercial hop yards throughout the Midwest and Eastern US have likely arrived via infected hop planting material, which harbors a PNW US derived P. macularis strain, as opposed to introductions occurring via wind-dispersal from nearby P. macularis

iii populations residing on feral hop. This project also created a novel set of qPCR markers for P. macularis mating type, which were used to provide an updated distribution map of P. macularis mating types within the US. This dissertation also greatly improved understanding of P. macularis overwintering potential via chasmothecia, demonstrating that early season disease incidence can reach levels of up to 70% when conditions are favorable for ascosporic infection. A pair of regressions models were developed to improve the timing at which control measures are taken during the early spring growing season, with the first model predicting the window of chasmothecial maturation based upon degree day accumulation, and the second model assigning an ascosporic infection risk level to rain events based upon the temperature and duration of the precipitation. These studies on the epidemiology of the disease indicate that existing grower practices such as spring pruning, early season fungicide applications, and basal foliage removal are likely even more crucial when the ascigerous P. macularis growth form is present within a hop yard.

iv BIOGRAPHICAL SKETCH

William (Bill) Andrew Weldon was born and raised in Upper Arlington, Ohio and attended Butler University in Indianapolis, Indiana on both an academic and athletic (M- tennis) scholarship, where he earned a B.S. in Biology, with minors in Chemistry and Spanish. Bill was first introduced to biological research as an undergraduate at Butler in the lab of Dr. Philip Villani. An interest in plant pathology was then fully fostered during the summer of 2014 as an undergraduate summer research scholar in the lab of Dr. David Gadoury at Cornell University. His professional interests are in applied plant pathology, with a special focus on pathogen ecology, epidemiology, and population genetics. The translation of research findings into readily applicable disease management behaviors and strategies is incredibly important aspect of Bill’s research philosophy, and as such, the land grant mission and extension efforts will be inherently intertwined with any research undertakings during his career. Throughout his time at Cornell, he was a familiar face for the hop growers of New York, speaking at field meetings, grower conferences, and making numerous field visits to address individual disease management issues. Bill is highly active in the American Phytopathological Society. He has attended every annual meeting since 2015, delivering two symposium session oral presentations, two technical session oral presentations, and a suite of poster presentations during this time. He also served as vice-chair and chair of the APS Graduate Student Committee during 2018 and 2019. As chair, he led multiple initiatives focused on establishing novel, inclusive graduate student networking opportunities with the national society membership at large, as well as specifically within the agricultural industry sector. While at Cornell, he also mentored four undergraduate research scholars, all of whom have gone on to pursue a higher education degree in the field of plant pathology. Outside of research, he is an active individual that has a passion for athletics, especially tennis and football, as well as the outdoors. Over the past five years, you could frequently find Bill walking along the lakefront or in the numerous creeks of the Finger Lakes region with his loyal companion, a black Labrador retriever named Linus.

Dedicated to

my mother and father, for their unwavering support in all that I pursue; and my grandmother, Anne Weldon, who was truly a saint in my life.

ACKNOWLEDGEMENTS

I am and will be eternally grateful to my graduate mentor, Dr. David M Gadoury, for the guidance that he has provided me throughout my time at Cornell University, which started in 2014, when I was a nervous undergraduate summer research intern still trying to pinpoint my passion within the field of Biology. He has opened countless doors for me to pursue almost any research direction of interest and has pushed me to extend my contributions to the field of plant pathology beyond scientific research and into service of the national well-being of the discipline as an active leader within the American Phytopathological Society. I will never again consider Lowe’s to be just a home improvement store, as at least a dozen times, David and I created custom pieces of equipment needed for various projects from loose items at Lowe’s. I also thank my committee member Dr. Lance E. Cadle-Davidson for his mentorship. He is one of the most creative and most compassionate scientists that I have ever met, and I have truly appreciated his presence during my graduate education. Thank you to Dr. David H. Gent, who, as a USDA Research Plant Pathologist located over 2,700 miles from Cornell University, went out of his way to welcome me into the hop pathology research world, making a sincere investment in my professional growth throughout my time as a graduate student. I seriously doubt that there is an adjunct member of a graduate student committee that has put forth more time and effort in a student than Dave has for me. Lastly, a profound thank you to Dr. Gregory M. Loeb and Dr. Stephen Reiners, my minor committee members for the fields of Entomology and Horticulture, respectively. They have both pushed me to grow as a scientist beyond the field of plant pathology and have served as shining examples for how to conduct extension at the highest level for Cornell University and the growers of New York. I also want to sincerely thank the members of the Gadoury, Cadle-Davidson, and Gent labs, past and present, for their support, collaboration, and friendship. A special thank you to Mary Jean Welser, who has been a kind, patient, and understanding pillar of support as the Gadoury laboratory technician upon which all lab members rely. I could not have successfully

vii gotten through my graduate research projects without her presence. A second special thank you to Camille Sisto, who started in our lab as a high-school student mentee and has since progressed to take on a wide variety of technician roles within the lab. Again, I could not have successfully completed a lot of my field research without her help and positive spirit. Thank you to the undergraduates who assisted with this research and put up with me as I continued to work on my skills as a mentor – Michael Riga, Matthew Cullen, Teddy Borland, and Owen Washam. Thank you to the faculty and staff within the Plant Pathology and Plant-Microbe Biology section for their inspiration, especially Dr. Christine Smart, Holly Lange, Dr. William Fry, and Dr. Jason Londo. I am also extremely grateful to the Field Research Unit for their support during the design and execution of my various field trials, as well as Dr. Lina Quesada- Ocampo and Dr. Amanda Gevens for their willingness to collaborate on a multi-state pathogen overwintering project. I also want to acknowledge Dr. Philip Villani and Dr. Nathanael Hauck for their mentorship while I was an undergraduate student at Butler University. They are the true reason for my interest in plant pathology research and I owe the entirety of this journey that I have taken since then to those two scientists. Lastly, and more on a personal note, I extend a never-ending appreciation to the friendships that I have developed at Cornell, in particular Chase Crowell, Katrin Ayer, Chris Peritore, Elizabeth Cieniewicz, Angela Kruse, Michael Fulcher, and Greg Vogel. Our adventures have been some of the greatest joys of my life, and these people are huge part of what makes Cornell so special to me. Thank you to my parents and grandparents, and my one wonderful sister, Elizabeth for their unwavering support throughout my Ph.D. Thank you to Elaina Galea for sharing this chaotic journey with me. And finally, thank you to Linus, for his reliable wet- nose and wagging tail, no matter the circumstances. This research was financially supported by USDA-NIFA Specialty Crop Research Initiative funds (project number 2014-51181-2238), USDA-NIFA Predoctoral Fellowship funds (project number 2019-67011-29734), and funding via the Engaged Cornell Engaged Graduate Student Fellowship.

viii TABLE OF CONENTS

BIOGRAPHICAL SKETCH ……………………………………………………………………………………………. v DEDICATION …………………………………………………………………………………………………………….. vi ACKNOWLEDGEMENTS …………………………………………………………………………………………….. vii LIST OF FIGURES ……………………………………………………………………………………………………….. xi LIST OF TABLES …………………………………………………………………………………………………………. xiv LIST OF ABBREVIATIONS …………………………………………………………………………………………… xvii PREFACE …………………………………………………………………………………………………………………… xviii INTRODUCTION: THE HOP POWDERY MILDEW PATHOSYSTEM WITHIN THE CONTINENTAL UNITED STATES …………………………………………………………………………………. 1 HOP PRODUCTION IN THE UNITED STATES …………………………………………………………. 1 PATHOGENS OF HOP …………………………………………………………………………………………… 3 HOP POWDERY MILDEW – PATHOGEN LIFESTYLE AND GROWTH FORMS …………… 3 HOP POWDERY MILDEW – CURRENT UNDERSTANDING OF DISEASE MANAGEMENT PRACTICES …………………………………………………………………………………. 7 HOP POWDERY MILDEW – GAPS IN UNDERSTANDING OF THE PATHOSYSTEM …… 11 SUMMARIZING STATEMENT INTRODUCING CONTENTS OF DISSERTATION …………. 15 REFERENCES ……………………………………………………………………………………………………….. 16 CHAPTER 1: TRANSCRIPTOME-DERIVED AMPLICON SEQUENCING (AMPSEQ) MARKERS ELUCIDATE THE U.S. PODOSPHAERA MACULARIS POPULATION STRUCTURE ACROSS FERAL AND COMMERCIAL PLANTINGS OF HUMULUS LUPULUS . 21 ABSTRACT …………………………………………………………………………………………………………… 21 INTRODUCTION …………………………………………………………………………………………………… 22 METHODS …………………………………………………………………………………………………………… 29 RESULTS ……………………………………………………………………………………………………………… 52 DISCUSSION ………………………………………………………………………………………………………… 76 REFERENCES ……………………………………………………………………………………………………….. 85 CHAPTER 2: A COMPREHENSIVE CHARACTERIZATION OF ECOLOGICAL AND EPIDEMIOLOGICAL FACTORS DRIVING PERENNATION OF PODOSPHAERA MACULARIS CHASMOTHECIA ……………………………………………………………………………………. 96 ABSTRACT …………………………………………………………………………………………………………… 96 INTRODUCTION …………………………………………………………………………………………………… 97 METHODS …………………………………………………………………………………………………………… 102 RESULTS ……………………………………………………………………………………………………………… 122 DISCUSSION ………………………………………………………………………………………………………… 162 REFERENCES ……………………………………………………………………………………………………….. 170

ix CHAPTER 3: CROSS INFECTIVITY OF POWDERY MILDEW ISOLATES ORIGINATING FROM HEMP (CANNABIS SATIVA) AND JAPANESE HOP (HUMULUS JAPONICUS) IN NEW YORK ……………………………………………………………………………………………………………….. 177 ABSTRACT …………………………………………………………………………………………………………… 178 INTRODUCTION …………………………………………………………………………………………………… 179 ACQUISITION OF ORIGINAL INFECTION MATERIAL AND MAINTENANCE OF ISOLATES IN CULTURE …………………………………………………………………………………………. 181 TAXONOMIC CLASSIFICIATION OF THE HEMP PM AND HOP PM ISOLATES …………. 183 CROSS INFECTIVITY ASSAYS OF THE G. SPADICEUS AND P. MACULARIS ISOLATES . 189 SEXUAL MATING COMPATIBILITY ASSAYS …………………………………………………………… 196 LARGER IMPLICATIONS ON THE MANAGEMENT OF HOP AND HEMP POWDERY MILDEWS ……………………………………………………………………………………………………………. 202 REFERENCES ……………………………………………………………………………………………………….. 205 APPENDIX I: HOP EXTENSION – COMMUNICATING DISEASE MANAGEMENT STRATEGIES FOR HOP POWDERY MILDEW AND HOP DOWNY MILDEW …………………… 209 REFERENCES ……………………………………………………………………………………………………….. 218 CONCLUDING STATEMENTS AND FUTURE DIRECTIONS ……………………………………………. 219 REFERENCES ……………………………………………………………………………………………………….. 225

LIST OF FIGURES

Figure 1-1 Hop stem infected with Podosphaera macularis ……………………………….. 25 Figure 1-2 PCoA plots of P. macularis AmpSeq haplotype profiles ……………………… 60 Figure 1-3 Distribution of P. macularis mating types within the United States, the United Kingdom, and continental Europe ……………………………………. 74 Figure S1-1 Summary of AmpSeq marker design pipeline ……………………………………. 32 Figure S1-2 Filtering summary for the output AmpSeq haplotype dataset …………… 55 Figure S1-3 Marker heterozygosity across the 330 filtered P. macularis samples … 56 Figure S1-4 MSA of the P. macularis wildtype and V6-virulent marker sequences .. 69 Figure S1-5 PCR and qPCR validation data confirming accuracy of P. macularis qPCR mating type assay …………………………………………………………………….. 72 Figure S1-6 PCoA plots of P. macularis AmpSeq haplotype profiles that were not filtered based on marker heterozygosity …………………………………………… 81 Figure 2-1 Various asexual and sexually produced growth forms of P. macularis . 100 Figure 2-2 P. macularis chasmothecial maturation curves across geography and year …………………………………………………………………………………………………… 130 Figure 2-3 Observed versus predicted ascospore release values for three regression equations modelling P. macularis chasmothecial maturation ……………………………………………………………………………………….. 136 Figure 2-4 Observed versus predicted ascospore released values for two regression equations modelling P. macularis ascospore release in response to varied temperature and duration of a wetting event …….. 141 Figure 2-5 Observed versus predicted ascospore release values for the GLMM and modified-Weibull regression models predicting P. macularis ascospore release in response to varied temperature and duration of leaf wetting ………………………………………………………………………………………. 148

xi Figure 2-6 Germination and colony formation success of P. macularis ascospores released and incubated across a range of temperatures …………………… 152 Figure S2-1 Experimental design of the P. macularis ascosporic infection field trial 106 Figure S2-2 Outlier analyses for the P. macularis temperature X duration of wetting ascospore release dataset ……………………………………………………. 142 Figure S2-3 Histogram of the residual values for two regression equations modelling P. macularis ascospore release in response to varied temperature and duration of a wetting event …………………………………… 143 Figure S2-4 Three-dimensional visualizations of the training and validation datasets for the P. macularis temperature X duration of leaf wetting project ………………………………………………………………………………………………. 149 Figure S2-5 Emerged seedlings of H. lupulus and experimental design of P. macularis chasmothecial infection assay …………………………………………… 168 Figure 3-1 G. spadiceus colonies growing on C. sativa leaves and flowers …………. 182 Figure 3-2 Neighbor-Joining consensus tree of the 28S and ITS regions of various powdery mildew species sampled …………………………………………………….. 187 Figure 3-3 G. spadiceus growing with differential success on various hemp and hop cultivars ……………………………………………………………………………………… 191 Figure 3-4 Incompatible host-resistance interaction between the hemp cultivar ‘TJ’s CBD’ and P. macularis ………………………………………………………………… 194 Figure 3-5 P. macularis growing on hemp leaf tissue of various origin ……………….. 195 Figure 3-6 Formation of P. macularis chasmothecia on the hemp cultivar ‘Wild Horse’ ……………………………………………………………………………………………….. 201 Figure A1-1 Screenshot of the Cornell SIPS Hops web page …………………………………. 211 Figure A1-2 Grower Essentials Hop Extension Document 1: Proper scouting and identification of hop downy mildew …………………………………………………. 212 Figure A1-3 Grower Essentials Extension Document 2: Proper scouting and identification of hop powdery mildew ……………………………………………… 213

xii Figure A1-4 Grower Essentials Extension Document 3: Proper scouting and identification of hop powdery mildew chasmothecia and a summary on the current understanding of hop host resistance to hop powdery mildew ……………………………………………………………………………………………… 214 Figure A1-5 Grower Essentials Extension Document 4: Explanation of the complicated dynamics of host resistance to hop downy mildew, which involves both an aboveground foliar resistance threshold and a below ground systemic resistance threshold …………………………………….. 215 Figure A1-6 Grower Essentials Extension Document 5: Five considerations before ever planting your first hop: a document of vital decisions to make in preparing to plant a hop yard ……………………………………………………………. 216 Figure A1-7 Grower Essentials Extension Document 6: Differentiating hop powdery mildew and hop downy mildew and why correctly differentiating the two diseases is so critical …………………………………….. 217

xiii

LIST OF TABLES

Table 1-1 Summary of phenotypic metadata for the 320 P. macularis samples surviving quality filtering analysis …………………………………………………………. 57 Table 1-2 Pairwise comparisons between P. macularis AmpSeq haplotype profiles grouped by cumulative metadata profile …………………………………………….. 63 Table 1-3 Distribution of P. macularis V6-virulent genotypes returned for virulence marker Pm2407 ……………………………………………………………………. 70 Table 1-4 Mating type idiomorph profiles for P. macularis samples collected throughout the US and Europe ……………………………………………………………. 75 Table S1-1 Metadata profile for all 514 P. macularis samples genotyped via AmpSeq ……………………………………………………………………………………………….. 35 Table S1-2 P. macularis qPCR assay primer set sequences …………………………………….. 51 Table S1-3 Pairwise comparisons between P. macularis AmpSeq haplotype profiles group by sampling geographic origin ……………………………………………………. 65 Table S1-4 Pairwise comparisons between P. macularis AmpSeq haplotype profiles group by type of hop planting ………………………………………………………………. 66 Table S1-5 Pairwise comparisons between P. macularis AmpSeq haplotype profiles grouped by cumulative metadata profile (long table form) ………………….. 67 Table 2-1 Disease incidence and severity ratings by week for the P. macularis chasmothecia overwintering field study (2019 and 2020) ……………………. 123 Table 2-2 Chasmothecial retention proportions of varying powdery mildew species …………………………………………………………………………………………………. 126 Table 2-3 Estimates of the fixed effects for GLM models fit to chasmothecial maturation tracked by degree days with base temperatures of 0C, 5C, or 10C ………………………………………………………………………………………………….. 132

xiv Table 2-4 Adjusted correlation coefficients and RSS values comparing the fit of three GLM models that vary by degree days with base temperatures of 0C, 5C, or 10C ………………………………………………………………………………………. 133 Table 2-5 Estimates of the fixed effects for three separate regression equations fit to model the seasonal maturation of P. macularis chasmothecia as defined by degree day accumulation …………………………………………………… 137 Table 2-6 Adjusted correlation coefficients and RMSE values between three separate regression equations fit to model the seasonal maturation of P. macularis chasmothecia as defined by degree day accumulation ……. 138 Table 2-7 Estimates of the fixed effects for a GLMM model describing P. macularis ascospore release in response to a combination of varied temperature and duration of leaf wetting ……………………………………………. 144 Table 2-8 Parameter estimates for the combined effects of temperature and duration of leaf wetting in predicting P. macularis ascospore release using a modified-Weibull non-linear regression …………………………………… 145 Table 2-9 Adjusted correlation coefficients and RMSE values between two separate regression equations fit to model P. macularis ascospore release in response to varied temperature and duration of leaf wetting 150 Table S2-1 ANOVA for host/ powdery mildew species as a predictor of chasmothecial retention ………………………………………………………………………. 127 Table S2-2 ANOVA for degree day accumulation as a predictor of P. macularis chasmothecial maturation …………………………………………………………………… 134 Table S2-3 ANOVA for the effects of temperature and duration of leaf wetting on predicting the release of P. macularis ascospores ……………………………….. 146 Table S2-4 Assessment of P. macularis ascospore-derived colony formation success across a range of temperatures ………………………………………………. 153 Table S2-5 P. macularis ascospores viability when exposed to a range of temperatures associated with the drying of hop cones ………………………… 155

xv Table S2-6 Summary of P. macularis chasmothecia incidence and severity on two populations of H. lupulus seed …………………………………………………………….. 158 Table S2-7 Retention rates of P. macularis chasmothecia on H. lupulus seed when subjected to a physical agitation in water ……………………………………………. 159 Table S2-8 Microscopic analysis of P. macularis chasmothecia originating from H. lupulus seed coats ………………………………………………………………………………… 160 Table S2-9 Disease incidence of P. macularis ascosporic infection on H. lupulus seedlings ……………………………………………………………………………………………… 161 Table 3-1 Primers used to conduct taxonomic analyses of P. macularis and G. spadiceus isolates ………………………………………………………………………………… 185 Table 3-2 Infection compatibility of G. spadiceus and P. macularis isolates on hemp and hop ……………………………………………………………………………………… 192 Table 3-3 Sexual reproductive compatibility between isolates of P. macularis and G. spadiceus ………………………………………………………………………………………… 198

xvi

LIST OF ABBREVIATIONS

P. macularis Podosphaera macularis

G. spadiceus Golovinomyces spadiceus

H. lupulus Humulus lupulus

C. sativa Cannabis sativa

PNW Pacific Northwest

US United States

MSA Multiple Sequence Alignment

PCR Polymerase Chain Reaction qPCR Quantitative Real Time Polymerase Chain Reaction

PCoA Principal Coordinates Analysis

MDS Multidimensional Scaling

ANOVA Analysis of Variance

RSS Residual Sum of Squares

RMSE Root Mean Square Error

GLM Generalized Linear Model

GLMM Generalized Linear Mixed Model

AmpSeq Amplicon Sequencing

RNAseq RNA sequencing

xvii

PREFACE

Podosphaera macularis is an obligately biotrophic ascomycete fungus that causes powdery mildew of hop (Humulus lupulus), a plant of which the flower, i.e. the hop cone, is a critical component of the craft brewing process. Minimal research efforts have been directed towards understanding the ecology and epidemiology of the ascigerous growth form of the pathogen, especially within North America. The contents of this dissertation will provide the reader with an enhanced understanding of P. macularis epidemiology and population structure within United States hop production regions, as well as a commentary on future directions and critical aspects of pathogen biology that remain unaddressed. An introduction section familiarizes the reader to the history of hop production and disease management within the US, as well as a current understanding of the hop powdery mildew pathosystem, current disease management practices, and gaps in current understanding. Chapter 1 describes a suite of studies that address aspects of P. macularis overwintering and early season re-emergence with a specific focus on the sexually produced ascigerous growth form of the fungus. These studies combine to generate multiple predictive models in regard to the pathogens overwintering potential, which will directly improve the way the pathogen is managed during the early spring season. Chapter 2 focuses on a large-scale project to generate two novel molecular toolkits for tracking P. macularis population structure, with an emphasis on high- throughput and cost-effective capacities. Chapter 3 characterizes the host range of P. macularis, indicating that the fungus is pathogenic toward other Cannabaceae species aside from H. lupulus, namely industrial hemp (Cannabis sativa). Appendix I describes a suite of online disease management tools and fact sheets produced during this dissertation that are housed within the Cornell SIPS web domain, serving as a novel, reliable, and curated reference point for hop growers of the Northeast.

xviii

An Introduction: The hop powdery mildew pathosystem within the continental United States William Weldon

Hop production in the US

Humulus lupulus is a perennial bine within the plant family Cannabaceae that is used in the brewing process to add both bittering and aromatic components to beer, depending upon when added to the brew (Neve 1991). The H. lupulus flower naturally produces lupulin, which is comprised of concentrated amounts of hop alpha and beta acids, as well as a suite of essential oils that vary by variety (Eyck and Gehring 2015). It is this collection of plant secondary metabolites upon which brewers rely to impart unique flavor profiles into beer, with each variety having an expected bittering and aromatic profile. Any stressors that the hop plant encounters during the growing season can cause the hop cone metabolite profile to stray from its varietal standard, which threatens its utility for brewing. In extreme cases, such as physical deformation or mortality due to the growth of pathogenic microorganisms, or feeding by insect pests, entire lots of hop cone product can be deemed unacceptable for the brewing process (Neve 1991; Eyck and Gehring 2015). As such, hops are a high-value specialty crop with little room for quality defects in physical appearance or cone chemistry.

According to the US Brewers Association 2019 National Beer Sales Data Report, since

2010 craft brewing has grown by 260% in terms of barrels of beer produced (Brewers

Association 2019). United States (US) hop acreage has expanded in parallel, increasing by

98.7% from 29,683 acres of hop planted in 2012 to now having 58,977 acres planted in 2019

(USA Hops 2019). In 2019, the Pacific Northwest (PNW) US region (Washington, Idaho,

Oregon) comprised 96.0% of US hop production, with Washington representing 73.2% of that

production. New York was reported to have planted 400 acres of commercial hops in 2019, which has been a consistent number since 2017 (USA Hops 2019). The relatively small, but locally-relevant NY hop industry is supported in part by the New York Farm Brewing Law, which provides a substantial tax incentive for breweries that use at least 60% of NY-produced hops and other ingredients in their craft beer products (Ritchie et al. 2012). In 2023, this minimum requirement of NY-produced ingredients will increase to 90%. While somewhat controversial in its ultimate benefits to the NY brewing industry, this legislation has nonetheless stimulated interest in local production of hops to meet the existing demand by craft breweries for locally sourced ingredients. The entire US hop acreage outside of the PNW

US region was reported to be 2,386 acres, yielding approximately 1,000,000 pounds (USA Hops

2019). This represents a regional hop industry that does not primarily exist to compete with the markets and yields of the PNW US region, but instead does so to provide locally sourced hops to breweries interested in such a product.

Interestingly, up until the early nineteenth century, the Northeast US, especially New

York, was the leading hop production region in North America (Holmes 1912). Reports from this era document the presence of hop powdery mildew (caused by Podosphaera macularis) in the region (first reported in NY in 1909) indicating that the hop producing regions east of the Rocky Mountains harbor an “ancestral” P. macularis population, thought to be of

European origin (F. Blodgett 1913). It is reported that successive, devastating epidemics of hop powdery mildew and hop downy mildew gave the final motivation for hop production to migrate westward to the “dryer” climates of northern California and Pacific Northwest, the latter which resides along a similar latitude but tends to accumulate growing degree days

earlier in the year and reach a greater cumulative degree day growing season total (Mahaffee

2003; Eyck and Gehring 2015). Following the repeal of the Volstead Act in 1933 via the passing of the 21st Amendment, the industry re-emerged almost exclusively within the PNW US and has dominated the landscape of US hop production since then (US Constitution Amendment

XXI 1933; USA Hops 2019).

Pathogens of hop

The primary pathogens affecting hop production are fungal and oomycete pathogens including Podosphaera macularis (hop powdery mildew), Pseudoperonospora humuli (hop downy mildew), Fusarium avenaceum (cone tip blight), F. sambucinum (Fusarium wilt and cone tip blight), and Verticillium dahliae (verticillium wilt), although the viral diseases caused by Apple mosaic virus, Hop mosaic virus, and Hop stunt viroid can be significant when certified virus-free hop planting material is not used (Mahaffee et al. 2009). In North America, hop powdery mildew and hop downy mildew are the two diseases that most consistently impact hop yield and quality. They often vary in their presence by geography and seasonal weather conditions, with more humid, temperate locations such as Oregon consistently affected by downy mildew, and dryer locations such as Washington more consistently dealing with heightened powdery mildew pressure (Gent et al. 2015). That said, all hop growing regions in the United States have the potential for a given growing season to be favorable for one or both diseases reaching epidemic levels, if left unmanaged.

Hop Powdery Mildew – Pathogen lifestyle and growth forms

In general, powdery mildew fungi are obligately biotrophic plant pathogens that infect a wide range of agriculturally and horticulturally important angiosperms (Yarwood 1957). The group collectively has a wide host range, but individual species of powdery mildew fungi are highly specialized, and each pathogen species often infects only a single host species (Yarwood

1957; Braun 2002). Hop powdery mildew is caused by the ascomycete fungus Podosphaera macularis, and its host range is thought to be limited to plants of the genus ‘Humulus’

(Mahaffee et al. 2009). In the late nineteenth century, the fungus that causes powdery mildew on strawberry, raspberry, and blackberry (Sphaerotheca macularis) was thought to possibly be the same fungus that causes hop powdery mildew. However, thorough host range and pathogenicity experiments by Blodgett (1913) clearly demonstrated that not to be the case.

The taxonomy and nomenclature of the strawberry powdery mildew pathogen has since been recognized as a distinct species (Braun and Cook 2012).

The polycyclic nature of the disease can result in destructive epidemics when populations of highly susceptible hosts are cultivated in climates favorable to disease, and are not otherwise protected from infection (Yarwood 1957; Gent 2008). P. macularis infection often starts early in the growing season, with hop shoots often emerging infected by the pathogen at cryptic levels between 0.02 to 0.67% of all plants (Gent 2008). As hyphal colonies expand on above-ground green hop tissue, they produce hundreds of thousands of wind- dispersed infectious conidia (Peetz 2007). These conidia spread to adjacent leaves as the disease increases in foliar incidence and severity throughout the first half of the growing season (Peetz 2007). As days shorten (after the summer solstice), lateral hop shoots will begin to develop and produce immature inflorescences (burrs), which is the greatest risk period for

the disease to transition from a foliar infection to one harming hop cone quality and yield

(Twomey et al. 2015).

While the majority of the above-ground hop plant material is removed during hop harvest, the remaining basal foliage (approximately lowest two feet) remains untouched into the late autumn in order to allow for sugar reserves to transition into the hop crown in preparation for winter dormancy (Gent et al. 2016). As powdery mildew often establishes within the basal foliage and spreads vertically up the hop bine architecture, any disease present during the growing season will often remain in this post-harvest basal foliage and have the potential to overwinter in one of two ways: (i) asexually, via hyphal growth on to hop buds just below the soil surface, which go dormant during the winter and re-emerge in the spring heavily infected, termed a flag shoot due to its strikingly white appearance (Gent et al. 2018); or (ii) sexually, via the formation of chasmothecia in the presence of both pathogen mating types (Blodgett 1913; Liyanage and Royle 1976). While chasmothecia require about a month of time on living host tissue to physically mature the external ascocarp, the internal ascospore maturation process that occurs over the course of the winter does not require living host tissue (Liyanage and Royle 1976; Wolfenbarger et al. 2015). As such, these structures are thought to form on any above ground hop tissue when high levels of P. macularis are present, mature over winter without the need for a living host, and then release infectious ascospores onto emerging hop tissue the following spring (Liyanage and Royle 1976; Wolfenbarger et al.

2015). However, the exact degree to which ascosporic infection contributes to early season disease incidence is not fully known.

