EXPLORATION OF THE SWEET CHERRY MICROBIOME AND POTENTIAL BIOLOGICAL CONTROLS OF PSEUDOMONAS SYRINGAE PV. SYRINGAE By Tammy K. Wilkinson A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Horticulture – Doctor of Philosophy 2023 ABSTRACT Bacterial canker, a disease caused by the bacteria Pseudomonas syringae pv. syringae (Pss), remains a critical economic hurdle for sweet cherry production. Young trees are most susceptible, while older trees with bacterial canker are sources of inoculum. Pss can be found throughout the environment and living on all plant surfaces. Infections can occur through multiple avenues including blossoms, wounds, and various natural openings including leaf scars. Pss at low population numbers does not cause pathogenesis but becomes an issue as populations build during periods of cool, wet weather that are typical of spring and fall in Michigan. The Pss isolated from Michigan sweet cherry flowers was found to be phylogenetically diverse, with 20 strains belonging to 3 out of the 7 clades in phylogroup (PG) 2 (PG2b, PG2c, PG2d). Virulence tests with the 20 strains on green fruit and sweet cherry wood from two sweet cherry cultivars, ‘Sweetheart’ and ‘Coral Champagne’, demonstrated a range of virulence: strains in PG2d were the most virulent, PG2b strains were moderate to avirulent, and PG2c strains were avirulent (PG2d>PG2b>PG2c). The variability of the Pss strains also was reflected in their ice nucleation activity (INA) with strains having INA+, INA−, and INA+/− phenotypes, GATTa+ + − − and GATTa+ + + − profiles, and amplification of the genes encoding for the production and secretion of the phytotoxin syringomycin being syrB+/syrD+ and syrB−/syrD+. Though much of its epiphytic lifestyle, biology, and ability to live in mixed populations with varying levels of virulence has been elucidated for Pss, very little was known about the other bacteria that exist with Pss within the sweet cherry leaf microbiome. A survey of the sweet cherry leaf microbiome of three cultivars, ‘Benton’, ‘Gold’, and ‘Sweetheart’ was conducted from Fall 2017- Fall 2019 in two regions of Michigan. There were no regional or cultivar differences in the sweet cherry leaf microbiome. The clear drivers of microbiome stability were season and Pseudomonas, as the microbiome was variable in spring and became more stable in the summer and fall. The microbiome was dominated by the Phylum Proteobacteria (67 to 100% relative abundance) and the genera Pseudomonas (26 to 95% relative abundance). The core microbiome was composed of Pseudomonas and Sphingomonas OTUs. All top taxa have the potential to be beneficial to the sweet cherry tree. As control options for the management of Pss populations are limited there is keen fruit grower interest in the potential for biological control agents. Current commercial biological controls (BCAs) moderate to avirulent Michigan Pss. The moderately virulent and avirulent Michigan Pss were surveyed via in-vitro co-inoculations for their potential to decrease growth of the virulent to moderately virulent Pss using the same method. In-vitro co-inoculations were an acceptable way to initially assess potential BCAs. All commercially available BCAs were able to reduce the mean total area of Pss growth compared to when they were next to water, with up to 50% suppression in some cases. No single commercial BCA stood out as the premier choice for Pss growth suppression. None of the Michigan Pss were found to be ideal candidates for the potential biological control of virulent Pss, with no statistical differences in growth compared to when inoculated next to water. This dissertation is dedicated to my children, Gregory & Grant iv ACKNOWLEDGEMENTS I would like to thank Greg Lang, George Sundin, Todd Einhorn, and Quan Zeng for their guidance and patience as I worked towards my Doctor of Philosophy degree at Michigan State University. Thank you to Feiran Li for all of the hours that she put in as my assistant on all of the various experiments and for all the picture wrangling, you are a wizard. Thank you to Erin Lauwers and Audrey Sebolt who gave me a crash course in MLST, phenotype assays and DNA extraction respectively. I also thank Sakib Hasan, and Guanqi Lu for your statistical consulting support. Thanks to Sarah Westcott for answering millions of my Mothur questions. I thank Kurt, Tom, and Scott Wells for allowing me to use their orchard. Thank you to the staff at the Clarksville Research Center, the Horticulture Teaching and Research Center, the Southwest Michigan Research Center, and the Research Technology Support Facility Genomics core. Thank you to Bob Van Buren, Pat Edger, Gou-Qing Song and Amy Iezzoni for the use of their labs and equipment. I would also like to thank the Michigan Cherry Committee for partial funding of this work. I give special thanks to my husband, Fred, and my children, Gregory and Grant, for all of their support, understanding and patience. I would also like to thank my parents and the rest of my family and friends for the never wavering encouragement on my long trek as a career student. v TABLE OF CONTENTS CHAPTER 1 PSEUDOMONAS SYRINGAE PV. SYRINGAE AND 55 YEARS OF BACTERIAL CANKER EPIDEMICS IN MICHIGAN: A LITERATURE REVIEW .........................................1 1.1: Michigan Cherry Production and Pseudomonas syringae pv. syringae .......................1 1.2: Pseudomonas syringae pv. syringae .............................................................................3 1.3: Pss Management ...........................................................................................................8 1.4: Pss Epiphytic Living and the Leaf Microbiome .........................................................11 1.5: Biological Control.......................................................................................................16 1.6: Objectives of Study .....................................................................................................21 REFERENCES ..................................................................................................................23 CHAPTER 2 VIRULENCE AND PHYLOGENY OF MICHIGAN PSEUDOMONAS SYRINGAE PV. SYRINGAE ISOLATES FROM SWEET CHERRY FLOWERS HIGHLIGHT VARIABILITY, THE NEED FOR CAUTION, AND AREAS OF FUTURE RESEARCH .......38 2.1: Abstract .......................................................................................................................38 2.2: Introduction .................................................................................................................39 2.3: Methods ......................................................................................................................46 2.4: Results .........................................................................................................................58 2.5: Discussion ...................................................................................................................63 2.6: Conclusions .................................................................................................................70 REFERENCES ..................................................................................................................94 CHAPTER 3 EXPLORATION OF THE SWEET CHERRY LEAF MICROBIOME REVEALS DOMINATION BY PSEUDOMONAS AND SEASONAL COMMUNITY DIRECTIONALITY ....................................................................................................................104 3.1: Abstract .....................................................................................................................104 3.2: Introduction ...............................................................................................................105 3.3: Methods ...................................................................................................................113 3.4: Results .......................................................................................................................119 3.5: Discussion .................................................................................................................127 3.6: Conclusions ...............................................................................................................136 REFERENCES ................................................................................................................167 CHAPTER 4 IN VITRO CO-INOCULATIONS FOR ASSESSMENT OF POTENTIAL PSEUDOMONAS SYRINGAE PV. SYRINGAE BIOLOGICAL CONTROL AGENTS ..............177 4.1: Abstract .....................................................................................................................177 4.2: Introduction ...............................................................................................................177 4.3: Methods ....................................................................................................................182 4.4: Results .......................................................................................................................185 4.5: Discussion .................................................................................................................190 4.6: Conclusions ...............................................................................................................195 REFERENCES ................................................................................................................219 APPENDIX A: PSS CHARACTERIZATION ...........................................................................224 APPENDIX B: SWEET CHERRY MICROBIOME ..................................................................359 vi APPENDIX C: PSS VS. BIOLOGICAL CONTROL AGENTS ................................................443 vii CHAPTER 1 PSEUDOMONAS SYRINGAE PV. SYRINGAE AND 55 YEARS OF BACTERIAL CANKER EPIDEMICS IN MICHIGAN: A LITERATURE REVIEW 1.1: Michigan Cherry Production and Pseudomonas syringae pv. syringae Michigan is an agriculturally diverse state, with 46,000 farms and 300 agricultural commodities (MDARD, 2023; USDA-Nass, 2021). This agricultural diversity is due to the protection of the Laurentian Great Lakes and fertile soils from glacial deposits (Brown, 1941; Warren and Vermette, 2022). Though orchards are scattered throughout the state, the “fruit belt” region along Lake Michigan is where the majority of fruit production occurs (Brown, 1941; Warren and Vermette, 2022). Michigan is fourth in sweet cherry (Prunus avium) production with 7,807 total acres and remains the nation’s top tart cherry (Prunus cerasus) producer (70% of the nation’s supply) with 33,381 total acres (Lang, 2019a; MDARD, 2018; USDA-Nass, 2019). The United States of America (USA) produced 275,000 tons of cherries in 2022, just behind Turkey for the top spot in global cherry production (Lang, 2019a; Bujdosό and Hrotkό, 2017; USDA- Nass, 2022). Global cherry production and consumer demand has increased dramatically over the past 20 years, driven by several factors, including marketing that touts cherries as flavorful “super fruits” that confer a wide range of health benefits (Lang, 2019a). According to an early census, cultivated cherries were established in Michigan sometime in the 1870s (Brown, 1941). The modern sweet cherry orchard (150+ years later) looks markedly different, moving away from orchards of trees reminiscent of their forested counterparts to high- density plantings grafted on dwarfing to semi-vigorous precocious rootstocks, such as the ‘Gisela’ series (Gi.6; Gi.5, and Gi3) supported by trellis (Lang et al., 2019). Michigan State University researchers and progressive cherry growers have led the charge toward the adoption 1 of the high-density “pedestrian orchards” featuring “two-dimensional/planar” production systems like that of the UFO (Upright Fruiting Offshoots) that are trained to form a “fruiting wall” and are easily picked from one side of the tree (i.e., less labor intensive) compared to the traditional “three-dimensional” canopy architectures (Lang et al., 2022). Various covering systems (e.g., row covers, high tunnels, and retractable roofed greenhouses) are also being explored to reduce rain-induced fruit cracking and disease pressure, and promote earlier fruit harvest potential (Lang et al., 2016; Lang, 2019a). These innovative approaches address grower concerns over production challenges due to labor issues (lack of workers and costs) and climatic events (frosts and rain) (Lang, 2019a). Another major limitation for sweet cherry production is significant disease pressures (e.g., bacterial canker, brown rot, and cherry leaf spot) linked to Michigan’s climate. Cool, wet springs and late falls are typical, and are ideal for the proliferation and infection of the bacterial phytopathogen Pseudomonas syringae pv. syringae van Hall (Pss), the causal agent of bacterial canker disease (Hirano and Upper, 1990; Hirano and Upper, 2000; Jones and Sutton, 1996; Kennelly et al. 2008). Management of this bacterial disease remains difficult and can be devastating to the sweet cherry industry, as young trees are highly susceptible (Spotts, 2010). Historically, Michigan had a lengthy spring with cool temperatures that delay bud break, and warmer falls that avoid late and early “killing frosts,” respectively, as a climatic influence of the Great Lakes (Warren and Vermette, 2022). Due to global climate change and the warming of the lake waters (including decreased ice cover), the occurrence of these previous protections has become quite volatile (Warren and Vermette, 2022). Lang (2019a) summarized climatic data (from J. Andresen, personal communication) from the Traverse City growing region that compared pre- to post-1940, indicating that bud development (side green) is now ~ 10 days 2 earlier, the number of potential frost events at side green has increased significantly from 5 events to ≥ 20, and annual rainfall has increased 10%. These statistics are particularly alarming when considering the biology of the Pss pathogen: in addition to the increase in population size and subsequent infection of cherry during cool, wet weather events, the pathogen is closely linked to the water cycle, being rain splash dispersed and capable of facilitating frost damage via ice nucleation which enables its entry into the plant (Hirano and Upper, 1990; Hirano and Upper, 2000; Jones and Sutton, 1996; Morris et al., 2008; Morris et al., 2013). Growers maintain high- density orchard systems with intensive pruning to promote appropriate leaf area-to-fruit ratios (LA: F), and increase canopy light penetration, yet such pruning can increase the risk of infection by Pss (Ayala and Lang, 2017; Whiting and Lang 2004). Dormant, early spring and late spring pruning often occurs concurrently with prime environmental conditions for Pss population increases and opportunistic infections via the pruning wounds (Kennelly et al, 2008; Jones and Sutton, 1996; Lang, 2019b; Spotts et al. 2010). 1.2: Pseudomonas syringae pv. syringae Jones (1971) reported Pss to be present in sweet cherry in Michigan for the first time following a 1968 epidemic (ca. 55 years ago). Most recently, three significant outbreaks have occurred (2002, 2012, and 2021), all occurring after unusually early warm spring weather that induced early bloom followed by intense frost events (Kennelly et al., 2007; Lauwers, 2022; Renick et al., 2008; Sundin and Rothwell, 2012). These large-scale events resulted in significant current and future year crop losses from frost damage as well as infection by Pss, leading to blossom blast followed by systemic disease and subsequent spur, shoot, limb, and young tree death (Kennelly et al., 2007; Lauwers, 2022; Renick et al., 2008; Sundin and Rothwell, 2012). Other Pss infection symptoms include sunken cankers, gumming, lesions on fruit, and “shot 3 holes” in leaves (Jones and Sutton, 1996; Kennelly et al., 2007). In addition to Pss, Jones (1971) isolated Pseudomonas syringae pv. morsprunorum (Psm) from sweet cherry. This pathogen has since been determined to be two different pathogens, P. amygdali pv. morsprunorum (race 1) and P. avellanae pv. morsprunorum (race 2); it is recovered more often on tart cherry and is less virulent than Pss in Michigan (Gomila et al., 2017; Hulin et al., 2020; Jones, 1971; Jones and Sutton, 1996; Renick et al., 2008). Pss can be differentiated from Psm via the biochemical assays GATTa (Gelatin liquefaction, Aesculin hydrolysis, Tyrosinase activity, and Tartrate utilization) and the amplification of the syrB or syrD gene (syringomycin phytotoxin); Pss is generally G+ A+ T − Ta− while Psm is G− A− T + Ta+, and Psm does not produce syringomycin (Bultreys and Gheysen, 1999; Latorre and Jones, 1979b; Lelliot et al., 1966; Renick et al., 2008; Sorensen et al., 1998). Psm produces the phytotoxin coronatine (detected through amplification of the cfl gene) which Pss does not; this toxin is able to interfere with stomatal closure, allowing bacteria to enter the host plant (Bultreys and Gheysen, 1999; Renick et al., 2008; Xin et al., 2018). Pss is a generalist phytopathogen and the causal agent for bacterial canker, blister bark, and blossom blast in stone fruit (all economically important Prunus spp.) and pome fruit (apple and pear) and is pathogenic to over 180 plant species globally (Kennely et al., 2007). Pseudomonas syringae (P.s.) has been found to have broad overlapping host ranges with phylogroup 2 (the Pss clade) having a greater extent of host ranges compared to other P.s. (Morris et al., 2019). Pss is also an opportunistic pathogen that can infect through wounds (pruning cuts and trellis wire rubs) and natural openings (leaf scars and stomates), but only when populations are high; otherwise, it is well-documented to live benignly on host and non-host plants (Hirano and Upper, 1990; Hirano and Upper, 2000; Kennelly et al., 2007; Latorre and Jones, 1979a; Lillrose et al., 2017). 4 Pss belong to the same phylogenetic lineage as Pseudomonas fluorescens and will fluoresce under UV light when grown on iron-deficient media; this is due to the production of iron (Fe) chelating molecules or siderophores (e.g., pyoverdines) and is often used in the initial steps toward the identification of Pss (Cornelis, 2010; Gomila et al., 2017; Latorre and Jones, 1979b; Lelliot et al., 1966). The ability to produce these siderophores confers epiphytic fitness to Pss as Fe is limited in the phyllosphere and Wensing et al. (2010) demonstrated that the strain 22d/93, a strong siderophore producer, was able to outcompete/out-colonize P.s pv. glycinea which produces a lesser amount of the same siderophores. Pss is a gram-negative bacterium in the class Gammaproteobacteria, that is phylogenetically placed within genomospecies 1, phylogroup 2, which is part of a much larger species complex composed of 13 phylogroups (Berge et al., 2014; Gardan et al., 1999; Gomila et al., 2017; Gutiérrez-Barranquero et al., 2019; Kennelly et al., 2007; Pulawska et al., 2017). Phylogroup (PG) 2 is composed of 7 clades (2a, 2b, 2c, 2d, 2e, 2f, and 2g) that contain pathogenic Pss (PG2b and PG2d), non-pathogenic Pss (PG2c) and Pss isolated from water and snow (PG2a and PG2e) (Abdellatif et al., 2020; Berge et al., 2014; Hall et al., 2019). The phylogroups 2f and 2g from characterizations of Pss strains isolated from grapes (Vitis vinifera) and oranges (Citrus sinensis) respectively are new additions to the clades originally described in Berge et al. (2014) (Abdellatif et al., 2020; Hall et al., 2019). The Pss in PG2c are all non-pathogens and have an atypical type III secretion system (T3SS), an important virulence component (Clarke et al., 2010; Mohr et al. 2008). As inferred by the former, Lauwers (2022) and Renick et al. (2008) classified Michigan Pss in PG2a, PG2b, and PG2d. These phylogroups were determined by Lauwers (2022) using multilocus sequence typing/analysis (MLSA/MLST) comparing 4 “housekeeping” genes to those of reference sequences. Current phylogenomic classifications of Pss are popularly being constructed utilizing 5 this method, using from 1-7 “housekeeping” genes (Abdellatif et al., 2019; Almeida et al., 2010; Berge et al., 2014; Bophela et al., 2020; Hall et al., 2019; Hulin et al., 2018a; Hwang et al., 2005; Iličić et al., 2021; Ivanović et al., 2022; Lauwers, 2022; Oksel, 2022; Popović et al., 2021; Sarkar and Guttman, 2004; Vasebi et al., 2020; Visnovsky et al., 2019). The ability of Pss to ice nucleate in plant tissues facilitates frost injury in the spring (Kennely et al., 2007). Pss cells are able to ice nucleate due to proteins embedded in their outer membrane and can do so at 0 to -5°C causing frost damage to plants at a higher temperature than would occur in the plant with no INA bacteria (i.e., in cherry, during bud break 50% of buds are killed at -6°C and 90% at -7°C with prolonged temperatures) (Hirano and Upper, 2000; Lindow et al., 1983a; Lindow et al., 1983b; Lukas et al. 2022; Salazar-Gutiérrez et al., 2004). The ice nucleation genes (ina genes) encode for outer membrane proteins (e.g., inaZ) which form as aggregates with aggregate size correlating to ice nucleus efficiency (Hirano and Upper 2000). The INA-induced frost injury allows Pss to enter and infect the plant causing initial blossom blast symptoms, which was a major contributor to a critical state-wide bacterial canker epidemic in Michigan in 2012 (Sundin and Rothwell, 2012). Renick et al. (2008) and Lauwers (2022) found a high level of INA strains in their surveys of Michigan Pss collected from cherry flowers with 94% INA and 100% INA respectively. Ice nucleation frequency of P.s cells in Pacific Northwest orchards was found to be variable, and strains with an INA phenotype may not be active all at the same time, thereby populations are composed of a combination of INA+/− phenotypes (Hirano and Upper, 2000; Gross et al., 1983; Renick et al., 2008). This is one of the reasons why the creation of recombinant Ice− strains did not persist as a viable biological control option as Ice− strains could not eliminate the mixed Ice+/− wild populations and control by the recombinant strain was based on prior colonization and density of the recombinant strains 6 (Hirano and Upper, 2000). It has been suggested that ancestrally the ability to ice nucleate may have been a mechanism for preventing cell injury during freezing in aquatic environments and appears to have been a part of the P.s. genome for a much longer time than the Type Three Secretion System (T3SS) (Morris et al., 2008; Morris et al. 2010; Morris et al. 2013). Pss overwinters in flower buds, cankers, bark, and systemically within the plant (Sundin et al., 1988; Jones and Sutton, 1996). Once Pss enters the plant, it inhabits the apoplast, where the bacteria can produce virulence factors through the Type Three Secretion System (T3SS), allowing the bacteria to inject effector proteins into plant cells (Alfano and Collmer, 2004). Evidence through whole genome comparisons points to shared genes/proteins in common among strains that have led to the adaptation for pathogenicity on woody hosts, cherry specifically, and proposed co-evolution with Prunus spp. (Hulin et al., 2018b; Hulin et al., 2020; Nowell et al., 2016; Ruinelli et al., 2022). These shared genes and proteins are part of the T3SS repertoire; the T3SS consists of an apparatus made by Pss (once inside the plant apoplast) that injects effector proteins into the plant cells, releasing nutrients to promote the proliferation of Pss and thwart the plants basal immune response (i.e., plant triggered immunity or PTI) (Alfano and Collmer, 2004; Hulin et al., 2018b; Hulin et al., 2020; Nowell et al., 2016; Ruinelli et al., 2022; Ruiz-Bedoya et al., 2023). These effector proteins also are considered “double agents” as they have the potential to elicit an additional plant immune response, or effector-triggered immunity (ETI), which adds another level of infection complexity dependent on both pathogen and plant host genetics (Alfano and Collmer, 2004; Hulin et al., 2018b; Hulin et al., 2020; Nowell et al., 2016; Ruinelli et al., 2022; Ruiz-Bedoya et al., 2023). Effectors are thought to be acquired through horizontal gene transfer, and pathogenicity is a result of the convergent gain and loss of these genes (Hulin et al., 2018b). In the simplest terms, genes encoding for T3SS are the hrp and hrc genes (part of 7 a pathogenicity island) while the genes encoding for the effector proteins are the avr and hop genes (Alfano and Collmer, 2004; Hulin et al., 2018; Kennelly et al., 2007). Pss has fewer effector proteins but more phytotoxin gene clusters coding from one to all three toxins (syringomycin, syringolin, and syringopeptin) (Hulin et al., 2018b; Ruinelli et al., 2019). These phytotoxins cause necrosis in plant tissues, and evidence points to a “trade-off” between bacteria phytotoxin production and effector proteins (Hulin et al., 2018b; Kennelly, 2007). The syringomycin phytotoxin is considered to be a significant virulence factor in Pss in addition to causing the release of plant nutrients it contributes to symptom development and aids in bacteria movement, and the genes for the production and delivery of the syringomycin phytotoxin, syrB and syrD, respectively are used as rapid identifiers of Pss via polymerase chain reaction (PCR) (Bultreys and Gheysen, 1999; Hulin et al., 2020; Sorensen et al., 1998; Xin et al., 2018). 1.3: Pss Management Management practices for bacterial canker have been limited to copper (Cu) spray applications and cultural practices. Copper-resistant Pss populations have been detected throughout Michigan cherry orchards for over 3 decades [89% of strains (137/154) in 1987, 31.7% of strains (100/315) in 2003-2004, and 13.8% of strains (9/65) in 2021] (Lauwers, 2022; Sundin et al., 1989; Renick et al., 2008). Thus, copper is not a long-term sustainable option for bacterial canker management in Michigan. Copper resistance of Pss on cherry is due to a 61-kb plasmid that can facilitate the spread of Cu resistance via conjugation (Sundin et al., 1989). Copper resistance genes (copABCDRS) are a four-gene operon (copA, copB, copC, and copD) that encode outer membrane and periplasmic space proteins that can sequester Cu molecules, regulated by the genes copS (signals) and copR (expresses) (Puig et al., 2002). While Cu in trace amounts can be beneficial to plants, long-term use in agriculture has led to excessive Cu 8 accumulation in soils and can have deleterious effects on earthworms and other beneficial invertebrates, dependent on soil PH and the sensitivity of species (Duque et al., 2023; Karimi et al., 2021). Other antibiotics such as oxytetracycline, streptomycin, and kasugamycin have been unavailable to Michigan growers for management of Pss on sweet cherry, with use restricted to apple (Malus x domestica) only, for control of the aggressive fire blight bacterial pathogen, Erwinia amylovora, due to resistance fears of resistance development (McGhee and Sundin, 2011; Slack et al., 2021; Sundin and Wang, 2018). However, as of 2020, kasugamycin became available for use in cherry, and no resistance has been detected yet in Michigan (Lauwers, 2022). Resistance to streptomycin is prevalent in Michigan, especially in E. amylovora populations, but could be acquired easily by Pss populations as well (via spontaneous mutation, or via plasmid conjugation or horizontal transfer) since streptomycin resistance has been detected in Pseudomonas syringae pv. papulans (Jones et al., 1991; McGhee et al., 2011; McGhee and Sundin, 2011; Slack et al., 2021; Sundin and Bender, 1996; Sundin and Wang, 2018). The cultural practices used by growers to reduce potential Pss infection consist of weed control (to reduce host plant reservoirs), avoidance of overhead irrigation (to reduce dissemination of the bacteria), planting less susceptible cultivars and rootstocks, and pruning during warm, dry weather (Lattore and Jones, 1979; Spotts et al., 2010; Carroll et al., 2010). Currently, there are no sweet cherry cultivars commercially available that are known to be resistant to Pss; however, wild, ornamental, and hybrid Prunus are being tested for their ability to hinder Pss establishment/proliferation, and results are promising (Hulin et al., 2022). Adherence to cultural control methods does not guarantee disease avoidance. Spotts et al. (2010) found that heading cuts were susceptible to Pss infection under field conditions for <1 9 week in the summer and for 2-3 weeks in the winter. Law (2017) found that sweet cherry trees could be susceptible to infection 17-24 days after pruning with no statistical differences in temperature effect (10°C v. 20°C) in growth chamber experiments. Spotts et al. (2010) found that heading cuts had a high rate of severe Pss infections, while Carroll et al. (2010) found that leaving a stub during pruning could lower the risk of infections that eventually spread into the main trunk; both studies recommended summer pruning during warm dry weather. Infection by Pss often is observed in Michigan orchards following summer pruning even during “dry” periods. Summer pruning can help to partition photosynthetic carbon towards fruit during pre- harvest, reduce shading, and increase light penetration into the canopy after harvest for improved flower bud formation (Lang, 2019b). Michigan, especially the lower peninsula, frequently experiences periods of high relative humidity (≥ 60%) and extreme high relative humidity (≥ 85%), with the probability of an extended event (≥36 h) being 70% to > 90% (within 14 days) regardless of the time of year (Komoto et al., 2021). Monier and Lindow (2003) showed that Pss strain B728a formed more and larger aggregates of cells on leaves in high relative humidity. Lindemann and Upper (1985) also reported that more significant upward fluxes of bacteria into aerosols occurred during warm sunny days (wind speeds >1 m/s) at midday when bean (Phaseolus vulgaris) leaves were dry, but not when leaves were wet with dew. They also reported instances in which a significant downward flux of bacteria occurred in the evening during temperature inversions (Lindemann and Upper, 1985). As cherry leaves are sources of inoculant and heavy dew could facilitate dispersal via drip into pruning wounds, further research needs to be conducted in relation to summer infection following pruning, relative to the high humidity and dew points typical in Michigan. Recommendations to Michigan growers may need to include avoiding pruning during periods with high humidity and heavy dew formation. 10 The quest to find alternative methods for Pss management (other than antibiotics) that would complement cultural practices is on the rise. A current hot research initiative is the use of bacteriophage as a possible Pss management approach (Akbaba and Ozaktan, 2021; Lauwers, 2022; Luo et al., 2022; Oueslati et al., 2022; Pinheiro et al., 2019; Rabiey et al., 2020). While previously available commercial biological control agents have not been tested as thoroughly for sweet cherry bacterial canker as for fire blight management in apples, in general, biological control of bacterial diseases has had some variable success and is worthy of further investigation (Kennely et al., 2007; Sundin et al., 2009). The sweet cherry microbiome is an additional untapped and extremely valuable area of research for sustainable Pss management tactics. 1.4: Pss Epiphytic Living and the Leaf Microbiome Epiphytic populations of bacteria are subject to extreme microclimate shifts; are susceptible to desiccation, UV degradation, and fluctuations in nutrient availability; and are vulnerable to plant immune responses (Vorholt, 2012). Pss, however, is successful at living epiphytically due to several complex adaptations such as motility, biosurfactant production, quorum sensing, UV damage repair, siderophore production, and aggregate formations (Burch et al., 2014; Gunasekera and Sundin, 2006; Haefele and Lindow, 1987; Hirano and Upper, 2000; Lindow and Brandl, 2003; Monier and Lindow, 2003; Quiñones et al., 2005; Vorholt, 2012; Wensing et al., 2010). Thirty-one genes significant for epiphytic colonization and fitness have been identified in Pss strain B728a, most of which are also important for the occupation of the apoplast, and which encode for amino acid and polysaccharide production (Helmann et al., 2019). Epiphytic bacteria colonize protected regions of leaf surfaces, such as stomatal openings and the base of trichomes, to escape desiccation and access possible nutrient sources (Beattie and 11 Lindow, 1999; Mansvelt and Hattingh, 1987; Mansvelt and Hattingh, 1989; Monier and Lindow, 2004; Roos and Hattingh, 1983). Pss strain B728a inoculated on bean plants had a greater survival rate when colonized in larger aggregates than solitary cells, and resident populations facilitated the survival of immigrating cells, while aggregates of mixed bacteria populations (Pss with Pantoea agglomerans) varied in size and appeared to spatially segregate (Beattie and Lindow, 1999; Monier and Lindow, 2004; Monier and Lindow, 2005a; Monier and Lindow, 2005b). Images from scanning electron microscopy of apple and sweet cherry leaves have shown Pss and Psm respectively, to aggregate near and within stomata, propagating within the “substomatal chambers” and expelling back out onto the leaf surface, while Pss on pear (Pyrus communis) was found near stomata and propagation appeared to occur at the base of trichomes and in cuticle depressions (Mansvelt and Hattingh, 1987; Mansvelt and Hattingh, 1989; Roos and Hattingh, 1983). Evapotranspiration from open stomata can cool leaves when temperatures are high, and Pss likely aggregates near them to escape desiccating conditions (increased temperature and dryness) (Monier and Lindow, 2004; Gommers, 2020; Kostaki et al., 2020). Bacteria carrying capacity corresponds to nutrient availability on leaves, and while the majority of sugars are depleted during bacteria growth, residual amounts remain, likely in regions of the leaf not occupied by bacteria (Mercier and Lindow, 2000). Mercier and Lindow (2000) found that sugar availability on leaves was species-specific and not evenly distributed across the leaf surface. While sweet cherry leaves have visibly apparent extrafloral nectary glands (which exude sugars) on the junction between the petiole and leaf, there is no mention in the literature of these being a source of nutrients or an infection point for Pss. Yee and Chapman (2008) assessed the influx of leaf nutrients available to a Rhagoletis sp. on cherry leaves from the extrafloral 12 nectaries and juice from bird-pecked fruit and Escalante-Péreza et al. (2012) have shown that the sugars from the extrafloral nectaries in Poplar (Populus spp.) have antimicrobial properties. It would be prudent to assess these influences on Pss population dynamics, especially considering Pss populations reportedly are low in the summer (Spotts et al., 2010). Leaf bacterial populations vary according to plant host species, season, leaf age, location within the canopy, and leaf surface, but they are primarily dependent on “immigration, emigration, growth, and death,” with the upper leaf surface communities mainly being influenced by dispersal (Hirano and Upper, 2000; Kinkel, 1997; Lindow and Brandl, 2003; Smets et al., 2022; Vorholt, 2012). Interactions of epiphytic bacteria are complex and can be positive with the production of “common goods” or negative with the production of antibiotic metabolites, as is true with the plant host’s experience as well with bacteria causing disease or promoting plant growth (Bashir et al., 2022; Glick and Gamalero, 2021; Mina et al., 2019; Pattnaik et al., 2021; Vortholt, 2012; Xin et al., 2018). Synergisms between Pseudomonas spp. pathogens and non-pathogens are apparent. Pss and P. viridiflava frequently are isolated with the kiwi (Actinidia spp.) bacterial canker pathogen P. syringae pv. actinidiae (Psa), which has been reported to enhance Psa infections (Purahong et al., 2018). Colonization and infection by Pseudomonas savastanoi pv. savastanoi (Psv), the olive knot pathogen, is enhanced by the presence of the non-pathogens, Erwinia toletana, E. oleae, and Pantoea agglomerans (Buonaurio et al., 2015; Marchi et al., 2006). Michigan Pss is phylogenetically diverse (Lauwers 2022; Rennick et al., 2008). However, it is unclear how they interact with one another, if they co- occur on multiple plant organs (they were isolated from flowers), and if they can influence bacterial canker disease severity in certain combinations. Other bacteria occupants of the sweet cherry leaf microbiome are unknown, as are their potential functions or their relationship to Pss. 13 Although bacterial communities on leaves vary, a few species are commonly found (Hirano and Upper 2000; Lindow and Brandl, 2003; Vorholt, 2012). Common and consistent bacteria shared across habitats are known as the core microbiome (Neu et al., 2021; Shade and Handelsman, 2012; Vandenkoornhuyse et al., 2015; Laforest-Lapointe et al., 2016). Laforest- Lapointe et al. (2016) defined a core microbiome for temperate forest tree species as “OTUs (Operational Taxonomic Units) present on 99% or more of all trees sampled”. Core microbiomes, however, can be selected via occurrence, relative abundance, or occurrence- abundance with each method having various caveats, neatly described by Neu et al. (2021). Defined core microbiomes can be used as a marker of plant health and are a way to simplify the complex array of bacteria inhabiting the leaf and can be useful to measure responses to various manipulations (Shade and Handelsmann, 2012.) Currently microbiomes are being surveyed at a greater and more thorough rate via culture-independent methods specifically by next-generation sequencing (NGS) technologies and sequencing of the 16S rDNA variable regions of bacteria (Knief, 2014; Kozich et al., 2013). Knowledge of the Michigan sweet cherry epiphytic bacteria that are present with Pss could be used to monitor how the community changes along with Pss population fluctuations/infections, and conceivably could be manipulated to assess how the community influences Pss as well as the potential for biological control of Pss. Evidence of plant “recruitment” of bacteria and the drivers of bacterial community composition on leaves [season/time, host plant species/genotype, plant age, location/region, and neighbors (i.e., environment)] have been elucidated by research in forest/urban trees, grasses (pertinent to biofuels), wild perennials, and cultivated annuals and perennials (Grady et al., 2019; Jackson and Denney, 2011; Kembel et al., 2014; Kim et al., 2012; Laforest-Lapointe et al., 2016a; Laforest-Lapointe et al., 2016b; Laforest-Lapointe et al., 2017; Lajoie and Kembel, 14 2021a; Lajoie and Kembel, 2021b; Meyer et al., 2022; Noble et al., 2020; Redford et al., 2010; Redford and Fierer, 2009; Stone and Jackson, 2021; Wagner et al., 2015). The apple phyllosphere (bark to flower) microbiome, which harbors the fire blight bacterial disease pathogen, Erwinia amylovora, has been explored for community assemblages, responses to antimicrobials (streptomycin, kasugamycin, and copper), responses to microbial inoculations, and differing orchard management styles (organic vs. conventional) (Aleklett et al., 2014; Arrigoni et al., 2018; Cui et al., 2021; Glenn et al., 2015; He et al., 2021; He et al., 2012; Shade et al., 2013; Steven et al., 2018; Tancos and Cox, 2017; Wallis et al., 2021; Yashiro and McManus, 2012). The leaf microbiomes of tea (Camellia sinensis), citrus, and grapes have also been analyzed (Carvalho et al., 2020; Cernava et al., 2019; Gobbi et al., 2020; Mina et al., 2020; Miura et al., 2019; Singh et al. 2019; Wu et al. 2020). Ares et al. (2021), assessed the kiwi epiphytic microbiome in male and female plants to determine if the communities varied by sex and how the communities changed with the presence of the kiwi bacterial canker (Psa). The leaf microbiomes of the four Prunus spp. (apricot, tart cherry, peach, and plum) have also been surveyed and compared in bulk leaf samples (Jo et al., 2015). O’Gorman et al. (2023) recently studied the microbiome communities of sweet cherry fruit and flowers in relation to the cherry slip-skin-maceration disorder (cherry-SSMD), and Zhang et al. (2021) assessed post-harvest sweet cherry fruit microbiomes at room temperature and under cold storage conditions. No surveys of the sweet cherry, Prunus avium L., leaf microbiome have been conducted. There is very little understanding of how other bacteria on the leaf surface impact Pss virulence or trigger plant immune responses. Xin et al. (2018) discusses this as the 4th vertex of the disease triangle comprised of host, environment, and pathogen. Characterization of the sweet cherry leaf microbiome would further add to the body of knowledge of Pss and orchard ecology and create a 15 foundation for further exploration into community interactions with Pss, possibly aiding in the future management of Pss populations and perhaps suggesting potential biological control options. There is increasing interest by fruit growers and the crop protection industry in the use and production of biological controls or biopesticides for plant diseases is increasing, as part of the desire to “take a more environmentally friendly approach” (Herrick 2018). 1.5: Biological Control The key to biocontrol success in managing plant pathogens will likely be an amalgamation of synergistic factors. One important aspect is to choose and isolate the best biocontrol agent for the environment and habitat in which the pathogen resides. For Pss, which is ubiquitous in the environment, it is important for biological control to maintain pathogen populations at a level too low for infection to occur. It is possible that through a survey of the bacterial populations that reside epiphytically alongside Pss, one could find bacteria that either facilitate or hinder the population growth of Pss. Kotan and Sahin (2006) assessed 206 bacteria strains as potential Pss biocontrol agents that were recovered from pome fruits in Turkey, using in vitro, in vivo (on ‘Golden Delicious’ apple), and carbon utilization assays. Their efforts yielded 10 strains that were most effective in reducing the severity of Pss infection in various combinations (Kotan and Sahin 2006). Kotan and Sahin (2006) found that there was an 83% similarity in carbon utilization between the stronger potential biocontrols and Pss, as opposed to a 66% similarity in carbon utilization between weaker potential biocontrols and Pss. Arrigoni et al. (2018) found that bark of apple and pear is a reservoir of potential biological control agents as bacteria genera from their microbiome analysis are known for their production of secondary metabolites as well as growth promotion and resistance induction capabilities. Bacteria previously isolated from the apple flower microbiome were found to reduce the level of the fire 16 blight caused by Erwinia amylovora dependent on the strains in the inoculum which were altering the community structure of the flower (Cui et al., 2021). Exploration of the sweet cherry microbiome via characterization of Pss strains as well as determining the community constituents alongside Pss, could possibly lead to the same potential biological control options for the management of Pss populations and, by extension, reduce the incidence of bacterial canker. Biological controls of plant pathogens are thought to function in two major ways: 1) competitive exclusion of the pathogen from niche or nutrient resources, and 2) through antibiosis (Lindow and Brandl, 2003). Wilson and Lindow (1994) found that the ice nucleating strain Pss Ice+ and the non-ice nucleating strain Pss Ice– share the same niche, compete for the same nutrient resources, and can replace each other. Wilson and Lindow (1993) found that Pseudomonas fluorescens A506, which colonizes flower pistils, was able to reduce the amount of infection in pear by Erwinia amylovora; 17% of the blossoms were infected when P. fluorescens was inoculated 72 h prior to E. amylovora, while 50% of blossoms were infected when E. amylovora and P. fluorescens were co-inoculated, and 71% of blossoms were infected when E. amylovora was inoculated alone. Pseudomonas fluorescens A506 is in the commercial formulation of BlightBan® A506 (Nufarm, Alsip, Illinois), registered as a biological control of Erwinia amylovora, in apples (Stockwell and Stack, 2007). Vanneste et al. (1992) showed that the biocontrol Erwinia herbicola Eh252 (Pantoea agglomerans) was able to inhibit infection by Erwinia amylovora in pear fruit due to antibiotic production. Burr et al. (1996) showed that the isolate Pseudomonas syringae 508 was able to prevent conidia of the apple scab pathogen Venturia inequalis from germinating in in vitro experiments. Wickasono et al. (2017) found that endophytic Pseudomonas spp. isolated from the medicinal plant, Mānuka (Leptospermum 17 scoparium), were able to reduce colonization of Psa and subsequently kiwi bacterial canker severity through the production of antibiotic compounds. Current research has expanded biocontrol options to include compounds or non-pathogenic bacteria that can induce natural plant defense systems and/or promote plant growth (Lillrose et al. 2017, O’Brien, 2017; Stroud et al. 2022, Syed-Ab-Rahman et al., 2018). Actigard™, is one such product that can induce the plant's immune response as it mimics the defense hormone salicylic acid and is actively used for the management of Psa in kiwi (Stroud et al., 2022). Though biocontrols have been shown experimentally to have the potential for managing plant pathogens, their overall success in the field has been largely inconsistent. Broniarek- Niemiec et al. (2023) tested several biological control/biopesticide products and found that those containing copper were still the most effective at managing bacterial canker in stone fruit. Lillrose et al. (2017) tested two plant-resistant inducers as well as three antibiotics and four biological controls applied to sweet cherry blossoms prior to wounding and inoculation with Pss and found that the antibiotic kasugamycin had the best result with 90% reduction in infection followed by the other two antibiotics (copper and oxytetracycline) and the biocontrol Blossom Protect (Aureobasidium pullulans) with 45-49% infection reduction. Sundin et al. (2009) showed that biocontrols currently available for fire blight control were not highly effective when tested in Michigan, Virginia, and New York, though success was better when the biocontrols were used with streptomycin. Braun-Kiewnick et al. (2000) had success controlling Pss, the basal kernel blight pathogen of barley (Hordeum vulgare), in the field when the biocontrol, Pantoea agglomerans (Erwinia herbicola) was applied before Pss infection, reducing the amount of blight 36-74% compared with the standard controls. Though biocontrols currently available for Erwinia amylovora have been assessed for blossom infection in apple and sweet cherry, no such 18 assessments have been documented for the management of epiphytic populations of Pss in Michigan cherry orchards (Sundin et al., 2009; Lillrose et al., 2017). The variability of biological agents to control plant pathogens could be due to the unsuitability of the biocontrol for the environment or climatic conditions in which they are applied (Cook, 1993; Guetsky et al., 2001; Sundin et al., 2009). Guetsky et al. (2001) studied mixing of different types of biocontrols to improve the consistency of control of the fungal pathogen, Botrytis cinerea, in strawberry (Fragaria ananassa). They found that the yeast, Pichia guikermondii, and the bacteria, Bacillus mycoides, had different temperature and humidity requirements for optimal growth. When used together, they were able to better control B. cinerea under varying conditions (Guetsky et al., 2001). Stockwell et al. (2011) found that mixing biocontrol agents improved control of Erwinia amylovora only when the biocontrols did not interfere with one another or were “mechanistically compatible” with each other. The biocontrol Pseudomonas fluorescens A506 produces a protease that degrades the antibiotics that are produced by the biocontrols Pantoea vagans C9-1 and Pantoea agglomerans Eh252, and when applied in combination, did not improve the variation of control of E. amylovora (Stockwell et al., 2011). However, when the mutant of P. fluorescens A506 (A506 aprX::Tn5) that lacked the enzyme was used with the Pantoea sp., fire blight was reduced by 86-78%. Another likely reason for the variable success of biological control agents in the field could be due to the native pathogen or non-pathogens populations already present on the plants at unknown concentrations that could confound the results of the inoculated pathogen population and the assessment of their possible biological controls. Pss and Pseudomonas, in general, are a diverse group of organisms with pathogenic and non-pathogenic strains that are able to cohabitate commensally and synergistically with other bacteria (Hulin et al., 2020; Lauwers, 19 2022; Melnyk et al., 2019; Purahong et al., 2018; Shalev et al., 2021; Shalev et al., 2022; Xin et al. 2018). With this in mind and given the fact that Pss is like a wolf in sheep’s clothing in that even pathogenic strains can live epiphytically without causing pathogenesis, it could be hypothesized that Pss populations would be difficult to manage with biological controls because they are able to adapt to co-existence with a broad range of other bacteria. Even with the inconsistent nature of available biological controls, grower and industry interest in the use and production of these products (biological controls or biopesticides) for plant diseases is increasing as part of the desire to “take a more environmentally friendly approach”, and many companies that produce biocontrols are part of the Biological Products Industry Alliance (Herrick, 2018). Lytic bacteriophage is currently garnering a lot of research attention as a biological control of Pss (single phage and cocktails) along with the commercially available AgriPhage™ ( Akbaba and Ozaktan, 2021; Lauwers, 2022; Luo et al., 2022; Oueslati et al., 2022; Pinheiro et al., 2019; Rabiey et al., 2020). Rabiey et al. (2020) isolated 70 phages from soil and tested them (singly and in cocktails) against Pss populations on bean and cherry leaves and found reductions of ~20-100% in Pss populations at specific time points throughout the course of the experiments. Studies in this area are already underway in Michigan (Lauwers, 2022). A thorough understanding of the Pss that inhabits the sweet cherry microbiome, along with the elucidation of the other sweet cherry microbiome Pseudomonas, could lead to a broader range of possible hosts for use in bacteriophage research in Michigan. Stroud et al. (2022) highlight the difficulties in making assessments of biological products in field settings and the importance of small-scale laboratory-type studies, such as using tissue culture to better understand how Actigard™ affects Psa populations. As field studies and even growth chamber studies with live plants are often time-consuming, resource-intensive, and 20 often yield inconclusive results, simpler in vitro studies would be ideal to initially screen an organism's potential as a biological control. Dual culture or co-inoculation Petri dish assays are frequently used to assess the potential biological control activity of a bacteria versus a pathogenic fungus (Chaouachi et al., 2021; Cirvilleri et al., 2005, Derikvan et al., 2023; Hammami et al., 2022, Lu et al., 2022; Niem et al., 2020, Nysanth et al. 2022). Mina et al. (2020b) have also demonstrated that this assay type was a viable way to assess the effect of a bacterial biological control agent against the olive knot pathogen Pseudomonas savastanoi pv. savastanoi. This would also be useful to survey potential biological control agents for Pss as well as study how two Pss strains that had been isolated from sweet cherry may interact with each other in co-culture. 1.6: Objectives of Study The objectives of this research dissertation are to: 1) phenotypically and phylogenomically characterize Pss strains isolated from cherry flowers via biochemical, virulence assays, and Multilocus sequence analysis (MLST); 2) characterize the sweet cherry leaf microbiome over time (seasons), and across cultivars, in two regions of Michigan to determine a) if there are regional and seasonal differences in the sweet cherry microbiome, b) if the leaf microbiome varies across sweet cherry cultivars, and c) what bacterial species are consistently present with Pss; and 3) to evaluate commercially available biological control agents a) for their ability to decrease Pss growth in vitro, b) determine if avirulent/moderately virulent Pss recovered from Michigan sweet cherry orchards can decrease virulent/moderately virulent Pss growth in vitro, and c) determine whether in vitro co-inoculations are a viable, easily employed option for the initial assessment of biological controls for Pss. 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SYRINGAE ISOLATES FROM SWEET CHERRY FLOWERS HIGHLIGHT VARIABILITY, THE NEED FOR CAUTION, AND AREAS OF FUTURE RESEARCH 2.1: Abstract Michigan’s sweet cherry industry is seriously impacted by the susceptibility of young sweet cherry trees to Pseudomonas syringae pv. syringae (Pss), the causal agent of the disease bacterial canker. Characterization of Pss strains by biochemical, phenotypical, and molecular analyses is critical for understanding the variation that exists within Michigan orchards and monitoring for emergent pathogens. Pss strains isolated from cherry flowers were found to be phylogenomically diverse, determined by multilocus sequence analysis (MLSA) using four housekeeping genes (gap1, gltA, gyrB, and rpoD) with 20 strains belonging to three different phylogroups (PG2b, PG2c, and PG2d). Only a single Pss strain was resistant to copper and no strains were resistant to streptomycin. Pss strains were variable in their GATTa profiles, in their ice nucleation activity (INA), and amplification of the syringomycin gene syrB. Notably, the Pss strains also exhibited a range of virulence levels determined by measurement of lesion area on green cherry fruit and virulence rating on sweet cherry wood. No new P.s pathogens were detected. The strains that were moderate to non-virulent require further investigation of their possible virulence in other Prunus spp. and how they impact or interact with the more virulent Pss. Without thorough virulence testing, the inadvertent use of moderate to avirulent strains could impact future research where virulence level could influence treatment responses. 