As is the case in all pathogen populations, geographic areas where the sexual spore is present and viable exhibit greater genotypic diversity, which is a simple biological result of the meiotic process driving sexual recombination of two distinct parental genotypes (Brewer et al. 2011). While not yet clearly demonstrated within the hop powdery mildew pathosystem, the presence of both mating types also presumably increases early season disease pressure, as chasmothecial overwintering and spring ascospore release is a more efficient overwintering process in many other powdery mildew pathosystems (Pearson and Gadoury 1987; Gadoury et al. 2010). As such, for any region where both P. macularis mating types have not yet been introduced, there is great incentive to keep it that way. Currently in the PNW, only the MAT1-

1 mating type is present, while east of the Rocky Mountains both mating types were sampled in an approximately 1:1 ratio (Wolfenbarger et al. 2015). As such, within the PNW US hop production region, chasmothecia have not been reported, and the only mode of overwintering observed thus far has been via flag shoots. Conversely, in all locations east of the Rocky

Mountains where both mating types are documented, chasmothecial formation has been routinely observed (Wolfenbarger et al. 2015).

The Oregon, Washington, and Idaho Departments of Agriculture have issued state- wide quarantines against the import of hop plant material outside of these three PNW states for the specific purpose of preventing introduction of the second P. macularis mating type

(Gent 2015). However, even with quarantine measures in place to restrict the distribution of hop material across state lines, the potential for inadvertent introduction of this second mating type is very real. With microbreweries opening at a rate of a new brewery every 20 hours (Brewers Association 2019), it is becoming increasingly difficult to effectively regulate

distribution of hop material (Berman 2014). All hop plant parts are included in this quarantine, except for kiln dried hops and hop seed (Gent 2015).

In both the 1913 report by Blodget, and the 1976 report by Liyanage and Royle, they describe the frequent presence of chasmothecia on hop cones in yards with high disease pressure. Bales of dried hop cones originating from Europe are frequently imported into the

PNW by hop pelletizing facilities, which are often operations owned by PNW hop growers and situated near their hop yards (Bill Weldon, direct personal observation). The propensity of chasmothecia to survive temperatures associated with the drying of hop cones, and therefore potentially be viable within these imported hop bales, has not yet been investigated. Similarly, hop seed is frequently imported from around the world into the PNW for hop breeding purposes, often arriving from regions that possess the MAT1-2 P. macularis mating type. P. macularis chasmothecia have been reported to form on hop seed (Claassen et al. 2017), however their viability and propensity to survive the seed stratification process used by hop breeders has also not yet been addressed.

Hop Powdery Mildew – Current understanding of disease management practices

Because the vast majority (< 96%) of US hop production resides within the PNW, and since the arrival of P. macularis into the region in 1996, only MAT1-1 strains have been reported, the majority of hop powdery mildew research over the past couple of decades has focused on a better understanding of the vegetative and asexual growth forms of the fungus and the creation of novel management practices to control its spread (Gent 2008; Mahaffee et al. 2009). As stated previously, in the presence of just the asexual form of P. macularis, a

very low percentage (<1%) of plants give rise to shoots bearing sporulating colonies, known as flag shoots (Gent 2008). This small level of overwintering success is sufficient for a polycyclic pathogen such as P. macularis to reach epidemic levels by mid-season due to the sheer volume of conidia produced during each generation and its potential to complete a generation approximately every 5 to 10 days (Peetz 2007). However, this initial low level of P. macularis is also a crucial time for disease management, as reducing or completely eliminating the first wave of infected hop tissue can have major downstream effects that delay disease development and the amount of P. macularis present in the yard at harvest (Gent et al. 2012).

As such, the timing and number of times a grower prunes away the initial hop shoot growth has been shown to significantly reduce the seasonal severity of hop powdery mildew under high disease pressure conditions, as is experienced in regions such as Washington (Gent et al.

2012; Probst et al. 2016). Moreover, yards that receive excellent pruning, which is often attained via pruning twice, regardless of whether it is done chemically or mechanically, require on average 1.1 to 1.5 fewer fungicide applications per season by delaying the need for the first fungicide application by 7.5 – 14.2 days (Gent et al. 2012). Similarly, another cultural practice, the removal of hop basal foliage once shoots reach a height exceeding 2m, can allow for fungicide applications to be terminated in late July (as opposed to late August) without significantly affecting the disease severity on hop cones observed during harvest (Gent et al.

2016). These two aforementioned studies also combined to demonstrate that fungicide applications made during the early stages of hop cone development have the strongest effect on suppressing powdery mildew on cones, and therefore the most efficacious fungicides

should be reserved for use during this phenological growth period (Gent et al. 2012, 2016;

Nelson et al. 2015).

Fungicide applications are currently a major component of a successful hop powdery mildew disease management program (Gent et al. 2015). Applications are often made on a prophylactic basis during seasons where there is a risk of powdery mildew, which for most hop growing regions is every year. Initiation of fungicide applications usually starts one to two weeks after the final pruning event in mid-Spring (Probst et al. 2016). Sulfur and other broad- spectrum fungicides such as bicarbonates and oils are used heavily during the first half of the growing season in order to keep mildew pressure at bay, while reserving the more efficacious, single-site fungicides for application during the early stages of hop cone development (Gent, personal communication). The most effective fungicides against P. macularis include products from Fungicide Resistance Action Committee (FRAC) groups 3 (DMI’s, aka Sterol Biosynthesis

Inhibitors)), 7 (SDHI’s), and 11 (QoI’s), or with products that mix two modes of action such as

Luna Sensation (FRAC 7/11) or Luna Experience (FRAC 3/11) (Gent 2020, Nelson at al. 2015).

These single site fungicides must be used in moderation and are at high risk for resistance development within populations of P. macularis, which is why these chemistries are reserved specifically for use during cone development and must be rotated in their application within a growing season (Gent 2020). Single site amino acid mutations in ascomycetes have been identified and associated with resistance phenotypes to QoI, DMI, and SDHI fungicides (Jones et al. 2014; Stevenson et al. 2019). Hop powdery mildew is often managed with all of these modes of action. Therefore, the selection pressure for isolates to develop resistances is present. To further complicate chemical disease management strategies, the use of some

single-site products such as Quintec (FRAC 13, AZN inhibitors) and Topguard (FRAC 3/11) have been proposed for cancellation within the European Union (EU), and as their use may be discontinued in the US, as many hop growers sell their hop products to breweries within the

EU (Gent 2020). Once hop cones have reached 3 weeks post-bloom, they begin to acquire partial age-related resistance, termed ontogenic resistance, where the cones are increasingly resistant to any new infections by P. macularis (Twomey et al. 2015). At this point, fungicide applications often transition back to utilizing less effective fungicide chemistries, which may be cheaper.

Not only does the industry have to manage the risk of P. macularis populations acquiring resistance to the most effective fungicide chemistries, but also to the resistance genes (R-genes) underlying varying forms of host resistance in the most widely deployed hop varieties. Even though only the asexual state of the fungus is currently observed in the PNW, fungal strains virulent on specific resistance alleles have appeared in pathogen populations over the past decade. Both Cascade and Nugget are widely grown hop cultivars that were bred

(inadvertently) with different genetic-based resistance to powdery mildew (Gent et al. 2015).

Since 2012, two isolate strains of P. macularis have emerged within the PNW US region that can overcome either the R6 qualitative resistance associated with Nugget or the partial resistance associated with Cascade, respectively (Wolfenbarger et al. 2016; Gent et al. 2017).

Because sexual reproduction is not currently possible within P. macularis populations of the

PNW, a strain possessing both virulence traits has not yet been reported (Gent et al. 2020).

The presence of either P. macularis strain has not yet been reported in NY, or any other hop growing region east of the Rocky Mountains, even though hop cultivars with R6-based

resistance (Nugget, Apollo, Newport), as well as the partially resistant cultivar Cascade are all widely planted (Gent et al. 2020).

Quite soon after the arrival of P. macularis into the PNW US in 1996, a risk index called

HOPS was developed based on the existing Gubler-Thomas Powdery Mildew Risk Index Model for management of grape powdery mildew (Erysiphe necator). This index largely considers daily rainfall and temperatures exceeding, falling below, and meeting the optimal growth range of P. macularis (Mahaffee 2003). Briefly, the index is categorized into three levels; low

(0 – 30 points), moderate (40 -60 points), and high (> 60 points), which are proposed to dictate the interval at which fungicides should be applied. Daily indices are calculated as a running total throughout the duration of the production season, according to the following sequential set of rules: (1) if there were a minimum of six continuous hours where the temperature was between 16C to 27C, there were less than 6 hours with temperatures above 30C, and less than

2.5mm of rainfall occurred on that day, add 20 points; (2) on days where these three conditions were not met, subtract 10 points (Mahaffee, 2003). The maximum that the index can reach is +100 and the minimum index is zero. Typically, daily indices start being recorded for a given location when 50% of the hop hills have produced shoots that exceed 15cm in growth. A slightly updated model that weighs temperature extremes more heavily as a negative factor, and sub-optimal temperatures as an additional threshold for index subtraction, is currently being validated at Oregon State University (Gent 2020).

Hop Powdery Mildew – Gaps in understanding of the pathosystem

Currently, the population structure of P. macularis within the United States has been described from a phenotypic standpoint (Gent et al. 2017; Wolfenbarger et al. 2016), but little work has been done to enable any large-scale, cost effective genotyping efforts. It is understood from some recent large-scale genotyping efforts that within the PNW US region there are unique stains of P. macularis, which appear to be exclusively clonal and therefore likely derived from a single introduction event in the late twentieth century (Gent et al. 2020).

Population clustering analyses indicated that the founding isolate introduced into the PNW in

1996 was likely of European origin, specifically from the United Kingdom, as opposed to being derived from the Eastern US population. This study, and a report by Wolfenbarger et al. 2015 that identified genomic loci that correspond to the MAT1-1 and MAT1-2 mating type loci, are the only two studies to date that have analyzed P. macularis from a genotyping perspective.

As such, there is plenty of room to develop molecular tools and resources within the P. macularis pathosystem. There is great incentive for the PNW US to prevent the introduction of the MAT1-2 mating type into the region, just as there is great incentive for all US hop producing regions east of the Rocky Mountains to prevent the introduction of either the R6- virulent (V6) or the Cascade-adapted strains of P. macularis. The advent of genetic tools that can differentiate these unique genotypes in a high-throughput, cost-effective manner would allow researchers to monitor pathogen movement proactively via seasonal sample collection and genotyping, as opposed to the current practice of waiting for reported failures in varietal resistance or a fungicide chemistry, or the physical observation of chasmothecial formation within a hop yard to indicate a change in population structure.

It is also critical that a better understanding of P. macularis chasmothecial biology and epidemiology be established, especially as it relates to environmental conditions within hop producing regions of the US. Currently, there are no epidemiological models available that factor in chasmothecial overwintering and early season levels of disease due to ascosporic infection. Effectively no research has been done to understand the epidemiological drivers of ascospore maturation and subsequent ascospore release in the early spring within the United

States. The most recent study characterized the temperatures (in a controlled laboratory setting) at which chasmothecia can form in the presence of hyphal growth from both P. macularis mating types, but proceeded no further (Wolfenbarger et al. 2015). Prior to that study, the next most recent work focusing on P. macularis chasmothecial biology within the

US was conducted by FM Blodgett at Cornell University in the early 1900’s (Blodgett 1913;

Blodgett 1915). His lab characterized basic structural morphology of hop powdery mildew ascocarps throughout their maturation and outlined a rough period of ascospore release, spanning from late March to mid-April. Outside of the US, work done by Salmon, as well as

Liyanage and Royle in Great Britain during the mid-twentieth century established the two major maturation periods of the fungus, November and subsequent March respectively, and confirmed a peak ascospore release period in mid-April in Europe (Salmon 1924; Liyanage and

Royle 1976). However, even these experiments failed to clearly demonstrate the infective capabilities of P. macularis ascospores alone and did not generate any sort of epidemiological models that could be applied by hop growers to aid in early season disease management.

Many questions are unanswered, such as: what induces formation, how does winter weather impact chasmothecial survival, what spring weather conditions promote the release

of ascospores, what are the ultimate risks these structures pose to breaking varietal resistance, and what is their potential to overcome currently efficacious fungicides? From an economic and environmental perspective, prolonging the lifespan of current varieties, and limiting fungicide use to specific periods of high risk are end goals. If we can determine which factors promote the success of these structures, from the point that hyphae of compatible individuals fuse to the dehiscence and release of viable ascospores, we can more effectively advise growers on how they can protect their crop from this second source of infection.

Questions also remain as to what factors govern maturation of ascospores. What role does the host substrate play in this process? It is unknown if ascocarps disperse to any secondary substrates in addition to hop during the overwintering period. Can P. macularis infect any other close relatives of H. lupulus? If so, can the pathogen reproduce sexually on these alternative hosts? What impact does the harvest process have on the dispersal of chasmothecia? What are the environmental drivers of ascospore discharge? These are all questions that also remain to be addressed.

Lastly, and mostly in specific reference to hop producing regions East of the Rocky

Mountains, the rapid influx of newly established hop yards has resulted in a hop grower base that is committed to, but relatively inexperienced in producing and processing hops. As such, there is a pressing need to continue and further establish hop extension systems that provide digestible resources focused on appropriate disease management techniques for not only powdery mildew, but the suite of pests and diseases that can be devastating to a growers hop yield. A specific example would be the confusion associated with properly identifying the presence of hop powdery mildew versus hop downy mildew within a hop yard, and

subsequently selecting an appropriate fungicide chemistry that has efficacy against the given pathogen present. Podosphaera macularis (hop powdery mildew) is a fungus, while

Pseudoperonospora humuli is an oomycete, which is a completely different class of organisms on the phylogenetic “tree of life” (Mahaffee et al. 2009). As such, the fungicides designed to control hop powdery mildew often have little to no effect on hop downy mildew, and vice versa. So, if a grower mistakenly diagnosis an infection as hop powdery mildew, when in actuality the pathogen is P. humuli, then not only are they wasting money on a fungicide spray that will have no effect, but they are delaying any measures that will actually slow the progression of the actual pathogen within their yard. These types of identification confusions are easy to make, but are also easy to avoid, and represent the “low hanging fruit” information that is critical to communicate to growers in an effective manner.

Summarizing statement introducing contents of dissertation

The chapters and appendices herein are the cumulative effort of approximately five years to address some of the aforementioned unknowns with respect to P. macularis population structure and epidemiology, translate these lessons into tools that will directly benefit the US hop industry, and improve the manner in which these tools are communicated and made available to the hop growers themselves. I believe that the best science starts with applied questions from the grower community itself and projects should be designed with specific deliverables that improve disease management in mind. Chapter 1 summarizes a project that established a set of amplicon sequencing molecular markers capable of differentiating P. macularis populations based on geographic origin, hop planting type

(cultivated versus feral), and possession of V6 virulence. Chapter 2 is a comprehensive analysis of biological and epidemiological factors that drive the maturation of P. macularis chasmothecia and release of ascospores during the early spring. Chapter 3 is a study that characterized the cross-infective capability of P. macularis onto certain varieties of Cannabis sativa, which is rapidly increasing in acreage throughout the US for the production of CBD- hemp. And lastly, the appendices describe the outreach and extension efforts during my graduate student tenure, which have focused on supporting both the national plant pathology graduate student community, as well as the hop grower community of New York. I hope that these findings will not only provide immediately applicable strategies for improved disease management, but also build on the base of existing knowledge to continue to push the field of plant pathology forward.

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Transcriptome-derived amplicon sequencing (AmpSeq) markers elucidate the U.S.

Podosphaera macularis population structure across feral and commercial plantings of

Humulus lupulus

Authors: William A. Weldon1, Brian J. Knaus2, Niklaus J. Grünwald3, Joshua S. Havill4, Mary H.

Block2, David H. Gent4, Lance E. Cadle-Davidson1,6, and David M. Gadoury1.

*Collaborator Affiliations:

1Section of Plant Pathology and Plant-Microbe Biology, Cornell AgriTech, Cornell University, Geneva,

NY 14456

2Department of Botany and Plant Pathology, Corvallis, OR 97331

3USDA-ARS Horticultural Crops Research Unit, Corvallis, OR 97330

4Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108

5USDA-ARS Forage Seed and Cereal Research Unit, Corvallis, OR 97331

6USDA-ARS Grape Genetics Research Unit, Geneva, NY 14456

Abstract

Obligately biotrophic plant pathogens pose challenges in population genetic studies due to their genomic complexity and elaborate culturing requirements with limited biomass. Hop powdery mildew (Podosphaera macularis) is an obligately biotrophic ascomycete that threatens sustainable hop production. P. macularis populations of the Pacific Northwest

(PNW) United States (US) differ from those of the Midwest and Northeastern US, lacking one of two mating types needed for sexual recombination and harboring two strains that are

differentially aggressive on the cultivar ‘Cascade’ and able to overcome the Humulus lupulus

R-gene R6 (V6), respectively. To develop a high-throughput marker platform for tracking the flow of genotypes across the US and internationally, we used an existing transcriptome of diverse P. macularis isolates to design a multiplex of 54 Amplicon Sequencing markers, validated across a panel of 391 US samples and 123 international samples. The results suggest that P. macularis from US commercial hop yards forms one population closely related to P. macularis of the UK, while P. macularis from US feral hop locations grouped with P. macularis of Eastern Europe. Included in this multiplex was a marker that successfully tracked V6- virulence in 65 of 66 samples with a confirmed virulence phenotype. A new qPCR assay for high-throughput genotyping of P. macularis mating type generated the highest resolution distribution map of P. macularis mating type to-date. Together, these genotyping strategies enable the high-throughput and inexpensive tracking of pathogen spread among geographical regions from single-colony samples and provide a roadmap to develop markers for other obligate biotrophs.

Introduction

Pathogen population structure can inform decisions on disease management, such as the timing and efficacy of fungicides (Villani et al. 2016; Mengistu et al. 2020) or the deployment of resistant host varieties (Moreira et al. 2019; Jones et al. 2014; Parada-Rojas and Quesada-Ocampo 2019). With understanding and monitoring, actions are possible to mitigate expansion of a pathogen group (Ali et al. 2014), prolong the longevity of fungicides and host resistance (Lichtner et al. 2020; Wolfenbarger et al. 2016; Gent et al. 2017), or select

targets for future breeding programs (Teh et al. 2017). Surveys dependent on molecular tools have quantified the dynamics of Phytophthora infestans clonal lineages worldwide, which has enabled the local, high resolution tracking of isolate populations each growing season (Dey et al. 2018; Hansen et al. 2016; Forbes et al. 1998; Knaus et al. 2016; Fry et al. 2015). The advent of molecular tools characterizing fine-scale population structure also facilitated quarantine efforts enacted toward Pyricularia graminis-tritici in Bangladesh and surrounding Middle

Eastern countries in response to the pathogens expansion of geographic range in 2016 (Islam et al. 2016; Ceresini et al. 2019).

Powdery mildew fungi are amongst the most widespread and problematic plant pathogens in modern agriculture (Dean et al. 2012). Comprised of hundreds of unique, often but not always host-specific species (Weldon et al. 2020; Gadoury and Pearson 1991), they are collectively capable of infecting thousands of monocot and dicot plants (Yarwood 1957).

Powdery mildew fungi cost growers billions of dollars in yield losses each year (Sambucci et al. 2014; Braun 2002). These polycyclic fungi reproduce rapidly, are wind-dispersed, and can germinate in the absence of water, and are thus not limited by sparse rainfall. Indeed, they reach their greatest potential within controlled environments (Janisiewicz et al. 2016; Rossi et al. 2020). They have the potential to both rapidly spread across geography and a propensity to adapt to fungicides and host resistance genes in many crops (Gadoury et al. 2012; Heyden and Lefebvre 2014; Gent et al. 2019; Peetz et al. 2009).

Podosphaera macularis, the causal agent of powdery mildew of hop (Figure 1-1), is no different. The pathogen has been endemic to England since at least the 1700s (Neve 1991;

Royle 1978) and in eastern North America since the early 1800s (Blodgett 1915). The Pacific

Northwest (PNW) region of North America, where over 98% of US hop production occurs, remained free of the pathogen until 1996 (Ocamb et al. 1999). Within two years of the first reports of the pathogen’s arrival the disease had spread throughout all hop producing areas of the PNW region (Gent 2008). The pathogen population in the PNW has been differentiated into three distinct virulence groups: (i) one that was prevalent prior to 2012, (ii) one differentially aggressive on the cultivar ‘Cascade’ (Cascade-adapted), and (iii) a group capable of overcoming host resistance conditioned by the R-gene R6 (V6), as found in the cultivar

‘Nugget’ (Wolfenbarger et al. 2016; Gent et al. 2017, 2020).

Figure 1-1. Humulus lupulus cv. ‘Symphony’ stem infected with Podosphaera macularis, which has erected hundreds of conidiophores that bear chains of conidia.

Throughout the Midwest US and Northeastern US, hop production has re-emerged to compliment a rapidly expanding craft brewing industry, and a consequent demand for locally sourced hops (Hop Growers of America 2018). Recent findings indicate that P. macularis populations found within Midwest and Northeastern US commercial hop yards may be genetically distinct from feral hop in the same regions (Gent et al. 2020). Isolates possessing

V6-virulence have not been confirmed in the US outside of the PNW (Wolfenbarger et al.

2016). Only the MAT1-1 mating-type idiomorph has been reported from within any US commercial hop plantings in the western U.S., and the ascigerous state has not been observed west of the Rocky Mountains in North America. Both mating-type idiomorphs have been detected in an approximate 1:1 ratio on feral hop sampled from a small number of locations in the Midwest and Northeastern US (Wolfenbarger et al. 2015). The distribution of virulence groups and partitioning and isolation of sexual reproduction are both relevant to sustainable

US hop production and also represent a perhaps unique spatial diversity of pathogen and host population characteristics (e.g., asexual versus sexual, commercial versus feral, and virulence versus avirulence) to which molecular methods could be deployed in the study and management of population diversity.

The ability to monitor a pathogen’s population structure relies upon two related, but distinct processes: (i) the identification of genetic variation within a population that correlates with phenotypes of interest; and (ii) the conversion of this observed genetic variance into a molecular marker system that can genotype future collections of the pathogen population in a timely, cost-effective, and high-throughput manner. Historically, developing comprehensive molecular marker libraries for obligately biotrophic pathogen groups such as the powdery

mildew fungi has presented certain limitations. The highly expanded, repetitive genomes and obligately biotrophic growth behavior of powdery mildews makes the assembly of even a single, high-confidence genome complicated. As such, genomes are currently unavailable for most powdery mildew species (Wicker et al. 2013; Jones et al. 2014), particularly given the amount of time, effort and plant material needed to culture and extract sufficiently large DNA quantity from these obligate biotrophs. Because of this bottleneck transcriptomic approaches have been used as an alternative sequencing methodology that reduces genome complexity, identifies sources of genetic variation exclusively within expressed gene sequences, and reduces the amount of biological material required for nucleic acid extraction (Gent et al.

2020; Vela-Corcía et al. 2016; Tollenaere et al. 2012; Rahman et al. 2019). It is important to note that due to the obligately biotrophic nature of their growth, all sequencing approaches for obligate biotrophs come with the inherit disadvantage that in addition to the target pathogen, sequence data will be returned for the host and any other epiphytic microorganisms present in the culture, which must be carefully filtered out prior to any alignment. While a transcriptomic approach reasonably resolves the hurdle of identifying genetic variation within obligate biotrophs, the need to convert this variation into a robust molecular marker genotyping platform remains.

Amplicon sequencing (AmpSeq) is a recently developed genotyping methodology that satisfies the cost, limited biomass, and high-throughput-capacity needs of an effective molecular marker platform (Yang et al. 2016; Zou et al. 2020). The genotyping pipeline was originally optimized to function within a plant-breeding framework, but more recently has been demonstrated to show promise in genotyping of obligately biotrophic plant pathogenic

fungal populations with low quantities of DNA (Kisselstein et al. 2017). In short, AmpSeq is a low-cost marker system for genotyping up to 2000 loci per sample in an initial multiplexed

PCR-1 (Yang et al. 2016). This reaction is then followed by a second PCR on the amplicons of

PCR-1 in order to add linker sequences that uniquely barcode samples by their location within dozens of 96-well plates. The barcoding enables all targeted loci of thousands of samples to be pooled and sequenced, often within a single Illumina sequencing lane depending on read depth needs, typically reducing the sequencing costs to near $1 per sample. When longer amplicons are sequenced (typically up to 280bp for Illumina 150 paired-end, base pair sequencing, variance data is returned not only at the specific nucleotide position for which the given marker was originally designed, but also for all other nucleotides within the amplicon, resulting in a marker system with higher information content than a single SNP (Zou et al. 2020). Thus, the AmpSeq platform may identify new, potentially phenotypically relevant variants linked to known variants when genotyping a population.

Application of an AmpSeq-based marker system would be extremely useful for obligately biotrophic organisms such as powdery mildew fungi because of the high costs of maintaining live cultures and phenotyping hundreds of isolates each year. Podosphaera macularis is subject to these constraints, as well as the additional current limitation that an assembled, whole genome sequence is not available. A recent study by Gent et al. (2020) characterized variation within the transcriptomic assemblies of 104 diverse P. macularis isolates in order to discern a likely European origin for the clonal P. macularis population of the PNW US, which first arrived to the region in 1996 (Ocamb et al. 1999). We saw this published dataset as an opportunity to re-purpose transcriptomic sequence data for the

creation of AmpSeq markers capable of monitoring changes in P. macularis population structure over time. Due to a very likely expanded and highly repetitive genome structure, P. macularis serves as a highly complex model that, if successful, opens the door for AmpSeq methodology across many organisms, which would be especially useful in obligately biotrophic pathosystems. As such, our stated objective was to employ a pre-existing P. macularis transcriptome dataset to develop a library of AmpSeq markers that is capable of genotyping the US P. macularis population across a range of relevant phenotypic parameters including geographic origin, mating type, and virulence toward the widely deployed H. lupulus

R-gene “R6”.

Methods

Generating AmpSeq local haplotype markers that characterize P. macularis population structure

RNA extraction, sequencing, and reference assembly. For this study, a previously assembled transcriptome and resequencing data library (Gent et al. 2020) were used to design SNP markers. Briefly, a collection of 104 P. macularis isolates were collected from the Pacific

Northwest (PNW) (representing pre-2012, V6-virulent, and Cascade-adapted strains), US regions east of the Rocky Mountains, the United Kingdom, and continental Europe. Since the arrival of P. macularis into the PNW US region in 1996, two uniquely virulent strains of the pathogen have emerged within this population since 2012; one strain that has a measurably enhanced ability to infect and grow on the hop cultivar ‘Cascade’ (Gent et al. 2017), and another that overcomes the R6-based host resistance in widely planted hop cultivars including

‘Nugget’, ‘Mt. Hood’, and ‘TriplePearl’ (Wolfenbarger et al. 2016). In addition to diversity in

geographic origin and virulence profile of samples, this sampling scheme captured diversity in

P. macularis samples originating from both commercially cultivated and feral hop plants. All isolates were transferred to six to nine leaves of the powdery mildew susceptible cultivar

‘Symphony’ upon receipt and kept in culture for 14 to 17 days. Acetate film was used to peel

P. macularis colonies from the leaf surface, then films of the same isolate were pooled and ground in liquid nitrogen for subsequent RNA extraction with an Ambion PureLink RNA mini kit (Thermo Fisher Scientific, Waltham, MA). Paired-end 2x150bp sequencing was performed on an Illumina HiSeq 3000 instrument at the Oregon State University Center for Genome

Research and Biocomputing. Sequence reads for 103 P. macularis isolates were independently quality filtered and mapped to a de novo reference transcriptome based on the pre-2012 PNW

P. macularis isolate HPM-663 (Gent et al. 2020).

Population level SNP variant calling and generation of AmpSeq primers. As detailed in Gent et al. (2020), reads of the 103 non-reference P. macularis isolates were mapped to the HPM-663 reference transcriptome assembly using bwa 0.7.10 (Li and Durbin 2009) and duplicate reads were marked and filtered out using the Picard toolkit 2.5.0 (Broad Institute and GitHub

Repository 2019). The ensuring amplicon sequencing marker design pipeline is summarized in

Figure S1-1. Using the GATK 3.5 HaplotypeCaller, variants including both single nucleotide polymorphisms (SNPs) and insertion-deletion mutations (INDELS) were called from resulting bam files that had been converted to a genomic variant call format (gVCF). The resulting library was filtered manually for variants that fell between the 1st and 8th deciles in read depth. Of these remaining variants, a second filtering step returned only those that were SNPs for which

the alternate allele was present in at least two of the 104 P. macularis assemblies. These remaining variants were then converted to a FASTA sequence file with requirements that the sequence encompassing each variant must be at least 240 base pairs (bp) in length, ideally

260bp, and no more than 500bp; and the SNP variant must be at least 30bp from the end of the sequence. Sequences housing multiple SNPs were permitted when these SNPs adhered to the aforementioned specifications. This FASTA file of P. macularis SNP variant sequences was then input into BatchPrimer3 (Untergasser et al. 2012) to obtain desirable polymerase chain reaction (PCR) primers for each sequence. Within BatchPrimer3, we again required that forward and reverse primers be located at least 30bp away from the SNP variant position and added requirements that amplicons be between 220 to 260bp long and only return primers between 18 to 25bp long with 57 – 63C annealing temperatures. All other parameters were kept as default. In addition to the primers designed around these polymorphisms, one additional locus was added as a genotyping target that included the SNP correlating with presence/absence of the V6-virulence phenotype (Block et al. 2020). Sequences for which forward and reverse primers were successfully designed by BatchPrimer3 were then modified for use in an amplicon sequencing platform, as described by (Yang et al. 2016). An AmpSeq universal linker sequence was added to the 5’ end of each BatchPrimer3-generated, locus- specific primer sequence: 5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-3’ for each forward primer, or 5’-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-3’ for each reverse primer.

Figure S1-1: A summary of the design and validation pipeline for an amplicon sequencing

(AmpSeq) SNP marker set when starting with a transcriptomic sequence library of 104

Podosphaera macularis samples.

Collection of a diverse P. macularis isolate DNA library. In order to assess the AmpSeq markers’ ability to differentiate P. macularis samples based on geographic origin, hop cultivation type, or V6-virulence phenotype, we assembled an extensive, novel collection of 514 P. macularis

DNA samples for genotyping (Table S1-1). These samples were similarly diverse in their geographic origin, hop cultivation type, and V6-virulence phenotype to the original P. macularis used for AmpSeq marker design. A subset (n = 61) of the collection had also been manually phenotyped for V6-virulence on the differential cultivar ‘Nugget’. A hierarchical sampling approach was taken, in which a minimum of 5 distinct P. macularis samples were collected from each hop sampling location and a minimum of three hop plantings were sampled from each geographic location. All samples were collected using 1cm Tough-Spot labeling stickers (Diversified BioTech, Dedham, MA) as an adhesive to peel mycelium and conidia of P. macularis colonies from the hop leaf. The sticker samples were then placed into

2mL microcentrifuge tubes for storage and subsequent DNA extraction. Collaborators shipped the collected P. macularis samples in padded envelopes via express mail at ambient temperature. Upon arrival samples were stored at -20C until processing for DNA extraction.

Sample tubes were placed on a metal platform over liquid nitrogen to maintain freezing conditions and supplemented with two stainless steel beads (SPEX sample prep item number

2150, Metuchen, NJ) and approximately 30 0.4-mm glass beads (Scientific Industries, Inc,

Bohemia, NY), which were then flash frozen in liquid nitrogen and ground using a GenoGrinder

(SPEX SamplePrep 2000 Geno/Grinder, Mutechen, NJ) for three cycles of 30s at 300 strikes/min. DNA was extracted via a modified CTAB protocol, using 24:1 chloroform: isoamyl

alcohol as the solution to separate organic soluble molecules from the DNA-containing liquid phase (Healey et al. 2014).

Table S1-1. Complete metadata profile for all 514 unique P. macularis samples submitted for

SNP marker genotyping via amplicon sequencing.

Sample No. Sample ID Geographic Origin Cultivation Type AmpSeq 1 GER001 Germany 1 Commercial 2 GER002 Germany 1 Commercial 3 GER003 Germany 1 Commercial 4 GER004 Germany 1 Commercial 5 GER005 Germany 1 Commercial *** 6 GER006 Germany 1 Commercial 7 GER007 Germany 1 Commercial 8 GER008 Germany 2 Commercial 9 GER009 Germany 2 Commercial 10 GER010 Germany 2 Commercial 11 GER011 Germany 2 Commercial 12 GER012 Germany 2 Commercial *** 13 GER013 Germany 2 Commercial 14 GER014 Germany 2 Commercial *** 15 GER016 Germany 3 Commercial 16 GER017 Germany 3 Commercial 17 GER018 Germany 3 Commercial 18 GER019 Germany 3 Commercial 19 GER020 Germany 3 Commercial 20 GER021 Germany 3 Commercial 21 GER022 Germany 3 Commercial 22 GER023 Germany 4 Commercial *** 23 GER024 Germany 4 Commercial *** 24 GER025 Germany 4 Commercial 25 GER026 Germany 4 Commercial *** 26 GER027 Germany 4 Commercial *** 27 GER028 Germany 4 Commercial 28 GER029 Germany 4 Commercial *** 29 GER033 Germany 5 Commercial *** 30 GER034 Germany 5 Commercial *** 31 GER035 Germany 5 Commercial 32 GER036 Germany 5 Commercial *** 33 GER037 Germany 5 Commercial *** 34 GER038 Germany 5 Commercial *** 35 GER039 Germany 5 Commercial 36 GER042 Germany 6 Commercial 37 GER043 Germany 6 Commercial 38 GER044 Germany 6 Commercial 39 GER045 Germany 6 Commercial 40 GER046 Germany 6 Commercial 41 GER047 Germany 6 Commercial 42 GER048 Germany 6 Commercial 43 GER051 Germany 7 Commercial

44 GER052 Germany 7 Commercial 45 GER053 Germany 7 Commercial 46 GER054 Germany 7 Commercial 47 GER055 Germany 7 Commercial 48 GER056 Germany 7 Commercial 49 GER057 Germany 8 Commercial 50 GER058 Germany 8 Commercial 51 GER059 Germany 8 Commercial 52 GER060 Germany 8 Commercial 53 GER061 Germany 8 Commercial 54 GER062 Germany 8 Commercial 55 GER063 Germany 8 Commercial 56 SLV001 Slovenia 1 Commercial *** 57 SLV002 Slovenia 1 Commercial *** 58 SLV003 Slovenia 1 Commercial *** 59 SLV004 Slovenia 1 Commercial *** 60 SLV005 Slovenia 1 Commercial *** 61 SLV006 Slovenia 1 Commercial *** 62 SLV007 Slovenia 1 Commercial *** 63 SLV009 Slovenia 2 Commercial *** 64 SLV010 Slovenia 2 Commercial *** 65 SLV011 Slovenia 2 Commercial *** 66 SLV012 Slovenia 2 Commercial *** 67 SLV024 Slovenia 2 Commercial *** 68 SLV025 Slovenia 2 Commercial *** 69 SLV026 Slovenia 2 Commercial *** 70 SLV013 Slovenia 3 Commercial *** 71 SLV014 Slovenia 3 Commercial *** 72 SLV015 Slovenia 3 Commercial *** 73 SLV016 Slovenia 3 Commercial *** 74 SLV017 Slovenia 3 Commercial *** 75 SLV027 Slovenia 3 Commercial *** 76 SLV028 Slovenia 3 Commercial *** 77 SLV018 Slovenia 4 Commercial *** 78 SLV019 Slovenia 4 Commercial *** 79 SLV020 Slovenia 4 Commercial *** 80 SLV021 Slovenia 4 Commercial *** 81 SLV022 Slovenia 4 Commercial *** 82 SLV023 Slovenia 4 Commercial *** 83 SLV030 Slovenia 4 Commercial *** 84 UK001 United Kingdom 1 Commercial *** 85 UK002 United Kingdom 1 Commercial 86 UK003 United Kingdom 1 Commercial 87 UK004 United Kingdom 1 Commercial *** 88 UK005 United Kingdom 1 Commercial *** 89 UK006 United Kingdom 1 Commercial 90 UK007 United Kingdom 1 Commercial 91 UK009 United Kingdom 2 Feral

92 UK010 United Kingdom 2 Feral *** 93 UK011 United Kingdom 2 Feral *** 94 UK012 United Kingdom 2 Feral *** 95 UK013 United Kingdom 2 Feral 96 UK014 United Kingdom 2 Feral *** 97 UK015 United Kingdom 2 Feral 98 UK017 United Kingdom 3 Commercial 99 UK018 United Kingdom 3 Commercial *** 100 UK019 United Kingdom 3 Commercial *** 101 UK020 United Kingdom 3 Commercial 102 UK021 United Kingdom 3 Commercial *** 103 UK022 United Kingdom 3 Commercial 104 UK023 United Kingdom 3 Commercial 105 MI007 Michigan Commercial 106 MI008 Michigan Commercial 107 MI009 Michigan Commercial 108 MI010 Michigan Commercial 109 MI011 Michigan Commercial 110 MI012 Michigan Commercial 111 MI013 Michigan Commercial 112 MN-19-001 Wisconsin Feral 113 MN-19-002 Wisconsin Feral 114 MN-19-003 Wisconsin Feral 115 MN-19-004 Wisconsin Feral 116 MN-19-005 Wisconsin Feral 117 MN-19-006 Minnesota Feral 118 MN-19-007 Minnesota Feral 119 MN-19-008 Minnesota Feral 120 MN-19-009 Minnesota Feral *** 121 MN-19-010 Minnesota Feral 122 MN-19-011 Indiana Feral *** 123 MN-19-012 Indiana Feral 124 MN-19-013 Indiana Feral *** 125 MN-19-014 Indiana Feral 126 MN-19-015 Indiana Feral 127 MN-19-016 New York Feral 128 MN-19-017 New York Feral 129 MN-19-018 New York Feral 130 MN-19-019 New York Feral 131 MN-19-020 New York Feral *** 132 MN-19-021 Indiana Feral *** 133 MN-19-022 Indiana Feral 134 MN-19-023 Indiana Feral *** 135 MN-19-024 Indiana Feral *** 136 MN-19-025 Indiana Feral 137 MN-19-031 Illinois 1 Feral 138 MN-19-032 Illinois 1 Feral *** 139 MN-19-033 Illinois 1 Feral

140 MN-19-034 Illinois 1 Feral 141 MN-19-035 Illinois 1 Feral *** 142 MN-19-036 Illinois 2 Feral *** 143 MN-19-037 Illinois 2 Feral 144 MN-19-038 Illinois 2 Feral *** 145 MN-19-039 Illinois 2 Feral 146 MN-19-040 Illinois 2 Feral *** 147 MN-19-056 Minnesota Feral 148 MN-19-057 Minnesota Feral 149 MN-19-058 Minnesota Feral 150 MN-19-059 Minnesota Feral 151 MN-19-060 Minnesota Feral 152 MN-19-066 Kansas Feral 153 MN-19-067 Kansas Feral 154 MN-19-068 Kansas Feral 155 MN-19-069 Kansas Feral 156 MN-19-070 Kansas Feral 157 MN-19-071 Minnesota Feral *** 158 MN-19-072 Minnesota Feral 159 MN-19-073 Minnesota Feral 160 MN-19-074 Minnesota Feral 161 MN-19-075 Minnesota Feral *** 162 MN-19-076 Massachusetts 1 Feral 163 MN-19-077 Massachusetts 1 Feral *** 164 MN-19-078 Massachusetts 1 Feral 165 MN-19-079 Massachusetts 1 Feral 166 MN-19-080 Massachusetts 1 Feral *** 167 MN-19-081 Massachusetts 2 Feral 168 MN-19-082 Massachusetts 2 Feral 169 MN-19-083 Massachusetts 2 Feral 170 MN-19-084 Massachusetts 2 Feral 171 MN-19-085 Massachusetts 2 Feral 172 MN-19-086 Indiana Feral 173 MN-19-087 Indiana Feral 174 MN-19-088 Indiana Feral 175 MN-19-089 Indiana Feral 176 MN-19-090 Indiana Feral 177 CZR001 Czech Republic Commercial *** 178 CZR002 Czech Republic Commercial *** 179 CZR003 Czech Republic Commercial *** 180 CZR004 Czech Republic Commercial *** 181 CZR005 Czech Republic Commercial *** 182 CZR006 Czech Republic Commercial *** 183 CZR007 Czech Republic Commercial *** 184 NY002 New York Feral *** 185 NY005 New York Feral *** 186 PNW-1216 Oregon Commercial 187 PNW-1217 Oregon Commercial

188 PNW-1218 Oregon Commercial 189 PNW-1220 Washington Commercial 190 PNW-1221 Washington Commercial 191 PNW-1222 Washington Commercial 192 PNW-663 Oregon Commercial *** 193 PNW-956 Washington Commercial *** 194 PNW-1040 Washington Commercial *** 195 PNW-1223 Oregon Commercial 196 PNW-1224 Oregon Commercial *** 197 OH001 Ohio 1 Commercial 198 OH002 Ohio 1 Commercial *** 199 OH003 Ohio 1 Commercial 200 OH004 Ohio 1 Commercial 201 OH005 Ohio 1 Commercial 202 OH006 Ohio 1 Commercial *** 203 OH007 Ohio 1 Commercial *** 204 OH013 Ohio 2 Commercial *** 205 OH014 Ohio 2 Commercial *** 206 OH015 Ohio 2 Commercial *** 207 OH016 Ohio 2 Commercial *** 208 OH017 Ohio 2 Commercial 209 OH018 Ohio 2 Commercial *** 210 OH019 Ohio 2 Commercial *** 211 NY025 New York Feral *** 212 NY026 New York Feral *** 213 NY027 New York Feral *** 214 NY028 New York Feral *** 215 NY029 New York Feral *** 216 MD036 Maryland 1 2018 Feral 217 MD037 Maryland 1 2018 Feral 218 MD038 Maryland 1 2018 Feral 219 MD039 Maryland 1 2018 Feral *** 220 MD040 Maryland 1 2018 Feral *** 221 MD047 Maryland 2 2018 Feral 222 MD048 Maryland 2 2018 Feral 223 MD049 Maryland 2 2018 Feral *** 224 MD050 Maryland 2 2018 Feral 225 MD045 Maryland 2 2018 Feral 226 MD003 Maryland 1 2019 Feral *** 227 MD004 Maryland 1 2019 Feral *** 228 MD005 Maryland 1 2019 Feral 229 MD006 Maryland 1 2019 Feral *** 230 MD007 Maryland 1 2019 Feral 231 MD013 Maryland 2 2019 Feral 232 MD014 Maryland 2 2019 Feral 233 MD015 Maryland 2 2019 Feral *** 234 MD016 Maryland 2 2019 Feral *** 235 MD017 Maryland 2 2019 Feral ***

236 MD023 Maryland 3 2019 Feral *** 237 MD024 Maryland 3 2019 Feral *** 238 MD025 Maryland 3 2019 Feral *** 239 MD026 Maryland 3 2019 Feral 240 MD027 Maryland 3 2019 Feral *** 241 2018 PED 10 New York 2018 Feral 242 2018 PED 11 New York 2018 Feral *** 243 2018 PED 12 New York 2018 Feral *** 244 2018 PED 18 New York 2018 Feral *** 245 2018 PED 19 New York 2018 Feral 246 2018 PED 20 New York 2018 Feral *** 247 NY016 New York 2019 Feral 248 NY017 New York 2019 Feral 249 NY018 New York 2019 Feral 250 NY019 New York 2019 Feral *** 251 NY020 New York 2019 Feral 252 NY021 New York 2019 Feral 253 NY022 New York 2019 Feral *** 254 VT001 Connecticut Commercial *** 255 VT002 Connecticut Commercial 256 VT003 Connecticut Commercial *** 257 VT004 Connecticut Commercial *** 258 NY054 New York Commercial *** 259 NY055 New York Commercial *** 260 NY032 New York Commercial *** 261 NY033 New York Commercial *** 262 NY034 New York Commercial *** 263 NY043 New York Commercial *** 264 NY044 New York Commercial *** 265 MN001 Indiana Feral 266 MN002 Indiana Feral 267 MN003 Indiana Feral 268 MN004 Indiana Feral 269 MN005 Indiana Feral *** 270 MN006 Minnesota 1 Feral 271 MN007 Minnesota 1 Feral 272 MN008 Minnesota 1 Feral 273 MN009 Minnesota 1 Feral 274 MN010 Minnesota 1 Feral 275 MN011 Minnesota 2 Feral 276 MN012 Minnesota 2 Feral 277 MN013 Minnesota 2 Feral 278 MN014 Minnesota 2 Feral 279 MN015 Minnesota 2 Feral 280 HPM-1114 Oregon Commercial *** 281 MN016 Washington Commercial 282 MN017 Washington Commercial 283 MN018 Washington Commercial

284 MN019 Washington Commercial 285 MN020 Washington Commercial 286 NY013 - 1 New York Feral 287 NY013 - 2 New York Feral 288 NY013 - 3 New York Feral 289 NY013 - 4 New York Feral 290 NY013 - 5 New York Feral 291 NY014 - 1 New York Commercial *** 292 NY014 - 2 New York Commercial *** 293 NY014 - 3 New York Commercial *** 294 NY014 - 4 New York Commercial *** 295 NY014 - 5 New York Commercial 296 MI001 - 1 Michigan Commercial *** 297 MI001 - 2 Michigan Commercial 298 MI001 - 3 Michigan Commercial 299 MI001 - 4 Michigan Commercial 300 NY012 - 1 New York Commercial 301 NY012 - 2 New York Commercial 302 NY012 - 3 New York Commercial 303 NY012 - 4 New York Commercial *** 304 NY012 - 5 New York Commercial *** 305 NY012 - 6 New York Commercial 306 NY015 - 1 New York Feral 307 NY015 - 2 New York Feral 308 NY015 - 3 New York Feral 309 NY015 - 4 New York Feral *** 310 NY015 - 5 New York Feral 311 NY015 - 6 New York Feral 312 NY011 - 1 New York Commercial 313 NY011 - 2 New York Commercial 314 NY011 - 3 New York Commercial *** 315 NY011 - 4 New York Commercial *** 316 NY011 - 5 New York Commercial 317 NY011 - 6 New York Commercial *** 318 NY011 - 7 New York Commercial *** 319 PNW 1 Washington Commercial *** 320 PNW 2 Washington Commercial *** 321 PNW 3 Washington Commercial 322 PNW 4 Washington Commercial *** 323 PNW 5 Washington Commercial *** 324 PNW 6 Washington Commercial *** 325 PNW 7 Washington Commercial 326 PNW 34 Washington Commercial 327 PNW 35 Washington Commercial *** 328 PNW 36 Washington Commercial 329 PNW 37 Washington Commercial *** 330 PNW 38 Washington Commercial *** 331 PNW 39 Washington Commercial ***

332 PNW 40 Washington Commercial *** 333 PNW 65 Oregon Commercial *** 334 PNW 66 Oregon Commercial *** 335 PNW 67 Oregon Commercial *** 336 PNW 68 Oregon Commercial *** 337 PNW 69 Oregon Commercial *** 338 PNW 70 Oregon Commercial 339 PNW 71 Oregon Commercial *** 340 PNW 100 Oregon Commercial *** 341 PNW 101 Oregon Commercial 342 PNW 102 Oregon Commercial 343 PNW 103 Oregon Commercial *** 344 PNW 104 Oregon Commercial *** 345 PNW 105 Oregon Commercial *** 346 PNW 106 Oregon Commercial *** 347 PNW 125 New York Commercial *** 348 PNW 126 New York Commercial *** 349 PNW 127 New York Commercial *** 350 PNW 128 New York Commercial 351 PNW 129 New York Commercial 352 PNW 138 Wisconsin Commercial *** 353 PNW 140 Wisconsin Commercial *** 354 PNW 142 Wisconsin Commercial *** 355 PNW 144 Wisconsin Commercial 356 PNW 146 Wisconsin Commercial *** 357 PNW 148 Wisconsin Commercial *** 358 PNW 149 New York Commercial *** 359 PNW 150 New York Commercial *** 360 PNW 151 New York Commercial *** 361 PNW 152 New York Commercial *** 362 PNW 153 New York Commercial 363 2019 PNW 1 Oregon Commercial 364 2019 PNW 2 Oregon Commercial *** 365 2019 PNW 3 Oregon Commercial *** 366 2019 PNW 4 Oregon Commercial 367 2019 PNW 5 Oregon Commercial *** 368 2019 PNW 6 Oregon Commercial 369 2019 PNW 7 Oregon Commercial 370 2019 PNW 8 Oregon Commercial 371 2019 PNW 9 Oregon Commercial *** 372 2019 PNW 10 Oregon Commercial 373 2019 PNW 11 Oregon Commercial *** 374 2019 PNW 12 Oregon Commercial *** 375 2019 PNW 13 Oregon Commercial *** 376 2019 PNW 14 Oregon Commercial 377 2019 PNW 15 Oregon Commercial 378 2019 PNW 16 Oregon Commercial 379 2019 PNW 17 Oregon Commercial

380 2019 PNW 18 Oregon Commercial 381 HPM 201 Germany Commercial 382 HPM 202 Germany Commercial *** 383 HPM 205 Germany Commercial *** 384 HPM 1023 Colorado Commercial 385 HPM 1028 Slovenia Commercial 386 HPM 1030 Washington Commercial *** 387 HPM 1057 Washington Commercial *** 388 HPM 1073 Washington Commercial *** 389 HPM 1079 Oregon Commercial *** 390 HPM 1085 Colorado Commercial *** 391 HPM 1089 Wisconsin Commercial *** 392 HPM 1097 New York Commercial *** 393 HPM 1104 Minnesota Feral *** 394 HPM 1117 Oregon Commercial *** 395 HPM 1120 Oregon Commercial *** 396 HPM 1126 Washington Commercial *** 397 HPM 1128 Washington Commercial *** 398 HPM 1137 Maryland Feral *** 399 HPM 1152 Minnesota Commercial *** 400 HPM 1154 North Carolina Commercial *** 401 HPM 1173 Oregon Commercial *** 402 HPM 1182 Wisconsin Commercial *** 403 HPM 1187 Michigan Commercial *** 404 HPM 1206 Michigan Commercial *** 405 HPM 663 Oregon Commercial 406 HPM 865 Slovenia Commercial 407 HPM 870 United Kingdom Commercial *** 408 HPM 952 Slovenia Commercial *** 409 HPM 1058 Washington Commercial *** 410 HPM 1063 Washington Commercial *** 411 HPM 1071 Washington Commercial *** 412 HPM 1076 Washington Commercial *** 413 HPM 1082 Oregon Commercial *** 414 HPM 1090 Wisconsin Commercial 415 HPM 1094 Oregon Commercial *** 416 HPM 1098 New York Commercial *** 417 HPM 1101 Minnesota Feral *** 418 HPM 1103 Minnesota Feral *** 419 HPM 1114 Oregon Commercial *** 420 HPM 1118 Oregon Commercial *** 421 HPM 1122 Oregon Commercial *** 422 HPM 1123 Oregon Commercial *** 423 HPM 1138 Maryland Feral *** 424 HPM 1151 Minnesota Commercial *** 425 HPM 1155 North Carolina Commercial *** 426 HPM 1184 Wisconsin Commercial *** 427 HPM 1189 Michigan Commercial ***

428 HPM 1205 Michigan Commercial *** 429 HPM-198 Germany Commercial *** 430 HPM-199 Germany Commercial *** 431 HPM-200 France Commercial *** 432 HPM-203 England Commercial *** 433 HPM-527 Minnesota Feral *** 434 HPM-1088 Colorado Commercial *** 435 HPM-1113 Oregon Commercial *** 436 HPM-1142 West Virginia Feral *** 437 HPM-1181 Wisconsin Commercial *** 438 HPM-1188 Michigan Commercial *** 439 HPM-1170 Michigan Commercial *** 440 HPM-866 England Commercial *** 441 NY0013-1 New York Feral 442 NY0013-2 New York Feral 443 NY0013-3 New York Feral *** 444 NY0013-4 New York Feral *** 445 NY014-1 New York Commercial *** 446 NY014-2 New York Commercial *** 447 NY014-3 New York Commercial *** 448 NY014-4 New York Commercial *** 449 MD001-1 Maryland Feral 450 MD001-2 Maryland Feral *** 451 MD001-3 Maryland Feral 452 MD001-4 Maryland Feral *** 453 MI001-1 Michigan Commercial *** 454 MI001-2 Michigan Commercial *** 455 MI001-3 Michigan Commercial *** 456 MI001-4 Michigan Commercial *** 457 NY015-1 New York Feral 458 NY015-2 New York Feral 459 NY015-3 New York Feral 460 NY015-4 New York Feral *** 461 PNW-149 New York Commercial *** 462 PNW-150 New York Commercial *** 463 PNW-151 New York Commercial 464 PNW-125 New York Commercial *** 465 PNW-126 New York Commercial *** 466 PNW-127 New York Commercial *** 467 PNW-128 New York Commercial 468 PNW-129 New York Commercial 469 PNW-130 New York Commercial *** 470 PNW-131 New York Commercial *** 471 PNW-137 Wisconsin Commercial 472 PNW-139 Wisconsin Commercial *** 473 PNW-141 Wisconsin Commercial 474 PNW-143 Wisconsin Commercial *** 475 PNW-145 Wisconsin Commercial ***

476 PNW-147 Wisconsin Commercial *** 477 PNW-68 Oregon Commercial *** 478 PNW-69 Oregon Commercial *** 479 PNW-70 Oregon Commercial *** 480 PNW-71 Oregon Commercial *** 481 PNW-72 Oregon Commercial *** 482 PNW-73 Oregon Commercial *** 483 PNW-1 Washington Commercial *** 484 PNW-2 Washington Commercial *** 485 PNW-3 Washington Commercial 486 PNW-4 Washington Commercial *** 487 PNW-5 Washington Commercial *** 488 PNW-6 Washington Commercial *** 489 PNW-7 Washington Commercial *** 490 PNW-8 Washington Commercial *** 491 PNW-9 Washington Commercial *** 492 PNW-10 Washington Commercial 493 PNW-11 Washington Commercial *** 494 PNW-12 Washington Commercial *** 495 PNW-32 Washington Commercial *** 496 PNW-33 Washington Commercial *** 497 PNW-34 Washington Unknown 498 PNW-35 Washington Unknown *** 499 PNW-36 Washington Wild 500 PNW-37 Washington Wild *** 501 PNW-38 Washington Wild *** 502 PNW-39 Washington Wild *** 503 PNW-40 Washington Wild *** 504 PNW-41 Washington Wild *** 505 PNW-42 Washington Wild 506 PNW-43 Washington Wild *** 507 PNW-94 Oregon Wild *** 508 PNW-95 Oregon Wild *** 509 PNW-96 Oregon Wild *** 510 PNW-97 Oregon Wild *** 511 PNW-98 Oregon Wild 512 PNW-99 Oregon Wild *** 513 HPM_E New York Feral *** 514 HPM_F New York Feral ***

Amplicon sequencing and haplotype calling. No-template controls, technical replicates, and biological replicates, for a total of 575 samples were submitted for amplicon sequencing at the Cornell University Biotechnology Resource Center as previously described by Yang et al.

(2016), but with the following modifications. During locus-specific amplification with universal adapters in PCR-1, a touchdown PCR program was adopted which consisted of a 10 min denaturation step at 95C, followed by 10 cycles of denaturation at 94C for 30 sec, primer annealing starting at 62C for 30 sec for the first cycle and recurrently decreasing by 1C for the ensuing cycles, and extension at 72C for 1 min. The 10 touchdown PCR cycles were then followed by 24 cycles of 94C for 30 sec, 56C for 30 sec, and 72C for 1 min. The reaction was completed with a post-extension incubation at 72C for 7 minutes. This touchdown PCR modification was adopted because an initial run using the default AmpSeq PCR-1 annealing temperature of 62C for every cycle yielded a poor amplification across the marker library.

After PCR-2, the indexed PCR products were pooled, cleaned with Agencourt AMPure beads, quantified, and sequenced on an IlluminaNextSeq500 (2x150bp) sequencer (Illumina, San

Diego, CA, USA). Raw sequence reads were then processed through a previously described, custom AmpSeq haplotyping pipeline (Fresnedo-Ramirez et al. 2017). Parameters were set to minimize sequencing errors by requiring a minimum of five samples per haplotype, a maximum of 10 unique haplotypes per sample in the first pass, and a maximum read count ratio of five between the two reported alleles for a given sample. All other input parameters were set to default. The raw sequencing reads have been deposited in the National Center for

Biotechnology Information Sequence Read Archive (SRA) and are accessible through

BioProject ID PRJNA638926 [data can be accessed by reviewers with

https://dataview.ncbi.nlm.nih.gov/object/PRJNA638926?reviewer=566bff7qg25l4cmtg45qu5ct4k.

These data will be publicly released upon acceptance of the manuscript].

Quality control and filtering of sequencing haplotype outputs. The haplotype file output from the AmpSeq haplotype analysis pipeline was subjected to four steps of additional quality control and filtering prior to any downstream analyses. Step 1 filtered P. macularis samples for those with at least 60% of loci having returned sequence data. Step 2 filtered markers with missing data, to remove loci that returned sequence data in fewer than 80% of P. macularis

DNA samples. Step 3 removed inconsistent markers by requiring at least 5 out of the 8 biological and technical replicate P. macularis DNA samples to have returned matching haplotype data. Only the technical/ biological replicates for which both P. macularis DNA samples survived filtering steps 1 and 2 were considered for use in filtering step 3. Step 4 removed heterozygous markers that were likely either the result of admixed genotypes in the

P. macularis colony sampled or were paralogous sequences within the P. macularis transcriptome by requiring markers to have returned heterozygous haplotypes in 10% or fewer of the remaining P. macularis DNA samples.

Global structure of hop powdery mildew populations. In order to assess the performance of the SNP marker library in accurately genotyping the global population structure of P. macularis, a classical multidimensional scaling (MDS) approach, also referred to as a principal coordinate analysis (PCoA), was utilized. The VCF datafile was read into TASSEL5 (Bradbury et al. 2007) and converted into a square distance matrix based on the cumulative haplotype

profile of all samples across all loci, calculated as 1 – Identity by State (IBS) similarity, with IBS defined as the probability that alleles drawn at random from two individuals at the same locus are the same. This distance matrix file was then read into R, where vegdist() (Oksanen et al.

2019) was used to convert the dataset into a Euclidian dissimilarity index format, cmdscale()

(R Core Team 2017) to run a classical multidimensional scaling of the Euclidian dissimilarity index, and ggplot2() (Wickham 2016) to visualize scatter plots of the first and second principal coordinate values. The PCoA calculations were paired with a permutational multivariate analysis of variance using distance matrices (PERMANOVA) (n=1000 permutations) to discern significant differences between the grouping of sample phenotype metadata groups, including geographic origin, hop planting type, and sample V6-virulence. With the TASELL5-derived distance matrix as the input, the R package adonis2() was used to run a permanova calculation and the package pairwise.adonis() to calculate pairwise comparisons and assign levels of statistical significance (Oksanen et al. 2019).