38 2.2: Introduction Michigan’s temperate climate (afforded by the protection of the Great Lakes) and fertile glacial soils is home to a diverse array of crops (Brown, 1941; Warren and Vermette, 2022). Fruit production is largely concentrated in the “fruit belt” region, located along the western coast nearer to Lake Michigan (Brown, 1941; Warren and Vermette, 2022). However, orchards can be found scattered throughout the state. Michigan is one of the most agriculturally diverse states in the U.S., with 46,000 farms and 300 different agrarian commodities (MDARD, 2023; USDA- Nass, 2021). Michigan is the nation’s top tart cherry (Prunus cerasus) producer (70% of U.S. supply) with 33,381 acres and is ranked fourth in sweet cherry (Prunus avium) production with 7,807 acres (Lang, 2019; MDARD, 2018; USDA-Nass, 2019). According to early census data, cultivated cherries were established in Michigan in the 1870s (Brown, 1941). More than 150 years later, the modern sweet cherry orchard looks markedly different from those early orchards of large trees that mirrored their forest counterparts, replaced with high-density plantings of small trees on dwarfing to semi-vigorous precocious rootstocks, such as the ‘Gisela’ series (Gi.6, Gi.5, and Gi3), often supported by trellising (Lang et al., 2019). Michigan State University researchers have led the push for progressive cherry growers to adopt high-density “pedestrian orchards” featuring “two-dimensional/planar” training systems like that of the UFO (Upright Fruiting Offshoots, Lillrose et al., 2017) that are trained to form a “fruiting wall” that can be mechanically hedged and easily picked from one side of the tree, thereby requiring less labor compared to the traditional “three-dimensional” tree architectures (Lang et al., 2022). Various covering systems (e.g., row covers, high tunnels and retractable roofed greenhouses) are also have been shown to reduce fruit cracking and disease pressure, and promote earlier fruit harvest potential (Lang et al., 2016; Lang, 2019). These 39 innovative approaches are grower-driven due to rising concerns over labor issues (lack of workers and costs) and climatic events (frosts and rain) (Lang, 2019). A major limitation for sweet cherry growers is disease pressures (e.g., bacterial canker, brown rot, and cherry leaf spot) linked to Michigan’s climate. The cool, wet spring and late fall weather typical in Michigan are ideal for the proliferation and infection of the bacterial phytopathogen Pseudomonas syringae pv. syringae van Hall (Pss), the causal agent of bacterial canker that can devastate young trees that are highly susceptible (Hirano and Upper, 1990; Hirano and Upper, 2000; Jones and Sutton, 1996; Spotts, 2010). Historically, Michigan had a lengthy spring with cool temperatures that delay bud break and warm falls due to the moderating influence of the Great Lakes, avoiding late and early “killing frosts,” respectively, as an influence of the Great Lakes (Warren and Vermette, 2022). Due to global climate change and the warming of the lake waters (including decreased winter ice cover), these previous protections have become quite volatile. Lang (2019) summarized comparisons of pre- to post-1940 climate data from the Michigan state climatologist (J. Andresen) that was recorded near Traverse City, a major cherry-growing region, noting that bud development (side green) is now ~ 10 days earlier, the number of potential frost events (at side green or later) has increased from 5 to over 20, and annual rainfall has increased 10% since 1940. These statistics are particularly alarming considering the biology of Pss; in addition to the increase in population size and subsequent potential for infection of cherry during cool, wet weather events, the pathogen is closely linked to the water cycle via rain splash dispersal, and is capable of facilitating frost damage via ice nucleation which enables its entry into the plant tissues (Hirano and Upper, 1990; Hirano and Upper, 2000; Jones and Sutton, 1996; Morris et al., 2008; Morris et al., 2013). Pss is also an opportunistic pathogen that can infect through wounds (pruning cuts and rubbing of bark on 40 trellis wires) and natural openings (leaf scars and stomates), but only when populations are high; otherwise, it is well-documented to live benignly on host and non-host plants (Hirano and Upper, 1990; Hirano and Upper, 2000; Kennelly et al., 2007; Latorre and Jones, 1979a; Lillrose et al., 2017). In the fall, when cool, wet weather persists and leaves drop, higher Pss populations can infect trees through leaf scars and subsequently can overwinter in cankers, buds, bark, and systemically (Jones and Sutton, 1996; Sundin et al., 1988). Jones (1971) was the first to report Pss to be present in sweet cherry in Michigan following a 1968 epidemic. Since then, three significant outbreaks have occurred (2002, 2012, and 2021), all occurring after unusually warm early spring weather that induced early bloom followed by numerous frost events (Kennelly et al., 2007; Lauwers, 2022; Renick et al., 2008; Sundin and Rothwell, 2012). These state-wide events resulted in significant current and future year crop losses from frost damage as well as infection by Pss, resulting in blossom blast followed by systemic disease and subsequent spur, shoot, limb, and young tree death (Kennelly et al., 2007; Lauwers, 2022; Renick et al., 2008; Sundin and Rothwell, 2012). Other symptoms of Pss infection include sunken cankers, gumming, lesions on fruit, and “shot holes” in leaves (Jones and Sutton, 1996; Kennelly et al., 2007). In addition to Pss, Jones (1971) isolated Pseudomonas syringae pv. morsprunorum (Psm) from sweet cherry. This pathogen has since been determined to be two different pathogens, P. amygdali pv. morsprunorum (race 1) and P. avellanae pv. morsprunorum (race 2) and is recovered more often on tart cherry and is less virulent than Pss in Michigan (Gomila et al., 2017; Hulin et al., 2020; Jones, 1971; Jones and Sutton, 1996; Renick et al., 2008). Pss is a generalist phytopathogen that infects more than 180 plant species, and phylogenetically belongs to genomospecies 1, phylogroup 2, and is part of a large species 41 complex composed of 13 phylogroups (Berge et al., 2014; Gardan et al., 1999; Gomila et al., 2017; Gutiérrez-Barranquero et al., 2019; Kennelly et al., 2007; Pulawska et al., 2017). Phylogroup (PG) 2 is composed of 7 clades (2a, 2b, 2c, 2d, 2e, 2f, and 2g) that contain non- pathogenic Pss (PG2c) and Pss isolated from water and snow (Abdellatif et al., 2020; Berge et al., 2014; Hall et al., 2019). In Michigan, Pss and Prunus spp. are found together in many sweet cherry orchards, and it can be difficult to find a symptomless tree. On a global scale, Pss infects all economically important Prunus spp. Evidence through whole genome comparisons points to shared genes/proteins in common among strains that have led to the adaptation for pathogenicity on woody hosts, cherry specifically, and proposed co-evolution with Prunus spp. (Hulin et al., 2018b; Hulin et al., 2020; Nowell et al., 2016; Ruinelli et al., 2022). These shared genes and proteins are part of the type III secretion system (T3SS) repertoire; the T3SS consists of an apparatus made by Pss (once inside the plant apoplast) that injects effector proteins into the plant cells, releasing nutrients to promote the proliferation of Pss and to thwart the plant’s basal immune response (i.e., PAMP-triggered immunity or PTI) (Alfano and Collmer, 2004; Hulin et al., 2018b; Hulin et al., 2020; Nowell et al., 2016; Ruinelli et al., 2022; Ruiz-Bedoya et al., 2023). These effector proteins also are considered “double agents” as they have the potential to elicit an additional plant immune response, or effector triggered immunity (ETI), which adds another level of infection complexity dependent on both pathogen and plant host genetics (Alfano and Collmer, 2004; Hulin et al., 2018b; Hulin et al., 2020; Nowell et al., 2016; Ruinelli et al., 2022; Ruiz-Bedoya et al., 2023). Effectors are thought to be acquired through horizontal gene transfer, and pathogenicity is a result of the convergent gain and loss of these genes (Hulin et al., 2018b). Pss has fewer effector proteins but more phytotoxin gene clusters coding from one to all three toxins (syringomycin, syringolin, and syringopeptin) (Hulin et al., 2018b; 42 Ruinelli et al., 2019). These phytotoxins cause necrosis in plant tissues, and evidence points to a “trade-off” between bacteria phytotoxin production and effector proteins (Hulin et al., 2018b; Kennelly, 2007). The genes for the production and delivery of the syringomycin phytotoxin, syrB, and syrD, respectively, are part of the rapid characterization/identification of Pss strains using polymerase chain reaction (PCR) (Bultreys and Gheysen, 1999; Sorensen et al., 1998). Management of Pss in Michigan orchards in problematic, as nothing can be done once the bacteria enter the plant. Strategies for reducing epiphytic Pss populations have been limited to cultural practice adjustments and unsustainable copper sprays. Pss resistance to copper is prevalent, and use is limited to applications during plant dormancy due to phytotoxicity in cherry (Lamichhane et al., 2018; Lauwers, 2022; Renick et al., 2008; Sundin et al., 1989). However, recent developments have started expanding the Pss population management arsenal. Though no bacterial canker-resistant cultivars are currently available, rapid screening protocols are being utilized to assess wild, ornamental, and hybrid Prunus for their ability to reduced bacterial multiplication after inoculation with promising results (Hulin et al., 2022). Use of antibiotics such as streptomycin, oxytetracycline, and kasugamycin had previously been restricted for use only in apples to manage the extremely aggressive fire blight pathogen Erwinia amylovora (McGhee and Sundin, 2011; Slack et al., 2021; Sundin and Wang, 2018). However, kasugamycin is now available to use in cherry for Pss population management, and resistance has yet to be detected in Michigan (Lauwers, 2022). Screening for streptomycin resistance in Pss should be done, as E. amylovora resistance is prevalent in Michigan and could be transferred to Pss via horizontal gene transfer (McGhee and Sundin, 2011; Slack et al., 2021; Sundin and Wang, 2018). Most of the biological control agents developed for E. amylovora management have had no definitive success and require more testing for possible management of Pss populations 43 (Kennely et al., 2007; Sundin et al., 2009). However, research using bacteriophage shows some potential, but research is still ongoing (Lauwers, 2022; Rabiey et al., 2020). Monitoring of P. syringae (P.s) in sweet cherry orchards is critical for detecting emergent pathogens and understanding the diversity within resident Pss populations. Characterization of Pss strains entails a combination of biochemical, phenotypical, and molecular tests/analyses. Fluorescence and oxidase enzyme activity often are used in the initial screening of Pss since 1) it belongs to the same phylogenetic lineage as Pseudomonas fluorescens, 2) will fluoresce under UV light, and 3) lacks the oxidase enzyme thereby eliciting no reaction with Kovacs’ oxidase reagent (Gomila et al., 2017; Kovacs, 1956; Latorre and Jones, 1979b; Lelliot et al., 1966). Pss is differentiated from Psm via the GATTa (Gelatin liquefaction, Aesculin hydrolysis, Tyrosinase activity, and Tartrate utilization) biochemical assays and the amplification of the syrB or syrD gene (syringomycin phytotoxin); Pss is generally G+ A+ T − Ta− while Psm is G− A− T + Ta+, and Psm does not produce syringomycin (Bultreys and Gheysen, 1999; Latorre and Jones, 1979b; Lelliot et al., 1966; Renick et al., 2008; Sorensen et al., 1998). These initial tests also may be followed up with INA, pathogenicity (on immature fruit), and antibiotic resistance assays (Renick et al. 2008; Lauwers 2022). Phylogenomic trees for genomic characterization of Pss are popularly being constructed utilizing multilocus sequence analysis/typing (MLSA/MLST) (Abdellatif et al., 2019; Almeida et al., 2010; Berge et al., 2014; Bophela et al., 2020; Hall et al., 2019; Hulin et al., 2018a; Hwang et al., 2005; Iličić et al., 2021; Ivanović et al., 2022; Lauwers, 2022; Oksel, 2022; Popović et al., 2021; Sarkar and Guttman, 2004; Vasebi et al., 2020; Visnovsky et al., 2019). Both MLST and MLSA compare 1 to 7 different “housekeeping” genes (conserved genes necessary for life) of novel strains with reference strains to group them according to similarity and construct phylogenies; MLST does this by assigning the alleles a 44 number (sequence type) while MLSA (utilized in this study) uses the actual DNA sequences (Almeida et al., 2010; Berge et al., 2014; Hwang et al., 2005; Iličić et al., 2021; Sarkar and Guttman 2004). Sakar and Guttman (2004) began using MLST for characterizing P.s spp. with 7 “housekeeping” genes, then Hwang et al. (2005) found that 4 genes [gapA (syn. gap1), gltA (syn. cts), gyrB, and rpoD] were sufficient, while Berge et al. (2014) and Iličić et al. (2021) make the case for using the single genes cts (syn. gltA) and recG respectively, for more rapid classifications. The genes (used in this paper’s study), gap1, and gltA (used in this study) are important for glycolysis and the citric acid cycle encoding for glyceraldehyde-3-phosphate dehydrogenase and citrate synthase respectively, while gyrB and rpoD are genes encoding for DNA gyrase B, and sigma factor 70, respectively, important for replication and transcription respectively (Hwang et al., 2005; Lauwers, 2022; Vasebi et al. 2020). Previously, Lauwers (2022), did not find any non-pathogenic Pss, while Rennick et al. (2008) mentioned finding non-pathogenic isolates that were not included in the DNA fingerprinting. Rennick et al. (2008) also found four isolates that were pathogenic but clustered outside of the other Pss groupings; P.s. outgroups were not included in the analysis. Pathogenicity in both studies was determined by the presence or absence of lesion formation on inoculated green fruit but differences in virulence level were not measured, nor was the pathogenicity or virulence level of strains inoculated on cherry wood evaluated (Lauwers, 2022; Rennick et al., 2008). Hulin et al. (2018a, 2022) highlight the importance of determining differences in strain virulence as being critical for use in and interpretation of future research, especially in the screening for resistant cherry cultivars. Both Lauwers (2022) and Renick et al. (2008) studied strains that were resistant to copper (13.8% and 31.7% respectively) and had a high number with INA (100% and 94% respectively). The focus of this paper’s study was to 45 evaluate the phylogenetic, biochemical, and phenotypic variability of Pss strains collected from sweet cherry flowers in Michigan and to characterize the range of virulence when inoculated on sweet cherry green fruit and wood. 2.3: Methods Bacteria Isolation Bacteria were isolated from sweet cherry flowers collected in May 2019 from orchards located in three regions of Michigan: Site 1, Wells Orchards, (Grand Rapids, Ottawa Co.); Site 2, Michigan State University (MSU) Clarksville Research Center (Clarksville, Ionia Co.); and Site 3, MSU Horticulture Teaching and Research Center (Holt, Ingham Co.) (Figure 2.1). A fourth site, MSU Southwest Michigan Research and Extension Center (Benton Harbor, Berrien Co.) was used for virulence testing. Ten flowers/tree were collected by clipping the pedicel with scissors and sanitized with 10% sodium hypochlorite and 75% ethanol into sterile Whirl-Pak® bags (Nasco, Madison WI). Flowers were selected from 8, 4, and 4 sweet cherry trees at site 1, site 2, and site 3 respectively. Flowers were held on ice in a cooler for transport to the laboratory. In the laboratory, flowers were transferred into 20 ml cold sterile 0.5x phosphate buffered saline (PBS) in 50 ml conical tubes (8 tubes/site1, 4 tubes/site 2, 4 tubes/site3). Which were then shaken at 200 rpm for 10 min. in a G24 Environmental Incubator Shaker (New Brunswick Scientific Co. Inc., Edison, NJ) and sonicated for 7 min. in an ultrasonic bath (Cole Parmer, Chicago, IL). A 100µl portion of the flower microbiome PBS wash stored at -70°C in 15% glycerin. The remaining flower wash was vortexed, serially diluted to 0, 10-1, 10-2, and 10-3, and plated onto King’s B (KB) media amended with 50 μg/ml cycloheximide (3 plates/sample) in four 25 μl drops/dilution (Figure 2.2). Plates were allowed to dry overnight at room temperature and were then placed into a 28°C incubator (Precision Scientific Model 805, Chicago IL) until 46 the following day when colonies were assessed visually for their likeness to Pss (i.e., cream- colored and circular in shape). Single colonies that were considered to be potential Pss were selected and re-streaked onto KB media amended with 50 μg/ml cycloheximide and were allowed to grow at 28°C for 2 days. Single colonies were selected from these streak plates for oxidase reaction testing. Colonies that had oxidase– reactions (characteristic of Pss) were stored from lawns at - 70°C in 15% glycerol. Bacteria strains were labeled according to site number (1, 2, or 3), cultivar name abbreviated (e.g., Gold= G, Black York=BY, Black Pearl=BP, Ulster=U, Van=V, Brooks=Br, R=Rainier), and isolate number, so that the final strain name, for example, would be 1-U-9 (site 1, Ulster, isolate 9). Oxidase Reaction To test for the presence of the enzyme cytochrome oxidase, a droplet of a 1% solution of N, N, N´, N´-tetra-methyl-p-phenylenediamine dihydrochloride was placed onto Whatman® filter paper (no. 1), then a single colony from a streak plate was picked up with a sterile toothpick and smeared onto the filter paper (Kovacs, 1956; Shields and Cathcart, 2010). Reactions were recorded as + (purple color = enzyme detected), or – (no color change = no enzyme detected) (Figure 2.3). Only the oxidase– bacteria were used for further characterization, as Pss is oxidase– (Janse, 2010; Latorre and Jones, 1979). syrD and syrB Gene Amplification Oxidase– strains were streaked onto KB media amended with 50 μg/ml cycloheximide using a sterile toothpick. Strains were allowed to grow for two days at 28°C. From a single colony for each strain, a small number of cells were scooped up using a sterile toothpick and washed into 20 μl sterile distilled water in polymerase chain reaction (PCR) tubes and vortexed. 47 The PCR tubes were inserted into a Bio Rad T100™ Thermal Cycler (Bio-Rad Laboratories, Hercules, CA) and incubated at 95°C for 10 min., causing lysis of the bacteria cells (ergo DNA extraction) via boiling. The extracted DNA was used for PCR amplification of the syrD gene that encodes for export/secretion of the phytotoxin syringomycin in Pss (Bultreys and Gheysen, 1999; Sorensen et al., 1998). Known Pss strains (13-7, 6-9, 26-3, 19-6, FF5-1, 6491, 1680, and 847) from the collection of Dr. George Sundin were also included, as was a sterile water control. The PCR reaction consisted of 2 μl of DNA, 1 μl each of syrD forward and reverse primer, 25 μl of One Taq® Hot Start 2X Master Mix with Standard Buffer (New England BioLabs®, Ipswich, MA), and Invitrogen™ UltraPure™ distilled water (ThermoFisher Scientific) up to a total volume of 50 μl. The primers used are the same as those in Bultreys and Gheysen (1999) as cited by Renick et al. (2008) (Table 2.1). PCR amplification occurred in the Bio Rad T100™ Thermal Cycler (Bio-Rad Laboratories Inc., Hercules, CA) under the thermal conditions described in Renick et al. (2008) (Table 2.1). Gel electrophoresis of the PCR product was done using a 1% Invitrogen™ UltraPure™ Agarose gel (ThermoFisher Scientific), stained with 1μl Ethidium Bromide (EtBr) (Bio-Rad Laboratories, Hercules, CA), in 1x Tris-Acetate-EDTA buffer (TAE). A 6x ficoll- based loading dye (10 µl) was added to the PCR product before gel loading. A 100 bp DNA ladder (New England BioLabs®) was included. The gel was submerged in 1X TAE and electrophoresis was run in a Horizontal System for Submerged Gel Electrophoresis series 1087, model H5 (Bethesda Research Laboratories, Life Technologies, Gaithersburg MD) at ~60 volts for ~80 min. Gel imaging was done using a Bio Rad Gel Doc™ EZ Imager. Only the strains that had amplification of the syrD gene (syrD+) were used in further classification, as the presence of 48 syrD is typical of Pss (Bultreys and Gheysen, 1999; Janse, 2010; Renick et al., 2008; Sorensen et al., 1998). Only oxidase– and syrD+ strains were used for the PCR amplification of the syrB gene that encodes the synthesis of the phytotoxin syringomycin in Pss (Bultreys and Gheysen, 1999; Sorensen et al.,1998). Stored DNA from a -20°C freezer was used; DNA was extracted as described in the syrD gene amplification; however, cell lysis occurred in sterile 0.5x sterile PBS via boiling. No additional known Pss reference strains were included as two of this study’s strains (1-U-9 and 3-BY-37) that were included were verified as Pss via whole genome sequencing (Dr. Michelle Hulin, East Malling, U.K., unpublished). The Psm strain 627 was included (from Dr. George Sundin’s collection) and a sterile water control was included. The PCR reaction volumes were the same as in the syrD amplification, the forward and reverse syrB primers used were those developed by Sorensen et al. 1998 as cited in Vasebi et al. (2020), using the same thermal profile used was the same as Vasebi et al. (2020) (Table 2.1). Gel electrophoresis and imaging were as described in the syrD amplification; however, the gel electrophoresis was run at ~116 V for 20 min., and a 100 bp GoldBio® DNA ladder (Gold Biotechnology, St. Louis, MO) was included. GATTa Profile and Fluorescence To separate putative Pss from Psm the GATTa biochemical profile was determined using 3 day old oxidase– and syrD+ strains and a Psm strain 627 control (Figure 2.4). Results were reported as positive (+) or negative (−). Gelatin liquefaction (G) was tested via stabbing cells into 1ml refrigerated (i.e., solid) 12% gelatin media with a sterile toothpick (Latorre and Jones, 1979). Cells were incubated for 7 days at 28°C and then placed in a refrigerator (~4°C) for 15 min before the assessment for liquefaction. All remaining tests involved streaking cells onto the 49 appropriate media types. Aesculin hydrolysis (A) was evaluated on media containing 0.1% aesculin and 0.5% ferric citrate where a color change (brown color or none) occurred after incubation at 28°C for 24h (Latorre and Jones, 1979; Sneath, 1956). Media containing 0.1% L- tyrosine (0.1%) was used to determine tyrosinase activity (T) consisting of a reddish-brown color change or none after 24h (Lelliot, 1966; Latorre and Jones, 1979). Tartrate utilization (Ta) was tested on Simmons media with 0.2% sodium tartrate, wherein a blue color change after 24 h at 28°C is a – result and no change is a + result (Holding and Collee, 1971; Latorre and Jones, 1979) (Figure 2.4). Fluorescence was evaluated for two day old oxidase– and syrD+ strains on KB media amended with 50 μg/ml cycloheximide. Plates were then placed in an ultraviolet (UV) light box, where fluorescence was observed through the oculars and recorded as + or – (Figure 2.4). Included were Pseudomonas fluorescens (strain GSPB1714), Erwinia Amylovora (strain Ca11-7 SmR), and Psm strain 627 were included as controls. Ice Nucleation Activity The capacity for ice nucleation activity (INA) was assayed by methods similar to Lindow et al. (1982b). The oxidase– and syrD+ strains (4 days old), including Psm strain 627 were washed into 900μl sterile PBS and vortexed. The bacteria in PBS (~109 to 1010 cfu/ml) were pipetted as ten 10μl drops/strain onto two floats that were made from aluminum foil coated with a thin layer of paraffin wax. A row of 10 μl PBS-only drops were also included. The floats were set in a refrigerated circulating water bath (Brinkmann MGW Lauda RMS, Delran, NJ) containing 50:50 ethylene glycol and distilled water (Figure 2.5). The initial bath temperature was set at -3°C, the temperature was then dropped by 0.5°C until reaching -5°C; each temperature was held for 15 min, and the number of frozen drops was recorded. Strains at -5°C 50 were observed at 15 min and 30 min. Strains were reported to be INA +, –, or +/– depending on the final observations at -5°C. Copper and Streptomycin Resistance To evaluate copper (Cu) resistance, a small number of cells of a single colony of two day-old oxidase–/syrD+ strains growing on KB amended with 50 μg/ml cycloheximide were streaked with a sterile toothpick onto mannitol-glutamate (MG) media with and without 250 μg/ml copper sulphate (CuSO4) (Renick et al., 2008). A known Cu resistant Pss strain (BC 43(390) CuR) and Cu susceptible Pss strain (26-3) were included (from Dr. George Sundin’s collection). The putative Pss strains were tested simultaneously alongside the resistant and susceptible Pss (Figure 2.6). Plates were assessed after two days of growing at 28°C, recorded as + (growth = resistant) or – (no growth = susceptible). This was repeated for strain 1-U-9 (Appendix A). Strains resistance to streptomycin were evaluated similarly to the Cu resistance assay, except that cells were streaked onto KB media amended with and without 50μg/ml streptomycin (Sundin and Bender, 1993). A known streptomycin resistant Erwinia amylovora strain (Ca11-7 SmR) and a streptomycin susceptible Pantoea agglomerans strain (UMAF3067) were included (from Dr. George Sundin’s collection). Plates were evaluated and reported as with the Cu resistance assay plates (Figure 2.6). Multilocus Sequence Analysis (MLSA) To further characterize the oxidase–/syrD+ strains and further verify that they are Pss a phylogeny was created based on MLSA using 4 housekeeping genes (gap1, gltA, gyrB, rpoD). The DNA of two day-old putative Pss strains was extracted via where cells were washed into 20µl of sterile 0.5x PBS and incubated at 95°C for 10 min in a Bio Rad T100™ Thermal Cycler. 51 The DNA was stored temporarily at 4°C for use in the following steps, as well as permanently stored long term at -20°C for later use. The genes gap1, gltA, gyrB, and rpoD were amplified for each strain in a total 25μl PCR reaction with 12.5 μl One Taq® Hot Start 2x Master Mix with Standard Buffer (New England BioLabs®), 0.5 μl each of forward and reverse primer, 2 μl DNA, and 9.5 μl Invitrogen™ UltraPure™ distilled water. The PCR primers were the same as those modified by Hwang et al. (2005) as cited by Vasebi et al. (2020) (Table 2.2). PCR amplification occurred in the Bio Rad T100™ Thermal Cycler under thermal conditions that were modified by Vasebi et al. (2020) (Table 2.2). Amplification of each gene was verified with gel electrophoresis using 5 μl of PCR product and 1 μl 6x ficoll-based loading dye on a 1% Agarose gel stained with 1μl Ethidium Bromide (EtBr) in 1x TAE run for 20 min at ~120 V. A 100 bp GoldBio® DNA ladder was included. Gel imaging was done using a Bio Rad Gel Doc™ EZ Imager. The remaining 20 μl of PCR product was cleaned using a QIAquick® PCR Purification Kit (Qiagen, Germantown, MD) following the steps provided in the kit instructions. The purified PCR product (9μl) was combined with 3 μl forward or reverse sequencing primer for each gene and strain in their own respective wells of a 96 well PCR plate (or strip of 8 PCR tubes in one instance). Sequencing primers were those cited in Vasebi et al. (2020) that had been modified by Hwang et al. (2005) (Table 2.2). A total of 160 gene x strain x primer combinations were submitted to the Genomics Core at the MSU Research Technology Support Facility (RTSF) (East Lansing, MI) for Sanger sequencing using the Applied Biosystems 3730xl DNA Analyzer (ThermoFisher Scientific). Returned sequences were viewed as chromatograms for sequence quality assessment (no gaps/missing bases and single, clear peaks) using FinchTV v.1.4.0 (Geospiza Inc., 2004-2006). 52 The forward and reverse sequences of each gene x strain were aligned using NCBI (National Center for Biotechnology Information) BLAST® (Basic local alignment search tool) (Altschul et al., 1990). BLAST® aligned sequences were trimmed to include base pairs (bp) only present in both reads. The trimmed forward reads for each gene x strain were used in all subsequent analyses. Sequences for each strain were aligned together by gene in MEGA11: Molecular Evolutionary Genetics Analysis version 11 (MEGA11) using the MUSCLE algorithm (Tamura et al., 2021; Edgar, 2004). Reference sequences were downloaded from NCBI GenBank® or the Plant Associated and Environmental Microbes Database (PAMDB.org) for each of the 4 genes from 40 Pss and P.s pathovars as well as P. fluorescens (strain A506), the type strain of P. viridiflava (CFBP2107T), P. savastanoi glycinea R4, and P. cersi (strain H346-S) (Almeida et al. 2010; Clark et al., 2016) (Table 2.3). Genes of reference strains that were gleaned from the whole genome were trimmed to the forward and reverse primers used by Vasebi et al. (2020). Reference strains were aligned by gene alone and then combined into a FASTA file with this study’s sequences using Linux command line and were aligned using MUSCLE in MEGA11. All strains were trimmed to be the same length, 473 bp, 501 bp, 507 bp, and 501 bp for the genes gap1, gltA, gyrB, and rpoD, respectively. The 4 genes were concatenated by strain using Linux command line, all the strains (reference and this study’s) which were then aligned a final time using MUSCLE in MEGA11. This final alignment with the 4 concatenated genes for each strain (reference and this study’s) was used to create a Maximum Likelihood (ML) phylogenetic tree in MEGA11. The best fit model for the ML tree was determined by selecting the “Find Best DNA/Protein Models (ML)” option in MEGA11 (Hall 2018). The ML tree was then constructed under the conditions 53 of the TN93+G+I model, with 1000 bootstrap replications, partial deletion, 95% site cut off, and the default tree inference options (Hall, 2018). The tree was rooted on P. fluorescens A506, bootstraps <75% were hidden, the tree was widened and expanded for better viewing in MEGA11 and FigTree v.1.4.4 (2006-2018, Andrew Rambaut, Institute of Evolutionary Biology, University of Edinburgh). The final aesthetics of the tree (Font color, brackets, phylogroup designations, and bootstrap values determined by MEGA11 added manually) were done using InkScape v.1.2 (InkScape.org). Green Fruit Virulence Assay Virulence of the oxidase–/syrD+ strains on sweet cherry fruit harvested with stems on from site 1 were tested in 2019 (3 fruit/strain ) and in 2021 (5 fruit/strain). Bacteria suspensions (10µl of 107 cfu/ml) were inoculated into wounds of green to straw colored fruit made via a sterile dissecting probe following surface sterilization with 0.5% sodium hypochlorite for 5 min, then rinsed in sterile distilled water followed by a swab with a sterile pad saturated with 70% isopropyl alcohol (Covidien™ Webcol™, Cardinal Healthcare, Dublin, OH). Bacteria suspensions in 2019 were from cells grown on KB media washed into 900 µl sterile PBS while in 2021 they were from cells grown overnight in KB broth (3 tubes/strain) that were washed 2x in equal volume of sterile PBS then serially diluted to the desired cfu/ml. Fruit stems were placed through a hole in the lid of a 14 ml round bottom test tubes (Falcon®, Corning, Corning, NY) held in test tube racks so that the stem was submerged in distilled water and the fruit sat on top of the lid (Figure 2.7). The bacteria controls in 2019 consisted of Pantoea agglomerans (strain UMAF3067), Escherichia coli dh5α, and Psm strain 627, as well as PBS only while in 2021 only Psm 627 and PBS were included. 54 The racks of inoculated fruit were placed inside a plastic tub (with lid) containing wet paper towels to keep humidity high. The tub with fruit was placed into a growth chamber with a 16:8 light:dark photoperiod at 24°C. Each fruit was photographed 4 days after inoculation (DAI) next to a U.S. quarter coin (as a known reference size) to measure the area of any lesions formed on the fruit using the ImageJ software (Schneider et al. 2012). In 2019 to obtain the best threshold contrasts and measurements in ImageJ the background was changed to white using Adobe® Photoshop (Elements 14 Photo Editor, Adobe®, San Jose, CA) so that only the fruit and quarter are the main focus of the image, and in some instances, the lesion color was darkened (without altering lesion size). In 2021 it was unnecessary to remove the background as the contrast was sufficient. In ImageJ, the image type was set to 8-bit, and the quarter reference was measured by, setting the known distance to 24.6 mm (coin diameter), and checking the Global box. The bottom threshold was then adjusted and recorded for each image so that the lesion on the fruit was prominent (the top threshold remained 0), and a selection tool was then used to select the lesion outline. The Analyze-analyze particles option was selected with size (mm2) set as 0, circularity set as 0.00-1.00, and show outlines, display results, and include holes were selected to measure the area of the lesion (mm2) via the software (Figure 2.7). All images used for data analysis for 2019 and 2021 can be found in Appendix A. Wood Virulence Assessment Strain virulence on sweet cherry wood was assessed in the field using methods similar to Hulin et al. (2018). In November 2019, six ‘Sweetheart’ trees and seven ‘Coral Champagne’ trees located at site 1 were wounded (~1-1.5 cm in size) via sterile razor blade (sanitized with 10% sodium hypochlorite and 75% ethanol between wounds) and inoculated with the oxidase– 55 /syrD+ strains (5 reps/strain) on two-to-three-year-old wood (Figure 2.8). All trees were trained as tall spindle axe (TSA) (Figure 2.9). The wood at the inoculation site was thoroughly swabbed with a 70% isopropyl alcohol pad before wounding. The inoculant was from strains (stored at ~- 70°C) grown overnight in 3 ml KB broth in 15 ml conical tubes (3 tubes/strain) shaken at 200 rpm and 28°C. Cells from the three tubes were combined and washed 2x in 9 ml sterile PBS and then serially diluted to 107 cfu/ml (determined by spectrophotometer at 600nm OD) and verified by plate counts. Due to the limited of the number of trees available and tree size, the strains inoculated on the trees were split between the 2 cultivars, 10 strains on ‘Sweetheart’ (i.e., 1-U-9, 1-V-13, 1-V-14, 1-V-15, 1-G-16, 1-G-17, 1-G-18, 1-R-21, 1-Br-22, and 1-Br-23) and 10 strains on ‘Coral Champagne’ (i.e., 2-BP-25, 2-BP-26, 2-BP-27, 2-BP-29, 2-BP-30, 2-BP-32, 2-BP-33, 2-BP-34, 3-BY-37, 3-BY-38). Strains were assigned randomly to 4 branches/tree and 3 site locations/branch (basal, mid, and distal) with at least 4 nodes between wounds (Appendix A). Inoculum and PBS only control (3 reps/tree) were applied via pipette in 20μl drops and were covered promptly with parafilm and duct tape for a total of 139 inoculations. Inoculated wood was left wrapped until May 2020 (~ 6 months) when they were unwrapped and assessed for lesion formation. Lesions were rated on the same 4 point scale (4=strong brown/black lesion, spread from inoculation site, gumming; 3= brown/black lesion, gumming; 2=some brown coloration; 1=no lesion) as in Hulin et al. (2018) (Figure 2.10). This study was repeated in 2021 with some modifications. In March 2021, all strains (5 reps/strain) and PBS only controls (3/tree) were inoculated on 2-to-3-year-old wood of 12 ‘Sweetheart’ trees trained as bi-axis espalier located at site 2 (Ionia Co.) and 20 ‘Coral Champagne’ trees (trained as upright fruiting offshoots [UFO]) at site 4 (Berrien Co.) (Figure 2.1 and Figure 2.9). As with the 2019/2020 study, the strains and reps were assigned to trees 56 randomly (Appendix A). The trees at site 2 were stronger and had more branching than at site 4 trees, ergo more trees were used at site 4. A total of 296 inoculations (136 at site 2, and 160 at site 4) were made using the same methods as described for 2019, except at site 4, inoculations were in four locations/branch (basal, mid, mid, distal) as only two branches/tree were available (Figure 2.9; Appendix A). Lesion formation was assessed after only 3 months, in June 2021, using the same scale described previously. All images documenting the inoculation and lesion rating assessments for both years can be found in Appendix A. Statistical Analysis of Virulence Studies Green fruit lesion area and wood lesion rating data were analyzed in the statistical program R v4.2.2 (R core team 2022). Data were collapsed by phylogenetic groupings (phylogroup) of strains (determined by MLSA) for both experiments due to like values. For analysis of the green fruit lesion area data the two years (2019 and 2021) were combined to improve the robustness of the model and the R code used was modified from Mangiafico (2016). A non-parametric Kruskal-Wallis test was used, followed by pairwise multiple comparisons using Dunn’s all pairs test with Bonferroni adjusted p-values (Mangiafico, 2023; Pohlert, 2022). The wood lesion rating data were analyzed using a cumulative links model (clm) for ordinal regression (Christensen, 2022). Model fitness was tested using McFadden’s pseudo-R-squared values (Andri et al., 2022; Venables and Ripley, 2002). Post hoc analysis was done with pairwise comparisons of strains using the lsmeans function with Tukey adjusted p-values (Graves et al., 2019; Lenth, 2023,). All graphical representations of data from both experiments were created using ‘ggplot2’ and ‘ggpubr’ (Kassambara, 2022; Wickham, 2016). Fonts, colors, and brackets were added or adjusted to the graphs using InkScape v.1.2 (InkScape.org). 57 2.4: Results Isolation, Oxidase Reaction, and syrD/syrB Amplification A total of 48 plates were used for the flower microbiome dilutions, 47 plates contained dilutions with single colonies (only one was overgrown at all dilutions). Forty-three produced colonies visually similar to Pss (i.e., cream-colored and circular in shape). A single colony from each of these 43 plates was selected for streaking to obtain pure cultures for further characterization of strains. Of the 43 pure cultures four were found to be oxidase+ and were discarded, having turned purple when exposed to Kovac’s oxidase reagent and therefore were not Pss. The remaining 39 putative Pss strains were oxidase− (no color change) and were stored and used for further analysis. The remaining 39 strains were then used in the PCR reaction for determining the presence of the syrD gene (gene encoding the export of syringomycin) (Appendix A). Of these, 20 strains had amplification of the syrD gene (syrD+), (strain nos. 9, 13, 14, 16, 17, 18, 21, 22, 23, 25, 26, 27, 29, 30, 32, 33, 34, 37, and 38) and were considered possible Pss strains (Table 2.4). These 20 oxidase−/syrD+ strains were named and used in all further experiments. Although the strains were given unique identifiers according to the location and cultivar from which they were collected (helpful for strain and data sharing/comparisons), in most cases only the strain number will be reported for simplicity in this paper. The 20 oxidase−/syrD+ strains were also screened for amplification of the syrB gene, with varied results (Appendix A). Eleven had amplification of the syrB gene (syrB+) (nos. 9, 22, 23, 25, 26, 27, 32, 33, 34, 37, and 38) while 9 failed to amplify (strain nos. 13, 14, 15, 16, 17, 18, 21, 29, 30) (Table 2.4). GATTa, Fluorescence, INA, and Cu/Streptomycin Resistance 58 The 20 oxidase−/syrD+ strains differed in their GATTa profiles. Fourteen (nos. 9, 25, 26, 27, 37, 38, 22, 23, 32, 33, 34, 16, 17, and 18) had the typical Pss GATTa profile of + + − − (Table 2.4). These strains (excluding 16, 17, and 18) were also syrB+. Strains 16, 17, and 18 were unique in that they exhibited the standard Pss GATTa profile but are syrB−. Six strains (13, 14, 15, 21, 29, and 30) had an atypical GATTa profile of + + + − and were all syrB− . The control strain of Psm 627 had the usual GATTa profile of Psm (− − + +), and thus none of the 20 strains were Psm. All 20 of the oxidase−/syrD+ strains fluoresced under UV light when grown on iron (Fe) deficient KB media. Strains were also variable for ice nucleation activity (INA). All strains that had the typical GATTa+ + − − profile were INA+, except for 22, 23, and 18. Strains 22 and 23 were INA−, while strain 18 was INA+/− (i.e., 30% were INA+ (n=10)) (Table 2.4). For the atypical GATTa+ + + − strains, three were INA− (13, 29, 30), two were INA+/− (14, 15; 10% were INA+ (n=10)), and one was INA+ (strain 21). A single strain, 1-U-9, had resistance to Cu while the other 19 oxidase−/syrD+ strains were susceptible to Cu at 250 μg/ml (Table 2.4). Strain 9 is GATTa+ + − −, syrB+/syrD+, and INA+. No strains were found to be resistant to the antibiotic streptomycin at 50 μg/ml. All images of the Cu and streptomycin media plates can be found in Appendix A. MLSA The ML phylogenetic tree produced as a result of the MLSA consisted of 3 main phylogroups (PG) 2, 3, and 1, rooted at P. fluorescens A506 (Figure 2.11). The best fit model for the ML tree was the Tamura-Nei (TN93) model with Gamma distributed (5 categories, parameter = 0.2623) invariant sites (37.27%) (G+I), (TN93+G+I) (Tamura and Nei, 1993). The PG2 in this tree are further sub-divided into 5 additional clades (2a, 2b, 2c, 2d, and 2f). All 20 strains are in 59 PG2, and are found within clades 2b, 2c, and 2d. None of this study’s strains were found in PG2a, PG2f, PG1, or PG3, nor did any cluster near P. viridiflava. Of the 20 strains, six are in PG2d (strains 37, 38, 9, 26, 27, and 25) with the well-known virulent Pss strain B728a, five are in PG2b (strains 33, 34, 32, 22, and 23) with the well-known virulent Pss strain FF5, and nine are in PG2c (strains 21, 17, 18, 16, 29, 30, 13, and 15) with the atypical, non-virulent Pss strain 642 (Figure 2.11). Those in PG2d are GATTa+ + − −, syrB+/syrD+, and INA+ (Table 2.4). The PG2d strains 37 and 38 clustered together (95% bootstrap certainty) nearest to a Hungarian (HUN) Pss strain 2340, while strain 9 was nearest to a Montenegro (MNE) Pss strain K3 (99% bootstrap certainty), and strains 26, 27, and 25 clustered together (96% bootstrap certainty) nearest to a Great Britain (GBR) Pss strain HRI-W7924 (98% bootstrap certainty) (Figure 2.11 and Table 2.3). The PG2b strains were also GATTa+ + − − and syrB+/syrD+, but varied in INA (i.e., 33, 34, and 32 are INA+, while 22, 23 are INA−). The PG2b strains 33, 34, and 32 clustered together with 100% bootstrap certainty, arising from the same branch as the Pss type strain CFBP4702 (98% bootstrap certainty). Strains 22 and 23 grouped together with 99% bootstrap certainty nearest to the Pss strain FF5-1 (99% bootstrap certainty). Strains in PG2c were variable in their GATTa profiles (67% GATTa+ + + −, 33% GATTa + + − −) and INA (equal no. strains in the INA+, INA−, and INA+/− categories); however, they were all syrB−/syrD+. The strains 16, 17, and 18 clustered together (99% bootstrap certainty) in PG2c between strain 21 (from this study) and the atypical Pss strain 642 with 100% and <75% bootstrap certainty, respectively (Figure 2.11, Table 2.3). Strains 29 and 30 (from this study) clustered together in PG2c with 100% bootstrap support nearest to the atypical Pss strain 508 (76% bootstrap certainty), while the strains 13, 14, and 15 clustered together with 100% 60 bootstrap certainty nearest to the Australian (AUS) Pss strain DAR82450 with less than 75% bootstrap support. Virulence on Green Fruit and Cherry Wood The lesion area data for strain virulence on green fruit was not normally distributed (Shapiro-Wilk normality test p=3.52e-16), with unequal variances (Levene’s test p=0.0003). The Kruskal-Wallis test for non-parametric data showed that the green fruit lesion area was statistically different for strains in the 3 PGs (χ²=152.98, df=4, p< 2.20e-16). There was a clear trend in the virulence level of the strains inoculated on green fruit (Figure 2.12). The strains 9, 37, 27, 38, 25, and 26 in PG2d were the most virulent strains on green fruit, with median lesion area ranging between 76.6 to 14.2 mm2. Strains 23, 22, 32, 33, and 34 in PG2b were moderately virulent, with median lesion area ranging from 11.5 to 5.8 mm2, and strains (13, 14, 15, 16, 17, 18, 21, 29, and 30) in phylogroup 2c are avirulent (0 mm2). The pairwise analysis of the phylogroups for lesion area supported the graphical interpretations, in which the strain lesion area or virulence in PG2d was statistically different (p<0.05) (i.e., more virulent) than strains in PG2b and PG2c, while strains in PG2b were more virulent than strains in PG2c (Appendix A). Strains in PG2c were avirulent, with no statistical difference from PBS (control). This gradation of virulence is also mirrored in the virulence ratings from strains wound- inoculated on sweet cherry wood (Figure 2.13). Where strains in PG2d (9, 37, 27, 38, 25, and 26) were the most virulent with median virulence ratings that ranged from 4 to 3 for both ‘Coral Champagne’ (2020 and 2021) and ‘Sweetheart’ (2020), and from 4 to 1 for ‘Sweetheart’ in 2021. The strains in PG2b (23, 22, 32, 33, and 34) were moderate to avirulent, with median virulence ratings of 2 to1 for ‘Coral Champagne’ (2020 and 2021) and 3 to1 for ‘Sweetheart’ (2020 and 2021). As in the green fruit assay, the strains in PG2c were avirulent, with a median virulence 61 rating of 1 for both ‘Coral Champagne’ and ‘Sweetheart’ (2020 and 2021), the same as the PBS controls. A proportionally low level of wild Pss contamination did occur in PBS control wounds for both cultivars and years, accounting for the single points when the virulence rating was >1. These graphical interpretations of the virulence rating data were further supported by cumulative link model (clm) ordinal regression analysis and post hoc pairwise comparisons of estimated marginal means. The best-fit model for the ordinal regression of the virulence rating data for the 20 Pss strains inoculated on sweet cherry wood is presented in Appendix A. There was a statistical effect of wood virulence rating overall for strains within the 3 phylogroups: PG2d virulence ratings were higher than those in PG2b while the PG2c and PBS control virulence ratings were less than those in PG2b based on the estimate coefficient (Table 2.5). The PG2b strains were variable in virulence and thereby are considered moderately virulent to avirulent. There were instances when PG2b strains were not statistically different from the avirulent 2c strains (on ‘Coral Champagne’ in 2020 and on ‘Sweetheart’ in 2021) and instances where they were statistically different from the PG2c strains (on ‘Sweetheart’ in 2020 and ‘Coral Champagne’ in 2021). There was a singular instance where PG2b strains were not statistically different than the more virulent PG2d strains (on ‘Sweetheart’ in 2020). The PG2c inoculations never differed statistically from the PBS controls for either cultivar or year, and therefore were avirulent. Pairwise comparisons of the marginal estimated means (lsmeans) of the virulence ratings of strains on wood by phylogroups again show virulence from most virulent to least virulent as PG2d > PG2b > PG2c (Appendix A). 62 2.5: Discussion syrB and syrD Amplification All 20 oxidase− strains amplified for the syrD gene (syrD+) however, there was pronounced variation in the strains that amplified for syrB (11 syrB+ and 9 syrB− strains) (Table 2.4). The amplification of syrB and syrD is standard for differentiating Pss from Psm and is included as part of the positive identification of P.s spp. (Bultreys and Gheysen, 1999; Renick et al. 2008; Sorensen et al., 1998; Quigley and Gross 1993). Many studies to characterize Pss have only used the amplification of the syrB gene to show the presence of the syringomycin phytotoxin (Hall et al. 2019; Ivanović et al., 2022; Lauwers, 2022; Olsel et al., 2022; Popović et al., 2021; Vasebi et al. 2020). This is likely due to the results of research from Sorensen et al. (1998), in which amplification of syrD did not always correlate with the presence of the gene in southern blot analyses. However, Bultreys and Gheysen (1999) used a separate set of primers and did not have specificity issues. Consequently those syrD primers were used in this study (as well as in Renick et al., 2008) and if only the syrB primers had been used then the 9 atypical Pss strains may have been discarded. For the 9 syrB− strains (strain nos. 13, 14, 15, 16, 17, 18, 21, 29, 30) there are two possible explanations that should be explored further: 1) these strains lack syrB, or 2) the syrB gene was not detected with the Sorensen et al. (1998) primers. The latter is entirely plausible given that the syrB gene region has been found to be quite diverse in addition to evidence showing variable results with the different syrD primers. However, given that the 9 syrB− strains were all found to be non-pathogenic (on cherry) and belong to the same phylogroup (PG2c), it is more likely that they lack syrB, as confirmed Pss strains lacking the syrB gene have been reported previously (Bultreys and Kaluzna, 2010; Hall et al., 2019; Popović et al., 2020; Quigley and Gross 1993; Scortichini et al., 2003). The production of syringomycin can be 63 verified in the Petri dish bioassay described in Latorre and Jones (1979) or the syrB− strains 16S rDNA sequences could be compared to other globally known syrB− strains (Popović et al., 2021). Lauwers (2022) did not find any syrB− strains in their characterization of Michigan Pss and considered syrB+ strains as verified Pss (and did not check for syrD), while Renick et al. (2008) only amplified syrD and considered syrD+ strains as verified Pss. The syringomycin phytotoxin is considered to be a significant virulence factor in Pss; in addition to causing the release of plant nutrients, it contributes to symptom development and aids in bacteria movement (Hulin et al., 2020; Xin et al., 2018). All 20 strains in this study contained syringomycin genes, either syrB+/syrD+ or syrB−/syrD+, and therefore have the potential to be virulent either on cherry or another Prunus species. GATTa, INA, and Cu/Streptomycin resistance The 20 oxidase−/syrB+/−/syrD+ strains isolated in this study also had diverse GATTa profiles and INA. The majority were GATTa+ + − − (strain nos. 9, 25, 26, 27, 37, 38, 22, 32, 33, 34, 16, 17, and 18) or GATTa + + + − (strain nos. 13, 14, 15, 21, 29, and 30) (Table 2.4). The standard GATTa profile for Pss is GATTa+ + − − , GATTa + + + − is atypical, and Psm is GATTa− − + + (Latorre and Jones 1979). While GATTa is a good test to differentiate between Pss and Psm, it is not a good test to use alone for Pss identification since tyrosinase activity and tartrate utilization by strains often is variable (Bultreys and Kaluzna, 2010; Hall et al., 2019; Latorre and Jones 1979). In this study six atypical GATTa + + + − strains (13, 14, 15, 21, 29, and 30) were syrB− non-pathogens (on cherry), and in PG2c, three strains (16, 17, 18) were typical GATTa+ + − −, syrB−, non-pathogens (on cherry), in PG2c, and 14 strains were typical GATTa+ + − − strains (9, 25, 26, 27, 37, 38, 22, 23, 32, 33, 34, 16, 17, and 18), syrB+ that ranged in pathogenicity in PG2b and PG2d (Table 2.4). Hall et al. (2019) reported Pss isolated from grapes (Vitis vinifera) in 64 Australian had atypical GATTa profiles that were both pathogenic and non-pathogenic. Lauwers (2022) did not find any Pss from Michigan sweet cherries with atypical GATTa profiles, and Renick et al. (2008) did not include GATTa tests in their characterizations of Michigan Pss. Pss strains isolated from sweet cherry flowers in Michigan have been shown to have a high degree of INA. Renick et al. (2008) had 94% INA in their Michigan Pss strains while Lauwers (2022) had 100% INA. The 20 strains in the current study were a mix of INA+ (60%), INA− (25%), and INA+/− (15%) (Table 2.4). Pss strains with a variable INA phenotype may not be active all at the same time, thereby populations are composed of a combination of INA+, −, and +/− (Hirano and Upper, 2000; Gross et al. 1983, Renick et al. 2008). Pss cells are able to ice nucleate due to proteins embedded in their outer membrane and can do so at 0 to -5°C, causing frost damage to plants at a higher temperature than would occur in the plant with no INA bacteria (i.e., in cherry, during bud break 50% of buds are killed at -6°C and 90% at -7°C with prolonged temperatures) (Hirano and Upper, 2000; Lindow et al., 1983a; Lindow et al., 1983b; Lukas et al. 2022; Salazar-Gutiérrez et al., 2004). This injury allows Pss to enter and infect the plant, causing initial blossom blast symptoms, which was a major contributor to a critical state- wide bacterial canker epidemic in Michigan in 2012 (Sundin and Rothwell, 2012). In this study, only a single strain, 1-U-9 (9), was found to be copper resistant (CuR); this strain was also GATTa+ + − −, syrB+/syrD+, INA+, and pathogenic (PG2d) (Table 2.4). It was surprising to find such a low incidence of CuR since Cu has been the most widely used and primary control measure for bacterial diseases since the mid-1800s as well as being the primary control option for bacterial canker disease (Lamichhane et al., 2018; Lauwers, 2022; Renick et al., 2008; Sundin et al., 1989). Lauwers (2022) and Renick et al. (2008) had Pss strains that were 13.8% and 31.7% CuR respectively. Sundin et al. (1989) found that CuR Pss strains from 65 Michigan cherry orchards contained a 61kb plasmid that was transferrable along with resistance to copper-susceptible (CuS) strains. None of the strains in the current study had resistance to the antibiotic streptomycin (streptomycin susceptible [SmS]) (Table 2.4). This is also a surprise since streptomycin is widely used in Michigan apple orchards, Michigan is ranked 3rd nationally for apple production, and streptomycin resistance (SmR) is prevalent in the fire blight pathogen E. amylovora (McGhee and Sundin, 2011; MDARD, 2018; Slack et al., 2021; Sundin and Wang, 2018). Jones et al. (1991) also isolated SmR P. syringae pv. papulans from Michigan apple orchards. MLSA and Virulence on Green Fruit and Cherry Wood Michigan’s Pss are also phylogenetically diverse. The ML tree in this study showed that Michigan Pss belonged to 3 out of the 7 PG2 clades (PG2b, PG2c, and PG2d) (Figure 2.11). Previous MLSA phylogenomic analyses of Michigan Pss had placed strains isolated from cherry flowers in phylogroups 2a, 2b, and 2d (Lauwers, 2022). Though both studies had Pss strains that belong to PG2b and PG2d, only this study found strains belonging to PG2c, and Lauwers (2022) found strains that belonged to PG2a. The strains 9, 25, 26, 27, 37, and 38 were in PG2d, and strains 9 and 37 had been confirmed previously as Pss from whole genome sequences (Dr. Michelle Hulin, East Malling, U.K., unpublished). These PG2d strains were also virulent on green sweet cherry fruit and wood (Figures 2.12, 2.13) and would be excellent for future experiments for which differences in infection vs. treatment need to be demonstrated. The strains in PG2b (22, 23, 32, 33, and 34) also can be considered to be verified Pss isolates since they were also GATTa + + − −, syrB+/syrD+, and INA+/INA− (Table 2.4). They were, however, moderately to avirulent on green sweet cherry fruit and wood, their pathogenicity on other Prunus spp., such as plum, apricot, or peach, has yet to be 66 assayed. Hulin et al. (2018a) showed reduced aggressiveness of some strains when looking at pathogenicity on plum and cherry wood. Since the current study utilized more thorough virulence tests, it is now known that the use of some of these strains in experiments could result in inconsistent infections and make treatments differences harder to discern. The strains in PG2c (13, 14, 15, 16, 17, 18, 21, 29, 30) were all syrB−/syrD+ but had variable GATTa profiles and INA and were avirulent on both the green sweet cherry fruit and sweet cherry wood. Phylogroup 2c is known as a non-pathogenic clade (Berge et al., 2014; Clarke et al., 2010). The strains Pss 642 and P.s 508, two reference strains included in this MLSA are in PG2c, are capable of INA and phytotoxin production, but have an atypical T3SS (Clarke et al., 2010; Mohr et al. 2008). All of the strains in this phylogroup were syrB−/syrD+ and varied in INA (Table 2.4). Strains 21, 17, 18, and 16 are closely related to Pss 642, while strains 29 and 30 are closely related to P.s 508, so these strains could be considered to be atypical Pss. Further sequencing and genome comparisons need to be made to examine the T3SS of these strains. The strains 13, 14, and 15, however, clustered together away from the atypical T3SS strains nearest to the avirulent Australian Pss strain DAR82450 (which was also GATTa+ + + −), but with <75% bootstrap certainty (Figure 2.11) (Hall et al., 2019). Strain 14 had been sequenced previously via the whole genome and was found to belong to a P.s outgroup (Dr. Michelle Hulin, East Malling, U.K., unpublished). These three strains should be labeled conservatively as P.s. strains rather than Pss. It is interesting that they did not classify with any of the P.s outgroups included in this MLSA with the 4 “housekeeping” genes (gap1, gltA, gyrB, and rpoD) used. While tools like PCR amplification and MLSA with a select subset of genes can give a more “rapid” picture of what Pss populations look like, the use of whole genome comparisons (e.g., Hulin et al., 2018b; Ruinelli et al., 2020) would give a more accurate insight into such a diverse 67 clade of organisms. Gomila et al. (2017) implemented the reorganization of some of the spp. within the P.s spp. complex with whole genome sequence comparisons. Baltrus and Orth (2018) discuss this concept in their review of Hulin et al. (2018b) and highlight how changes in technology and affordability will provide for more sophisticated experimental results. This MLSA also showed how Michigan strains are closely related to Pss strains from around the world. A prime example is the Michigan Pss strain 9 and the K3 Pss strain from Montenegro that grouped together in PG2d (Figure 2.11). Both strains are GATTa + + − −, INA+, syrB+, SmS, and CuR, and it would be interesting to compare them further at the genomic level, especially their copper resistance (this studies Table 2.4; Popović et al. 2021). Strains consistent virulence level on both the green fruit and cherry wood, seem to correspond to phylogroup, since PG2d was the most virulent overall while PG2b had moderate to avirulent strains and PG2c had avirulent strains (Tables 2.5, Figures 2.12-2.13). Hulin et al. (2018a), did not report this putative relationship, whose methodology guided this study’s approach. The gradient of virulence for the strains within the 3 phylogroups is also clear. Further virulence assays would be required, especially on wood, to confirm a correlation between phylogroup and virulence, since in 2019, this study split the 20 strains between two cherry cultivars. The use of rapid pathogenicity tests for new pathogen identification only reports a +/− response, so the nuance in virulence levels can be missed, requiring more in-depth characterizations. Virulence gradients may not be of interest to researchers focused on the most virulent Pss isolates. For research applicable to Michigan cherry growers, the range demonstrated by these 20 strains should definitely be considered since they all have virulence genes (i.e., syrD+, and several were INA+ or INA+/−). 