Continental distribution of Podosphaera macularis mating-type idiomorphs

Design and validation of real-time quantitative PCR (qPCR) HPM mating assay. AmpSeq markers targeting the MAT1-1 and MAT1-2 loci were included in preliminary runs of the genotyping pipeline but failed to return acceptable haplotype and sequence data (data not presented). As such, the decision was made to exclude the mating-type locus markers from the AmpSeq library and instead design qPCR primers for genotyping of mating type. The biological objective was to provide a thorough description of the distribution of P. macularis mating-type idiomorphs across the U.S., as the MAT1-2 idiomorph has yet to be reported in

the PNW and would have major implications on disease management should it arrive. The technical objective was to redesign agarose gel PCR markers around the mating type loci reported in Wolfenbarger et al. (2015) as a presence/ absence qPCR assay. In this assay format, both mating type idiomorphs could be determined in multiplex and be more time efficient when scaled up to processing hundreds of P. macularis samples. The qPCR primer sets (PCR primers and fluorescent probe primer sequences) were designed and ordered from

Integrated DNA Technologies (Coralville, IA) using their PrimerQuest primer design tool for a standard qPCR assay. Mating-type locus sequences for primer design were downloaded from

P. macularis isolate HPM-175 sequence (GenBank KJ922755.1) for the MAT1-1-1 locus and from P. macularis isolate HPM-200 sequence (GenBank KJ741396.1) for the MAT1-2-1 locus.

The qPCR primer set sequences are summarized in Table S1-2. Reactions were conducted in a

BioRad CFX96 Touch Real-time PCR Detection System (Bio-Rad, Hercules, CA), using a 25uL reaction volume with PrimeTime Gene Expression Master Mix (2X) (Integrated DNA

Technologies, Coralville, IA) and 2uL of sample template DNA. Cycling conditions were set at the default recommendation of a single cycle of polymerase activation for 3 min at 95C, followed by 40 amplification cycles containing a denaturation step at 95C for 15 sec and an annealing/extension step at 60C for 1 min. A sample was considered having returned a positive result for a given mating-type locus if the quantitation cycle (Cq) value (for which the fluorescence threshold was surpassed) was within 7 cycles of the positive control sample Cq value during that specific reaction run, excluding samples that failed to surpass the CFX

Maestro analysis software (Bio-Rad, Hercules, CA) minimum fluorescence threshold.

Specificity of the assay was determined using a validation subset of 16 independent P.

macularis samples of diverse geographic origin (Bustin et al. 2009). The mating type of these samples was first determined using the published P. macularis mating-type primers from

Wolfenbarger et al. (2015). These mating-type designations were then compared to those returned in the qPCR assay as means for marker validation.

Table S1-2. qPCR primer sequences designed to target the Podosphaera macularis MAT1-1-1 and MAT1-2-1 mating type loci.

Target Locus Primer Set Name Primer F (5'-3') Primer R (5' - 3') Probe (5' - 3') MAT1-1-1 PmMAT1-1-1 AGCGCCGATCGTTACATTTC CCGTCTCATCAGTGTAGCTAGT -/5HEX/AAGAATCCC/ZEN/AATGTGCGGGCAAAC/3IABkFQ/- MAT1-2-1 PmMAT1-2-1 CAACCCTGGTCTTAGCAATAATC GCAAGATCCTTGTAGGCATTTC -/56-FAM/ACCTTGATC/ZEN/TCTTCAATGTGGGCCA/3IABkFQ/-

Screening the continental populations of Podosphaera macularis for mating-type idiomorphs.

The same library of diverse P. macularis samples genotyped with AmpSeq was surveyed for mating type using the novel qPCR mating-type idiomorph assay described above. However, the assay was run on only the 320 P. macularis samples that survived all post-sequencing filtering steps, which indicated DNA of sufficient quantity and quality. These 320 samples were supplemented with 177 additional P. macularis samples that were either collected in years prior to 2018 or were collected at a location where fewer than five distinct P. macularis samples were submitted. This disqualified them from being included in the AmpSeq marker project, but these samples were still valuable for the purpose of describing mating-type distribution across hop producing regions. A one proportion Z-test with continuity correction using the R function prop.test() (R Core Team 2017) was conducted on the observed P. macularis mating-type ratios to test for adherence to a 1:1 distribution of the MAT1-1 and

MAT1-2 mating-type idiomorphs within a geographic region.

Results

Generating AmpSeq haplotype markers that characterize P. macularis population structure

Population level SNP variant calling and generation of AmpSeq primers. When transcriptome sequence reads for all 103 P. macularis samples were aligned to the de novo reference transcriptome of P. macularis isolate HPM-663, 142,623 variants were returned, which included both single nucleotide polymorphisms (SNPs) and insertion/ deletion (INDEL) mutations (Fig. S1-1). When these reads were filtered for only those that fell between the 2nd and 8th read depth deciles, 21,407 variants remained. After filtering to keep only SNP variants

with the alternate allele present in at least two of the 104 P. macularis isolate transcriptomes,

330 variants across 196 loci remained. We targeted SNP’s present in at least two samples to eliminate sequencing errors, increase likely relevance to different collections of P. macularis, and because rare alleles are typically filtered out before analyzing population genetic data.

BatchPrimer3 returned PCR primer sets for 168 of the 196 loci. Two of the 168 PCR primer sets were removed during a percent identity matrix analysis in an attempt to limit the likelihood of any primer-primer interactions, resulting in a final 166 PCR primer sets for addition of the

AmpSeq linker sequences.

Quality control and filtering of sequencing haplotype outputs. The post-Illumina AmpSeq filtering steps are summarized in Figure S1-2. The original sequencing submission contained

575 P. macularis DNA samples (including biological replicates, technical replicates, and water controls). The amplicon.py AmpSeq data processing and haplotype calling pipeline returned haplotype data for all 575 P. macularis samples, with 151 of the 166 SNP markers having successfully returned some data. Quality filtering resulted in a final data set of 320 P. macularis samples (56% of the original, Table 1-1) and 54 AmpSeq local haplotype markers. Filter step

4, which removed marker loci that exceeded a 10% heterozygosity level across the 320 P. macularis samples, removed the greatest number of markers of all filtering steps (Figure S1-

3). The output haplotype matrix file, sample and primer key file, the forward and reverse primer sequences, and a file containing the major allele sequence for each of the 54 AmpSeq loci are uploaded to the GitHub repository project “Podosphaera macularis AmpSeq marker

project 2020”, [URL is available to the reviewers by request and will be made publicly available upon acceptance of the manuscript].

Figure S1-2: A summary of steps and rationale taken to filter the Illumina sequence outputs based on Podosphaera macularis DNA sequence quality and SNP marker locus performance.

Figure S1-3: Of the 105 loci that remained after filtering steps 1 – 4, the percentage of the filtered Podosphaera macularis samples returning heterozygous SNPs for a given locus, termed ‘locus heterozygosity’. In the fifth and final filtering step, marker loci that had heterozygosity levels of less than 10% were chosen as the final SNP marker set, the cutoff denoted by a horizontal black dotted line.

Table 1-1. Summary of phenotypic metadata for the 320 Podosphaera macularis samples passing all quality filtering parameters.

Sampling Location Commercial Samples Feral Samples Total United States Northeast 49 46 95 Midwest 41 15 56 Pacific Northwest 65 0 65 Europe United Kingdom 10 4 14 France 1 0 1 Germany 19 0 19 Slovenia 33 0 33 Czech Republic 7 0 7 Cumulative Total 320

Global structure of hop powdery mildew populations. The first two principal coordinate eigenvalues accounted for 95.2% of the variation explained within the amplicon sequencing haplotype dataset (Figure 1-2). The first phenotypic metadata category that we used to describe clustering patterns within the principal coordinate analyses was geographic sampling origin (Figure 1-2A). Geographic origin alone was not capable of explaining the clustering patterns observed within the US-derived samples, specifically those from the Northeastern US and Midwestern US. The confidence ellipses encompassing both the Midwest US and

Northeastern US derived samples clearly span across two unique clusters of samples within the larger two ellipses. When grouped by the type of hop planting from which the sample was collected, two distinct clusters emerged (Figure 1-2B). One ellipse encompassed samples primarily originating from cultivated hop yards, while the other encompassed samples derived largely from feral plantings of hop. When these two metadata phenotypes were merged into a single cumulative passport profile of each sample (Figure 1-2C), the discrepancy observed in

Figure 1-2A of a single confidence ellipse spanning two groupings for both the Midwest US and Northeastern US samples became clearer. In this case, the ellipses belonging to Eastern

US and Midwestern US samples collected from feral hop plants occupied an overlapping but distinct space in the upper left of the PCoA plot, which overlapped with P. macularis isolates from Eastern Europe, notably Slovenia and the Czech Republic. In contrast, the ellipses of

Midwest US and Eastern US P. macularis samples collected exclusively from cultivated hop plants occupied a second overlapping, but distinct space in the upper right portion of the PCoA plot. These two “cultivated” US groupings also overlapped in their ordination with the

cultivated-hop derived samples of the Pacific Northwest US, as well as samples derived from the United Kingdom (Figure 1-2C).

Figure 1-2. Principal coordinate analyses (PCoA) of Podosphaera macularis samples based upon Euclidian distances of the returned amplicon sequencing haplotype profiles. Across all three plots, confidence ellipses are only included for samples originating from the U.S. (A) P. macularis samples are grouped by geographic origin alone. Both Midwest and Eastern US sample groups have two distinct clusters, indicating geography alone doesn’t describe the observed ordination pattern. (B) P. macularis samples grouped by the type of hop planting from which the sample were collected. Samples collected from commercial plantings and from feral hop plantings roughly group into two distinct clusters. (C) P. macularis samples grouped by both geographic origin and hop planting type. Samples from Midwest and Eastern U.S. feral hop plants are distinct from samples originating from commercial plants, independent of region of origin.

Pairwise comparisons of the P. macularis cumulative phenotype groupings, based on the PERMANOVA output, provided an additional statistical test for significant differences between sampling groups that followed the same pattern observed in the PCoA plots (Tables

S1-3, S1-4, S1-5). In this case the most interesting results are the cumulative phenotype pairings that are not statistically significant from one another, which are shaded in light grey in Table 1-2. These results further support the distinct clustering patterns observed in the

PCoA plots, where the P. macularis samples derived from US cultivated hop yards grouped together and those derived from feral hop plantings throughout the US clustered in a distinctly separate space (Table S1-5).

Table 1-2. Pairwise comparisons (adjusted P-value) between Podosphaera macularis samples grouped by geographic origin and the type of hop planting from which the sample was derived, based on PERMANOVA. All samples from Europe and the United Kingdom were collected in commercial plantings. Pairings that are not significantly different are highlighted in light grey.

i a l

Table S1-3. (top) Pairwise comparisons (p-adjusted value) between P. macularis samples based on a PERMANOVA and grouped by their sampling location phenotype. (bottom)

Summary statistics of the PERMANOVA.

PERMANOVA Sample Pairwise Comparisons Sum of Squares F.Model R2 p.value p.adjusted sig Europe vs United Kingdom 0.638 40.905 0.359 0.001 0.01 * Europe vs Midwest 1.607 33.227 0.226 0.001 0.01 * Europe vs Northeast 0.808 12.441 0.075 0.001 0.01 * Europe vs PNW 5.907 666.421 0.814 0.001 0.01 * United Kingdom vs Midwest 0.049 0.721 0.010 0.42 1 United Kingdom vs Northeast 0.163 1.937 0.018 0.165 1 United Kingdom vs PNW 0.431 90.049 0.457 0.001 0.01 * Midwest vs Northeast 0.371 4.099 0.027 0.048 0.48 Midwest vs PNW 1.096 33.180 0.183 0.001 0.01 * Northeast vs PNW 3.619 72.641 0.280 0.001 0.01 * adonis2(formula = seqData ~ sample_origin, permutations = 1000, method = "euclidian" Df Sum of Squares R2 F Pr(>F) sample_origin 4 1.6302 0.25913 27.544 0.000999 *** Residual 315 4.6607 0.74087 Total 319 6.2908 1 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table S1-4. (top) Pairwise comparisons (p-adjusted value) between P. macularis samples based on a PERMANOVA and grouped by the type of hop planting from which the sample was collected. (bottom) Summary statistics of the PERMANOVA.

PERMANOVA Sample Pairwise Comparisons Sum of Squares F.Model R2 p.value p.adjusted sig Commercial vs Feral 9.224 229.805 0.420 0.001 0.001 ** adonis2(formula = seqData ~ yard_type, permutations = 1000, method = "euclidian" Df Sum of Squares R2 F Pr(>F) yard_type 1 2.5212 0.40078 212.69 0.000999 *** Residual 318 3.7696 0.59922 Total 319 6.2908 1 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table S1-5. (top) Pairwise comparisons (p-adjusted value) between P. macularis samples based on a PERMANOVA and grouped by their combined phenotype of sampling location and type of hop planting. (bottom) Summary statistics of the PERMANOVA.

PERMANOVA Sample Pairwise Comparisons Sum of Squares F.Model R2 p.value p.adjusted sig Europe vs United Kingdom 0.638 40.905 0.359 0.001 0.021 . Europe vs Midwest - Feral 0.490 24.571 0.252 0.001 0.021 . Europe vs Northeast - Feral 0.983 48.222 0.317 0.001 0.021 . Europe vs Midwest - Commercial 3.152 139.548 0.585 0.001 0.021 . Europe vs PNW - Commercial 5.907 666.421 0.814 0.001 0.021 . Europe vs Northeast - Commercial 3.957 214.842 0.668 0.001 0.021 . United Kingdom vs Midwest - Feral 1.093 49.393 0.638 0.001 0.021 . United Kingdom vs Northeast - Feral 1.648 75.608 0.562 0.001 0.021 . United Kingdom vs Midwest - Commercial 0.229 8.829 0.141 0.004 0.084 United Kingdom vs PNW - Commercial 0.431 90.049 0.457 0.001 0.021 . United Kingdom vs Northeast - Commercial 0.313 17.066 0.216 0.001 0.021 . Midwest - Feral vs Northeast - Feral 0.018 0.665 0.011 0.523 1.000 Midwest - Feral vs Midwest - Commercial 2.810 88.369 0.621 0.001 0.021 . Midwest - Feral vs PNW - Commercial 3.860 498.479 0.823 0.001 0.021 . Midwest - Feral vs Northeast - Commercial 3.222 137.571 0.689 0.001 0.021 . Northeast - Feral vs Midwest - Commercial 5.711 203.698 0.706 0.001 0.021 . Northeast - Feral vs PNW - Commercial 9.452 872.617 0.863 0.001 0.021 . Northeast - Feral vs Northeast - Commercial 6.837 300.186 0.763 0.001 0.021 . Midwest - Commercial vs PNW - Commercial 0.057 4.733 0.034 0.014 0.294 Midwest - Commercial vs Northeast - Commercial 0.018 0.721 0.008 0.414 1.000 PNW - Commercial vs Northeast - Commercial 0.024 2.533 0.018 0.096 1.000

adonis2(formula = seqData ~ sample_combined, permutations = 1000, method = "euclidian" Df Sum of Squares R2 F Pr(>F) sample_combined 6 3.7809 0.60101 78.581 0.000999 *** Residual 313 2.51 0.39899 Total 319 6.2908 1 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Detecting the V6-virulence locus. The AmpSeq marker targeting the V6-virulence locus

(Pm_2407) passed all sequence quality filtering steps described above. The major alternate allele returned in the haplotype dataset matched the expected SNP mutation associated with a V6-virulence phenotype, which was confirmed via a Clustal Omega alignment of the sequence in comparison to the wildtype allele (Figure S1-4). The V6-virulence primer data agreed with 24 out of 25 P. macularis samples that had been manually phenotyped and confirmed as possessing the V6-virulence phenotype (Table 1-3). Additionally, the V6 SNP locus returned no false positive genotypes, correctly assigning 41 out of 41 samples that had been manually phenotyped as non-V6-virulent.

Figure S1-4: Multiple sequence alignment of the top two amplicon sequencing haplotypes for

Podosphaera macularis marker Pm2407, which differentiates samples based on V6-virulence.

The highlighted SNP at position 115 is the expected SNP that differentiates V6 virulence, as reported by Block et al. (2020).

Table 1-3. Distribution of Podosphaera macularis V6-virulence genotypes returned for marker

Pm2407. An ‘R6 hop variety derived’ sample was collected from either the hop cultivar

‘Nugget’ or ‘TriplePerl’. An ‘Unknown’ sample phenotype indicates a sample received where the hop cultivar was not able to be identified, nor was the sample phenotyped for V6- virulence.

Returned V6 genotype Isolate V6-virulence Phenotype Wild-type V6 positive Other haplotype Total Confirmed V6 1 10 0 11 R6 hop variety derived 0 14 0 14 Confirmed non-V6 41 0 0 41 Non-R6 hop variety derived 103 2 2 107 Unknown 54 2 2 58 Total 231

Continental distribution of Podosphaera macularis mating-type idiomorphs

Design and validation of real-time quantitative PCR (qPCR) HPM mating-type assay. Real-time quantitative PCR primer sets were successfully designed for both the MAT1-1 and MAT1-2 P. macularis mating-type idiomorphs. These primer sets were confirmed to function properly together in a multiplexed qPCR assay as the qPCR assigned mating types matched those returned in the traditional PCR-based mating-type assay (Figure S1-5). The qPCR assay was also able to genotype one additional P. macularis sample within the validation sample set that failed to amplify via traditional PCR, suggesting a possible increase in assay sensitivity.

Figure S1-5: Real-time quantitative PCR amplification curves (above) validated by comparison against the Wolfenbarger et al. (2015) PCR mating type primer set (right), confirming that qPCR mating type assay returns robust amplification results capable of differentiating diverse

Podosphaera macularis isolates by mating type. Mating type designations summarized in table to the left. Cq value is the qPCR cycle at which the detected sample fluorescence level passed the minimum threshold. NY005 is a P. macularis isolate previously confirmed as a MAT1-1 mating type and NY002 is a P. macularis isolate previously confirmed for a MAT1-2 mating type (Weldon et al. 2020).

Screening the continental populations of Podosphaera macularis for mating-type idiomorphs.

The mating-type idiomorphs of 497 P. macularis samples were determined using the HPM qPCR mating-type assay. Although we requested that collaborators aim to only collect

ToughSpot sticker peels of a single P. macularis colony with clear, distinct margins around the entire colony, there was no guarantee all isolates were comprised of a single pure genotype.

As such, we designated sample mating-type idiomorph profiles as either “MAT1-1”, “MAT1-

2”, or “mixed” based on the observed qPCR amplification curves. The observed P. macularis population mating-type distribution is summarized in Table 1-4 and visualized across geography in Figure 1-3. A one-sample test of equal proportions indicated that the ratio of P. macularis mating-type idiomorphs observed in Northeastern and Midwestern US feral hop plantings did not significantly differ from a 1:1 ratio (P = 0.397), while the observed P. macularis mating-type ratio in commercial hop plantings was significantly different from 1:1 in the Northeastern/ Midwestern US and the PNW US, but not in Europe (P = 2.2X10-16, P =

2.2X10-16, and P = 0.515 respectively).

Figure 1-3. Distribution of Podosphaera macularis mating type idiomorphs sampled within (A) the continental United States and (B) the United Kingdom and continental Europe. For a given location, the mating type of each individual sample was determined (range of samples genotyped within a given location, n = 2 to 20) and then a cumulative mating type profile for the location was assigned.

Table 1-4. Mating type idiomorph profiles of Podosphaera macularis samples collected throughout the Eastern US, Midwest US, Pacific Northwest US, and Europe, as determined by a multiplexed qPCR targeting the MAT1-1-1 and MAT1-2-1 loci. Parenthetical values denote the total number of distinct locations from where P. macularis was sampled within a given sampling location category. A one sample test of equal proportions indicated that the ratio of

P. macularis mating type idiomorphs observed in Eastern and Midwestern US feral hop plantings did not significantly differ from a 1:1 ratio (P = 0.397), while the observed P. macularis mating type ratio in commercial hop plantings was significantly different from 1:1 in the Eastern/ Midwestern US and the PNW US, but not in Europe (P = 2.2X10-16, P = 2.2X10-

16, and P = 0.515 respectively).

P. macularis collected from cultivated hop yards P. macularis collected from feral plantings of hop Sampling Location MAT1-1 MAT1-2 Mixed Total MAT1-1 MAT1-2 Mixed Total Eastern US 126 0 0 126 (9) 25 21 61 107 (16) Midwest US 39 0 1 40 (6) 13 4 8 25 (9) PNW US 139 0 0 139 (15) NA NA NA NA Europe 19 12 27 58 (13) 1 1 0 2 (1) Cultivated Total 363 Feral Total 134 Cumulative Total 497

Discussion

Using Podosphaera macularis as a model, we have outlined a novel methodology for re-purposing transcriptomic sequence datasets to build a highly-multiplexed amplicon sequencing (AmpSeq) variant library, which is amenable for SNP marker genotyping without the need for pathogen culturing. P. macularis belongs to a class of obligately biotrophic fungi that are difficult to culture en masse and often possess large genomes with high transposable element activity (Jones et al. 2014; Wicker et al. 2013). As such, this approach serves as a template for the generation of low-input, high-throughput AmpSeq genotyping libraries for other difficult-to-culture or obligately biotrophic fungi and oomycetes, including other powdery mildews, downy mildews, and rusts. AmpSeq reactions, which may be comprised of up to 2000 different markers (or likely more) as a PCR multiplex (Zou et al. 2020), provide greater population-wide resolution than other common low-input population genotyping strategies such as the sequencing of a small collection of microsatellites (SSRs), AFLP markers,

SNPs, or differentiation via more recent technologies such as high-resolution melt curve analysis (Forcelini et al. 2018; Hansen et al. 2016; Rafiei et al. 2018; Ordóñez et al. 2019; Forbes et al. 1998; Lees et al. 2006; Markell and Milus 2008). In fact, a wide range of existing markers including SSRs, SNPs, and INDELs reported within any number of studies could be pooled into a single AmpSeq library and genotyped together as long as primers are compatible and amplicon sizes are similar. As stated previously, a transcriptomic sequence dataset was used as the source of genetic variation from which the AmpSeq SNP markers were designed.

However, any existing dataset containing sequence variance information across a representative sample population could likely be re-purposed in a similar manner. It has been

shown to work equally well in the re-purposing of existing GBS (Yang et al. 2016) and whole genome sequencing (WGS) (Zou et al. 2020) data to create AmpSeq molecular marker sets.

The molecular tools presented here greatly enhance our ability to monitor P. macularis individuals for genetic diversity, putative geographic origin, mating type, and virulence towards a widely deployed R-gene. The AmpSeq markers differentiated P. macularis population structure across the same phenotypic parameters described in (Gent et al. 2020).

This is perhaps unsurprising, but nonetheless the desired outcome. When plotted by the principal coordinate values, grouping samples based on their geographic origin alone was inadequate in differentiating samples collected within the Northeast US and Midwest US (Fig

1-2A). However, accounting for planting type clearly resolved populations of P. macularis with a commercial hop origin from those collected on feral hop (Fig 1-2B). Combining both geographic region of origin and hop yard type into a single cumulative phenotype designation ultimately provided the resolution necessary to explain the conflicting clustering patterns observed within Fig 1-2A. Interestingly, the Northeastern US and Midwestern US P. macularis samples derived from feral hop locations occupied a unique space within the PCoA plot

(encapsulated by overlapping 95% confidence ellipses), overlapping slightly with Eastern

European samples from the Czech Republic and Slovenia. Conversely, the commercially- derived Northeastern US and Midwestern US P. macularis clustered in a space that overlapped with the commercial PNW US P. macularis samples, with UK-derived samples grouping nearby.

This same phenomenon was reported by (Gent et al. 2020), albeit with a much smaller sample size. The subsequent PERMANOVA and pair-wise comparisons of cumulative passport

phenotype designations confirmed these same clustering patterns, providing strong evidence for the AmpSeq markers generating local haplotypes with biological relevance (Table 2).

The AmpSeq SNP marker set was also able to differentiate P. macularis samples based on V6-virulence phenotype. The subset of P. macularis samples that had a previously confirmed V6-phenotype based on a compatible infection result on the differential cultivar

‘Nugget’ clustered very tightly with P. macularis samples from the PNW US. This is to be expected since V6-virulent isolates have not been reported outside of the region

(Wolfenbarger et al. 2016). Multiple transcriptome variants are associated with V6-virulence in the PNW population of P. macularis (Gent et al. 2020). However, not all of these variants reliably differentiate isolates that lack or possess V6-virulence (Block et al. 2020). We used a single AmpSeq SNP locus (Pm2407) that is well correlated with V6-virulence. The AmpSeq haplotype data for this specific locus (Table 3) demonstrates the good performance of this marker in properly differentiating wild-type and V6-virulent isolates of P. macularis. As other markers associated with virulence are identified, these can easily be incorporated into the

AmpSeq framework, providing a more encompassing profile of P. macularis population structure and phenotypic information important for management. Specifically, it would be of great benefit to identify a locus or set of loci that associate with ‘Cascade-adapted’ isolates of

P. macularis isolates (Gent et al. 2017) to allow for complete genotyping of all known uniquely virulent P. macularis races in the U.S. in a single, high-throughput assay.

One interesting aspect of the AmpSeq haplotype data output from this P. macularis population was the high number of loci for which samples returned a heterozygous haplotype.

Like all other powdery mildew fungi, P. macularis is taxonomically categorized as an

ascomycete fungus, which are thought to be universally haploid organisms (Braun 2002; Braun and Cook 2012). Four ways that a marker locus could be returned as heterozygous in a sample from a haploid fungus (after the dataset has been filtered for acceptable sequence read quality) are: (1) that the P. macularis colony sampled was actually an admixture of multiple genotypes overlapping in their growth; (2) that one or multiple gene duplication events have happened within the P. macularis genome, resulting in returned sequence data that actually corresponds to reads from multiple paralogous genes that have diverged slightly (Jones et al.

2014); (3) that the SNP marker primers were inadvertently targeting multiple, distinct gene loci; or (4) error within a variant caller that was originally designed to call diploids. In other powdery mildew fungi, namely Erysiphe necator and Blumeria graminis, aligned genome sequences indicate that upwards of 90% of the genome may be comprised of gene duplications and transposable element activity (Jones et al. 2014; Wicker et al. 2013).

However, because a P. macularis genome is not available, it was not possible to confidently map our sequence reads to a known genomic location, and therefore not possible to differentiate whether a locus that returned an appreciable level of heterozygosity (>10%) was due to genetic admixture, gene duplication, or non-specific amplification. As such, in the interest of being as conservative as possible and presenting a finalized AmpSeq SNP marker library that is the most likely to return informative SNP data into the future, we decided to filter out all SNP loci that exceeded a 10% heterozygosity level across the 320 P. macularis samples that survived quality filtering steps. It is worth noting that the clustering patterns output from the PCoA plots and the PERMANOVA were the same when this filtering step was not applied (Figure S1-6), indicating that the most relevant variants reside within the final set

of 54 SNP loci. This step would not be necessary in cases where a fully sequenced, aligned genome is available. But in the current case of P. macularis, and for other fungi where a sequenced genome is unavailable, we propose this approach as a reasonable, conservative framework in selecting SNP loci for which to adhere.

Figure S1-6: Principal coordinate analyses (PCoA) of the P. macularis sample library, based upon calculated Euclidian distances of the returned amplicon sequencing haplotypes of the

105 loci that remained prior to filtering loci based on heterozygosity level. Samples are grouped based on their combined metadata profile of geographic origin and hop planting type.

In order to provide an updated and high resolution classification of the P. macularis mating-type idiomorph distribution across the US and Europe, we surveyed the AmpSeq SNP marker validation sample set for mating type, as well as some additional pre-existing P. macularis DNA samples in our collection. In the process, we updated the existing P. macularis mating-type markers for use in a multiplexed real-time quantitative PCR format. Wolfenbarger et al. (2015) assayed 56 samples collected from geographies with sexually reproducing P. macularis populations. Here we expanded that sampling depth to 358 P. macularis samples from geographies with sexually reproduction populations, as well as an additional 139 P. macularis samples from the PNW US region to provide the highest resolution understanding of P. macularis mating-type distribution to date. Given that there were 139 P. macularis samples collected from within the PNW and no positive MAT1-2 detections, a one-sided 95% confidence interval yields a possible MAT1-2 idiomorph frequency of between 0 to 2.6% of the population. As such, the PNW US hop growing region still likely harbors an exclusively

MAT1-1 P. macularis population, indicating that quarantine in place to prevent the import of foreign hop plant material into the region has largely been successful. In further support of the idea that there are distinct P. macularis populations within commercial versus feral hop plantings in the Northeastern US and Midwestern US, all but one of the 166 P. macularis samples collected from commercial hop yards returned a MAT1-1 mating-type profile, while both mating types were identified in approximately a 1:1 ratio within the populations derived from feral hops (Table 4).

These data, in combination with the ordination and PERMANOVA data from the

AmpSeq SNP marker analysis, provides further support for a relatively recent introduction of

P. macularis from the PNW into commercial hop yards of the Northeastern and Midwestern

U.S. While these population structure analyses cannot discern which of the commercially derived P. macularis clusters (Northeastern US, Midwestern US, and PNW US) is the founder, the historical timing of the pathogen’s emergence across geographies suggests only one likely scenario. The Northeastern US and Midwestern US hop industries have only re-emerged within the past decade, while P. macularis arrived into the PNW US in 1996. As such, and as suggested previously (Gent et al. 2020), the most plausible scenario is a dissemination of the fungus on infected hop planting material that was originally sourced out of the PNW region, distributed to a handful of hop propagation facilities east of the Rockies, and ultimately distributed to a large portion of the new hop yards of the Northeastern US and Midwest US as their initial planting material. It is entirely possible, and to some extent, expected, that as the density of hop yards established throughout the Northeastern and Midwestern US increases, effectively shrinking the “natural bridge” that the pathogen must travel from one hop planting to another, P. macularis introduction events will occur via pathogen spread from feral hop plantings into commercial yards, or between yards via long-distance dispersal (Gent et al. 2019). If this happens, we hypothesize that future analyses utilizing this AmpSeq marker set would reflect such a transition in population structure. We also expect a drastic shift in mating type ratios would occur within commercial hop yards, resulting in a similar 1:1 ratio to that currently observed in feral hop plantings. Presently, our data re-emphasizes the pressing need for hop growers to thoroughly inspect hop planting material, as well as the first shoots that emerge in spring, in order to limit introduction of the pathogen into new plantings in the near and long-term.