68 Further research should look at the T3SS/effectors that these strains may possess (especially the PG2b and 2c strains) in addition to looking at those of the most virulent strains. Recently Ruiz-Bedoya et al. (2023) demonstrated that mutant P.s strains with a single effector alone are non-virulent but when strains are combined, they can be just as virulent as wild-type pathogens with multiple effectors. Pss is known to have few T3SS effectors, and a diverse array of strains can be isolated from the same niche as seen in this study. It is entirely possible that these moderate to avirulent strains could produce some factor that bolsters the more virulent strains and enhances their effectiveness as pathogens (Hulin et al., 2018b; Ruinelli et al., 2019; Ruiz-Bedoya et al., 2023). This concept was also noted by Barrett et al. (2011), who considered pathogen “cooperators” vs. non-pathogen “cheats” in which the fitness of the non- pathogen is improved by the “goods” produced by the pathogen. They also suggested that non-pathogens have a “fitness advantage in non-host environments”. Pss is a generalist and requires a diverse set of genes to function in the multiple environments in which it is known to exist (Bell and Bell, 2021; Morris et al., 2019). The evidence of “synergisms” between strains in P.s populations (albeit in pathogen-pathogen interactions) is also mounting. Petriccione et al. (2017) isolated Pss with P.s. pv. actinidiae and found that co-inoculations enhanced kiwi canker infections. Bophela et al. (2020) isolated P. viridiflava with Pss on plum, and Lipps and Samac (2021) noted several more “synergisms” involving P. viridiflava. Co-inoculation studies could be done in tandem with further virulence-level studies on cherry wood and other Prunus species. These moderate to avirulent strains should be assessed as possible biological control agents, perhaps as compatible vectors for dissemination of bacteriophage, or perhaps they could compete for resources. This study has provided an initial foundation for the sweet cherry flower microbiome with respect to Pss diversity. Elucidation of the cherry microbiome certainly will require 69 extensive, long-term study. Xin et al. (2018) discussed the importance of the microbiome as part of the classic disease triangle, ostensibly as a “4th vertex” considering that the interactions in the microbiome can “influence plant immunity and/or pathogen virulence”, so further study of what are the potential impacts of these moderate to avirulent Pss strains in Michigan cherry orchards is warranted. It also would be interesting to see if these moderate to avirulent strains could be recovered from cherry flowers at other locations throughout Michigan and other cherry growing regions to determine the geographic and/or ecological limits of their resident populations. 2.6: Conclusions This characterization of the Pss strains isolated from sweet cherry flowers has highlights the variability in Michigan orchards. The 20 Pss strains in this study belong to 3 phylogenomic groups (PG2d, PG2b, and PG2c), exhibited a gradient of virulence on green cherry fruit and wood, had variable GATTa profiles, amplified the syringomycin production gene (syrB), and showed INA. This study also demonstrated the importance of not forgoing thorough virulence tests, as Pss researchers could inadvertently select strains with reduced virulence capabilities. Caution also should be used when interpreting MLSA phylogenomic trees and PCR amplification results, since primer/gene sequence selection may bias results. This was evident from the MLSA placement of strain 14 in the atypical Pss PG2c, but upon whole genome sequencing it was found to belong in a P.s outgroup (Dr. Michelle Hulin, East Malling, U.K., unpublished). Future research questions that should be addressed with these 20 Pss strains include: Do the syrB− strains produce syringomycin or other phytotoxins? What virulence genes or T3SS/effectors do these Michigan Pss strains possess (typical/atypical)? What does the phylogenetic tree look like with whole genome analyses? Are the Pss strains in PG2b or PG2c virulent or more virulent on other Prunus spp.? How do the strains with varying degrees of 70 virulence interact with each other on cherry: cheaters, producers, mutualism/synergisms? Can co-inoculations increase virulence in cherry wood or on other Prunus? Can these moderate to avirulent strains be isolated from orchards throughout Michigan and from tissues other than flowers? Can they be useful as biological control agents? Answers to these questions may help facilitate the development of future Pss population management strategies and tools. 71 Figure 2.1. Michigan orchards where bacteria strains were isolated from cherry flowers (sites 1, 2, and 3) and where strains were tested for their virulence on wood (sites 1, 2, and 4). Site 1 is Wells Orchards (Grand Rapids, Ottawa Co.), site 2 is Michigan State University (MSU) Clarksville Research Center (Clarksville, Ionia Co.), site 3 is MSU Horticulture Teaching and Research Center (Holt, Ingham Co.), and site 4 is MSU Southwest Michigan Research and Extension Center (Benton Harbor, Berrien Co.). 72 A B Figure 2.2. Isolation of putative Pseudomonas syringae pv. syringae (Pss) strains from sweet cherry flowers: A) an example of the drop plates made from the serial dilutions (0, 10-1, 10-2, 10- 3) of the flower wash, and B) an example of the streak plates made from the selection of a single colony from the drop plate. Single colonies of this pure culture were then used for oxidase testing and making bacterial lawns for storing strains for further use. 73 Figure 2.3. Bacterial strains that were positive for the cytochrome oxidase enzyme (oxidase+) turned purple when smeared onto filter paper with Kovacs’ oxidase reagent. These strains were not characterized any further since Pseudomonas syringae pv. syringae (Pss) is oxidase– (no reaction/color change). 74 Table 2.1. Primers and thermal cycler parameters used to amplify the genes syrD and syrB involved in the export and synthesis of the phytotoxin syringomycin in Pseudomonas syringae. Gene Primer Thermal profile Product size (bp) Reference syrD Forward 5′- CAGCGGCGTTGCGTCCATTGC-3′ Reverse 5′- TGCCGCCGACGATGTAGACCAGC-3′ syrB Forward 5′- CTTTCCGTGGTCTTGATGAGG-3′ Reverse 5′- TCGATTTTGCCGTGATGAGTC-3′ 1,040 Bultreys and Gheysen (1999), Renick et al. (2008) 752 Sorensen et al. (1998), Vasebi et al. (2020) 95 °C 2 min. 35 cycles: 94 °C 1 min. 60 °C 1 min. 72 °C 1:30 min. 72 °C 5 min. 12 °C ∞ 94 °C 4 min. 35 cycles: 94 °C 1:30 min. 60 °C 1:30 min. 72 °C 3 min. 72 °C 10 min. 12 °C ∞ 75 B C C D A E Figure 2.4. Examples of how oxidase–/syrD+ strains were tested biochemically using various media types for their GATTa profile and the phenological expression of fluorescence commonly used to determine Pseudomonas syringae pv. syringae (Pss) strains. A) Gelatin liquefaction [G] tubes, B) Aesculin hydrolysis plates [A], C) L-tyrosinase activity [T] plates, D) Tartrate Utilization [Ta] plates, and E) strains tested for fluorescence on KB plates in a UV light box. 76 Figure 2.5. Floats coated with a thin layer of paraffin wax with 10 replicates of each bacterial strain in a 50% ethylene glycol refrigerated water bath used for testing ice nucleation activity (INA). 77 A C B D Figure 2.6. Examples of Pseudomonas syringae pv. syringae (Pss) copper (Cu) and streptomycin (Sm) resistance assay plates. A) mannitol-glutamate (MG) media amended with 250 μg/ml copper sulphate (CuSO4) with this study’s strains streaked in the left side of the plate, the Cu resistant Pss BC 43(390) CuR strain in the center, and the susceptible Pss 26-3 on the right side of the plate, B) an MG plate without Cu, C) King’s B (KB) media amended with 50 μg/ml streptomycin with this study’s strains streaked on the left side of the plate, the Sm resistant Erwinia amylovora strain (Ca11-7 SmR) in the center, and the susceptible Pantoea agglomerans strain (UMAF3067) on the right side of the plate. D) KB media without streptomycin. 78 Table 2.2. The PCR primers, sequencing primers, and thermal cycler parameters for the amplification and sequencing of the four housekeeping genes (gap1, gltA, gyrB, and rpoD), using DNA extracted from the oxidase-/syrD+ bacterial strains isolated in this study from sweet cherry flowers to be used in multilocus sequencing typing analysis (MLSA) and construction of a phylogenetic tree. Application Gene Primer Thermal profile PCR gap1 Forward 5′-CCGGCSGARCTGCCSTGG-3′ Reverse 5′-GTGTGRTTGGCRTCGAARATCGA-3′ gltA Forward 5′-CCTCBTGCGAGTCGAAGATCACC-3′ Reverse 5′-CTTGTAVGGRCYGGAGAGCATTTC-3′ gyrB Forward 5′-CBGCRGCVGARGTSATCATGAC-3′ Reverse 5′-TTGTCYTTGGTCTGSGAGCTGAA-3′ rpoD Forward 5′-AGGTGGAAGACATCATCCGCATG-3′ Reverse 5′-CCGATGTTGCCTTCCTGGATCAG-3′ Sanger Sequencing gap1 Forward 5′-CGARTGCACSGGBCTSTTCACC-3′ Reverse 5′-GTGTGRTTGGCRTCGAARATCGA-3′ 94 °C 3 min. 35 cycles: 94 °C 2 min. 63 °C 1 min. 72 °C 1min. 72 °C 5 min. 12 °C ∞ Reference Hwang et al. (2005), Vasebi et al. (2020) Product size (bp) 606 664 748 894 gltA Forward 5′-CTGRTCGCCAAGATGCCGAC-3′ Reverse 5′-CGAAGATCACGGTGAACATGCTGG-3′ gyrB Forward 5′-CBGCRGCVGARGTSATCATGAC-3′ Reverse 5′-TTGTCYTTGGTCTGSGAGCTGAA-3′ rpoD Forward 5′-YGAAGGCGARATYGRAATCG-3′ Reverse 5′-CCGATGTTGCCTTCCTGGATCAG-3′ 79 Table 2.3. The reference Pseudomonas strains used for multilocus sequence analysis (MLSA), with the four housekeeping genes (gap1, gltA, gyrB, rpoD), including the NCBI GenBank accession numbers. Strain Host Country Accession no. x gene gap1, gltA gyrB, rpoD Sequence Reference Sequence Location P.s Cit7 Citrus (Orange) USA1 CP073636 P.s tomato DC3000 P.s syringae 642 Solanum lycopersicum (Tomato) GBR2 AE016853 Unknown weed USA NZ_ADGB01000062, NZ_ADGB01000077 NZ_ADGB01000027, NZ_ADGB01000033 P.s actinidiae CFBP4909PT Actinidia deliciosa (Kiwi) P.s syringae CFBP4702T Syringa vulgaris (Lilac) P. viridiflava CFBP2107T Phaseolus (Bean) P. cerasi H346-S Corylus avellana (Hazelnut) P.s syringae B728a P.s syringae DAR82441 Phaseolus (Bean) Vitis vinifera (Grape) JPN3 GBR CHE4 CAN5 USA AUS6 KF937408, KF937504 KF937601, KF937698 KF937407, KF937504 KF937601, KF937698 KF937401, KF937498 KF937595, KF937692 NZ_JACSZQ010000001, NZ_JACSZQ010000002 NZ_JACSZQ010000004, NZ_JACSZQ010000016 Ellouze et al. (2020) CP000075 KP127645, KP136850 KP192349, KP229318 80 Feil et al. (2005) Hall et al. (2019) Baltrus et al. (2011) Buell et al. (2003) Clarke et al. (2010) Cunty et al. (2014) Cunty et al. (2014) Cunty et al. (2014) NCBI NCBI NCBI NCBI NCBI NCBI NCBI NCBI NCBI Table 2.3. (cont’d) P.s syringae DAR82442 P.s syringae DAR82446 P.s syringae DAR82447 P.s syringae DAR82450 P.s syringae DAR82452 Vitis vinifera (Grape) Vitis vinifera (Grape) Vitis vinifera (Grape) Vitis vinifera (Grape) Vitis vinifera (Grape) P.s 9643 Prunus domestica (Plum) P.s syringae 9654 Prunus domestica (Plum) P.s syringae 9656 Prunus avium (Sweet cherry) P.s 508 P.s papulans LMG5076PT P.s syringae FF5 Malus (Apple) Malus (Apple) Pyrus calleryana (Ornamental Pear) AUS AUS AUS AUS AUS GBR GBR GBR USA CAN USA KP127646, KP136851 KP192350, KP229319 KP127655, KP136860 KP192359, KP229328 KP127656, KP136861 KP192360, KP229329 KP127659, KP136864 KP192363, KP229332 KP127661, KP136866 KP192365, KP229334 Hall et al. (2019) Hall et al. (2019) Hall et al. (2019) Hall et al. (2019) Hall et al. (2019) NZ_MLET01000002, NZ_MLET01000001 NZ_MLET01000018, NZ_MLET01000007 Hulin et al. (2018) NZ_MLES01000019, NZ_MLES01000005 NZ_MLES01000010, NZ_MLES01000016 Hulin et al. (2018) NZ_MLEM01000003, NZ_MLEM01000001 NZ_MLEM01000006, NZ_MLEM01000010 Hulin et al. (2018) NCBI NCBI NCBI NCBI NCBI NCBI NCBI NCBI NA NA NA 81 Hwang Schema Hwang Schema Hwang Schema PAMDB.org PAMDB.org PAMDB.org Table 2.3. (cont’d) P.s TLP2 Solanum tuberosum (Potato) P.s syringae KBI222 Rubus idaeus (Raspberry) P.s syringae KFB422 Rubus fruticosus (Blackberry) Pyrus (Pear) Prunus avium (Sweet cherry) P. fluorescens A506 P.a morsprunorum HRI-W5261R2 P.a morsprunorum HRI-W5269R1 USA SRB7 SRB USA GBR NA Hwang Schema PAMDB.org MW407117, MW407127 MW407137, MW407147 MW407115, MW407125 MW407135, MW407145 NC_017911 Ivanović et al. (2022) Ivanović et al. (2022) Loper et al. (2012) LIIA01000145, NZ_LIIA01000028 NZ_LIIA01000152, NZ_LIIA01000135 Nowell et al. (2016) Prunus cerasus (Tart cherry) GBR LIHZ01000103, NZ_LIHZ01000100 NZ_LIHZ01000082, NZ_LIHZ01000058 P.s avii CFBP3846PT Prunus avium (Wild cherry) P.s syringae 2339 P.s syringae 2340 Prunus avium (Sweet cherry) Pyrus (Pear) FRA HUN8 HUN LIIJ01000007, NZ_LIIJ01000301 NZ_LIIJ01000227, NZ_LIIJ01000241 LIHU01000032, NZ_LIHU01000030 NZ_LIHU01000033, NZ_LIHU01000021 LIHT01000059, NZ_LIHT01000069 NZ_LIHT01000055, NZ_LIHT01000078 P.s syringae HRI-W7872 Prunus domestica (Plum) GBR LIHS01000009, NZ_LIHS01000071 NZ_LIHS01000040, NZ_LIHS01000028 82 Nowell et al. (2016) Nowell et al. (2016) Nowell et al. (2016) Nowell et al. (2016) Nowell et al. (2016) NCBI NCBI NCBI NCBI NCBI NCBI NCBI NCBI NCBI Table 2.3. (cont’d) P.s syringae HRI-W7924 Prunus cerasus (Tart cherry) Prunus dulcis (Almond) Prunus persica (Peach) Prunus persica (Peach) Prunus salicina (Japanese plum) Prunus armeniaca (Apricot) Prunus persica (Nectarine) Prunus avium (Sweet cherry) Glycine max (Soybean) P.s B7 P.s BR1 P.s BR2 P.s JS1 P.s K3 P.s N1 P.s T1 P. savastanoi glycinea R4 P.s persicae NCPPB2254 GBR MNE9 MNE MNE MNE MNE MNE MNE USA LIHR01000002, NZ_LIHR01000107 NZ_LIHR01000053, NZ_LIHR01000025 MT295211, MT345293 MT265357, MT240941 MT295229, MT345311 MT265375, MT250597 MT295230, MT345312 MT265376, MT250598 MT295232, MT345314 MT265378, MT250600 MT295221, MT345303 MT265367, MT250589 MT295231, MT345313 MT265377, MT250599 MT295228, MT345310 MT265374, MT250596 AEGH01000053, AEGH01000062 AEGH01000036, AEGH01000001 Nowell et al. (2016) Popović et al. (2021) Popović et al. (2021) Popović et al. (2021) Popović et al. (2021) Popović et al. (2021) Popović et al. (2021) Popović et al. (2021) Qi et al. (2007) NCBI NCBI NCBI NCBI NCBI NCBI NCBI NCBI NCBI NCBI Prunus persica (Peach) FRA10 LAZV01000005, NZ_LAZV01000082 NZ_LAZV01000085, NZ_LAZV01000167 Zhoa et al. (2015) 1United States of America (USA), 2Great Britain (GBR), 3Japan (JPN), 4Switzerland (CHE), 5Canada(CAN), 6Australia (AUS), 7Serbia (SRB), 8Hungary (HUN), 9Montenegro (MNE), 10France (FRA) 83 A B C Figure 2.7. Assay to test the virulence of the oxidase–/syrD+ putative Pseudomonas syringae pv. syringae (Pss) strains on green cherry fruit. A) cherries were wounded via stabbing with a sterile dissecting probe before inoculation, B) how lesions area was measured in 2019 via ImageJ, and C) how lesion area was measured in 2021. 84 A D B E C F Figure 2.8. The inoculation of sweet cherry wood with oxidase–/syrD+ putative Pseudomonas syringae pv. syringae (Pss) strains. A) wood following sanitizing with a 70% isopropyl alcohol, B) wounding with a sterile razor blade, C) the wound, D) inoculations with 107 cfu/ml of bacteria in 20μl drops, E) wound following inoculation, and F) the wound covered by parafilm and duct tape with the strain number and rep written on it. 85 A B C Figure 2.9. Examples of the trees that were used to test for oxidase–/syrD+ putative Pseudomonas syringae pv. syringae strain virulence on the sweet cherry cultivars Sweetheart and Coral Champagne. A) a TSA tree located at site 1 (Ottawa Co., MI), used for inoculations in 2019, B) a Bi-Espalier tree (cv. Sweetheart) located at site 2 (Ionia Co. MI) used for inoculations in 2021, and C) a Bi-UFO tree (cv. Coral Champagne) located at site 4 (Berrien Co., MI) in 2021. 86 4 3 2 1 Figure 2.10. The lesion rating scale as in Hulin et al. (2018) used to assess oxidase–/syrD+ putative Pseudomonas syringae pv. syringae (Pss) strain virulence on sweet cherry wood (cv. Sweetheart and Coral Champagne), with 4= black/brown lesion, spread away from the inoculation site, gumming; 3= black/brown lesion, gumming; 2= browning; and 1=no lesion. 87 Table 2.4. Results of the biochemical tests GATTa (Gelatin liquefaction, Aesculin hydrolysis, Tyrosinase activity, Tartrate utilization) and oxidase enzyme reaction, and the phenotype tests for INA (Ice nucleation activity), fluorescence, copper resistance (Cu R) at 250 μg/ml, and streptomycin resistance (Sm R) at 50 μg/ml. The amplification of the syringomycin-related genes syrB/syrD, and phylogroup designation was determined by MLSA for the identification of 20 possible Pseudomonas syringae pv. syringae (Pss) strains (labeled according to isolation collection information: location [site 1, 2, or 3] – sweet cherry cultivar (U=Ulster, V=Van, G=Gold, R=Rainier, Br=Brooks, BP=Black Pearl, BY=Black York) - strain no.). Controls include PBS and the bacteria strains: P.s morsprunorum (Psm 627), P. fluorescens (P.flu GSPB 1714), Erwinia amylovora (E.a Ca11-7 SmR), Pss (BC43(390) CuR, 26-3, 19- 6, 13-7, 6-9, 847, 6491, 1680, and FF5-1), and Pantoea agglomerans (P.ag UMAF3067). Phylogroup 2d 2b 2c ctrl Strain 1-U-9 2-BP-25 2-BP-26 2-BP-27 3-BY-37 3-BY-38 1-Br-22 1-Br-23 2-BP-32 2-BP-33 2-BP-34 1-G-16 1-G-17 1-G-18 1-V-13 1-V-14 1-V-15 1-R-21 2-BP-29 2-BP-30 PBS GATTa + + − − + + − − + + − − + + − − + + − − + + − − + + − − + + − − + + − − + + − − + + − − + + − − + + − − + + − − + + + − + + + − + + + − + + + − + + + − + + + − NA INA Fluorescence + + + + + + − − + + + + + +/− − +/− +/− + − − − + + + + + + + + + + + + + + + + + + + + NA 88 syrB + + + + + + + + + + + + + + + + + + + + + + + − + − + − + − + − + − + − + − − + NA NA syrD Oxidase Cu R − − − − − − − − − − − − − − − − − − − − NA + − − − − − − − − − − − − − − − − − − − NA Sm R − − − − − − − − − − − − − − − − − − − − NA Table 2.4. (cont’d) Psm 627 P.flu GSPB 1714 E.a Ca11-7 SmR Pss BC43(390) CuR Pss 26-3 Pss 19-6 Pss 13-7 Pss 6-9 Pss 847 Pss 6491 Pss 1680 Pss FF5-1 P.ag UMAF3067 − − + + NA NA NA NA NA NA NA NA NA NA NA NA − NA NA NA NA NA NA NA NA NA NA NA NA − + − NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA + NA + NA − NA + NA + NA NA + + NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA + − NA NA NA NA NA NA NA NA NA NA + NA NA NA NA NA NA NA NA NA − 89 Figure 2.11. A maximum likelihood (ML) phylogenetic tree using the Tamura-Nei model (1000 bootstraps) for MLSA of 4 concatenated housekeeping genes (gap1, gltA, gyrB, and rpoD) of 20 possible Pseudomonas syringae pv. syringae (Pss) strains (in red) rooted by P. fluorescens, including reference strains from 7 P.s phylogroups (PG), PG2 (2a, 2b, 2c, 2d, and 2f), PG1, PG3, and P. viridiflava. A distance scale with the number of bp substitutions/site and bootstrap values >75% are shown. 90 Figure 2.12. Graphs for the green fruit assay for virulence of the 20 Pseudomonas syringae strains on immature sweet cherry fruit, including strain Psm 627 (Psm) and PBS as controls. A) Bar graph of the median lesion area (mm2) for each strain number (Strain no.) within the phylogroup (PG) 2d, 2b, and 2c, letters (a, b, and c) above the bars indicate statistical differences of phylogroup for lesion area or virulence (p<0.05) determined by Dunn’s all pairs test (Bonferroni adjusted p-values). B) Box plot showing the distribution of the lesion areas (mm2) produced on the individual fruit for each strain (colored points (n=8)) within the 3 phylogroups, and the horizontal black bar within the box signifies the median lesion area (mm2). 91 Figure 2.13. Box plots showing the distribution of the virulence rating for Pseudomonas syringae strains (strain no.) and the trend of most to least virulent in the phylogroups (PG), 2d, 2b, and 2c, inoculated on sweet cherry wood of the cultivars A) Coral Champagne (Coral) in 2020, B) Sweetheart in 2020, C) Coral in 2021, and D) Sweetheart in 2021, with PBS controls. Black horizontal bars are the median rating value for each strain, colored points are the rating value for each rep (n=5). Letters (a, and b) indicate statistical differences of phylogroup for virulence (p<0.05) from pairwise comparisons of estimated marginal means (Tukey adjusted p-values). 92 Table 2.5. Summary statistics from the clm ordinal regression (rating~phylogroup), in which the estimate value indicates an increase (↑, + estimate value) or decrease (↓, − estimate value) in virulence (rating) for phylogroups 2c, 2d, and ctrl (PBS control) in relation to the phylogroup 2b, with standard error (SE), z-value, and the p-value indicating significance when p<0.05, for the Pseudomonas syringae strains that were rated for virulence on the sweet cherry cultivars Coral Champagne and Sweetheart in 2020 and 2021. Year Cultivar Phylogroup (vs. 2b) Virulence Estimate SE z-value p-value 2020 Coral Champagne Sweetheart 2021 Coral Champagne Sweetheart 2c 2d ctrl 2c 2d ctrl 2c 2d ctrl 2c 2d ctrl ↓ ↑ ↓ ↓ ↑ ↓ ↓ ↑ ↓ ↓ ↑ ↓ -0.35 2.78 -2.39 -3.56 2.26 -3.55 -2.56 3.01 -2.84 -0.63 1.86 -1.54 0.86 0.75 1.16 0.99 1.29 1.22 1.11 0.64 1.11 0.86 0.72 1.19 -0.41 3.70 -2.06 -3.61 1.76 -2.90 -2.30 4.67 -2.56 -0.74 2.59 -1.30 0.6832 0.0002 0.0396 0.0003 0.0787 0.0038 0.0213 3.03e-6 0.0105 0.4612 0.0095 0.1946 93 REFERENCES Abdellatif, E., Kałużna, M., Ferrante, P., Scortichini, M., Bahri, B., Janse, J.D., van Vaerenberg, J., Baeyen, S., Sobiczewski, P., and Rhouma, A. 2020. Phylogenetic, genetic, and phenotypic diversity of Pseudomonas syringae pv. syringae strains isolated from citrus blast and black pit in Tunisia. Plant Pathol. 69: 1414– 1425. Alfano, J.R., and Collmer, A. 2004. Type III secretion system effector proteins: double agents in bacterial disease and plant defense. Annu. Rev. 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Laurentian Great Lakes warming threatens northern fruit belt refugia. Int. J. Biometeorol. 66: 669–677. Wickham, H. 2016. ggplot2: Elegant graphics for data analysis. Springer-Verlag, New York. Xin, X-F., Kvitko, B., and He, S.Y. 2018. Pseudomonas syringae: what it takes to be a pathogen. Nat. Rev. Microbiol. 16: 316–328. Zhao, W., Jiang, H., Tian, Q., and Hu, J. 2015. Draft genome sequence of Pseudomonas syringae pv. persicae NCPPB 2254. Genome Announc. 3(3):e00555-15. doi: 10.1128/genomeA.00555- 15. 103 CHAPTER 3 EXPLORATION OF THE SWEET CHERRY LEAF MICROBIOME REVEALS DOMINATION BY PSEUDOMONAS AND SEASONAL COMMUNITY DIRECTIONALITY 3.1: Abstract The pathogen Pseudomonas syringae pv. syringae (Pss), causal agent of a major cherry disease, bacterial canker, is a well-known and much-studied epiphyte. Little is known, however, about the other bacteria that also are present in the leaf microbiome. Knowledge of these other bacteria potentially could lend some insight into how to manage Pss populations in the future. The sweet cherry leaf microbiome was surveyed in two regions of Michigan (Ottawa Co. and Ingham Co.) from three cultivars of sweet cherry (‘Benton’, ‘Gold’, and ‘Sweetheart’) over three seasons (Fall 2017 to Fall 2019). The microbiome was dominated by the Phylum Proteobacteria (67% − 100% relative abundance) and the genera Pseudomonas (26% − 96% relative abundance). The core microbiome was composed of Pseudomonas (OTUs 00001, 00002, 00003, 0001, and 0002) and Sphingomonas (OTUs 00004, 0003, and 0007). Season and Pseudomonas are the most important drivers of the Michigan sweet cherry leaf microbiome in Michigan, where springs were quite variable, and summer and fall had the most stable communities. Bacterial populations are lowest in the spring and increase during the summer and fall. There were no regional or cultivar differences in the bacterial communities of this study’s leaf microbiome. Many of the top bacteria taxa have the potential to be growth-promoting bacteria, and Sphingomonas was the second most consistently present taxa in all seasons. This study can serve as the foundation for expanding research of the sweet cherry leaf microbiome with respect to the relationship between Pss and its microbiome denizens. 104 3.2: Introduction Sweet cherry (Prunus avium L.) leaves are well-studied organs imperative for gas exchange/photosynthesis and transpiration, essential for fruit tree growth and development, but they also harbor previously underappreciated microbial communities that are fundamental components of tree health or disease (Bashir et al., 2022; Glick and Gamalero, 2021; Leveau, 2019; Lindow and Brandl, 2003; Mina et al., 2019; Pattnaik et al., 2021; Vorholt, 2012). In Michigan, the primary microbial focus in sweet cherry orchards has been on individual disease- causing organisms like the bacterial canker causal agent Pseudomonas syringae pv. syringae (Pss) (Kennelly et al., 2008; Jones, 1971; Jones and Sutton, 1996; Latorre and Jones, 1979b; Sundin et al., 1988). Pss is a well-known entity within the plant phyllosphere (aerial portions of the plant), a well-adapted epiphyte on leaf surfaces, and can live as non-pathogens at low population levels (Hirano and Upper, 1990; Hirano and Upper, 2000; Jones and Sutton, 1996; Kennelly et al., 2008; Latorre and Jones, 1979b; Lindow and Brandl, 2003; Sundin et al., 1988; Vorholt, 2012). Pss is ubiquitous in Michigan cherry orchards, isolated from weeds, grasses, plant detritus, cherry leaves, flowers, buds, and bark, and is linked to the water cycle, rain splash dispersed, and genetically adapted to Prunus spp. (Hirano and Upper, 1990; Hirano and Upper, 2000; Hulin et al., 2018; Hulin et al., 2020; Kennelly et al., 2008; Latorre and Jones, 1979a; Lattore and Jones, 1979b; Nowell et al., 2016; Sundin et al., 1988; Morris et al., 2008; Morris et al., 2013; Ruinelli et al., 2022). Morris et al. (2019) found that Pseudomonas syringae (P.s.), in general, have broad and overlapping host ranges and that phylogroup 2 (PG2), the clade to which Pss belongs, has a greater host range compared to other P.s. This is not surprising, considering that Pss is a known pathogen in over 180 plant species and can infect through wounds (e.g., pruning, trellis rubs) and 105 natural openings (e.g., stomata and leaf scars) (Jones and Sutton, 1996; Kennelly et al., 2008; Lillrose et al., 2017). Michigan cherry growers must consider the potential economic benefits of high-density orchard systems and the potential risks of infection by the opportunistic Pss pathogen. In the late 1990s, as sweet cherry growers began the initial move toward converting acreage to dwarfing rootstocks and high-density plantings, a conundrum arose in which fruit size and quality, critical market parameters, appeared to be limited in parallel with tree vigor, prompting extensive investigations into source/sink dynamics (Ayala and Lang 2017). These studies not only elucidated the movement of carbon through the tree from shoots, flowers, and fruits throughout the growing season (bud break to dormancy) but also highlighted the importance of the leaf area to fruit ratio (LA:F) for optimizing marketable fruit production (Ayala and Lang, 2017; Whiting and Lang, 2004). This entails achieving a favorable balance between the amount of leaf area that provides photosynthetic assimilates (carbon) and the number of fruit (crop load) that utilizes those assimilates during development, and optimization is influenced by the combination of scion, rootstock, and environment (Ayala and Lang, 2017; Whiting and Lang, 2004). Precision in training and pruning promotes the balance through the manipulation of leaf area (removal or promotion of growth), the thinning of fruiting sites (reduced flowers per spur), and increasing light distribution to leaves and fruit within the canopy architecture (Ayala and Lang, 2017). Pruning, however, comes with a cost as dormant, early spring, and late spring pruning that is meant to direct and stimulate growth, improve LA: F, and reduce tree vigor, respectively, occurs concurrently with Michigan’s cool wet (rain/snow) weather conditions that are ideal for Pss population growth and subsequent infection via the pruning wounds (Kennelly et al., 2008; 106 Jones and Sutton, 1996; Lang, 2019; Spotts et al., 2010;). Local populations of Pss are already present in the spring at and before bud break, as the bacteria overwinter in buds, and as leaf expansion takes place, deposition of Pss can occur from adjacent vegetation and rain/snow/air (Hirano and Upper, 2000; Latorre and Jones, 1979a; Lindemann and Upper, 1985; Sundin et al., 1988; Morris et al., 2008). Current pruning recommendations are to wait for periods of dry weather with warmer air temperatures, with summer being ideal, as Pss infections can cause necrosis even at temperatures as low as -10 °C (Carroll et al., 2010; Spotts et al., 2010; Sobiczewski and Jones, 1995). Summer pruning via hedging (pre-harvest and post-harvest) can reduce shade and increase light penetration into the tree canopy while reducing tree vigor and partitioning carbon toward fruits (Lang, 2019). Summer infections by Pss still may occur and pruning wounds can be susceptible to Pss infections ranging from < 1 week to 24 days (Law, 2017; Spotts et al., 2010). Michigan is subjected to frequent periods of high (≥ 60%) and extremely high (≥ 85%) relative humidity; regardless of the time of year, the probability of an extended (>36 h) humidity event within any 14 day period is 70-90% (Komoto et al., 2021). Monier and Lindow (2003) found that bacterial aggregates of Pss strain B728a were more extensive and more numerous at high relative humidity (100%) than at low relative humidity (<50%). In Michigan, even during a “dry” period in the summer, it is possible that Pss on leaves covered in heavy dew could be dispersed to pruning wounds via drip and subsequently cause infection. Lindemann and Upper (1985) also reported that more significant upward fluxes of bacteria into aerosols occurred during warm sunny days (wind speeds >1 m/s) at midday when leaves (Phaseolus vulgaris) were dry, but not when leaves were wet with dew. While deposition occurred throughout the day, there were instances in which a significant downward flux of bacteria occurred in the evening 107 during temperature inversions. In a cherry orchard, it could be possible that following one of these events, a higher concentration of Pss trapped in dew on leaf surfaces could lead to a higher incidence of infection through pruning wounds. Recommendations to Michigan growers may need to include avoidance of pruning whenever there is high humidity and heavy dew formation (though this hypothesis requires testing). Bacteria on leaf surfaces aggregate in protected sites and near possible areas of increased nutrient availability, near or within stomata, along leaf veins, trichomes/glandular trichomes, and cuticular fissures (Beattie and Lindow, 1999; Mansvelt and Hattingh, 1987; Mansvelt and Hattingh, 1989; Monier and Lindow, 2004; Roos and Hattingh, 1983). Bacterial aggregates can be mixed populations of varying sizes that have been observed to segregate spatially, and resident populations seem to facilitate the survival of immigrant cells (Beattie and Lindow, 1999; Monier and Lindow, 2004; Monier and Lindow, 2005a; Monier and Lindow, 2005b). On apple and sweet cherry leaves, Pss and Pseudomonas syringae pv. morsprunorum (Psm), respectively, were observed to aggregate near and within stomata, propagating within the substomatal chambers and expelling back out onto the leaf surface, while Pss on pear was found near stomata and multiplication appeared to occur at the base of trichomes and in cuticle depressions (Mansvelt and Hattingh, 1987; Mansvelt and Hattingh, 1989; Roos and Hattingh, 1983). Pss likely aggregate near stomata to escape desiccating conditions (increased temperature and dryness) on the leaf surface, as stomata can cool leaves during high temperatures (Monier and Lindow, 2003; Gommers, 2020; Kostaki et al., 2020). Mercier and Lindow (2000) found that nutrient availability on leaves corresponded to bacteria carrying capacity, and while the majority of sugars were depleted during bacteria growth, residual amounts remained, likely on regions of the leaf not occupied by bacteria, while sugar availability on leaves was species-specific and not 108 evenly distributed across the leaf surface. Bacterial leaf populations vary according to leaf age, location within the canopy, and leaf side, but they are dependent primarily on “immigration, emigration, growth, and death,” with the upper leaf surface communities influenced mainly by dispersal (Kinkel, 1997; Smets et al., 2023). Curiously, there is never any mention in the literature of the visibly apparent extrafloral nectary glands (which exude sugars) on sweet cherry leaves at the junction between the petiole and leaf, which could be a source of nutrients, or an infection point for Pss. Perhaps the nectar has antimicrobial properties as described for Poplar (Escalante-Péreza et al., 2012), and it would be prudent to assess these in sweet cherries. Nectar could also be a potential Pss population regulator on leaves during summer months when populations reportedly are low. Yee and Chapman (2008) assessed the influx of cherry leaf nutrients available to fruit flies (Rhagoletis sp.) from the extrafloral nectaries and juice from bird-pecked fruit. It would also be worthwhile to do the same for Pss population dynamics. Though bacteria on leaf surfaces are subjected to wild fluctuations in microclimate and nutrient availability, they have an arsenal of adaptations to ensure survival, such as motility, biosurfactant production, quorum sensing, UV damage repair, siderophore production, as well as aggregate formations (Burch et al., 2014; Gunasekera and Sundin, 2006; Haefele and Lindow, 1987; Hirano and Upper, 2000; Lindow and Brandl, 2003; Monier and Lindow, 2003; Quiñones et al., 2005; Vorholt, 2012; Wensing et al., 2010). Helmann et al. (2019) have identified 31 genes significant to epiphytic colonization and fitness in Pss strain B728a, which included genes encoding for amino acid and polysaccharide production. Most of these genes were also important for occupying the leaf apoplast. Bacterial interactions on leaf surfaces are complex and can be positive (e.g., the production of “common goods”) and negative (e.g., the production of antibiotic metabolites), 109 which is also true for the plant since bacteria could cause disease (negative) or promote growth (positive) (Bashir et al. 2022; Glick and Gamalero, 2021; Mina et al. 2019; Pattnaik et al. 2021; Vorholt, 2012; Xin et al. 2018). Pseudomonas spp. have been found to have close associations with other bacteria (pathogens and nonpathogens) that enhance disease severity and colonization, such as the olive knot pathogen Pseudomonas savastanoi pv. savastanoi (Psv) and the non- pathogens Erwinia toletana, E. oleae, and Pantoea agglomerans (Buonaurio et al. 2015; Marchi et al. 2006). P. syringae pv. actinidiae (Psa), the causal agent of bacterial canker in kiwi, has been frequently isolated with the pathogens Pss and P. viridiflava, which seem to have a synergistic relationship that boosts Psa infections (Purahong et al., 2018). Pss isolated from flowers in Michigan cherry orchards is a phylogenetically diverse mix of strains with varying degrees of pathogenicity on green cherry fruit and wood (Wilkinson, Chapter 2). However, it is unclear what type of interactions these strains may have with one another, how they may influence the severity of canker disease, and whether or not this same mix of strains occurs on other plant organs. Michigan sweet cherry orchards are susceptible to bacterial canker infections as the three vertices of the traditional disease triangle (host, environment, and pathogen) converge throughout the year. At the same time, other bacterial occupants of the sweet cherry microbiome, their function, and their relationship to Pss remain unknown. Xin et al. (2018) highlighted the microbiome as a fourth critical component of the disease triangle since members can trigger plant immune responses and impact pathogen virulence. Management of Pss populations on sweet cherry is problematic, and elucidation of microbiome community members will add to the body of knowledge of Pss orchard ecology and may aid the development of future management strategies. 110 Next-generation sequencing (NGS) technologies and sequencing of the 16S rDNA variable regions for bacteria have stimulated thorough culture-independent exploration of plant microbiomes (Knief, 2014; Kozich et al., 2013). Members of leaf communities have recurring genera (likely due to adaptation to the epiphytic lifestyle) but can vary in community composition (Hirano and Upper, 2000; Lindow and Brandl, 2003; Vorholt, 2012). Often, core microbiomes are determined. They are defined as being common and consistently occurring members of like habitats and are a way to simplify a complex array of organisms that can be used as a marker for plant health and how they respond to various manipulations (Neu et al., 2021; Shade and Handelsman, 2012). Core microbiomes can be determined in several ways, via occurrence, relative abundance, or occurrence-abundance, each method with its own set of caveats, the largest being the arbitrary cut-offs assigned by individual researchers (Custer et al., 2023; Neu et al., 2021). Information about the core bacteria living epiphytically with Pss on sweet cherry leaves in Michigan could be a possible way to monitor how the community changes with Pss population fluctuations/infections and opens the door for strategic manipulation of the bacteria community (how the community influences Pss) and potential biological management of Pss populations in the future. Research on the leaf microbial communities of forest/urban trees, grasses (pertinent to biofuels), wild perennials, and cultivated annuals and perennials have revealed the essential drivers of community composition to be season (time), host plant species (genotype), plant age, and environment (location/ region and neighbors), with some evidence of plant recruitment (Grady et al., 2019; Jackson and Denney, 2011; Kembel et al., 2014; Kim et al., 2012; Laforest- Lapointe et al., 2016a; Laforest-Lapointe et al., 2016b; Laforest-Lapointe et al., 2017; Lajoie and Kembel, 2021a; Lajoie and Kembel, 2021b; Meyer et al., 2022; Noble et al., 2020; Redford et 111 al., 2010; Redford and Fierer, 2009; Stone and Jackson, 2021; Wagner et al., 2015). Recent studies of fruit crop phyllosphere microbiomes have been driven by associated disease pressures, the quest for alternative management strategies, and a better understanding of pathogen/non- pathogen ecology. The apple phyllosphere has been surveyed from bark to blossom since the fire blight disease causal agent, Erwinia amylovora, can devastate orchards, and research efforts have ranged from community characterization to how the communities respond to antibiotic treatments (streptomycin, kasugamycin and copper), microbial inoculations, and orchard management styles (organic vs. conventional) (Aleklett et al., 2014; Arrigoni et al., 2018; Cui et al., 2021; Glenn et al., 2015; He et al., 2021; He et al., 2012; Shade et al., 2013; Steven et al., 2018; Tancos and Cox, 2017; Wallis et al., 2021; Yashiro and McManus, 2012). Microbiomes of other crops, including tea leaves, citrus, and grapes, have been assessed similarly to apples (Carvalho et al., 2020; Cernava et al., 2019; Gobbi et al., 2020; Mina et al., 2020; Miura et al., 2019; Singh et al., 2019; Wu et al., 2020). Kiwi leaf microbial communities have been analyzed according to plant sex and the presence of the pandemic causal agent of kiwi bacterial canker, Psa (Ares et al., 2021). Jo et al. (2015) examined the epiphytic microbiomes of several Prunus spp. (apricot, tart cherry, peach, and plum). While Zhang et al. (2021) analyzed sweet cherry fruit microbiomes post-harvest, and O’Gorman et al. (2023) surveyed the microbiomes of sweet cherry fruit and flowers in relation to the emergent disease, cherry slip-skin-maceration disorder (cherry-SSMD), to the author’s knowledge no surveys have included the sweet cherry epiphytic bacteria populations. The objectives of this paper are to survey the sweet cherry leaf microbiome bacterial community across three cultivars, three seasons, and in two regions of Michigan to determine: a) if there are seasonal and regional differences in the sweet cherry leaf microbiome, 112 b) if the leaf microbiome varies across sweet cherry cultivars, and c) the core members of the microbiome. This work will provide a foundation for future microbiome research (e.g., soil/roots, bark, flower) in sweet cherry. It will identify the bacteria that reside epiphytically with Pss in Michigan orchards, information that is needed for future work on community interactions with Pss and the potential management of Pss populations. 3.3: Methods Orchard Plantings and Sample Collection In May 2017, three-year-old sweet cherry trees previously grown in pots were planted in two regions in Michigan: Wells Orchards, Grand Rapids, Ottawa Co. (site 1), and at the Michigan State University (MSU) Horticulture Teaching and Research Center, Holt, Ingham Co. (site 2) (Figure 3.1). The sweet cherry cultivars, ‘Gold’, ‘Sweetheart’, and ‘Benton’ on Mazzard rootstock were interplanted with 23 other cultivars in a completely randomized design (Appendix B). The three cultivars were selected based on their susceptibility to Pss infection: ‘Benton’ and ‘Sweetheart’ are susceptible, while ‘Gold’ is reported to have an “observable level of tolerance” (Hulin et al., 2022; Spotts et al., 2010; Sundin and Rothwell, 2012). Orchards in both locations were managed using traditional integrative pest management (IPM) strategies for commercial orchards in Michigan. Sites varied in planting orientation, soil composition, and cropping history (Appendix B). Site 1 had sandy clay loam soil and had been planted with peaches for ten years before their removal in 2016. Site 2 had fine sandy loam soil and had been on a corn/soybean crop rotation with soybeans present in 2016. At both sites, 4 trees of ‘Benton’ and ‘Sweetheart’ were sampled, while 3 trees of cultivar ‘Gold’ were sampled (Fall 2017). The same 11 trees were sampled at site 1 throughout the duration of the experiment (2017- 2019), this was not the case at site 2 due to tree mortality over 113 the course of the experiment (Appendix B). Leaves (4/tree) were sampled for microbiome analysis in October (Fall) 2017, 2018, and 2019 at 10-25% leaf drop, in May (Spring) 2018 and 2019 when new leaves were fully expanded, and in August (Summer) 2018 and 2019, corresponding with summer pruning timing. A total of 44 leaves/site were harvested Fall 2017 and Spring 2018, 44 leaves at site 1 and 40 leaves at site 2 in Summer and Fall 2018, as well as Spring, Summer, and Fall 2019. Individual leaves with petioles were harvested while wearing gloves sprayed with 75% ethanol, collected from the terminal end of branches and placed into sterile Whirl-Pak® bags (Nasco, Madison, WI) (Figure 3.2). Leaf width and length were measured according to Demirsoy and Lang (2010) to calculate leaf area and then placed into a cooler for transport to the lab. Leaves were collected on the same day for both sites, excluding Spring 2019 when they were collected a day apart due to the safe re-entry period following a spray application at site 2. Each leaf sample was assigned a number for labeling simplicity in all subsequent sample processing steps (Appendix B). Sample Processing and Metadata Collection In the laboratory, individual leaves were placed into a 50 ml conical tube with 45 ml sterile 0.5x phosphate-buffered saline (PBS) and were then shaken at 200 rpm for 10 min in a G24 Environmental Incubator Shaker (New Brunswick Scientific, Edison NJ) and sonicated for 7 min in an ultrasonic bath (Cole Parmer, Chicago, IL). For long-term storage, a 100 µl portion of the leaf microbiome wash from each leaf sample was pipetted into sterile 15% glycerol in a cryovial and stored at -70°C. For counting culturable bacteria, an additional 100 µl portion of the leaf wash for each leaf sample was serially diluted in 900 µl sterile PBS to 0, 10-1, and 10-2 for samples collected in 2017 and 2018 and plated onto King’s B (KB) media amended with 50 μg/ml cycloheximide 114 with 2 leaf samples/plate (Figure 3.3). In Spring 2019, serial dilutions were expanded to 0, 10-1, 10-2, and 10-3 with one plate/leaf sample. Summer and Fall 2019 serial dilutions were expanded even further to 0, 10-1, 10-2, 10-3, 10-4, 10-5, 10-6, 10-7 (2 plates/sample). All plates were allowed to dry overnight at room temperature and then were then placed into a 28°C incubator (Precision Scientific Model 805, Chicago, IL) until the following day when pictures were taken of plates for colony counting. Colony forming units (cfu)/leaf area (cm2) are reported for each sample. Weather data was collected from the MSU Enviro-weather system (https://mawn.geo.msu.edu/) for the two weeks prior to each sampling date, including mean daily temperature (°C), mean daily relative humidity (%), and precipitation (mm). For site 1, data were obtained from the nearest weather station located at Kraft Orchards, Sparta, MI. (Kent Co.), ~ 24 km north (Figure 3.1). The data for site 2 was obtained from the on-farm weather station. Spray records were also obtained for each site for the two-week period prior to each leaf sampling date. In Fall 2019, trees used for microbiome sampling were rated for bacterial canker lesions as follows: For trunks, 3=healthy, 2=minor infection (sunken/gumming lesion ≤ 2.5 cm), 1=major infection (multiple sunken/gumming lesions > 2.5 cm), 0=dead; for shoots or branches, 3=healthy, 2=isolated infections, 1=many infections, 0=dead. DNA Extraction and Sequencing For each leaf sample, 3 ml of the leaf wash was pipetted into 15 ml conical tubes for DNA extraction. For 2017-2018 no technical replications were made. However, in 2019 technical replications (3 reps) were included with three 15 ml conical tubes of 3 ml leaf wash/leaf sample. All tubes with leaf wash were kept at 4 °C until the time of DNA extraction. Extraction of DNA was accomplished via the E.Z.N.A® Bacterial DNA Kit (OMEGA Bio-tek, Norcross, GA) following the manufacturer’s protocol. Samples that were combined or spilled 115 during the DNA extraction process were omitted from further analysis (6 combined samples in Spring 2018, 1 spilled sample in Fall 2018, and 2 combined samples in Rep 1 and 2 samples in Rep 3 Spring 2019). A “mock” community (+ control) was included consisting of four Pss strains (6-9.3, 13-7, 19-6, and 26-3) (from G. Sundin’s collection) and a negative control (−was included containing no DNA. The extracted DNA samples were stored at -20°C. The quality of DNA was assessed using the Qubit™ 3.0 fluorometer, polymerase chain reaction (PCR), and gel electrophoresis. The Qubit™ fluorometer assays were conducted in 2017 using the Qubit™ dsDNA BR Assay Kit (Invitrogen, ThermoFisher Scientific, Waltham, MA) and in 2018-2019 using the Qubit™ 1x dsDNA HS Assay Kit following the manufacturer’s protocols. The 16S V4 rDNA conserved region of the bacterial DNA was amplified using PCR in a Bio Rad T100™ Thermal Cycler (Bio-Rad Laboratories, Hercules, CA), with the following reaction components; 4 µl of sample DNA, 12.5 μl of One Taq® Hot Start 2x Master Mix with Standard Buffer (New England BioLabs® , Ipswich, MA), 0.5 µl forward and reverse primer, brought up to a 25 µl total reaction volume with Invitrogen™ UltraPure™ distilled water. The 16S V4 primers, forward: GTGCCAGCMGCCGCGGTAA and reverse: GGACTACHVGGGTWTCTAAT and the modified thermal conditions, 94 °C for 3 min., 35 cycles of 94°C for 45 s, 50°C for 60 s, 72°C for 90 s, final 72 °C for 10 min and 4°C infinite, were from Kozich et al. (2013). The PCR product was run on a 1% Invitrogen™ UltraPure™ Agarose gel, stained with 1μl Ethidium Bromide (EtBr) (Bio-Rad Laboratories), in 1x Tris- Acetate-EDTA buffer (TAE) at ~60 V for ~80 min. A 100bp DNA ladder (New England BioLabs®) was included. Gel imaging was done using a Bio Rad Gel Doc™ EZ Imager. The DNA samples that produced bands were sent to the MSU Research Technology Support Facility Genomics Core (RTSF) for sequencing (Appendix B). Sequencing occurred on 116 the Illumina MiSeq platform using the 16S V4 rDNA primers (515f/806r) as in Kozich et al. (2013), following the normalization of samples. Sequencing resulted in two 250 bp paired-end reads returned for sequence analysis. Sequence and Statistical Analysis Sequences were processed using Mothur software version 1.44.2 following the “MiSeq SOP” (https://mothur.org/wiki/miseq_sop/) with modifications pertinent to this specific data set (Kozich et al., 2013; Schloss et al., 2009). Mothur v.1.44.2 was accessed through the MSU Institute of Cyber-Enabled Research (ICER) High-Performance Computing Center (HPCC) and ran in “batch mode” and in “interactive mode”. Sequences from 2017, 2018, and 2019 (Rep 3 only) were processed together, as 2017 and 2018 did not include technical replications. Sequences from 2019 (all reps) were then processed together without 2017 and 2018. All sequences were aligned, and operational taxonomic units (OTUs) were classified with 97% sequence identity against the SILVA database release 132 (Quast et al. 2013; Yilmaz et al. 2014). Chloroplasts, mitochondria, unknowns, Archaea, and Eukaryota were removed from the sequence analysis following the “Miseq SOP” (Schloss et al., 2009). The sequence error rate was determined with the known sequences of P. syringae pv. syringae strain FF5 (GenBank accession ACXZ00000000.1) set as the reference (Lindeberg, 2011; Sohn et al., 2012). The 2017-2019 and the 2019 sequence data sets were rarefied and subsampled to the same number of sequences (1162); Goods coverage, Alpha diversity (number of OTUs, Shannon Diversity Index, and Shannon Evenness Index), and Beta diversity (Bray-Curtis Index) were calculated via Mothur (Schloss et al., 2009). Relative abundance was calculated (no. OTUs/1162 sequences) using R version 4.3.1 (R core team, 2023). The core microbiome was determined via the Mothur, 117 with parameters set for relative abundance (≥ 0.01) and the number of samples (at least 50%) (Schloss et al., 2009). In order to include Fall 2017 in the statistical analysis, year and season were combined in a single parameter time for the 2017-2019 data set. Data was analyzed using R version 4.3.1 (R core team, 2023). Distributions of the data are in Appendix B. These statistics were fit to a robust linear mixed model with site, cultivar, and time (2017-2019 data) or season (for 2019 data) as fixed effects and site, row, and tree as nested random effects (for 2017-2019 data) or site, row, tree, and leaf as nested random effects (for the 2019 data) (Bates et al., 2015). This was followed by analysis of variance (ANOVA) table as well as pairwise comparisons of the estimated marginal means (Lenth, 2023). The Bray-Curtis dissimilarity index was tested for the homogeneity of variances (dispersions) followed by a permutational multivariate analysis of variance (PERMANOVA) with 999 permutations (Oksanen et al., 2022). Nonmetric multidimensional scaling (NMDS) was also performed on the Bray-Curtis dissimilarity for graphical visualization of distances between samples (Oksanen, 2022). Weather data, excluding precipitation, was analyzed using a simple linear model with site and time as fixed effects followed with ANOVA and pairwise comparisons of the estimated marginal means (Lenth, 2023; R core team, 2023). The precipitation data was analyzed via Kruskal-Wallis test (data were not normally distributed), followed by pairwise multiple comparisons using Dunn’s test with Bonferroni adjusted p-values (Mangiafico, 2023; Ogle et al., 2023). All graphs were made in R v. 4.3.1 using a combination of base R and ggplot2 and ggpubr (Kassambara, 2023; R core team, 2023; Wickham, 2015). Graphic fonts were edited, and the letters indicating statistical differences were added to graphs via InkScape v.1.2 (InkScape.org). 118 3.4: Results Sequencing A total of 596 leaves were sampled from both orchard sites combined from 2017-2019, 74% (n=443) were sent for sequencing following DNA extraction. For 2019 alone, 252 leaves were collected from both sites with 3 technical DNA extraction replications for a total of 756 samples, of these, 62% (n=471) were sent for sequencing. Only samples that had amplification of the 16S rDNA region following DNA extraction were sent for sequencing (Appendix B). For Spring 2018, only samples from site 1 were sequenced as site 2 did not have PCR amplification of the 16S rDNA region following DNA extraction. Following the processing of sequences at the 97% sequencing identity through the Mothur v. 1.44.2 pipeline for 2017-2019 leaf samples, a total of 17,795,034 sequences were returned ranging from 17 to 135,128 sequences/sample, 61 samples contained < 1,000 sequences (potentially mis-assembled sequences). For 2019 a total of 6,757,926 sequences were returned ranging from 17 to 58,818 sequences/sample, and 151 samples contained <1,000 sequences. Samples that contained <1, 000 sequences/sample were filtered out via subsampling. Both data sets were subsampled to 1162 sequences as it was the smallest number of sequences for the 2017-2019 data set and only excluded two additional samples from the 2019 data set. Sampling effort for both the 2017-2019 and 2019 data sets was very good, with Good’s coverage values ranging from 96 to 100% and 98 to 100%, respectively for all the samples with >1000 sequences. Rarefaction curves also demonstrate good sampling effort many are nearing or have reached their species richness asymptotes (Figure 3.4). Following subsampling, a total of 384 and 325 samples remained for the 2017-2019 and 2019 datasets, respectively. The negative control samples did contain sequences however they consisted of <1% of the total sequences for both data sets, therefore, no OTUs were removed 119 from the data and the negative control samples were used only as a reference and not included in further statistical analysis. Both datasets were rarefied (1000 iterations) via Mothur v. 1.44.2 when calculating the alpha and beta diversity statistics. Alpha-Diversity The alpha diversity statistics for 2017-2019, bacteria plate counts (log cfu/cm2 leaf area), number of OTUs, Shannon diversity index, and the Shannon evenness index ANOVA’s revealed that neither site nor cultivar was significant for any of these metrics and could be combined, while time was significant for all four metrics (Table 3.1). For the 2017-2019 plate counts for estimating the number of bacteria in the sweet cherry microbiome of the 596 samples 23% (n=140) of the plates contained too many to count (TMTC), 21% (n=123) of plates had no growth, and 55% (n=333) of plates had countable bacterial growth. The plates that had TMTC were omitted from the analysis, 456 total samples remained in the analysis (n=61, 2017 fall; n=77, 2018 spring; n=53, 2018 summer; n=14, 2018 fall; n=84, 2019 spring; n=83, 2019 summer; n=84, 2019 fall). The mean number of bacteria present on the leaf microbiome was lowest in the spring (2018 and 2019) and continued to increase from summer into fall (2018 and 2019) (Figure 3.5). Species richness or number of OTUs/sample (n=84, 2017 fall; n=36, 2018 spring (only site 1); n=65 2018 summer; n=54, 2018 fall; n=35, 2019 spring; n=64, 2019 summer; n=46, 2019 fall) had a more variable trend over time where spring, summer, and fall 2018 were not statistically different from each other (Figure 3.5). While a trend similar to that seen for the plate counts occurred where the mean number of OTUs increased from spring to fall for 2019. Overall the spring was the most variable where spring 2018 had the greatest number of OTUs while 120 spring 2019 had the lowest number of OTUs and was statistically different from all other time points. The summer and fall had more statistically similar numbers of OTUs over time. The species diversity (Shannon Diversity index) in the sweet cherry leaf microbiome is relatively stable over time, excluding Springtime points, where Spring 2018 had the greatest amount of diversity and Spring 2019 had the least amount of diversity and they were statistically different (Figure 3.5). The Springtime points were also statistically different from all other Summer and Fall time points. The Summer and Fall time points had a species diversity that ranged from a mean of 1.4 to 1.8 and were not statistically different from each other. The sweet cherry leaf microbiome has approximately the same Shannon evenness values at all summer and fall time points (0.4 to 0.5) and are not statistically different (Figure 3.5). While Spring is variable where 2018 has the greatest evenness value and was statistically different from all other time points excluding fall 2019, while spring 2019 had the lowest species evenness and was statistically different from all other time points. Beta Diversity The Bray-Curtis dissimilarity index was used to determine if there are clear differences in the community composition in the sweet cherry leaf microbiome. The ANOVA tables for the test for beta-dispersion or homogeneity of variances for each model parameter show that cultivar has homogeneous variances, while site, and time are heterogeneous (Table 3.2). The PERMANOVA table shows that site is not significant Bray-Curtis dissimilarity, however cultivar, and time was significant. Time (season) however, has greatest explanatory value as it is significant on its own and in interactions and accounts for 19.6% of the variability (R2=0.196). The NMDS ordination of the Bray-Curtis dissimilarities support this as there were no clear dissimilarities for either site or cultivar (Figure 3.6). While the NMDS ordination of the Bray-Curtis dissimilarities clearly 121 show differences in time where springtime has the greatest amount of dispersion (furthest distance from the centroid) and has the most dissimilar communities, while summer becomes less dissimilar, and fall communities are the most similar (least distance to the centroid) (Figure 3.7). Relative Abundances The ANOVAs for the relative abundances of the Top four Phyla and “other” and the relative abundances of Pseudomonas and all “other” taxa were not significant for site, cultivar or time, but were significant for Phylum, time*Phylum and genus, time*genus respectively (Table 3.3). Thereby the site and cultivars were combined and means separations were for only Phylum, time*Phylum and genus, time*genus (n=84, 2017 Fall; n=36, 2018 Spring (only site 1); n=65 2018 Summer; n=54, 2018 Fall; n=35, 2019 Spring; n=64, 2019 Summer; n=46, 2019 Fall) (Appendix B). The sweet cherry leaf microbiome is dominated by the Phylum Proteobacteria at all time points ranging in mean relative abundance of 0.67 (67%) to 1.00 (100%) (Figure 3.8). The other Phyla were present at much lower levels at all time points and included Actinobacteria, Bacteroidetes, Firmicutes, and “other”. Within each time point Proteobacteria was statistically different than all of the other Phyla. Across time points the relative abundance of the Proteobacteria in spring is the most variable and is statistically different from all Proteobacteria at the other time points while there again appears to be similarities in summer and fall relative abundance of Proteobacteria. All other Phyla (Actinobacteria, Bacteroidetes, Firmicutes and “other”) are considerably lower in relative abundance compared to Proteobacteria with overall little statistical difference from each other within and across time and no consistency in their positions as the next most abundant phylum. 122 When focusing on the relative abundances of Pseudomonas and all “other” taxa, Pseudomonas has a clearly defined presence within the sweet cherry leaf microbiome and a seasonal trend in dominance (Figures 3.9, 3.10). The relative abundance of Pseudomonas is more variable in the Spring, where “other” taxa was dominate in 2018 while Pseudomonas dominated in 2019 (i.e., the greatest differential occurred between the “other” taxa and Pseudomonas in spring). During the summer the differential decreases and Pseudomonas becomes more dominate while the most stability occurs during the fall where Pseudomonas and the “other” taxa are close to equal in relative abundance. Within each time point for 2017-2019 the relative abundances at the deepest taxonomic level (genus or family) were examined further i.e., picking apart the “other” taxon present in the leaf microbiome where the top taxa were selected via their mean values and combined into “other” if they had a relative abundance <0.03. The core microbiome that was selected by the occurrence of OTUs in at least 50% of the samples at relative abundance of ≥0.01 and returned four core taxa (Pseudomonas OTU00001, Pseudomonas OTU00002, Pseudomonas OTU00003, Sphingomonas OTU00004) (Schloss et al., 2009). Under more stringent parameters such as occurrence in 97% of the samples no core microbiome was returned. The ANOVA table for the top deepest taxonomic level at each time revealed no significance of site or cultivar but was significant for taxa while the core microbiome was not significant for site, cultivar, or time but was significant for genus and time*genus (Table 3.4). The relative abundances at the top deepest taxonomic levels taxa show Pseudomonas to be strikingly dominant at all time points (Fall 2017 to Fall 2019), excluding Spring 2018 when the “other” taxa had a greater relative abundance and Pseudomonas had the 2nd highest relative abundance, followed by the rest of the taxa which were not statistically different that one another 123 (Figure 3.11). Besides Pseudomonas the only other taxa that makes the top relative abundances consistently at all time points is Sphingomonas, excluding Spring 2019 where it was not included in the top taxa. The 2017-2019 core microbiome (Pseudomonas OTU00001, Pseudomonas OTU00002, Pseudomonas OTU00003, Sphingomonas OTU00004) relative abundance were compared to the relative abundances of the rest of the combined Pseudomonas and Sphingomonas with the remaining taxa combined into “other” (Figure 3.12). The core Pseudomonas OTU00001 has relative abundances that range from 0.50 (50%) to 0.08 (8%), Pseudomonas OTU00002 ranged from 0.30 to 0.02 and Pseudomonas OTU00003 ranged from 0.21 to 0.1, while Sphingomonas OTU00004 ranged from 0.18 to 0.0. These taxa by themselves have relatively low relative abundances but when the Pseudomonas core OTUs are combined they make up the majority of the Pseudomonas at each time. While the Sphingomonas OTU00004 is often statistically the same as all other Sphingomonas with a few exceptions at all time points. 2019 Alpha-Diversity The ANOVA table for the 2019 plate counts (log cfu/cm2), number of OTUs, Shannon diversity index, and the Shannon evenness index show no significant effect of cultivar and while there is some variability with site and site*season effects, there is a significant effect of season for all four alpha-diversity metrics (Table 3.5). All four alpha diversity metrics follow a trend of having lower values in the Spring and then increase into the Summer and Fall (Figure 3.13). 2019 Beta Diversity The ANOVA for the beta-dispersion for the Bray-Curtis dissimilarity index for 2019 shows that cultivar has homogeneity of variance while site, and season have heterogenous variances (Table 3.6). The PERMANOVA of the Bray-Curtis dissimilarity index is not 124 significant for site but is for cultivar, and season. Season like 2017-2019 seems to have the most influence over community composition accounting for 15.6% of the variability (R2=0.156). The NMDS ordination of the Bray-Curtis index for 2019 does not distinguish dissimilarity between communities for sites or cultivars while there is a clear clustering of points by season with summer and fall having less dissimilar community compositions than spring which has more dispersion than the other two seasons. Fall is much more similar in composition (least mean distance to the centroid) than that of summer and spring (which has the furthest mean distance to the centroid) indicating stabilization of community in the Fall (Figure 3.14). 2019 Relative Abundance As with the 2017-2019 data set the ANOVA for the relative abundances (n=88, spring; n=130, summer; n=107, fall) of the 2019 top Phylum and Pseudomonas and “other” was not significant for site, cultivar, or season but was significant for Phylum, season*Phylum, genus, and season*genus respectively (Table 3.7). As with the 2017-2019 data the 2019 top Phylum is dominated by the Proteobacteria with the other top Phyla being the Actinobacteria, Bacteroidetes, Firmicutes and “other” (Figure 3.15). Bacteroidetes was the second most abundant in the fall and summer, but spring was almost entirely composed of Proteobacteria. The relative abundance of Pseudomonas dominated in the spring with the greatest differential from the “other” taxa, while still dominate in Summer the differential was much reduced between it and the “other” taxa (Figure 3.16). The fall Pseudomonas was about equal to the “other” bacteria as with the 2017-2019 data set indicating stabilization by the fall season. The 2019 ANOVA tables for both the deepest taxonomic level (genus or family) and the core microbiome did not have site or cultivar (nor season for the latter) as significant effects 125 while genus was significant (Table 3.8). At the deepest taxonomic level for 2019 the taxa were similar to that of those in 2017-2019 (Figure 3.17). All three seasons were dominated by Pseudomonas while Sphingomonas was the next most abundant. As with the 2019-2017 data set the Sphingomonas were also consistently in the top taxa present in summer and fall but included in the “other” taxa for spring. The 2019 core microbiome (Pseudomonas OTU0001, Pseudomonas OTU0002, Sphingomonas OTU00003, Sphingomonas OTU00007) relative abundance were compared to the relative abundances of the rest of the combined Pseudomonas and Sphingomonas with the remaining taxa combined into “other” (Figure 3.18). As with the 2017-2019 data set the core microbiome was selected via the same parameters of occurrence in 50% of the samples and at relative abundance of ≥0.01 and when more stringent parameters were used (97% occurrence) no taxa were returned. The Pseudomonas OTU0001 was the most abundant taxa in all three seasons while Pseudomonas OTU0002 was the next most abundant in the spring and summer but was 4th in the fall. As with the core Pseudomonas in the 2017-2019 data the 2019 core Pseudomonas OTUs appear to make up most of the total Pseudomonas relative abundance. This is true for the 2019 core microbiome Sphingomonas OTUs when compared to the other Sphingomonas combined as well. Weather, Spray Frequency, and Canker Ratings Precipitation, it was analyzed using the Kruskal-Wallis test and was not significant for site (χ²=0.356, df=1, p=0.5506) but was significant for time (season) (χ²=22.877, df=6, p=0.0008). The ANOVA tables for the relative humidity and mean daily temperature also show that site was not significant, while they were both significant for time (season) (Table 3.9). Overall, the amount of precipitation was low, with median values ranging from 0.0 to 2.3 mm, 126 with little statistical difference (Figure 3.19) The mean relative humidity for the two-week periods was high, ranging from 72.8%-80.6%. Only Fall 2018 was statistically different from Spring and Summer 2018, and Summer 2019, while all other times were not statistically different for relative humidity. The Spring and Fall mean daily temperatures ranged from 12.6-15.4 °C and were not statistically different from one another, excluding Fall 2017 and Spring 2019. Spring and Fall mean daily temperatures were statistically different from all Summer temperatures, which ranged between 21.2-22.6 °C. The Summer temperatures were not statistically different from each other. The spray records were examined for the two-week periods leading up to each leaf sampling date, spray frequency was quite low at both sites, with site 1 having more spray events when compared to site 2 (Appendix B). By Fall 2019, two years after planting, all trees that were part of the microbiome leaf sampling had bacterial canker (Appendix B). 3.5: Discussion It is well understood that the leaf microbiome is a harsh environment and generally bears a low amount of biomass, with the microbiome being variable from leaf to leaf, in canopy location, and in tissue age, as well as abaxial and adaxial surfaces (Hirano and Upper, 2000; Kinkel, 1997; Lindow and Brandl, 2003; Smets et al., 2023; Vorholt, 2012). It was not surprising that not all of the leaves sampled in this study yielded DNA or countable bacteria. This is one caveat of single-leaf sampling versus bulk-leaf sampling. However, bulk-leaf sampling could overinflate the mean population size (Donegan et al., 1991; Hirano and Upper, 2000; Kinkle et al., 1995). Sampling coverage via the culture-independent methods was excellent, as indicated by the rarefaction curves and the Good’s coverage values (96-100%, 2017-2019; 98-100%, 2019), even with using 3 ml of the leaf wash for the DNA extractions, these results should be an 127 accurate estimate of the sweet cherry leaf microbiome diversity. There were no striking differences between the 2017-2019 data set and the 2019 alone data set when looking at the alpha and beta diversity as well as relative abundances, as they were reflective of each other. The technical replications of 2019 did ensure that DNA data was available for each site for all seasons, whereas during the Spring of 2018, there was only DNA recovered from site 1 and no DNA recovered from site 2 leaves. It was determined that though some the data distributions were not entirely normal, the structure of the experimental design should be upheld, which contained of multiple fixed effects and random effects, therefore the data was analyzed with a robust linear mixed model. While the use of non-parametric tests would ignore the experimental design, the random effects in particular. Schielzeth et al. (2020) showed that linear mixed models are robust and capable of handling even severe violations of normality. There were no regional differences (site1 v. site 2) or cultivar differences (‘Benton’, ‘Gold’, ‘Sweetheart) in the Michigan sweet cherry leaf microbiomes for the 2017-2019 data set when looking at both alpha and beta diversity as well as the relative abundances of Phyla, and Pseudomonas. When looking at a single year (2019), some differences in region (sites) were apparent for some alpha diversity metrics but not all. However, the cultivars in 2019 were not statistically different for all metrics, and neither site nor cultivar was different for the Bray-Curtis dissimilarity index (beta diversity). Steven et al. (2018) found no differences in apple flower communities between cultivars (Braeburn, McIntosh, and Sunrise), while host-specific bacterial communities were found in the Olive cultivars (Cobrançosa and Verdeal Transmontana) (Mina et al. 2020). Work in temperate and tropical forests indicates that bacterial community differences are tree species-driven (Kim et al., 2012; Laforest-Lapointe et al. 2016a; Redford et al., 2010). While Singh et al. (2019) found bacterial community differences between species of grapes, they 128 found that the effect of season was stronger. Researchers have found sites (environment) or nearest neighbors to influence bacterial populations as well (Noble et al. 2020; Laforest-Lapointe et al. 2017; Lajoie and Kemble, 2021b). Muira et al. (2018) found that leaves sampled from surrounding forest tree species shared common OTUs with vineyard grape leaves. It would be interesting to determine if cultivated sweet cherries in Michigan share common bacterial communities with their forested counterparts. Lajoie and Kemble (2021a) found both the phylogenies of the tree host and the leaf bacteria influence associations with each other. Jo et al. (2015), however, found that Prunus spp. (P. armeniaca, P. cerasus, P. persica, and P. domestica) from bulk leaf samples harbored unique bacterial diversities with only 23 shared genera. When looking at the weather data for the two sites, there were also no differences in the mean daily temperatures, precipitation, or humidity as well which was surprising as site 1 is ~34 miles from Lake Michigan while site 2 is ~109 miles from Lake Michigan. Stone and Jackson (2021) found that rainfall did not influence the communities on a limited time scale for the broadleaf cattail (Typha latifolia) while “long-term seasonal patterns” did. As both these sites share similar weather when looking at the two-week period leading up to the sampling dates, and both were managed as commercial orchards, though site 1 had more springtime spray events, these sites appear to be identical at this scale and lack explanatory value. Perhaps looking at the sweet cherry trees grown in the more southern or northern regions of Michigan would exhibit site differences in their bacteria communities, which would need to be investigated further. Research has been done in apple and citrus to determine if orchard management strategy (conventional v. organic or ecologically managed) influences bacterial communities with mixed results where citrus bacteria were highly variable between strategies while apple had greater 129 differences in communities over time than over management practices (Carvalho et al., 2020; Glenn et al., 2015). Wallis et al. (2021) and Yashiro and McManus (2012) looked at the effect of Streptomycin treatments in apples on endophytic and epiphytic bacterial communities, respectively, and both found treatment had little effect, while farm location had greater effect for the former and time and location had more effect for the latter. Sweet cherries in Michigan, however, are not grown organically, nor are antibiotics currently being used. There are markedly clear trends in the Michigan sweet cherry leaf microbiome driven by time (season). The plate counts of the culturable bacteria (log cfu/cm2) indicate that bacteria populations are low in the spring and increase through Summer and into Fall. The lack of statistical differences between Summer and Fall for alpha diversity suggests that the population numbers stabilize between Summer and Fall, and the trend appears to be consistent over time points. Summer and Fall also have a similar number of species (OTUs), species diversity (Shannon Diversity Index), and evenness (Shannon evenness index), while the number of OTUs and Shannon diversity and evenness index for spring can vary from being its highest (40.6 OTUs, 2.4 Shannon, and 0.6 evenness ) in 2018 to the lowest (6.0 OTUs and 0.3 Shannon, and 0.2 evenness) in 2019. The non-metric dimensional scaling (NMDS) also demonstrates the decrease in dissimilarity of the bacteria community from Spring through Fall, where communities are the most similar/stable. Spring 2019’s Shannon diversity index indicates that it was nearing zero (0.3, 2017-2019; 0.2 to 0.5, 2019) where an H′ value=0 would be comprised of a single species, Shannon values typically range between 1.5 to 3.5 (Magurran, 2004). The relative abundance data show that the sweet cherry leaf samples of Spring 2019 consist almost entirely of Pseudomonas with a mean of 0.96 (96%), and 0.97 (97%) in the 2017-2019 and 2019 data sets, respectively. 130 It is not surprising that Spring has the most variability as leaves are newly emergent, have less surface area, nutrient availability, and bacteria populations are just becoming established (Kinkel, 1997). Smets et al. (2022) sampled London plane tree leaves (Platanus acerifolia) weekly over a six-week period covering the span of leaf emergence and found clear successional patterns of early, mid, and late communities that shared the greatest dissimilarity and high rate of change right after leaf emergence and stabilized as they neared the end of the sampling period. Though outside the scope of this experiment (i.e., not measured), it should be noted that Spring is also the prime season for Pss population increases and subsequent disease pressure, which could influence population variability. Pss is known to overwinter in buds and is likely one of the earliest colonizers of the sweet cherry microbiome (Sundin et al., 1988). Perhaps the variability of the springtime microbiome could also be related to the sweet cherry trees' carbon (C) and nitrogen (N) availability to the newly emergent leaves, as these nutrients are just mobilizing from storage tissues, and there is strong competition for these resources, and there is evidence that bacterial communities can be influenced by plant C-N status (Ayala and Lang, 2017; Bringel and Couée, 2015). This could also be included in the future microbiome and Pss population work in sweet cherry. Excluding the variability of Spring, the Michigan sweet cherry leaf microbiome shows stability and predictability over the Summer and Fall time points where the Shannon evenness index shows no statistical differences indicating the relative abundances of the bacteria community are unchanged between time points Summer and Fall 2018 and Summer and Fall 2019 as well as the species richness (no. OTUs and the Shannon diversity index) (Magurran, 2004). The leaf microbiomes of perennial grasses, common bean, soybean, and canola also had microbiomes influences by time where earlier communities were similar to the soil microbiomes 131 while later communities were distinct leaf communities that were similar among all plants (Copeland et al. 2015; Grady et al. 2019). Grady et al. (2019) bacterial communities could be defined as early, mid, and late with “directional assembly” over the growing season in perennial grasses. While Redford and Fierer (2009) showed that Cottonwood leaves also had early, mid, and late communities, the early and late communities were more similar to one another. Both studies demonstrated predictability from season to season (Grady et al., 2019; Redford and Fierer, 2009). For Magnolia leaf microbiomes, while they were found to have distinct seasonal communities, they were not consistent (predictable) from year to year (Jackson and Denney, 2011). The Michigan sweet cherry leaf microbiome is dominated by Proteobacteria, the phylum in which Pseudomonas belongs, ranging from 67 to100% relative abundance across all time points, the lowest value being Spring 2018 and the maximum value being Spring 2019, a continuing theme of Springtime variability followed by the more stable Summer and Fall relative abundance values for the Proteobacteria. The additional top Phyla present in the sweet cherry leaf microbiome consists of low relative abundances of Actinobacteria (0 to11%), Bacteroidetes(0 to 8%), and Firmicutes (0 to 16%). These phyla are typical of leaf microbiomes (Vorholt, 2012). Jo et al. (2015) also recovered the same 4 phyla from the bulk leaf samples from four Prunus spp. where Proteobacteria was also dominant (93%), followed by Bacteroidetes (4%), Actinobacteria (2%), and Firmicutes (1%). Proteobacteria was also dominant in the leaves of olive (60.8%) and kiwi (76.9%) and in apple flowers (>90% total sequences) (Ares et al., 2021; Mine et al., 2020; Steven et al., 2018). The most dominant of all of the taxa are Pseudomonas in the Michigan sweet cherry leaf microbiome. When looking at only Pseudomonas again, there appears to be a trend of variability 132 in the Spring (26% in Spring 2018 vs. 96% in Spring 2019) and more stability in the Summer and Fall as there is no statistical difference between the Pseudomonas relative abundances at those time points. Pseudomonas is a clear driving force of modulation of the relative abundances of all “other” taxa, as Pseudomonas relative abundance increases the relative abundance of the “other” taxa is driven down while, when the relative abundance of Pseudomonas nears 50% it seems to have a “neutralizing” effect as the “other” taxa also approach 50% relative abundance which coincides with the Fall time points only to further lend strength to the evidence pointing to the stabilization of the bacterial community by the Fall season. The only time where the “other” taxa exceed Pseudomonas was in Spring 2018 when Pseudomonas dropped to 26% relative abundance, however, there was not a single taxa that took its place as the top genus at that time. Grady et al. (2019) found that Pseudomonas was replaced over time by Methylobacterium as part of a “compensatory relationship”. Ares et al. (2021) also saw a decrease in Pseudomonas and an increase in Methylobacterium in the Fall. In this study, Methylobacterium (3% relative abundance) was in the top taxa for Spring 2018 but did not replace Pseudomonas. Steven et al. (2018) found that Pseudomonas and Enterobacteriaceae were dominant in the apple flower microbiome and had a negative correlation as one increased, the other decreased. Though Enterobacteriaceae was present in the top taxa in Fall 2017 (16%), Summer 2018 (5%), Spring 2019 (3%), and Summer 2019 (8%), a relationship between the two is not evident (Figure 3.30 and Table 3.14). Steven et al. (2018) also emphasized that the ability of Pseudomonas spp. to produce antimicrobial compounds could be a likely explanation as to how Pseudomonas is regulating the microbiome. Pseudomonas is also capable of living commensally in the microbiome, has been isolated from sweet cherry previously with other virulent and avirulent Pseudomonas, is ubiquitous in the environment, with a broad host range, and in the case of Pss, a 133 strong evolutionary connection to agriculture and cherry, in particular, which could also potentially explain the large presence of this bacteria in the sweet cherry leaf microbiome (Hulin et al., 2018; Hulin et al., 2020; Morris et al., 2013; Morris et al., 2019; Nowell et al., 2016; Shalev et al., 2022; Ruinelli et al., 2022; Wilkinson, Chapter 2; Xin et al., 2018). An important next step would be to determine how many of these Pseudomonas recovered from the sweet cherry leaf microbiome are actually Pss, which could be determined via qPCR quantification of the syrB1 gene specific to Pss (Petriccione et al., 2017). Further “Omics” studies should also be undertaken in the future, as well as co-inoculations, as looking into the function of these bacteria and how they interact with each other could indicate how these bacteria are able to cohabitate or dominate in the phyllosphere as well (Vilanova and Porcar, 2016). Ares et al. (2021), found that inoculations of the pathogen Pseudomonas syringae pv. actinidiae on Kiwi leaves altered the microbiome community structure and relative abundances. While Cui et al. (2021) inoculations with known stigma colonizing bacteria in apple flowers could alter disease pressure by Erwinia amylovora. Cernava et al. (2019) also showed that biological control treatments increased tea leaf microbiome diversity. Jo et al. (2015), curiously did not find Pseudomonas to be the most dominant genera, while Sphingomonas and Methylobacterium were in the four Prunus spp. leaf microbiomes they analyzed. In their tart cherry leaf analysis, Pseudomonas was third amongst the top four bacterial genera recovered with 258 sequences, proceeded by Methylobacterium (865 sequences) and Sphingomonas (399 sequences) and followed by Mucilaginibacter (120 sequences). In this study analysis of the sweet cherry leaf microbiome, Sphingomonas was consistently present in the top taxa (ranging from 4% to 23% relative abundance) at all time points excluding the Spring 2019 (Figure 3.30 and Table 3.14). 134 The core microbiome of the Michigan sweet cherry leaf was also composed of Pseudomonas and Sphingomonas OTUs, with the Pseudomonas OTUs being dominant and, when combined, make up the lion’s share of all the Pseudomonas. The Mothur method for determining the core microbiome was opted for based on convenience and ease of use as well as the methods that it applies, which used the number of samples the bacteria are present in (or occupancy/occurrence), as well as the relative abundances (Schloss et al., 2009). Custer et al. (2023) compared four methods that can be used for defining core microbiomes and found that occurrence and abundance used together were the most accurate and that hard cutoff values caused over-inflated core memberships. In this study, analysis of the core microbiome hard cutoff values, such as 97% occurrence (present in 97% of samples), failed to yield a core microbiome, while the less stringent parameter of 50% present in samples did return a core microbiome. In addition to quantifying the number of Pss, via qPCR, in the sweet cherry leaf microbiome, it should also be ascertained if any of the core microbiome Pseudomonas OTUs (OTU 00001, OTU 00002, OTU 00003 from 2017-2019, and OTU 0001, OTU 0002 from 2019) are possibly Pss via phylogenetic comparisons with known Pss 16S sequences. The Sphingomonas core microbiome OTUs (OTU 00004 from 2017-2019 and OTU 0003, OTU 0007 from 2019) should also be investigated further compared to known 16S sequenced Sphingomonas in regard to their potential to be beneficial to sweet cherry itself or as potential biological control agents of Pss. Sphingomonas recovered from the plant Arabidopsis thaliana have been found to promote plant growth, boost plant defense, suppress pathogen growth and disease progression, and grow more slowly than Pseudomonas (Innerebner et al., 2011; Lundberg et al., 2022). In terms of hunting additional biological control agents for Pss, 135 microbiome work should be expanded to other sweet cherry tree tissues, as Arrigono et al. (2018) found bark to be a potential reservoir for biological control agents in apple and pear. Several of the other top taxa found in the sweet cherry leaf microbiome are possible plant growth promoters, such as Acinetobacter, Chryseobacterium, and Bradyrhizobium, which are also found on the rhizosphere (Kuor et al., 2019). While Xanthomonas, a known Prunus pathogen, and Curtobacterium, a bacteria wilt pathogen, also in the top taxa, could be plant growth promoters or possible pathogens on other plants (Chase et al., 2016; Jones and Sutton, 1996). It is likely that many of these top taxa have multiple possible functional roles in the sweet cherry phyllosphere, many of which could likely be beneficial. In addition to being a plant growth promoter, Methylobacterium is able to utilize methanol, a volatile organic compound emitted by plants, Curtobacterium can degrade organic compounds, while Pseudomonas can promote plant growth and suppress bacterial growth (Chase et al., 2016; Bringel and Couée, 2015; Preston, 2004; Sah et al., 2021). 3.6: Conclusions This is the first time the sweet cherry leaf microbiome has been surveyed to identify the bacteria present in the Michigan sweet cherry orchards, likely living alongside Pss as it is a well- known epiphyte. In addition to adding to the previously unknown body of knowledge for the sweet cherry leaf microbiome and ecology, the results of this study provide a foundation for further detailed studies of the sweet cherry microbiome in relation to Pss. There were no regional or cultivar differences in the sweet cherry leaf microbiomes, while season exerted a strong influence on the microbiome. Bacteria populations were low in the spring and increased during summer and fall, whereas the spring communities were quite variable; they began to stabilize during summer and were most stable by fall. Proteobacteria was 136 the most prevalent phylum in all seasons. The genera Pseudomonas was consistent and a dominant presence within the microbiome, serving as an additional driver along with season in directing the stabilization of the microbiome into fall. While all other top taxa present were variable, Sphingomonas was consistently present in the top taxa in all seasons, excluding the Spring of 2019, when Pseudomonas was nearing 100% relative abundance. The Michigan sweet cherry leaf microbiome had a core microbiome (present in 50% of samples with at least 1% relative abundance) that also consisted of Pseudomonas and Sphingomonas OTUs. These OTUs should be studied further to determine if any of the Pseudomonas OTUs are Pss by phylogenetic comparisons with known Pss 16S sequences (100% bootstrap certainty). The Sphingomonas OTUs also should be studied to determine if they share any phylogenetic relationships with known beneficial Sphingomonas spp. as possible biological control agents for Pss or as plant beneficials. Further analysis of the microbiome DNA from this study could also quantify the number of the Pss present in the samples via qPCR. Continuing research into the sweet cherry microbiome could expand to other tissues (flowers, bark, buds, roots) to determine if Pseudomonas is strongly present in all organs of the tree across seasons. Leaf microbiomes from Michigan’s southern and northern fruit belt regions could be assessed for regional differences. Future work should determine how the microbiome shifts with the inoculation of virulent Pss (and vice versa), and how the microbiome changes with the addition of biological control agents. The spring microbiome should be examined for C- N movement within the leaves in correlation to the microbiome (or core microbiome) and the presence of Pss and disease pressure, alone and relative to the harshness of winter or frost events. This could explain the variability in spring populations more definitively. Other “omics” type studies and co-inoculation experiments could determine the function or role that these bacteria 137 play within the leaf microbiome, as many could potentially be beneficial to the sweet cherry tree. This could also shed light on how they are all able to live together in the same environment, especially with Pseudomonas as the strongest presence. This research is the first documentation of the sweet cherry leaf microbiome bacterial community that Xin et al. (2018) considers to be the 4th vertex of the disease triangle. Much has yet to be ascertained as to how these bacteria could be manipulated in order to manage Pss populations and subsequently reduce disease incidence. 138 Figure 3.1. Michigan orchards where the sweet cherry cultivars were planted for surveying the sweet cherry leaf microbiome. Red highlighted counties are where site 1: Wells Orchards, Grand Rapids MI, (Ottawa Co.), and site 2: Michigan State University Horticulture Teaching and Research Center, Holt MI, (Ingham Co.) is located, the gray highlighted county site *: Kraft Orchards, Sparta MI, (Kent Co.) is where the weather station is located used for site 1 weather data (~24 km, North of site 1). 139 A B Figure 3.2. Sampling sweet cherry leaves for microbiome analysis, A) wearing gloves sanitized with 75% ethanol, leaves were selected individually from the terminal end of branches and placed into sterile Whirl-Pak® bags, and B) the length and width of each leaf was measured and then placed into a cooler for transport to the laboratory. 140 Figure 3.3. Examples of the King’s B (KB) media amended with 50 μg/ml cycloheximide used for counting culturable bacteria from the sweet cherry leaf microbiome, A-D) colonies from serial dilutions of 0, 10-1, and 10-2 with 2 leaf samples/plate in Fall 2017, Spring, Summer, and Fall 2018 respectively, E) colonies from serial dilutions of 0, 10-1, 10-2, and 10-3 with a single leaf sample/plate in Spring 2019, Fa-Gb) colonies from serial dilutions of 0, 10-1, 10-2, and 10-3 with a single leaf sample/plate and dilutions of 10-4, 10- 5, 10-6, and 10-7 from the same single leaf sample in Summer (Fa, Fb) and Fall 2019 (Ga, Gb) respectively. 141 Figure 3.4. The rarefaction curves demonstrate the sampling effort with the number of OTUs and number of sequences per sample from the sweet cherry cultivars ‘Benton’, ‘Gold’, and ‘Sweetheart’ sampled from two sites in Michigan, with OTUs assigned according to 97% sequence identity A) includes the curves for the 2017-2019 sampling seasons (Fall 2017, Spring, Summer, Fall 2018, and Spring, Summer, Fall 2019) and B) includes the curves for the 2019 sampling season (Spring, Summer, and Fall) which included 3 technical replications/ leaf sample, the vertical line in both graphs denotes subsampling at 1162 sequences. 142 Table 3.1. The ANOVA tables for the α-diversity statistics, bacteria plate counts (log cfu/cm2), the number of OTUs (no. OTUs), the Shannon diversity, and Shannon evenness indices. Following the linear mixed model with site, cultivar, and time as fixed effects and nested random effects of (1 | site:row) + (1 | site:row:tree), with significance determined by p-value ≤ 0.05. α-statistic Model term df1 df2 F-ratio p-value log cfu/cm2 no. OTUs Shannon diversity index Shannon evenness index site cultivar time site cultivar time site cultivar time site cultivar time 1 2 6 1 2 6 1 2 6 1 2 6 5.63 12.43 440.50 1.100 0.411 108.805 6.28 12.68 363.55 6.81 11.77 364.62 6.83 11.74 364.68 1.031 0.368 36.369 0.156 0.846 29.206 0.000 1.101 24.127 0.3373 0.6714 <0.0001 0.3475 0.6993 <0.0001 0.7048 0.4535 <0.0001 0.9996 0.3646 <0.0001 143 Figure 3.5. Mean alpha diversity metrics ± SE for the sweet cherry leaf microbiome over time with letters indicating statistical differences for each individual statistic: A) cultured bacteria colony counts (log cfu/m2 of leaf area), B) the observed number of OTUs (spp. richness), C) the Shannon diversity index (spp. richness and evenness) and D) the Shannon evenness index (spp. evenness) calculated from 16S rDNA sequenced from 3 ml of leaf wash. 144 Table 3.2. The ANOVA tables following the test for Beta-dispersion (homogeneity of variances) and the Permutational analysis of variances (PERMANOVA) (999 permutations) of the Bray- Curtis dissimilarity index to determine the differences in community composition in the sweet cherry leaf microbiome sampled at different times (seasons). Groups Residuals Groups Residuals Groups Residuals Groups Residuals Groups Residuals Model term cultivar site time row tree Model term cultivar time site cultivar*time time*site site*row site*row*tree Residual Total df 2 381 1 382 6 377 3 380 16 367 df 2 6 1 12 5 6 15 336 383 Beta-dispersion ANOVA Mean squares Sum of Squares F-value p-value 0.009 4.390 0.1504 4.0463 1.808 4.465 0.033 4.240 0.425 4.507 0.004 0.012 0.150 0.011 0.301 0.012 0.011 0.011 0.027 0.012 0.368 0.6921 14.203 0.0002 25.442 < 2.2e-16 0.995 0.3951 2.162 0.0060 PERMANOVA Sum of Squares 0.984 23.009 0.778 4.568 8.406 1.525 5.441 72.610 117.320 R2 F-value p-value 2.277 17.746 3.598 1.762 7.779 1.176 1.679 0.0090 0.0010 0.9990 0.0010 0.0010 0.1820 0.0010 0.008 0.196 0.007 0.039 0.072 0.013 0.046 0.619 1.000 145 Figure 3.6. The Nonmetric multidimensional scaling (NMDS) ordination of the Bray-Curtis dissimilarities to show the relationship between bacteria community composition of the sweet cherry leaf microbiome from A) two sites in Michigan (Ottawa Co. and Ingham Co.) and from B) the three sweet cherry cultivars (‘Benton’, ‘Gold’, and ‘Sweetheart’) that the leaves and leaf microbiomes were sampled from over time (Fall 2017, and Spring, Summer, and Fall 2018 and 2019). The ellipses represent the 95% confidence intervals for each parameter (site or cultivar). Stress k=0.216. 146 Figure 3.7. The Nonmetric multidimensional scaling (NMDS) ordination of the Bray-Curtis dissimilarities (stress k=0.216) to show the relationship between bacteria community composition of the Michigan sweet cherry leaf microbiome over time for A) Spring 2018 and 2019, B) Summer 2018 and 2019, C) and Fall 2017, 2018, and 2019 where the ellipses represent the 95% confidence intervals for each time, and D) is a box plot of the distributions of the distances from the centroid for samples at each time point where the solid black bar is the median, and the colored square points indicate the mean distance from the centroid. 147 Table 3.3. The ANOVA tables for the relative abundance of the top four Phyla and rarer Phyla combined into “other”, and the relative abundance at the deepest taxonomic level (genus) of Pseudomonas, and all “other” taxa at the deepest taxonomic level (genus or family) combined. Following the linear mixed model with site, cultivar, time, phylum (or genus), and time*phylum (or time*genus) as fixed effects and nested random effects of (1 | site:row) + (1 | site:row:tree), with significance determined by p-value ≤ 0.05. α-statistic Model term df1 df2 F-ratio p-value Relative abundance Top Phyla Relative abundance Pseudomonas site cultivar time phylum time*phylum site cultivar time genus time*genus 1 2 6 4 24 1 2 6 1 6 6.97 11.58 1425.47 1864.07 1864.07 0.000 0.000 0.000 8894.394 31.653 6.97 11.58 694.12 733.07 733.07 0.000 0.000 0.000 57.870 48.879 1.0000 1.0000 1.0000 <0.0001 <0.0001 1.0000 1.0000 1.0000 <0.0001 <0.0001 148 Figure 3.8. The mean relative abundance ± SE of the top 4 Phyla and the rarer Phyla combined into “other” that make up the sweet cherry leaf microbiome for Fall 2017, Spring, Summer, and Fall 2018, and Spring, Summer, and Fall 2019. Lower-case letters indicate statistical differences in Phyla within each time (season), and the upper-case letters indicate statistical differences of each Phyla across each time. With significance determined by p-value ≤0.05 149 Figure 3.9. The mean relative abundances of Pseudomonas and all “other” taxa at the genus/family level ± SE in the sweet cherry leaf microbiome. Letters indicate statistical differences, lower-cased letters indicate statistical differences within each time, while upper- cased letters indicate statistical differences of each Pseudomonas or “other” across time, with significance determined by p-value ≤ 0.05. 150 Figure 3.10. Visualization highlighting the modulating influence that Pseudomonas appears to have on the “other” taxa in the sweet cherry leaf microbiome with evident community stabilization by Fall. Values are the mean relative abundances of Pseudomonas and all “other” taxa at the genus/family level ± SE over each time point (season), lower-cased letters indicate statistical differences within each time, with significance determined by p-value ≤ 0.05. 151 Table 3.4. The ANOVA tables of the top deepest taxonomic level (genus or family) by time (season) where taxa of relative abundance <0.03 were combined into “other”. Following the linear mixed model with site, cultivar, and taxa as fixed effects and nested random effects of (1 | site:row) + (1 | site:row:tree), with significance determined by p-value ≤ 0.05. Spring 2018 is for site 1 only with the linear mixed model sans site. The ANOVA table of the core microbiome relative abundances (genus and OTU identifier ), the rest of the combined Pseudomonas and combined Sphingomonas, and the remaining taxa combined as “other”. Following the linear mixed model with site, cultivar, time, genus, and time*genus as fixed effects and nested random effects of (1 | site:row) + (1 | site:row:tree). Model term df1 df2 F-ratio p-value α-statistic Relative abundance Top deepest taxa by time Fall 2017 Spring 2018 Summer 2018 Fall 2018 Spring 2019 Summer 2019 Fall 2019 site cultivar taxa cultivar taxa site cultivar taxa site cultivar taxa site cultivar taxa site cultivar taxa site cultivar taxa 1 2 4 2 7 1 2 4 1 2 4 1 2 2 1 2 5 1 2 6 5.51 12.15 394.16 4.40 270.28 4.72 10.26 302.47 4.32 8.58 300.70 7.72 3.97 88.95 6.28 10.27 359.74 5.17 9.26 300.43 0.000 0.000 179.468 0.000 38.533 0.000 0.000 68.751 0.000 0.000 119.306 0.000 0.000 1438.871 0.000 0.000 141.591 0.000 0.000 79.168 0.000 0.000 0.000 122.153 17.591 1.0000 1.0000 <0.0001 1.0000 <0.0001 1.0000 1.0000 <0.0001 1.0000 1.0000 <0.0001 1.0000 1.0000 <0.0001 1.0000 1.0000 <0.0001 1.0000 1.0000 <0.0001 1.0000 1.0000 1.0000 <0.0001 <0.0001 Relative abundance Core Taxa site cultivar time genus time*genus 6.97 11.58 1774.48 2618.07 2618.07 1 2 6 6 36 152 Figure 3.11. The mean relative abundances ± SE of the top deepest taxonomic level (genus or family) for the sweet cherry leaf microbiome by time (season) in which taxa of relative abundance <0.03 were combined into “other”. Letters indicate statistical differences, with significance determined by p-value ≤ 0.05. 153 Figure 3.12. The mean relative abundances of the core microbiome taxon (genus [P. (Pseudomonas) and S. (Sphingomonas)] and OTU identifier), the rest of the combined Pseudomonas and combined Sphingomonas, and the remaining combined “other” taxa ± SE over time (season). Letters indicate statistical differences, lower-cased letters are within each time, while upper-cased letters indicate statistical differences of each genus/OTU across time, with significance determined by p-value ≤ 0.05. 154 Table 3.5. The 2019 ANOVA tables for the α-diversity statistics, bacteria plate counts (log cfu/cm2), the number of OTUs (no. OTUs), the Shannon diversity, and Shannon evenness indices. Following the linear mixed model with site, cultivar, season, and site*season as fixed effects and nested random effects of (1|site:row)+(1|site:row:tree) for the log cfu/cm2 and (1 | site:row) + (1 | site:row:tree)+(1|site:row:tree:leaf) for the remaining α-diversity statistics, with significance determined by p-value ≤ 0.05. α-statistic Model term df1 df2 F-ratio p-value log cfu/cm2 no. OTUs Shannon diversity index Shannon evenness index site cultivar season site*season site cultivar season site*season site cultivar season site*season site cultivar season site*season 1 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2 5.54 11.75 226.00 226.00 7.39 11.22 289.16 289.71 7.26 11.36 287.63 288.10 7.28 11.34 287.90 288.39 0.888 1.456 231.684 10.968 27.343 0.380 157.640 4.985 11.215 0.257 125.751 2.046 4.226 0.077 79.149 4.141 0.3854 0.2722 <0.0001 <0.0001 0.0010 0.6921 <0.0001 0.0074 0.0116 0.7780 <0.0001 0.1312 0.0773 0.9268 <0.0001 0.0129 155 Figure 3.13. Mean alpha diversity metrics ± SE, 2019, for the sweet cherry leaf microbiome for the two sites in Michigan, over three seasons. For each individual statistic the lower-cased letters indicate statistical differences in season within each site and the upper-cased letters indicate statistical differences between the two sites for each season, with an exception for the Shannon diversity index for which the two lower-cased letters at the end of each line graph indicated statistical differences between the two sites and the letter above the line graphs indicate statistical differences between seasons. A) cultured bacteria colony counts (log cfu/m2 of leaf area), B) the observed number of OTUs (spp. richness), C) the Shannon diversity index (spp. richness and evenness) and D) the Shannon evenness index (spp. evenness) calculated from 16S rDNA sequenced from 3 ml of leaf wash. 156 Table 3.6. The 2019 ANOVA tables following the test for Beta-dispersion (homogeneity of variances) and the Permutational analysis of variances (PERMANOVA) (999 permutations) of the Bray-Curtis dissimilarity index to determine the differences in community composition in the sweet cherry leaf microbiome sampled at different seasons. Model term Beta-dispersion ANOVA df Sum of Squares Mean squares F-value p-value cultivar site season row tree leaf 2 Groups Residuals 322 0.041 6.560 1 Groups Residuals 323 0.336 6.162 Groups 2 Residuals 322 0.798 5.813 Groups 3 Residuals 321 0.247 6.080 Groups 14 Residuals 310 0.626 6.380 Groups 3 Residuals 321 0.021 6.349 0.021 0.020 0.336 0.019 0.399 0.018 0.082 0.019 0.045 0.021 0.007 0.020 1.009 0.3658 17.632 3.47e-05 22.095 1.018e-09 4.340 0.0051 2.172 0.0088 0.351 0.7888 Model term cultivar season site cultivar*season season*site site*row site*row*tree site*row*tree*leaf Residual Total df 2 2 1 4 2 6 12 60 235 324 Sum of Squares 1.615 11.436 1.034 3.248 4.578 1.860 4.478 17.277 28.006 73.531 PERMANOVA R2 F-value p-value 6.778 47.979 8.675 6.813 19.206 2.601 3.131 2.416 0.0010 0.0010 0.3560 0.0010 0.0010 0.0010 0.0010 0.0010 0.022 0.156 0.014 0.044 0.062 0.025 0.061 0.235 0.381 1.000 157 Figure 3.14. The 2019 Nonmetric multidimensional scaling (NMDS) ordination of the Bray- Curtis dissimilarities (stress k=0.136) to show the relationship between bacteria community composition of the Michigan sweet cherry leaf microbiome for A) the two sites where leaves were sampled, B) the three sweet cherry cultivars (‘Benton’, ‘Gold’, and ‘Sweetheart’) that the leaves and leaf microbiomes were sampled from, and C) the three sampling seasons (Spring, Summer, and Fall) for which the ellipses represent the 95% confidence intervals for each time, and D) is a box plot of the distributions of the distances from the centroid for samples in each season for which the solid black bar is the median, and the colored square points indicate the mean distance from the centroid. 158 Table 3.7. The 2019 ANOVA tables for the relative abundance of the top four Phyla and rarer Phyla combined into “other”, and the relative abundance at the deepest taxonomic level (genus) of Pseudomonas, and all “other” taxa at the deepest taxonomic level (genus or family) combined. Following the linear mixed model with site, cultivar, season, phylum (or genus), and season*phylum (or season*genus) as fixed effects and nested random effects of (1|site:row)+(1|site:row:tree)+(1|site:row:tree:leaf),with significance determined by p-value ≤ 0.05. α-statistic Model term df1 df2 F-ratio p-value Relative abundance Top Phyla Relative abundance Pseudomonas site cultivar season phylum season*phylum site cultivar season genus season*genus 1 2 2 4 8 1 2 2 1 2 7.17 10.08 564.66 1543.34 1543.34 0.000 0.000 0.000 20916.890 33.282 7.17 10.08 411.89 577.34 577.34 0.000 0.000 0.000 707.545 257.777 1.0000 1.0000 1.0000 <0.0001 <0.0001 1.0000 1.0000 1.0000 <0.0001 <0.0001 159 Figure 3.15. The 2019 mean relative abundance ± SE of the top 4 Phyla and the rarer Phyla combined into “other” that make up the sweet cherry leaf microbiome for Spring, Summer, and Fall. Lower-case letters indicate statistical differences in Phyla within each season, and the upper-case letters indicate statistical differences of each Phyla across each season, with significance determined by p- value ≤0.05. 160 Figure 3.16. The 2019 mean relative abundances of Pseudomonas and all “other” taxa at the genus/family level ± SE in the sweet cherry leaf microbiome for A) Spring, B) Summer, C) Fall, and D) the visualization highlighting the influence that Pseudomonas appears to have on the “other” taxa in the sweet cherry leaf microbiome with evident community stabilization by Fall. Letters indicate statistical differences, lower-cased letters indicate statistical differences within each season, while upper-cased letters indicate statistical differences of each Pseudomonas or “other” across seasons, with significance determined by p-value ≤ 0.05. 161 Table 3.8. The 2019 ANOVA tables of the top deepest taxonomic level (genus or family) by season where taxa of relative abundance <0.03 were combined into “other”. Following the linear mixed model with site, cultivar, and genus as fixed effects and nested random effects of (1|site:row)+(1|site:row:tree)+(1|site:row:tree:leaf) with significance determined by p-value ≤ 0.05. The 2019 ANOVA table of the core microbiome relative abundances (genus and OTU identifier ), the rest of the combined Pseudomonas and combined Sphingomonas, and the remaining taxa combined as “other”. Following the linear mixed model with site, cultivar, season, genus, and season*genus as fixed effects and nested random effects of (1|site:row)+(1|site:row:tree)+(1|site:row:tree:leaf). Model term df1 df2 F-ratio p-value α-statistic Relative abundance Top deepest taxa by season Spring Summer Fall site cultivar genus site cultivar genus site cultivar genus Relative abundance Core Taxa site cultivar season genus season*genus 1 2 1 1 2 6 1 2 6 1 2 2 6 12 13.62 5.04 137.37 0.000 0.000 7563.751 7.49 8.96 848.97 5.23 8.99 692.36 7.17 10.08 604.09 2187.34 2187.34 0.000 0.000 410.924 0.000 0.000 227.867 0.000 0.000 0.000 245.136 28.563 1.0000 1.0000 <0.0001 1.0000 1.0000 <0.0001 1.0000 1.0000 <0.0001 1.0000 1.0000 1.0000 <0.0001 <0.0001 162 Figure 3.17. The 2019 mean relative abundances ± SE of the top deepest taxonomic level (genus or family) for the sweet cherry leaf microbiome by season in which taxa of relative abundance <0.03 were combined into “other”. Letters indicate statistical differences within season, with significance determined by p-value ≤ 0.05. 163 Figure 3.18. The 2019 mean relative abundances of the core microbiome taxon (genus and OTU identifier), the rest of the combined Pseudomonas and combined Sphingomonas, and the remaining combined “other” taxa ± SE over season. Letters indicate statistical differences, lower-cased letters are within each season, while upper-cased letters indicate statistical differences of each genus/OTU across the seasons, with significance determined by p-value ≤ 0.05. Core taxa were selected via occurrence and relative abundance when they occurred in 50% of all samples and had a relative abundance of ≥ 0.01. 164 Table 3.9. The ANOVA tables for the relative humidity (%), daily temperature (°C), and the and Kruskal-Wallis test result for the precipitation (mm) compiled from the two-week period leading up to the microbiome leaf sampling dates. Metric Relative humidity (%) Daily temperature (°C) Metric Model term site time site time Model term df1 df2 F-ratio p-value 1 6 1 6 188 188 0.648 3.436 0.4220 0.0030 188 188 1.265 0.2621 35.477 <0.0001 χ2 df p-value Precipitation (mm) site time 0.356 22.877 1 6 0.5506 0.0008 165 Figure 3.19. The weather data collected from 2017-2019 over a two-week period leading up to each leaf microbiome sampling date, A) The distribution of the precipitation (mm) data, B) The mean daily temperature (°C), and C) the mean relative humidity (%). 166 REFERENCES Aleklett, K., Hart, M., and Shade, A. 2014. The microbial ecology of flowers: an emerging frontier in phyllosphere research. Botany 92(4): 253-266. Ares, A., Pereira, J., Garcia, E., Costa, J., and Tiago, I. 2021. The leaf bacterial microbiota of female and male kiwifruit plants in distinct seasons: assessing the impact of Pseudomonas syringae pv. actinidiae. Phytobiomes J. 5:275-287. 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SYRINGAE BIOLOGICAL CONTROL AGENTS 4.1: Abstract In vitro co-inoculation experiments can be used to initially assess how putative commercial biological control agents (BCAs) may influence the growth of Pseudomonas syringae pv. syringae (Pss) strains found on flowers in Michigan sweet cherry orchards. Strains of Pss that were identified previously for differences in virulence on green fruit and wood also varied in their strength of growth in-vitro. All commercial BCAs tested (Serenade® Opti, Blossom Protect™, BlightBan®A506, Bloomtime™, and Double Nickel55™) decreased the mean total area of growth of Pss compared to a water control, with up to a 50% decrease in growth in some cases. When the more virulent Pss strains were co-inoculated with the less virulent or avirulent Pss strains, there were no significant decreases in their mean total area of growth. This demonstrated their ability to coexist commensally and revealed that these particular less virulent to avirulent strains would not be effective candidates for potential biological control of the more virulent strains. 