Moving forward, the AmpSeq SNP marker library and analysis pipeline, as well as the updated qPCR mating-type markers are tools available to track some of the most pressing threats to sustainable hop production due to P. macularis. This study has validated the baseline structure of the US P. macularis population to which future AmpSeq runs can compare to in order to discern shifts in structure. The arrival of MAT1-2 P. macularis individuals into the PNW US would likely mean that the population would no longer overwinter solely through asexual means (Gent et al. 2019; Gent et al. 2018). The escape of

V6-virulent P. macularis strains out of the PNW US and into other US hop production systems would threaten the viability of some select hop cultivars, especially ‘Nugget’, ‘TriplePearl’,

‘Triumph’, and others that appear to have powdery mildew resistance based on R6. In future years, any new clustering patterns returned by the AmpSeq SNP markers that were previously unique to a specific sampling geography or cultivation type could suggest a new mode of pathogen spread or a local introduction event that could be targeted for control. The more we transition to forms of proactive monitoring of P. macularis population structure, the better we will be able to manage the pathogen as a whole.

AmpSeq has been demonstrated to be functional in genotyping other types of sequence variation (INDELS, SSRs, etc.) in breeding applications (Yang et al. 2016; Fresnedo-

Ramírez et al. 2017). Here, AmpSeq successfully genotyped SNP variants of the obligately biotrophic plant pathogen, P. macularis. In synthesizing these two findings, we expect additional variant types would perform equally well in genotyping plant pathogenic fungi via

AmpSeq. For example, one could likely create a single AmpSeq marker library that combines a set of SSR markers differentiating a pathogen population by clonal lineages reported from

one study with a set of SNP markers that correspond to pathogen QoI and SDHI fungicide resistance reported in a separate study into a single genotyping assay. We also expect other sequencing technologies where trait-marker data exists (GBS and WGS) to function as the source dataset for calling genetic variants to create AmpSeq marker libraries. The pathosystems that may benefit most from an approach such as this are those that lack extensive genomic resources, where culturing and nucleic acid extraction is labor-intensive and difficult to scale. This approach may therefore be especially useful in genotyping populations of obligately biotrophic pathogens.

Acknowledgements: The authors would like to thank Mary Jean Welser for her technical support and Elisabeth Seigner, Sebastjan Radišek, Peter Glendinning, and Josef Patzak for their collaboration in the collection of Podosphaera macularis samples.

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A comprehensive characterization of ecological and epidemiological factors driving perennation of Podosphaera macularis chasmothecia

Authors: William A. Weldon1, Michelle E. Marks2, Amanda J. Gevens2, Kimberly D’Arcangelo3, Lina M.

Quesada-Ocampo3, Stephen Parry4, David H. Gent5, Lance E. Cadle-Davidson1,6, and David M.

Gadoury1.

*Collaborator Affiliations:

1Section of Plant Pathology and Plant-Microbe Biology, Cornell AgriTech, Cornell University, Geneva,

NY 14456

2Department of Plant Pathology, University of Wisconsin – Madison, Madison, WI 53706

3Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695

4Cornell Statistical Consulting Unit, Cornell University, Ithaca, NY 14850

5USDA-ARS Forage Seed and Cereal Research Unit, Corvallis, OR 97331

6USDA-ARS Grape Genetics Research Unit, Geneva, NY 14456

Abstract Hop powdery mildew, caused by the ascomycete fungus Podosphaera macularis is a consistent threat to sustainable production. The pathogen utilizes two reproductive strategies for overwintering and perennation: (i) asexual vegetative hyphae on dormant hop buds that emerge the following season as hop shoots fully parasitized by the fungus; or (ii) sexual ascocarps (chasmothecia) that harbor four to eight infectious ascospores that presumably mature over winter and are released on to newly emerged hop tissue the following spring.

Little work has been done to understand the biological and epidemiological factors driving the

winter maturation of P. macularis chasmothecia and subsequent spring release of ascospores within the context of North American hop production systems. Herein, this is the first study to confirm that P. macularis chasmothecia, in the absence of any asexual P. macularis growth forms, is a highly viable overwintering source that is capable of causing early season infection at levels between 31 – 78% incidence. Additionally, two distinct epidemiological models were defined and validated that describe (i) P. macularis chasmothecial maturation, independent of overwintering geography; and (ii) the risk of P. macularis ascospore release in response to varying durations of rain and temperature. P. macularis ascospores were confirmed to be infectious to some degree at all temperatures typically observed during springtime rainfall events. Lastly, we found no evidence to support the possibility for introduction of P. macularis into new geographies via chasmothecia-infested hop seed, but ascospores did remain viable and infectious at very low, non-zero levels when exposed to temperatures at or below 51.7C, which resides within the lower-end of the spectrum of post-harvest hop drying temperatures.

Overall, these tools and insights into pathogen ecology will directly improve early season management of hop powdery mildew in the presence of P. macularis chasmothecia.

Introduction For plant pathogens that cause disease in temperate climates, and therefore face months of freezing temperatures and limited host availability, the ability to successfully overwinter and perennate into the following year is crucial to the long-term fitness of the organism (Moyer et al. 2014; Machardy et al. 2001; Suffert et al. 2011). Fungal pathogens with saprophytic and/or necrotrophic phases such as Fusarium spp.., Verticillium spp., and

Aspergillus spp. often overwinter as soil saprophytes or embedded within necrosed plant

debris (McKeen and Thorpe 1981; Gent et al. 2012; Manzo and Claflin 1984; Wickow, et al.

1993). For obligate biotrophs such as the powdery and downy mildews, which require living host tissue in order to actively grow and reproduce, overwintering can be on, in, or apart from living host tissue depending on the pathosystem (Gadoury et al. 2010; Pearson and Gadoury

1987; Asalf et al. 2013; Rossi et al. 2010; Pirondi et al. 2015; Kennelly et al. 2007; Coley-Smith

1962).

Powdery mildew of hop, caused by Podosphaera macularis, is documented to overwinter in one of two ways: (i) mycelial (asexual) infection of hop bud tissue that resides just below the soil surface; enters winter dormancy; and re-emerges the following spring as an infected flag shoot (Figure 2-1d-e) (Blodgett 1915; Gent et al. 2018; Gent et al. 2009), or (ii) the ascigerous growth stage, in which sexually produced ascocarps (chasmothecia) house infectious ascospores released during the early spring (Figure 2-1a-c) (Liyanage and Royle

1976). P. macularis is a heterothallic organism, meaning that for chasmothecia formation to occur, both mating types of the fungus must be present and overlap in their growth

(Wolfenbarger et al. 2015; Braun 2002; Braun and Cook 2012). The Pacific Northwest US, where over 98% of US commercial hop production resides, has only documented the presence of the MAT1-1 mating type (Wolfenbarger et al. 2015; Weldon et al. 2020). As such, the ascigerous stage of the pathogen has not been reported in the region. Accordingly, the majority of current research in regard to early season management of P. macularis in the US has focused on predicting and managing primary inoculum densities resulting from asexual hyphal growth on hop flagshoots, which are documented to occur in less than 1% of all emerging hop plants during the spring (Gent et al. 2019, 2016, 2012; Probst et al. 2016; Peetz

et al. 2009). However, in almost all hop growing regions east of the Rocky Mountains, both P. macularis mating types have been documented in approximately a 1:1 distribution ratio on wild and feral plants and chasmothecial formation has been observed at all locations where both mating types were found (Wolfenbarger et al. 2015; Weldon et al. 2020).

Figure 2-1: Sexually produced fruiting bodies, ascospores, and conidia of Podosphaera macularis. (a) Scanning electron micrograph of a mature chasmothecium, approximately 90

µm in diameter; (b) P. macularis colony populated with chasmothecia of varied maturation levels; (c) crush mount of a chasmothecium releasing an ellipsoid ascus that contains eight mature ascospores; (d) recently dormant hop bud emerging co-infected with P. macularis as a flag shoot; (e) hop stem infected with P. macularis, which has erected hundreds of conidiophores that bear chains of conidia.

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While hop production throughout the Midwest and Eastern US has re-emerged at a small scale in parallel with the growth of the craft brewing industry over the past decade (Hop

Growers of America 2019), research focused on characterizing the biological and epidemiological factors that drive P. macularis ascosporic infection in the early spring has been largely absent since the early 1900s, which corresponds to when the hop industry moved westward and established in the PNW US upon cessation of the National Prohibition Act via the passing of the 21st Amendment in 1933 (US Constitution Amendment XXI 1933). P. macularis chasmothecia were shown to be infectious in greenhouse conditions within the US

(Blodgett 1913; Blodgett 1915), and were later circumstantially suggested to be infectious in field conditions in the United Kingdom, with chasmothecial maturation and subsequent ascospore release occurring no earlier than April (Liyanage and Royle 1976). The timing of chasmothecia maturation within US hop production regions, as well as the impact of epidemiological factors such as temperature, duration of wetting events, duration and low temperature extremes during the winter, and early spring degree day accumulation have all yet to be explored in depth for P. macularis ascocarps, yet all are factors that have been demonstrated to be predictive of early season disease pressure within other fungal plant pathosystems (Gadoury 1986; Asalf et al. 2013; Gadoury 1990; Gubler and Leavitt 1994; Cao et al. 2015; Rossi et al. 2008).

As such, this research set out to comprehensively characterize critical epidemiological factors associated with P. macularis ascocarp perennation. Herein, these projects are described in a chronological order with respect to the natural ascigerous overwintering process, starting with chasmothecial formation in the autumn and progressing toward spring

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ascospore release, infection, and establishment of a primary inoculum population. Our specific objectives included, in biological order; (i) characterizing the role of the timing of chasmothecial formation in the autumn in contributing to springtime ascosporic infection events; (ii) quantifying the propensity of chasmothecia to remain adhered to hop leaf foliage while overwintering; (iii) modeling early spring maturation of P. macularis chasmothecia; (iv) modeling of the interaction between temperature and duration of wetting events in promoting ascospore release, (v) quantifying the infection success of released P. macularis ascospores across the same temperature range; and lastly characterizing the risk of viable P. macularis chasmothecia being distributed into new hop production regions via either transport of (vi) dried hop cone material and (vii) hop seed.

Methods

Overwintering P. macularis-infested hop leaf disks for use in ascospore release assays.

Podosphaera macularis chasmothecia infested Humulus japonicus (syn. H. scandens) leaf tissue was collected at a field location in Geneva, NY during the autumn of each growing season. This leaf tissue was cut into hundreds of 1cm diameter leaf disks, ensuring there was a surplus of leaf disks available for all assays to be conducted during the spring of the following year. Each disk was individually placed into a 90mm diameter filter paper that had been twice- folded into a conical envelope. The leaf-disk envelopes were then placed into mesh window screen bags, 100 per bag maximum. These mesh bags were placed outside in Geneva, NY (or another overwintering location for select specific experiments) at ground-level to overwinter naturally until they were used in various experiments during the following spring and summer,

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as detailed below. A cage constructed from chicken wire was placed over the mesh bags to prevent tampering by any small animals.

Podosphaera macularis ascospore release assay.

A 90mm glass Petri dish was used for each individual assay. Two straws were secured in parallel at the base of a glass Petri dish and a microscope slide was rested securely on top of the straws using double-sided tape. A damp 90mm diameter filter paper disk was secured to the inner lid of the Petri-dish. On this piece of filter paper, a 1cm diameter H. japonicus leaf disk bearing mature chasmothecia, generated as described above, was placed slightly off of center. The leaf disk was misted with fine droplets of distilled water, such that the disk was damp but there was no obvious pooling of water. The lid of the dish was then closed, rotated such that the leaf disk was suspended directly over the glass microscope slide, and sealed with parafilm. The leaf disk was left to incubate for a predetermined amount of time and at a specific temperature, which varied by experiment. After the incubation period, the microscope slide was allowed to air dry, and then a 0.05% Cotton Blue in lactoglycerol staining solution was used to mount a 22x40mm glass cover slip across the slide. The slide was visualized via brightfield microscopy at 200x to quantify the number of ascospores discharged onto the glass slide surface.

Early season disease incidence and severity resulting from P. macularis ascosporic infection events.

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Design and execution of the field experiment. The objective of this experiment was to quantify the initial incidence and severity of disease exclusively associated with P. macularis ascosporic infection on H. lupulus during the early spring growing season. To accomplish this, we installed an irrigated vertical, two wire trellis (1.5m high) plot where a pot-in-pot nursery system was adopted to house potted ‘Symphony’ cultivar hop plants in a square grid pattern with 5m spacing (Figure S2-1). All hop plants were started via rooted vegetative cuttings of the third or fourth internodes and up-potted progressively to ultimately a 3.1 gallon pot destined for the field. Using a completely randomized experimental design, ninety potted hop plants were randomly assigned to one of three treatment groups, 30 plants in each: (1) an “early chasmothecia” cohort, (2) a “late chasmothecia” cohort, or (3) a non-treated control group.

All 90 potted hop plants were moved to a secondary location free of powdery mildew in late-

June and only moved into the experimental yard when appropriate. The “early chasmothecia” cohort plants were moved into the experimental yard in late August, when approximately 5g of dried hop leaf litter bearing P. macularis chasmothecia was seeded at the base of each pot.

This group represented hop leaf litter bearing chasmothecia that may fall to the ground during the late-August harvest process. In late October the 30 “late chasmothecia” cohort hop plants were moved to the experimental hop yard and seeded with hop leaf litter, as described above.

This group represented hop leaf litter bearing chasmothecia that may fall to the ground during leaf senescence in response to the first freeze events of the late autumn. The non-treated control plants were also moved to the experimental yard at this time but did not receive any added hop leaf litter.

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The chasmothecia-infested hop leaf litter was generated on two month old potted

‘Symphony’ hop plants that were maintained in a walk-in growth chamber at 19C and 16 h daylengths in Geneva, NY. During the first week of July, these plants were spray inoculated with a P. macularis conidial suspension (1X105 conidia/ mL) twice. The conidial suspension consisted of two P. macularis isolates (NY005 and NY002) that were of opposite mating type

(MAT1-1-1 and MAT1-2-1, respectively, Weldon et al. (2020)), which were added in equal density to the suspension. These plants were incubated for approximately 40 days, and then all leaf tissue that was infected with P. macularis and showed signs of chasmothecial formation was placed into large paper bags and allowed to desiccate at ambient room temperature

(~21C) for 7 days. This desiccation ensured that all vegetative, asexual growth forms of P. macularis (hyphae and conidia) were non-viable and that chasmothecia were the only living source of the pathogen. All potted plants in the experimental hop yard were allowed to overwinter naturally. Starting at bud break during the ensuing spring season, disease incidence and severity measurements were taken on a weekly basis. For a given potted hop plant, disease incidence was defined as presence/absence of P. macularis, while disease severity was defined as the estimated percentage of the green leaf tissue with powdery mildew. Each potted hop plant was considered an individual sampling unit. Data collection ceased when powdery mildew was observed on the non-treated control plants.

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Figure S2-1: Field plot layouts of (left) the clean hop storage site and (center, right) the experimental hop yard where potted hop plants of the early and late chasmothecia cohorts were populated with dried hop leaf tissue infested with Podosphaera macularis chasmothecia.

The chasmothecia-infested hop leaf tissue was incorporated into the existing hop leaf tissue from above, with the mesh screening ensuring that the leaf material would stay confined within the given pot over the winter and subsequent early spring. In the experimental hop yard, the potted hops were placed in a secondary structural plastic pot of slightly larger volume, similar to as is done in tree nurseries. This allowed for the potted hop plants to be flush in the ground while overwintering, for adequate drainage, and allowed for the potted hop plants to easily be removed and replaced with new, clean hop plants for the two repeated seasons of the trial.

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Data Analysis. Means of each response variable (disease incidence and severity) were taken across all plants that survived the winter and produced viable shoots. Disease incidence was calculated as the number of potted hop plants with powdery mildew symptoms divided by the total number of potted hop plants that had produced shoot growth. Disease severity was calculated as the mean estimated proportion of green hop tissue covered with powdery mildew. Year was a significant effect in predicting disease incidence, and as such the two seasons were analyzed separately. A logistic regression models was fit to disease incidence data due to the binomial nature of this data using glm(link = “logit”) in R (version 3.6.3) and subjected to a likelihood ratio test to assess the significance of chasmothecia cohort in predicting disease incidence the following spring. A general linear regression model (GLM) was fit to the severity data and subjected to analysis of variance to determine the effect of the timing of chasmothecial introduction as leaf litter during the autumn on subsequent disease severity observed the ensuing spring. This model was then also subjected to a Tukey’s Honest

Significant Differences (HSD) test via HSD.test() in R (version 3.6.3) to identify any treatment group means that were significantly different from one another.

Adherence of Podosphaera macularis chasmothecia to the leaf surface:

Experimental design. In September and October of 2018, chasmothecia-infested leaves from dichotomous plant species including hop (H. lupulus), grape (Vitis vinifera), lilac (Syringa vulgaris), and sycamore (Platanus occidentalis) were collected and cut into 1cm leaf disks.

Additionally, P. macularis chasmothecia were generated on ‘Symphony’ hop leaves in detached-leaf culture using a fine-tipped paint brush for inoculation of P. macularis isolates

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NY002 and NY005, which are mating types MAT1-2-1 and MAT1-1-1, respectively (Weldon et al. 2020). These leaves were kept in culture for approximately 30 days, and during that time the P. macularis colonies produced chasmothecia that progressed to an externally mature form with a brown-black ascus approximately 90µm in diameter and dozens of attachment hyphae, as described in Wolfenbarger et al 2015. This experiment was set up in a completely randomized design, with leaf disk as the individual sampling unit. Using a dissecting microscope at a magnification of 25x, the number of chasmothecia per leaf disk was quantified for three leaf disks per host leaf type. Each leaf disk was then individually vigorously shaken for one minute in 200mL of distilled water within a 1L narrow mouth glass Erlenmeyer flask, as described in Gadoury et al. (2010). The leaf disk was removed from the water suspension, gently dried, and the number of chasmothecia remaining adhered to the leaf surface were counted. For each leaf disk, the proportion of chasmothecia retained value was quantified by dividing the numerical count of chasmothecia adhered to the leaf surface after physical agitation by the numerical count of chasmothecia adhered to the leaf surface after physical agitation. The entire experiment was repeated three independent times.

Data analysis. The means of chasmothecial adherence to the leaf surface of various dichotomous plant hosts were subjected to analysis of variance to determine the effect of host/ powdery mildew species combination on the propensity of the given chasmothecia structure to remain firmly adhered to its given host leaf surface. Host was considered a fixed effect within the GLM model. The response variable was also subjected to a Tukey’s HSD test

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within R (version3.6.3) via HSD.test() to identify treatment group means that were significantly different from one another.

Seasonal maturation of P. macularis chasmothecia across geography:

Experimental design. In early October of each season, Humulus japonicus leaves heavily infested with P. macularis chasmothecia were collected, cut in to 1cm leaf disks, and placed into filter paper envelopes as described above. Sets of 80 leaf disk envelopes were then placed into large mesh bags fashioned from window screening, sealed, and sent via ground shipping services to the University of Wisconsin – Madison (Madison, WI) and North Carolina State

University (Raleigh, NC). A third set of 80 leaf disk envelopes was kept at Cornell AgriTech

(Geneva, NY). These three locations were designed to represent a range of winter durations and low temperature extremes. During the winters of 2018 – 2020, Wisconsin had a January mean low temperature of -10.1C, while in New York the January mean low temperature was

-7.6C, and in North Carolina it was 0.2C. At each location in late October, the leaf disks were placed outside at ground level in a research hop yard to overwinter. A cage fashioned from chicken wire was placed overtop the leaf disks to protect the samples over winter. Starting in late December to early January, three leaf disks were collected at random and subjected to the ascospore release assay described above, recording the total number of ascospores discharged onto the microscope slide. As such, individual leaf disk was the sampling unit. At this same timing, all leaf disk envelopes were removed from the mesh envelope and stapled to a wooden board just above ground level, such that samples were easily accessible, safe from rodent and earthworm activity, and still exposed to the weather. The ascospore release

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assay was set up at ambient room temperature (~21C), allowed to incubate overnight for approximately 18 h until ascospore release counts were taken the following morning. Samples were collected weekly at each location until three consecutive weeks of ascospore release counts of “zero” were recorded, at which the sampling interval was adjusted to every other week until the counts at all locations had reached three consecutive weeks of “zero” ascospore release counts. The project was conducted in New York in 2018, 2019, and 2020 and in North Carolina and Wisconsin in 2019 and 2020.

Data analysis and model development. For each location, the weekly ascospore release counts were used to calculate the proportion of ascospores released to date, as compared to the cumulative total ascospores counted over the course of the entire season. This value was calculated by dividing the summed count of ascospores released up to a given sampling week by the total number of ascospores counted at a given location for the entirety of the given season. This cumulative ascospore proportion was plotted over Gregorian calendar date, providing seasonal chasmothecia maturation curves over time across NY, NC, and WI for 2018,

2019, and 2020. We then converted the Gregorian calendar dates to thermal time accumulation, obtained via the Cornell Network for Environment and Weather Applications

(NEWA), using the Baskerville-Emin degree day formula (sine-wave algorithm) and a biofix starting at the first observed mature ascospore release event for the given sampling location.

This data transformation was calculated three separate times for each location, testing degree day base temperatures of 0C, 5C, and 10C, which corresponded to the freezing point of water, the approximate minimum temperature for hop tissue growth, and the minimum temperature

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for P. macularis hyphal growth, respectively. The cumulative ascospore release response variable was fit to three separate GLM models which utilized degree day accumulation inputs with base temperatures of 0C, 5C, or 10C, respectively, as the fixed effect. Both year and location were not significant when included as fixed effects within any model, and as such, the two to three years of data across the three locations were combined for all further regression analyses. Ascospore release data was filtered for only values that fell between the range of

0.05 to 0.95 proportion of total ascospore release. These three GLMs were compared to one another via analysis of variance, which indicated that both the 0C and 5C GLM models were a significantly better fit to the dataset than the 10C GLM model (P < 0.0001, 0.0001, respectively). The 0C GLM model was ultimately selected as the best fit to the dataset, as discussed below, and as such was the focus of all later model selection and validation analyses.

The 0C base degree day derived dataset was then fit to two other putative models within R

(version 3.6.3) in addition to the GLM regression. Model 1 (GLM) was y = 0.0032g + 0.063, where y is the cumulative ascospore release proportion and g is degree day). A GLM regression was also fit to a logit transformation of the ascospore release proportion data, defined as:

푦 푀표푑푒푙 2: log ( ) = 훽 푔 − 훽 , 1 − 푦 푔 푖푛푡푒푟푐푒푝푡 where y is the cumulative ascospore release proportion, g is degree day, and each  is an estimate of each given fixed effect. Lastly, a Beta regression (Ferrari and Cribari-Neto 2004), via betareg() (Cribari-Neto and Zeileis 2010), was fit to the ascospore release proportion data, defined as:

Γ(휙) 푀표푑푒푙 3: 푓(푦, 휇, 휙) = 푦휇휙−1(1 − 푦)(1−휇)휙−1, 0 < 푦 < 1, Γ(휇휙)Γ((1 − 휇)휙)

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where  is the variate mean and  is a precision parameter. The adjusted correlation coefficient (r2) and the root mean square error (RMSE) were calculated for each model in comparison to the original input dataset via the predict() and cor() functions in R (version

3.6.3).

As an additional step to compare the performances of the three putative models describing ascospore release in response to degree day accumulation, an internal validation analysis was performed in 10 independent iterations, each using a randomly selected subset of the data (Steyerberg et al. 2001). Within each iteration, 15% of the original ascospore release dataset was selected at random held out during parameter estimation. The three regression models were then fit to the remaining 85% of the original dataset. Then, for each model, predicted ascospore release response values were calculated for the same degree day predictor values that comprised the validation dataset, such that r2 and RMSE values between the model predicted ascospore release values and those of the internal validation dataset could then be calculated. A cumulative mean r2 and RMSE value of each model was then calculated to summarize average model performance across the ten internal validation iterations.

The role of temperature and duration of leaf wetting in driving P. macularis ascospore release.

Experimental design. The biological objective of the experiment was to track ascospore release over a 24 h timeframe across four different temperatures: 5C, 10C, 15C, and 20C and use these values to generate a predictive model for ascospore release based on the fixed effects of

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temperature and duration of leaf wetting, similar to as described in Gadoury 1990. As such,

H. japonicus leaves bearing P. macularis chasmothecia were collected, cut into leaf discs, and overwintered as described above. In mid-April, once the chasmothecia population was mature but before it had been completely exhausted, a subset of 100 leaf disk envelopes were brought into a 4C growth chamber and stored until their prompt use in ascospore release assays. The glass Petri dish ascospore release assay described above was used, with the following modifications. Ascospore release assays were incubated for a 24 period at either 5C,

10C, 15C, or 20C. Within each assay, ascospore release counts were taken at 1hr, 3hr, 6hr,

12hr, and 24hr by exchanging the microscope slide in the bottom of the Petri dish. After the

24 h incubation at a given temperature, all disks were then incubated at 20C for an additional

24 h and a final ascospore release count was taken. This additional incubation was done to account for any ascospores that may have not released in response to the given treatment temperature but were still capable of release at a very suitable temperature, such as 20C.

Within each experimental run, each temperature group had 6 replications and the entire experiment was repeated four times: twice with 2018-generated chasmothecia and twice with

2019-generated chasmothecia. As a way to account for any variation in ascospore release between leaf disks, each leaf disk was cut in half and assigned randomly to two ascospore release assays of varying temperature. These data were used as the training dataset for model generation. A validation dataset of ascospore release values was then developed for model validation by randomly pairing temperatures between 5-20C and leaf wetting durations between 0-24h to create 26 different treatment groups, two replications within each group, and conducting an ascospore release assay for each. Cumulative ascospore release was

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calculated as the total number of counted ascospores up to a given timepoint divided by the total number of ascospores released during the 24 h time course assay plus any ascospores counted during the subsequent 24 h incubation at 20C.

Data analysis and model development. Two different regression models were fit to the training dataset; (1) a generalized linear mixed model (GLMM) and (2) a modified form of the

Weibull function (WF), which is a nonlinear regression model as described in Arauz et al. 2010.

Unless stated otherwise, italicized lower- and uppercase letters represent fixed effects and parameters, respectively.

The GLMM regression equation was of the form:

2 푀표푑푒푙 4: 푓(푑, 푡) = 훽푡푡 + 훽푑푑 − 훽푑^2푑 − 훽푑푡푑푡 − 훽푖푛푡푒푟푐푒푝푡 , where the fixed effects d and t are equal to duration of leaf wetting (h) and temperature (C), respectively, and each  is an estimate of each given fixed effect. Leaf disk and the interaction of leaf disk by temperature treatment were included as random effects within the GLMM. The regression was conducted via the lmer() function in R. Both t and d were treated as continuous variables. Analysis of variance was calculated to assess for the effects of temperature and duration of leaf wetness on the release of P. macularis ascospores.

The modified WF, calculated via the nls() function in R was of the form:

(푡−퐹)퐺 푀표푑푒푙 5: 푓(푑, 푡) = [1 − 푒푥푝{−(퐵 × 푑)2}]/푐표푠 ℎ [ ], 2 where fixed effects d and t are the same as above, and B, F, and G are parameter estimates.

Outlier analyses were performed by calculating Cooks Distance, cooks.distance() within R, and plotting boxplots of observed ascospore release values, grouped by both timepoint and

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temperature treatment via ggplot2() (Wickham 2016). Histograms of sample residual values were plotted for both models via resid() to confirm a largely normal distribution of the input values, centered around zero. Performance of these two models was then compared in two phases. The first comparison was between the model predicted ascospore release values versus the original input ascospore release values for which the models were fit. The second was a comparison between observed ascospore release values of an independent validation dataset versus the model predicted ascospore release values for the same 26 temperature and duration combinations of the validation dataset. Adjusted correlation coefficients and

RMSE values were calculated with R for both the training and validation phases as metrics to compare model performance.

Germination and colony development of P. macularis ascospores across a range of temperatures.

Experimental design. The objective of this experiment was to quantify the effect of various low temperatures on ascospore germination and initial growth during the first 48 h after discharge (19C). Twenty-four hop leaves, cv. ‘Symphony’ were collected, surface sterilized, and placed into a detached-leaf double-Petri dish culture, as described above. Six leaves were assigned to each of the three experimental groups, or the non-treated control group. In all cases, a 1cm P. macularis chasmothecia-infested hop leaf disk was mounted to a damp circular

(90mm diameter) piece of filter paper, which was placed on the inner lid of the petri-dish, such that when the lid was closed, the leaf disk was suspended above the detached hop leaf, as described above. Leaf disk was considered the individual sampling unit. Leaf disks were

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assigned to temperature treatments in a completely randomized design. The chasmothecia- infested leaf disks were incubated above the hop leaves for 48 hours at either 4C, 8C, 12C or a control temperature of 19C. After the 48 h, the filter paper and leaf disk were removed.