4.2: Introduction The bacterial canker disease causal agent, Pseudomonas syringae pv. syringae (Pss), is a globally economically devastating pathogen in the cherry industry, with young trees particularly being susceptible to whole tree death (Marroni et al., 2023; Spotts et al., 2010). Spotts et al. (2010) reported up to 75% loss of trees in Oregon, U.S.A., while Marroni et al. (2023) cited losses of 20-50% in New Zealand orchards. Copper has been the standard for management of Pss and new formulations are being tested continually (Broniarek-Niemiec et al., 2023). Broniarek- 177 Niemiec et al., (2023) field tested various copper formulations along with biological control agents and found the copper-containing products to be the most efficacious for reducing Pss on leaves and fruit of Prunus spp. The use of copper for Pss population management in Michigan orchards, however, is not a sustainable option as the phylogenetically diverse Pss strains have been found to be resistant to copper (Lauwers, 2022; Renick et al., 2008; Wilkinson, Chapter 2). Copper accumulations in agricultural soils also has been found to have deleterious effects on beneficial invertebrates (Duque et al., 2023; Karimi et al., 2021). For many years, antibiotics such as oxytetracycline, streptomycin, and kasugamycin were unavailable to cherry growers for management of Pss because use was restricted to control of the highly aggressive fire blight bacterial pathogen, Erwinia amylovora, to reduce the potential for development of resistance (Lauwers, 2022; McGhee and Sundin, 2011; Slack et al., 2021; Sundin and Wang, 2018). Recently (2020), however kasugamycin has become available for use in cherry and has been shown to reduce flower infection by Pss in sweet cherry by 90% (Lillrose et al., 2017). Although no Pss resistance to kasugamycin has been detected in Michigan thus far, resistance development is still a serious concern, so kasugamycin is not used routinely for pre- emptive Pss management (Lauwers, 2022; Lillrose et al., 2017; Sundin and Wang, 2018). In addition to the use of copper for Pss management, cultural practices are widely used, such as weed control, drip (rather than sprinkler) irrigation, and timing of pruning, none of which are not guaranteed to prevent Pss infection (Lattore and Jones, 1979; Spotts et al., 2010; Carroll et al., 2010). There are also no sweet cherry cultivars resistant to Pss, although wild, ornamental, and hybrid Prunus are being tested to identify sources of genetic resistance (Hulin et al., 2022). There is a keen fruit grower interest in finding alternatives to antibiotics that are sustainable and “environmentally friendly” and could complement cultural practices for 178 management of Pss. The most recent research trend for management of Pss is with lytic bacteriophage (single phage and cocktails) along with the commercially available AgriPhage™ (Akbaba and Ozaktan, 2021; Brown, 2023; Lauwers, 2022; Luo et al., 2022; Oueslati et al., 2022; Pinheiro et al., 2019; Rabiey et al., 2020). In Michigan, bacteriophage cocktails including native bacteriophage isolated from Michigan soils have been tested for efficacy of Pss population management and have been found to be limited by UV exposure (Brown, 2023; Lauwers 2022). A boom in the bio-pesticide, bio-protectant, bio-control industry has created confusion as to what is the true definition of biological control is (Lahlali et al., 2022; Stenberg et al., 2021). Stenberg et al. (2021) proposed that products that contain plant growth promoting bacteria and incite systemic acquired resistance (SAR) or induced systemic resistance (ISR) should belonging under the bioprotectant umbrella but remain a separate entity compared to what is traditionally considered a biological control. Actigard™ is a type of SAR, that “primes” the plant’s innate immunity, as it mimics the defense hormone salicylic acid, and has been found to be an effective complement to other management strategies for Pseudomonas syringae pv. actinidiae (Psa) in kiwi production, but it was not efficacious on sweet cherry for Pss (Lillrose et al., 2017; Stroub et al., 2022). The overlying theme of all of these products is that they are inconsistently effective in the field and in spite of potential demand, they are slowly produced and have not been adopted widely by growers (Lahlali et al., 2022). The biocontrols currently available for Erwinia amylovora have been assessed for blossom infection in apple and sweet cherry however, no such assessments have been documented for the management of epiphytic Pss populations of in Michigan cherry orchards (Sundin et al., 2009; Lillrose et al., 2017). As Pss is a known epiphyte, the ability to manage populations on leaves may be a key component of successful reduction in disease incidence. The 179 sweet cherry leaf microbiome could be an untapped source of possible biological control agents (BCAs) since the sweet cherry leaf microbiome has been shown to be heavily dominated by Pseudomonas spp. (Wilkinson, Chapter 3), which are capable of plant growth promotion, ISR, the production of secondary metabolites and volatile organic compounds that elicit antibiosis, as well as competitive exclusion (Dimkić et al., 2022). Potential BCAs for Pss have been assessed from apple fruits, and the pear and apple bark has been identified as a reservoir for potential BCAs as well (Kotan and Sahin, 2006; Arrigoni et al., 2018). Cui et al. (2021) demonstrated that bacteria isolated from the apple flower microbiome could influence the prevalence of the bacterial canker pathogen when reintroduced to the flower in certain compositions. Vasebi et al. (2023) found 18 strains that had been isolated from the almond and apricot phyllosphere and endosphere that were antagonistic to Pss in in vitro bioassays. The Pss isolated from Michigan cherry flowers are also a phylogenetically diverse group belonging to phylogroups 2a, 2b, 2c, and 2d, and having a range in their level of virulence on green fruit and wood, should also be assessed for their biological control potential (Lauwers, 2022; Renick et al., 2008; Wilkinson, Chapter 2). Biological controls of plant pathogens are thought to function in two major ways: 1) competitive exclusion of the pathogen from niche or nutrient resources, and 2) through antibiosis (Lindow and Brandl, 2003). Though in vitro assays do not necessarily translate into field efficacy, they are suitable for the initial determination of whether potential BCAs are capable of limiting the growth of the pathogen via either competition or antibiosis. Stroud et al. (2022) touted the importance of small-scale in vitro assays since the actual way a BCA affects the target pathogen can be lost in the complexity of a field study. As field and growth chamber studies with live plants are time-consuming, resource-intensive, nearly impossible for tree species, and 180 often yield inconclusive results, simpler in vitro studies are valuable for initial screening of potential BCAs. Stroud et al. (2022) used tissue culture to determine how Actigard™ was able to limit Psa population sizes. Dual culture or co-inoculation in vitro assays, in Petri plates have long been used to assess the biological control potential of bacteria against fungal pathogens (Chaouachi et al., 2021; Cirvilleri et al., 2005, Derikvan et al., 2023; Hammami et al., 2022; Lu et al., 2022; Niem et al., 2020; Nysanth et al. 2022). Often a mycelium plug is co-inoculated with either a pipetted amount of bacteria or a streak of bacteria, and in some cases multiple co- inoculations may be made on the same Petri plate (Chaouachi et al., 2021; Cirvilleri et al., 2005, Derikvan et al., 2023; Hammami et al., 2022; Lu et al., 2022; Niem et al., 2020; Nysanth et al. 2022). Vasebi et al. (2023) made a lawn of Pss first, followed by 5µl of each of the bacteria being assessed for their antagonistic properties. In these cases, the zones of inhibition were measured. Mina et al. (2020) showed that this method also worked well for testing the interaction between two bacteria, in which the olive knot bacteria pathogen Pseudomonas savastanoi pv. savastanoi was co-inoculated next to potential BCAs. In vitro dual culture or co-inoculation should be a viable method to assess the currently available commercial biological control agents having potential for managing Pss populations, as well as a way to initially determine how the Pss strains isolated from sweet cherry flowers may interact with one another. The objectives of this experiment were to a) demonstrate that in vitro co-inoculations are a viable, easily employed option for the initial assessment of BCAs for Pss, b) evaluate commercially available BCAs for their ability to decrease Pss growth in vitro, and c) determine if avirulent or moderately virulent Pss strains recovered from Michigan sweet cherry orchards can decrease growth of virulent or moderately virulent Pss strains in vitro. 181 4.3: Methods 2019 Co-Inoculations Pss PG 2d vs. BP, SO, and H2O During 2019, Pss strains 9, 25, 37, and 38 from phylogroup (PG) 2d, all belonging to the most virulent phylogroup recovered from sweet cherry flowers in Michigan (Wilkinson, Chapter 2) were co-inoculated next to the commercially available biocontrol agents (BCAs) Serenade® Opti (SO), Bacillus subtilis, a bacterium (Bayer CropScience LP, St. Louis, MO), and Blossom Protect™ (BP), Aureobasidium pullulans, (Westbridge, Chelsea Vista, CA), a yeast-like fungus, or sterile water in Petri dish assays (Table 4.1). Sterile filter paper discs ~34 mm² in area were placed ~32 mm apart onto 100 mm x 15 mm Petri plates (VWR®, Radnor PA) containing sterile King’s B media (KB) (Figure 4.1). For each Petri dish (5 replications/strain/treatment [n= 60 plates]), one filter paper disk was inoculated with a 5 µl droplet of 108 cfu/ml of one of the virulent Pss strains, and the other disk was inoculated with a 5 µl droplet of the biocontrol agent at the maximum recommended field rate or a sterile water control. All co-inoculations occurred on the same day. Inoculations were allowed to dry overnight on the lab bench before the plates were sealed on trays in a plastic bag (to prevent drying out) and were placed into an incubator at 28°C (Precision Scientific Model 805, Chicago IL). Photographs of each plate were taken at 4 days after inoculation (DAI) and the area of growth of the Pss next and the co-inoculated biocontrol or water control could be measured using the software ImageJ (Schneider et al. 2012). To obtain the best threshold contrasts and measurements in ImageJ, in certain instances Adobe® Photoshop (Elements 14 Photo Editor, Adobe®, San Jose, CA) was used to highlight the area of bacterial growth (without altering area size). In ImageJ, the image type was set to 8-bit, and the plate diameter (85 mm) was used as a known reference size. The top threshold was then adjusted so that the area of Pss vs. biocontrol growth was prominent (the bottom threshold remained 182 constant, 255), a selection tool was then used to select each growth area outline. Then Analyze- analyze particles option was selected with size (mm2) set as 20 (and 1 for smaller pipette splatter areas), circularity set as 0.00-1.00, and show outlines, display results, and include holes were selected. Pipette splatter was included as the part of the total area of growth as long as it was distinguishable as being either Pss or BCA as it is considered part of the original 5µl of inoculum. The area of Pss growth (mm2) was reported. All images can be viewed in Appendix C. 2021 Co-Inoculations Pss PG 2b, 2c, 2d vs. BB, BLT, BP, DN, SO, and H2O In 2021 the co-inoculations of Pss vs. biocontrols was expanded to include ten Pss strains that had been recovered from sweet cherry flowers in Michigan (Wilkinson, Chapter 2) from the moderately virulent PG 2b strains 22, 23, 34, the avirulent PG 2c strain 14, and the virulent PG2d strains 9, 25, 26, 27, 37, 38. The commercially available biological controls were also expanded to include BlightBan®A506 (BB) (a bacterium, Pseudomonas fluorescens) (Nufarm Americas Inc., Alsip,, IL), and Bloomtime™ (BLT) (a bacterium, Pantoea agglomerans) (Verdesian Life Science, US, LLC, Cary, NC), and Double Nickel55™ (DN) (a bacterium, Bacillus amyloliquefaciens) (Certis USA LLC, Columbia, MD), in addition to BP and SO (Table 4.1). All co-inoculations were done as described previously for the 2019 Petri dish assays; however the biological controls were applied at 3 different rates: 1.0x which was the maximum recommend rate, then 0.5x (half the recommended rate) and 1.5x (1.5 times the maximum recommended rate) and plates for Pss vs. BP were also co-inoculated on Potato Dextrose Agar (PDA) (BD Difco™, Becton Dickinson, Franklin Lakes, NJ) in addition to KB media. As BP is the only fungus the PDA media may better support BP growth and could result in better Pss suppression by BP. A total of 1,200 plates were co-inoculated. Due to space limitations in growth chambers and an effort to control inoculation error or contamination, co-inoculations 183 occurred in groups according to strain and growth chamber, with strains being randomly assigned to a group (Table 4.2). The area of growth of the Pss strains grown next to the challenging BCA was measured as described above using ImageJ (Schneider et al. 2012). 2022 Co-Inoculations Pss PG 2b, 2d vs. Pss PG 2c, 2b, and H2O In 2022 co-inoculation assays were conducted to determine what effect the Pss strains collected from Michigan sweet cherry flowers belonging to the phylogroups 2b (strains 23 and 33) and 2c (strains 14 and 18) moderate virulence and avirulent strains respectively may have on the most virulent PG2d (strains 9 and 25) and the moderately virulent PG2b strains when co- inoculated in petri assays. The Pss co-inoculations plus sterile water controls were applied to KB plates as described for 2019 (5 reps [150 plates total]). The PG2d (virulent) and PG2b (moderate- virulence) Pss strains were inoculated at 108, 106, and 104 cfu/ml (assigned to different days to prevent error/contamination) while the challenging PG2c (avirulent) and PG2b (moderate- virulence) strains were inoculated at a constant rate of 108 cfu/ml (Table 4.3). The area of growth for the PG 2b and PG 2d strains were measured as described previously using ImageJ (Schneider et al. 2012). Statistical Analysis All data was analyzed using R version 4.3.1 with a linear model with ANOVA and pairwise comparisons of the estimated marginal means (Kuznetsova et al., 2017; Lenth, 2023; R core team, 2023). Distribution of data can be found in Appendix C for all co-inoculation experiments. Data for 2021 was analyzed separately according to group assignment. The 2021 data containing BP was analyzed first with all the other BCAs on KB media separately for each group and then with BP and H2O treatments only on KB and PDA media. The data for 2022 was also analyzed separately for each group. Plates that were contaminated were omitted from the 184 analysis. When an effect of treatment was evident the % decrease in the total area of Pss growth (mm2) compared to the growth next H2O was calculated using the formula: % decrease=[(old value – new value)/(old value)] *100 (i.e. [(mean area Pss next to H2O – mean area Pss next to BCA)/(mean area Pss next to H2O)]*100). All graphs were made in R v. 4.3.1 using a combination of base R and ggplot2 and ggpubr (Kassambara, 2023; R core team, 2023; Wickham, 2015). Graphic fonts were edited, and the letters indicating statistical differences were added to graphs via InkScape v.1.2 (InkScape.org). 4.4: Results 2019 Pss PG 2d vs. BP, SO, and H2O There were no statistical differences between the virulent PG2d strains (9, 25, 37, and 38) mean total areas of growth when co-inoculated next to the BCAs BP, SO or H2O. There were statistical treatment effects, where the mean area of growth of the PG2d strains was suppressed by 25% and 27% when co-inoculated next to BP and SO respectively compared to when next to water (Table 4.4 and Figure 4.2). There were no statistical differences between the two BCAs. 2021 Pss PG 2b, 2c, 2d vs. BB, BLT, BP, DN, SO, and H2O Group1: Pss 14 (PG2c), 25 (PG2D), 38 (PG2d) For the three Pss strains 14 (PG2c), 25 (PG2D), and 38 (PG2d) grown next to the BCAs BB, BLT, BP, DN, SO, and H2O there was only an effect of BCA rate on the mean total area of growth (mm2) of the Pss strains (Table 4.5). Where Pss strains grown next to BCAs inoculated at a rate of 1.5x had the greatest mean total area of growth followed by Pss strains grown next to BCAs inoculated at a rate of 0.5x, while strains grown next to BCAs inoculated at a rate of 1.0x had the least amount of growth (i.e., the most suppression of growth) (Figure 4.3). Only the 1.0x and 1.5x BCA rates were statistically different. 185 Group2: Pss 23 (PG2b), 34 (PG2b) For the two PG2b strains 23 and 34 when co-inoculated next to the five BCAs or water there were statistically significant effects of treatment, strain, rate*treatment, and treatment*strain (Table 4.5). All BCA treatments were able to suppress the mean total area of growth of Pss strain 23 and 34 (23% to 36%) when compared to water (Figure 4.4). SO was the only BCA that was not statistically different than water. Strain 34 was the strongest of the two strains when grown next to BCAs with a larger mean total area of growth than strain 23. When considering the rate* treatment effect the greatest suppression of Pss growth occurred when BCAs were inoculated at the 1.0x rate (27% to 50%) when compared to the Pss next to H2O, however only BP was statistically different from water at this rate. The Pss next to BCAs inoculated at 0.5x had a 21% to 50% decrease in growth when compared to those next to water, with only BB and BLT being statistically different than water at that rate. While the Pss next to BCAs inoculated at 1.5x had no suppression (SO) to very little suppression of growth (3% to 15%) and no statistical differences from water. For the treatment*strain effect Pss strain 34 had a 32% to 49% decrease in the mean total area of growth when co-inoculated next to BCAs compared to when next to water (all statistically different from water) while strain 23 had no suppression (BP) to very little suppression (1% to 12%) in the mean total area of growth when co-inoculated next to BCAs compared to when next to water with no statistical differences from water. Group3: Pss 22 (PG2b), 26 (PG2d), 37 (PG2d) The in vitro co-inoculation of the two PG2d strains 26 and 33 and the PG2b strain next to BCAs or water had an effect of BCA rate, Pss strain, and treatment*strain (Table 4.5). The mean total area of growth of the Pss next to BCAs at three rates can be ranked as 0.5x<1.0x<1.5x, 186 where only the 0.5x and 1.5x rates were statistically different (Figure 4.5). While the PG2b strain and the PG2d strains can be ranked according to their mean total area of growth (mm2) next to BCAs as 22<26<37 with all being statistically different from one another. For the treatment*strain interaction the mean total area of growth of strain 37 was suppressed by 11 to 36% in co-inoculations with BCAs with only SO being statistically different from water. While strain 26 and 22 had no statistical difference from water with suppression of growth (strain 26) and only 1% suppression of growth next to BB and BLT (strain 22). Group4: Pss 9 (PG2d), 27 (PG2d) The PG2d strains (9 and 27) only had an effect of BCA rate and Pss strain (Table 4.5). Where the mean area of Pss growth was greatest at the BCA rate of 1.0x, followed by 1.5x and the least at 0.5x with only 0.5x and 1.0x being statistically different (Figure 4.6). Strain 27 had a greater mean area of growth and was statistically different from strain 9 when co-inoculated next to BCAs. 2021 Pss PG 2b, 2c, 2d vs. BP and H2O on KB and PDA Media Group 1: Pss 14 (PG2c), 25 (PG2d), 38 (PG2d) For the two PG2d strains, 25 and 38, and the PG2c strain 14 co-inoculated with BP or water on KB or PDA media there was an effect of strain and media*rate*treatment(trt)*strain (Table 4.6). Strains can be ranked for their mean total area of growth as 25<38<14 with statistical difference only between PG2c strain 14 and PG2d strain 25 (Figure 4.7). Though there was an effect of media*rate*trt*strain, no statistical differences could be discerned however, unless the interaction of rate*trt*strain was nested within media. Strain 38 (PG2d) had the greatest suppression of growth when co-inoculated next to BP at all rates on KB (16% to 44%) and on PDA at 0.5x and 1.0x (31% and 28%) but there were no statistical differences from water. Strain 187 25 (PG2d) had suppression of growth when BP was inoculated at 0.5x (19%) and 1.0x (29%) rates on KB and at the rate 0.5x (40%) on PDA however there were no statistical differences when compared to water. Strain 14 (PG2c) had a 66% suppression of growth with BP at 1.5x rate on KB which was statistically different than water, while there was suppression of growth (2% to 18%) of this strain at all BP rates on PDA there were not statistical differences from water. Group 2: Pss 23 (PG2b), 34 (PG2b) The PG2b strains 23 and 34 had significant effects of media, rate, treatment (trt), media*rate, media*strain, trt*strain, and media*trt*strain (Table 4.6). Overall, these Pss strains when co-inoculated next to either BP or H2O had a statistically greater mean total area of growth on KB media than on PDA (Figure 4.8). The mean total area of growth of the strains when grown next to BP at the three rates can be ranked as 1.5x<0.5x<1.0x where only 1.5x and 1.0x are statistically different. The PG2b strains had a 19% decrease in growth when grown next to BP compared to when next to H2O. Overall Pss grown on KB media had a greater mean total area of growth at all inoculation rates of BP compared to those on PDA, however they were only statistically different at the 0.5x rate (Figure 4.9). Strain 23 and 34 were only statistically different on KB media where 34 had the strongest mean total area of growth. Strain 23 had 4% suppression of growth however only strain 34 was statistically different from water with a 30% suppression of growth next to BP. On KB strain 34 was also statistically different from water with a 44% suppression in growth next to BP, while 23 was decreased by 15% on PDA there was no statistical difference when next to water. Group 3: Pss 22 (PG2b), 26 (PG2d), 37 (PG2d) For Pss strain 22 (PG 2b) and the two PG2d strains 26 and 37 co-inoculated with the BCA BP or water on KB or PDA media there was only an effect of BP inoculation rate and an 188 effect of Pss strain (Table 4.6). Where strains grown next to BP or H2O at 1.0x and 1.5x had statistical differences in their mean total areas of growth as strains next to BP at 1.0x had the least amount of total area of growth while those next to BP at the rate 1.5x had the greatest mean total area of growth (Figure 4.10). The PG2d strains (26 and 37) had the strongest total mean area of growth while strain 22 was statistically different with the weaker amount of growth. Group 4: Pss 9 (PG2d), 27 (PG2d) For the PG2d Pss strains 9 and 27 there was a significant effect of media*rate, rate*strain, and media*trt (Table 4.6). The strains on KB media had stronger growth than on PDA at the 1.0x and 1.5x BP rates however the only statistical differences between the two medias occurred at the 0.5x BP rate when strains on PDA had stronger growth than when on KB (Figure 4.11). Strain 27 (with the greater mean area of growth) was statistically different from strain 9 only when BP was inoculated at the 1.0x rate. Although there was an effect of media*trt pairwise comparisons of the estimated marginal means did not show any statistical differences unless nested within rate (Appendix C). Even with the nested rate parameter there were no significant differences between media and treatment compared to water though Pss growth was suppressed by 4% at 0.5x BP and 15% at 1.0x BP on KB media (Figure 4.12). 2022 Co-Inoculations Pss PG 2b, 2d vs. Pss PG 2c, 2b, and H2O Group 1: Pss v. Pss at 108 cfu/ml When all strains were co-inoculated at 108 cfu/ml there was only a significant effect of strain (Table 4.7). The virulent phylogroup 2d Pss strains 9 and 25 had the greater mean area of growth when grown next to a PG 2c/2b Pss strains or water compared to the moderately virulent strains PG2b Pss strains 23 and 33 when co-inoculated with PG2c. Strain 23 with the weakest 189 growth was statistically different than all strains, while strain 9 with the strongest mean area of total growth was statistically different from both PG2b strains (23 and 33) (Figure 4.13). Group 2: Pss at 106 cfu/ml v. Pss at 108 cfu/ml Pss PG2d/2b strains 9, 25, 23 and 33 at the rate 106 cfu/ml next to the Pss PG2c/PG2b strains 14, 18, 23, 33 at 108 cfu/ml had an effect of strain and a weak treatment effect (Table 4.7). The PG2d strains 9 and 25 grew stronger and were statistically different than the PG2b strains 23 and 33 (Figure 4.14). Though no treatments were statistically different than water the PG2d strains 9 and 25 at 106 cfu/ml had a 20% and 8% decrease in mean total area of growth when grown next PG2b strain 33 and PG2c strain 14 at 108 cfu/ml respectively. Group 3: Pss at 104 cfu/ml v. Pss at 108 cfu/ml For the Pss PG2d/2b strains 9, 25, 23 and 33 at the rate 104 cfu/ml next to the Pss PG2c/PG2b strains 14,18, 23, 33 at 108 cfu/ml there was only an effect of treatment (Table 4.7). Though no treatments were statistically different than water there was suppression of growth of the PG2d and PG2c strains (14% to 33%) when challenged with PG2b and PG2c strains (Figure 4.15). 4.5: Discussion Though this experiment’s methodology was most similar to that employed by Mina et al. (2020) it was simpler, more time efficient, and more precise. While Mina et al. (2020) measured the radial growth (toward the antagonist) daily until the growth stopped this study measured the total area of growth at a single time point (4 DAI). In this study the total area of growth was precisely measured using the ImageJ software while Mina et al. (2020) does not specify exactly how the growth was physically measured, likely via ruler, they estimated the growth/day via the slope from regression analysis. The percent decrease in growth for both this study and Mina et al. 190 (2020) used the same equation which used the growth of the pathogen next to water or alone and the growth of the pathogen next to the BCA. Mina et al. (2020) also used additional assays to measure siderophore, lipase, and protease production which was not done in this study but should be done in future assays especially with the microbiome or flower isolated Pss bacteria strains. A clever assay to prove that the volatiles produced by the BCA was a mode of action that reduced the pathogens growth was employed by removing a strip of the media that was between the co-inoculants (Mina et al., 2020). This type of assay should be used as a follow up experiment for both the commercial BCAs and the Pss used in this study’s experiment. The experimental design of this study was not conducive to measuring competitive exclusion and the modes of action of the BCAs were either the production of secondary metabolites or volatiles. The BCAs BB, BLT and BP are thought to function as competitive excluders of the fire blight pathogen, E. amylovora (Sundin et al., 2009; Wilson and Lindow 1994; Zeng et al., 2023). However, strains of Pseudomonas fluorescens, Bacillus amyloliquefaciens, Bacillus subtilis, and Aureobasidium pullulans which are the bacteria or fungi in the BCAs BB, DN, SO, and BP respectively, have been documented to produce volatile organic compounds as a mode of action for the suppression of plant pathogens (Dimkić et al., 2022; Köhl et al., 2019; Tilocca et al., 2020). While strains of Pseudomonas fluorescens, Bacillus amyloliquefaciens, Bacillus subtilis, and Pantoea agglomerans which are the organisms in the BCAs BB, DN, SO, and BLT and are well known producers of secondary metabolites with antibiotic activity (Dimkić et al., 2022; Köhl et al., 2019; Lorenzi et al., 2022; Sundin et al., 2009; Tilocca et al., 2020). Again, it should be emphasized that this type of experiment is very simplistic and only can give an initial glimpse into what may occur between the pathogen and the BCA, and it excludes the nuances of additional benefits or interactions the BCA may provide in- 191 planta. For example, BP is also thought to induce host resistance of the plant to E. amylovora due to the apparent pathogenesis of A. pullulans itself, which could only be deduced from in-planta experiments (Zeng et al., 2023). Additionally, a major caveat in the execution of this specific experiment was the way in which the co-inoculated plates were stored within the growth chamber. The individual Petri plates were not sealed but were stacked on trays and then sealed en masse within a plastic bag with the intention of preventing drying of the media which may have resulted in the unintended gassing, via volatiles, of the entire experiment which may have influenced results. As mentioned above Pseudomonas spp. themselves are capable of producing volatiles and the Pss strains in these experiments also contributed to the malodorous malady (Dimkić et al., 2022; Tilocca et al., 2020). Seiber et al. (2020) found that a volatile is likely the signal to induce the production of mangotoxin in the Pss causal agent of bacterial apical necrosis in Mango. This experiment was successful in demonstrating that the commercially available biological controls can affect the mean total area of growth of Pss at all experimental time points (2019-2021). Where in 2019, there was an overall 25 to 27% decrease for Pss PG2d next to BP and SO, in 2021, there was an overall decrease for the PG2b strains Pss 23 and 34 next to BB, BLT, BP, DN, and SO, while strain 34 on its own had a 32 to 49% decrease in growth. This was inconsistent across the strains where strains 14 (PG2c), 25 (PG2d), and 38 (PG2d) did not have a treatment effect only rate, and the PG2d strains 9 and 27 also had no effect of treatment only strain and BCA rate. No BCA stood out from all the others as a better contender for decreasing Pss growth as they were not statistically different from one another and equally capable of suppressing growth or not. 192 The effect of strain only demonstrates that certain strains are stronger when in-vitro next to BCAs or water but does not indicate any clear level of suppression. For example, strain 34 grew stronger than strain 23, however strain 34 had a greater level of suppression (32 to 49%) than strain 23 which had a range of no suppression to 12% suppression compared to when next to water. This was also true for strain 37 (PG2d) which was a stronger strain compared to strains 26 (PG2d) and 22 (PG2b) but it had a higher level of suppression when grown next to all BCAs ranging from 11to 36% while strain 26 and 22 did not have any suppression of growth and in some instances had a greater mean area of growth the next to the BCA than when next to water. These strain differences in their responses to BCAs also highlights the importance of understanding the types of strains that are present in an orchard. Where if strains like 22, 23, and 26 that were resistant to the influence of BCAs were to be dominate in the sweet cherry microbiome then the use of BCAs for population management would be and exercise in futility while if the microbiome were dominated by the strains 34 and 37 that had suppression of growth when challenged with BCAs there could be a stronger possibility for successful usage of BCAs for population management. Pss and Pseudomonas, in general, are a diverse group of organisms with pathogenic and non-pathogenic strains and are able to cohabitate commensally and synergistically with other bacteria it could be that some of these strains are better at co-existing with a broader range of other bacteria genera and that is why there are differences in effects of the BCAs (Hulin et al., 2020; Lauwers, 2022; Melnyk et al., 2019; Purahong et al., 2018; Shalev et al., 2021; Shalev et al., 2022; Xin et al. 2018). These strains should be analyzed further at the molecular to determine if differences exist genetically between strains as it has been determined that some Pseudomonas have had genetic gains and losses in relation to pathogenicity, their 193 ability to live commensally, as well as their ability induce plant protection (Melnyk et al., 2019; Shalev et al., 2022). Overall, the 1.5x rate did not give any advantage to those treatments where the lowest mean total area of growth was at 0.5x and 1.0x with no statistical differences between them. For strains 9 and 27, however, the 1.5x and 0.5x rates had a lower mean total area of growth but then there was no statistical difference between 1x and 1.5x rates. The 1x rate is a suitable rate for this type of experiment. There was no advantage conferred to the lone fungal BCA, Blossom Protect™ (BP), by including assay on the PDA media as overall there were no differences between strains grown next to water than when grown next to BP. It was demonstrated that the strains grew stronger on KB and the effect of the BCAs on KB were more pronounced which indicates that KB is a more suitable media for this experiment, and the assays on KB were more informative. For the 2022 in-vitro experiments were the virulent to moderately virulent Pss strains were co-inoculated next to the moderate to avirulent Pss strains that were isolated from the sweet cherry flower there were no effects of treatment when the virulent to moderate strains were inoculated at 108, 106, or 104 cfu/ml next to the moderate to avirulent Pss inoculated at 108 cfu/ml. Strain differences in their mean total area of growth demonstrated that the more virulent PG2d strains (9, 25) had stronger mean area of growth than the PG2b strains (23, 33), with strain 23 being the weakest. When the virulent to moderately virulent strains were inoculated at 104 cfu/ml next to the moderate to avirulent Pss inoculated at 108 though not statistically different, the strains 33 and 23 caused a 33% decrease in growth of the strains 9 and 25, while the treatment strains 18 and 14 had a 14% to 28% decrease in the growth of the strains PG2d/2b strains (9, 25, 33, 23) when compared to those co-inoculated with water. Considering that these 194 strains were already capable of living side by side on the sweet cherry flower in the field it may again be a demonstration of their ability to co-exist with one another it is curious that they do so while having different levels of virulence. Again, these strains should be analyzed at the genetic level to determine what genes they have in common. 4.6: Conclusions This experiment demonstrated that in-vitro co-inoculations assays are a good initial way to look at the potential interactions between Pss and BCAs. All the BCAs tested had the ability to decrease the mean total area of growth of Pss likely via secondary metabolite or volatiles while no one particular BCA excelled over the other. None of the avirulent to moderately virulent Pss were able to significantly decrease the mean total area of growth of the virulent to moderately virulent Pss, but they were closer to achieving an effect when the virulent to moderately virulent Pss were inoculated at a lower rate (104 cfu/ml) while the virulent to moderately virulent strains were at the higher rate (108 cfu/ml). However, it is clear that these avirulent to moderately virulent strains are also not good candidates as potential biological control agent of pathogenic Pss. Further experiments done using this method should include the assays for volatiles, enzymes and secondary metabolites like those used in Mina et al. (2020), and all plates should be individually sealed. The Pss strains should be investigated further on the genetic level comparing virulence genes and the genes necessary for commensal living. 195 Table 4.1. The rates of the commercial biological control agents (BCA) with the abbreviations used in tables and figures (per 100 gallons of water), organism, and manufacturer used in all of the co-inoculation experiments in which Pss was grown next to a BCA or next to H2O, including the year (Exp. year) when the experiment took place or in which the BCA was used, where 1x is the maximum recommended label rate (in bold), 0.5x is half that, and 1.5x is that above the maximum recommended. BCA Organism/Manufacturer Type Rate/100 gal. Exp. Year BlightBan®A506 (BB) Pseudomonas fluorescens A506 Nufarm Americas Inc., Alsip,, IL Bacteria Bloomtime™ (BLT) Pantoea agglomerans E325, NRRL B-21586 Verdesian Life Science, US, LLC, Cary, NC Bacteria 2.65 oz 5.30 oz 7.95 oz 75.0 g 150.0 g 225.0 g Blossom Protect™ : Aureobasidium pullulans DSM 14940, DSM 14941 Buffer Protect (BP) Westbridge, Chelsea Vista, CA Fungus 0.63 : 4.38 lb 1.25 : 8.75 lb 1.88 : 13.13 lb Double Nickel55™ (DN) Bacillus amyloliquefaciens D747 Certis USA LLC, Columbia, MD Serenade®Opti (SO) Bacillus subtilis QST 713 Bayer CropScience LP, St. Louis, MO Bacteria Bacteria 1.5 lb 3.0 lb 4.5 lb 10.0 oz 20.0 oz 30.0 oz 0.5x 1.0x 1.5x 0.5x 1.0x 1.5x 0.5x 1.0x 1.5x 0.5x 1.0x 1.5x 0.5x 1.0x 1.5x 2021 2021 2021 2021 2021 2021 2021 2019, 2021 2021 2021 2021 2021 2021 2019, 2021 2021 196 Figure 4.1. An example of co-inoculated plates: A1) Pss strain 25 next to the biological control agent (BCA) Serenade®Opti (SO), A2) the threshold image produced by ImageJ that is used for defining the area to be measured, A3) is the outline of the measured area of growth (mm2) for the Pss strain 25 used in the analysis and SO (area not included in the statistical analysis), B) the filter paper alone (no growth) that was inoculated with H2O and an example of pipette splatter during inoculation of strain 38 for which the total area of growth (mm2) was measured, and C) an example of a plate that was contaminated with an unknown fungus and was excluded from the statistical analysis. 197 Table 4.2. The 2021 grouping of Pss strains that were co-inoculated next to the biological control agents BlightBan®A506 (BB), Bloomtime™ (BLT), Blossom Protect™ (BP), Double Nickel55™ (DN), Serenade® Opti (SO), or H2O, their phylogroup (PG), inoculation timing and growth chamber assignment. Year Group Pss strains PG Week Day Growth chamber 2021 1 2 3 4 14 25 38 23 34 22 26 37 9 27 2c 2d 2d 2b 2b 2b 2d 2d 2d 2d A 1 B 2 1 2 1 2 1 2 198 Table 4.3. The 2022 group assignment of Pss strains 9, 25, 23, 33 (the virulent to moderately virulent strains) that were co-inoculated next to the avirulent and moderately virulent (strains 14, 18, 23, 33), their phylogroups, the rates at which they were inoculated, and day of inoculation. Water (ctrl) was co-inoculated next to strains 9 , 25, 23, 33 for each group. Group Day 1 1 Pss strain 9 PG 2d Rate (cfu/ml) 108 Pss strain 33 v. v. 18 v. H2O PG 2b 2c Rate (cfu/ml) 108 108 25 2d 108 33 2b 108 23 2b 108 2 2 9 2d 106 25 2d 106 33 2b 106 23 2b 106 3 3 9 2d 104 25 2d 104 33 2b 104 23 2b 104 108 108 108 108 108 108 108 108 108 108 108 108 108 108 108 108 2b 2c 2c 2c 2b 2c 2b 2c 2c 2c 2b 2c 2b 2c 2c 2c 23 v. v. 14 v. H2O 18 v. v. H2O v. 14 v. H2O 33 v. v. 18 v. H2O 23 v. v. 14 v. H2O v. 18 v. H2O 14 v. v. H2O 33 v. 18 v. v. H2O 23 v. 14 v. v. H2O v. 18 v. H2O v. 14 H2O 199 virulence virulent v. moderate virulent v. avirulent ctrl virulent v. moderate virulent v. avirulent ctrl moderate v. avirulent ctrl moderate v. avirulent ctrl virulent v. moderate virulent v. avirulent ctrl virulent v. moderate virulent v. avirulent ctrl moderate v. avirulent ctrl moderate v. avirulent ctrl virulent v. moderate virulent v. avirulent ctrl virulent v. moderate virulent v. avirulent ctrl moderate v. avirulent ctrl moderate v. avirulent ctrl Table 4.4. The 2019 ANOVA for co-inoculations of the virulent Pss phylogroup (PG) 2d strains 9, 25, 37, and 38 grown next to the commercial biological controls Serenade® Opti (SO), Blossom Protect™ (BP) or water (H2O) in Petri plates with KB media. Year Model term df 1 df 2 F-ratio p-value 2019 trt strain trt * strain 2 3 6 48 48 48 6.246 0.458 0.680 0.0039 0.7132 0.6663 200 Figure 4.2. The mean total area of growth (mm2) ± SE of virulent Pss phylogroup (PG) 2d strains 9, 25, 37, and 38 at 108 cfu/ml grown next to the commercial biological control agents (BCAs) Serenade® Opti (SO), Blossom Protect™ (BP) or water (H2O). The % decrease in growth for the strains in co-inoculated with the BCAs vs. H2O is shown. The letters indicate statistical differences of the treatments with significance determined by p-value ≤0.05. 201 Table 4.5. The 2021 ANOVA tables for co-inoculations of ten Pss strains (9, 14, 22, 23, 25, 26, 27, 34, 37, 38) from three phylogroups (PG) ranging in virulence level (PG 2d virulent, PG 2b moderate virulence, 2c avirulent) grown next to the commercial biological control agents (BCAs) BlightBan®A506 (BB), Bloomtime™ (BLT), Blossom Protect™ (BP), Double Nickel55™ (DN), Serenade® Opti (SO), or water (H2O) in Petri plates with KB media; “group” corresponds to the Pss strains that were inoculated next to each BCA or H2O as the 3 to 2 strains for each group were challenged by the BCA on separate occasions. Year group strains/PG Model term df 1 df 2 F-ratio p-value 2021 1 2 3 4 14 2c 25 2d 38 2d 23 2b 34 2b 22 2b 26 2d 37 2d rate trt strain rate*trt rate*strain trt*strain rate*trt*strain rate trt strain rate*trt rate*strain trt*strain rate*trt*strain rate trt strain rate*trt rate*strain trt*strain rate*trt*strain 9 2d 27 2d rate trt strain trt*strain 2 5 2 10 4 10 20 2 5 1 10 2 5 10 2 5 2 10 4 10 20 2 5 1 5 199 199 199 199 199 199 199 119 119 119 119 119 119 119 189 189 189 189 189 189 189 144 144 144 144 3.995 1.066 2.006 1.074 1.688 0.675 1.031 1.123 5.353 6.557 1.914 0.813 4.204 1.12 0.0199 0.3802 0.1372 0.3841 0.1543 0.7467 0.4270 0.3288 0.0002 0.0117 0.0496 0.4459 0.0015 0.3533 5.103 0.0069 0.3058 1.210 15.802 <0.0001 0.2483 1.273 0.4490 0.928 0.0297 2.060 0.4265 1.032 0.0200 4.022 2.003 0.0816 19.039 <0.0001 0.4845 0.898 202 Figure 4.3. Group 1 2021, mean total area of growth (mm2 ± SE) of Pss strains 14, 25, and 38, belonging to phylogroup (PG) 2c, 2d, and 2d, respectively, co-inoculated at 108 cfu/ml next to the five biological control agents (BCAs; BlightBan®A506, Bloomtime™, Blossom Protect™, Double Nickel55™, Serenade® Opti) or H2O at the rates 0.5x, 1.0x, and 1.5x. Letters indicate statistical differences in Pss growth by BCA rate with significance determined by p-value ≤0.05. 203 Figure 4.4. Group 2 (2021) Pss strains 23 and 34, belonging to phylogroup (PG) 2b ± SE co- inoculated at 108 cfu/ml next to the five biological control agents [BCAs; BlightBan®A506 (BB), Bloomtime™ (BLT), Blossom Protect™ (BP), Double Nickel55™ (DN), Serenade® Opti (SO)] or H2O at the rates 0.5x, 1.0x, and 1.5x. A) mean total area of growth (mm2 ± SE) of Pss by BCA (trt), B) mean total area of growth (mm2 ± SE) by Pss strain overall when next to each BCA or H2O, C) mean total area of growth (mm2 ± SE) of Pss by BCA(trt) and BCA rate (0.5x, 1.0x, 1.5x) (rate*trt )and D) mean total area of growth (mm2) of Pss next to each BCA (trt*strain). Letters indicate statistical differences in Pss growth, with significance determined by p-value ≤0.05 and the % decrease in growth of Pss next to each BCA vs. H2O for trt (BCA), rate*trt, and trt*strain. 204 Figure 4.5. Group 3 (2021) Pss strains 22, 26, and 37, belonging to phylogroup (PG) 2b, 2d, and 2d, respectively, co-inoculated at 108 cfu/ml next to the five biological control agents [BCAs; BlightBan®A506 (BB), Bloomtime™ (BLT), Blossom Protect™ (BP), Double Nickel55™ (DN), Serenade® Opti (SO)] or H2O at the rates 0.5x, 1.0x, and 1.5x. A) mean total area of growth (mm2 ± SE) of Pss by BCA rate, B) mean total area of growth (mm2 ± SE) by Pss strain overall when next to each BCA or H2O, and C) mean total area of growth (mm2 ± SE) of each Pss strain next to each BCA(trt) (trt*strain). Letters indicate statistical differences in Pss growth, with significance determined by p-value ≤0.05 and the % decrease in growth of Pss next to each BCA vs. H2O for trt*strain. 205 Figure 4.6. Group 4 (2021) Pss strains 9 and 27, belonging to phylogroup (PG) 2d ± SE co- inoculated at 108 cfu/ml next to the five biological control agents [BCAs; BlightBan®A506, Bloomtime™, Blossom Protect™, Double Nickel55™, Serenade® Opti] or H2O at the rates 0.5x, 1.0x, and 1.5x. A) mean total area of growth (mm2 ± SE) of Pss by BCA rate, and B) mean total area of growth (mm2 ± SE) by Pss strains overall when next to each BCA or H2O. Letters indicate statistical differences in Pss growth with significance determined by p-value ≤0.05. 206 Table 4.6. The 2021 ANOVA tables for co-inoculations of ten Pss strains (9, 14, 22, 23, 25, 26, 27, 34, 37, 38) from three phylogroups (PG) ranging in virulence level (PG 2d virulent, PG 2b moderate virulence, 2c avirulent) grown next to the commercial biological control agent (BCA), Blossom Protect™ (BP) or water (H2O) in Petri plates with KB or PDA media; “group” corresponds to the Pss strains that were inoculated next to each BCA or H2O as the 3 to 2 strains for each group were challenged by the BCA on separate occasions. Year group strains/PG Model term df 2 F-ratio p-value df 1 2021 1 14 2c 25 2d 38 2d 2 23 2b 34 2b media rate trt strain media*rate media*trt media*strain rate*trt rate*strain trt*strain media*rate*trt media*rate*strain media*trt*strain rate*trt*strain media*rate*trt*strain media rate trt strain media*rate media*trt media*strain rate*trt rate*strain trt*strain media*rate*trt media*rate*strain media*trt*strain rate*trt*strain media*rate*trt*strain 3 22 2b 26 2d 37 2d media rate trt strain media*rate 207 1 2 1 2 2 1 2 2 4 2 2 4 2 4 4 1 2 1 1 2 1 1 2 2 1 2 2 1 2 2 1 2 1 2 2 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 93 93 93 93 93 93 93 93 93 93 93 93 93 93 93 1.134 3.025 3.522 3.104 0.152 1.263 0.183 0.854 1.291 0.597 2.991 1.077 0.808 0.485 3.302 0.2888 0.0518 0.0627 0.0480 0.8591 0.2631 0.8333 0.4280 0.2767 0.5518 0.0535 0.3703 0.4477 0.7466 0.0128 36.438 <0.0001 0.0200 4.079 0.0110 6.728 0.1117 2.578 0.0270 3.755 0.0574 3.701 0.0034 9.064 0.2872 1.264 0.2565 1.381 0.0354 4.560 0.3468 1.071 0.1821 1.735 0.0027 9.518 0.8693 0.140 0.1686 1.815 126 126 126 126 126 0.183 7.021 0.708 9.185 0.249 0.6693 0.0013 0.4016 0.0002 0.7802 Table 4.6. (cont’d) 4 9 2d 27 2d media*trt media*strain rate*trt rate*strain trt*strain media*rate*trt media*rate*strain media*trt*strain rate*trt*strain media*rate*trt*strain media rate trt strain media*rate media*trt media*strain rate*trt rate*strain trt*strain media*rate*trt media*rate*strain media*trt*strain rate*trt*strain media*rate*trt*strain 1 2 2 4 2 2 4 2 4 4 1 2 1 1 2 1 1 2 2 1 2 2 1 2 2 126 126 126 126 126 126 126 126 126 126 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 1.127 1.841 1.497 1.037 2.232 0.896 1.460 1.396 1.228 0.152 0.455 1.626 1.428 2.746 9.652 4.282 3.641 0.272 6.877 0.015 1.198 1.223 0.102 1.656 0.614 0.2904 0.1630 0.2277 0.3909 0.1116 0.4108 0.2182 0.2513 0.3022 0.9616 0.5015 0.2021 0.2351 0.1009 0.0002 0.0413 0.0594 0.7621 0.0016 0.9043 0.3063 0.2991 0.7501 0.1965 0.5434 208 Figure 4.7. Group 1 (2021) Pss strains 14, 25, and 38 belonging to phylogroup (PG) 2c, 2d, and 2d, respectively, co-inoculated at 108 cfu/ml next to Blossom Protect™ (BP) or H2O on KB or PDA media at the rates 0.5x, 1.0x, and 1.5x. A) mean total area of growth (mm2 ± SE) of Pss strains overall, B) the mean total area of growth (mm2 ± SE) of each Pss strain next to each BCA(trt) and rate nested within media (rate*trt*strain|media). Letters indicate statistical differences in Pss growth, with significance determined by p-value ≤0.05 and the % decrease in growth of Pss next to each BP vs. H2O for rate*trt*strain|media. 209 Figure 4.8. Group 2 (2021) Pss strains 23 and 34, belonging to phylogroup (PG) 2b co- inoculated at 108 cfu/ml next to the biological control agent (BCA) Blossom Protect™ (BP) or H2O at the rates 0.5x, 1.0x, and 1.5x on KB or PDA media. A) mean total area of growth (mm2 ± SE) of Pss when grown next to BP or H2O on KB or PDA media, B) mean total area of growth (mm2 ± SE) of Pss by BP rate (0.5x, 1.0x, 1.5x), and C) mean total area of growth (mm2 ± SE) by Pss strain next to BP or H2O. Letters indicate statistical differences in Pss growth with significance determined by p-value ≤0.05 and the % decrease in growth of Pss next to BP vs. H2O. 210 Figure 4.9. Group 2 (2021) Pss strains 23 and 34, belonging to phylogroup (PG) 2b co- inoculated at 108 cfu/ml next to the biological control agent (BCA) Blossom Protect™ (BP) or H2O at the rates 0.5x, 1.0x, and 1.5x on KB or PDA media. A) mean total area of growth (mm2 ± SE) of Pss when grown next to BP or H2O at the various rates on each media (media*rate), B) mean total area of growth (mm2 ± SE) of each Pss strain on each media (media*strain), C) mean total area of growth (mm2 ± SE) by Pss strain next to BP or H2O (trt*strain), and D) mean total area of growth (mm2 ± SE) by Pss strain and media type next to BP or H2O (media*trt*strain). Letters indicate statistical differences in Pss growth, with significance determined by p-value ≤0.05 and the % decrease in growth of Pss next to BP vs. H2O for trt*strain and (media*trt*strain). 211 Figure 4.10. Group 3 (2021) Pss strains 22, 26, and 37, belonging to phylogroup (PG) 2b, 2d, and 2d, respectively, co-inoculated at 108 cfu/ml next to Blossom Protect™ (BP) or H2O at the rates 0.5x, 1.0x, and 1.5x on KB and PDA media. A) mean total area of growth (mm2 ± SE) of Pss by BP rate, B) mean total area of growth (mm2 ± SE) by Pss strain overall when next to BP or H2O. Letters indicate statistical differences in Pss growth, with significance determined by p- value ≤0.05. 212 Figure 4.11. Group 4 (2021) Pss strains 9 and 27, belonging to phylogroup (PG) 2d co- inoculated at 108 cfu/ml next to Blossom Protect™ (BP) or H2O at the rates 0.5x, 1.0x, and 1.5x on KB and PDA media. A) mean total area of growth (mm2 ± SE) of Pss for each media by BP rate (media*rate), and B) mean total area of growth (mm2 ± SE) by Pss strains overall at each BP rate (rate*strain). Letters indicate statistical differences in Pss growth, with significance determined by p-value ≤0.05. 213 Figure 4.12. Group 4 (2021) Pss strains 9 and 27, belonging to phylogroup (PG) 2d co- inoculated at 108 cfu/ml next to Blossom Protect™ (BP) or H2O at the rates 0.5x, 1.0x, and 1.5x on KB and PDA media: mean total area of growth (mm2 ± SE) of Pss next to BP or H2O for each media by BP rate (media*trt|rate). Letters indicate statistical differences in Pss growth, with significance determined by p-value ≤0.05 and the % decrease in growth of Pss strains next to BP vs. H2O. 214 Table 4.7. The 2022 ANOVA tables for co-inoculations of Pss strains 9, 25 (PG 2d virulent) and 23, 33 (PG2b moderate virulence) grown next to the strains 14, 18 (PG 2c avirulent), and 23,33 (PG2b moderate virulence) or water (H2O) in Petri plates with KB media; “group” corresponds to the rate combination at which the PG2d and PG2b strains were inoculated next to the PG2c and PG2b strains or H2O, for which group 1 strains were inoculated at 108 cfu/ml, group 2 strains at 106 v. 108 cfu/ml, and group 3 strains at 104 v. 108 cfu/ml. Year group strains/PG 2022 1 2 3 9 (2d) v. 33 (2b) /18 (2c) or H2O 25 (2d) v. 23 (2b) /14 (2c) or H2O 23 (2b) v. 14 (2c) or H2O 33 (2b) v. 18 (2c) or H2O 9 (2d) v. 33 (2b) /18 (2c) or H2O 25 (2d) v. 23 (2b) /14 (2c) or H2O 23 (2b) v. 14 (2c) or H2O 33 (2b) v. 18 (2c) or H2O 9 (2d) v. 33 (2b) /18 (2c) or H2O 25 (2d) v. 23 (2b) /14 (2c) or H2O 23 (2b) v. 14 (2c) or H2O 33 (2b) v. 18 (2c) or H2O Rate (cfu/ml) 108 v. 108 106 v. 108 104 v. 108 Model term df 1 df 2 F-ratio p-value strain trt strain trt strain trt 3 4 3 4 3 4 41 41 42 42 42 42 20.525 0.689 <0.0001 0.6038 22.128 2.584 <0.0001 0.0507 2.467 2.669 0.0753 0.0452 215 Figure 4.13. Group 1 (2022) co-inoculations of Pss strains 9, 25 (PG 2d virulent) or 23, 33 (PG2b moderate virulence) grown next to the treatment strains 14, 18 (PG 2c avirulent), or 23,33 (PG2b moderate virulence) or water (H2O) in Petri plates with KB media, in which all strains were inoculated at 108 cfu/ml. A) distribution of the total area of growth (mm2 ± SE) for each strain next to each treatment Pss (Pss trt), and B) mean total area of growth (mm2 ± SE) for the strains overall. Letters indicate statistical differences, with significance determined by p-value ≤0.05. 216 Figure 4.14. Group 2 (2022) co-inoculations of Pss strains 9, 25 (PG 2d virulent) or 23, 33 (PG2b moderate virulence) at 106 cfu/ml that are grown next to the treatment strains 14, 18 (PG 2c avirulent), or 23, 33 (PG2b moderate virulence) at 108 cfu/ml, or water (H2O) in Petri plates with KB media. A) distribution of the total area of growth (mm2 ± SE) for each strain next to each treatment Pss (Pss trt), and B) mean total area of growth (mm2 ± SE) for the strains overall, and C) mean total area of growth (mm2 ± SE) of the Pss strains 9, 25, or 23, 33 according to the treatment Pss. 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Site cv./Year Sweetheart 2019/2020 1 Coral Champagne 2019/2020 2 Sweetheart 2021 Branch no./location Row/Tree 1/16 1/40 2/18 2/48 3/14 4/28 1/18 1/36 2/14 2/30 3/16 3/30 4/3 3/4 4/4 5/24 6/23 7/9 8/10 9/4 10/4 12/23 12/24 1 2 3 4 23/4 21/5 18/1 18/2 15/2 16/3 21/4 17/2 ctrl 27/5 ctrl 15/3 ctrl 21/3 9/3 ctrl ctrl 30/2 ctrl 23/2 22/1 ctrl ctrl 21/2 26/2 ctrl 27/1 38/2 9/5 ctrl 17/3 17/4 17/5 9/1 34/1 ctrl ctrl 33/3 ctrl 34/5 bas. mid. dist. bas. mid. dist. bas. mid. dist. bas. mid. dist. Strain no./Rep 9/4 13/5 14/4 15/4 ctrl 18/4 33/5 26/4 ctrl 38/4 37/4 38/3 ctrl ctrl 15/4 15/2 25/4 34/3 38/4 ctrl 22/4 14/2 16/1 22/4 23/5 14/5 15/5 16/5 18/5 ctrl 25/5 27/4 34/4 ctrl 33/4 32/4 18/3 18/1 ctrl 13/4 29/4 ctrl 22/1 9/2 32/3 13/1 22/2 13/4 9/2 22/3 ctrl 16/2 26/3 ctrl 30/3 32/3 ctrl 32/2 29/2 18/2 33/3 29/2 29/3 ctrl 9/1 ctrl 21/3 ctrl 25/5 14/3 ctrl 13/3 ctrl 16/4 18/3 25/4 37/3 27/3 ctrl 30/4 25/3 29/3 9/5 27/2 34/1 ctrl 15/5 29/1 38/2 ctrl 16/5 22/3 13/1 ctrl 21/1 13/2 14/2 ctrl 27/2 34/2 33/2 ctrl 37/2 ctrl ctrl 26/1 ctrl ctrl ctrl 37/2 34/2 ctrl ctrl 22/5 ctrl 23/1 17/1 ctrl 15/1 14/1 16/1 30/1 25/1 ctrl 37/1 29/1 32/1 33/1 ctrl 26/5 34/4 38/1 21/1 9/4 33/5 14/5 13/2 ctrl ctrl 23/3 ctrl ctrl 22/5 ctrl 37/5 32/5 38/5 26/5 30/5 ctrl 29/5 21/4 ctrl 13/3 23/5 23/3 ctrl 38/1 26/1 37/1 ctrl 32/5 22/2 27/1 27/3 37/5 32/2 ctrl 16/2 ctrl 38/3 30/3 ctrl 23/2 ctrl 27/5 32/4 33/4 30/1 14/4 37/4 29/5 25/1 ctrl 15/3 14/3 17/4 34/5 16/4 ctrl ctrl 33/2 25/3 17/3 ctrl ctrl 9/3 32/1 ctrl 17/5 13/5 25/2 23/4 18/5 ctrl 29/4 ctrl 15/1 33/1 26/4 25/2 34/3 ctrl 224 Table A1. (cont’d) Site cv./Year 4 Coral Champagne 2021 13/10 14/8 Branch no./location Row/Tree 1/5 1/6 1/7 1/16 1/17 1/25 1/26 1/27 2/2 2/3 2/16 2/17 2/24 3/8 3/9 3/10 3/18 3/19 3/29 3/30 23/1 14/1 21/2 17/1 38/5 30/2 1 (North) bas. mid. mid. dist. 27/4 ctrl ctrl 37/3 ctrl 21/5 16/3 26/2 17/2 30/5 2 (South) bas. mid. mid. dist. ctrl 26/3 ctrl ctrl 30/4 18/4 Strain no./Rep 32/3 ctrl ctrl 34/4 27/1 ctrl 38/3 17/1 ctrl 15/1 30/5 30/1 14/2 ctrl 26/2 18/2 ctrl 17/5 ctrl ctrl 17/2 ctrl 26/4 26/5 18/4 ctrl 29/5 18/3 34/2 ctrl 33/3 9/1 15/5 16/4 ctrl 27/2 37/1 ctrl 29/2 25/1 ctrl 37/3 ctrl 9/5 26/3 21/5 38/2 ctrl 16/3 ctrl 17/3 13/2 ctrl 14/3 27/4 22/2 17/4 18/1 9/3 26/1 25/4 38/5 ctrl ctrl 16/1 34/1 34/3 ctrl 21/4 ctrl 30/3 33/2 ctrl 21/1 30/2 38/1 ctrl ctrl 13/4 32/2 ctrl 33/5 23/1 ctrl 13/3 ctrl 23/2 22/1 33/1 ctrl ctrl 22/4 ctrl ctrl 18/5 25/2 9/2 ctrl 29/3 ctrl ctrl 27/3 14/4 22/5 ctrl ctrl 23/4 34/5 ctrl ctrl 15/4 21/2 38/4 29/1 ctrl ctrl 37/2 37/5 30/4 ctrl 37/4 13/1 ctrl ctrl ctrl ctrl ctrl 27/5 14/5 ctrl ctrl ctrl ctrl 15/2 ctrl 21/3 32/4 16/5 ctrl 22/3 ctrl 23/5 33/4 ctrl 32/1 32/5 16/2 ctrl 9/4 25/3 ctrl ctrl 25/5 23/3 29/4 ctrl 13/5 14/1 15/3 ctrl 225 Figure A1. Gel images showing bands for the amplified syrD gene (present in P.s spp.) for the 39 oxidase− putative Pseudomonas syringae pv. syringae (Pss) strains that were isolated from sweet cherry flowers. Strains with the syrD gene and possible Pss are highlighted by arrows; water controls and known Pss isolates are included (B-D). A) the New England BioLabs® (NEB) 100 bp DNA ladder included in the gel electrophoresis runs, denoting that the DNA fragment amplified for syrD is 1040 bp, as indicated by the white lines on the gel images (B-D); B) the oxidase− strains no.1-18 along with the known Pss strains 6491, 1680, and 847; C) the oxidase− strains 19-35 along with known Pss strains 13-7, 6-9, 19-6, and 26-3; D) the oxidase− strains 36- 39 along with the known Pss strain FF5-1. 226 Figure A2. Gel images showing bands for the amplified syrB gene (present in P.s spp.) for the 20 oxidase−/syrD+ strains. Strains with the syrB gene are highlighted by arrows; water controls are included (B-C). A) The GoldBio® 100 bp DNA ladder included in the gel electrophoresis runs, denoting that the DNA fragment amplified for syrB is 752 bp, as indicated by the white lines on the gel images (B-C); B) the 20 oxidase−/syrD+ strains no. 9-38; C) the gel from the repeated PCR reaction of strains 9-38 to verify weaker gel bands seen in image B. 227 Table A2. Results of the Dunn’s all pairs test for the green fruit lesion area data for the combined years 2019 and 2021, where Pseudomonas syringae strains were collapsed by phylogroup and Psm 627 and PBS are included as controls. Letters a, b, and c indicate overall significant differences (p<0.05) in virulence based on Bonferroni adjusted p-values. Test comparison p-value phylogroup letter Dunn’s all pairs 2b - 2d 2c - 2b 2c - 2d Psm - 2b Psm - 2c Psm - 2d PBS - 2b PBS - 2c PBS - 2d PBS - Psm 0.0398 1.96e-13 3.79e-29 1.0000 0.0008 0.8670 0.0010 1.0000 2.59e-07 0.0325 2d 2b 2c Psm627 PBS a b c ab c 228 Table A3. Model fitting for the cumulative links model (clm) ordinal regression for the virulence ratings of Pseudomonas syringae strains (collapsed by phylogroup) that were inoculated on sweet cherry wood of cultivars Coral Champagne (Coral) and Sweetheart, assessed in 2020 and 2021, where the lower AIC and p-values are the best-fit models used in further analysis. Year Model *rating ~ 1 rating ~ phylogroup rating ~ phylogroup + time rating ~ phylogroup + branchloc rating ~ phylogroup + branchno rating ~ phylogroup + time + branchno rating ~ phylogroup + time + branchno + branchloc *rating ~ 1 rating ~ phylogroup rating ~ phylogroup + branchloc Cultivar Coral Sweetheart Coral Sweetheart Coral Sweetheart Coral Sweetheart Coral Sweetheart Coral Sweetheart Coral Sweetheart Coral Sweetheart Coral Sweetheart Coral Sweetheart no. par 3 6 7 8 9 10 12 3 6 7 8 AIC 169.51 102.59 128.54 71.94 130.14 73.25 131.01 75.45 132.16 74.94 133.82 76.28 136.89 79.33 224.28 152.33 135.24 133.46 137.18 135.89 2020 Log Likelihood -81.75 -48.29 -58.27 -29.97 -58.07 -29.63 -57.51 -29.72 -57.08 -28.47 -56.91 -28.14 -56.44 -27.67 2021 -109.14 -73.17 -61.62 -60.73 -61.59 -59.95 Log Ratio df p-value 46.97 36.65 0.39 0.69 1.13 -0.20 0.85 2.51 0.34 0.66 0.93 0.95 95.04 24.87 0.06 1.57 3 1 1 1 1 2 3 1 2 3.53e-10 5.45e-8 0.5298 0.4075 0.2882 1.0000 0.3556 0.1135 0.5585 0.4161 0.6270 0.6218 <2.00e-16 1.64e-5 0.7998 0.4563 229 Table A3. (cont’d) rating ~ phylogroup + branchno rating ~ phylogroup + branchloc + branchno *Null model Coral Sweetheart Coral Sweetheart 8 9 9 11 138.17 139.18 140.09 141.68 -61.09 -60.59 -61.05 -59.84 1.00 -1.28 0.08 1.50 1 1 2 0.3163 1.0000 0.7732 0.4732 230 Table A4. Pairwise comparisons (contrast) of virulence ratings for the Pseudomonas syringae strains collapsed by phylogroups (2b, 2d, 2c) and the control (ctrl), that were inoculated on the cultivars Coral Champagne and Sweetheart in 2020 and 2021, using estimated marginal means (lsmeans) analysis, following clm ordinal regression (rating ~ phylogroup). The group letters a and b indicate overall significant differences (p<0.05) in virulence based on adjusted Tukey p-values. Cultivar/Year Coral Champagne 2020 Contrast estimate SE df z-ratio p-value phylogroup lsmean SE df 2b-2c 2b-2d 2b-ctrl 2c-2d 2c-ctrl 2d-ctrl 2b-2c 2b-2d 2b-ctrl 2c-2d 2c-ctrl 2d-ctrl 2b-2c 2b-2d 2b-ctrl 2c-2d 2c-ctrl 2d-ctrl 0.35 -2.78 2.39 -3.13 2.04 5.17 3.56 -2.26 3.55 -5.82 -0.02 5.81 2.56 -3.01 2.84 -5.57 0.28 5.85 0.86 0.75 1.16 0.85 1.23 1.16 0.99 1.29 1.22 1.42 1.26 1.59 1.11 0.65 1.11 1.11 1.43 1.11 Inf Inf Inf 0.41 -3.70 2.06 -3.68 1.66 4.45 3.61 -1.76 2.90 -4.10 -0.01 3.65 2.30 -4.67 2.56 -5.00 0.20 5.26 0.9771 0.0012 0.1671 0.0013 0.3434 0.0001 0.0017 0.2937 0.0197 0.0002 1.0000 0.0015 0.0975 <0.0001 0.0514 <0.0001 0.9974 <0.0001 2d 2b 2c ctrl 0.92 -1.86 -2.22 -4.25 0.39 0.60 0.72 1.06 Sweetheart 2020 2d 2b 2c ctrl 2.27 0.01 -3.55 -3.53 1.14 0.61 0.78 1.06 Coral Champagne 2021 2d 2b 2c ctrl 1.25 -1.76 -4.32 -4.60 0.40 0.47 1.02 1.02 Inf Inf Inf Asymp. LCL 0.16 -3.03 -3.62 -6.34 Asymp. UCL 1.67 -0.70 -0.81 -2.17 0.05 -1.19 -5.07 -5.61 0.48 -2.69 -6.32 -6.59 4.50 1.21 -2.03 -1.45 2.03 -0.83 -2.32 -2.60 group a b b b a a b b a b b b 231 Table A4. (cont’d) 2b-2c 2b-2d 2b-ctrl 2c-2d 2c-ctrl 2d-ctrl 0.63 -1.86 1.54 -2.49 0.91 3.39 0.86 0.72 1.19 0.70 1.18 1.08 Inf 0.74 -2.59 1.30 -3.54 0.77 3.14 0.8822 0.0470 0.5648 0.0023 0.8679 0.0091 Sweetheart 2021 2d 2b 2c ctrl -1.45 -3.31 -3.94 -4.84 0.42 0.68 0.66 1.06 Inf -2.27 -4.63 -5.24 -6.91 -0.63 -1.98 -2.64 -2.77 a b b b 232 Figure A3. Gel images showing the amplified DNA bands of four housekeeping genes used in the Multilocus sequence analysis (MLSA) with A) the GoldBio® 100 base pair (bp) DNA ladder with gene fragment size of each gene indicated, B) gap1 gene encoding for glyceraldehyde-3-phosphate dehydrogenase in glycolysis, C) gltA gene encoding for citrate synthase in the citric acid cycle, D) gyrB gene encoding for DNA gyrase B in replication, E) rpoD encoding for sigma factor 70 in transcription, and F) all four genes. 233 Figure A4. 2019 copper resistance assay plates two days after inoculation. A1) Pss 9, Pss 13 and Pss14 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media amended with 250 μg/ml CuSO4. A2) Pss 9, Pss 13 and Pss14 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media alone. B1) Pss 15, Pss 16 and Pss17 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media amended with 250 μg/ml CuSO4. B2) Pss 15, Pss 16 and Pss17 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media alone. C1) Pss 18, Pss 21 and Pss22 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media amended with 250 μg/ml CuSO4. C2) Pss 18, Pss 21 and Pss 22 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media alone. D1) Pss 23, Pss 25 and Pss 26 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media amended with 250 μg/ml CuSO4. D2) Pss 23, Pss 25 and Pss 26 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media alone. 234 Figure A5. 2019 copper resistance assay plates two days after inoculation. A1) Pss 27, Pss 29 and Pss 30 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media amended with 250 μg/ml CuSO4. A2: Pss 27, Pss 29 and Pss 30 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media alone. B1) Pss 32, Pss 33 and Pss34 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media amended with 250 μg/ml CuSO4. B2) Pss 32, Pss 33 and Pss34 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media alone. C1) Pss 37, Pss 38 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media amended with 250 μg/ml CuSO4. C2) Pss 37, Pss 38 (left), Cu resistant Pss strain (middle), and Cu susceptible Pss strain (right) on MG media alone. 235 Figure A6. 2020 copper resistance assay plates two days after inoculation. A1-A5) Cu resistant Pss strain (left), Pss 9 (middle) and Cu susceptible Pss strain (right) on MG media amended with 250 μg/ml CuSO4. B1-B5) Cu resistant Pss strain (left), Pss 9 (middle) and Cu susceptible Pss strain (right) on MG media without CuSO4. 236 Figure A7. 2019 green cherry fruit (‘Hedelfinger’) lesions four days after inoculation. A1-A3) Pss 9 inoculated in stab wound. B1-B3) Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 237 Figure A8. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 13 inoculated in stab wound, with no lesions for measuring in ImageJ. 238 Figure A9. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 14 inoculated in stab wound, with no lesions for measuring in ImageJ. 239 Figure A10. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 22 inoculated in stab wound. B1-B3)Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 240 Figure A11. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 23 inoculated in stab wound. B1-B3)Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 241 Figure A12. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 25 inoculated in stab wound. B1-B3)Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 242 Figure A13. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 26 inoculated in stab wound. B1-B3)Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 243 Figure A14. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 27 inoculated in stab wound. B1-B3)Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 244 Figure A15. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 32 inoculated in stab wound. B1-B3)Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 245 Figure A16. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 33 inoculated in stab wound. B1-B3)Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 246 Figure A17. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 34 inoculated in stab wound. B1-B3)Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 247 Figure A18. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 37 inoculated in stab wound. B1-B3)Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 248 Figure A19. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) Pss 38 inoculated in stab wound. B1-B3) Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 249 Figure A20. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3Psm inoculated stab wound. B1-B3) Images after threshold adjustment in ImageJ. C1-C3) Image of measured area. A1, B1, C1. Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3. 250 Figure A21. 2019 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A3) PBS (control) inoculated stab wound, with no lesions for measuring in ImageJ. 251 Figure A22. 2019 wound inoculations on ‘Sweetheart’ cherry wood with Pss 9. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. Images with NA are not available. 252 Figure A23. 2019 wound inoculations on ‘Sweetheart’ cherry wood with Pss 13. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 253 Figure A24. 2019 wound inoculations on ‘Sweetheart’ cherry wood with Pss 14. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. Images with NA are not available. 254 Figure A25. 2019 wound inoculations on ‘Sweetheart’ cherry wood with Pss 15. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 255 Figure A26. 2019 wound inoculations on ‘Sweetheart’ cherry wood with Pss 16. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. Images with NA are not available. 256 Figure A27. 2019 wound inoculations on ‘Sweetheart’ cherry wood with Pss 17. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 257 Figure A28. 2019 wound inoculations on ‘Sweetheart’ cherry wood with Pss 18. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 258 Figure A29. 2019 wound inoculations on ‘Sweetheart’ cherry wood with Pss 21. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 259 Figure A30. 2019 wound inoculations on ‘Sweetheart’ cherry wood with Pss 22. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. Images with NA are not available. 260 Figure A31. 2019 wound inoculations on ‘Sweetheart’ cherry wood with Pss 23. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 261 Figure A32. 2019 wound inoculations on ‘Coral’ cherry wood with Pss 25. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 262 Figure A33. 2019 wound inoculations on ‘Coral’ cherry wood with Pss 26. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 263 Figure A34. 2019 wound inoculations on ‘Coral’ cherry wood with Pss 27. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 264 Figure A35. 2019 wound inoculations on ‘Coral’ cherry wood with Pss 29. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 265 Figure A36. 2019 wound inoculations on ‘Coral’ cherry wood with Pss 30. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 266 Figure A37. 2019 wound inoculations on ‘Coral’ cherry wood with Pss 32. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 267 Figure A38. 2019 wound inoculations on ‘Coral’ cherry wood with Pss 33. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 268 Figure A39. 2019 wound inoculations on ‘Coral’ cherry wood with Pss 34. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 269 Figure A40. 2019 wound inoculations on ‘Coral’ cherry wood with Pss 37. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. Images with NA are not available. 270 Figure A41. 2019 wound inoculations on ‘Coral’ cherry wood with Pss 38. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by razor blade. A3- E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after being covered with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the summer. A6-E6) The inoculation site interior assessment in the spring of 2020. A1-A6. Replication 1. B1-B6. Replication 2. C1-C6. Replication 3. D1-D6. Replication 4. E1-E6. Replication 5. 271 Figure A42. 2019 wound inoculations on ‘Sweetheart’ cherry wood with PBS. A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by razor blade. A3-F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after being covered with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the summer. A6-F6) The inoculation site interior assessment in the spring of 2020. Images with NA are not available. 272 Figure A43. 2019 wound inoculations on ‘Sweetheart’ cherry wood with PBS. A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by razor blade. A3- F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after being covered with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the summer. A6-F6) The inoculation site interior assessment in the spring of 2020. 273 Figure A44. 2019 wound inoculations on ‘Sweetheart’ cherry wood with PBS. A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by razor blade. A3- F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after being covered with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the summer. A6-F6) The inoculation site interior assessment in the spring of 2020. Images with NA are not available. 274 Figure A45. 2019 wound inoculations on ‘Coral’ cherry wood with PBS. A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by razor blade. A3-F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after being covered with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the summer. A6-F6) The inoculation site interior assessment in the spring of 2020. 275 Figure A46. 2019 wound inoculations on ‘Coral’ cherry wood with PBS.A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by razor blade. A3-F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after being covered with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the summer. A6-F6) The inoculation site interior assessment in the spring of 2020. Images with NA are not available. 276 Figure A47. 2019 wound inoculations on ‘Coral’ cherry wood with PBS. A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by razor blade. A3-F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after being covered with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the summer. A6-F6) The inoculation site interior assessment in the spring of 2020. 277 Figure A48. 2019 wound inoculations on ‘Coral’ cherry wood with PBS. A1-C1) The inoculation site before wounding. A2-C2) The inoculation site after wounding by razor blade. A3-C3) The inoculation site after pipetting inoculum into the wound. A4-C4) The inoculation site after being covered with Parafilm and tape. A5-C5) The inoculation site exterior assessment in the summer. A6-C6) The inoculation site interior assessment in the spring of 2020. 278 Figure A49. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 9 inoculated in stab wound. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. 279 Figure A50. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 13 inoculated in stab wound, with no lesions for measuring in ImageJ. 280 Figure A51. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 14 inoculated in stab wound, with no lesions for measuring in ImageJ. 281 Figure A52. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 15 inoculated in stab wound, with no lesions for measuring in ImageJ. 282 Figure A53. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 16 inoculated in stab wound, with no lesions for measuring in ImageJ. 283 Figure A54. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 17 inoculated in stab wound, with no lesions for measuring in ImageJ. 284 Figure A55. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 18 inoculated in stab wound, with no lesions for measuring in ImageJ. 285 Figure A56. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 21 inoculated in stab wound, with no lesions for measuring in ImageJ. 286 Figure A57. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 22 inoculated in stab wound. B1- B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. 287 Figure A58. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 23 inoculated in stab wound. B1- B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. 288 Figure A59. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 25 inoculated in stab wound. B1- B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. 289 Figure A60. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 26 inoculated in stab wound. B1- B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. 290 Figure A61. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 27 inoculated in stab wound. B1- B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. 291 Figure A62. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 29 inoculated in stab wound, with no lesions for measuring in ImageJ. 292 Figure A63. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 30 inoculated in stab wound, with no lesions for measuring in ImageJ. 293 Figure A64. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 32 inoculated in stab wound. B1- B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. 294 Figure A65. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 33 inoculated in stab wound. B1- B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. 295 Figure A66. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 34 inoculated in stab wound. B1- B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. 296 Figure A67. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 37 inoculated in stab wound. B1- B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. 297 Figure A68. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Pss 38 inoculated in stab wound. B1- B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. 298 Figure A69. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) PBS inoculated in stab wound, with no lesions for measuring in ImageJ. 299 Figure A70. 2021 green cherry fruit (‘Hedelfinger’) lesion four days after inoculation. A1-A5) Psm inoculated in stab wound. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication4; A5, B5, C5: Replication 5. Images with NA are not available. 300 Figure A71. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 9. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 301 Figure A72. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 13. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 302 Figure A73. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 14. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. Images with NA are not available. 303 Figure A74. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 15. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 304 Figure A75. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 16. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 305 Figure A76. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 17. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. Images with NA are not available. 306 Figure A77. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 18. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 307 Figure A78. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 21. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 308 Figure A79. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 22. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 309 Figure A80. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 23. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 310 Figure A81. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 25. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 311 Figure A82. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 26. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. Images with NA are not available. 312 Figure A83. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 27. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 313 Figure A84. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 29. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 314 Figure A85. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 30. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 315 Figure A86. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 32. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 316 Figure A87. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 33. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 317 Figure A88. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 34. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 318 Figure A89. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 37. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 319 Figure A90. 2021 wound inoculation on ‘Sweetheart’ cherry wood with Pss 38. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 320 Figure A91. 2021 wound inoculation on ‘Sweetheart’ cherry wood with PBS. A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by sterile razor blade A3-F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after covering with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the spring. A6-F6) The inoculation site interior assessment in the spring. 321 Figure A92. 2021 wound inoculation on ‘Sweetheart’ cherry wood with PBS. A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by sterile razor blade A3-F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after covering with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the spring. A6-F6) The inoculation site interior assessment in the spring. 322 Figure A93. 2021 wound inoculation on ‘Sweetheart’ cherry wood with PBS. A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by sterile razor blade A3-F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after covering with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the spring. A6-F6) The inoculation site interior assessment in the spring. 323 Figure A94. 2021 wound inoculation on ‘Sweetheart’ cherry wood with PBS. A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by sterile razor blade A3-F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after covering with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the spring. A6-F6) The inoculation site interior assessment in the spring. 324 Figure A95. 2021 wound inoculation on ‘Sweetheart’ cherry wood with PBS. A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by sterile razor blade A3-F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after covering with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the spring. A6-F6) The inoculation site interior assessment in the spring. 325 Figure A96. 2021 wound inoculation on ‘Sweetheart’ cherry wood with PBS. A1-F1) The inoculation site before wounding. A2-F2) The inoculation site after wounding by sterile razor blade A3-F3) The inoculation site after pipetting inoculum into the wound. A4-F4) The inoculation site after covering with Parafilm and tape. A5-F5) The inoculation site exterior assessment in the spring. A6-F6) The inoculation site interior assessment in the spring. 326 Figure A97. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 9. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 327 Figure A98. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 13. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 328 Figure A99. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 14. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 329 Figure A100. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 15. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 330 Figure A101. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 16. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 331 Figure A102. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 17. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 332 Figure A103. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 18. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 333 Figure A104. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 21. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 334 Figure A105. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 22. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 335 Figure A106. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 23. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 336 Figure A107. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 25. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 337 Figure A108. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 26. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 338 Figure A109. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 27. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 339 Figure A110. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 29. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 340 Figure A111. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 30. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 341 Figure A112. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 32. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 342 Figure A113. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 33. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 343 Figure A114. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 34. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 344 Figure A115. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 37. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 345 Figure A116. 2021 wound inoculation on ‘Coral’ cherry wood with Pss 38. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. A1-A6: Replication 1. B1-B6: Replication 2. C1-C6: Replication 3. D1-D6: Replication 4. E1-E6: Replication 5. 346 Figure A117. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. 347 Figure A118. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. 348 Figure A119. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. 349 Figure A120. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. 350 Figure A121. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. 351 Figure A122. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. 352 Figure A123. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. 353 Figure A124. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. 354 Figure A125. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. Images with NA are not available. 355 Figure A126. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. 356 Figure A127. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. 357 Figure A128. 2021 wound inoculation on ‘Coral’ cherry wood with PBS. A1-E1) The inoculation site before wounding. A2-E2) The inoculation site after wounding by sterile razor blade A3-E3) The inoculation site after pipetting inoculum into the wound. A4-E4) The inoculation site after covering with Parafilm and tape. A5-E5) The inoculation site exterior assessment in the spring. A6-E6) The inoculation site interior assessment in the spring. 358 APPENDIX B: SWEET CHERRY MICROBIOME Figure B1. Map of the sweet cherries ‘Benton’ (4 trees), ‘Gold’ (3 trees), and ‘Sweetheart’ (4 trees), sampled for leaf microbiome analysis. Interplanted with 23 other cultivars (see key) in a completely randomized design in 4 rows, 50 trees/row, 1.2 m between trees, and 6.1 m alleywaysat site 1, Wells Orchards, Grand Rapids, MI (Ottawa county). 359 Figure B2. Map of the sweet cherries ‘Benton’ (4 trees), ‘Gold’ (3 trees), and ‘Sweetheart’ (4 trees), sampled for leaf microbiome analysis. Interplanted with 23 other cultivars (see key) in a completely randomized design in 4 rows, 49 trees/row, with 1.2 m between trees and 6.1 m alleyways at site 2, Michigan State University Horticulture Teaching and Research Center, Holt, MI, (Ingham county.), with asterisks indicating trees replaced due to mortality (see key). 360 Table B1. The sample numbers assigned to each leaf collected from the three sweet cherry cultivars (Benton, Gold, and Sweetheart) and controls (+ and −) for microbiome determination at the orchards in two regions of Michigan (site 1 and 2) with the corresponding row and tree number, in each season/year (Fall 2017, Spring 2018, Summer 2018, Fall 2018, Spring 2019, Summer 2019, and Fall 2019). Season/Year Site Row Tree Leaf sample nos. Cultivar Site Row Tree Sample nos. Cultivar Fall 2017 1 Spring 2018 2 1 1 1 2 2 2 3 3 3 4 4 1 1 1 2 2 2 3 3 3 4 4 4 31 40 5 8 48 6 14 48 4 28 4 31 40 5 8 48 6 14 48 4 28 1 - 4 5 - 8 9 - 12 13 -16 17-20 21-24 25-28 29-32 33-36 37-40 41-44 1 - 4 5 - 8 9 - 12 13 -16 17-20 21-24 25-28 29-32 33-36 37-40 41-44 Benton Gold Sweetheart Gold Benton Sweetheart Gold Sweetheart Benton Benton Sweetheart Benton Gold Sweetheart Gold Benton Sweetheart Gold Sweetheart Benton Benton Sweetheart 1 1 2 2 2 3 3 3 4 4 4 1 1 2 2 2 3 3 3 4 4 4 27 46 26 31 38 6 31 38 5 30 44 27 46 31 35 38 6 31 38 5 17 44 45-48 49-52 53-56 57-60 61-64 65-68 69-72 73-76 77-80 81-84 85-88 89 90 45-48 49-52 53-56 57-60 61-64 65-68 69-72 73-76 77-80 81-84 85-88 Benton Sweetheart Gold Benton Sweetheart Gold Sweetheart Benton Sweetheart Gold Benton + Ctrl “mock” − Ctrl Benton Sweetheart Benton Gold Sweetheart Gold Sweetheart Benton Sweetheart Gold Benton 361 Table B1. (cont’d) Summer 2018 1 Fall 2018 1 1 1 2 2 2 3 3 3 4 4 1 1 1 2 2 2 3 3 3 4 4 4 31 40 5 8 48 6 14 48 4 28 4 31 40 5 8 48 6 14 48 4 28 Benton Gold Sweetheart 1 - 4 5 - 8 9 - 12 13 -16 Gold 17-20 Benton 21-24 25-28 Gold 29-32 33-36 Benton 37-40 Benton 41-44 Sweetheart Sweetheart Sweetheart Benton Gold Sweetheart 1-4 5 - 8 9 - 12 13 -16 Gold 17-20 Benton 21-24 25-28 Gold 29-32 33-36 Benton 37-40 Benton 41-44 Sweetheart Sweetheart Sweetheart 1 1 2 2 2 3 3 4 4 4 1 1 2 2 2 3 3 4 4 4 27 46 31 35 38 6 38 5 17 44 27 46 31 35 38 6 38 5 17 44 2 362 89 90 + Ctrl “mock” − Ctrl Sweetheart Sweetheart 45-48 Benton 49-52 53-56 Benton 57-60 Gold 61-64 65-68 Gold 69-72 Benton 73-76 77-80 Gold 81-84 Benton − Ctrl + Ctrl “mock” Sweetheart 85 86 Sweetheart Sweetheart 45-48 Benton 49-52 53-56 Benton 57-60 Gold 61-64 65-68 Gold 69-72 Benton 73-76 77-80 Gold 81-84 Benton Sweetheart 85 86 + Ctrl “mock” − Ctrl Table B1. (cont’d) Spring 2019 1 Summer 2019 1 1 1 2 2 2 3 3 3 4 4 1 1 1 2 2 2 3 3 3 4 4 4 31 40 5 8 48 6 14 48 4 28 4 31 40 5 8 48 6 14 48 4 28 Benton Gold Sweetheart 1-4 5 - 8 9 - 12 13 -16 Gold 17-20 Benton 21-24 25-28 Gold 29-32 33-36 Benton 37-40 Benton 41-44 Sweetheart Sweetheart Sweetheart Benton Gold Sweetheart 1-4 5 - 8 9 - 12 13 -16 Gold 17-20 Benton 21-24 25-28 Gold 29-32 33-36 Benton 37-40 Benton 41-44 Sweetheart Sweetheart Sweetheart 1 1 2 2 2 3 3 4 4 4 1 1 2 2 2 3 3 4 4 4 27 46 31 35 38 6 38 5 17 44 27 46 31 35 38 6 38 5 17 44 2 363 Sweetheart 45-48 Benton 49-52 53-56 Benton 57-60 Gold 61-64 65-68 Gold 69-72 Benton 73-76 77-80 Gold 81-84 Benton Sweetheart Sweetheart 85 86 + Ctrl “mock” − Ctrl Sweetheart Sweetheart 45-48 Benton 49-52 53-56 Benton 57-60 Gold 61-64 65-68 Gold 69-72 Benton 73-76 77-80 Gold 81-84 Benton Sweetheart 85 86 + Ctrl “mock” − Ctrl Sweetheart 45-48 Benton 49-52 53-56 Benton 57-60 Gold 61-64 65-68 Gold 69-72 Benton 73-76 77-80 Gold 81-84 Benton Sweetheart Sweetheart 85 86 + Ctrl “mock” − Ctrl Table B1. (cont’d) Fall 2019 1 1 1 1 2 2 2 3 3 3 4 4 4 31 40 5 8 48 6 14 48 4 28 Benton Gold Sweetheart 1-4 5 - 8 9 - 12 13 -16 Gold 17-20 Benton 21-24 25-28 Gold 29-32 33-36 Benton 37-40 Benton 41-44 Sweetheart Sweetheart Sweetheart 2 1 1 2 2 2 3 3 4 4 4 27 46 31 35 38 6 38 5 17 44 364 Figure B3. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Fall 2017 from sweet cherry orchards in two regions of Michigan, A-C) where the arrows (↓) indicate the DNA samples that were sent for sequencing. The leaf sample numbers (above the bands) 1-44 are from orchard site one, numbers 45-88 are from orchard site two, and the controls (samples 89 and 90) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1, D) are some repeated amplifications of leaf sample DNA with the 100 bp DNA ladder (L) present. 365 Figure B4. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Spring 2018 from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-C) are from orchard site one with the DNA sample nos. (above the bands) 1-44 (missing combined samples 27 and 28) with the 100 bp DNA ladder (L) present in the first lane of each gel, and D-E) are from orchard site two with the DNA sample nos. 45-88 (missing combined samples 67, 68, 69, and 70), the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 89 and 90) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 366 Figure B5. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Summer 2018 from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-B) are from orchard site one with the DNA sample nos. (above the bands) 1-44 with the 100 bp DNA ladder (L) present in the first lane of each gel, and C-D) are from orchard site two with the DNA sample nos. 45-84, the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 86 and 85) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 367 Figure B6. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Fall 2018 from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-B) are from orchard site one with the DNA sample nos. (above the bands) 1-44 with the 100 bp DNA ladder (L) present in the first lane of each gel, and C-D) are from orchard site two with the DNA sample nos. 45-84 (missing spilled sample 58), the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 85 and 86) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 368 Figure B7. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Spring 2019 (technical Rep 1) from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-B) are from orchard site one with the DNA sample nos. (above the bands) 1-44 with the 100 bp DNA ladder (L) present in the first lane of each gel, and C-D) are from orchard site two with the DNA sample nos. 45-84 (missing combined samples 83 and 84), the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 85 and 86) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 369 Figure B8. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Spring 2019 (technical Rep 2) from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-B) are from orchard site one with the DNA sample nos. (above the bands) 1-44 with the 100 bp DNA ladder (L) present in the first lane of each gel, and C-D) are from orchard site two with the DNA sample nos. 45-84, the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 85 and 86) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 370 Figure B9. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Spring 2019 (technical Rep 3) from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-B) are from orchard site one with the DNA sample nos. (above the bands) 1-44 (missing combined samples 6 and 7) with the 100 bp DNA ladder (L) present in the first lane of each gel, and C-D) are from orchard site two with the DNA sample nos. 45-84, the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 85 and 86) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 371 Figure B10. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Summer 2019 (technical Rep 1) from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-B) are from orchard site one with the DNA sample nos. (above the bands) 1-44 with the 100 bp DNA ladder (L) present in the first lane of each gel, and C-D) are from orchard site two with the DNA sample nos. 45-84, the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 85 and 86) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 372 Figure B11. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Summer 2019 (technical Rep 2) from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-B) are from orchard site one with the DNA sample nos. (above the bands) 1-44 with the 100 bp DNA ladder (L) present in the first lane of each gel, and C-D) are from orchard site two with the DNA sample nos. 45-84, the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 85 and 86) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 373 Figure B12. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Summer 2019 (technical Rep 3) from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-B) are from orchard site one with the DNA sample nos. (above the bands) 1-44 with the 100 bp DNA ladder (L) present in the first lane of each gel, and C-D) are from orchard site two with the DNA sample nos. 45-84, the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 85 and 86) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 374 Figure B13. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Fall 2019 (technical Rep 1) from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-B) are from orchard site one with the DNA sample nos. (above the bands) 1-44 with the 100 bp DNA ladder (L) present in the first lane of each gel, and C-D) are from orchard site two with the DNA sample nos. 45-84 (missing combined samples 75 and 76), the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 85 and 86) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 375 Figure B14. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Fall 2019 (technical Rep 2) from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-B) are from orchard site one with the DNA sample nos. (above the bands) 1-44 (missing spilled sample 20) with the 100 bp DNA ladder (L) present in the first lane of each gel, and C-D) are from orchard site two with the DNA sample nos. 45-84, the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 85 and 86) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 376 Figure B15. Gel images showing the amplification of the bacteria 16 S rDNA from the leaf samples collected in Fall 2019 (technical Rep 3) from sweet cherry orchards in two regions of Michigan, where the arrows (↓) indicate the DNA samples that were sent for sequencing, A-B) are from orchard site one with the DNA sample nos. (above the bands) 1-44 with the 100 bp DNA ladder (L) present in the first lane of each gel, and C-D) are from orchard site two with the DNA sample nos. 45-84, the 100 bp DNA ladder (L) present in the first lane of each gel, and the controls (samples 85 and 86) are indicated with a + or – sign indicating the positive/“mock” control and negative/sans DNA control, respectively. The tree and cultivar identities of each sample no. are defined in Table B1. 377 A C B D Figure B16. The quantile-quantile plots (qqplot) of the residuals for graphical visualization of the distribution of the alpha diversity statistics data, A) bacteria plate counts (log cfu/cm2), B) the number of otus (no. otus), C) the Shannon diversity, and D) Shannon evenness indices. 378 Table B2. The mean log cfu/cm2 of leaf area ± SE of bacteria plate counts for each sampling time point, including the pairwise comparisons of the estimated marginal means with the confidence limits (lower and upper C.L.) and the p-value. Letters indicate statistical differences, with significance determined by p-value ≤ 0.05. Time 2017 Fall 2018 Spring 2018 Summer 2018 Fall 2019 Spring 2019 Summer 2019 Fall Mean log cfu/cm2 SE df lower C.L. upper C.L. 4.9 1.0 4.4 6.7 1.5 5.4 5.6 84 0.2 0.2 60 0.2 105 0.5 371 53 0.2 53 0.2 53 0.2 4.5 0.6 4.0 5.8 1.1 5.0 5.2 5.4 1.4 4.9 7.6 1.9 5.8 6.0 bc a b d a cd cd Contrast estimate SE df t-ratio p-value 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer -1.8 3.9 0.5 -0.7 3.4 -0.4 5.7 2.2 1.1 5.2 1.3 -3.4 -4.6 -0.5 -4.4 -1.2 2.9 -0.9 4.1 0.2 -3.8 0.5 444 0.3 443 0.3 441 0.3 440 0.3 440 0.3 441 0.5 444 0.5 444 0.5 443 0.5 443 0.5 443 0.3 436 0.3 434 0.3 434 0.3 435 0.3 432 0.3 432 0.3 432 0.3 427 0.3 427 0.3 427 -3.576 13.989 1.591 -2.424 12.346 -1.588 11.803 4.502 2.267 10.795 2.743 -11.819 -17.927 -2.066 -16.985 -4.054 10.194 -3.244 16.310 0.915 -15.344 0.0071 <0.0001 0.6881 0.1910 <0.0001 0.6901 <0.0001 0.0002 0.2627 <0.0001 0.0902 <0.0001 <0.0001 0.3751 <0.0001 0.0012 <0.0001 0.0215 <0.0001 0.9701 <0.0001 379 Table B3. The mean number of OTUs ± SE at each sampling time point including the pairwise comparisons of the estimated marginal means with the confidence limits (lower and upper C.L.) and the p-value. Letters indicate statistical differences, with significance determined by p-value ≤ 0.05. Time 2017 Fall 2018 Spring 2018 Summer 2018 Fall 2019 Spring 2019 Summer 2019 Fall Mean no. OTUs SE df lower C.L. upper C.L. 18.3 40.6 35.7 34.7 6.0 22.2 31.2 1.6 43 2.4 167 1.8 63 84 1.9 2.4 162 1.8 69 2.1 111 15.1 35.9 32.2 30.9 1.3 18.7 27.1 21.4 45.3 39.2 38.5 10.7 25.8 35.3 a b bc bc d a c contrast estimate SE df t-ratio p-value 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer -16.4 -22.4 -17.4 -12.9 12.3 -3.9 -6.0 -1.0 3.5 28.7 12.5 4.9 9.4 34.6 18.4 4.5 29.7 13.5 25.2 9.0 -16.2 2.3 371 2.7 364 2.2 371 2.5 369 2.7 368 2.2 370 2.9 360 2.4 363 2.7 365 2.9 366 2.5 363 2.9 360 3.0 363 3.2 363 2.9 359 2.5 364 2.8 367 2.3 362 3.0 371 2.6 364 2.8 365 -7.075 -8.234 -7.933 -5.273 4.568 -1.788 -2.042 -0.425 1.309 9.877 5.094 1.726 3.101 10.970 6.450 1.778 10.523 5.822 8.318 3.518 -5.769 <0.0001 <0.0001 <0.0001 <0.0001 0.0001 0.5573 0.3901 0.9995 0.8475 <0.0001 <0.0001 0.5988 0.0337 <0.0001 <0.0001 0.5637 <0.0001 <0.0001 <0.0001 0.0088 <0.0001 380 Table B4. The mean Shannon diversity index ± SE at each sampling time point, including the pairwise comparisons of the estimated marginal means with the confidence limits (lower and upper C.L.) and the p-value. Letters indicate statistical differences, with significance determined by p-value ≤ 0.05. Time 2017 Fall 2018 Spring 2018 Summer 2018 Fall 2019 Spring 2019 Summer 2019 Fall Mean Shannon diversity index SE df lower C.L. upper C.L. 1.4 2.4 1.6 1.8 0.3 1.4 1.8 0.1 81 0.1 255 0.1 111 0.1 143 0.1 246 0.1 127 0.1 187 1.3 2.1 1.4 1.6 0.1 1.2 1.6 1.6 2.6 1.8 1.9 0.6 1.6 2.0 ab c ab ab d b a contrast estimate SE df t-ratio p-value 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer -0.3 -0.9 -0.2 -0.4 1.1 0.0 -0.6 0.2 0.0 1.4 0.4 0.8 0.6 2.1 1.0 -0.2 1.3 0.2 1.5 0.4 -1.1 0.1 372 0.1 365 0.1 372 0.1 367 0.1 371 0.1 370 0.2 363 0.1 366 0.1 370 0.2 370 0.1 367 0.2 363 0.2 367 0.2 368 0.2 362 0.1 369 0.1 371 0.1 366 0.2 372 0.1 369 0.1 369 -2.691 -6.565 -1.423 -2.807 7.955 0.373 -3.975 1.273 -0.234 9.485 2.869 5.156 3.609 12.394 6.515 -1.464 8.661 1.691 9.321 2.996 -7.297 0.1033 <0.0001 0.7892 0.0768 <0.0001 0.9998 0.0016 0.8638 1.0000 <0.0001 0.0651 <0.0001 0.0064 <0.0001 <0.0001 0.7658 <0.0001 0.6227 <0.0001 0.0457 <0.0001 381 Table B5. The mean Shannon evenness index ± SE at each sampling time point, including the pairwise comparisons of the estimated marginal means with the confidence limits (lower and upper C.L.) and the p-value. Letters indicate statistical differences, with significance determined by p-value ≤ 0.05. Time 2017 Fall 2018 Spring 2018 Summer 2018 Fall 2019 Spring 2019 Summer 2019 Fall Mean Shannon evenness index SE df lower C.L. upper C.L. 0.5 0.6 0.4 0.5 0.2 0.4 0.5 0.02 0.03 0.02 0.02 0.03 0.02 0.03 83 257 112 145 248 130 190 0.5 0.6 0.4 0.4 0.1 0.4 0.5 0.5 0.7 0.5 0.5 0.2 0.5 0.6 a b a a c a ab contrast estimate SE df t-ratio p-value 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer 0.01 -0.15 0.05 -0.03 0.33 0.05 -0.15 0.04 -0.04 0.32 0.04 0.20 0.12 0.47 0.19 -0.08 0.28 0.00 0.35 0.08 -0.28 0.03 0.04 0.03 0.03 0.04 0.03 0.04 0.03 0.04 0.04 0.03 0.04 0.04 0.04 0.04 0.03 0.04 0.03 0.04 0.03 0.04 372 365 372 367 371 370 363 367 370 370 367 363 367 368 362 369 371 366 372 369 369 0.236 -4.044 1.724 -0.912 9.156 1.647 -3.944 1.322 -1.038 8.270 1.252 5.166 2.877 11.248 5.100 -2.353 7.369 -0.066 8.862 2.283 -7.449 1.0000 0.0012 0.6006 0.9706 <0.0001 0.6515 0.0018 0.8413 0.9449 <0.0001 0.8729 <0.0001 0.0638 <0.0001 <0.0001 0.2220 <0.0001 1.0000 <0.0001 0.2549 <0.0001 382 A B Figure B17. The quantile-quantile plots (qqplot) of the residuals for graphical visualization of the distribution of the relative abundance data for: A) the top 4 Phyla and the rare Phyla combined into “other” relative abundance, and B) the Pseudomonas and all “other” taxa relative abundances. 383 A D G B E C F Figure B18. The quantile-quantile plots (qqplot) of the residuals for graphical visualization of the distribution of the relative abundance data at the deepest taxonomic level (genus or family), in which taxa that had a relative abundance <0.03 were combined into “other”. For each time (season) A) Fall 2017, B) Spring 2018, C) Summer 2018, D) Fall 2018, E) Spring 2019, F) Summer 2019, and G) Fall 2019. 384 Figure B19. The quantile-quantile plot (qqplot) of the residuals for graphical visualization of the distribution of the relative abundance data of the core microbiome (genus and OTU). Includes the four core genera Pseudomonas Otu00001, Pseudomonas Otu00002, Pseudomonas Otu00003 and Sphingomonas Otu00004, with the other Pseudomonas combined and Sphingomonas combined and remaining additional taxa combined into “other”. Core taxa were selected via occurrence and relative abundance when they occurred in 50% of all samples and had a relative abundance of ≥ 0.01. 385 Table B6. The mean relative abundances of the top 4 Phyla and “other” rarer Phyla combined ± SE, with the confidence limits (lower and upper C.L.) and the p-value. Letters indicate statistical differences, superscripted letters following Phyla indicate significance within each time while upper-cased letters indicate statistical differences of each Phyla across time, with significance determined by p-value ≤ 0.05. Time Phylum Mean SE df 2017 Fall 2018 Spring Proteobacteria b Actinobacteria a Bacteroidetes a Firmicutes a Other a Proteobacteria d Firmicutes c Actinobacteria b Other a Bacteroidetes a 2018 Summer Proteobacteria c Actinobacteria b Bacteroidetes a Firmicutes a Other a Proteobacteria c Bacteroidetes b Actinobacteria a Firmicutes a Other a Proteobacteria b Firmicutes a Actinobacteria a Bacteroidetes a Other a 2019 Spring 2018 Fall 2019 Summer Proteobacteria c Bacteroidetes b Actinobacteria ab Firmicutes a Other a Proteobacteria c Bacteroidetes b Actinobacteria a Firmicutes a Other a 2019 Fall 0.95 0.03 0.01 0.01 0.00 0.67 0.16 0.09 0.04 0.04 0.84 0.11 0.02 0.02 0.01 0.88 0.08 0.03 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.90 0.07 0.03 0.00 0.00 0.90 0.08 0.01 0.00 0.00 1167 1167 1167 1167 1167 1725 1725 1725 1725 1725 1334 1334 1334 1334 1334 1475 1475 1475 1475 1475 1723 1723 1723 1723 1723 1418 1418 1418 1418 1418 1613 1613 1613 1613 1613 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 386 lower C.L. 0.94 0.02 -0.01 -0.01 -0.01 0.65 0.13 0.07 0.02 0.01 0.82 0.09 0.00 0.00 -0.01 0.86 0.06 0.01 -0.02 -0.02 0.97 -0.02 -0.02 -0.03 -0.03 0.88 0.05 0.01 -0.02 -0.02 0.88 0.06 -0.01 -0.02 -0.02 upper C.L. 0.97 0.05 0.02 0.02 0.02 0.70 0.18 0.12 0.07 0.06 0.86 0.13 0.04 0.04 0.03 0.90 0.10 0.05 0.02 0.02 1.02 0.03 0.03 0.03 0.03 0.92 0.08 0.05 0.02 0.02 0.92 0.11 0.04 0.02 0.02 D A A A A A B B A AB B B A A A BC B A A A E A A A A C B A A A C B A A A Table B7. The pairwise comparison of the estimated marginal means of relative abundances of the top 4 Phyla and “other” Phyla for each time point and for each Phyla across each time point with the p-values, with significance determined by p-value ≤ 0.05. Time 2017 Fall Contrast Actinobacteria v. Bacteroidetes Actinobacteria v. Firmicutes Actinobacteria v. Other Actinobacteria v. Proteobacteria Bacteroidetes v. Firmicutes Bacteroidetes v. Other Bacteroidetes v. Proteobacteria Firmicutes v. Other Firmicutes v. Proteobacteria Other v. Proteobacteria 2018 Spring Actinobacteria v. Bacteroidetes Actinobacteria v. Firmicutes Actinobacteria v. Other Actinobacteria v. Proteobacteria Bacteroidetes v. Firmicutes Bacteroidetes v. Other Bacteroidetes v. Proteobacteria Firmicutes v. Other Firmicutes v. Proteobacteria Other v. Proteobacteria 2018 Summer Actinobacteria v. Bacteroidetes Actinobacteria v. Firmicutes Actinobacteria v. Other Actinobacteria v. Proteobacteria Bacteroidetes v. Firmicutes Bacteroidetes v. Other Bacteroidetes v. Proteobacteria Firmicutes v. Other df 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 t-ratio 2.116 2.255 2.458 -79.182 0.139 0.342 -81.298 0.203 -81.437 -81.639 3.012 -3.803 2.837 -32.718 -6.814 -0.175 -35.730 6.640 -28.915 -35.555 6.695 6.866 7.537 -55.520 0.171 0.842 -62.215 0.671 p-value 0.2135 0.1602 0.1009 <0.0001 0.9999 0.9971 <0.0001 0.9996 <0.0001 <0.0001 0.0221 0.0014 0.0371 <0.0001 <0.0001 0.9998 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.9998 0.9175 <0.0001 0.9627 Estimate 0.02 0.03 0.03 -0.92 0.00 0.00 -0.95 0.00 -0.95 -0.95 0.05 -0.07 0.05 -0.58 -0.12 0.00 -0.63 0.12 -0.51 -0.63 0.09 0.09 0.10 -0.73 0.00 0.01 -0.82 0.01 SE 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 387 Table B7. (cont’d) 2018 Fall Firmicutes v. Proteobacteria Other v. Proteobacteria Actinobacteria v. Bacteroidetes Actinobacteria v. Firmicutes Actinobacteria v. Other Actinobacteria v. Proteobacteria Bacteroidetes v. Firmicutes Bacteroidetes v. Other Bacteroidetes v. Proteobacteria Firmicutes v. Other Firmicutes v. Proteobacteria Other v. Proteobacteria 2019 Spring Actinobacteria v. Bacteroidetes Actinobacteria v. Firmicutes Actinobacteria v. Other Actinobacteria v. Proteobacteria Bacteroidetes v. Firmicutes Bacteroidetes v. Other Bacteroidetes v. Proteobacteria Firmicutes v. Other Firmicutes v. Proteobacteria Other v. Proteobacteria 2019 Summer Actinobacteria v. Bacteroidetes Actinobacteria v. Firmicutes Actinobacteria v. Other Actinobacteria v. Proteobacteria Bacteroidetes v. Firmicutes Bacteroidetes v. Other Bacteroidetes v. Proteobacteria 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 -62.386 -63.057 -3.316 2.072 2.317 -58.304 5.388 5.633 -54.989 0.245 -60.377 -60.622 0.015 -0.016 0.019 -55.405 -0.031 0.004 -55.420 0.035 -55.389 -55.424 -2.546 2.171 2.312 -65.306 4.717 4.859 -62.760 <0.0001 <0.0001 0.0083 0.2325 0.1397 <0.0001 <0.0001 <0.0001 <0.0001 0.9992 <0.0001 <0.0001 1.0000 1.0000 1.0000 <0.0001 1.0000 1.0000 <0.0001 1.0000 <0.0001 <0.0001 0.0811 0.1912 0.1413 <0.0001 <0.0001 <0.0001 <0.0001 -0.83 -0.83 -0.05 0.03 0.03 -0.85 0.08 0.08 -0.80 0.00 -0.88 -0.88 0.00 0.00 0.00 -1.00 0.00 0.00 -1.00 0.00 -1.00 -1.00 -0.03 0.03 0.03 -0.87 0.06 0.06 -0.84 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 388 Table B7. (cont’d) 2019 Fall Firmicutes v. Other Firmicutes v. Proteobacteria Other v. Proteobacteria Actinobacteria v. Bacteroidetes Actinobacteria v. Firmicutes Actinobacteria v. Other Actinobacteria v. Proteobacteria Bacteroidetes v. Firmicutes Bacteroidetes v. Other Bacteroidetes v. Proteobacteria Firmicutes v. Other Firmicutes v. Proteobacteria Other v. Proteobacteria Phylum Contrast Actinobacteria 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 df 1881 1874 1881 1874 1880 1879 1872 1875 1879 1879 1876 1872 1876 1877 1871 1878 0.141 -67.477 -67.618 -4.442 0.884 0.915 -56.222 5.326 5.357 -51.780 0.031 -57.106 -57.137 t-ratio -0.172 -3.920 -6.198 1.208 2.057 0.046 -3.493 -5.396 1.253 2.043 0.203 -1.136 4.494 5.049 3.778 6.466 0.9999 <0.0001 <0.0001 0.0001 0.9028 0.8912 <0.0001 <0.0001 <0.0001 <0.0001 1.0000 <0.0001 <0.0001 p-value 1.0000 0.0018 <0.0001 0.8913 0.3792 1.0000 0.0088 <0.0001 0.8728 0.3876 1.0000 0.9170 0.0002 <0.0001 0.0031 <0.0001 0.00 -0.90 -0.90 -0.07 0.01 0.01 -0.88 0.08 0.08 -0.81 0.00 -0.90 -0.90 Estimate 0.00 -0.06 -0.08 0.02 0.03 0.00 -0.06 -0.08 0.02 0.03 0.00 -0.02 0.08 0.09 0.06 0.09 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 SE 0.01 0.02 0.01 0.01 0.02 0.01 0.02 0.01 0.02 0.02 0.01 0.02 0.02 0.02 0.02 0.01 389 Table B7. (cont’d) 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 0.11 0.08 0.01 -0.02 -0.03 -0.07 -0.03 -0.01 -0.08 0.01 -0.06 0.04 0.06 0.00 0.08 0.02 0.02 -0.05 0.04 -0.03 -0.06 0.02 -0.04 0.08 0.02 -0.06 0.00 -0.15 -0.01 0.00 Bacteroidetes Firmicutes 1880 1875 1879 1878 1878 1881 1874 1881 1874 1880 1879 1872 1875 1879 1879 1876 1872 1876 1877 1871 1878 1880 1875 1879 1878 1878 1881 1874 1881 1874 6.834 5.859 0.857 -1.108 -1.931 -5.695 -2.010 -1.072 -5.609 0.457 -4.630 2.728 4.431 -0.180 4.988 1.219 1.078 -2.794 2.083 -1.738 -4.424 1.279 -3.357 4.980 1.352 -4.080 0.119 -10.117 -1.021 0.317 <0.0001 <0.0001 0.9787 0.9259 0.4599 <0.0001 0.4084 0.9362 <0.0001 0.9993 0.0001 0.0920 0.0002 1.0000 <0.0001 0.8871 0.9345 0.0773 0.3634 0.5907 0.0002 0.8616 0.0141 <0.0001 0.8268 0.0009 1.0000 <0.0001 0.9494 0.9999 0.02 0.01 0.02 0.01 0.02 0.01 0.02 0.01 0.01 0.02 0.01 0.02 0.01 0.02 0.02 0.01 0.02 0.02 0.02 0.02 0.01 0.02 0.01 0.02 0.01 0.02 0.01 0.02 0.01 0.01 390 Table B7. (cont’d) Other 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 0.00 0.00 -0.15 -0.01 0.00 0.00 0.00 0.14 0.16 0.16 0.16 0.02 0.02 0.02 0.00 0.00 0.00 0.00 -0.04 -0.01 0.00 0.00 0.00 -0.04 -0.01 0.00 0.00 0.00 0.03 0.04 1880 1879 1872 1875 1879 1879 1876 1872 1876 1877 1871 1878 1880 1875 1879 1878 1878 1881 1874 1881 1874 1880 1879 1872 1875 1879 1879 1876 1872 1876 0.313 0.261 -9.469 -1.028 0.187 0.195 0.122 8.879 9.308 8.797 9.864 1.178 1.101 1.204 0.022 -0.077 -0.094 0.210 -2.478 -0.498 0.183 0.201 0.223 -2.465 -0.646 -0.016 0.017 0.002 1.979 2.366 0.9999 1.0000 <0.0001 0.9476 1.0000 1.0000 1.0000 <0.0001 <0.0001 <0.0001 <0.0001 0.9026 0.9279 0.8929 1.0000 1.0000 1.0000 1.0000 0.1678 0.9989 1.0000 1.0000 1.0000 0.1727 0.9952 1.0000 1.0000 1.0000 0.4284 0.2138 0.02 0.01 0.02 0.01 0.02 0.02 0.01 0.02 0.02 0.02 0.02 0.01 0.02 0.01 0.02 0.01 0.02 0.01 0.02 0.01 0.01 0.02 0.01 0.02 0.01 0.02 0.02 0.01 0.02 0.02 391 Table B7. (cont’d) 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer Proteobacteria 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer 1877 1871 1878 1880 1875 1879 1878 1878 1881 1874 1881 1874 1880 1879 1872 1875 1879 1879 1876 1872 1876 1877 1871 1878 1880 1875 1879 1878 1878 2.260 2.542 0.601 0.583 0.678 0.031 0.018 -0.016 5.537 18.525 8.789 3.901 -3.028 4.100 12.700 2.638 -1.245 -7.244 -1.546 -10.800 -13.375 -18.188 -14.446 -3.820 -9.797 -4.385 -5.890 -0.186 6.121 0.2644 0.1451 0.9968 0.9973 0.9938 1.0000 1.0000 1.0000 <0.0001 <0.0001 <0.0001 0.0019 0.0400 0.0008 <0.0001 0.1152 0.8764 <0.0001 0.7167 <0.0001 <0.0001 <0.0001 <0.0001 0.0026 <0.0001 0.0002 <0.0001 1.0000 <0.0001 0.04 0.04 0.01 0.01 0.01 0.00 0.00 0.00 0.07 0.28 0.11 0.05 -0.05 0.05 0.21 0.04 -0.02 -0.12 -0.02 -0.17 -0.23 -0.33 -0.23 -0.06 -0.16 -0.06 -0.10 0.00 0.10 0.02 0.02 0.01 0.02 0.01 0.02 0.01 0.02 0.01 0.02 0.01 0.01 0.02 0.01 0.02 0.01 0.02 0.02 0.01 0.02 0.02 0.02 0.02 0.01 0.02 0.01 0.02 0.01 0.02 392 Table B8. The mean relative abundances of Pseudomonas and all “other” taxa at the genus/family level ± SE, with the confidence limits (lower and upper C.L.). Superscripted letters following Pseudomonas or “other” indicate statistical differences within each time while upper- cased letters indicate statistical differences of each Pseudomonas or “other” across time, with significance determined by p-value ≤ 0.05. Time Genera Mean SE df 2017 Fall Pseudomonas b Other a 0.61 0.39 0.03 0.03 2018 Spring Other b Pseudomonas a 2018 Summer Pseudomonas b Other a 0.74 0.26 0.04 0.04 0.58 0.42 0.03 0.03 2018 Fall Pseudomonas a Other a 0.51 0.49 0.03 0.03 2019 Spring Pseudomonas b Other a 0.96 0.04 0.04 0.04 2019 Summer Pseudomonas b Other a 0.61 0.39 0.03 0.03 2019 Fall Other a Pseudomonas a 0.52 0.48 0.04 0.04 297 297 620 620 371 371 444 444 613 613 414 414 530 530 lower C.L. upper C.L. 0.55 0.34 0.65 0.18 0.52 0.35 0.44 0.42 0.66 B 0.45 B 0.83 C 0.35 A 0.65 B 0.48 B 0.58 B 0.56 B 0.87 -0.04 1.04 C 0.13 A 0.55 0.32 0.45 0.40 0.68 B 0.45 B 0.60 B 0.55 B 393 Table B9. The pairwise comparison of the estimated marginal means of relative abundances of Pseudomonas and “other” taxa at the genus or family level for each time point and for each taxon across each time point with the p-values, with significance determined by p-value ≤ 0.05. Time Contrast Estimate SE 2017 Fall Other v. Pseudomonas 2018 Spring Other v. Pseudomonas 2018 Summer Other v. Pseudomonas 2018 Fall Other v. Pseudomonas 2019 Spring Other v. Pseudomonas 2019 Summer Other v. Pseudomonas 2019 Fall Other v. Pseudomonas -0.21 0.48 -0.17 -0.02 -0.92 -0.23 0.04 0.04 0.06 0.04 0.05 0.06 0.05 0.05 Genus Other Contrast Estimate SE 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer -0.09 -0.34 -0.02 -0.13 0.35 0.01 -0.25 0.07 -0.03 0.45 0.10 0.32 0.22 0.70 0.35 0.04 0.05 0.04 0.05 0.05 0.04 0.06 0.05 0.05 0.06 0.05 0.05 0.06 0.06 0.05 394 df 733 733 733 733 733 733 733 df 750 742 750 743 749 748 741 744 748 748 745 741 745 746 740 t-ratio p-value -5.359 <0.0001 7.967 <0.0001 -3.717 -0.485 0.0002 0.6282 -15.062 <0.0001 -5.022 <0.0001 0.786 0.4323 t-ratio p-value -2.095 -6.659 -0.528 -2.688 6.837 0.183 -4.500 1.511 -0.637 7.998 2.139 5.946 3.769 11.453 6.483 0.3564 <0.0001 0.9984 0.1028 <0.0001 1.0000 0.0002 0.7384 0.9956 <0.0001 0.3307 <0.0001 0.0033 <0.0001 <0.0001 Table B9. (cont’d) 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer Pseudomonas 