Within each temperature treatment group, 3 leaves were collected for microscopic analysis of ascospore growth after 48 h. The remaining 3 leaves within each treatment group were placed in the same growth chamber and incubated at 19C for the remainder of the experiment. For the first set of 3 leaves, a 15mm leaf disk was cut from the portion of the hop leaf oriented directly beneath the chasmothecia-infested leaf disk, and therefore inoculated with the highest proportion of discharged ascospores. These leaf disks were cleared of their chlorophyll content via incubation in a 3:1 EtOH: glacial acetic acid solution for at least 6 hours and then rinsed for 5 min in 25% EtOH. Any P. macularis present on the leaf disks was then stained via a 12-hour incubation in 0.5% chlorazol black E aqueous solution. After this incubation the disks were mounted on glass slides in a 25% glycerol aqueous solution and visualized via compound light microscopy at 100X – 400x magnification. Any observed P. macularis ascospores were rated for (1) germination and (2) colony formation success, which was defined as an ascospore that had germinated and produced branching hyphae. The second set of 3 detached leaves were monitored for macroscopic evidence of P. macularis colony formation over a 14-day period, recording P. macularis incidence per leaf, the number of colonies formed per leaf, and the pathogen latency period per leaf.

Data analysis. Germination rate was calculated as the count of germinated ascospores divided by the total number of ascospores counted on the leaf disk. Colony success rate was calculated

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as the total count of ascospores that had germinated and produced branching hyphae, divided by the total number of germinated ascospores. The P. macularis colony formation incidence data was analyzed in a logistic regression model due to its binomial nature via glm(link =

”logit”) in R and then subjected to a likelihood ratio test to assess significance of varying incubation temperatures in predicting colony formation by P. macularis ascospores. All other response variables (germination rate, colony formation success, number of colonies formed, and latency duration) were analyzed by fitting generalized linear regression models to the data via glm(). Analysis of variance was used to test for differences among incubation temperature treatments in predicting the given aspect of P. macularis ascospore growth. These models were also subjected to a Tukey’s Honest Significant Differences (HSD) test via HSD.test() to identify any treatment group means that were significantly different from one another.

Viability of chasmothecia after exposure to hop cone drying conditions.

Experimental design. The biological objective of this experiment was to determine if P. macularis chasmothecia could remain viable after exposure to temperatures commonly used within the hop industry for the post-harvest drying of hop cones. In October of 2018 and 2019,

P. macularis chasmothecia infested H. japonicus leaf tissue was collected from a field location in Geneva, NY. Twenty-four 2cm diameter leaf disks were cut from these leaves and assigned randomly to one of three experimental groups (51.7C, 62.8C, 76.7C) or a non-treated control group. Leaf disk was treated as the individual sampling unit. Each set of six leaf disks within a given experimental group was placed on to an approximately 20cm x 20cm sheet of thin cardboard and a 50mm diameter filter paper disk was pinned overtop each individual leaf disk

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to prevent the disk from shriveling up during heat exposure. The cardboard sheet bearing the leaf disks was then placed in a hybridization oven set to the appropriate temperature for the given treatment group, and the disks were incubated for one hour. Fifteen minutes prior to introducing the leaf disks into the oven, a small weigh boat was filled with distilled water and placed inside to bring the relative humidity level inside the oven above 95% relative humidity, as evidenced by the formation of water droplets on the oven door. This was done to reflect the high humidity conditions that are typical when hops are dried on a commercial scale. The non-treated control group leaf disks were not exposed to a one-hour heating event. All leaf disks were then individually placed into 90mm conical filter paper envelopes and overwintered in Geneva, NY as described above. In mid-April, the samples were brought into the lab for assessment of ascospore viability. Within each treatment group, 3 leaf disks were set up in a glass Petri dish ascospore release assay, as described above, and the raw number of ascospores discharged during the overnight assay were recorded. The remaining 3 leaf disks of each treatment group were set up in a detached leaf ascospore release assay, as described above, and the incidence and number of P. macularis colonies formed on the hop leaves over a 14-day period was recorded. The experiment was conducted independently three times.

Data analysis. The means of each response variable were subjected to analysis of variance to determine the effect of drying temperature on the survival of P. macularis chasmothecia and discharge of viable ascospores. The three independent experimental replications were not returned as a significant effect in an analysis of variance in regard to P. macularis colony formation success but were significant in regard to the mean ascospore release counts

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observed. As such, the data was only pooled in the case of the colony formation success analyses. Colony formation success was calculated as the number of leaves where P. macularis growth was observed divided by the total number of leaves tested. A logistic regression model was fit to the colony formation incidence data and analyzed similar to the binomial datasets described above. The ascospore release counts and colony formation counts were also subjected to a Tukey’s HSD test via HSD.test() to identify any treatment group means that were significantly different from one another.

Podosphaera macularis chasmothecial viability on hop seed.

Experimental design. The objective of this study was to assess whether P. macularis chasmothecia could perennate on H. lupulus seed via ascosporic infection of germinated seedlings. In October of 2017, two populations of wild H. lupulus seed infested with P. macularis chasmothecia were collected near St. Paul, MN and sent to Geneva, NY for assessment of viability and infection potential. Upon receipt, the two seed populations, “Pop

1” and “Pop 2” were visually inspected under a dissecting microscope at 25x magnification for incidence of chasmothecial infestation (n=100), mean number of chasmothecia observed per infested seed, and mean severity, calculated as an estimated proportion of the area of the seed coat occupied by P. macularis chasmothecia. Twenty-four seeds from “Pop 2”, the population with the greatest chasmothecia infestation severity, were categorized into three groups based on the severity of infestation and subjected to the same water-based physical agitation assay described above. Twenty three and 28 chasmothecia were manually scraped from the surface of seeds from Pop 1 and Pop 2, respectively, mounted on glass slides in a

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0.05% Tween 20 aqueous solution, and rated for chasmothecia dehiscence and ascosporic viability, the latter of which was conducted via epifluorescence microscopy, using 0.1% fluorescein diacetate as the viability stain. The remaining seed collections were then set up for stratification at 4C and no light exposure for 9 weeks. Seeds were not surface sterilized prior to stratification in order to avoid negatively impacting P. macularis chasmothecia viability. The two seed populations were distributed across 90mm Petri dishes that were lined with five layers of wetted germination filter paper disks at their base, allocating approximately

200 seeds per dish. After 2, 5 and 8 weeks of stratification, the Petri-dish trays were temporarily removed from the 4C incubator, the lids were opened for 2 min to provide a brief exposure to the air, and the filter paper disks were re-wetted before being returned back into the 4C incubator. After the 9-week stratification was completed, the seeds were set up in a greenhouse germination experiment. The two seed populations (n = 3300 seeds in total) were tracked separately for germination and the incidence of powdery mildew. In addition to these two seed groups, two control groups consisting of seed from each population were generated.

The control groups consisted of H. lupulus seed that was visually confirmed to be free of any

P. macularis chasmothecia. The control seed was also agitated for 5 minutes in a 1:2:5 solution of 95% EtOH: 6% NaOCl (aq): dH2O solution, followed by two 5 minute rinses in dH2O. For the greenhouse assay, each seed population was planted in its entirety into 50-cell seedling trays, with three seeds per well for Pop 1 and two seeds per well for Pop 2. Over a 6-week timeframe,

H. lupulus seedling germination and the incidence of plants with powdery mildew were recorded. All germinated H. lupulus seedlings were removed from the experiment after 4

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weeks of growth by the given seedling to limit the amount of plant material present that was at risk to secondary infection from any exogenous sources P. macularis colonies.

Data Analysis. There was no data analysis performed because all of the data returned zero disease incidence across the two populations.

Results

Early season disease incidence and severity resulting from P. macularis ascosporic infection events. In both the 2019 and 2020 seasons, the late chasmothecia cohort resulted in powdery mildew incidence of 0.78 and 0.32, respectively, which were significantly greater (P < 0.0001,

0.0001, respectively) than that of both the early chasmothecia and non-treated control cohorts (Table 2-1). Although not significantly different from the NTC group, the early chasmothecia cohort did produce a non-zero mean disease incidence value of 0.064 across the two seasons. Both the early and late chasmothecia cohorts had non-zero disease severity ratings across both years, but neither was significantly different from a zero severity, as observed in the non-treated control group (P = 0.3004).

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Table 2-1: Disease incidence and severity ratings by week for the Podosphaera macularis chasmothecia overwintering field study in 2019 and 2020. Within each cell, the fraction of diseased hop plants out of the total number of hop plants within the treatment group that had produced shoots thus far (maximum of 30) is indicated in parentheses, followed by the disease incidence value and the mean disease severity rating of the diseased hop plants, both as proportions. At the last rating date, disease incidence but not disease severity of the “Late

Cohort” group were significantly different than those of the “Early Cohort” and non-treated control groups (P < 0.0001 and 0.3004, respectively).

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Adherence of Podosphaera macularis chasmothecia to the leaf surface: Analysis of variance indicated that the host-powdery mildew combination was a significant predictor of chasmothecial adherence to the leaf surface (Table S2-1). A Tukey’s Honest Significant

Differences (HSD) test indicated that P. macularis chasmothecia on hop, from both lab- generated and field-collected chasmothecia, adhered to the leaf surface to a significantly greater degree than the other perennial hosts tested (P < 0.0001), with a mean chasmothecial retention proportion of 0.963 – 0.974 (Table 2-2).

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Table 2-2: A comparison of the steadfastness of chasmothecial adherence to host leaves across several powdery mildew species. Proportion of chasmothecia retained was determined

퐵−퐴 by the formula 푃푟표푝표푟푡𝑖표푛 푅푒푡푎𝑖푛푒푑 = 1 − ( ), where A = the numerical count of 퐵 chasmothecia remaining adhered to the hop leaf disk surface after having been vigorously shaken in 200mL of water for 60 seconds and B = the numerical count of chasmothecia adhered to the hop leaf disk surface prior to the vigorous shaking treatment. Parenthetical values indicate standard error. The HSD Grouping column denotes significantly different (P <

0.05) chasmothecial retention proportions (across all three experimental replications), denoted by the pathogen host, as determined with a Tukey’s honest significant difference

(HSD) test for multiple comparisons.

Host Proportion of Chasmothecia Retained HSD Grouping Hop - Cultured 0.974 (0.024) a Hop - Field Collected 0.963 (0.017) a Grape 0.457 (0.059) c Lilac 0.366 (0.064) c Sycamore 0.612 (0.109) b

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Table S2-1. Analysis of variance for the propensity of various powdery mildew species chasmothecia to remain adhered to the given host leaf surface under vigorous physical disturbance.

Source df Sum Sq P > F Exp Rep (e) 2 0.0053 0.89 Host (h) 4 7.1085 2.20E-16 e X h 7 1.3645 4.73E-08

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Seasonal maturation of Podosphaera macularis chasmothecia across geography: When plotted by Gregorian calendar day, the seasonal progression of P. macularis chasmothecia maturation differed by geographic sampling location (Figure 2-2a). Across the three years of this project, the Raleigh, NC overwintering location had a significantly smaller cumulative number of P. macularis ascospores released (mean = 531.5 ascospores) per season than that of both the Geneva, NY (mean = 2713 ascospores, P = 0.009) and Madison, WI (mean = 3427 ascospores, P = 0.005) locations. However, when the data was transformed to reflect weekly ascospore counts as a percentage of the cumulative ascospore release, both year and location were not significant fixed effects within any model, and as such, the three years of data across the three locations were combined for all regression analyses presented here (Table S2-2).

Degree day accumulation was a significant predictor for the cumulative maturation rate of P. macularis chasmothecia, regardless of whether degree day was calculated using a base temperature of 0C, 5C, 10C (P < 0.0001; 0.0001; 0.0001, respectively, Table 2-3). Estimates of fixed effects of the three models are also summarized in Table 2-3. Of these three GLMs, the model utilizing growing degree days of a 0C base temperature as the fixed effect best normalized chasmothecia maturation curves across geography (Figure 2-2b), also returning the largest adjusted correlation coefficient (r2) value (0.955) and smallest residual sum of squares (RSS) output (0.225) observed when compared to the training dataset (Table 2-4).

Degree day also had a slightly greater effect in predicting chasmothecial maturation within the 0C GLM model (P = 1.9X10-9), as opposed to the 5C GLM (P = 2.8X10-8). By virtue of how base temperature is factored into the degree day calculation, the original input data of the 0C

GLM also had fewer non-zero degree day observations than the base 5C GLM. These outputs

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were cumulatively used as the rationale to proceed with the 0C base temperature degree day dataset for further model optimization.

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Figure 2-2: Podosphaera macularis ascospore maturation curves in the early spring across three geographic locations; New York (NY), North Carolina (NC), and Wisconsin (WI) and three growing seasons (2018, 2019, 2020). (A) P. macularis ascospore maturation tracked by

Gregorian calendar date. (B) P. macularis ascospore maturation tracked by growing degree day accumulation (base 0C) since the date of first recorded ascospore release at the given location/ year. The y-axis reflects cumulative ascospore release as a proportion in comparison to the total number of ascospores released over the entire season at that location.

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Table 2-3: Estimates of fixed effects for three separate generalized linear model regression models fit to describe the seasonal maturation of Podosphaera macularis as defined by degree day accumulation (base 0C) since the date of first ascospore release across three sampling locations (New York, North Carolina, Wisconsin) and three years (2018, 2019, and 2020).

GLM (base temp 0C) Estimate (b) Asymptotic SE P > |z| Asymptotic CI_L Asymptotic CI_U (intercept) 0.1146 0.0516 3.49E-02 0.0009 0.2204 Degree Day 0.0030 0.0034 1.86E-09 0.0023 0.0037 GLM (base temp 5C) Estimate (b) Asymptotic SE P > |z| Asymptotic CI_L Asymptotic CI_U (intercept) 0.2045 0.0492 2.92E-04 0.1036 0.3055 Degree Day 0.0053 0.0007 2.84E-08 0.0039 0.0067 GLM (base temp 10C) Estimate (b) Asymptotic SE P > |z| Asymptotic CI_L Asymptotic CI_U (intercept) 0.2883 0.0506 4.71E-06 0.1845 0.3921 Degree Day 0.0141 0.0024 2.17E-06 0.0093 0.0189

a SE is the standard error. CIL and CIU are the lower and upper limits, respectively, of the 95% confidence interval around the fixed effect and parameter estimates. GLM = Generalized linear regression model. Logit transformation is a log odds ratio of the original dataset that was subsequently fit to a generalized linear regression model.

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Table 2-4: Comparison of generalized linear model (GLM) regressions fit to three early spring

Podosphaera macularis ascospore release datasets. The input datasets varied by whether ascospore release counts were tracked over time using cumulative growing degree days with either a 0 ℃, 5 ℃, or 10 ℃ base temperature. Each GLM used the same fixed effects – degree day, location, and year. Degree Day was a significant fixed effect in each of the three models, as determined via an analysis of variance (ANOVA). The adjusted correlation coefficient (r2) for each model was calculated as a correlation calculation between the model predicted ascospore release values and the input ascospore release values for which the model was fit.

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Table S2-2. Analysis of variance for the effects of degree day accumulation (base 0C), location, year, and their interactions on the maturation of Podosphaera macularis chasmothecia.

Source df Sum Sq P > F Degree Day (g) 48 3.9319 7.49E-15 Location (l) 2 0.0657 0.14 Year (y) 2 0.0361 0.33 g X l 95 0.1843 0.01 g X y 95 0.0363 0.33 l X y 3 0.0354 0.34

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Two additional models were fit to the base 0C degree day dataset; (2) a logit transformation followed by a fit to a GLM, and (3) a beta regression. Estimates of fixed effects and parameters of the three models are summarized in Table 2-5. Again, in all three models

(GLM, Logit GLM, and Beta), degree day was a significant predictor of the chasmothecial maturation rate (P < 0.0001, 0.0001, and 0.0001, respectively). As a visual comparison of model performance, the predicted values of each regression model were plotted against the original input ascospore release values from which the models were generated (Figure 2-3).

Adjusted r2 and RMSE values comparing the training dataset to model predicted values for the

Beta regression model were 0.916 and 0.1401, respectively, which were the highest correlation coefficient and lowest RMSE values returned across the three models (Table 2-6).

Similarly, when model performance was calculated in comparison to ten iterations of a randomly sub-set internal validation set, the Beta regression returned the highest correlation coefficient (0.925) and the lowest RMSE value (0.157) across the three candidate models

(Table 2-6).

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Figure 2-3: Performance assessment of the three model candidates, each reporting predicted ascospore release proportions over time (growing degree day, base 0C), as compared to the observed values of the original input dataset, which also has a 95% confidence band plotted along the curve. “LR” stands for a linear regression model that was generated using logit transformed values of the original ascospore release input dataset. The correlation coefficients (adjusted r2) of the model predicted values as compared to the original input dataset are 0.92, 0.91, and 0.80 for the beta regression, linear regression, and linear regression of logit transformed input data, respectively.

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Table 2-5: Estimates of fixed effects and parameters for three separate regression models fit to describe the seasonal maturation of Podosphaera macularis as defined by degree day accumulation (base 0C) since the date of first ascospore release across three sampling locations (New York, North Carolina, Wisconsin) and three years (2018, 2019, and 2020).

Beta Regression Estimate (b) Asymptotic SE P > |z| Asymptotic CI_L Asymptotic CI_U (intercept) -2.4824 0.2512 2.00E-16 -2.9748 -1.9900 Degree Day 0.0179 0.0018 2.00E-16 0.0143 0.0214 (phi coefficient) 6.001 1.3950 1.69E-05 3.2670 8.7344 Logit Transformation Estimate (b) Asymptotic SE P > |z| Asymptotic CI_L Asymptotic CI_U (intercept) -4.0493 0.4491 5.60E-11 -4.9295 -3.1690 Degree Day 0.0254 0.0031 6.22E-10 0.0193 0.0314 GLM Estimate (b) Asymptotic SE P > |z| Asymptotic CI_L Asymptotic CI_U (intercept) 0.0631 0.0341 7.24E-02 -0.0060 0.1322 Degree Day 0.0032 0.0002 3.90E-16 0.0027 0.0037

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Table 6: Comparison of adjusted correlation (r2) and root mean square error (RMSE) between the Beta regression model, generalized linear regression model (GLM), and generalized linear regression of a logit transformed ascospore release response. r2 and RMSE are calculated based on the model predictions for ascospore release proportions in comparison to both the original input ascospore release values (training dataset) and an internal validation dataset that consisted of 10 iterations of at random, sub-setting 15% of the original dataset as a validation dataset, building each model on the remaining 85% and then comparing model performance against the validation dataset. The r2 and RMSE values reported for the validation dataset are the calculated mean of each value across the 10 iterations of sub- setting.

2 Model r2 – Training dataset RMSEt r – Validation dataset RMSEv Beta Regression 0.916 0.1401 0.923 0.1780 GLM 0.910 0.1426 0.887 0.2126 Logit Transformation 0.799 0.1994 0.925 0.1570 a r2 is the adjusted correlation coefficient; subscript t = training dataset and subscript v = validation dataset.

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The role of temperature and duration of leaf wetting in driving Podosphaera macularis ascospore release. Temperature, the duration of leaf wetting, and their interacting effects all significantly (P < 0.0001) affected the release of Podosphaera macularis ascospores (Table S2-

3). No significant differences in ascospore release were observed between the four independent repetitions of the experiment, and as such the data were pooled for all analyses, including model optimization and validation. Cook’s distance indicated 20 datapoints fell above the outlier threshold of 4*mean (all sample Cook’s distance values, n = 480) (Figure S2-

2a). Box plots of ascospore release proportions grouped by a combination of temperature and wetting duration returned 31 datapoints that fell outside of 1.5*interquartile range (IQR), of either the 1st or 3rd quartiles (Figure S2-2b). The 20 datapoints that overlapped between the two outlier analyses were removed from the study, retaining 460 datapoints across the temperature and wetting duration treatments for all downstream model optimization and validation. Using this filtered dataset, both a GLMM and a modified Weibull non-linear regression were generated. Estimates of the combined effects of temperature and duration of leaf wetting within the GLMM are summarized in Table 2-7. Parameter estimates for the combined effects of temperature and duration of leaf wetting are summarized in Table 2-8.

The residual values within each model were plotted as a histogram, in each case to check for a normal distribution of residuals centered around zero (Figure S2-3). Model predicted ascospore release values over time were plotted against the original input dataset, with plots subdivided by the four temperature treatments, allowing for assessment of model accuracy in predicting ascospore release over time specific to either 5C, 10C, 15C or 20C (Figure 2-4).

Adjusted r2 values for the GLMM model corresponding to these four plots (5C, 10C, 15C, and

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20C) were 0.851, 0.950, 0.891, and 0.816, respectively, while the adjusted r2 values for the modified Weibull model in the same plot order were 0.649, 0.880, 0.931, and 0.903, respectively.

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Figure 2-4: Comparison of the predicted ascospore release values over time (24hrs) for model

1, the generalized linear mixed model, and model 2, the modified Weibull nonlinear distribution, as compared to the original observed input dataset. In order to visualize the differential performance of the two models at varying temperatures, the predicted values are subset into four graphs (a-d) that correspond to ascospore release estimates over time at 5C,

10C, 15C, and 20C, respectively. Adjusted r2 values for the GLMM model corresponding to the

5C, 10C, 15C, and 20C plots were 0.851, 0.950, 0.891, and 0.816, respectively. Adjusted r2 values for the modified Weibull model in the same plot order by temperature were 0.649,

0.880, 0.931, and 0.903, respectively.

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Figure S2-2: Outlier analyses of the original temperature X duration ascospore release training dataset. (a) Cook’s Distance calculation of all 480 original ascospore release datapoint inputs.

The red abline runs across the plot at a y-intercept equal to 4*mean (sum of the 480 Cook’s distance values), slope = 0. All datapoints above this abline were considered for removal from the dataset as an outlier. (b) Box and whisker plot of the 480 original ascospore release datapoint inputs, categorized by both temperature and duration of leaf wetting. Black points indicate samples that reside outside of 1.5 times the interquartile range (IQR) and were considered for removal from the dataset as an outlier.

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Figure S2-3: Histogram of the residual values for (a) the generalized linear mixed model and

(b) the modified Weibull non-linear distribution form.

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Table 2-7: Estimates of the fixed effects for the generalized linear mixed model, 푓(푑, 푡) =

0.0397푡 + 0.1215푑 − .0031푑2 − .0009푑푡 − 0.4522, describing ascospore release by

Podosphaera macularis in response to a combination of varied temperature and duration of leaf wetness.

Fixed Effect Estimate (b) Asymptotic SE Asymptotic CIL Asymptotic CIU (intercept) -0.4522 0.0364 -0.5237 -0.3811 t 0.0397 0.0023 0.0351 0.0442 d 0.1215 0.0050 0.1118 0.1313 d2 -0.0031 0.0002 -0.0034 -0.0028 t X d -0.0009 0.0002 -0.0012 -0.0005

a Fixed effect (t) = temperature and (d) = duration of leaf wetting. SE is the standard error and CIL and CIU are the lower and upper limits, respectively, of the 95% confidence interval around the parameter estimates.

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Table 2-8. Parameter estimates for the combined effects of temperature and duration of leaf

(푡−퐹)퐺 wetting based on model 2, 푓(푑, 푡) = [1 − exp{−(퐵 × 푑)2}]/cosh [ ], describing 2 ascospore release by Podosphaera macularis.

Parameter Estimate Asymptotic SE Asymptotic CIL Asymptotic CIU B 0.288 0.01 0.266 0.312 F 18.229 0.814 17.24 19.523 G 0.191 0.016 0.167 0.214

a Parameters B, F, and G are defined in the main text; d = duration of leaf wetting, t = temperature. SE is the standard error, CIL and CIU are the lower and upper limits, respectively, of the 95% confidence interval around the parameter estimates.

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Table S2-3. Analysis of variance for the effects of temperature and the duration of leaf wetness on the release of ascospores by Podosphaera macularis.

Source df Sum Sq P > F Experiment 3 0.14 0.2265 Temperature (t) 3 9.73 2.20E-16 Duration (d) 4 19.70 2.20E-16 t x d 11 0.65 9.25E-06 d^2 15 11.50 2.20E-16

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The cumulative ascospore release values observed in the original training dataset, as well as the validation dataset are summarized in Figure S2-4. In the validation analyses for the

GLMM and modified Weibull regressions, the regression line for observed versus predicted ascospore release values was not significantly different (P < 0.604; 0.650, respectively) from a slope of 1 (i.e. a slope where observed values = predicted values) (Figure 2-5). Adjusted r2 and

RMSE values for the GLMM model in comparison to the original ascospore release training dataset values were 0.900 and 0.1772, respectively, and were 0.864 and 0.2073 for the modified Weibull nonlinear regression model in comparison to the original training dataset observations (Table 2-9). In comparison to the observed ascospore release values of the independent validation dataset, the GLMM predicted values had an adjusted r2 of 0.893 and an RMSE of 0.1801, while the modified Weibull model had an adjusted r2 of 0.849 and an RMSE of 0.1887 (Table 2-9).

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2 Figure 2-5. Validation of model 1 (left): 푓(푑, 푡) = 훽푡푡 + 훽푑푑 − 훽푑^2푑 − 훽푑푡푑푡 − 훽푖푛푡푒푟푐푒푝푡

(푡−퐹)퐺 and model 2 (right): 푓(푑, 푡) = [1 − exp{−(퐵 × 푑)2}]/cosh [ ], both describing 2 ascospore release by Podosphaera macularis in response to the combined effects of varied temperature and duration of leaf wetting. Comparison between observed versus model predicted ascospore release values for a validation dataset consisting of 26 random combinations of temperature and wetting duration. The regression line (solid line) is not significantly different from the observed = predicted abline (slope = 1). The correlation coefficients (adjusted r2) are 0.89 and 0.85 for models 1 and 2, respectively.

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Figure S2-4. (left) There dimensional graphical visualizations of (a) the original input dataset of Podosphaera macularis ascospore release over 24 hours (1, 3, 6, 12, and 24hr timepoints) for the temperatures 5C, 10C, 15C, and 20C and (b) the observed P. macularis ascospore release values of the validation dataset, used to test the predictive ability of the model produced using the training dataset values. The validation dataset was comprised of 26 random combinations of temperatures between 5 – 20C and timepoints between 1 – 24 hours.

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Table 9: Comparison of adjusted correlation and root mean square error (RMSE) between the generalized linear mixed model and the modified Weibull nonlinear distribution. The two models are compared in their predictive outputs in relation to both the original input dataset

(Training dataset) and the observed values of the validation dataset.

2 – 2 Model r Training dataset RMSEt r – Validation dataset RMSEv GLMM 0.900 0.1772 0.893 0.1801 modified Weibull 0.864 0.2073 0.849 0.1887

a 2 r is the adjusted correlation coefficient; subscript t = training dataset and subscript v = validation dataset.

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Germination and colony development of Podosphaera macularis ascospores across a range of temperatures. The temperature at which recently discharged ascospores were exposed to during the first 48 h had a significant effect on germination and colony formation success rates

(Figure 2-6). At 4C, a small percentage (2.9%) of P. macularis ascospores germinated and none of the ascospores that germinated successfully established a colony within the first 48 h of growth, as evidenced through no observed production of branching hyphae. All three other temperature groups tested had an appreciable amount of ascospore germination and growth observed, however, the 8C treatment group had significantly lower germination and colony formation success rates than the 19C temperature treatment (Figure 2-6). When monitoring for macroscopic evidence of colony formation after all detached leaves were transitioned to

19C, 48 h after inducing ascospore release, there were no significant impact of initial incubation temperature on limiting eventual successful colony formation (P < 0.721) (Table

S2-4). There was also no significant impact of the acute low temperature treatments on the number of colonies that formed per leaf (P < 0.163), nor the latency period (P < 0.544).

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Figure 2-6: Germination and colony formation success rates of Podosphaera macularis ascospores released and incubated across a range of temperatures. Hop leaf disks infested with P. macularis chasmothecia were suspended over hop leaves, cv ‘Symphony’ for 48 hours prior to being rated for germination and colony formation success via staining with

0.5% Chlorazol Black E and subsequent brightfield microscopy. Successful germination was defined as a P. macularis ascospore that had produced a hypha greater than half the length of the ascospore. Colony formation was defined as a P. macularis ascospore that was observed to have successfully germinated and produced branching hyphae. The experiment was repeated (Rep) three times. Lowercase letters above the bars denote significantly different (P < 0.05) treatment groups (across all 3 Reps combined), as determined with a

Tukey’s HSD test for multiple comparisons.

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Table S2-4: Assessment of Podosphaera macularis ascospore-derived colony formation across a range of temperatures. Temperature treatments were applied for the first 48 hours of culture, and then all groups were transitioned to 19C for the remainder of the experiment.

Colony formation success was defined as any P. macularis colony that was macroscopically visible within 14 days of being kept in culture. Latency duration was defined as the number of days since ascospore release at which the first evidence of conidiophore formation or production of conidia was observed. A logistic regression was used to model the binomial colony formation success data, which when subjected to a Chi Square goodness of fit test, indicated that there was no significant difference between the temperatures to which P. macularis ascospores were exposed during the first 48 hours of growth in limiting the ultimately successful formation of macroscopically visible colonies (P > 2 = 0.721). There was also no significant difference across the 48-hour temperature treatments in the number of distinct colonies formed, or the mean latency period (P > F = 0.163 and P > F = 0.544, respectively).

Colony Formation Success (%) Mean Number of Distinct Colonies Mean Latency Duration (Days) Treatment Rep 1 Rep 2 Rep 3 Rep 1 Rep 2 Rep 3 Rep 1 Rep 2 Rep 3 4C 0.33 1.00 1.00 1.00 9.67 10.50 8.00 8.33 7.00 8C 0.33 1.00 0.67 6.00 12.67 13.67 8.00 7.00 6.33 12C 0.67 1.00 1.00 2.50 15.00 8.00 8.00 7.33 6.50 19C 0.67 1.00 0.67 2.50 11.67 5.33 8.00 7.00 6.67

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Viability of chasmothecia after exposure to hop cone drying conditions. The mean number of ascospores discharged from non-drying temperature treated (NTC) P. macularis chasmothecia was significantly greater than any of the temperature treated groups, with the

51.7C temperature treatment group being the only experimental group that had an appreciable level of ascospore release (mean = 12.2 ascospores released) across the three independent replications of the assay (Table S2-5). When considering colony formation success, both the NTC group and the 51.7C drying temperature group had instances where P. macularis colonies were formed (Table S2-5). The NTC colony formation success proportion was the only value that was significantly different from that of any other treatment groupings

(P < 0.0197), with nine of twelve detached leaf assays resulting in compatible ascosporic infection. The mean number of colonies formed in the NTC and 51.7C groups were the only non-zero value, with the NTC group mean number of colonies formed being significantly greater than only the 62.8C and 76.7C drying temperature treatments.

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Table S2-5. Podosphaera macularis ascospore viability when exposed to a range of temperatures used in the drying of hop cones post-harvest. Rep 1, 2, and 3 are the three independent replications of both the ascospore release assay and the detached leaf colony formation assay. Rep was not a significant predictor of ascospore release (P < 0.5051) but was a significant effect in predicting mean colony formation success (P < 0.0024). Parentheses within the “Mean Ascospore Release Count” columns indicate (standard error, Tukey’s HSD significance grouping). Parentheses within the “Colony Formation Success” column indicate

(mean number of unique P. macularis colonies formed during the detached leaf ascospore release assay, Tukey’s HSD significance grouping). The NTC group colony formation success proportion was significantly greater than that of all drying temperature treatments (P < 0.0197

(ANOVA))

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Podosphaera macularis chasmothecial viability on hop seed: While P. macularis chasmothecial seed infestation incidence did not differ greatly between populations, the chasmothecial infestation severity of seed population 2 was significantly greater than that of population 1 (P < 0.0001), determined via a Tukey’s HSD test (Table S2-6). P. macularis chasmothecia demonstrated a steadfast adherence to the H. lupulus seed coat surface, ranging between 76.7 – 92.1% of chasmothecia remaining adhered to the seed coat after physical agitation in water, varying slightly, by chasmothecia infestation level (Table S2-7). As evidenced through the FDA viability assay, both seed populations 1 and 2 had largely non- viable chasmothecia (Table S2-8). All P. macularis asci observed under epifluorescence microscopy were deemed either previously dehisced (86.7% of total) (containing no ascospores) or housed ascospores that did not metabolize the FDA stain and emit a fluorescence signal, indicating non-viability (13.3%). Powdery mildew did not develop on any seedlings grown from infested H. lupulus seed (Table S2-9).

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Table S2-6: Summary of Podosphaera macularis chasmothecia incidence (n = 100), counts per seed, and mean severity within the two Humulus lupulus seed populations surveyed. Mean severity was taken as an estimated proportion of the seed coat that was infested with chasmothecia.

Population Bag Incidence Mean Chasmo Count Mean Severity 1 1 0.39 4.13 0.0344 1 2 0.35 2.90 0.0242 1 3 0.33 3.97 0.0331 1 4 0.32 3.73 0.0311 2 1 0.25 25.37 0.2114

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Table S2-7: Retention rates of Podosphaera macularis chasmothecia on Humulus lupulus seed coats when subjected to physical agitation in water, seeds grouped by chasmothecial infestation level.

Infestation Level Chasmothecia Range Mean Chasmothecial Retention Light 7 - 23 0.767 (0.047) Moderate 36 - 66 0.882 (0.024) Heavy 68 - 172 0.921 (0.015)

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Table S2-8: Microscopic analysis of Podosphaera macularis chasmothecia originating from

Humulus lupulus seed coats. Dehiscence counts are a categorization of chasmothecia dehiscing with (natural) or without pressure having been applied to the structures via pressure placed on the glass coverslip while mounted on a microscope slide. The Fluorescein Diacetate

(FDA) viability assay is used as an indicator of ascospore viability, as viable ascospores are able to uptake the dye and metabolize the compound, resulting in a fluorescence signal at 526nm.

Dehiscence Counts FDA Viability Assay Population Natural Dehiscence Pressure Dehiscence Viable Ascospores Previously dehisced Non-viable 1 5 18 0 2 17 2 11 17 0 4 22

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Table S2-9: Disease incidence data resulting from the greenhouse seed germination experiment. Control groups consisted of Humulus lupulus seed from the given population that was visually inspected and confirmed to be free of any P. macularis chasmothecia on the seed coat.

Population Planted Germinated Disease Incidence 1 2700 279 0 1 - Control 300 14 0 2 200 0 0 2 - Control 100 8 0

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Discussion This collection of experiments addressed critical gaps in the understanding of the biological and epidemiological drivers of P. macularis chasmothecial overwintering and early spring ascosporic infection. Collectively, these models and characterizations of P. macularis ascosporic infection will enhance grower ability to manage early season ascosporic disease pressure in targeted manners that move away from prophylactic based control.

While Liyanage and Royle 1976 provided circumstantial evidence for the infective capability of P. macularis ascospores, our overwintering field study is the first to demonstrate that these are capable of perennating the disease into the ensuing spring. Gent et al. (2008,

2018) reported that in an exclusively asexual P. macularis population, as is the case in the PNW

US, the pathogen exclusively overwinters on dormant hop buds within the crown that emerge as infected flag shoots at a rate of between 0.02 – 0.69%. Our findings indicate that in the presence of a sexually reproducing population, as is the case in Europe and most hop growing regions in the U.S. east of the Rocky Mountains, the initial incidence of plants with powdery mildew can be substantially greater. The incidence of plants with powdery mildew due to ascosporic infection reached upwards of 78% in an environmentally favorable season

(temperate, occasional rain and cloud cover) such as 2019. Ascosporic infection may still produce an appreciable level of initial infection even in less environmentally favorable seasons

(hot, extended periods of drought) such as the 2020 season, which peaked at 32% disease incidence before declining as drought conditions continued.

It is also significant that the late chasmothecia cohort, which was seeded as leaf litter in late October, caused much higher levels of disease incidence the following spring than did the early cohort, which was seeded as leaf litter in late August. Gent et al. (2018) reported

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that post-harvest fungicide applications may reduce the incidence of leaves with powdery mildew but did not affect the amount of flag shoot emergence the following year. It is unknown whether that reported reduction of foliar powdery mildew incidence could limit late season chasmothecial formation, and therefore lessen the disease incidence due to ascosporic infection the following spring. In the grape powdery mildew pathosystem, chasmothecia typically aren’t observed at high density until disease incidence exceeds 40% within a vineyard

(Gadoury 1988). It also remains to be seen whether current early season disease management practices such as pruning away the first wave of emergent hop shoots (Gent et al. 2012; Probst et al. 2016), or stripping the lower 2m of hop leaf tissue (Gent et al. 2016) will be sufficient to keep primary powdery mildew levels under control when chasmothecia are present. There is a chance that control measures may need to be revised in spring when dealing with the presence of P. macularis chasmothecia.

The chasmothecial adherence assay demonstrated that P. macularis chasmothecia, both from field-collected and lab-cultured hop leaves, have a steadfast adherence to hop leaf tissue. Chasmothecia of P. macularis are embedded within dense, pannose mycelium that develops in colonies, which effectively anchors the ascocarps to affected tissue due to their long appendages (Wolfenbarger et al. 2015). When put in context of how hop plants senesce during late autumn, the vast majority of above ground hop plant tissue falls to the ground and becomes leaf litter. From an evolutionary fitness standpoint, it would seem advantageous for the P. macularis chasmothecia to remain tightly adhered to leaf and cone tissue and end up at the soil surface, positioned exactly where annual hop shoots emerge from the perennial root system the following spring season. Gadoury et al. (2010) performed a similar comparison

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of chasmothecial adherence, noting that the chasmothecia of powdery mildew species infecting perennial woody hosts tended to have fewer attachment hyphae and more readily detach from the leaf surface, while those from perennial herbaceous hosts, such strawberry, produced profuse numbers of attachment hyphae and had a similarly steadfast attachment (>

90%) to their host as did the P. macularis chasmothecia.

In tracking the seasonal maturation of P. macularis chasmothecia across three distinct locations with varying climates, we designed and validated a predictive model for chasmothecia maturation that is robust across a spectrum of weather conditions, such that it may be applicable to many hop growing regions. A distinct order to chasmothecial maturation by location was associated with a progression from the most temperate winter (Raleigh, NC) to the longest, coldest winter (Madison, WI). Interestingly, the cumulative number of matured ascospores counted during each season increased with winter intensity, with only 13 – 16% as many matured ascospores recorded in North Carolina as compared to New York or Wisconsin.

This hints that there may be an overwintering, chilling, or dormancy requirement involved in

P. macularis ascospore maturation. While significant to report that chasmothecial maturation rates do vary by intensity of winter weather, our main objective was to normalize this data such that a cumulative model for chasmothecial maturation could be used to predict the window of possible ascospore release regardless of location. The GLM utilizing degree days of a 0C base temperature was selected over GLMs utilizing degree day base temperatures of 5C or 10C based on it having the greatest r2 (0.955) and smallest RSS (0.225) of the three GLM models. From there, a Beta regression model was chosen as the best fit to the 0C degree day dataset, selected over a GLM model that no longer included year and location as fixed effects,

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as well as a second GLM fit to Logit transformed ascospore release values. The 0C degree day

2 Beta regression model had the greatest r (0.916t, 0.925v) and lowest RMSE (0.1401t, 0.1570v) values of any model tested against the original training dataset (t), or the iterative internal validation datasets (v), respectively. We expect this model to be useful in estimating the window of time, measured in thermal time, when P. macularis chasmothecia within a hop yard are likely to contain viable ascospores. This ability to estimate the start, peak, and end of the viability of a chasmothecial population within a hop yard provides a general timeframe to monitor for putative ascosporic infection events.

Now that a general window of chasmothecial maturation was defined, our next objective was to quantify how temperature and duration of wetting events, such as rain or dew, interact to induce varying amounts of ascospore release. In practice this second model was designed to output a general ascospore release risk level specific to each rain event that occurs within the predicted period of chasmothecia maturity. The performance of two models, one being a GLMM (Model 4) and the other being a modified Weibull regression (Model 5) adapted from Arauz et al. (2010), were compared both in their predictive capacity in regard to the original datasets from which with models were fit, as well as an independent validation dataset. In both cases, the GLMM was a slightly better predictor of P. macularis ascospore release, as evidenced through the GLMM model returning greater r2 (0.900, 0.864) and smaller

(0.1772, 0.2073) RMSE values in both model training and model validation, respectively. While the application of the GLMM model limits ascospore release predictions to be made exclusively within the range of temperatures (5C – 20C) and wetting durations (1hr – 24hrs) from which the models were generated, in practice, this encompasses the vast majority of rain

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events that hop growers might encounter during the early spring growing season when susceptible hop tissue is present. The formula itself is also much more simple to calculate and thus appropriate given its intended application.

We recommend that the model be used to assign risk of ascospore release qualitatively to low (approximately 0 – 20% ascospore release), medium (approximately 20% - 50%), or high

(> 50%) risk categories. This is because disease management responses will likely vary categorically in a similar manner, from potentially applying a broad spectrum product such as sulfur (low risk) to applying a higher-efficacy single site fungicide or pruning away the given wave of emerged hop buds in a high risk categorization. As an example, according to Model 4

(GLM), a rain event lasting for 5.5 hours that was an average temperature of 14C would be predicted to induce 61.1% (58.2% - 64.0% range for a 95% confidence interval) of the undehisced, matured ascospores present within the hop yard to discharge. A moderately high level of ascospore release such as this may prompt a more aggressive management response in order to mitigate the ascosporic infection that likely occurred in response to the rain, such as the prompt application of a highly efficacious fungicide. Alternatively, if it rained for 2hrs at

8C, and only 8.2% of mature ascospores were predicted to have been released, a low risk response such as visual scouting for disease could possibly be utilized.

Once the propensity of P. macularis ascospores to be discharged was defined across a relevant range of temperatures and durations of wetting events, it was necessary to confirm that these structures could actually germinate and establish viable colonies under the same environmental conditions. While the germination and colony formation success rates certainly increased with increasing temperature (maximum of 19C in the present study), ascospores

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released across all temperatures developed into macroscopically visible colonies of similar incidence and severity. This indicates that should a population of P. macularis ascospores be released in response to a rain event, it is probable that disease will develop should temperatures return to an optimal range (approximately 12 – 19C) within 48 hours of the rain event.

The final set of experiments were designed to address unexpected ways the hop powdery mildew pathogen may disseminate. As mentioned previously, the PNW US harbors only one of two P. macularis mating types. As evidenced through the newly reported heightened overwintering potential of P. macularis chasmothecia, there is legitimate reason to prevent the introduction of the second mating type into the region. Quarantine measures are in place that prevent the import of living hop plant material into the PNW US (Gent 2015), however, this does not apply to the import of hop seed for breeding purposes or the import of dried hop cones, which are often imported from Europe and pelletized, processed, and resold (Gent, personal communication). All previous research to date in other powdery mildew pathosystems reports an inability of powdery mildew species to be seed transmissible

(Enright, S. and Cipollin, D. 2007; Jarvis et al. 2002) Though limited to one year, across two independent populations, we found no evidence (across 3000 cumulative hop seeds surveyed) that P. macularis chasmothecia could survive the hop seed stratification process and infect emerged hop seedlings. It is worth noting that in numerous instances, the dehisced hop seed coat clung to hop cotyledons for an extended period of time, theoretically lingering in an ideal position for ascosporic infection, had there been any viable chasmothecia present on the seed coat (Figure S2-5).

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Figure S2-5: (a-c) Germinated hop seedlings, each in a conformation where the dehisced seed coat is hanging on to the expanding hop cotyledons. (d) Experimental greenhouse set up of the hop seedling germination assay, designed to allow for airflow while minimizing risk of any tray-tray transfer of P. macularis ascospores.

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In addressing the second concern for transport of P. macularis chasmothecia into new regions, being via the import of dried hop cone material infested with the ascigerous state, we mimicked the hop cone drying process on leaf disks infested with chasmothecia across a range of industry relevant temperatures. Contrary to our hypothesis that all drying temperatures would kill P. macularis, chasmothecia exposed to 51.7C (125F) for 1 h consistently returned minimal, but non-zero, ascospore release counts and 25% of the detached leaf ascospore release assays at this temperature resulted in viable, macroscopically visible colonies. In commercial hop drying situations, hops are often dried upwards of 8 to 12 hours. In that regard, a 1hr drying treatment, as was done in this experiment, is minimal.

However, when hop are kiln dried, cone piles often reach depths of 1m (Gent, personal communication). As the cones dry, moisture is released that evaporates upward and prevents the topmost layer from reaching the actual drying temperature until later into the drying process, which is the condition that was mimicked in our design. While the odds of a series of events occurring that would result in a viable chasmothecium imported into the PNW region on dried hop material, it is certainly not zero, and therefore low level survival of P. macularis ascospores exposed to 51.7C drying temperatures is worth noting.

These projects collectively address critical aspects of P. macularis overwintering biology and disease management, starting with the impact of the timing of chasmothecial formation in the autumn on initial levels of disease the subsequent season, and progressing through the spring and early summer to describe the epidemiological effects of factors including overwintering geography, the temperature during and duration of wetting events, and degree day accumulation in predicting springtime ascospore release and infection events.

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The importance of late season chasmothecia cohorts in determining early spring disease, as well as a knowledge of where P. macularis chasmothecia are likely to overwinter establishes a potential rationale and target for post-harvest control of powdery mildew. The two models, which describe (i) chasmothecia maturation as determined by degree day accumulation; and

(ii) the interaction of temperature and duration of wetting events in driving ascospore release, should provide general guidelines to better align early season disease management strategies with P. macularis biology, as opposed to a typical reliance on prophylactic management. Many of these factors are proven to be crucial aspects of successful early season disease management programs of other pathosystems (Arauz et al. 2010; Pfender 2001; Gubler and

Leavitt 1994; Caffi et al. 2012; Cao et al. 2015), and may serve as a template for the design of similar tools within other powdery mildew pathosystems.

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Wolfenbarger, SN, Twomey, MC., Gadoury, DM., Knaus, BJ., Grünwald, NJ., and Gent, DH.

2015. “Identification and Distribution of Mating‐type Idiomorphs in Populations of

Podosphaera Macularis and Development of Chasmothecia of the Fungus.” Plant

Pathology 1997: 1–9. https://doi.org/10.1111/ppa.12344.

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Cross Infectivity of Powdery Mildew Isolates Originating from Hemp (Cannabis sativa) and

Japanese Hop (Humulus japonicus) in New York.

William A. Weldon1, Maire R. Ullrich2, Lawrence B. Smart3, Christine D. Smart1, and David M.

Gadoury1*

1Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science,

Cornell AgriTech, Cornell University, Geneva, NY 14456

2Cornell Cooperative Extension Orange County, Middletown NY 10940

3Horticulture Section, School of Integrative Plant Science, Cornell AgriTech, Cornell

University, Geneva, NY 14456

*Corresponding author: David M. Gadoury, email: [email protected]

Keywords: Cannabis sativa, Humulus lupulus, powdery mildew, host resistance, Podosphaera macularis, Golovinomyces spadiceus

Funding: New York State Department of Agriculture and Markets (C00195GG), USDA-ARS

SCRI (2014-51181-22381), the Empire State Development Corporation (128884).

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Abstract

In the recent decade, agricultural production of both hemp (Cannabis sativa) and hop

(Humulus lupulus) have expanded throughout the Pacific Northwest, Midwest, and Eastern US to support the growing industries for which these plants are key components. The significant and rapidly expanding overlap of production regions of these two Cannabaceae plant family members creates a potential dispersal route for organisms that are pathogenic to both hosts.

Powdery mildew is a disease of high economic impact in both hemp and hop production systems, yet it was largely unknown whether the powdery mildew fungi commonly associated with hemp could also be pathogenic on hop, and vice versa. We isolated Golovinomyces spadiceus growing upon hemp in New York production greenhouses, and Podosphaera macularis from feral hop (H. japonicus) plantings also in New York. Herein, we report the pathogenicity of P. macularis associated with hop to C. sativa cultivars ‘Anka’ and ‘Wild Horse’, and pathogenicity of G. spadiceus towards hop. The potential for P. macularis to establish, produce viable, infectious conidia, and undergo sexual recombination on hemp could complicate efforts to exclude the MAT1-2 mating type of P. macularis from western North

America, and could facilitate the spread of races pathogenic towards ‘Cascade’ hop, and hop cultivars with R6-based resistance to P. macularis, including ‘Nugget’. Further assessment of the pathogenicity of diverse P. macularis isolates, in both geographic origin and the range of hop species, is necessary to better understand the dispersal risk of P. macularis on hemp.

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Introduction: The rise of agricultural hemp production in the United States – new avenues for pathogen dispersal.

Production of hemp (Cannabis sativa) in the United States has steadily increased since re-introduction was allowed under the 2014 Farm Bill (Vote Hemp 2018a). Between 2017 and

2018, the acreage of hemp in production tripled from 25,713 acres to 78,176 acres (Vote

Hemp 2018b). Following passage of the 2018 Farm Bill, hemp was removed from the US Drug

Enforcement Agency Controlled Substance List. Additionally, the definition of hemp was expanded to broadly cover all parts of C. sativa plants including seeds, derivatives, extracts, and cannabinoids with a tetrahydrocannabinol level of 0.3% or less (McConnell et al. 2018).

Hemp acreage is expected to continue rapid growth (Empire State Development 2019; Vote

Hemp 2018b), with 45 states having now enacted legislation to allow or encourage its production. Montana, the 2018 US hemp acreage leader, alone reported 22,000 acres of harvested hemp in 2018 (Vote Hemp 2018b; Washington State Department of Agriculture

2019) and over 500,000 acres have been licensed for production in 2019 (Vote Hemp 2019).

Hemp is a member of the Cannabaceae plant family, along with the hop species

Humulus lupulus (Brewer’s hop) and the broadly-dispersed invasive hop species H. japonicus

(Japanese hop) (USDA Natural Resources Conservation Service 2019). All are hosts of powdery mildew pathogens: Golovinomyces spadiceus in the case of hemp (Braun and Cook 2012), and

Podosphaera macularis in the case of hop (Braun and Takamatsu 2000). Cross infectivity of these pathogens on the foregoing hosts has not been previously reported. Although most powdery mildew pathogens are highly species-specific, cross infectivity of powdery mildews between host genera within the same botanic family is possible, in particular when they grow

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within a common range, or their production is comingled with wild hosts. For example, within the Vitaceae, certain isolates of Erysiphe necator can move freely between Vitis labrusca, V. vinifera, V. riparia, Ampelopsis glandulosa, A. brevipenunculata, Parthenocissus tricuspidata, and P. quinquefolia (Gadoury and Pearson 1991).

The rapid expansion of hemp acreage, in particular within the Pacific Northwest

(PNW), a region comprising > 95% of US hop production, should heighten concern with respect to the possibility of cross infectivity within the Cannabaceae family. The expansion of hemp production in the Midwest and Eastern US has been paralleled by a resurgence of local hop production due to growth of the craft brewing industry, thus creating the widespread and comingled production of hemp and hop (Hop Growers of America 2018; Vote Hemp 2018b).

Quarantine of hop powdery mildew is already a national issue. Podosphaera macularis has been present in the Pacific Northwest only since 1996 (Ocamb et al. 1999), caused great destruction to US hop production upon its introduction (Gent 2008), and the pathogen populations established in the region contain only one of two possible mating type idiomorphs: MAT1-1 (Wolfenbarger et al. 2015). The primary emphasis of quarantine efforts has been to contain the other P. macularis mating type idiomorph (MAT1-2) to prevent its spread to western North America, and the consequent increase in genotypic diversity of western populations that would inevitably result (Gent 2015). However, there is also a significant motivation to prevent certain PNW-derived P. macularis isolates from escaping the region and establishing in eastern US hop production. More recently, P. macularis isolates virulent upon Cascade, a historically major US cultivar, as well as isolates able to overcome

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R6-based resistance to P. macularis in hop cultivars such as Nugget, have emerged in the

Pacific Northwest.

Accordingly, we initiated a series of experiments to examine the potential for cross infectivity of powdery mildew pathogens found naturally occurring on hemp and hop in New

York. Herein we report the ability of P. macularis isolates to grow and reproduce on hemp, and G. spadiceus isolates to weakly grow and reproduce on hop, but to a degree unlikely to be commercially relevant.

Acquisition of original infection material and maintenance of isolates in culture.

Two hemp powdery mildew (PM) isolates identified growing on potted hemp plants in greenhouse production systems were used in this study. Hemp PM Isolate 19001 was collected from the hemp cultivar ‘Lifter’ from a hemp greenhouse operation in Orange County,

NY. Hemp PM isolate 19002 was collected from the hemp cultivar line ‘17GH-F1’ from a greenhouse operation in Erie County, NY (Figure 3-1). No chasmothecia were observed on powdery mildew infected hemp plant material in either greenhouse. Infected leaves were incubated in growth chambers for two days to allow for production of new, viable conidia.

Both isolates were then single spore transferred from the original source leaves to young hemp leaves, cultivar “TJ’s CBD’, in a double Petri-dish detached leaf culture design (Pearson and Gadoury 1987). Isolates were subjected to two additional rounds of single spore transfer, after which each culture was considered pure and maintained via a paint brush conidial inoculation thereafter (Quinn and Powell Jr. 1982; Gadoury and Pearson 1991).

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Figure 3-1. Golovinomyces spadiceus colonies growing on Cannabis sativa (a) leaves and (b) female flowers, cultivar ‘17GH-F1’.

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Hemp cultivars used in this study include ‘Anka’ (seed from UniSeeds, Cobden Ontario,

Canada), ‘Wild Horse’ (seed from Winterfox Farms Eagle Point, OR), and ‘TJ’s CBD (clones from

Stem Holdings Agri Inc, Eugene, OR).

Two hop powdery mildew isolates, hereafter referred to as NY002 and NY005, from our culture collection were used for the cross infection assays. Both isolates were collected from feral H. japonicus plants in Geneva, NY. Both were reduced to clonal isolates from transfers of single conidial chains on detached leaves of the H. lupulus cultivar ‘Symphony’, using the same methods as described above for the hemp PM isolates, and maintained on H. lupulus ‘Symphony’ detached leaves via paint brush conidia inoculations thereafter. Both the hemp PM and hop PM isolates were transferred to new leaves of their respective host every

11-15 days. Isolates were kept in growth chambers at 19C, 50% relative humidity, and 14-hour daylengths.

Taxonomic classification of the hemp PM and hop PM isolates.

In order to confirm the identify of hemp PM isolates 19001 and 19002, the 28S and ITS regions were PCR amplified using the primers PM5G/NLP2 for the 3’ half of the ITS and 28S, and ITS5/PM6G for the 5’ half of the ITS (Bradshaw et al. 2017). Primer sequences are re-listed here for future convenience (Table 3-1). PCR products were Sanger sequenced in both the forward and reverse directions, and consensus sequences for both isolates were deposited into GenBank (GenBank Accessions MN365027 and MN381107). A NCBI BLAST search indicated the sequenced ITS and 28S regions of isolates 19001 and 19002 were identical and returned a 98% query coverage and 99.92 - 100% identity with Golovinomyces spadiceus. All

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other significant Golovinomyces sp. alignments were less than 96.5% identity. These BLAST alignments agree with species classifications in previous reports of hemp powdery mildew in

North America (Szarka et al. 2019; Pépin et al. 2018).

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Table 3-1. Primers used in this study. Forward and reverse primer pairings grouped by the double lined border.

Primer Name Sequence (5' - 3') Citation ITS_5 GGAAGTAAAAGTCGTAACAAGG White et al 1990 PM6G CGAGCCCCAACACCAA Scholler et al 2016 PM5G GACCCTCCACCCGTGT Scholler et al 2016 NLP2 GGTCCCAACAGCTATGCTCT Mori et al 2000 PmMAT1-1-1_BW_F AGCGCCGATCGTTACATTTC this study PmMAT1-1-1_BW_R CCGTCTCATCAGTGTAGCTAGT this study EnF2 AAAGATGCACCTCTCGATGAA Brewer et al 2011 EnR3 AAGTTATAGAAGACATCGCAGTCA Brewer et al 2011 PmMAT1-2-1_BW_F CAACCCTGGTCTTAGCAATAATC this study PmMAT1-2-1_BW_R GCAAGATCCTTGTAGGCATTTC this study G_o_MAT1-2_F ACATGCCGAAACTGTTTTGC this study G_o_MAT1-2_R GCCTTGTATGAATTCCGTCCT this study

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The hop PM isolates NY002 and NY005 were identical and confirmed as Podosphaera macularis through sequencing of the PM5G/ NLP2 region of the ITS. Both isolates returned a single high yield amplification product (GenBank Accessions MN381106 and MN381108) with

100% query coverage and 100% identity to P. macularis. The Golovinomyces specific primer pairing PM5G (F)/ NLP2 (R) yielded multiple PCR products in hop PM isolates NY002 and

NY005, indicating potential off-target amplification. As such, these amplicons were not used to further confirm species.

To further visualize the phylogenetic relationship between the hemp and hop associated powdery mildew isolates, a consensus Neighbor-Joining Tree of the 28S and ITS region was constructed using the Tamura-Nei genetic distance model (Tamura and Nei 1993) with a bootstrap resampling method and 1000 replications (Geneious Prime 2019.2.3). The G. spadiceus and P. macularis isolates closely grouped with other deposited GenBank isolates of their respective species, on distinct branches from other related mildew species including G. tabaci, G. macrocarpus, P. xanthii, P. aphanis, and Blumeria graminis (Figure 3-2).

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Figure 3-2. Neighbor-Joining consensus tree of the 28S and ITS regions of various powdery mildew species, including the two G. spadiceus and two P. macularis isolates of interest in this study. Samples are labeled by species, isolate name (if relevant), and their associated GenBank accession number. Puccinia graminis (GenBank Accession MN385567.1) was used as an outgroup to root the tree. Branches are labeled with the consensus support percentage based on 1000 replications of the Tamura-Nei model.

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We paired the ITS classification of the hemp PM and hop PM isolates with an analysis of powdery mildew colony morphology. Hemp PM isolate 19001 and hop PM isolate NY005 were surveyed for conidia size, conidiophore dimensions, and morphological production of conidial chains (Zeiss Axiophot Trinocular Compound Light Microscope). Under a dissecting microscope (Wild Makroskop M420) both hemp PM isolate 19001 and hop PM isolate NY005 were also examined on each isolate’s respective original host for latency period and daily conidial production numbers. Each inoculation set contained three biological replicates, and the entire inoculation protocol was repeated in triplicate.

Similar to previous reports (Szarka et al. 2019; Pépin et al. 2018; Braun 2002), mycelia on the original infected plant material from which both the hemp PM and hop PM isolates were obtained were amphigenous and caulicolous. In rare occasions, mildew growth extended on to short succulent stems. Conidiophores of hemp PM isolate 19001 were singular, hyaline structures that measured 131.46 m in length (n = 15, SD = 11.07). Conidia were ellipsoid to ovoid and measured 34.13 m (n = 20, SD = 2.69) X 19.36 m (n = 20, SD = 2.17). Once conidiophores were produced, multiple (6-10) mature conidia were produced over a 24-hour period in single chains. Conidiophores of hop PM isolate NY005 were similarly singular, hyaline erect structures that measured 59.32 m in length (n = 15, SD = 11.51). Conidia were ellipsoid to ovoid and measured 30.25 m (n = 20, SD = 1.23) X 21.01 m (n = 20, SD = 1.99). Hop PM isolate NY005 also produced multiple mature conidia in single chains over each course of a 24- hour sporulation period. Morphological characteristics for hemp PM isolate 19001 were consistent with descriptions of Golovinomyces spadiceus (Szarka et al. 2019; Pépin, Punja, and

Joly 2018) and isolate morphology of hop PM isolate NY005 was consistent with descriptions

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of Podosphaera macularis (U. Braun 2002). The convergence of the molecular and morphological data confirmed that hemp PM isolates 19001 and 19002 are G. spadiceus and hop PM isolates NY002 and NY005 are P. macularis.