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer 747 749 744 748 747 747 750 742 750 743 749 748 741 744 748 748 745 741 745 746 740 747 749 744 748 747 747 -2.111 6.939 0.668 8.280 2.712 -6.386 2.095 6.659 0.528 2.688 -6.837 -0.183 4.500 -1.511 0.637 -7.998 -2.139 -5.946 -3.769 -11.453 -6.483 2.111 -6.939 -0.668 -8.280 -2.712 6.386 0.3473 <0.0001 0.9943 <0.0001 0.0967 <0.0001 0.3564 <0.0001 0.9984 0.1028 <0.0001 1.0000 0.0002 0.7384 0.9956 <0.0001 0.3307 <0.0001 0.0033 <0.0001 <0.0001 0.3473 <0.0001 0.9943 <0.0001 0.0967 <0.0001 -0.10 0.38 0.03 0.48 0.13 -0.35 0.09 0.34 0.02 0.13 -0.35 -0.01 0.25 -0.07 0.03 -0.45 -0.10 -0.32 -0.22 -0.70 -0.35 0.10 -0.38 -0.03 -0.48 -0.13 0.35 0.05 0.05 0.05 0.06 0.05 0.05 0.04 0.05 0.04 0.05 0.05 0.04 0.06 0.05 0.05 0.06 0.05 0.05 0.06 0.06 0.05 0.05 0.05 0.05 0.06 0.05 0.05 395 Table B10. The mean relative abundances of the top deepest taxonomic level (genus or family) by time (season) where taxa of relative abundance <0.03 were combined into “other” ± SE, with the confidence limits (lower and upper C.L.). Letters indicate statistical differences, with significance determined by p-value ≤ 0.05. Time Genera Mean SE 2017 Fall Pseudomonas Enterobacteriaceae Sphingomonas Massilia Other 2018 Spring Other Pseudomonas Bradyrhizobium Acinetobacter Sphingomonas Methylobacterium Streptococcus Xanthomonas 2018 Summer Pseudomonas 2018 Fall Other Sphingomonas Curtobacterium Enterobacteriaceae Pseudomonas Sphingomonas Other Rhizobiaceae Chryseobacterium Massilia 2019 Spring Pseudomonas Enterobacteriaceae Other 2019 Summer Pseudomonas Sphingomonas Other Enterobacteriaceae Pedobacter Rhizobiaceae Pseudomonas Sphingomonas Rhizobiaceae 2019 Fall 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.61 0.16 0.11 0.07 0.06 0.50 0.26 0.05 0.05 0.04 0.03 0.03 0.03 0.58 0.17 0.11 0.08 0.05 0.51 0.23 0.13 0.07 0.03 0.03 0.96 0.03 0.01 0.61 0.12 0.09 0.08 0.05 0.05 0.48 0.22 0.10 396 df 111 111 111 111 111 112 112 112 112 112 112 112 112 91 91 91 91 91 116 116 116 116 116 116 23 23 23 143 143 143 143 143 143 144 144 144 lower C.L. 0.57 0.12 0.08 0.03 0.02 0.45 0.21 0.00 0.00 -0.02 -0.02 -0.03 -0.03 0.53 0.11 0.06 0.03 0.00 0.48 0.19 0.09 0.03 0.00 0.00 0.92 -0.01 -0.03 0.58 0.08 0.05 0.04 0.02 0.01 0.44 0.19 0.07 upper C.L. 0.64 0.19 0.15 0.10 0.09 0.56 0.32 0.11 0.11 0.09 0.09 0.08 0.08 0.64 0.22 0.17 0.14 0.11 0.55 0.26 0.16 0.10 0.07 0.07 1.00 0.07 0.05 0.65 0.16 0.12 0.12 0.09 0.09 0.52 0.26 0.14 c b ab a a c b a a a a a a c b ab ab a d c b ab a a b a a b a a a a a d c b Table B10. (cont’d) Other Hymenobacter Massilia Chryseobacterium 0.09 0.04 0.03 0.03 0.02 0.02 0.02 0.02 144 144 144 144 0.06 0.00 0.00 -0.01 0.13 0.08 0.07 0.06 ab ab ab a 397 Table B11. The pairwise comparison of the estimated marginal means of relative abundances of the deepest taxonomic level (genus or family) by time (season) where taxa of relative abundance <0.03 were combined into “other” with significance determined by p-value ≤ 0.05. Contrast Estimate SE df t-ratio p-value Time 2017 Fall Enterobacteriaceae v. Massilia Enterobacteriaceae v. Other Enterobacteriaceae v. Pseudomonas Enterobacteriaceae v. Sphingomonas Massilia v. Other Massilia v. Pseudomonas Massilia v. Sphingomonas Other v. Pseudomonas Other v. Sphingomonas Pseudomonas v. Sphingomonas 2018 Spring Acinetobacter v. Bradyrhizobium Acinetobacter v. Methylobacterium Acinetobacter v. Other Acinetobacter v. Pseudomonas Acinetobacter v. Sphingomonas Acinetobacter v. Streptococcus Acinetobacter v. Xanthomonas Bradyrhizobium v. Methylobacterium Bradyrhizobium v. Other Bradyrhizobium v. Pseudomonas Bradyrhizobium v. Sphingomonas Bradyrhizobium v. Streptococcus Bradyrhizobium v. Xanthomonas Methylobacterium v. Other Methylobacterium v. Pseudomonas Methylobacterium v. Sphingomonas 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 394 394 394 394 394 394 394 394 394 394 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 270 3.702 4.014 -18.506 1.742 0.312 -22.208 -1.960 -22.519 -2.271 20.248 -0.094 0.455 -11.557 -5.343 0.306 0.561 0.634 0.549 -11.463 -5.249 0.400 0.655 0.728 -12.012 -5.798 -0.149 0.0023 0.0007 <0.0001 0.4093 0.9979 <0.0001 0.2879 <0.0001 0.1564 <0.0001 1.0000 0.9998 <0.0001 <0.0001 1.0000 0.9993 0.9984 0.9994 <0.0001 <0.0001 0.9999 0.9980 0.9961 <0.0001 <0.0001 1.0000 0.09 0.10 -0.45 0.04 0.01 -0.54 -0.05 -0.55 -0.06 0.49 0.00 0.02 -0.45 -0.21 0.01 0.02 0.02 0.02 -0.45 -0.21 0.02 0.03 0.03 -0.47 -0.23 -0.01 398 Table B11. (cont’d) Methylobacterium v. Streptococcus Methylobacterium v. Xanthomonas Other v. Pseudomonas Other v. Sphingomonas Other v. Streptococcus Other v. Xanthomonas Pseudomonas v. Sphingomonas Pseudomonas v. Streptococcus Pseudomonas v. Xanthomonas Sphingomonas v. Streptococcus Sphingomonas v. Xanthomonas Streptococcus v. Xanthomonas 2018 Summer Curtobacterium v. Enterobacteriaceae 2018 Fall Curtobacterium v. Other Curtobacterium v. Pseudomonas Curtobacterium v. Sphingomonas Enterobacteriaceae v. Other Enterobacteriaceae v. Pseudomonas Enterobacteriaceae v. Sphingomonas Other v. Pseudomonas Other v. Sphingomonas Pseudomonas v. Sphingomonas Chryseobacterium v. Massilia Chryseobacterium v. Other Chryseobacterium v. Pseudomonas Chryseobacterium v. Rhizobiaceae Chryseobacterium v. Sphingomonas Massilia v. Other Massilia v. Pseudomonas Massilia v. Rhizobiaceae 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 270 270 270 270 270 270 270 270 270 270 270 270 302 302 302 302 302 302 302 302 302 302 301 301 301 301 301 301 301 301 0.106 0.179 6.214 11.863 12.118 12.191 5.649 5.904 5.977 0.255 0.328 0.073 0.807 -2.207 -13.427 -0.824 -3.013 -14.234 -1.631 -11.221 1.382 12.603 0.018 -3.920 -20.028 -1.299 -8.023 -3.938 -20.046 -1.317 1.0000 1.0000 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 1.0000 1.0000 1.0000 0.9284 0.1801 <0.0001 0.9231 0.0233 <0.0001 0.4788 <0.0001 0.6394 <0.0001 1.0000 0.0015 <0.0001 0.7856 <0.0001 0.0014 <0.0001 0.7756 0.00 0.01 0.24 0.47 0.48 0.48 0.22 0.23 0.23 0.01 0.01 0.00 0.03 -0.08 -0.50 -0.03 -0.11 -0.53 -0.06 -0.42 0.05 0.47 0.00 -0.09 -0.48 -0.03 -0.19 -0.09 -0.48 -0.03 399 Table B11. (cont’d) Massilia v. Sphingomonas Other v. Pseudomonas Other v. Rhizobiaceae Other v. Sphingomonas Pseudomonas v. Rhizobiaceae Pseudomonas v. Sphingomonas Rhizobiaceae v. Sphingomonas Enterobacteriaceae v. Other Enterobacteriaceae v. Pseudomonas Other v. Pseudomonas 2019 Spring 2019 Summer Enterobacteriaceae v. Other Enterobacteriaceae v. Pedobacter Enterobacteriaceae v. Pseudomonas Enterobacteriaceae v. Rhizobiaceae Enterobacteriaceae v. Sphingomonas Other v. Pedobacter Other v. Pseudomonas Other v. Rhizobiaceae Other v. Sphingomonas Pedobacter v. Pseudomonas Pedobacter v. Rhizobiaceae Pedobacter v. Sphingomonas Pseudomonas v. Rhizobiaceae Pseudomonas v. Sphingomonas Rhizobiaceae v. Sphingomonas Chryseobacterium v. Hymenobacter Chryseobacterium v. Massilia Chryseobacterium v. Other Chryseobacterium v. Pseudomonas 2019 Fall 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 301 301 301 301 301 301 301 89 89 89 360 360 360 360 360 360 360 360 360 360 360 360 360 360 360 300 300 300 300 -8.041 -16.108 2.622 -4.103 18.729 12.005 -6.724 1.228 -45.832 -47.059 -0.234 0.961 -20.407 1.163 -1.573 1.195 -20.173 1.397 -1.339 -21.368 0.201 -2.534 21.570 18.834 -2.736 -0.503 -0.320 -2.565 -17.479 <0.0001 <0.0001 0.0952 0.0007 <0.0001 <0.0001 <0.0001 0.4401 <0.0001 <0.0001 0.9999 0.9297 <0.0001 0.8541 0.6170 0.8390 <0.0001 0.7291 0.7630 <0.0001 1.0000 0.1170 <0.0001 <0.0001 0.0708 0.9988 0.9999 0.1407 <0.0001 -0.19 -0.38 0.06 -0.10 0.45 0.29 -0.16 0.02 -0.93 -0.95 -0.01 0.03 -0.53 0.03 -0.04 0.03 -0.53 0.04 -0.04 -0.56 0.01 -0.07 0.56 0.49 -0.07 -0.01 -0.01 -0.07 -0.45 400 Table B11. (cont’d) Chryseobacterium v. Rhizobiaceae Chryseobacterium v. Sphingomonas Hymenobacter v. Massilia Hymenobacter v. Other Hymenobacter v. Pseudomonas Hymenobacter v. Rhizobiaceae Hymenobacter v. Sphingomonas Massilia v. Other Massilia v. Pseudomonas Massilia v. Rhizobiaceae Massilia v. Sphingomonas Other v. Pseudomonas Other v. Rhizobiaceae Other v. Sphingomonas Pseudomonas v. Rhizobiaceae Pseudomonas v. Sphingomonas Rhizobiaceae v. Sphingomonas -0.08 -0.20 0.00 -0.05 -0.44 -0.07 -0.18 -0.06 -0.44 -0.07 -0.19 -0.39 -0.01 -0.13 0.37 0.26 -0.12 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 -3.027 -7.572 0.183 -2.062 -16.976 -2.524 -7.069 -2.245 -17.159 -2.707 -7.252 -14.914 -0.462 -5.007 14.452 9.907 -4.546 0.0424 <0.0001 1.0000 0.3780 <0.0001 0.1545 <0.0001 0.2748 <0.0001 0.1001 <0.0001 <0.0001 0.9993 <0.0001 <0.0001 <0.0001 0.0002 401 Table B12. The mean relative abundances of the core microbiome taxon (genus and OTU identifier ), the rest of the combined Pseudomonas and combined Sphingomonas, and the remaining combined “other” taxa ± SE over time (season), with the confidence limits (lower and upper C.L.). Superscripted letters following genus/OTUs indicate statistical differences within each time, while upper-cased letters indicate statistical differences of each genus/OTU across time, with significance determined by p-value ≤ 0.05. Time 2017 Fall Genera Mean SE df Other d Pseudomonas Otu00003 cd Pseudomonas Otu00001 cd Pseudomonas Otu00002 c Sphingomonas Otu00004 b Sphingomonas ab Pseudomonas a 2018 Spring Other c Pseudomonas Otu00001 b Pseudomonas Otu00002 a Sphingomonas a Sphingomonas Otu00004 a Pseudomonas Otu00003 a Pseudomonas a 2018 Summer Pseudomonas Otu00002 c Other c Pseudomonas Otu00003 b Pseudomonas Otu00001 ab Sphingomonas ab Sphingomonas Otu00004 a Pseudomonas a 0.28 0.21 0.20 0.19 0.09 0.02 0.00 0.70 0.21 0.03 0.03 0.01 0.01 0.01 0.30 0.30 0.16 0.08 0.07 0.05 0.04 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02 1834 1834 1834 1834 1834 1834 1834 2472 2472 2472 2472 2472 2472 2472 2038 2038 2038 2038 2038 2038 2038 402 lower C.L. 0.241 0.165 0.163 0.153 0.052 -0.018 -0.037 0.638 0.150 -0.029 -0.035 -0.049 -0.053 -0.054 0.257 0.257 0.116 0.037 0.023 0.000 -0.009 upper C.L. 0.3211 B 0.2458 C 0.2432 B 0.2335 B 0.1321 ABC 0.0621 A 0.0436 A 0.7617 C 0.2736 BC 0.0947 A 0.0885 A 0.0744 A 0.0705 A 0.0695 AB 0.3486 C 0.3485 B 0.2070 BC 0.1281 A 0.1144 A 0.0913 AB 0.0822 AB Table B12. (cont’d) 2018 Fall Pseudomonas Otu00001 d Other c Sphingomonas Otu00004 b Sphingomonas ab Pseudomonas Otu00003 ab Pseudomonas Otu00002 a Pseudomonas a 2019 Spring Pseudomonas Otu00001 c Pseudomonas Otu00002 b Pseudomonas ab Pseudomonas Otu00003 ab Other a Sphingomonas Otu00004 a Sphingomonas a 2019 Summer Other b 2019 Fall Pseudomonas Otu00002 b Pseudomonas Otu00001 b Sphingomonas Otu00004 a Pseudomonas Otu00003 a Pseudomonas a Sphingomonas a Pseudomonas Otu00001 c Other c Sphingomonas Otu00004 b Pseudomonas Otu00003 ab Pseudomonas Otu00002 a Sphingomonas a Pseudomonas a 0.43 0.26 0.14 0.09 0.05 0.02 0.01 0.50 0.23 0.12 0.10 0.04 0.00 0.00 0.27 0.27 0.21 0.10 0.07 0.06 0.02 0.31 0.30 0.18 0.10 0.06 0.04 0.01 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 2202 2202 2202 2202 2202 2202 2202 2472 2472 2472 2472 2472 2472 2472 2136 2136 2136 2136 2136 2136 2136 2355 2355 2355 2355 2355 2355 2355 0.377 0.212 0.087 0.038 0.000 -0.029 -0.037 0.441 0.169 0.061 0.038 -0.021 -0.062 -0.062 0.221 0.219 0.168 0.050 0.023 0.018 -0.021 0.253 0.244 0.124 0.043 0.008 -0.010 -0.042 0.4775 D 0.3125 B 0.1876 BC 0.1385 A 0.1001 A 0.0718 A 0.0631 AB 0.5659 D 0.2936 BC 0.1857 B 0.1631 ABC 0.1038 A 0.0627 A 0.0625 A 0.3128 B 0.3116 BC 0.2600 BC 0.1417 ABC 0.1156 AB 0.1101 AB 0.0707 A 0.3618 C 0.3527 B 0.2329 C 0.1520 AB 0.1164 A 0.0984 A 0.0664 AB 403 Table B13. The pairwise comparison of the estimated marginal means of relative abundances of core microbiome taxon (genus and OTU identifier ) the rest of the combined Pseudomonas and combined Sphingomonas and the remaining combined “other” taxa for each time point and for each taxon across each time point with significance determined by p-value ≤ 0.05. Time 2017 Fall Contrast Other v. Pseudomonas Other v. Pseudomonas Otu00001 Other v. Pseudomonas Otu00002 Other v. Pseudomonas Otu00003 Other v. Sphingomonas Other v. Sphingomonas Otu00004 Pseudomonas v. Pseudomonas Otu00001 Pseudomonas v. Pseudomonas Otu00002 Pseudomonas v. Pseudomonas Otu00003 Pseudomonas v. Sphingomonas Pseudomonas v. Sphingomonas Otu00004 Pseudomonas Otu00001 v. Pseudomonas Otu00002 Pseudomonas Otu00001 v. Pseudomonas Otu00003 Pseudomonas Otu00001 v. Sphingomonas Pseudomonas Otu00001 v. Sphingomonas Otu00004 Pseudomonas Otu00002 v. Pseudomonas Otu00003 Pseudomonas Otu00002 v. Sphingomonas Pseudomonas Otu00002 v. Sphingomonas Otu00004 Pseudomonas Otu00003 v. Sphingomonas Pseudomonas Otu00003 v. Sphingomonas Otu00004 Sphingomonas v. Sphingomonas Otu00004 404 Estimate 0.28 0.08 0.09 0.08 0.26 0.19 -0.20 -0.19 -0.20 -0.02 -0.09 0.01 0.00 0.18 0.11 SE 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 df 2618 2618 2618 2618 2618 2618 2618 2618 2618 2618 2618 t-ratio 9.584 2.691 3.025 2.603 8.946 6.528 -6.893 -6.558 -6.980 -0.638 -3.056 p-value <0.0001 0.1009 0.0402 0.1253 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.9956 0.0367 0.03 2618 0.334 0.9999 0.03 2618 -0.088 1.0000 0.03 2618 6.255 <0.0001 0.03 2618 3.837 0.0024 -0.01 0.03 2618 -0.422 0.9996 0.17 0.10 0.18 0.11 0.03 2618 5.920 <0.0001 0.03 2618 3.503 0.0085 0.03 2618 6.342 <0.0001 0.03 2618 3.925 0.0017 -0.07 0.03 2618 -2.418 0.1914 Table B13. (cont’d) 2018 Spring Other v. Pseudomonas Other v. Pseudomonas Otu00001 Other v. Pseudomonas Otu00002 Other v. Pseudomonas Otu00003 Other v. Sphingomonas Other v. Sphingomonas Otu00004 Pseudomonas v. Pseudomonas Otu00001 Pseudomonas v. Pseudomonas Otu00002 Pseudomonas v. Pseudomonas Otu00003 Pseudomonas v. Sphingomonas Pseudomonas v. Sphingomonas Otu00004 Pseudomonas Otu00001 v. Pseudomonas Otu00002 Pseudomonas Otu00001 v. Pseudomonas Otu00003 Pseudomonas Otu00001 v. Sphingomonas Pseudomonas Otu00001 v. Sphingomonas Otu00004 Pseudomonas Otu00002 v. Pseudomonas Otu00003 Pseudomonas Otu00002 v. Sphingomonas Pseudomonas Otu00002 v. Sphingomonas Otu00004 Pseudomonas Otu00003 v. Sphingomonas Pseudomonas Otu00003 v. Sphingomonas Otu00004 Sphingomonas v. Sphingomonas Otu00004 2018 Summer Other v. Pseudomonas Other v. Pseudomonas Otu00001 405 0.69 0.49 0.67 0.69 0.67 0.69 -0.20 -0.03 0.00 -0.02 0.00 0.18 0.20 0.19 0.20 0.02 0.01 0.02 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 2618 2618 2618 2618 2618 2618 2618 2618 2618 2618 2618 15.650 11.034 15.080 15.626 15.220 15.537 -4.615 -0.570 -0.023 -0.430 -0.112 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0001 0.9976 1.0000 0.9995 1.0000 0.04 2618 4.046 0.0011 0.04 2618 4.592 0.0001 0.04 2618 4.185 0.0006 0.04 2618 4.503 0.0001 0.04 2618 0.546 0.9981 0.04 2618 0.139 1.0000 0.04 2618 0.457 0.9993 -0.02 0.04 2618 -0.407 0.9997 0.00 0.01 0.27 0.22 0.04 2618 -0.089 1.0000 0.04 2618 0.318 0.9999 0.03 0.03 2618 2618 8.088 6.696 <0.0001 <0.0001 Table B13. (cont’d) 2018 Fall Other v. Pseudomonas Otu00002 Other v. Pseudomonas Otu00003 Other v. Sphingomonas Other v. Sphingomonas Otu00004 Pseudomonas v. Pseudomonas Otu00001 Pseudomonas v. Pseudomonas Otu00002 Pseudomonas v. Pseudomonas Otu00003 Pseudomonas v. Sphingomonas Pseudomonas v. Sphingomonas Otu00004 Pseudomonas Otu00001 v. Pseudomonas Otu00002 Pseudomonas Otu00001 v. Pseudomonas Otu00003 Pseudomonas Otu00001 v. Sphingomonas Pseudomonas Otu00001 v. Sphingomonas Otu00004 Pseudomonas Otu00002 v. Pseudomonas Otu00003 Pseudomonas Otu00002 v. Sphingomonas Pseudomonas Otu00002 v. Sphingomonas Otu00004 Pseudomonas Otu00003 v. Sphingomonas Pseudomonas Otu00003 v. Sphingomonas Otu00004 Sphingomonas v. Sphingomonas Otu00004 Other v. Pseudomonas Other v. Pseudomonas Otu00001 Other v. Pseudomonas Otu00002 Other v. Pseudomonas Otu00003 Other v. Sphingomonas 406 0.00 0.14 0.23 0.26 -0.05 -0.27 -0.12 -0.03 -0.01 -0.22 -0.08 0.01 0.04 0.14 0.23 0.26 0.09 0.12 0.02 0.25 -0.17 0.24 0.21 0.17 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 2618 2618 2618 2618 2618 2618 2618 2618 2618 -0.003 4.298 7.110 7.815 -1.393 -8.092 -3.791 -0.978 -0.274 1.0000 0.0004 <0.0001 <0.0001 0.8059 <0.0001 0.0029 0.9588 1.0000 0.03 2618 -6.699 <0.0001 0.03 2618 -2.398 0.1997 0.03 2618 0.415 0.9996 0.03 2618 1.119 0.9225 0.03 2618 4.301 0.0004 0.03 2618 7.114 <0.0001 0.03 2618 7.818 <0.0001 0.03 2618 2.813 0.0734 0.03 2618 3.517 0.0081 0.03 2618 0.704 0.9924 0.04 0.04 0.04 0.04 0.04 2618 2618 2618 2618 2618 6.905 -4.569 6.664 5.881 4.817 <0.0001 0.0001 <0.0001 <0.0001 <0.0001 Table B13. (cont’d) 2019 Spring Other v. Sphingomonas Otu00004 Pseudomonas v. Pseudomonas Otu00001 Pseudomonas v. Pseudomonas Otu00002 Pseudomonas v. Pseudomonas Otu00003 Pseudomonas v. Sphingomonas Pseudomonas v. Sphingomonas Otu00004 Pseudomonas Otu00001 v. Pseudomonas Otu00002 Pseudomonas Otu00001 v. Pseudomonas Otu00003 Pseudomonas Otu00001 v. Sphingomonas Pseudomonas Otu00001 v. Sphingomonas Otu00004 Pseudomonas Otu00002 v. Pseudomonas Otu00003 Pseudomonas Otu00002 v. Sphingomonas Pseudomonas Otu00002 v. Sphingomonas Otu00004 Pseudomonas Otu00003 v. Sphingomonas Pseudomonas Otu00003 v. Sphingomonas Otu00004 Sphingomonas v. Sphingomonas Otu00004 Other v. Pseudomonas Other v. Pseudomonas Otu00001 Other v. Pseudomonas Otu00002 Other v. Pseudomonas Otu00003 Other v. Sphingomonas Other v. Sphingomonas Otu00004 0.12 -0.41 -0.01 -0.04 -0.08 -0.12 0.41 0.38 0.34 0.29 -0.03 -0.07 -0.12 -0.04 -0.09 -0.05 -0.08 -0.46 -0.19 -0.06 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 2618 2618 2618 2618 2618 2618 3.458 -11.474 -0.240 -1.024 -2.088 -3.447 0.0099 <0.0001 1.0000 0.9487 0.3603 0.0103 0.04 2618 11.233 <0.0001 0.04 2618 10.450 <0.0001 0.04 2618 9.386 <0.0001 0.04 2618 8.027 <0.0001 0.04 2618 -0.783 0.9866 0.04 2618 -1.847 0.5161 0.04 2618 -3.206 0.0230 0.04 2618 -1.064 0.9385 0.04 2618 -2.423 0.1893 0.04 2618 -1.359 0.8235 0.04 0.04 0.04 0.04 0.04 0.04 2618 2618 2618 2618 2618 2618 -1.827 -10.300 -4.230 -1.322 0.921 0.917 0.5299 <0.0001 0.0005 0.8418 0.9693 0.9700 407 Table B13. (cont’d) 2019 Summer Pseudomonas v. Pseudomonas Otu00001 Pseudomonas v. Pseudomonas Otu00002 Pseudomonas v. Pseudomonas Otu00003 Pseudomonas v. Sphingomonas Pseudomonas v. Sphingomonas Otu00004 Pseudomonas Otu00001 v. Pseudomonas Otu00002 Pseudomonas Otu00001 v. Pseudomonas Otu00003 Pseudomonas Otu00001 v. Sphingomonas Pseudomonas Otu00001 v. Sphingomonas Otu00004 Pseudomonas Otu00002 v. Pseudomonas Otu00003 Pseudomonas Otu00002 v. Sphingomonas Pseudomonas Otu00002 v. Sphingomonas Otu00004 Pseudomonas Otu00003 v. Sphingomonas Pseudomonas Otu00003 v. Sphingomonas Otu00004 Sphingomonas v. Sphingomonas Otu00004 Other v. Pseudomonas Other v. Pseudomonas Otu00001 Other v. Pseudomonas Otu00002 Other v. Pseudomonas Otu00003 Other v. Sphingomonas Other v. Sphingomonas Otu00004 Pseudomonas v. Pseudomonas Otu00001 408 -0.38 -0.11 0.02 0.12 0.12 0.27 0.40 0.50 0.50 0.13 0.23 0.23 0.10 0.10 0.00 0.20 0.05 0.00 0.20 0.24 0.17 -0.15 0.04 2618 -8.474 <0.0001 0.04 0.04 0.04 0.04 2618 2618 2618 2618 -2.403 0.505 2.748 2.744 0.1974 0.9988 0.0872 0.0881 0.04 2618 6.070 <0.0001 0.04 2618 8.978 <0.0001 0.04 2618 11.221 <0.0001 0.04 2618 11.217 <0.0001 0.04 2618 2.908 0.0564 0.04 2618 5.151 <0.0001 0.04 2618 5.147 <0.0001 0.04 2618 2.243 0.2729 0.04 2618 2.239 0.2749 0.04 2618 -0.004 1.0000 0.03 0.03 0.03 0.03 0.03 0.03 0.03 2618 2618 2618 2618 2618 2618 2618 6.110 1.592 0.037 5.944 7.298 5.159 -4.518 <0.0001 0.6878 1.0000 <0.0001 <0.0001 <0.0001 0.0001 Table B13. (cont’d) 2019 Fall Pseudomonas v. Pseudomonas Otu00002 Pseudomonas v. Pseudomonas Otu00003 Pseudomonas v. Sphingomonas Pseudomonas v. Sphingomonas Otu00004 Pseudomonas Otu00001 v. Pseudomonas Otu00002 Pseudomonas Otu00001 v. Pseudomonas Otu00003 Pseudomonas Otu00001 v. Sphingomonas Pseudomonas Otu00001 v. Sphingomonas Otu00004 Pseudomonas Otu00002 v. Pseudomonas Otu00003 Pseudomonas Otu00002 v. Sphingomonas Pseudomonas Otu00002 v. Sphingomonas Otu00004 Pseudomonas Otu00003 v. Sphingomonas Pseudomonas Otu00003 v. Sphingomonas Otu00004 Sphingomonas v. Sphingomonas Otu00004 Other v. Pseudomonas Other v. Pseudomonas Otu00001 Other v. Pseudomonas Otu00002 Other v. Pseudomonas Otu00003 Other v. Sphingomonas Other v. Sphingomonas Otu00004 Pseudomonas v. Pseudomonas Otu00001 Pseudomonas v. Pseudomonas Otu00002 Pseudomonas v. Pseudomonas Otu00003 Pseudomonas v. Sphingomonas 409 -0.20 -0.01 0.04 -0.03 -0.05 0.14 0.19 0.12 0.20 0.24 0.17 0.04 -0.03 -0.07 0.29 -0.01 0.24 0.20 0.25 0.12 -0.30 -0.05 -0.09 -0.03 0.03 0.03 0.03 0.03 2618 2618 2618 2618 -6.073 -0.166 1.188 -0.950 <0.0001 1.0000 0.8989 0.9641 0.03 2618 -1.555 0.7113 0.03 2618 4.352 0.0003 0.03 2618 5.706 <0.0001 0.03 2618 3.568 0.0067 0.03 2618 5.907 <0.0001 0.03 2618 7.261 <0.0001 0.03 2618 5.122 <0.0001 0.03 2618 1.354 0.8262 0.03 2618 -0.785 0.9865 0.03 2618 -2.138 0.3302 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 2618 2618 2618 2618 2618 2618 2618 2618 2618 2618 7.315 -0.233 6.037 5.129 6.498 3.061 -7.549 -1.278 -2.186 -0.818 <0.0001 1.0000 <0.0001 <0.0001 <0.0001 0.0361 <0.0001 0.8622 0.3032 0.9832 Table B13. (cont’d) Genus/OTU Other Pseudomonas v. Sphingomonas Otu00004 Pseudomonas Otu00001 v. Pseudomonas Otu00002 Pseudomonas Otu00001 v. Pseudomonas Otu00003 Pseudomonas Otu00001 v. Sphingomonas Pseudomonas Otu00001 v. Sphingomonas Otu00004 Pseudomonas Otu00002 v. Pseudomonas Otu00003 Pseudomonas Otu00002 v. Sphingomonas Pseudomonas Otu00002 v. Sphingomonas Otu00004 Pseudomonas Otu00003 v. Sphingomonas Pseudomonas Otu00003 v. Sphingomonas Otu00004 Sphingomonas v. Sphingomonas Otu00004 -0.17 0.25 0.21 0.26 0.13 -0.04 0.02 -0.12 0.05 -0.08 -0.13 Contrast Estimate 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 0.02 -0.42 -0.02 -0.02 0.24 0.01 -0.44 -0.04 -0.04 0.22 0.00 0.04 2618 -4.254 0.0004 0.04 2618 6.271 <0.0001 0.04 2618 5.362 <0.0001 0.04 2618 6.731 <0.0001 0.04 2618 3.295 0.0173 0.04 2618 -0.908 0.9713 0.04 2618 0.460 0.9993 0.04 2618 -2.976 0.0465 0.04 2618 1.369 0.8185 0.04 2618 -2.068 0.3723 0.04 2618 -3.437 0.0107 SE 0.03 0.04 0.03 0.03 0.04 0.03 0.04 0.03 0.04 0.04 0.03 df t-ratio p-value 2635 2628 2635 2628 2634 2633 2626 2629 2633 2633 2630 0.567 -11.144 -0.704 -0.504 6.331 0.455 -10.780 -1.169 -0.954 5.411 -0.127 0.9977 <0.0001 0.9924 0.9988 <0.0001 0.9993 <0.0001 0.9059 0.9635 <0.0001 1.0000 410 Table B13. (cont’d) Pseudomonas 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 411 0.40 0.40 0.66 0.43 0.00 0.26 0.04 0.26 0.03 -0.23 -0.01 0.00 -0.03 -0.01 -0.12 -0.06 0.01 -0.02 0.00 -0.11 -0.05 -0.03 0.00 -0.12 -0.06 0.02 -0.09 -0.03 -0.11 -0.05 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.04 0.04 0.04 0.03 0.04 0.03 0.03 0.04 0.03 0.04 0.03 0.04 0.04 0.03 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.04 0.04 2626 2630 2631 2625 2632 2634 2629 2633 2632 2632 2635 2628 2635 2628 2634 2633 2626 2629 2633 2633 2630 2626 2630 2631 2625 2632 2634 2629 2633 2632 10.115 9.556 14.751 11.003 0.123 6.619 1.089 6.082 0.869 -5.697 -0.291 -0.112 -1.066 -0.251 -3.167 -1.946 0.131 -0.681 0.024 -2.701 -1.471 -0.736 -0.105 -2.591 -1.433 0.676 -2.197 -0.833 -2.633 -1.432 <0.0001 <0.0001 <0.0001 <0.0001 1.0000 <0.0001 0.9314 <0.0001 0.9770 <0.0001 1.0000 1.0000 0.9378 1.0000 0.0260 0.4501 1.0000 0.9937 1.0000 0.0983 0.7621 0.9904 1.0000 0.1291 0.7838 0.9939 0.2972 0.9816 0.1167 0.7844 Table B13. (cont’d) Pseudomonas Otu00001 Pseudomonas Otu00002 2019 Spring v. 2019 Summer 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 412 0.06 -0.22 -0.01 0.12 -0.10 -0.30 -0.01 0.22 0.34 0.12 -0.08 0.21 0.13 -0.10 -0.29 0.00 -0.23 -0.42 -0.13 -0.20 0.09 0.29 0.17 0.16 -0.11 0.13 -0.04 -0.07 -0.01 0.04 2632 1.497 0.7467 0.03 0.04 0.03 0.03 0.04 0.03 0.04 0.03 0.04 0.04 0.03 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.04 0.04 0.04 0.03 0.04 0.03 0.03 0.04 0.03 0.04 2635 2628 2635 2628 2634 2633 2626 2629 2633 2633 2630 2626 2630 2631 2625 2632 2634 2629 2633 2632 2632 2635 2628 2635 2628 2634 2633 2626 -6.846 -0.233 3.888 -3.030 -7.938 -0.351 5.311 9.972 3.180 -1.862 6.145 3.297 -2.276 -6.533 -0.055 -6.220 -10.661 -3.979 -4.638 2.575 7.316 5.239 4.269 -3.530 3.808 -0.998 -2.317 -0.275 <0.0001 1.0000 0.0020 0.0397 <0.0001 0.9999 <0.0001 <0.0001 0.0251 0.5057 <0.0001 0.0172 0.2559 <0.0001 1.0000 <0.0001 <0.0001 0.0014 0.0001 0.1340 <0.0001 <0.0001 0.0004 0.0077 0.0027 0.9545 0.2363 1.0000 Table B13. (cont’d) Pseudomonas Otu00003 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 413 -0.28 -0.04 -0.21 -0.24 -0.27 -0.03 -0.20 -0.23 0.24 0.07 0.04 -0.17 -0.20 -0.03 0.16 0.20 0.04 0.11 0.10 0.14 0.04 -0.11 -0.05 -0.05 -0.02 -0.15 -0.09 -0.09 -0.06 0.06 0.03 0.04 0.04 0.03 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.04 0.04 0.04 0.03 0.04 0.03 0.03 0.04 0.03 0.04 0.03 0.04 0.04 0.03 0.04 0.04 0.04 0.04 0.04 2629 2633 2633 2630 2626 2630 2631 2625 2632 2634 2629 2633 2632 2632 2635 2628 2635 2628 2634 2633 2626 2629 2633 2633 2630 2626 2630 2631 2625 2632 -8.129 -1.072 -5.128 -7.023 -6.879 -0.696 -4.441 -5.911 6.654 1.817 1.130 -4.001 -5.604 -0.870 4.749 5.237 1.426 3.132 2.772 4.364 1.017 -3.219 -1.264 -1.242 -0.565 -3.888 -2.117 -2.060 -1.547 1.760 <0.0001 0.9364 <0.0001 <0.0001 <0.0001 0.9929 0.0002 <0.0001 <0.0001 0.5365 0.9191 0.0013 <0.0001 0.9770 <0.0001 <0.0001 0.7877 0.0291 0.0818 0.0003 0.9503 0.0221 0.8683 0.8779 0.9977 0.0020 0.3430 0.3768 0.7163 0.5755 Table B13. (cont’d) Sphingomonas Sphingomonas Otu00004 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 414 0.06 0.09 0.00 0.03 0.03 -0.07 0.00 -0.05 -0.02 0.02 0.00 0.06 0.02 0.04 0.09 0.06 -0.04 -0.02 0.03 0.00 0.02 0.07 0.04 0.04 0.02 -0.02 -0.05 0.08 0.05 0.04 0.03 0.04 0.04 0.04 0.03 0.04 0.03 0.03 0.04 0.03 0.04 0.03 0.04 0.04 0.03 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.04 0.04 0.04 0.03 0.04 0.03 2634 2629 2633 2632 2632 2635 2628 2635 2628 2634 2633 2626 2629 2633 2633 2630 2626 2630 2631 2625 2632 2634 2629 2633 2632 2632 2635 2628 2635 1.536 2.774 -0.072 0.773 0.786 -2.029 -0.127 -1.509 -0.643 0.578 -0.088 1.520 0.568 1.175 2.163 1.835 -1.071 -0.414 0.596 0.051 0.681 1.739 1.332 1.042 0.534 -0.622 -1.390 2.109 1.495 0.7233 0.0814 1.0000 0.9875 0.9864 0.3967 1.0000 0.7397 0.9954 0.9974 1.0000 0.7327 0.9977 0.9036 0.3165 0.5244 0.9366 0.9996 0.9969 1.0000 0.9936 0.5898 0.8367 0.9443 0.9983 0.9961 0.8075 0.3473 0.7482 Table B13. (cont’d) 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer -0.09 0.09 0.00 0.12 0.09 -0.04 0.14 0.04 -0.03 -0.17 0.01 -0.08 -0.13 0.05 -0.05 0.18 0.08 -0.10 0.03 0.04 0.03 0.04 0.03 0.04 0.04 0.03 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.04 0.04 0.04 2628 2634 2633 2626 2629 2633 2633 2630 2626 2630 2631 2625 2632 2634 2629 2633 2632 2632 -2.513 2.423 -0.118 3.076 2.657 -1.089 3.360 1.205 -0.839 -3.948 0.278 -2.108 -3.675 1.147 -1.514 4.220 2.284 -2.411 0.1549 0.1892 1.0000 0.0345 0.1099 0.9314 0.0139 0.8923 0.9809 0.0016 1.0000 0.3481 0.0045 0.9132 0.7367 0.0005 0.2519 0.1940 415 A C B D Figure B20. The 2019 quantile-quantile plots (qqplot) of the residuals for graphical visualization of the distribution of the alpha diversity statistics data, A) bacteria plate counts (log cfu/cm2), B) the number of OTUs, C) the Shannon diversity, and D) Shannon evenness indices 416 Table B14. The mean 2019 log cfu/cm2 of leaf area ± SE of bacteria plate counts for each site and season, including the pairwise comparisons of the estimated marginal means with the confidence limits (lower and upper C.L.) and the p-value. Superscripted letters following the season indicate statistical differences within each site and upper-case letters indicate statistical differences across sites for each season, with significance determined by p-value ≤ 0.05. Site Season Mean log cfu/cm2 SE df lower C.L. upper C.L. 1 2 Spring a Summer b Fall b Spring a Summer b Fall b 1.9 4.9 5.2 1.1 5.8 6.0 0.3 0.3 0.3 0.3 0.3 0.3 15 15 15 18 18 18 1.4 4.4 4.6 0.5 5.2 5.4 2.5 5.5 5.8 1.6 6.4 6.6 A A A B B B Site Contrast estimate SE df t-ratio p-value 1 2 Fall v. Spring Fall v. Summer Spring v. Summer Fall v. Spring Fall v. Summer Spring v. Summer 3.3 0.3 -3.0 5.0 0.2 -4.8 0.3 0.3 0.3 0.3 0.3 0.3 226 226 226 226 226 226 11.0 0.9 -10.1 16.0 0.7 -15.3 <0.0001 0.6666 <0.0001 <0.0001 0.7868 <0.0001 Season Contrast estimate SE df t-ratio p-value Spring Site 1 v. 2 Summer Site 1 v. 2 Site 1 v. 2 Fall -0.9 0.9 0.8 0.4 0.4 0.4 16 17 16 -2.305 2.290 2.166 0.0345 0.0355 0.0453 417 Table B15. The 2019 mean number of OTUs ± SE for each site and season including the pairwise comparisons of the estimated marginal means with the confidence limits (lower and upper C.L.) and the p-value. Superscripted letters following the season indicate statistical differences within each site and upper-case letters indicate statistical differences across sites for each season, with significance determined by p-value ≤ 0.05. Site Season 1 2 Spring a Summer b Fall c Spring a Summer b Fall c Mean no. OTUs 6.3 17.6 25.0 9.5 23.0 35.2 SE df lower C.L. upper C.L. 1.0 1.2 1.3 1.9 0.9 1.0 17 33 35 130 11 16 4.1 15.1 22.3 5.8 20.9 33.1 8.5 20.1 27.6 13.1 25.0 37.4 A A A A B B Site Contrast estimate SE df t-ratio p-value 1 2 Fall - Spring Fall - Summer Spring - Summer Fall - Spring Fall - Summer Spring - Summer 18.6 7.4 -11.2 25.8 12.3 -13.5 1.5 1.6 1.4 2.0 1.2 2.0 Season Contrast estimate SE Spring Summer Fall Site 1 v. 2 Site 1 v. 2 Site 1 v. 2 3.1 5.4 10.3 2.1 1.6 1.7 271 293 293 305 270 299 df 66 21 25 12.397 <0.0001 4.619 <0.0001 -7.987 <0.0001 12.811 <0.0001 10.621 <0.0001 -6.939 <0.0001 t-ratio p-value 0.1444 1.477 3.469 0.0023 6.184 <0.0001 418 Table B16. The 2019 mean Shannon diversity index ± SE for each site and season including the pairwise comparisons of the estimated marginal means with the confidence limits (lower and upper C.L.) and the p-value. Statistical differences by site and season are indicated by superscripted letters following site and season, with significance determined by p-value ≤ 0.05. Site Season Mean Shannon diversity index SE df lower C.L. upper C.L. 1 a 2 b Spring a Summer b Fall c Spring a Summer b Fall c 0.2 1.0 1.6 0.5 1.4 1.8 0.1 0.1 0.1 0.1 0.1 0.1 16.1 30.7 32.5 120.1 10.5 14.5 0.1 0.8 1.5 0.3 1.2 1.6 0.387 1.139 1.822 0.792 1.496 1.917 Contrast estimate SE df t-ratio p-value Fall - Spring Fall - Summer Spring - Summer 1.3 0.5 -0.8 0.1 0.1 0.1 Contrast estimate SE Site 1 v. 2 0.3 0.1 298 288 298 df 7 15.800 <0.0001 8.361 <0.0001 -9.733 <0.0001 t-ratio p-value 3.349 0.0116 419 Table B17. The 2019 mean Shannon evenness index ± SE for each site and season including the pairwise comparisons of the estimated marginal means with the confidence limits (lower and upper C.L.) and the p-value. Superscripted letters following the season indicate statistical differences within each site and upper-case letters indicate statistical differences across sites for each season, with significance determined by p-value ≤ 0.05. Site Season 1 2 Spring a Summer b Fall c Spring a Summer b Fall c Mean no. OTUs 0.2 0.3 0.5 0.2 0.4 0.5 SE df lower C.L. upper C.L. 0.02 0.02 0.03 0.04 0.02 0.02 16 31 33 122 11 15 0.1 0.3 0.5 0.2 0.4 0.5 0.2 0.4 0.6 0.3 0.5 0.5 A A A A B A Site Contrast estimate SE df t-ratio p-value 1 2 Fall - Spring Fall - Summer Spring - Summer Fall - Spring Fall - Summer Spring - Summer 0.4 0.2 -0.2 0.3 0.1 -0.2 0.03 0.03 0.03 0.04 0.02 0.04 274 293 291 303 269 298 12.170 5.552 -6.697 6.659 2.797 -5.231 <.0001 <.0001 <.0001 <.0001 0.0152 <.0001 Season Contrast estimate SE df t-ratio p-value Spring Summer Fall Site 1 v. 2 Site 1 v. 2 Site 1 v. 2 0.08 0.09 -0.02 0.04 0.03 0.03 61.6 19.5 23.2 1.834 2.965 -0.591 0.0715 0.0078 0.5600 420 A B Figure B21. The 2019 quantile-quantile plots (qqplot) of the residuals for graphical visualization of the distribution of the relative abundance data for A) the top 4 Phyla and the rare Phyla combined into “other” relative abundance, and B) the Pseudomonas and all “other” taxa relative abundances. 421 A B C Figure B22. The 2019 quantile-quantile plots (qqplot) of the residuals for graphical visualization of the distribution of the relative abundance data at the deepest taxonomic level (genus or family), where taxa that had a relative abundance <0.03 were combined into “other”. For each season A) Spring, B) Summer, C) Fall. 422 Figure B23. The 2019 quantile-quantile plot (qqplot) of the residuals for graphical visualization of the distribution of the relative abundance data of the core microbiome (genus and OTU). Includes the four core genera Pseudomonas Otu0001, Pseudomonas Otu0002, Sphingomonas Otu0003 and Sphingomonas Otu0007, with the other Pseudomonas combined and Sphingomonas combined and remaining additional taxa combined into “other”. Core taxa were selected via occurrence and relative abundance when they occurred in 50% of all samples and had a relative abundance of ≥ 0.01. 423 Table B18. The 2019 mean relative abundances of the top 4 Phyla and “other” rarer Phyla combined ± SE, with the confidence limits (lower and upper C.L.) and the p-value. Superscripted letters following Phyla indicate statistical differences within each season while upper-cased letters indicate statistical differences of each Phyla across season, with significance determined by p-value ≤ 0.05. Season Phylum Mean SE df Spring Summer Fall Proteobacteria b Firmicutes a Actinobacteria a Bacteroidetes a Other a Proteobacteria c Bacteroidetes b Actinobacteria b Firmicutes a Other a Proteobacteria c Bacteroidetes b Actinobacteria a Firmicutes a Other a 1.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.93 0.005 0.04 0.005 0.02 0.005 0.00 0.005 0.00 0.005 0.91 0.08 0.02 0.00 0.00 0.01 0.01 0.01 0.01 0.01 831 831 831 831 831 556 556 556 556 556 662 662 662 662 662 lower C.L. upper C.L. 0.99 -0.01 -0.01 -0.01 -0.01 0.93 0.03 0.01 -0.01 -0.01 0.90 0.07 0.01 -0.01 -0.01 1.01 0.01 0.01 0.01 0.01 0.94 0.05 0.03 0.01 0.01 0.92 0.09 0.03 0.01 0.01 C A A A A B B B A A A C AB A A 424 Table B19. The 2019 pairwise comparisons of the estimated marginal means of relative abundances of the top 4 Phyla and “other” combined Phyla for each season and for each Phyla across season with p-values, with significance determined by p-value ≤ 0.05. Season Spring Summer Fall Contrast Actinobacteria v. Bacteroidetes Actinobacteria v. Firmicutes Actinobacteria v. Other Actinobacteria v. Proteobacteria Bacteroidetes v. Firmicutes Bacteroidetes v. Other Bacteroidetes v. Proteobacteria Firmicutes v. Other Firmicutes v. Proteobacteria Other v. Proteobacteria Actinobacteria v. Bacteroidetes Actinobacteria v. Firmicutes Actinobacteria v. Other Actinobacteria v. Proteobacteria Bacteroidetes v. Firmicutes Bacteroidetes v. Other Bacteroidetes v. Proteobacteria Firmicutes v. Other Firmicutes v. Proteobacteria Other v. Proteobacteria Actinobacteria v. Bacteroidetes Actinobacteria v. Firmicutes Actinobacteria v. Other Actinobacteria v. Proteobacteria Bacteroidetes v. Firmicutes Bacteroidetes v. Other Bacteroidetes v. Proteobacteria Firmicutes v. Other Estimate 0.001 0.000 0.001 -0.997 -0.001 0.000 -0.998 0.001 -0.997 -0.998 -0.015 0.021 0.023 -0.911 0.036 0.038 -0.896 0.002 -0.933 -0.934 -0.060 0.014 0.015 -0.893 0.074 0.075 -0.833 0.000 df 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 1543 t-ratio 0.102 -0.009 0.108 -128.587 -0.111 0.006 -128.690 0.117 -128.579 -128.696 -2.323 3.365 3.628 -142.818 5.688 5.951 -140.495 0.263 -146.183 -146.446 -8.551 2.028 2.090 -127.012 10.579 10.640 -118.462 0.062 p-value 1.0000 1.0000 1.0000 <0.0001 1.0000 1.0000 <0.0001 1.0000 <0.0001 <0.0001 0.1380 0.0070 0.0027 <0.0001 <0.0001 <0.0001 <0.0001 0.9990 <0.0001 <0.0001 <0.0001 0.2530 0.2250 <0.0001 <0.0001 <0.0001 <0.0001 1.0000 SE 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007 425 Table B19. (cont’d) Firmicutes v. Proteobacteria Other v. Proteobacteria -0.907 -0.908 0.007 0.007 1543 1543 -129.040 -129.102 <0.0001 <0.0001 Phylum Contrast estimate SE df t-ratio p-value Actinobacteria Fall - Spring Fall - Summer Spring - Summer Bacteroidetes Fall - Spring Firmicutes Other Fall - Summer Spring - Summer Fall - Spring Fall - Summer Spring - Summer Fall - Spring Fall - Summer Spring - Summer Proteobacteria Fall - Spring Fall - Summer Spring - Summer 0.014 -0.009 -0.023 0.075 0.037 -0.038 0.000 -0.001 -0.001 0.000 0.000 -0.001 -0.090 -0.027 0.063 0.008 0.007 0.007 0.008 0.007 0.007 0.008 0.007 0.007 0.008 0.007 0.007 0.008 0.007 0.007 1530 1587 1577 1530 1587 1577 1530 1587 1577 1530 1587 1577 1530 1587 1577 1.882 -1.277 -3.145 9.935 5.453 -5.296 -0.012 -0.206 -0.178 0.051 -0.022 -0.073 0.1442 0.4085 0.0048 <0.0001 <0.0001 <0.0001 0.9999 0.9769 0.9826 0.9986 0.9997 0.9971 -11.855 -3.949 8.692 <0.0001 0.0002 <0.0001 426 Table B20. The 2019 mean relative abundances of Pseudomonas and all “other” taxa at the genus/family level ± SE, including the pairwise comparisons of the estimated marginal means with the confidence limits (lower and upper C.L.). Superscripted letters following Pseudomonas or “other” indicate statistical differences within each season, while upper-cased letters indicate statistical differences of each Pseudomonas or “other” across seasons, with significance determined by p-value ≤ 0.05. Season Genera Mean SE df Spring Summer Fall Pseudomonas b Other a Pseudomonas b Other a Pseudomonas a Other a 0.97 0.03 0.66 0.34 0.51 0.49 0.02 0.02 0.02 0.02 0.02 0.02 Season Contrast estimate SE Spring Summer Fall Other v. Pseudomonas Other v. Pseudomonas Other v. Pseudomonas -0.95 -0.33 -0.02 0.03 0.03 0.03 Genera Contrast estimate SE Pseudomonas Fall - Spring Other Fall - Summer Spring - Summer Fall - Spring Fall - Summer Spring - Summer -0.47 -0.15 0.31 0.47 0.15 -0.31 0.03 0.03 0.03 0.03 0.03 0.03 205 205 116 116 142 142 df 577 577 577 df 575 621 614 575 621 614 lower C.L. 0.928 -0.018 0.627 0.301 0.469 0.452 upper C.L. 1.0180 C 0.0719 A 0.6993 0.3734 B B 0.5483 A C 0.5313 t-ratio p-value -30.864 -12.924 -0.612 <0.0001 <0.0001 0.5406 t-ratio p-value -15.056 -5.780 10.465 15.056 5.787 -10.465 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 427 Table B21. The 2019 mean relative abundances of the top deepest taxonomic level (genus or family) by season where taxa of relative abundance <0.03 were combined into “other” ± SE, with the confidence limits (lower and upper C.L.). Letters indicate statistical differences within season, with significance determined by p-value ≤ 0.05. Season Spring Summer Fall Season Spring Summer Genera Mean SE df lower C.L. upper C.L. Pseudomonas Other Pseudomonas Sphingomonas Enterobacteriaceae Other Pedobacter Massilia Rhizobiaceae Pseudomonas Sphingomonas Rhizobiaceae Other Massilia Hymenobacter Methylobacterium 0.97 0.03 0.66 0.13 0.06 0.06 0.03 0.03 0.03 0.51 0.20 0.09 0.09 0.04 0.04 0.03 contrast Other v. Pseudomonas Enterobacteriaceae v. Massilia Enterobacteriaceae v. Other Enterobacteriaceae v. Pedobacter Enterobacteriaceae v. Pseudomonas Enterobacteriaceae v. Rhizobiaceae Enterobacteriaceae v. Sphingomonas estimate -0.95 0.03 0.01 0.03 -0.60 0.04 -0.07 428 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 SE 0.01 0.02 0.02 0.02 0.02 0.02 0.02 21 21 220 220 220 220 220 220 220 175 175 175 175 175 175 175 df 137 849 849 849 849 849 849 0.95 0.00 0.64 0.11 0.04 0.03 0.01 0.01 0.00 0.49 0.18 0.07 0.07 0.02 0.02 0.00 1.00 0.05 0.69 0.15 0.09 0.08 0.05 0.05 0.05 0.53 0.22 0.12 0.11 0.06 0.06 0.05 b a c b a a a a a d c b b a a a 2.149 0.492 1.934 p-value t-ratio -86.970 <0.0001 0.3251 0.9990 0.4579 -37.030 <0.0001 0.2918 0.0005 2.209 -4.222 Table B21. (cont’d) Fall Massilia v. Other Massilia v. Pedobacter Massilia v. Pseudomonas Massilia v. Rhizobiaceae Massilia v. Sphingomonas Other v. Pedobacter Other v. Pseudomonas Other v. Rhizobiaceae Other v. Sphingomonas Pedobacter v. Pseudomonas Pedobacter v. Rhizobiaceae Pedobacter v. Sphingomonas Pseudomonas v. Rhizobiaceae Pseudomonas v. Sphingomonas Rhizobiaceae v. Sphingomonas Hymenobacter v. Massilia Hymenobacter v. Methylobacterium Hymenobacter v. Other Hymenobacter v. Pseudomonas Hymenobacter v. Rhizobiaceae Hymenobacter v. Sphingomonas Massilia v. Methylobacterium Massilia v. Other Massilia v. Pseudomonas Massilia v. Rhizobiaceae Massilia v. Sphingomonas Methylobacterium v. Other Methylobacterium v. Pseudomonas Methylobacterium v. Rhizobiaceae Methylobacterium v. Sphingomonas -0.03 0.00 -0.63 0.00 -0.10 0.02 -0.61 0.03 -0.08 -0.63 0.00 -0.10 0.64 0.53 -0.10 0.00 0.01 -0.05 -0.47 -0.05 -0.16 0.01 -0.05 -0.47 -0.05 -0.16 -0.06 -0.48 -0.07 -0.17 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 849 849 849 849 849 849 849 849 849 849 849 849 849 849 849 692 692 692 692 692 692 692 692 692 692 692 692 692 692 692 0.6453 -1.657 -0.214 1.0000 -39.178 <0.0001 1.0000 0.060 -6.371 <0.0001 0.7785 1.442 -37.522 <0.0001 0.6050 1.717 -4.714 0.0001 -38.964 <0.0001 0.274 1.0000 -6.157 <0.0001 39.238 <0.0001 32.807 <0.0001 -6.431 <0.0001 1.0000 -0.184 0.9937 0.680 -3.265 0.0196 -29.249 <0.0001 -3.405 0.0124 -10.017 <0.0001 0.9776 0.864 -3.081 0.0348 -29.064 <0.0001 -3.221 0.0226 -9.833 <0.0001 -3.945 0.0017 -29.929 <0.0001 -4.085 0.0010 -10.697 <0.0001 429 Table B21. (cont’d) Other v. Pseudomonas Other v. Rhizobiaceae Other v. Sphingomonas Pseudomonas v. Rhizobiaceae Pseudomonas v. Sphingomonas Rhizobiaceae v. Sphingomonas -0.42 0.00 -0.11 0.42 0.31 -0.11 0.02 0.02 0.02 0.02 0.02 0.02 692 692 692 692 692 692 -25.983 <0.0001 -0.140 1.0000 -6.752 <0.0001 25.844 <0.0001 19.232 <0.0001 -6.612 <0.0001 430 Table B22. The 2019 mean relative abundances of the core microbiome taxon (genus and OTU identifier), the rest of the combined Pseudomonas and combined Sphingomonas, and the remaining combined “other” taxa ± SE over season, with the confidence limits (lower and upper C.L.). Superscripted letters following genus/OTUs indicate statistical differences within each season, while upper- cased letters indicate statistical differences of each genus/OTU across time, with significance determined by p-value ≤ 0.05. Season Spring Summer Fall Genera Mean SE df lower C.L. upper C.L. Pseudomonas Otu0001 d Pseudomonas Otu0002 c Pseudomonas b Other a Sphingomonas Otu0003 a Sphingomonas Otu0007 a Sphingomonas a Pseudomonas Otu0001 d Pseudomonas Otu0002 c Other c Sphingomonas Otu0003 b Pseudomonas ab Sphingomonas Otu0007 a Sphingomonas a Pseudomonas Otu0001 d Other c Sphingomonas Otu0003 b Pseudomonas Otu0002 a Sphingomonas Otu0007 a Pseudomonas a Sphingomonas a 0.66 0.20 0.11 0.03 0.00 0.00 0.00 0.36 0.25 0.21 0.11 0.05 0.02 0.00 0.43 0.29 0.15 0.06 0.04 0.02 0.00 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 1340 1340 1340 1340 1340 1340 1340 953 953 953 953 953 953 953 1113 1113 1113 1113 1113 1113 1113 0.62 0.17 0.07 -0.01 -0.04 -0.04 -0.04 0.33 0.22 0.17 0.08 0.02 -0.01 -0.03 0.39 0.26 0.12 0.03 0.01 -0.02 -0.03 0.70 0.24 0.15 0.07 0.04 0.04 0.04 0.40 0.28 0.24 0.14 0.08 0.05 0.03 0.46 0.33 0.19 0.10 0.08 0.05 0.04 C B B A A A A A B B B AB A A B C B A A A A 431 Table B23. The 2019 pairwise comparisons of the estimated marginal means of relative abundances of core microbiome taxon (genus and OTU identifier), the rest of the combined Pseudomonas and combined Sphingomonas and the remaining combined “other” taxa for each season, and for each taxon across each season with significance determined by p-value ≤ 0.05. Contrast Estimate SE df t-ratio p-value Season Spring Other v. Pseudomonas Other v. Pseudomonas Otu0001 Other v. Pseudomonas Otu0002 Other v. Sphingomonas Other v. Sphingomonas Otu0003 Other v. Sphingomonas Otu0007 Pseudomonas v. Pseudomonas Otu0001 Pseudomonas v. Pseudomonas Otu0002 Pseudomonas v. Sphingomonas Pseudomonas v. Sphingomonas Otu0003 Pseudomonas v. Sphingomonas Otu0007 Pseudomonas Otu0001 v. Pseudomonas Otu0002 Pseudomonas Otu0001 v. Sphingomonas Pseudomonas Otu0001 v. Sphingomonas Otu0003 Pseudomonas Otu0001 v. Sphingomonas Otu0007 Pseudomonas Otu0002 v. Sphingomonas Pseudomonas Otu0002 v. Sphingomonas Otu0003 Pseudomonas Otu0002 v. Sphingomonas Otu0007 Sphingomonas v. Sphingomonas Otu0003 Sphingomonas v. Sphingomonas Otu0007 Sphingomonas Otu0003 v. Sphingomonas Otu0007 -0.09 -0.63 -0.18 0.03 0.03 0.03 -0.55 -0.09 0.11 0.11 0.11 0.45 0.66 0.66 0.66 0.20 0.20 0.20 0.00 0.00 0.00 0.15 -0.16 -0.04 0.20 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02 0.02 0.02 0.02 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 -3.020 -22.364 -6.337 0.932 0.922 0.925 -19.344 -3.317 3.952 3.942 3.945 16.027 23.296 23.286 23.289 7.269 7.259 7.262 -0.010 -0.007 0.003 6.669 -6.856 -1.812 8.836 0.0409 <0.0001 <0.0001 0.9675 0.9691 0.9687 <0.0001 0.0161 0.0016 0.0016 0.0016 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 1.0000 1.0000 1.0000 <0.0001 <0.0001 0.5401 <0.0001 2019 Summer Other v. Pseudomonas Other v. Pseudomonas Otu0001 Other v. Pseudomonas Otu0002 Other v. Sphingomonas 432 Table B23. (cont’d) 2019 Fall Other v. Sphingomonas Otu0003 Other v. Sphingomonas Otu0007 Pseudomonas v. Pseudomonas Otu0001 Pseudomonas v. Pseudomonas Otu0002 Pseudomonas v. Sphingomonas Pseudomonas v. Sphingomonas Otu0003 Pseudomonas v. Sphingomonas Otu0007 Pseudomonas Otu0001 v. Pseudomonas Otu0002 Pseudomonas Otu0001 v. Sphingomonas Pseudomonas Otu0001 v. Sphingomonas Otu0003 Pseudomonas Otu0001 v. Sphingomonas Otu0007 Pseudomonas Otu0002 v. Sphingomonas Pseudomonas Otu0002 v. Sphingomonas Otu0003 Pseudomonas Otu0002 v. Sphingomonas Otu0007 Sphingomonas v. Sphingomonas Otu0003 Sphingomonas v. Sphingomonas Otu0007 Sphingomonas Otu0003 v. Sphingomonas Otu0007 Other v. Pseudomonas Other v. Pseudomonas Otu0001 Other v. Pseudomonas Otu0002 Other v. Sphingomonas Other v. Sphingomonas Otu0003 Other v. Sphingomonas Otu0007 Pseudomonas v. Pseudomonas Otu0001 Pseudomonas v. Pseudomonas Otu0002 Pseudomonas v. Sphingomonas Pseudomonas v. Sphingomonas Otu0003 Pseudomonas v. Sphingomonas Otu0007 Pseudomonas Otu0001 v. Pseudomonas Otu0002 0.10 0.18 -0.31 -0.20 0.05 -0.06 0.03 0.12 0.36 0.25 0.34 0.25 0.14 0.23 -0.11 -0.02 0.09 0.28 -0.14 0.23 0.29 0.14 0.25 -0.41 -0.05 0.01 -0.14 -0.03 0.36 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 2187 4.135 7.949 -13.525 -8.481 2.167 -2.534 1.279 5.044 15.692 10.991 14.805 10.648 5.947 9.760 -4.701 -0.888 3.813 10.777 -5.288 8.887 11.348 5.377 9.741 -16.065 -1.890 0.571 -5.400 -1.036 14.176 0.0007 <0.0001 <0.0001 <0.0001 0.3140 0.1476 0.8616 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0001 0.9744 0.0027 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.4873 0.9976 <0.0001 0.9456 <0.0001 433 Table B23. (cont’d) Pseudomonas Otu0001 v. Sphingomonas Pseudomonas Otu0001 v. Sphingomonas Otu0003 Pseudomonas Otu0001 v. Sphingomonas Otu0007 Pseudomonas Otu0002 v. Sphingomonas Pseudomonas Otu0002 v. Sphingomonas Otu0003 Pseudomonas Otu0002 v. Sphingomonas Otu0007 Sphingomonas v. Sphingomonas Otu0003 Sphingomonas v. Sphingomonas Otu0007 Sphingomonas Otu0003 v. Sphingomonas Otu0007 0.43 0.27 0.38 0.06 -0.09 0.02 -0.15 -0.04 0.11 Contrast Genus/OTU Other Pseudomonas Pseudomonas Otu0001 Pseudomonas Otu0002 Sphingomonas Fall - Spring Fall - Summer Spring - Summer Fall - Spring Fall - Summer Spring - Summer Fall - Spring Fall - Summer Spring - Summer Fall - Spring Fall - Summer Spring - Summer Fall - Spring Fall - Summer Spring - Summer Sphingomonas Otu0003 Fall - Spring Fall - Summer 434 Estimate 0.27 0.09 -0.18 -0.09 -0.03 0.06 -0.23 0.06 0.29 -0.14 -0.18 -0.04 0.00 0.00 0.00 0.15 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 SE 0.03 0.02 0.03 0.03 0.02 0.03 0.03 0.02 0.03 0.03 0.02 0.03 0.03 0.02 0.03 0.03 0.02 2187 2187 2187 2187 2187 2187 2187 2187 2187 df 2172 2232 2220 2172 2232 2220 2172 2232 2220 2172 2232 2220 2172 2232 2220 2172 2232 16.636 10.665 15.029 2.461 -3.511 0.853 -5.971 -1.607 4.364 t-ratio 9.721 3.536 -6.834 -3.473 -1.405 2.309 -8.397 2.562 11.139 -5.127 -7.473 -1.625 0.068 0.053 -0.022 5.643 1.837 <0.0001 <0.0001 <0.0001 0.1744 0.0083 0.9791 <0.0001 0.6777 0.0003 p-value <0.0001 0.0012 <0.0001 0.0015 0.3383 0.0547 <0.0001 0.0283 <0.0001 <0.0001 <0.0001 0.2352 0.9974 0.9984 0.9997 <0.0001 0.1578 Table B23. (cont’d) Sphingomonas Otu0007 Fall - Spring Spring - Summer Fall - Summer Spring - Summer -0.11 0.04 0.02 -0.02 0.03 2220 -4.168 0.0001 0.03 0.02 0.03 2172 2232 2220 1.565 0.891 -0.799 0.2613 0.6458 0.7035 435 B A C Figure B24. The qqplots for the residuals for A) precipitation, B) relative humidity, and C) temperature, over a two week period leading up to leaf sampling. 436 Table B24 The means ± SE with the lower and upper confidence limits for the relative humidity (%), daily temperature °C, and the median precipitation (mm) compiled from the two-week period leading up to the microbiome leaf sampling dates. Metric Time Mean Relative humidity (%) Daily temperature (°C) Metric Precipitation (mm) 2017 Fall 2018 Spring 2018 Summer 2018 Fall 2019 Spring 2019 Summer 2019 Fall 2017 Fall 2018 Spring 2018 Summer 2018 Fall 2019 Spring 2019 Summer 2019 Fall Time 2017 Fall 2018 Spring 2018 Summer 2018 Fall 2019 Spring 2019 Summer 2019 Fall SE 1.8 1.7 1.7 1.7 1.7 1.7 1.7 0.6 0.6 0.6 0.6 0.6 0.6 0.6 df 188 188 188 188 188 188 188 188 188 188 188 188 188 188 Lower C.L. 70.5 69.4 70.0 77.3 73.9 69.6 75.3 Upper C.L 77.4 76.1 76.7 84.0 80.6 76.3 82.0 11.3 13.3 21.4 13.0 14.1 19.8 13.9 13.8 15.7 23.9 15.5 16.6 22.3 16.4 ab b b a ab b ab a ab c ab b c ab 74.0 72.8 73.3 80.6 77.2 72.9 78.6 12.6 14.5 22.6 14.2 15.4 21.1 15.1 Median 0.0 2.3 0.0 0.6 2.2 0.0 0.8 ab a ab ab a b a 437 Table B25. The p-values for the pairwise comparisons of the estimated marginal means of the relative humidity (%), and daily temperature (°C), and the p-values and Bonferroni adjusted p-values for the pairwise comparisons of the distribution (medians) of the precipitation (mm) data following the Dunns Test; all data was assembled from the two-week period leading up to microbiome leaf sampling. Metric Relative humidity (%) Contrast 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer Daily Temperature (°C) 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall df 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 t-ratio -2.735 0.495 0.254 -1.913 -1.343 0.415 3.285 3.017 0.830 1.406 3.179 -0.242 -2.447 -1.867 -0.078 -2.186 -1.611 0.163 0.575 2.349 1.774 -1.840 -2.160 -11.102 -2.825 p-value 0.0953 0.9989 1.0000 0.4746 0.8309 0.9996 0.0205 0.0452 0.9815 0.7983 0.0282 1.0000 0.1852 0.5049 1.0000 0.3079 0.6754 1.0000 0.9974 0.2266 0.5674 0.5226 0.3226 <.0001 0.0759 SE 2.5 2.4 2.5 2.5 2.5 2.5 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 0.9 0.9 0.9 0.9 Estimate -6.7 1.2 0.6 -4.7 -3.3 1.0 7.9 7.3 2.0 3.4 7.7 -0.6 -5.9 -4.5 -0.2 -5.3 -3.9 0.4 1.4 5.7 4.3 -1.7 -1.9 -10.1 -2.6 438 Table B25. (cont’d) Precipitation (mm) 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer Comparison 2017 Fall v. 2018 Fall 2017 Fall v. 2018 Spring 2017 Fall v. 2018 Summer 2017 Fall v. 2019 Fall 2017 Fall v. 2019 Spring 2017 Fall v. 2019 Summer 2018 Fall v. 2018 Spring 2018 Fall v. 2018 Summer 2018 Fall v. 2019 Fall 2018 Fall v. 2019 Spring 2018 Fall v. 2019 Summer -2.8 -8.5 -0.3 -8.4 -0.9 -1.1 -6.9 -8.1 -0.6 -0.9 -6.6 7.5 7.3 1.6 -0.3 -6.0 -5.7 z-test -0.385 -1.071 -0.689 1.593 1.996 2.703 -0.912 -0.532 0.153 -2.528 -1.164 439 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 188 -3.104 -9.392 -0.307 -9.348 -0.994 -1.276 -7.622 -9.122 -0.696 -0.980 -7.381 8.354 8.072 1.726 -0.282 -6.628 -6.346 0.0352 <.0001 0.9999 <.0001 0.9548 0.8623 <.0001 <.0001 0.9927 0.9578 <.0001 <.0001 <.0001 0.5996 1.0000 <.0001 <.0001 p-value adj. p-value 0.7006 0.2843 0.4906 0.1111 0.0459 0.0069 0.3619 0.5947 0.8788 0.0115 0.2443 1.0000 1.0000 1.0000 1.0000 0.9647 0.1444 1.0000 1.0000 1.0000 0.2409 1.0000 Table B25. (cont’d) 2018 Spring v. 2018 Summer 2018 Spring v. 2019 Fall 2018 Spring v. 2019 Spring 2018 Spring v. 2019 Summer 2018 Summer v. 2019 Fall 2018 Summer v. 2019 Spring 2018 Summer v. 2019 Summer 2019 Fall v. 2019 Spring 2019 Fall v. 2019 Summer 2019 Spring v. 2019 Summer -0.787 -0.104 -2.783 -0.255 2.281 2.690 3.403 0.694 3.223 3.477 0.4313 0.9168 0.0054 0.7989 0.0225 0.0071 0.0007 0.4874 0.0013 0.0005 1.0000 1.0000 0.1132 1.0000 0.4731 0.1499 0.0140 1.0000 0.0267 0.0106 440 Figure B25. Timeline of the spray events that occurred at the two sites for each two-week period leading up to the leaf sampling date. The leaves indicate the sample date, while purple (site 1) and green (site 2) arrows indicate a spray event for each site. Sprays consisted of insecticides (I), fungicides (F), and foliar nutrients (N) at site 1 and insecticides (I) and fungicides (F) at site 2. 441 Figure B26. Fall 2019 bacterial canker ratings for trees planted at sites 1 and 2, two years after planting (Spring 2017). Ratings were recorded for structural wood, the trunk, and branches, where canker ratings range from no canker (3=healthy) to moderate infection (2=minor) to severe infection (1=major infection). 442 APPENDIX C: PSS VS. BIOLOGICAL CONTROL AGENTS Figure C1. The 2019 quantile-quantile plots (qqplot) of the residuals for graphical visualization of the distribution of the total area of growth (mm2) of Pss phylogroup (PG) 2d strains 9, 25, 37, and 38 at 108 cfu/ml co-inoculated next to the commercial biological controls Serenade® Opti (SO), Blossom Protect™ (BP) or water (H2O). 443 A C B D Figure C2. The 2021 quantile-quantile plots (qqplot) of the residuals for graphical visualization of the distribution of the total area of growth (mm2) of Pss strains at 108 cfu/ml co-inoculated next to the commercial biological controls BlightBan®A506 (BB), Bloomtime™ (BLT), Blossom Protect™ (BP), Double Nickel55™ (DN), Serenade® Opti (SO), or water (H2O) at 3 rates (0.5x, 1.0x, 1.5). A) Group 1 Pss strains 14 (PG 2c), 25 (PG2d), and 38 (PG2d), B) Group 2 Pss strains 23 and 34 (PG2b), C) Group 3 Pss strains 22 (PG2b), 26 (PG2d), and 37 (PG2d), and D) Group 4 Pss strains 9 and 27 (PG2d). 