Cross Infectivity Assays of the G. spadiceus and P. macularis isolates.

All hemp PM and hop PM isolates were assayed for evidence of pathogenicity on hemp and hop leaf tissue. For the hemp infection assays, inoculations were executed on the adaxial surface of detached leaves in double Petri-dish culture, on 10-14 day old seedlings, and on 3-

4 week-old potted hemp plants. The hemp cultivars ‘TJ’s CBD’, ‘Anka’, and ‘Wild Horse’ were collectively surveyed at various growth stages, depending on plant tissue availability. All inoculations were ‘dry’ inoculations using a fine tipped paint brush and 12-14 day old mildew colonies from detached leaves in double-Petri dishes as the inoculum source. For hop infection assays, inoculations were done both on detached hop leaves and potted plants of ‘Symphony’ and ‘Zeus’ (often also marketed as ‘CTZ’, a complex of the identical cultivars ‘Columbus’,

‘Tomahawk’ and ‘Zeus’). Both hop cultivars are known to be highly susceptible to P. macularis.

For all assays, the adaxial leaf surface was dry inoculated with conidia from 10-12 day old mildew colonies from detached leaves in double-Petri dishes using a fine tipped paint brush.

A compatible host – pathogen interaction was defined as colony formation through hyphal growth and completion of the latency period through production of infectious conidia. All cross-infectivity assays contained three biological replicates. Inoculation assays on detached leaves and seedlings were repeated in triplicate, while potted-plant inoculations were repeated twice.

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No difference in pathogenicity was observed between G. spadiceus isolates 19001 and

19002. On detached leaves of both hop cultivars ‘Symphony’ and ‘Zeus’, G. spadiceus was capable of initial infection, vegetative growth and production of viable conidia that could be returned to hemp and be pathogenic (Table 3-2). However, when compared to the level of colonization and sporulation on hemp over the same 14-day timeframe, G. spadiceus was a significantly slower developing and macroscopically less prolific pathogen on hop tissue

(Figure 3-3). The latency period of G. spadiceus was delayed by 1-3 days on hop in comparison to control inoculations on hemp leaf tissue. When G. spadiceus was inoculated on to second and third node leaves of potted hop ‘Symphony’ and ‘Zeus’ plants, we were unable to produce a compatible infection event that resulted in macroscopically visible powdery mildew colonies, and as such considered G. spadiceus to be non-pathogenic in a practical sense. When young leaf tissue is collected for use in a detached leaf culture system, the leaf remains consistent at its given growth stage. Alternatively, when that same aged leaf is inoculated while it is still attached to the rest of a potted plant, the leaf continues to grow – increasing in both size and cuticle thickness (Twomey et al. 2015; Ficke, Gadoury, and Seem 2007). As such, we hypothesize that as part of a potted plant, the leaf acquires a degree of ontogenic resistance in the days following inoculation, which suppresses G. spadiceus hyphal growth rate, preventing the fungus from growing well enough to complete its latency and produce conidia, as reported in other powdery mildew pathosystems (Twomey et al. 2015; Ficke,

Gadoury, and Seem 2007; Asalf et al. 2014). Based on these results, it is unlikely that the G. spadiceus isolates tested would become significant pathogens of hop.

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Figure 3-3. Golovinomyces spadiceus growing on (a) the first leaf node of the hemp cultivar

‘Anka’ seedling; (b) the hemp cultivar ‘TJ’s CBD’ 14 days post inoculation (dpi); and (c) on the hop cultivar ‘Symphony’ 14 dpi.

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Table 3-2. Compatibility of Golovinomyces spadiceus and Podosphaera macularis isolates on hemp and hop. (+) indicates compatible pathogen interaction (-) indicates a non-compatible pathogen interaction; (+/-) indicates a compatible interaction in detached leaf culture, but not so on a potted plant of the given cultivar. The mean latency period rounded to the nearest whole day is in parentheses.

Cannabis sativa Humulus lupulus Isolate Anka Wild Horse TJ's CBD Symphony Zeus Golovinomyces 19001 + (9) + (9) + (9) +/- (10) +/- (11) spadiceus 19002 + (8) + (9) + (9) +/- (10) NA Podosphaera NY002 + (9) + (8) - + (7) + (9) macularis NY005 + (8) + (7) - + (6) + (7)

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Podosphaera macularis isolates NY002 and NY005 inoculated on to ‘TJ’s CBD’ induced a non-host resistance type response, where P. macularis conidia germinated at a low rate, and those that germinated did not grow past production of an initial germination tube and primary appressorium (Figure 3-4). In some instances, a localized HR response could be observed in the plant host epidermal cell immediately below the P. macularis appressorium (Figure 3-4b).

Podosphaera macularis isolates were pathogenic on hemp ‘Anka’ and ‘Wild Horse’ (Table 3-

2). Ten to fourteen-day old seedling and detached leaf inoculations of both varieties yielded compatible infection events, with a cumulative P. macularis infection success rate of 53% (n =

36) across the two varieties. In many of the compatible infections, a notably dense hyphal mat developed prior to production of conidiophores (Figure 3-5a). Podosphaera macularis isolate

NY005 averaged a latency period one day shorter than isolate NY002 across all infection events, including the control infections on hop cultivar ‘Symphony’. Controlled inoculations on to potted hemp plants cultivar ‘Wild Horse’ and ‘Anka’ also produced macroscopically visible P. macularis colonies (Figure 3-5b, 3-5c).

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Figure 3-4. An incompatible host-resistance interaction between the hemp cultivar ‘TJ’s CBD’ and P. macularis. (1) is an example of a non-germinated conidium; (2) an example of a conidium that has germinated and terminated growth at the formation of an initial appressorium; and (3) a necrosed epidermal cell, part of a localized host hypersensitive response to presence of the powdery mildew conidium.

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Figure 3-5. Podosphaera macularis growing on (a) a first leaf node of the hemp cultivar ‘Anka’ seedling; (b) a young leaf from the secondary bud of a potted hemp plant cultivar ‘Wild Horse’;

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Sexual mating compatibility assays.

The mating types of the two hop PM isolates (NY002 and NY005) were confirmed through PCR amplification of the MAT1-1 and MAT1-2 loci using primers based on deposited

NCBI sequences of each (Wolfenbarger et al. 2015) and confirmation of sequence accuracy through Sanger sequencing of PCR products. To further confirm sexual compatibility the two hop PM isolates were crossed with one another on detached hop leaves (‘Symphony’) and surveyed for production of chasmothecia.

The hemp PM isolates (19001 and 19002) were surveyed for mating type through attempted PCR amplification of the MAT1-1-1 and MAT1-2-1 mating type loci. Due to limited sequence availability of the mating type regions in the genus Golovinomyces, the MAT1-1 locus was targeted using one set of primers designed from the Erysiphe necator MAT1-1 locus

(Brewer et al. 2011), and a second set of primers designed from the Podosphaera macularis

MAT1-1 locus (Wolfenbarger et al. 2015). Two MAT1-2 HMG domain locus primer sets were designed based on a G. orontii isolate (GenBank KJ909541, unpublished) and a P. macularis isolate (Wolfenbarger et al. 2015). The sexual compatibility of the two respective hemp PM isolates was checked by inoculating both isolates on to the same detached hemp leaf, varieties

‘TJ’s CBD’ and ‘Anka’. No co-inoculation of hemp PM isolates 19001 and 19002 resulted in the production of chasmothecia, providing evidence that they are likely the same mating type idiomorph (Table 3-3). Prior publications have reported the formation of viable G. spadiceus chasmothecia in hemp production systems of the eastern and southeastern United States, documenting structures which contain 5-15 asci that each bear two ovoid ascospores (Szarka et al. 2019). As the existence of sexual reproduction in G. spadiceus is not in question, nor is

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there any reported evidence across powdery mildew systems to suggest mating type as a driving factor of cross-host infection capability, additional G. spadiceus isolates of the alternate mating type were not considered necessary for the context of this study.

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Table 3-3. Sexual reproductive compatibility between isolates of Podosphaera macularis and

Golovinomyces spadiceus. (+) indicates sexual reproductive compatibility and formation of chasmothecia fruiting bodies; (-) indicates an incompatible sexual reproductive pairing of isolates; (NA) indicates the cultivar was not used for that isolate pairing because one or both isolates could not successfully colonize the given host.

Cannabis sativa Humulus lupulus Isolate pair Anka TJ's CBD Symphony 19001 + Golovinomyces spadiceus - - - 19002 NY002 + Podosphaera macularis + NA + NY005 19001 + - NA - Golovinomyces spadiceus NY002 + Podosphaera macularis 19002 + - NA - NY005

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When co-inoculated on to the same detached hop leaf ‘Symphony’, P. macularis isolates NY002 and NY005 transitioned from asexual growth to production of sexually reproduced chasmothecia at any point where mycelial growth of the two isolates intersected, confirming that the two isolates are of opposite mating type and are sexually compatible.

Targeted amplification of the MAT1-1-1 and MAT1-2-1 mating type loci confirmed that P. macularis isolate NY002 is mating type MAT1-2, while P. macularis isolate NY005 is mating type MAT1-1. None of the PCR primers targeting the MAT1-1 and MAT1-2 loci yielded amplified DNA products in either G. spadiceus isolate. Ultimately, whole genome sequencing based approaches will allow for targeted design of primers capable of differentiating G. spadiceus mating type, as well as many other important gene regions of powdery mildew organisms, such as the demethylase inhibitor (DMI) fungicide gene target, CYP51, for example

(Jones et al. 2014). While undoubtedly a worthwhile endeavor in improving our downstream ability to manage G. spadiceus, the timeline required to produce a high quality whole genome of an obligately biotrophic fungus such as G. spadiceus is significant and fell outside the scope of this specific cross-infection study.

The ability of P. macularis to undergo sexual reproduction on hemp plant tissue was investigated on 10-14 day old ‘Anka’ seedlings. P. macularis isolates NY002 and NY005 were co-inoculated on to ‘Anka’ seedlings and chasmothecia initials could be observed under a dissecting microscope as early as 18 days post-inoculation (Figure 3-6a). Over the next 14 days the chasmothecia initials matured from ascocarps appearing externally clear, to yellow, to brown, to black. Once the ascocarp was a brown-black color, the ascocarps were transferred to a glass slide, where pressure was applied to the coverslip to reveal the inner ascus. A single

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ascus was observed, which is consistent with P. macularis morphology (U. Braun 2002) (Figure

3-6b). Staining with Sudan Black B revealed the expected granularity and lipid droplets within a P. macularis ascus (Wolfenbarger et al. 2015) (Figure 3-6c).

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Figure 3-6. (a) Formation of P. macularis chasmothecia on the hemp cultivar ‘Wild Horse’. (b)

P. macularis ascocarp, isolated from a colony originating on hemp, that has dehisced and is releasing a single ascus. (c) Ascus contents are stained with 1% Sudan Black B to confirm presence of the expected lipid droplets within a maturing ascus.

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Co-inoculations of G. spadiceus isolate 19001 with each respective P. macularis isolate were made to investigate potential sexual compatibility between the two species (Table 3).

Crosses were made on detached hop leaves ‘Symphony’ and detached hemp leaves ‘Anka’. In the case of the inoculations on the detached hop leaves, to accommodate differing growth rates, the hemp PM isolates were inoculated and allowed to grow for 3 days before inoculating the same leaf with the respective hop PM isolate. No chasmothecia were formed in any of the co-inoculations between G. spadiceus and the P. macularis isolates of either mating type.

Larger Implications on the Management of Hop and Hemp Powdery Mildews.

Total planted hemp acreage is expected to continue to increase dramatically, and production regions now frequently overlap with regions of hop production (Vote Hemp

2018b). With the recent reports of hemp powdery mildew in Canada (Pépin, Punja, and Joly

2018) and the United States (Szarka et al. 2019) and the overlapping of production regions, understanding the cross infectivity of hemp-associated and hop-associated powdery mildew is highly relevant to future successful disease management of both pathogens.

Using G. spadiceus originally cultured on detached hemp leaves, and then inoculated on to detached leaves of hop ‘Symphony’ and ‘Zeus’, we were able to demonstrate pathogenicity and confirm cross infective capability on hop. The inability to induce G. spadiceus infection on potted hop plants of either cultivar suggests this powdery mildew species is unlikely to be a significant pathogen in hop production systems from a disease management perspective. However, a broader survey of additional hop cultivars is warranted.

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Using P. macularis originally cultured on detached hop leaves, and then inoculated on to a suite of hemp varieties, we were able to confirm the cross infective capability of P. macularis on hemp ‘Anka’ and ‘Wild Horse’. Inoculation events on hemp ‘TJ’s CBD’ yielded an incompatible reaction highly similar to the poor germination and HR response associated with host resistance. Additionally, when compatible mating types of P. macularis were paired on

‘Anka’ seedlings, chasmothecia formation occurred at growth points where the two isolates overlapped, demonstrating the pathogens ability to reproduce both sexually and asexually on hemp. Future research will survey additional hemp cultivars to determine if resistance to P. macularis is unique to ‘TJ’s CBD’.

Currently the MAT1-2 mating type idiomorph of P. macularis has yet to be documented within the PNW hop growing region, while both mating types are present in approximately a

1:1 ratio in hop production regions of the Midwest, Eastern US, and Europe (Wolfenbarger et al. 2015). The introduction of the second mating type would likely mean enhanced overwintering capability of the pathogen through sexual reproduction and formation of chasmothecia, as well as enhanced distribution and variation of virulent strains of P. macularis as a genetic result of sexual recombination. Because of these implications, a quarantine on the import of hop plant material from anywhere outside of the PNW was put in place in 2015

(Gent 2015). However, if P. macularis can also infect hemp to some degree, a possible alternative route for the MAT1-2 mating type to enter the region exists. Methods to monitor and prevent introduction of the P. macularis MAT1-2 mating type idiomorph through this route should be considered. Additional work investigating the extent to which P. macularis isolates derived from hop of various geographic origins and hop species can infect hemp would

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provide a clearer picture into the risks of P. macularis being an economically relevant pathogen of C. sativa.

Acknowledgements: We thank Holly Lange, Rebecca Wilk, and Mary Jean Welser for their technical support.

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Management: A Case Study of Grape Powdery Mildew.” Phytopathology 92 (6): 671–75.

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Gadoury, DM. and Pearson, RC. 1991. “Heterothallism and Pathogenic Specialization in

Uncinula Necator.” Phytopathology 81 (10): 1287–93.

Gent, DH. 2015. “Hop Quarantine Important for Hop Powdery Mildew Control.”

https://www.usahops.org/cabinet/data/Quarantine-HPM Gent article 2-15.pdf.

Gent, DH. 2008. “A Decade of Hop Powdery Mildew in the Pacific Northwest.” Plant Health

Progress 1998 (January). https://doi.org/10.1094/PHP-2008-0314-01-RV.

Hop Growers of America. 2018. “2018 Hop Statistical Report.”

Jones, L., Riaz, S., Morales-Cruz, A., Amrine, KC., McGuire, B., Gubler, WD., Walker, MA., and

Cantu, D. 2014. “Adaptive Genomic Structural Variation in the Grape Powdery Mildew

Pathogen, Erysiphe Necator.” BMC Genomics 15 (1). https://doi.org/10.1186/1471-

2164-15-1081.

McConnell, M., Wyden, R., Merkley, J., and Paul, R. 2018. Hemp Farming Act of 2018. United

States of America: United States Congress.

Ocamb, CM., Klein, R., Barbour, J., Griesbach, J., and Mahaffee, WF. 1999. “First Report of

Hop Powdery Mildew in the Pacific Northwest.” Plant Disease 83 (11): 1072–1072.

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Pearson, R., and Gadoury, DM. 1987. “Cleistothecia, the Source of Primary Inoculum for

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Pépin, N., Punja, ZK., and Joly, DL. 2018. “ Occurrence of Powdery Mildew Caused by

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Appendix I: Hop Extension – Communicating disease management strategies for hop powdery mildew and hop downy mildew

Over the past five years, an incredibly important aspect of my education as a plant pathologist has been the extension interactions that I’ve had with the hop growers and brewers of the Northeast and Pacific Northwest US, especially those from New York. Seeing research through to its incorporation into the disease management practices of the hop grower community is critically important to the long term viability of the industry. As such, as a graduate student at Cornell University, I’ve taken every opportunity that I could to support the hop industry through extension and help establish Cornell as a reliable source for hop pathology. I often learn just as much about hop production from the growers as I hope that they learn about disease management strategies from myself. In having presented at almost one dozen hop commodity meetings or grower conferences, including various “Hop Twilight” field meetings around New York, the American Hop Convention, the Northeast Hop Alliance annual meeting, Cornell’s Fruit Field Day, the Oregon Hop Commission’s seasonal board meeting, and numerous field visit to grower hop yards, I have accumulated well over one thousand extension contact hours as a Cornell PhD candidate.

These extension efforts had direct financial support in the form of a $15,000 Engaged

Cornell Graduate Student Grant that I was awarded in 2017, which outlined a two-year supplementary project to engage the Northeast hop growers, identify gaps in the communication of disease management strategies from the grower perspective, and work to generate resources that fill these information voids in media forms most desirable to this grower community. As such, the Cornell SIPS hops webpage was born (Figure A1), which

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houses a collection of six (currently) peer-reviewed documents that address specific grower needs relating to the management of hop powdery mildew and hop downy mildew (Figures

A2 – A7). In addition to these documents, there are two webinar presentations made available on the page that discuss aspects of proper identification and management of the “mildews” affecting hop, including hop powdery mildew and hop downy mildew. The domain also serves as a landing page to link other reputable, peer-reviewed, free-to-the-public sources of information regarding integrated pest management in hop production, such as the Field Guide for Integrated Pest Management in Hops (3rd edition) (Gent et al. 2015), as well as information connecting growers with Cornell’s Plant Disease Diagnostic Clinic and other reputable third party services that will accept NY-originating grower samples for hop petiole testing and hop cone quality analysis. The underlying goal of this web page is to establish Cornell as a reliable source of curated hop disease and pest management information, be that research conducted internally within Cornell or externally at other land grant universities. If we cannot be the university conducting all of the hop pathology research, we can at least be a reliable source for NY growers to look to when making disease management decisions. This web page is incorporated into the Cornell SIPS landing domain (sips.cals.cornell.edu), and therefore will be maintained long-term by the SIPS media team, ensuring information can easily be added and links will remain active. All documents and links within the page have been reviewed for compliance with the American Disabilities Act.

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Figure A1: A screenshot of the Cornell SIPS Hops web page. All content was provided by Bill Weldon and the website was designed in collaboration with Craig Cramer, Media Specialist for Cornell SIPS.

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Figure A2: Grower Essentials Extension Document 1: Proper scouting and identification of hop downy mildew.

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Figure A3: Grower Essentials Extension Document 2: Proper scouting and identification of hop powdery mildew.

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Figure A4: Grower Essentials Extension Document 3: Proper scouting and identification of hop powdery mildew chasmothecia and a summary on the current understanding of host resistance to hop powdery mildew.

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Figure A5: Grower Essentials Extension Document 4: Explanation of the complicated dynamics of host resistance to hop downy mildew, which involves both an aboveground foliar resistance and a below ground resistance to crown rot.

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Figure A6: Grower Essentials Extension Document 5: Five considerations before ever planting your first hop: a document of vital decisions to make in preparing to plant a hop yard.

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Figure A7: Grower Essentials Extension Document 6: Differentiating hop powdery mildew and hop downy mildew and why correctly differentiating the two diseases is critical.

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References:

Gent, DH., Walsh, D., Barbour, J., Boydston, R., George, A., James, D., and Sirrine, R. 2015.

Field Guide for Integrated Pest Management in Hops. 3rd ed. Hop Growers of America.

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Concluding Statements and Future Directions

In summary, this dissertation has addressed several critical, previously unexplored, aspects of the hop powdery mildew pathosystem. These projects have created both a substantial body of novel knowledge with respect to P. macularis overwintering biology, population structure, and host range, as well as a tangible collection of disease management tools that optimize the timing of control measures to better align with pathogen biology and real-time weather inputs. The marriage between the creation of new knowledge that moves the field of plant pathology forward and the generation of tools and strategies that are immediately applicable to growers is the driving force behind the land grant mission, which I sincerely hope is clearly embodied throughout this dissertation.

Each chapter described a meaningful amount of new knowledge with respect to the hop powdery mildew pathosystem. In chapter one, I described the distribution patterns of unique genetic profiles observed throughout the United States and Europe, which indicate that the P. macularis within Midwest and Eastern US commercial hop yards is at present, primarily introduced via hop planting material, as opposed to arriving from nearby feral hop plantings that harbor P. macularis. These genotyping efforts also suggested that the V6- virulant strain of P. macularis has yet to expand in range beyond the PNW US, while the MAT1-

2 mating type has yet to arrive into the region.

As described in chapter two, we now have a better understanding of how geographical differences, which associate with differences in the duration and intensity of winter weather, drive the maturation of P. macularis chasmothecia. The manner in which temperature and the duration of wetting events, such as rain or dew, interact to promote ascospore release was

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also described, and we observed germination and growth of P. macularis ascospores across the majority of these springtime temperatures. The steadfast adherence of P. macularis chasmothecia to host tissue indicates that these structures are likely to overwinter along the soil surface, as the vast majority of aboveground hop tissue senesces as plant debris in the late autumn. Chasmothecia are reported to form particularly well on hop cones (Gent, personal communication) and cones can remain attached to the remnants of the bines from the previous season over the winter. This would provide a means for the pathogen to remain associated with the general location of a hop host during the dormant period. Also, for the first time, P. macularis ascospores, in the absence of any vegetative or asexual growth forms, have been demonstrated to be a substantial source of early season disease incidence.

Finally, within the contents of chapter three, the pathogenicity of P. macularis on certain varieties of industrial hemp was described. The ability of P. macularis to both grow vegetatively and undergo sexual recombination to produce chasmothecia on Cannabis sativa represents a secondary pathway for pathogen dispersal that could affect existing quarantine efforts within the PNW US, especially as plantings of Cannabis sp. are increasing exponentially throughout the region (Vote Hemp 2019).

In addition to the generation of new knowledge, these projects also created a wide array of tools that researchers and growers may utilize in the management of hop powdery mildew. A collection of AmpSeq molecular markers now exists that may be widely used for the future high-throughput and cost effective genotyping of P. macularis populations.

Similarly, a quantitative real-time PCR (qPCR) assay is now available that can survey P. macularis mating type in a highly specific, multiplex-ready, and high-throughput manner. Two

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epidemiological models have been developed, which pair together to predict the seasonal maturation window of P. macularis chasmothecia in the spring and early summer, as well as the risk of ascosporic release during a wetting event that happens during this period of chasmothecial maturity. Lastly, and maybe most importantly, much of this information, as well as the pre-existing body of knowledge in regard to the management of hop powdery mildew and hop downy mildew, was published in a collection of grower-focused extension documents, podcasts, and webinars which are housed within the Cornell School of Integrative

Plant Sciences Hops Webpage, https://sips.cals.cornell.edu/extension-outreach/hops/.

Unsurprisingly, there is still much to be done in the continued effort to better understand P. macularis as a biological organism, more accurately describe how the pathogen interacts with its host and environment to complete its life cycle and perennate into subsequent seasons, and ultimately utilize this knowledge to employ disease management strategies that optimize inputs, prolong longevity of such tools, minimize the negative effects on the surrounding ecosystem, and more efficiently produce hops for use in the brewing process. Specific to the contents and focus of this dissertation, the following is a substantive, but not exhaustive, set of directions to which future research on the P. macularis pathosystem could go.

With respect to the creation and validation of an AmpSeq molecular marker library used in genotyping populations of P. macularis, there will continually be the need to add relevant markers when novel P. macularis phenotypes emerge anywhere within the hop production system. As a specific example, adding a marker or set of markers that differentiates isolates based on the possession of the Cascade-adapted phenotype would be an immediately

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valuable addition to this marker library, as this strain has had a significant impact on decreasing the acreage of Cascade hop planted in the PNW US region, as well as around the entire country (Gent et al. 2017; USA Hops 2019). In reference to this same marker library, a future effort to fully sequence the P. macularis genome utilizing a long read technology (Pac-

Bio, Nanopore, etc.) would allow us to determine the specific genomic location that each

AmpSeq marker is targeting. This would clarify questions regarding the ploidy of P. macularis, as well as the surprisingly high levels of heterozygosity returned for some markers, which could be the result of off-target amplification, a mixed culture having been sampled, or gene- duplication events that created genes with copy number variation. Additionally, due to the striking genetic differentiation between Northeastern US P. macularis isolates derived from commercial and feral hop plants, a survey a mating compatibility should be conducted between the two populations. It is entirely possible that the two sub-populations have diverged to a point where their reproductive fitness with the other respective sub-population has been compromised. If this were to be the case, it would have direct implications on how we may expect pathogen movement to occur in the future.

In the near future, it is important to incorporate the epidemiological models described herein into one or multiple environmental monitoring databases, such as the Cornell Network for Environment and Weather Applications (NEWA). This action would allow hop growers throughout the US to utilize these tools in real time to make disease management decisions with respect to P. macularis ascosporic infection and imminent weather conditions.

Implementing both the chasmothecial maturation and ascospore release models into the

NEWA framework will also enable these models, which have been developed and validated

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largely in controlled conditions, to have their performance tested against dynamic weather patterns, which is the ultimate step in proving their utility in improving disease management strategies. As such, when incorporated into NEWA, a beta version should first be tested over a growing season, and then made available to the grower community upon satisfactory performance.

Now that P. macularis ascospores are clearly demonstrated to be a viable overwintering source, and that the late season chasmothecia appear to perennate more successfully than those formed around harvest, it would make sense to investigate whether any post-harvest control measures impact the degree to which ascosporic infection occurs the following season. Logically, control measures such as late-season basal foliage removal or late season fungicide applications should be investigated for such an effect, paying special attention to the impact that these practices have on the vigor and yield of the hop plant itself, as the late season hop foliage is known to be critical in providing the rhizome with the energy to overwinter and re-emerge the following season. If late-season basal foliage removal did prove to be a promising practice, then additional studies optimizing the timing would be important. I would also examine whether a very minor, late-season crowning practice that incorporates hop plant material into the top few inches of soil would be of any benefit, as it may promote the degradation of chasmothecia.

Because there were such striking cumulative numbers of matured ascospores recorded at the most temperature overwintering location (NC) in comparison to the two generally colder winter locations (NY and WI), future studies investigating whether P. macularis ascospores have a chilling or dormancy requirement driving springtime viability is

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merited. This could be addressed by collecting a population of P. macularis chasmothecia in the autumn and overwintering a subset of samples at 2C, a subset in a low temperature chamber that has a mean temperature of 2C but fluctuates ± 5C on a daily interval (and therefore falls below 0C for a portion of each day), and a subset that is placed outdoors to overwinter naturally.

It has also been anecdotally reported (Gent, personal communication) that powdery mildew in organically managed hop yards is often much less severe, even non-existent, in comparison to the mildew levels of commercial hop yards nearby. Investigating how efficiently chasmothecia form on organic versus conventionally managed hop could a valuable future direction, paying special attention to differences in host vigor between the two management systems and how that relates to chasmothecial formation and overwintering success.

Proceeding into the early spring, field studies investigating how various existing cultural control practices perform in limiting the initial disease pressure caused by P. macularis ascospore release and infection would be critical, as this mode of overwintering appears to be much more efficient and successful than that of perennation via bud infection. These field studies would survey cultural practices such as pruning, varying at the timing, frequency, and mode (mechanical versus chemical), as well as the timing of fungicide applications and removal of basal foliage, again exploring various timings, frequency of removal, and mode.

Finally, it will be important to continue to monitor and sample for P. macularis within industrial (CBD) hemp and THC Cannabis sativa plantings as production expands into regions that overlap with hop production. The current quarantine measures in place within the PNW

US in an effort to prevent the arrival of the MAT1-2 mating type into the region have no

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mention of Cannabis sativa plant material (Gent 2015). As such, it could be an alternative route for the pathogen to arrive into the region, migrate into a nearby hop yard, and then very likely spread throughout the region, as was the case for P. macularis over the 1996 and 1997 hop growing seasons in the PNW US (Ocamb et al. 1999).

References:

Gent, DH. 2015. “Hop Quarantine Important for Hop Powdery Mildew Control.”

https://www.usahops.org/cabinet/data/Quarantine-HPM Gent article 2-15.pdf.

Gent, DH., Massie, ST., Twomey, MC., and Wolfenbarger, SN. 2017. “Adaptation to Partial

Resistance to Powdery Mildew in the Hop Cultivar Cascade by Podosphaera Macularis.”

Plant Disease 101 (6): 874–81. https://doi.org/10.1094/PDIS-12-16-1753-RE.

Ocamb, CM., Klein, R., Barbour, J., Griesbach, J., and Mahaffee, WF. 1999. “First Report of

Hop Powdery Mildew in the Pacific Northwest.” Plant Disease 83 (11): 1072–1072.

https://doi.org/10.1094/PDIS.1999.83.11.1072A.

USA Hops. 2019. “2019 US Hop Statistical Report.”

Vote Hemp. 2019. “2019 U.S. Hemp License Report.” U.S. Hemp Crop Report. 2019.

https://www.votehemp.com/u-s-hemp-crop-report/.

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