444 Table C1. The 2019 co-inoculations of the virulent Pss phylogroup (PG) 2d strains 9, 25, 37, and 38 grown next to the commercial biological controls Serenade® Opti (SO), Blossom Protect™ (BP) or water (H2O) in Petri plates with KB media, with the mean total area of growth (mm2) of the strains ± SE, confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each treatment, with letters indicating statistical differences with significance determined by p-value ≤0.05. Treatment Mean total area (mm2) SE df lower C.L. upper C.L. SO BP H2O 160.6 165.6 220.7 13.3 13.3 13.3 Contrast Estimate SE BP v. H2O BP v. SO H2O v. SO -55.1 5.0 60.1 18.9 18.9 18.9 48 48 48 df 48 48 48 133.8 138.8 193.9 187.4 192.4 247.5 a a b t-ratio p-value -2.920 0.265 3.185 0.0145 0.9621 0.0071 445 Table C2. Group 1 2021, mean total area of growth (mm2) of Pss strains 14, 25, and 38, belonging to phylogroup (PG) 2c, 2d, and 2d, respectively ± SE grown next to the five biological control agents (BCA) or H2O co-inoculated at the rates 0.5x, 1.0x, and 1.5x. With confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means at each rate, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG BCA rate Pss Mean total area (mm2) SE df lower C.L. upper C.L. 2021 1 14 2c 25 2d 38 2d 1.0x 0.5x 1.5x 178.0 192.9 223.4 11.4 199 11.3 199 11.7 199 155.4 170.6 200.4 200.6 215.2 246.4 b ab a Year Group Strain/PG contrast estimate SE df t-ratio p-value 2021 1 14 2c 25 2d 38 2d 0.5x v. 1.5x 0.5x v. 1.0x 1.5x v. 1.0x -30.5 14.9 45.4 16.3 199 16.1 199 16.3 199 -1.875 0.926 2.777 0.1486 0.6247 0.0165 446 Table C3. Group 2 2021, by treatment (trt, i.e., biological control agent, BCA) and by strain, mean total area of growth (mm2) of Pss strains 23 and 34, belonging to phylogroup (PG) 2b ± SE in co-inoculations of BCAs [BlightBan®A506 (BB), Bloomtime™ (BLT), Blossom Protect™ (BP), Double Nickel55™ (DN), Serenade® Opti (SO), or water (H2O)] at three rates (0.5x, 1.0x, 1.5x), with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each treatment and strain, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG 2021 2 23 2b 34 2b Treatment (BCA) Pss Mean total area (mm2) SE df lower C.L. upper C.L. BB BLT BP DN H2O SO 153.3 164.5 177.7 167.3 237.6 184.0 12.5 119 15.9 119 12.5 119 13.9 119 12.8 119 17.2 119 128.5 133.0 152.9 139.8 212.4 149.8 178.0 196.1 202.4 194.8 262.9 218.1 a a a a b ab Year Group Strain/PG contrast estimate SE df t-ratio p-value 2021 2 23 2b 34 2b BB v. BLT BB v. BP BB v. DN BB v. H2O BB v. SO BLT v. BP BLT v. DN BLT v. H2O BLT v. SO BP v. DN BP v. H2O BP v. SO DN v. H2O DN v. SO H2O v. SO -11.2 -24.4 -14.0 -84.4 -30.7 -13.2 -2.8 -73.1 -19.5 10.4 -59.9 -6.3 -70.3 -16.7 53.7 20.3 119 17.7 119 18.7 119 17.9 119 21.3 119 20.3 119 21.1 119 20.4 119 23.5 119 18.7 119 17.9 119 21.3 119 18.9 119 22.1 119 21.4 119 -0.555 -1.382 -0.751 -4.725 -1.442 -0.651 -0.132 -3.583 -0.829 0.556 -3.356 -0.295 -3.729 -0.753 2.502 0.9936 0.7375 0.9749 0.0001 0.7011 0.9867 1.0000 0.0064 0.9615 0.9935 0.0132 0.9997 0.0039 0.9746 0.1318 Year Group Strain 2021 2 23 34 Year Group 2021 2 contrast 23 v. 34 PG 2b Mean total area (mm2) SE df lower C.L. upper C.L. 165.8 195.6 estimate 8.4 8.1 SE 119 119 149.2 179.7 182.4 211.6 a b df t-ratio p-value -29.8 11.6 119 -2.561 0.0117 447 Table C4. Group 2 2021 mean total area of growth (mm2) of Pss strains 23 and 34, belonging to phylogroup (PG) 2b ± SE in co- inoculations of biological control agents (BCAs) [BlightBan®A506 (BB), Bloomtime™ (BLT), Blossom Protect™ (BP), Double Nickel55™ (DN), Serenade® Opti (SO), or water (H2O)] at three rates (0.5x, 1.0x, 1.5x), with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each rate*treatment, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG Rate Treatment (BCA) Pss Mean total area (mm2) SE df lower C.L. upper C.L. 2021 2 23 2b 34 2b 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x BB BB BB BLT BLT BLT BP BP BP DN DN DN H2O H2O H2O SO SO SO 21.6 21.6 21.6 27.9 26.1 28.6 21.6 21.6 21.6 26.1 23.0 23.0 23.0 21.6 21.6 26.1 23.0 38.3 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 106.8 105.4 118.9 81.4 110.5 137.8 171.1 119.7 113.6 117.1 122.8 119.3 226.9 131.2 223.6 155.9 165.3 57.7 192.6 191.2 204.7 192.1 214.0 251.3 256.9 205.5 199.3 220.6 213.7 210.3 317.8 216.9 309.4 259.4 256.3 209.3 a a ab a ab ab ab ab a ab ab ab b ab b ab ab ab 149.7 148.3 161.8 136.7 162.3 194.5 214.0 162.6 156.5 168.8 168.3 164.8 272.4 174.1 266.5 207.6 210.8 133.5 448 Table C4. (cont’d) Year Group Strain/PG contrast estimate SE df t-ratio p-value 2021 2 23 2b 34 2b 0.5x BB v. 1.5x BB 0.5x BB v. 1.0x BB 0.5x BB v. 0.5x BLT 0.5x BB v. 1.5x BLT 0.5x BB v. 1.0x BLT 0.5x BB v. 0.5x BP 0.5x BB v. 1.5x BP 0.5x BB v. 1.0x BP 0.5x BB v. 0.5x DN 0.5x BB v. 1.5x DN 0.5x BB v. 1.0x DN 0.5x BB v. 0.5x H2O 0.5x BB v. 1.5x H2O 0.5x BB v. 1.0x H2O 0.5x BB v. 0.5x SO 0.5x BB v. 1.5x SO 0.5x BB v. 1.0x SO 1.5x BB v. 1.0x BB 1.5x BB v. 0.5x BLT 1.5x BB v. 1.5x BLT 1.5x BB v. 1.0x BLT 1.5x BB v. 0.5x BP 1.5x BB v. 1.5x BP 1.5x BB v. 1.0x BP 1.5x BB v. 0.5x DN 1.5x BB v. 1.5x DN 1.5x BB v. 1.0x DN 1.5x BB v. 0.5x H2O 1.5x BB v. 1.5x H2O 30.6 30.6 35.4 33.9 35.9 30.6 30.6 30.6 33.9 31.6 31.6 31.6 30.6 30.6 33.9 31.6 44.0 30.6 35.4 33.9 35.9 30.6 30.6 30.6 33.9 31.6 31.6 31.6 30.6 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 0.046 -0.395 0.367 -0.370 -1.249 -2.100 -0.421 -0.221 -0.563 -0.588 -0.478 -3.887 -0.795 -3.815 -1.707 -1.936 0.369 -0.441 0.327 -0.411 -1.288 -2.147 -0.467 -0.267 -0.605 -0.633 -0.523 -3.931 -0.842 1.0000 1.0000 1.0000 1.0000 0.9986 0.8104 1.0000 1.0000 1.0000 1.0000 1.0000 0.0184 1.0000 0.0233 0.9616 0.8917 1.0000 1.0000 1.0000 1.0000 0.9980 0.7832 1.0000 1.0000 1.0000 1.0000 1.0000 0.0159 1.0000 1.4 -12.1 13.0 -12.6 -44.8 -64.3 -12.9 -6.8 -19.1 -18.6 -15.1 -122.7 -24.4 -116.8 -57.9 -61.1 16.2 -13.5 11.6 -14.0 -46.3 -65.7 -14.3 -8.2 -20.5 -20.0 -16.5 -124.1 -25.8 449 Table C4. (cont’d) 1.5x BB v. 1.0x H2O 1.5x BB v. 0.5x SO 1.5x BB v. 1.5x SO 1.5x BB v. 1.0x SO 1.0x BB v. 0.5x BLT 1.0x BB v. 1.5x BLT 1.0x BB v. 1.0x BLT 1.0x BB v. 0.5x BP 1.0x BB v. 1.5x BP 1.0x BB v. 1.0x BP 1.0x BB v. 0.5x DN 1.0x BB v. 1.5x DN 1.0x BB v. 1.0x DN 1.0x BB v. 0.5x H2O 1.0x BB v. 1.5x H2O 1.0x BB v. 1.0x H2O 1.0x BB v. 0.5x SO 1.0x BB v. 1.5x SO 1.0x BB v. 1.0x SO 0.5x BLT v. 1.5x BLT 0.5x BLT v. 1.0x BLT 0.5x BLT v. 0.5x BP 0.5x BLT v. 1.5x BP 0.5x BLT v. 1.0x BP 0.5x BLT v. 0.5x DN 0.5x BLT v. 1.5x DN 0.5x BLT v. 1.0x DN 0.5x BLT v. 0.5x H2O 0.5x BLT v. 1.5x H2O 30.6 33.9 31.6 44.0 35.4 33.9 35.9 30.6 30.6 30.6 33.9 31.6 31.6 31.6 30.6 30.6 33.9 31.6 44.0 38.3 40.0 35.4 35.4 35.4 38.3 36.2 36.2 36.2 35.4 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 -3.861 -1.749 -1.981 0.337 0.709 -0.014 -0.913 -1.706 -0.026 0.173 -0.208 -0.205 -0.096 -3.504 -0.401 -3.420 -1.351 -1.553 0.644 -0.667 -1.445 -2.186 -0.732 -0.559 -0.839 -0.872 -0.776 -3.750 -1.056 0.0200 0.9524 0.8722 1.0000 1.0000 1.0000 1.0000 0.9618 1.0000 1.0000 1.0000 1.0000 1.0000 0.0601 1.0000 0.0761 0.9965 0.9844 1.0000 1.0000 0.9927 0.7583 1.0000 1.0000 1.0000 1.0000 1.0000 0.0286 0.9998 -118.2 -59.4 -62.5 14.8 25.1 -0.5 -32.8 -52.2 -0.8 5.3 -7.0 -6.5 -3.0 -110.6 -12.3 -104.7 -45.9 -49.0 28.3 -25.5 -57.8 -77.3 -25.9 -19.8 -32.1 -31.5 -28.1 -135.6 -37.3 450 Table C4. (cont’d) 0.5x BLT v. 1.0x H2O 0.5x BLT v. 0.5x SO 0.5x BLT v. 1.5x SO 0.5x BLT v. 1.0x SO 1.5x BLT v. 1.0x BLT 1.5x BLT v. 0.5x BP 1.5x BLT v. 1.5x BP 1.5x BLT v. 1.0x BP 1.5x BLT v. 0.5x DN 1.5x BLT v. 1.5x DN 1.5x BLT v. 1.0x DN 1.5x BLT v. 0.5x H2O 1.5x BLT v. 1.5x H2O 1.5x BLT v. 1.0x H2O 1.5x BLT v. 0.5x SO 1.5x BLT v. 1.5x SO 1.5x BLT v. 1.0x SO 1.0x BLT v. 0.5x BP 1.0x BLT v. 1.5x BP 1.0x BLT v. 1.0x BP 1.0x BLT v. 0.5x DN 1.0x BLT v. 1.5x DN 1.0x BLT v. 1.0x DN 1.0x BLT v. 0.5x H2O 1.0x BLT v. 1.5x H2O 1.0x BLT v. 1.0x H2O 1.0x BLT v. 0.5x SO 1.0x BLT v. 1.5x SO 1.0x BLT v. 1.0x SO 35.4 38.3 36.2 47.4 38.8 33.9 33.9 33.9 37.0 34.8 34.8 34.8 33.9 33.9 37.0 34.8 46.3 35.9 35.9 35.9 38.8 36.7 36.7 36.7 35.9 35.9 38.8 36.7 47.8 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 -3.671 -1.853 -2.048 0.068 -0.833 -1.525 -0.010 0.170 -0.178 -0.172 -0.073 -3.164 -0.348 -3.071 -1.228 -1.395 0.621 -0.542 0.890 1.060 0.663 0.716 0.810 -2.120 0.571 -2.004 -0.338 -0.443 1.278 0.0366 0.9227 0.8390 1.0000 1.0000 0.9871 1.0000 1.0000 1.0000 1.0000 1.0000 0.1484 1.0000 0.1853 0.9989 0.9950 1.0000 1.0000 1.0000 0.9998 1.0000 1.0000 1.0000 0.7991 1.0000 0.8612 1.0000 1.0000 0.9982 -129.8 -70.9 -74.1 3.2 -32.3 -51.8 -0.3 5.8 -6.6 -6.0 -2.5 -110.1 -11.8 -104.2 -45.4 -48.6 28.8 -19.5 32.0 38.1 25.7 26.3 29.7 -77.8 20.5 -71.9 -13.1 -16.3 61.1 451 Table C4. (cont’d) 0.5x BP v. 1.5x BP 0.5x BP v. 1.0x BP 0.5x BP v. 0.5x DN 0.5x BP v. 1.5x DN 0.5x BP v. 1.0x DN 0.5x BP v. 0.5x H2O 0.5x BP v. 1.5x H2O 0.5x BP v. 1.0x H2O 0.5x BP v. 0.5x SO 0.5x BP v. 1.5x SO 0.5x BP v. 1.0x SO 1.5x BP v. 1.0x BP 1.5x BP v. 0.5x DN 1.5x BP v. 1.5x DN 1.5x BP v. 1.0x DN 1.5x BP v. 0.5x H2O 1.5x BP v. 1.5x H2O 1.5x BP v. 1.0x H2O 1.5x BP v. 0.5x SO 1.5x BP v. 1.5x SO 1.5x BP v. 1.0x SO 1.0x BP v. 0.5x DN 1.0x BP v. 1.5x DN 1.0x BP v. 1.0x DN 1.0x BP v. 0.5x H2O 1.0x BP v. 1.5x H2O 1.0x BP v. 1.0x H2O 1.0x BP v. 0.5x SO 1.0x BP v. 1.5x SO 30.6 30.6 33.9 31.6 31.6 31.6 30.6 30.6 33.9 31.6 44.0 30.6 33.9 31.6 31.6 31.6 30.6 30.6 33.9 31.6 44.0 33.9 31.6 31.6 31.6 30.6 30.6 33.9 31.6 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 1.679 1.879 1.331 1.450 1.559 -1.849 1.305 -1.714 0.188 0.102 1.832 0.200 -0.184 -0.179 -0.070 -3.478 -0.374 -3.394 -1.327 -1.528 0.662 -0.364 -0.373 -0.264 -3.672 -0.574 -3.593 -1.507 -1.722 0.9669 0.9138 0.9971 0.9924 0.9838 0.9241 0.9977 0.9601 1.0000 1.0000 0.9297 1.0000 1.0000 1.0000 1.0000 0.0646 1.0000 0.0819 0.9972 0.9868 1.0000 1.0000 1.0000 1.0000 0.0364 1.0000 0.0462 0.9885 0.9585 51.4 57.5 45.2 45.8 49.2 -58.4 40.0 -52.5 6.4 3.2 80.5 6.1 -6.2 -5.7 -2.2 -109.8 -11.5 -103.9 -45.1 -48.2 29.1 -12.4 -11.8 -8.3 -115.9 -17.6 -110.0 -51.2 -54.3 452 Table C4. (cont’d) 1.0x BP v. 1.0x SO 0.5x DN v. 1.5x DN 0.5x DN v. 1.0x DN 0.5x DN v. 0.5x H2O 0.5x DN v. 1.5x H2O 0.5x DN v. 1.0x H2O 0.5x DN v. 0.5x SO 0.5x DN v. 1.5x SO 0.5x DN v. 1.0x SO 1.5x DN v. 1.0x DN 1.5x DN v. 0.5x H2O 1.5x DN v. 1.5x H2O 1.5x DN v. 1.0x H2O 1.5x DN v. 0.5x SO 1.5x DN v. 1.5x SO 1.5x DN v. 1.0x SO 1.0x DN v. 0.5x H2O 1.0x DN v. 1.5x H2O 1.0x DN v. 1.0x H2O 1.0x DN v. 0.5x SO 1.0x DN v. 1.5x SO 1.0x DN v. 1.0x SO 0.5x H2O v. 1.5x H2O 0.5x H2O v. 1.0x H2O 0.5x H2O v. 0.5x SO 0.5x H2O v. 1.5x SO 0.5x H2O v. 1.0x SO 1.5x H2O v. 1.0x H2O 1.5x H2O v. 0.5x SO 44.0 34.8 34.8 34.8 33.9 33.9 37.0 34.8 46.3 32.5 32.5 31.6 31.6 34.8 32.5 44.6 32.5 31.6 31.6 34.8 32.5 44.6 31.6 31.6 34.8 32.5 44.6 30.6 33.9 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 0.523 0.017 0.116 -2.975 -0.154 -2.877 -1.050 -1.206 0.763 0.106 -3.206 -0.184 -3.113 -1.132 -1.310 0.779 -3.312 -0.293 -3.222 -1.231 -1.417 0.702 3.115 0.186 1.860 1.896 3.112 -3.019 -0.990 1.0000 1.0000 1.0000 0.2294 1.0000 0.2816 0.9999 0.9991 1.0000 1.0000 0.1340 1.0000 0.1680 0.9996 0.9976 1.0000 0.1019 1.0000 0.1286 0.9989 0.9941 1.0000 0.1671 1.0000 0.9205 0.9078 0.1683 0.2083 0.9999 23.0 0.6 4.0 -103.5 -5.2 -97.7 -38.8 -42.0 35.4 3.5 -104.1 -5.8 -98.2 -39.4 -42.6 34.8 -107.6 -9.3 -101.7 -42.8 -46.0 31.3 98.3 5.9 64.7 61.6 138.9 -92.4 -33.6 453 Table C4. (cont’d) 1.5x H2O v. 1.5x SO 1.5x H2O v. 1.0x SO 1.0x H2O v. 0.5x SO 1.0x H2O v. 1.5x SO 1.0x H2O v. 1.0x SO 0.5x SO v. 1.5x SO 0.5x SO – 1.0x SO 1.5x SO – 1.0x SO -36.8 40.6 58.8 55.7 133.0 -3.2 74.2 77.3 31.6 44.0 33.9 31.6 44.0 34.8 46.3 44.6 119 119 119 119 119 119 119 119 -1.165 0.923 1.734 1.765 3.025 -0.091 1.600 1.733 0.9994 1.0000 0.9559 0.9486 0.2055 1.0000 0.9790 0.9561 454 Table C5. Group 2 2021 mean total area of growth (mm2) of Pss strains 23 and 34, belonging to phylogroup (PG) 2b ± SE in co- inoculations of biological control agents (BCAs) [BlightBan®A506 (BB), Bloomtime™ (BLT), Blossom Protect™ (BP), Double Nickel55™ (DN), Serenade® Opti (SO), or water (H2O)] at three rates (0.5x, 1.0x, 1.5x), with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each trt*strain, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG Treatment (BCA) 2021 2 23 2b 34 2b BB BLT BP DN H2O SO BB BLT BP DN H2O SO Year Group 2021 2 Contrast BB strain 23 v. BLT strain 23 BB strain 23 v. BP strain 23 BB strain 23 v. DN strain 23 BB strain 23 v. H2O strain 23 BB strain 23 v. SO strain 23 BB strain 23 v. BB strain 34 BB strain 23 v. BLT strain 34 Pss Mean total area (mm2) SE df lower C.L. upper C.L. 151.0 158.1 181.7 170.3 172.4 161.6 155.5 171.0 173.7 164.3 302.9 206.4 estimate -7.0 -30.7 -19.3 -21.4 -10.5 -4.5 -19.9 17.7 20.2 17.7 19.5 18.4 27.9 17.7 24.6 17.7 19.8 17.7 20.2 SE 26.8 25.0 26.3 25.5 33.1 25.0 30.3 119 119 119 119 119 119 119 119 119 119 119 119 df 119 119 119 119 119 119 119 a a a a a a a a a a b a 116.0 118.1 146.7 131.6 136.0 106.2 120.5 122.2 138.7 125.2 267.9 166.4 t-ratio -0.263 -1.228 -0.731 -0.838 -0.319 -0.180 -0.658 186.0 198.0 216.7 209.0 208.8 216.9 190.5 219.8 208.7 203.5 337.9 246.4 p-value 1.0000 0.9856 0.9999 0.9995 1.0000 1.0000 1.0000 455 Table C5. (cont’d) BB strain 23 v. BP strain 34 BB strain 23 v. DN strain 34 BB strain 23 v. H2O strain 34 BB strain 23 v. SO strain 34 BLT strain 23 v. BP strain 23 BLT strain 23 v. DN strain 23 BLT strain 23 v. H2O strain 23 BLT strain 23 v. SO strain 23 BLT strain 23 v. BB strain 34 BLT strain 23 v. BLT strain 34 BLT strain 23 v. BP strain 34 BLT strain 23 v. DN strain 34 BLT strain 23 v. H2O strain 34 BLT strain 23 v. SO strain 34 BP strain 23 v. DN strain 23 BP strain 23 v. H2O strain 23 BP strain 23 v. SO strain 23 BP strain 23 v. BB strain 34 BP strain 23 v. BLT strain 34 BP strain 23 v. BP strain 34 BP strain 23 v. DN strain 34 BP strain 23 v. H2O strain 34 BP strain 23 v. SO strain 34 DN strain 23 v. H2O strain 23 DN strain 23 v. SO strain 23 DN strain 23 v. BB strain 34 DN strain 23 v. BLT strain 34 DN strain 23 v. BP strain 34 DN strain 23 v. DN strain 34 -22.7 -13.3 -151.9 -55.4 -23.6 -12.2 -14.3 -3.5 2.5 -12.9 -15.6 -6.3 -144.8 -48.3 11.4 9.3 20.2 26.2 10.7 8.0 17.4 -121.2 -24.7 -2.1 8.7 14.7 -0.7 -3.4 5.9 25.0 26.5 25.0 26.8 26.8 28.1 27.3 34.5 26.8 31.9 26.8 28.3 26.8 28.6 26.3 25.5 33.1 25.0 30.3 25.0 26.5 25.0 26.8 26.8 34.1 26.3 31.5 26.3 27.8 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 -0.907 -0.502 -6.076 -2.064 -0.881 -0.434 -0.524 -0.101 0.095 -0.405 -0.582 -0.222 -5.397 -1.693 0.434 0.365 0.609 1.048 0.354 0.321 0.655 -4.848 -0.920 -0.079 0.255 0.560 -0.022 -0.130 0.213 0.9989 1.0000 <0.0001 0.6490 0.9992 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 <0.0001 0.8681 1.0000 1.0000 1.0000 0.9962 1.0000 1.0000 1.0000 0.0002 0.9988 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 456 Table C5. (cont’d) DN strain 23 v. H2O strain 34 DN strain 23 v. SO strain 34 H2O strain 23 v. SO strain 23 H2O strain 23 v. BB strain 34 H2O strain 23 v. BLT strain 34 H2O strain 23 v. BP strain 34 H2O strain 23 v. DN strain 34 H2O strain 23 v. H2O strain 34 H2O strain 23 v. SO strain 34 SO strain 23 v. BB strain 34 SO strain 23 v. BLT strain 34 SO strain 23 v. BP strain 34 SO strain 23 v. DN strain 34 SO strain 23 v. H2O strain 34 SO strain 23 v. SO strain 34 BB strain 34 v. BLT strain 34 BB strain 34 v. BP strain 34 BB strain 34 v. DN strain 34 BB strain 34 v. H2O strain 34 BB strain 34 v. SO strain 34 BLT strain 34 v. BP strain 34 BLT strain 34 v. DN strain 34 BLT strain 34 v. H2O strain 34 BLT strain 34 v. SO strain 34 BP strain 34 v. DN strain 34 BP strain 34 v. H2O strain 34 BP strain 34 v. SO strain 34 DN strain 34 v. H2O strain 34 DN strain 34 v. SO strain 34 H2O strain 34 v. SO strain 34 -132.6 -36.1 10.8 16.9 1.4 -1.3 8.0 -130.5 -34.0 6.0 -9.4 -12.1 -2.8 -141.3 -44.9 -15.4 -18.2 -8.8 -147.4 -50.9 -2.7 6.6 -131.9 -35.4 9.4 -129.2 -32.7 -138.6 -42.1 96.5 26.3 28.1 33.5 25.5 30.8 25.5 27.0 25.5 27.3 33.1 37.3 33.1 34.2 33.1 34.5 30.3 25.0 26.5 25.0 26.8 30.3 31.6 30.3 31.9 26.5 25.0 26.8 26.5 28.3 26.8 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 119 -5.034 -1.286 0.324 0.661 0.046 -0.051 0.298 -5.116 -1.245 0.183 -0.252 -0.367 -0.081 -4.274 -1.301 -0.509 -0.727 -0.333 -5.896 -1.896 -0.090 0.210 -4.350 -1.112 0.353 -5.169 -1.219 -5.226 -1.489 3.595 0.0001 0.9794 1.0000 1.0000 1.0000 1.0000 1.0000 0.0001 0.9839 1.0000 1.0000 1.0000 1.0000 0.0022 0.9775 1.0000 0.9999 1.0000 <0.0001 0.7593 1.0000 1.0000 0.0017 0.9936 1.0000 0.0001 0.9864 <0.0001 0.9409 0.0229 457 Table C6. Group 3 2021, by rate and by strain, mean total area of growth (mm2) of Pss strains 22, 26, and 37, belonging to phylogroup (PG) 2b and 2d, ± SE, in co-inoculations of biological control agents (BCAs) [BlightBan®A506 (BB), Bloomtime™ (BLT), Blossom Protect™ (BP), Double Nickel55™ (DN), Serenade® Opti (SO), or water (H2O)] at three rates (0.5x, 1.0x, 1.5x), with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each treatment and strain, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG Rate 2021 3 22 2b 26 2d 37 2d 0.5x 1.5x 1.0x Pss Mean total area (mm2) 136.8 165.5 150.9 Year Group Strain/PG contrast estimate 2021 3 22 2b 26 2d 37 2d 0.5x v. 1.5x 0.5x v. 1.0x 1.5x v. 1.0x -28.8 -14.1 14.6 SE df lower C.L. upper C.L. 6.2 6.5 7.0 SE 9.0 9.3 9.5 189 189 189 124.5 152.6 137.2 149.0 178.4 164.6 a b ab df t-ratio p-value 189 189 189 -3.194 -1.518 1.532 0.0047 0.2849 0.2781 Year Group Strain PG 2021 3 22 26 37 2b 2d 2d Mean total area (mm2) 122.2 154.7 176.3 Year Group contrast estimate 2021 3 strain 22 v. strain 26 strain 22 v. strain 37 strain 26 v. strain 37 -32.5 -54.1 -21.6 SE df lower C.L. upper C.L. 7.0 6.0 6.6 SE 9.2 9.7 9.0 189 189 189 108.3 142.9 163.2 136.0 166.6 189.4 a b c df t-ratio p-value 189 189 189 -3.520 0.0016 -5.598 <0.0001 0.0443 -2.411 458 Table C7. Group 3 2021 mean total area of growth (mm2) of Pss strains 22, 26, and 37, belonging to phylogroup (PG) 2b and 2d, ± SE, in co-inoculations of biological control agents (BCAs) [BlightBan®A506 (BB), Bloomtime™ (BLT), Blossom Protect™ (BP), Double Nickel55™ (DN), Serenade® Opti (SO), or water (H2O)] at three rates (0.5x, 1.0x, 1.5x), with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each treatment*strain, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG Treatment (BCA) 2021 3 22 2b 26 2d 37 2d BB BLT BP DN H2O SO BB BLT BP DN H2O SO BB BLT BP DN H2O SO Pss Mean total area (mm2) SE df lower C.L. upper C.L. 117.7 118.0 120.5 131.4 119.0 126.7 164.0 149.7 147.7 160.0 134.2 172.8 184.8 149.3 150.2 201.1 226.4 146.0 14.9 17.5 14.9 22.2 17.5 14.9 14.3 14.9 14.9 14.3 15.5 14.3 14.3 21.9 14.9 14.9 16.0 14.3 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 88.3 83.4 91.1 87.5 84.5 97.3 135.8 120.3 118.3 131.8 103.7 144.5 156.6 106.1 120.8 171.7 194.9 117.8 147.1 152.6 149.8 175.2 153.6 156.1 192.2 179.1 177.1 188.2 164.7 201.0 213.1 192.4 179.6 230.5 258.0 174.2 a a a abc a ab abc abc ab abc ab abc abc abc abc bc c ab 459 Table C7. (cont’d) Year Group 2021 3 Contrast estimate SE df t-ratio p-value BB strain 22 v. BLT strain 22 BB strain 22 v. BP strain 22 BB strain 22 v. DN strain 22 BB strain 22 v. H2O strain 22 BB strain 22 v. SO strain 22 BB strain 22 v. BB strain 26 BB strain 22 v. BLT strain 26 BB strain 22 v. BP strain 26 BB strain 22 v. DN strain 26 BB strain 22 v. H2O strain 26 BB strain 22 v. SO strain 26 BB strain 22 v. BB strain 37 BB strain 22 v. BLT strain 37 BB strain 22 v. BP strain 37 BB strain 22 v. DN strain 37 BB strain 22 v. H2O strain 37 BB strain 22 v. SO strain 37 BLT strain 22 v. BP strain 22 BLT strain 22 v. DN strain 22 BLT strain 22 v. H2O strain 22 BLT strain 22 v. SO strain 22 BLT strain 22 v. BB strain 26 BLT strain 22 v. BLT strain 26 BLT strain 22 v. BP strain 26 BLT strain 22 v. DN strain 26 BLT strain 22 v. H2O strain 26 BLT strain 22 v. SO strain 26 BLT strain 22 v. BB strain 37 BLT strain 22 v. BLT strain 37 -0.3 -2.8 -13.7 -1.4 -9.0 -46.3 -32.0 -30.0 -42.3 -16.5 -55.1 -67.2 -31.6 -32.5 -83.4 -108.8 -28.3 -2.5 -13.4 -1.0 -8.7 -46.0 -31.7 -29.7 -42.0 -16.2 -54.8 -66.9 -31.3 23.0 21.1 26.8 23.0 21.1 20.7 21.1 21.1 20.7 21.5 20.7 20.7 26.5 21.1 21.1 21.9 20.7 23.0 28.3 24.8 23.0 22.6 23.0 23.0 22.6 23.4 22.6 22.6 28.0 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 -0.014 -0.132 -0.511 -0.060 -0.427 -2.243 -1.519 -1.425 -2.050 -0.769 -2.668 -3.252 -1.194 -1.543 -3.961 -4.975 -1.372 -0.107 -0.472 -0.042 -0.377 -2.033 -1.377 -1.291 -1.857 -0.693 -2.421 -2.954 -1.116 1.0000 1.0000 1.0000 1.0000 1.0000 0.7224 0.9883 0.9941 0.8407 1.0000 0.4104 0.1133 0.9993 0.9862 0.0124 0.0002 0.9962 1.0000 1.0000 1.0000 1.0000 0.8493 0.9960 0.9981 0.9237 1.0000 0.5930 0.2339 0.9997 460 Table C7. (cont’d) BLT strain 22 v. BP strain 37 BLT strain 22 v. DN strain 37 BLT strain 22 v. H2O strain 37 BLT strain 22 v. SO strain 37 BP strain 22 v. DN strain 22 BP strain 22 v. H2O strain 22 BP strain 22 v. SO strain 22 BP strain 22 v. BB strain 26 BP strain 22 v. BLT strain 26 BP strain 22 v. BP strain 26 BP strain 22 v. DN strain 26 BP strain 22 v. H2O strain 26 BP strain 22 v. SO strain 26 BP strain 22 v. BB strain 37 BP strain 22 v. BLT strain 37 BP strain 22 v. BP strain 37 BP strain 22 v. DN strain 37 BP strain 22 v. H2O strain 37 BP strain 22 v. SO strain 37 DN strain 22 v. H2O strain 22 DN strain 22 v. SO strain 22 DN strain 22 v. BB strain 26 DN strain 22 v. BLT strain 26 DN strain 22 v. BP strain 26 DN strain 22 v. DN strain 26 DN strain 22 v. H2O strain 26 DN strain 22 v. SO strain 26 DN strain 22 v. BB strain 37 DN strain 22 v. BLT strain 37 -32.2 -83.1 -108.4 -28.0 -10.9 1.4 -6.2 -43.5 -29.2 -27.2 -39.6 -13.7 -52.3 -64.4 -28.8 -29.7 -80.7 -106.0 -25.5 12.3 4.7 -32.6 -18.3 -16.3 -28.7 -2.8 -41.4 -53.5 -17.9 23.0 23.0 23.7 22.6 26.8 23.0 21.1 20.7 21.1 21.1 20.7 21.5 20.7 20.7 26.5 21.1 21.1 21.9 20.7 28.3 26.8 26.5 26.8 26.8 26.5 27.1 26.5 26.5 31.2 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 -1.399 -3.613 -4.569 -1.238 -0.407 0.062 -0.295 -2.108 -1.387 -1.293 -1.915 -0.639 -2.533 -3.117 -1.089 -1.410 -3.828 -4.847 -1.237 0.435 0.175 -1.234 -0.684 -0.610 -1.083 -0.104 -1.566 -2.022 -0.574 0.9952 0.0396 0.0012 0.9989 1.0000 1.0000 1.0000 0.8084 0.9957 0.9981 0.9027 1.0000 0.5088 0.1600 0.9998 0.9948 0.0196 0.0004 0.9989 1.0000 1.0000 0.9989 1.0000 1.0000 0.9998 1.0000 0.9839 0.8548 1.0000 461 Table C7. (cont’d) DN strain 22 v. BP strain 37 DN strain 22 v. DN strain 37 DN strain 22 v. H2O strain 37 DN strain 22 v. SO strain 37 H2O strain 22 v. SO strain 22 H2O strain 22 v. BB strain 26 H2O strain 22 v. BLT strain 26 H2O strain 22 v. BP strain 26 H2O strain 22 v. DN strain 26 H2O strain 22 v. H2O strain 26 H2O strain 22 v. SO strain 26 H2O strain 22 v. BB strain 37 H2O strain 22 v. BLT strain 37 H2O strain 22 v. BP strain 37 H2O strain 22 v. DN strain 37 H2O strain 22 v. H2O strain 37 H2O strain 22 v. SO strain 37 SO strain 22 v. BB strain 26 SO strain 22 v. BLT strain 26 SO strain 22 v. BP strain 26 SO strain 22 v. DN strain 26 SO strain 22 v. H2O strain 26 SO strain 22 v. SO strain 26 SO strain 22 v. BB strain 37 SO strain 22 v. BLT strain 37 SO strain 22 v. BP strain 37 SO strain 22 v. DN strain 37 SO strain 22 v. H2O strain 37 SO strain 22 v. SO strain 37 -18.8 -69.8 -95.1 -14.7 -7.6 -45.0 -30.6 -28.7 -41.0 -15.1 -53.7 -65.8 -30.2 -31.1 -82.1 -107.4 -27.0 -37.3 -23.0 -21.0 -33.3 -7.5 -46.1 -58.2 -22.6 -23.5 -74.4 -99.8 -19.3 26.8 26.8 27.4 26.5 23.0 22.6 23.0 23.0 22.6 23.4 22.6 22.6 28.0 23.0 23.0 23.7 22.6 20.7 21.1 21.1 20.7 21.5 20.7 20.7 26.5 21.1 21.1 21.9 20.7 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 -0.703 -2.605 -3.469 -0.554 -0.332 -1.987 -1.332 -1.245 -1.810 -0.648 -2.375 -2.908 -1.079 -1.353 -3.568 -4.525 -1.192 -1.807 -1.091 -0.997 -1.614 -0.350 -2.232 -2.816 -0.854 -1.115 -3.533 -4.563 -0.936 1.0000 0.4552 0.0615 1.0000 1.0000 0.8718 0.9973 0.9988 0.9381 1.0000 0.6276 0.2585 0.9998 0.9967 0.0456 0.0014 0.9993 0.9391 0.9998 0.9999 0.9783 1.0000 0.7299 0.3122 1.0000 0.9997 0.0507 0.0012 1.0000 462 Table C7. (cont’d) BB strain 26 v. BLT strain 26 BB strain 26 v. BP strain 26 BB strain 26 v. DN strain 26 BB strain 26 v. H2O strain 26 BB strain 26 v. SO strain 26 BB strain 26 v. BB strain 37 BB strain 26 v. BLT strain 37 BB strain 26 v. BP strain 37 BB strain 26 v. DN strain 37 BB strain 26 v. H2O strain 37 BB strain 26 v. SO strain 37 BLT strain 26 v. BP strain 26 BLT strain 26 v. DN strain 26 BLT strain 26 v. H2O strain 26 BLT strain 26 v. SO strain 26 BLT strain 26 v. BB strain 37 BLT strain 26 v. BLT strain 37 BLT strain 26 v. BP strain 37 BLT strain 26 v. DN strain 37 BLT strain 26 v. H2O strain 37 BLT strain 26 v. SO strain 37 BP strain 26 v. DN strain 26 BP strain 26 v. H2O strain 26 BP strain 26 v. SO strain 26 BP strain 26 v. BB strain 37 BP strain 26 v. BLT strain 37 BP strain 26 v. BP strain 37 BP strain 26 v. DN strain 37 BP strain 26 v. H2O strain 37 14.3 16.3 4.0 29.8 -8.8 -20.8 14.7 13.8 -37.1 -62.4 18.0 2.0 -10.3 15.5 -23.1 -35.2 0.4 -0.5 -51.4 -76.8 3.7 -12.3 13.5 -25.1 -37.2 -1.6 -2.5 -53.4 -78.7 20.7 20.7 20.2 21.1 20.2 20.2 26.1 20.7 20.7 21.5 20.2 21.1 20.7 21.5 20.7 20.7 26.5 21.1 21.1 21.9 20.7 20.7 21.5 20.7 20.7 26.5 21.1 21.1 21.9 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 0.694 0.790 0.197 1.415 -0.434 -1.030 0.564 0.670 -1.796 -2.908 0.889 0.094 -0.501 0.721 -1.119 -1.703 0.015 -0.024 -2.442 -3.511 0.177 -0.596 0.629 -1.215 -1.799 -0.060 -0.118 -2.536 -3.602 1.0000 1.0000 1.0000 0.9945 1.0000 0.9999 1.0000 1.0000 0.9420 0.2586 1.0000 1.0000 1.0000 1.0000 0.9997 0.9639 1.0000 1.0000 0.5774 0.0542 1.0000 1.0000 1.0000 0.9991 0.9414 1.0000 1.0000 0.5066 0.0411 463 Table C7. (cont’d) BP strain 26 v. SO strain 37 DN strain 26 v. H2O strain 26 DN strain 26 v. SO strain 26 DN strain 26 v. BB strain 37 DN strain 26 v. BLT strain 37 DN strain 26 v. BP strain 37 DN strain 26 v. DN strain 37 DN strain 26 v. H2O strain 37 DN strain 26 v. SO strain 37 H2O strain 26 v. SO strain 26 H2O strain 26 v. BB strain 37 H2O strain 26 v. BLT strain 37 H2O strain 26 v. BP strain 37 H2O strain 26 v. DN strain 37 H2O strain 26 v. H2O strain 37 H2O strain 26 v. SO strain 37 SO strain 26 v. BB strain 37 SO strain 26 v. BLT strain 37 SO strain 26 v. BP strain 37 SO strain 26 v. DN strain 37 SO strain 26 v. H2O strain 37 SO strain 26 v. SO strain 37 BB strain 37 v. BLT strain 37 BB strain 37 v. BP strain 37 BB strain 37 v. DN strain 37 BB strain 37 v. H2O strain 37 BB strain 37 v. SO strain 37 BLT strain 37 v. BP strain 37 BLT strain 37 v. DN strain 37 1.7 25.8 -12.8 -24.8 10.7 9.8 -41.1 -66.4 14.0 -38.6 -50.7 -15.1 -16.0 -66.9 -92.2 -11.8 -12.1 23.5 22.6 -28.3 -53.6 26.8 35.6 34.7 -16.3 -41.6 38.8 -0.9 -51.8 20.7 21.1 20.2 20.2 26.1 20.7 20.7 21.5 20.2 21.1 21.1 26.8 21.5 21.5 22.2 21.1 20.2 26.1 20.7 20.7 21.5 20.2 26.1 20.7 20.7 21.5 20.2 26.5 26.5 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 189 0.081 1.226 -0.631 -1.227 0.411 0.476 -1.989 -3.094 0.692 -1.832 -2.405 -0.563 -0.745 -3.117 -4.146 -0.561 -0.596 0.900 1.094 -1.371 -2.499 1.323 1.362 1.679 -0.787 -1.937 1.919 -0.034 -1.959 1.0000 0.9990 1.0000 0.9990 1.0000 1.0000 0.8706 0.1693 1.0000 0.9316 0.6051 1.0000 1.0000 0.1598 0.0063 1.0000 1.0000 1.0000 0.9998 0.9962 0.5342 0.9975 0.9965 0.9684 1.0000 0.8938 0.9010 1.0000 0.8842 464 Table C7. (cont’d) BLT strain 37 v. H2O strain 37 BLT strain 37 v. SO strain 37 BP strain 37 v. DN strain 37 BP strain 37 v. H2O strain 37 BP strain 37 v. SO strain 37 DN strain 37 v. H2O strain 37 DN strain 37 v. SO strain 37 H2O strain 37 v. SO strain 37 -77.2 3.3 -50.9 -76.3 4.2 -25.3 55.1 80.4 27.1 26.1 21.1 21.9 20.7 21.9 20.7 21.5 189 189 189 189 189 189 189 189 -2.848 0.125 -2.418 -3.488 0.202 -1.158 2.667 3.746 0.2929 1.0000 0.5953 0.0581 1.0000 0.9995 0.4106 0.0258 465 Table C8. Group 4 2021, by rate and by strain, mean total area of growth (mm2) of Pss strains 9 and 27, belonging to phylogroup (PG) 2d, ± SE, in co-inoculations of biological control agents (BCAs) [BlightBan®A506 (BB), Bloomtime™ (BLT), Blossom Protect™ (BP), Double Nickel55™ (DN), Serenade® Opti (SO), or water (H2O)] at three rates (0.5x, 1.0x, 1.5x), with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each rate and strain, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG Rate Pss Mean total area (mm2) SE df lower C.L. upper C.L. 2021 4 9 2d 27 2d 0.5x 1.5x 1.0x 190.9 234.2 258.3 16.4 144 16.5 144 17.9 144 158.6 201.6 223.0 223.3 266.8 293.6 a ab b Year Group Strain/PG contrast estimate SE df t-ratio p-value 2021 4 9 2d 27 2d 0.5x v. 1.5x 0.5x v. 1.0x 1.5x v. 1.0x -43.2 -67.4 -24.2 23.3 144 24.3 144 24.2 144 -1.856 -2.774 -0.997 0.1552 0.0172 0.5799 Year Group Strain PG Mean total area (mm2) SE df lower C.L. upper C.L. 2021 4 9 27 2d 2d 185.4 270.3 13.6 144 14.0 144 158.5 242.6 212.2 297.9 Year Group contrast estimate SE df t-ratio p-value 2021 4 strain 27 v. strain 9 84.9 19.5 144 4.363 <0.0001 466 A C B D Figure C3. The 2021 quantile-quantile plots (qqplot) of the residuals for graphical visualization of the distribution of the total area of growth (mm2) of Pss strains at 108 cfu/ml co-inoculated next to the commercial biological control Blossom Protect™ (BP) or water (H2O) on KB or PDA media at three rates (0.5x, 1.0x, 1.5x) A) Group 1 Pss strains 14 (PG 2c), 25 (PG2d), and 38 (PG2d), B) Group 2 Pss strains 23 and 34 (PG2b), C) Group 3 Pss strains 22 (PG2b), 26 (PG2d), and 37 (PG2d), and D) Group 4 Pss strains 9 and 27 (PG2d). 467 Table C9. The 2021 group 1 Pss strains v. Blossom Protect™ (BP) or water (H2O) in Petri plates with KB or PDA media, mean total area of growth (mm2) of Pss strains 14, 25, and 38, belonging to phylogroup (PG) 2c, 2d, and 2d, respectively ± SE co-inoculated with BP at the rates 0.5x, 1.0x, and 1.5x. With confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each strain, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain PG Pss Mean total area (mm2) SE df lower C.L. upper C.L. 2021 1 14 25 38 2c 2d 2d 212.9 166.6 184.3 13.4 13.1 13.3 139 139 139 186.5 140.7 158.0 239.4 192.5 210.7 a b ab Year Group contrast estimate SE df t-ratio p-value 2021 1 14 v. 25 14 v. 38 25 v. 38 46.3 28.6 -17.7 18.7 18.9 18.7 139 139 139 2.473 1.514 -0.947 0.0386 0.2876 0.6114 468 Table C10. The 2021 group 1 Pss strains v. Blossom Protect™ (BP) or water (H2O) in Petri plates with KB or PDA media, mean total area of growth (mm2) of Pss strains 14, 25, and 38, belonging to phylogroup (PG) 2c, 2d, and 2d, respectively ± SE co-inoculated with BP at the rates 0.5x, 1.0x, and 1.5x. With confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for media*rate*trt*strain with letters indicating statistical differences with significance determined by p- value ≤0.05. Year Group media Strain/PG Treatment Rate 2021 1 KB 14 2c 25 2d 38 2d BP BP BP H2O H2O H2O BP BP BP H2O H2O H2O BP BP BP H2O H2O H2O 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 469 Pss Mean total area (mm2) 276.5 120.0 164.9 229.8 351.7 146.5 157.7 201.2 150.1 195.1 162.2 212.3 150.9 196.1 140.9 270.0 234.2 170.4 SE df lower C.L. upper C.L. 50.3 45.0 50.3 45.0 45.0 45.0 45.0 45.0 50.3 45.0 50.3 45.0 45.0 45.0 58.1 45.0 45.0 45.0 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 177.1 31.1 65.5 140.9 262.8 57.6 68.7 112.3 50.7 106.1 62.7 123.4 62.0 107.1 26.1 181.1 145.3 81.5 375.9 209.0 264.4 318.7 440.6 235.4 246.6 290.1 249.5 284.0 261.6 301.2 239.8 285.0 255.7 359.0 323.1 259.3 ab a ab ab b ab ab ab ab ab ab ab ab ab ab ab ab ab Table C10. (cont’d) 2021 1 PDA Year Group Media 2021 KB 1 14 14 14 14 14 14 25 25 25 25 25 25 38 38 38 38 38 38 BP BP BP H2O H2O H2O BP BP BP H2O H2O H2O BP BP BP H2O H2O H2O 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 161.6 363.9 155.3 229.4 197.3 158.3 133.7 150.3 138.4 222.0 142.4 134.4 151.1 160.4 151.5 219.9 157.0 209.6 Contrast 0.5x BP strain 14 v. 1.5x BP strain 14 0.5x BP strain 14 v. 1.0x BP strain 14 0.5x BP strain 14 v. 0.5x H2O strain 14 0.5x BP strain 14 v. 1.5x H2O strain 14 0.5x BP strain 14 v. 1.0x H2O strain 14 0.5x BP strain 14 v. 0.5x BP strain 25 0.5x BP strain 14 v. 1.5x BP strain 25 0.5x BP strain 14 v. 1.0x BP strain 25 0.5x BP strain 14 v. 0.5x H2O strain 25 0.5x BP strain 14 v. 1.5x H2O strain 25 0.5x BP strain 14 v. 1.0x H2O strain 25 estimate 156.5 111.6 46.7 -75.2 130.0 118.8 75.3 126.4 81.4 114.3 64.2 470 45.0 50.3 45.0 45.0 45.0 45.0 45.0 45.0 45.0 45.0 38.0 45.0 45.0 45.0 45.0 45.0 45.0 45.0 SE 67.5 71.1 67.5 67.5 67.5 67.5 67.5 71.1 67.5 71.1 67.5 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 df 139 139 139 139 139 139 139 139 139 139 139 72.7 264.5 66.4 140.5 108.4 69.4 44.8 61.4 49.5 133.0 67.2 45.5 62.2 71.5 62.6 131.0 68.1 120.7 250.5 463.3 244.3 318.4 286.2 247.2 222.7 239.2 227.3 310.9 217.5 223.3 240.1 249.3 240.5 308.8 246.0 298.5 a a a a a a a a a a a a a a a a a a t-ratio p-value 0.6680 2.319 0.9831 1.569 1.0000 0.693 0.9997 -1.115 0.8962 1.927 0.9499 1.762 0.9997 1.117 0.9458 1.778 0.9991 1.207 0.9784 1.608 1.0000 0.952 Table C10. (cont’d) 0.5x BP strain 14 v. 0.5x BP strain 38 0.5x BP strain 14 v. 1.5x BP strain 38 0.5x BP strain 14 v. 1.0x BP strain 38 0.5x BP strain 14 v. 0.5x H2O strain 38 0.5x BP strain 14 v. 1.5x H2O strain 38 0.5x BP strain 14 v. 1.0x H2O strain 38 1.5x BP strain 14 v. 1.0x BP strain 14 1.5x BP strain 14 v. 0.5x H2O strain 14 1.5x BP strain 14 v. 1.5x H2O strain 14 1.5x BP strain 14 v. 1.0x H2O strain 14 1.5x BP strain 14 v. 0.5x BP strain 25 1.5x BP strain 14 v. 1.5x BP strain 25 1.5x BP strain 14 v. 1.0x BP strain 25 1.5x BP strain 14 v. 0.5x H2O strain 25 1.5x BP strain 14 v. 1.5x H2O strain 25 1.5x BP strain 14 v. 1.0x H2O strain 25 1.5x BP strain 14 v. 0.5x BP strain 38 1.5x BP strain 14 v. 1.5x BP strain 38 1.5x BP strain 14 v. 1.0x BP strain 38 1.5x BP strain 14 v. 0.5x H2O strain 38 1.5x BP strain 14 v. 1.5x H2O strain 38 1.5x BP strain 14 v. 1.0x H2O strain 38 1.0x BP strain 14 v. 0.5x H2O strain 14 1.0x BP strain 14 v. 1.5x H2O strain 14 1.0x BP strain 14 v. 1.0x H2O strain 14 1.0x BP strain 14 v. 0.5x BP strain 25 1.0x BP strain 14 v. 1.5x BP strain 25 1.0x BP strain 14 v. 1.0x BP strain 25 1.0x BP strain 14 v. 0.5x H2O strain 25 1.0x BP strain 14 v. 1.5x H2O strain 25 125.6 80.4 135.6 6.5 42.3 106.1 -44.9 -109.7 -231.7 -26.4 -37.6 -81.1 -30.0 -75.0 -42.1 -92.2 -30.9 -76.0 -20.9 -150.0 -114.2 -50.4 -64.8 -186.8 18.5 7.3 -36.2 14.9 -30.1 2.8 67.5 67.5 76.8 67.5 67.5 67.5 67.5 63.6 63.6 63.6 63.6 63.6 67.5 63.6 67.5 63.6 63.6 63.6 73.4 63.6 63.6 63.6 67.5 67.5 67.5 67.5 67.5 71.1 67.5 71.1 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 1.862 1.192 1.766 0.096 0.627 1.573 -0.666 -1.725 -3.643 -0.416 -0.591 -1.276 -0.445 -1.179 -0.624 -1.450 -0.485 -1.195 -0.284 -2.358 -1.795 -0.792 -0.961 -2.769 0.274 0.108 -0.537 0.209 -0.446 0.039 0.9206 0.9993 0.9490 1.0000 1.0000 0.9827 1.0000 0.9583 0.0384 1.0000 1.0000 0.9983 1.0000 0.9994 1.0000 0.9926 1.0000 0.9992 1.0000 0.6396 0.9413 1.0000 1.0000 0.3451 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 471 Table C10. (cont’d) 1.0x BP strain 14 v. 1.0x H2O strain 25 1.0x BP strain 14 v. 0.5x BP strain 38 1.0x BP strain 14 v. 1.5x BP strain 38 1.0x BP strain 14 v. 1.0x BP strain 38 1.0x BP strain 14 v. 0.5x H2O strain 38 1.0x BP strain 14 v. 1.5x H2O strain 38 1.0x BP strain 14 v. 1.0x H2O strain 38 0.5x H2O strain 14 v. 1.5x H2O strain 14 0.5x H2O strain 14 v. 1.0x H2O strain 14 0.5x H2O strain 14 v. 0.5x BP strain 25 0.5x H2O strain 14 v. 1.5x BP strain 25 0.5x H2O strain 14 v. 1.0x BP strain 25 0.5x H2O strain 14 v. 0.5x H2O strain 25 0.5x H2O strain 14 v. 1.5x H2O strain 25 0.5x H2O strain 14 v. 1.0x H2O strain 25 0.5x H2O strain 14 v. 0.5x BP strain 38 0.5x H2O strain 14 v. 1.5x BP strain 38 0.5x H2O strain 14 v. 1.0x BP strain 38 0.5x H2O strain 14 v. 0.5x H2O strain 38 0.5x H2O strain 14 v. 1.5x H2O strain 38 0.5x H2O strain 14 v. 1.0x H2O strain 38 1.5x H2O strain 14 v. 1.0x H2O strain 14 1.5x H2O strain 14 v. 0.5x BP strain 25 1.5x H2O strain 14 v. 1.5x BP strain 25 1.5x H2O strain 14 v. 1.0x BP strain 25 1.5x H2O strain 14 v. 0.5x H2O strain 25 1.5x H2O strain 14 v. 1.5x H2O strain 25 1.5x H2O strain 14 v. 1.0x H2O strain 25 1.5x H2O strain 14 v. 0.5x BP strain 38 1.5x H2O strain 14 v. 1.5x BP strain 38 -47.3 14.0 -31.1 24.0 -105.1 -69.3 -5.5 -121.9 83.3 72.1 28.6 79.7 34.7 67.6 17.5 78.9 33.7 88.9 -40.3 -4.4 59.4 205.2 194.1 150.5 201.6 156.7 189.6 139.4 200.8 155.6 67.5 67.5 67.5 76.8 67.5 67.5 67.5 63.6 63.6 63.6 63.6 67.5 63.6 67.5 63.6 63.6 63.6 73.4 63.6 63.6 63.6 63.6 63.6 63.6 67.5 63.6 67.5 63.6 63.6 63.6 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 -0.702 0.208 -0.461 0.313 -1.558 -1.027 -0.081 -1.917 1.310 1.134 0.450 1.182 0.546 1.002 0.275 1.240 0.530 1.210 -0.633 -0.069 0.934 3.227 3.051 2.367 2.989 2.463 2.810 2.192 3.157 2.447 1.0000 1.0000 1.0000 1.0000 0.9842 0.9999 1.0000 0.9004 0.9977 0.9996 1.0000 0.9993 1.0000 0.9999 1.0000 0.9988 1.0000 0.9991 1.0000 1.0000 1.0000 0.1247 0.1914 0.6335 0.2203 0.5620 0.3192 0.7551 0.1485 0.5739 472 Table C10. (cont’d) 1.5x H2O strain 14 v. 1.0x BP strain 38 1.5x H2O strain 14 v. 0.5x H2O strain 38 1.5x H2O strain 14 v. 1.5x H2O strain 38 1.5x H2O strain 14 v. 1.0x H2O strain 38 1.0x H2O strain 14 v. 0.5x BP strain 25 1.0x H2O strain 14 v. 1.5x BP strain 25 1.0x H2O strain 14 v. 1.0x BP strain 25 1.0x H2O strain 14 v. 0.5x H2O strain 25 1.0x H2O strain 14 v. 1.5x H2O strain 25 1.0x H2O strain 14 v. 1.0x H2O strain 25 1.0x H2O strain 14 v. 0.5x BP strain 38 1.0x H2O strain 14 v. 1.5x BP strain 38 1.0x H2O strain 14 v. 1.0x BP strain 38 1.0x H2O strain 14 v. 0.5x H2O strain 38 1.0x H2O strain 14 v. 1.5x H2O strain 38 1.0x H2O strain 14 v. 1.0x H2O strain 38 0.5x BP strain 25 v. 1.5x BP strain 25 0.5x BP strain 25 v. 1.0x BP strain 25 0.5x BP strain 25 v. 0.5x H2O strain 25 0.5x BP strain 25 v. 1.5x H2O strain 25 0.5x BP strain 25 v. 1.0x H2O strain 25 0.5x BP strain 25 v. 0.5x BP strain 38 0.5x BP strain 25 v. 1.5x BP strain 38 0.5x BP strain 25 v. 1.0x BP strain 38 0.5x BP strain 25 v. 0.5x H2O strain 38 0.5x BP strain 25 v. 1.5x H2O strain 38 0.5x BP strain 25 v. 1.0x H2O strain 38 1.5x BP strain 25 v. 1.0x BP strain 25 1.5x BP strain 25 v. 0.5x H2O strain 25 1.5x BP strain 25 v. 1.5x H2O strain 25 210.8 81.7 117.5 181.3 -11.2 -54.7 -3.6 -48.6 -15.7 -65.8 -4.4 -49.6 5.6 -123.6 -87.7 -23.9 -43.5 7.6 -37.4 -4.5 -54.6 6.8 -38.4 16.8 -112.4 -76.5 -12.8 51.1 6.1 39.0 73.4 63.6 63.6 63.6 63.6 63.6 67.5 63.6 67.5 63.6 63.6 63.6 73.4 63.6 63.6 63.6 63.6 67.5 63.6 67.5 63.6 63.6 63.6 73.4 63.6 63.6 63.6 67.5 63.6 67.5 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 2.871 1.284 1.848 2.851 -0.176 -0.860 -0.053 -0.764 -0.232 -1.035 -0.069 -0.780 0.076 -1.943 -1.379 -0.376 -0.684 0.112 -0.588 -0.067 -0.859 0.106 -0.604 0.228 -1.767 -1.203 -0.201 0.758 0.096 0.578 0.2832 0.9982 0.9254 0.2948 1.0000 1.0000 1.0000 1.0000 1.0000 0.9999 1.0000 1.0000 1.0000 0.8899 0.9957 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9486 0.9992 1.0000 1.0000 1.0000 1.0000 473 Table C10. (cont’d) 1.5x BP strain 25 v. 1.0x H2O strain 25 1.5x BP strain 25 v. 0.5x BP strain 38 1.5x BP strain 25 v. 1.5x BP strain 38 1.5x BP strain 25 v. 1.0x BP strain 38 1.5x BP strain 25 v. 0.5x H2O strain 38 1.5x BP strain 25 v. 1.5x H2O strain 38 1.5x BP strain 25 v. 1.0x H2O strain 38 1.0x BP strain 25 v. 0.5x H2O strain 25 1.0x BP strain 25 v. 1.5x H2O strain 25 1.0x BP strain 25 v. 1.0x H2O strain 25 1.0x BP strain 25 v. 0.5x BP strain 38 1.0x BP strain 25 v. 1.5x BP strain 38 1.0x BP strain 25 v. 1.0x BP strain 38 1.0x BP strain 25 v. 0.5x H2O strain 38 1.0x BP strain 25 v. 1.5x H2O strain 38 1.0x BP strain 25 v. 1.0x H2O strain 38 0.5x H2O strain 25 v. 1.5x H2O strain 25 0.5x H2O strain 25 v. 1.0x H2O strain 25 0.5x H2O strain 25 v. 0.5x BP strain 38 0.5x H2O strain 25 v. 1.5x BP strain 38 0.5x H2O strain 25 v. 1.0x BP strain 38 0.5x H2O strain 25 v. 0.5x H2O strain 38 0.5x H2O strain 25 v. 1.5x H2O strain 38 0.5x H2O strain 25 v. 1.0x H2O strain 38 1.5x H2O strain 25 v. 1.0x H2O strain 25 1.5x H2O strain 25 v. 0.5x BP strain 38 1.5x H2O strain 25 v. 1.5x BP strain 38 1.5x H2O strain 25 v. 1.0x BP strain 38 1.5x H2O strain 25 v. 0.5x H2O strain 38 1.5x H2O strain 25 v. 1.5x H2O strain 38 -11.1 50.3 5.1 60.3 -68.9 -33.0 30.8 -45.0 -12.1 -62.2 -0.8 -46.0 9.2 -120.0 -84.1 -20.3 32.9 -17.2 44.2 -1.0 54.2 -75.0 -39.1 24.6 -50.1 11.3 -33.9 21.3 -107.9 -72.0 63.6 63.6 63.6 73.4 63.6 63.6 63.6 67.5 71.1 67.5 67.5 67.5 76.8 67.5 67.5 67.5 67.5 63.6 63.6 63.6 73.4 63.6 63.6 63.6 67.5 67.5 67.5 76.8 67.5 67.5 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 -0.174 0.791 0.080 0.821 -1.083 -0.519 0.484 -0.667 -0.170 -0.922 -0.012 -0.682 0.120 -1.778 -1.247 -0.301 0.488 -0.271 0.694 -0.016 0.737 -1.179 -0.615 0.387 -0.743 0.167 -0.503 0.277 -1.599 -1.068 1.0000 1.0000 1.0000 1.0000 0.9998 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9457 0.9987 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9994 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9796 0.9998 474 Table C10. (cont’d) 2021 1 PDA 1.5x H2O strain 25 v. 1.0x H2O strain 38 1.0x H2O strain 25 v. 0.5x BP strain 38 1.0x H2O strain 25 v. 1.5x BP strain 38 1.0x H2O strain 25 v. 1.0x BP strain 38 1.0x H2O strain 25 v. 0.5x H2O strain 38 1.0x H2O strain 25 v. 1.5x H2O strain 38 1.0x H2O strain 25 v. 1.0x H2O strain 38 0.5x BP strain 38 v. 1.5x BP strain 38 0.5x BP strain 38 v. 1.0x BP strain 38 0.5x BP strain 38 v. 0.5x H2O strain 38 0.5x BP strain 38 v. 1.5x H2O strain 38 0.5x BP strain 38 v. 1.0x H2O strain 38 1.5x BP strain 38 v. 1.0x BP strain 38 1.5x BP strain 38 v. 0.5x H2O strain 38 1.5x BP strain 38 v. 1.5x H2O strain 38 1.5x BP strain 38 v. 1.0x H2O strain 38 1.0x BP strain 38 v. 0.5x H2O strain 38 1.0x BP strain 38 v. 1.5x H2O strain 38 1.0x BP strain 38 v. 1.0x H2O strain 38 0.5x H2O strain 38 v. 1.5x H2O strain 38 0.5x H2O strain 38 v. 1.0x H2O strain 38 1.5x H2O strain 38 v. 1.0x H2O strain 38 0.5x BP strain 14 v. 1.5x BP strain 14 0.5x BP strain 14 v. 1.0x BP strain 14 0.5x BP strain 14 v. 0.5x H2O strain 14 0.5x BP strain 14 v. 1.5x H2O strain 14 0.5x BP strain 14 v. 1.0x H2O strain 14 0.5x BP strain 14 v. 0.5x BP strain 25 0.5x BP strain 14 v. 1.5x BP strain 25 0.5x BP strain 14 v. 1.0x BP strain 25 -8.3 61.4 16.2 71.4 -57.8 -21.9 41.9 -45.2 10.0 -119.1 -83.3 -19.5 55.2 -74.0 -38.1 25.7 -129.1 -93.3 -29.5 35.8 99.6 63.8 -202.4 6.2 -67.9 -35.7 3.3 27.8 11.3 23.2 67.5 63.6 63.6 73.4 63.6 63.6 63.6 63.6 73.4 63.6 63.6 63.6 73.4 63.6 63.6 63.6 73.4 73.4 73.4 63.6 63.6 63.6 67.5 63.6 63.6 63.6 63.6 63.6 63.6 63.6 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 -0.122 0.965 0.255 0.972 -0.908 -0.345 0.658 -0.710 0.136 -1.873 -1.310 -0.307 0.751 -1.163 -0.600 0.403 -1.758 -1.270 -0.402 0.564 1.566 1.003 -3.000 0.098 -1.067 -0.561 0.052 0.438 0.178 0.364 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9167 0.9977 1.0000 1.0000 0.9995 1.0000 1.0000 0.9507 0.9984 1.0000 1.0000 0.9834 0.9999 0.2152 1.0000 0.9998 1.0000 1.0000 1.0000 1.0000 1.0000 475 Table C10. (cont’d) 0.5x BP strain 14 v. 0.5x H2O strain 25 0.5x BP strain 14 v. 1.5x H2O strain 25 0.5x BP strain 14 v. 1.0x H2O strain 25 0.5x BP strain 14 v. 0.5x BP strain 38 0.5x BP strain 14 v. 1.5x BP strain 38 0.5x BP strain 14 v. 1.0x BP strain 38 0.5x BP strain 14 v. 0.5x H2O strain 38 0.5x BP strain 14 v. 1.5x H2O strain 38 0.5x BP strain 14 v. 1.0x H2O strain 38 1.5x BP strain 14 v. 1.0x BP strain 14 1.5x BP strain 14 v. 0.5x H2O strain 14 1.5x BP strain 14 v. 1.5x H2O strain 14 1.5x BP strain 14 v. 1.0x H2O strain 14 1.5x BP strain 14 v. 0.5x BP strain 25 1.5x BP strain 14 v. 1.5x BP strain 25 1.5x BP strain 14 v. 1.0x BP strain 25 1.5x BP strain 14 v. 0.5x H2O strain 25 1.5x BP strain 14 v. 1.5x H2O strain 25 1.5x BP strain 14 v. 1.0x H2O strain 25 1.5x BP strain 14 v. 0.5x BP strain 38 1.5x BP strain 14 v. 1.5x BP strain 38 1.5x BP strain 14 v. 1.0x BP strain 38 1.5x BP strain 14 v. 0.5x H2O strain 38 1.5x BP strain 14 v. 1.5x H2O strain 38 1.5x BP strain 14 v. 1.0x H2O strain 38 1.0x BP strain 14 v. 0.5x H2O strain 14 1.0x BP strain 14 v. 1.5x H2O strain 14 1.0x BP strain 14 v. 1.0x H2O strain 14 1.0x BP strain 14 v. 0.5x BP strain 25 1.0x BP strain 14 v. 1.5x BP strain 25 476 -60.4 19.2 27.2 10.4 1.2 10.0 -58.3 4.5 -48.0 208.6 134.5 166.7 205.7 230.2 213.7 225.5 142.0 221.6 229.5 212.8 203.5 212.4 144.0 206.9 154.3 -74.1 -41.9 -2.9 21.6 5.1 63.6 58.9 63.6 63.6 63.6 63.6 63.6 63.6 63.6 67.5 67.5 67.5 67.5 67.5 67.5 67.5 67.5 63.0 67.5 67.5 67.5 67.5 67.5 67.5 67.5 63.6 63.6 63.6 63.6 63.6 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 -0.949 0.326 0.427 0.164 0.019 0.158 -0.917 0.071 -0.755 3.092 1.994 2.471 3.049 3.412 3.167 3.343 2.105 3.515 3.402 3.154 3.017 3.148 2.135 3.067 2.288 -1.165 -0.659 -0.046 0.340 0.080 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.1739 0.8672 0.5565 0.1925 0.0758 0.1450 0.0918 0.8088 0.0563 0.0780 0.1496 0.2068 0.1519 0.7908 0.1846 0.6903 0.9994 1.0000 1.0000 1.0000 1.0000 Table C10. (cont’d) 1.0x BP strain 14 v. 1.0x BP strain 25 1.0x BP strain 14 v. 0.5x H2O strain 25 1.0x BP strain 14 v. 1.5x H2O strain 25 1.0x BP strain 14 v. 1.0x H2O strain 25 1.0x BP strain 14 v. 0.5x BP strain 38 1.0x BP strain 14 v. 1.5x BP strain 38 1.0x BP strain 14 v. 1.0x BP strain 38 1.0x BP strain 14 v. 0.5x H2O strain 38 1.0x BP strain 14 v. 1.5x H2O strain 38 1.0x BP strain 14 v. 1.0x H2O strain 38 0.5x H2O strain 14 v. 1.5x H2O strain 14 0.5x H2O strain 14 v. 1.0x H2O strain 14 0.5x H2O strain 14 v. 0.5x BP strain 25 0.5x H2O strain 14 v. 1.5x BP strain 25 0.5x H2O strain 14 v. 1.0x BP strain 25 0.5x H2O strain 14 v. 0.5x H2O strain 25 0.5x H2O strain 14 v. 1.5x H2O strain 25 0.5x H2O strain 14 v. 1.0x H2O strain 25 0.5x H2O strain 14 v. 0.5x BP strain 38 0.5x H2O strain 14 v. 1.5x BP strain 38 0.5x H2O strain 14 v. 1.0x BP strain 38 0.5x H2O strain 14 v. 0.5x H2O strain 38 0.5x H2O strain 14 v. 1.5x H2O strain 38 0.5x H2O strain 14 v. 1.0x H2O strain 38 1.5x H2O strain 14 v. 1.0x H2O strain 14 1.5x H2O strain 14 v. 0.5x BP strain 25 1.5x H2O strain 14 v. 1.5x BP strain 25 1.5x H2O strain 14 v. 1.0x BP strain 25 1.5x H2O strain 14 v. 0.5x H2O strain 25 1.5x H2O strain 14 v. 1.5x H2O strain 25 16.9 -66.6 13.0 20.9 4.2 -5.0 3.8 -64.5 -1.7 -54.2 32.2 71.2 95.7 79.2 91.0 7.5 87.1 95.0 78.3 69.1 77.9 9.6 72.4 19.9 39.0 63.5 47.0 58.8 -24.7 54.9 63.6 63.6 58.9 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 58.9 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 58.9 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 0.266 -1.047 0.221 0.329 0.066 -0.079 0.060 -1.015 -0.027 -0.853 0.506 1.119 1.505 1.245 1.431 0.118 1.479 1.494 1.231 1.086 1.225 0.150 1.138 0.312 0.613 0.999 0.739 0.925 -0.388 0.933 1.0000 0.9999 1.0000 1.0000 1.0000 1.0000 1.0000 0.9999 1.0000 1.0000 1.0000 0.9997 0.9890 0.9987 0.9936 1.0000 0.9908 0.9898 0.9989 0.9998 0.9990 1.0000 0.9996 1.0000 1.0000 0.9999 1.0000 1.0000 1.0000 1.0000 477 Table C10. (cont’d) 1.5x H2O strain 14 v. 1.0x H2O strain 25 1.5x H2O strain 14 v. 0.5x BP strain 38 1.5x H2O strain 14 v. 1.5x BP strain 38 1.5x H2O strain 14 v. 1.0x BP strain 38 1.5x H2O strain 14 v. 0.5x H2O strain 38 1.5x H2O strain 14 v. 1.5x H2O strain 38 1.5x H2O strain 14 v. 1.0x H2O strain 38 1.0x H2O strain 14 v. 0.5x BP strain 25 1.0x H2O strain 14 v. 1.5x BP strain 25 1.0x H2O strain 14 v. 1.0x BP strain 25 1.0x H2O strain 14 v. 0.5x H2O strain 25 1.0x H2O strain 14 v. 1.5x H2O strain 25 1.0x H2O strain 14 v. 1.0x H2O strain 25 1.0x H2O strain 14 v. 0.5x BP strain 38 1.0x H2O strain 14 v. 1.5x BP strain 38 1.0x H2O strain 14 v. 1.0x BP strain 38 1.0x H2O strain 14 v. 0.5x H2O strain 38 1.0x H2O strain 14 v. 1.5x H2O strain 38 1.0x H2O strain 14 v. 1.0x H2O strain 38 0.5x BP strain 25 v. 1.5x BP strain 25 0.5x BP strain 25 v. 1.0x BP strain 25 0.5x BP strain 25 v. 0.5x H2O strain 25 0.5x BP strain 25 v. 1.5x H2O strain 25 0.5x BP strain 25 v. 1.0x H2O strain 25 0.5x BP strain 25 v. 0.5x BP strain 38 0.5x BP strain 25 v. 1.5x BP strain 38 0.5x BP strain 25 v. 1.0x BP strain 38 0.5x BP strain 25 v. 0.5x H2O strain 38 0.5x BP strain 25 v. 1.5x H2O strain 38 0.5x BP strain 25 v. 1.0x H2O strain 38 62.8 46.1 36.9 45.7 -22.6 40.2 -12.3 24.5 8.0 19.8 -63.7 15.9 23.8 7.1 -2.1 6.7 -61.6 1.2 -51.3 -16.5 -4.7 -88.2 -8.6 -0.7 -17.4 -26.6 -17.8 -86.1 -23.3 -75.8 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 58.9 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 58.9 63.6 63.6 63.6 63.6 63.6 63.6 63.6 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 0.988 0.725 0.580 0.719 -0.356 0.632 -0.194 0.386 0.126 0.312 -1.001 0.270 0.375 0.112 -0.033 0.106 -0.969 0.019 -0.807 -0.260 -0.074 -1.387 -0.146 -0.011 -0.273 -0.419 -0.280 -1.354 -0.366 -1.192 0.9999 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9999 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9955 1.0000 1.0000 1.0000 1.0000 1.0000 0.9965 1.0000 0.9993 478 Table C10. (cont’d) 1.5x BP strain 25 v. 1.0x BP strain 25 1.5x BP strain 25 v. 0.5x H2O strain 25 1.5x BP strain 25 v. 1.5x H2O strain 25 1.5x BP strain 25 v. 1.0x H2O strain 25 1.5x BP strain 25 v. 0.5x BP strain 38 1.5x BP strain 25 v. 1.5x BP strain 38 1.5x BP strain 25 v. 1.0x BP strain 38 1.5x BP strain 25 v. 0.5x H2O strain 38 1.5x BP strain 25 v. 1.5x H2O strain 38 1.5x BP strain 25 v. 1.0x H2O strain 38 1.0x BP strain 25 v. 0.5x H2O strain 25 1.0x BP strain 25 v. 1.5x H2O strain 25 1.0x BP strain 25 v. 1.0x H2O strain 25 1.0x BP strain 25 v. 0.5x BP strain 38 1.0x BP strain 25 v. 1.5x BP strain 38 1.0x BP strain 25 v. 1.0x BP strain 38 1.0x BP strain 25 v. 0.5x H2O strain 38 1.0x BP strain 25 v. 1.5x H2O strain 38 1.0x BP strain 25 v. 1.0x H2O strain 38 0.5x H2O strain 25 v. 1.5x H2O strain 25 0.5x H2O strain 25 v. 1.0x H2O strain 25 0.5x H2O strain 25 v. 0.5x BP strain 38 0.5x H2O strain 25 v. 1.5x BP strain 38 0.5x H2O strain 25 v. 1.0x BP strain 38 0.5x H2O strain 25 v. 0.5x H2O strain 38 0.5x H2O strain 25 v. 1.5x H2O strain 38 0.5x H2O strain 25 v. 1.0x H2O strain 38 1.5x H2O strain 25 v. 1.0x H2O strain 25 1.5x H2O strain 25 v. 0.5x BP strain 38 1.5x H2O strain 25 v. 1.5x BP strain 38 11.9 -71.7 7.9 15.9 -0.9 -10.1 -1.3 -69.6 -6.8 -59.3 -83.5 -3.9 4.0 -12.7 -22.0 -13.1 -81.5 -18.6 -71.2 79.6 87.5 70.8 61.6 70.4 2.1 64.9 12.4 7.9 -8.8 -18.0 63.6 63.6 58.9 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 58.9 63.6 63.6 63.6 63.6 63.6 63.6 63.6 58.9 63.6 63.6 63.6 63.6 63.6 63.6 63.6 58.9 58.9 58.9 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 0.186 -1.127 0.135 0.249 -0.013 -0.159 -0.020 -1.094 -0.106 -0.932 -1.313 -0.067 0.063 -0.200 -0.345 -0.206 -1.281 -0.293 -1.119 1.352 1.376 1.113 0.968 1.107 0.032 1.021 0.194 0.135 -0.149 -0.306 1.0000 0.9996 1.0000 1.0000 1.0000 1.0000 1.0000 0.9998 1.0000 1.0000 0.9976 1.0000 1.0000 1.0000 1.0000 1.0000 0.9982 1.0000 0.9997 0.9966 0.9958 0.9997 1.0000 0.9997 1.0000 0.9999 1.0000 1.0000 1.0000 1.0000 479 Table C10. (cont’d) 1.5x H2O strain 25 v. 1.0x BP strain 38 1.5x H2O strain 25 v. 0.5x H2O strain 38 1.5x H2O strain 25 v. 1.5x H2O strain 38 1.5x H2O strain 25 v. 1.0x H2O strain 38 1.0x H2O strain 25 v. 0.5x BP strain 38 1.0x H2O strain 25 v. 1.5x BP strain 38 1.0x H2O strain 25 v. 1.0x BP strain 38 1.0x H2O strain 25 v. 0.5x H2O strain 38 1.0x H2O strain 25 v. 1.5x H2O strain 38 1.0x H2O strain 25 v. 1.0x H2O strain 38 0.5x BP strain 38 v. 1.5x BP strain 38 0.5x BP strain 38 v. 1.0x BP strain 38 0.5x BP strain 38 v. 0.5x H2O strain 38 0.5x BP strain 38 v. 1.5x H2O strain 38 0.5x BP strain 38 v. 1.0x H2O strain 38 1.5x BP strain 38 v. 1.0x BP strain 38 1.5x BP strain 38 v. 0.5x H2O strain 38 1.5x BP strain 38 v. 1.5x H2O strain 38 1.5x BP strain 38 v. 1.0x H2O strain 38 1.0x BP strain 38 v. 0.5x H2O strain 38 1.0x BP strain 38 v. 1.5x H2O strain 38 1.0x BP strain 38 v. 1.0x H2O strain 38 0.5x H2O strain 38 v. 1.5x H2O strain 38 0.5x H2O strain 38 v. 1.0x H2O strain 38 1.5x H2O strain 38 v. 1.0x H2O strain 38 -9.2 -77.5 -14.7 -67.2 -16.7 -26.0 -17.1 -85.5 -22.6 -75.2 -9.2 -0.4 -68.8 -5.9 -58.4 8.8 -59.5 3.3 -49.2 -68.3 -5.5 -58.0 62.8 10.3 -52.5 58.9 58.9 58.9 58.9 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 63.6 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 139 -0.156 -1.317 -0.249 -1.142 -0.263 -0.408 -0.269 -1.344 -0.356 -1.182 -0.145 -0.006 -1.081 -0.093 -0.919 0.139 -0.936 0.053 -0.774 -1.075 -0.086 -0.913 0.988 0.162 -0.826 1.0000 0.9975 1.0000 0.9996 1.0000 1.0000 1.0000 0.9968 1.0000 0.9993 1.0000 1.0000 0.9998 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9998 1.0000 1.0000 0.9999 1.0000 1.0000 480 Table C11. Group 2 2021, by media, rate, and treatment (trt, i.e., biological control agent, BCA), mean total area of growth (mm2) of Pss strains 23 and 34, belonging to phylogroup (PG) 2b ± SE in co-inoculations of Blossom Protect™ (BP) or water (H2O) at three rates (0.5x, 1.0x, 1.5x) grown on KB or PDA media, with confidence limits (lower and upper C.L.) and the p- value for the pairwise comparisons of the estimated marginal means for each media, rate, and treatment, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG Media 2021 2 23 2b 34 2b KB PDA Pss Mean total area (mm2) 208.0 128.0 Year Group Strain/PG contrast estimate SE 2021 2 23 2b 34 2b KB v. PDA 80.1 13.3 SE df lower C.L. upper C.L. 9.3 9.4 93 93 df 93 189.0 109.0 226.0 146.0 a b t-ratio p-value 6.036 <0.0001 Year Group Strain/PG Rate 2021 2 23 2b 34 2b 0.5x 1.5x 1.0x Pss Mean total area (mm2) 177.0 141.0 184.0 Year Group Strain/PG 2021 2 23 2b 34 2b contrast 0.5x v. 1.5x 0.5x v. 1x 1.5x v. 1x estimate SE 16.3 16.3 16.1 35.9 -7.0 -43.0 Year Group Strain/PG Treatment (BCA) 2021 2 23 2b 34 2b BP H2O Pss Mean total area (mm2) 150.4 184.8 Year Group Strain/PG 2021 2 23 2b 34 2b contrast BP v. H2O estimate SE 13.3 -34.4 481 SE df lower C.L. upper C.L. 11.6 11.4 11.5 93 93 93 df 93 93 93 154.0 119.0 162.0 200.0 164.0 207.0 ab a b t-ratio p-value 0.0748 2.210 0.9034 -0.430 0.0246 -2.662 SE df lower C.L. upper C.L. 9.4 9.4 93 93 df 93 131.7 166.3 169.1 203.4 a b t-ratio p-value 0.0110 -2.594 Table C12. Group 2 2021 mean total area of growth (mm2) of Pss strains 23 and 34, belonging to phylogroup (PG) 2b ± SE in co- inoculations of biological control agent Blossom Protect™ (BP) or water (H2O) at three rates (0.5x, 1.0x, 1.5x) grown on KB or PDA media, with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each media*rate, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG Media Rate Pss Mean total area (mm2) SE df 2021 2 23 2b 34 2b KB PDA KB PDA KB PDA 0.5x 0.5x 1.5x 1.5x 1.0x 1.0x 243.2 111.3 168.3 114.3 211.5 157.1 16.5 16.5 16.0 16.1 16.0 16.5 Year Group Strain/PG contrast estimate SE 2021 2 23 2b 34 2b KB 0.5x v. PDA 0.5x KB 0.5x v. KB 1.5x KB 0.5x v. PDA 1.5x KB 0.5x v. KB 1.0x KB 0.5x v. PDA 1.0x PDA 0.5x v. KB 1.5x PDA 0.5x v. PDA 1.5x PDA 0.5x v. KB 1.0x PDA 0.5x v. PDA 1.0x KB 1.5x v. PDA 1.5x KB 1.5x v. KB 1.0x KB 1.5x v. PDA 1.0x PDA 1.5x v. KB 1.0x PDA 1.5x v. PDA 1.0x KB 1.0x v. PDA 1.0x 23.3 22.9 23.1 22.9 23.3 22.9 23.1 22.9 23.3 22.7 22.6 22.9 22.7 23.1 22.9 131.9 74.9 128.9 31.7 86.1 -57.0 -3.0 -100.2 -45.7 54.0 -43.2 11.3 -97.2 -42.8 54.4 482 93 93 93 93 93 93 df 93 93 93 93 93 93 93 93 93 93 93 93 93 93 93 lower C.L. 210.5 78.6 136.6 82.3 179.8 124.4 upper C.L. 275.9 144.0 200.1 146.4 243.2 189.8 a b bc b ac bc t-ratio p-value 5.662 3.263 5.589 1.382 3.698 -2.484 -0.129 -4.365 -1.964 2.378 -1.910 0.490 -4.279 -1.854 2.372 <0.0001 0.0187 <0.0001 0.7378 0.0048 0.1395 1.0000 0.0005 0.3706 0.1747 0.4025 0.9964 0.0006 0.4367 0.1771 Table C13. Group 2 2021 mean total area of growth (mm2) of Pss strains 23 and 34, belonging to phylogroup (PG) 2b ± SE in co- inoculations of biological control agent Blossom Protect™ (BP) or water (H2O)) at three rates (0.5x, 1.0x, 1.5x) grown on KB or PDA media, with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each media*strain and with letters indicating statistical differences with significance determined by p-value ≤0.05. SE df lower C.L. upper C.L. 93 93 93 93 df 93 93 93 93 93 93 150.6 109.9 212.4 92.2 203.5 163.9 264.2 144.3 a ab c b t-ratio p-value 2.111 -3.286 3.144 -5.386 0.987 6.485 0.1571 0.0077 0.0118 <0.0001 0.7572 <0.0001 Year Group Strain/PG Media Strain 2021 2 23 2b 34 2b KB PDA KB PDA 23 23 34 34 Pss Mean total area (mm2) 177.0 136.9 238.3 118.3 13.3 13.6 13.0 13.1 Year Group Strain/PG contrast estimate SE 2021 2 23 2b 34 2b KB strain 23 v. PDA strain 23 KB strain 23 v. KB strain 34 KB strain 23 v. PDA strain 34 PDA strain 23 v. KB strain 34 PDA strain 23 v. PDA strain 34 KB strain 34 v. PDA strain 34 40.1 -61.3 58.8 -101.4 18.6 120.0 19.0 18.6 18.7 18.8 18.9 18.5 483 Table C14. Group 2 2021 mean total area of growth (mm2) of Pss strains 23 and 34, belonging to phylogroup (PG) 2b ± SE in co- inoculations of biological control agent Blossom Protect™ (BP) or water (H2O) at three rates (0.5x, 1.0x, 1.5x) grown on KB or PDA media,, with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each treatment*strain and with letters indicating statistical differences with significance determined by p-value ≤0.05. SE df lower C.L. upper C.L. 93 93 93 93 df 93 93 93 93 93 93 127.0 133.6 121.0 183.6 180.9 186.4 172.8 235.7 a a a b t-ratio p-value -0.320 0.373 -2.950 0.704 -2.654 -3.390 0.9886 0.9822 0.0206 0.8955 0.0454 0.0056 Year Group Strain/PG Treatment (BCA) Strain 2021 2 23 2b 34 2b BP H2O BP H2O 23 23 34 34 Pss Mean total area (mm2) 153.9 160.0 146.9 209.6 13.6 13.3 13.0 13.1 Year Group Strain/PG contrast estimate SE 2021 2 23 2b 34 2b BP strain 23 - H2O strain 23 BP strain 23 - BP strain 34 BP strain 23 - H2O strain 34 H2O strain 23 - BP strain 34 H2O strain 23 - H2O strain 34 BP strain 34 - H2O strain 34 -6.1 7.0 -55.7 13.1 -49.6 -62.8 19.0 18.8 18.9 18.6 18.7 18.5 484 Table C15. Group 2 2021 mean total area of growth (mm2) of Pss strains 23 and 34, belonging to phylogroup (PG) 2b ± SE in co- inoculations of biological control agent Blossom Protect™ (BP) or water (H2O) at three rates (0.5x, 1.0x, 1.5x) grown on KB or PDA media,, with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each media*treatment*strain with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Media Treatment (BCA) Strain 2021 2 Year Group 2021 2 KB PDA KB PDA KB PDA KB PDA BP BP H2O H2O BP BP H2O H2O contrast 23 23 23 23 34 34 34 34 KB BP strain 23 v. PDA BP strain 23 KB BP strain 23 v. KB H2O strain 23 KB BP strain 23 v. PDA H2O strain 23 KB BP strain 23 v. KB BP strain34 KB BP strain 23 v. PDA BP strain34 KB BP strain 23 v. KB H2O strain34 KB BP strain 23 v. PDA H2O strain34 PDA BP strain 23 v. KB H2O strain 23 PDA BP strain 23 v. PDA H2O strain 23 PDA BP strain 23 v. KB BP strain34 PDA BP strain 23 v. PDA BP strain34 PDA BP strain 23 v. KB H2O strain34 a a a a a a b a SE df lower C.L. upper C.L. 18.4 19.9 19.2 18.4 18.4 18.4 18.4 18.7 SE 27.2 26.6 26.1 26.1 26.1 26.1 26.3 27.7 27.2 27.2 27.2 27.2 93 93 93 93 93 93 93 93 df 93 93 93 93 93 93 93 93 93 93 93 93 145.1 86.6 134.3 111.0 137.1 83.5 266.3 79.3 t-ratio 2.046 0.350 1.306 0.307 2.361 -4.646 2.486 -1.671 -0.792 -1.751 0.222 -6.509 218.3 165.7 210.5 184.3 210.3 156.7 339.5 153.5 p-value 0.4574 1.0000 0.8945 1.0000 0.2730 0.0003 0.2144 0.7058 0.9932 0.6543 1.0000 <0.0001 Pss Mean total area (mm2) 181.7 126.2 172.4 147.6 173.7 120.1 302.9 116.4 estimate 55.6 9.3 34.1 8.0 61.6 -121.2 65.3 -46.2 -21.5 -47.5 6.0 -176.8 485 Table C15.(cont’d) PDA BP strain 23 v. PDA H2O strain34 KB H2O strain 23 v. PDA H2O strain 23 KB H2O strain 23 v. KB BP strain34 KB H2O strain 23 v. PDA BP strain34 KB H2O strain 23 v. KB H2O strain34 KB H2O strain 23 v. PDA H2O strain34 PDA H2O strain 23 v. KB BP strain34 PDA H2O strain 23 v. PDA BP strain34 PDA H2O strain 23 v. KB H2O strain34 PDA H2O strain 23 v. PDA H2O strain34 KB BP strain34 v. PDA BP strain34 KB BP strain34 v. KB H2O strain34 KB BP strain34 v. PDA H2O strain34 PDA BP strain34 v. KB H2O strain34 PDA BP strain34 v. PDA H2O strain34 KB H2O strain34 v. PDA H2O strain34 9.8 24.7 -1.3 52.3 -130.5 56.0 -26.1 27.5 -155.3 31.3 53.6 -129.2 57.3 -182.8 3.7 186.5 27.3 26.6 26.6 26.6 26.6 26.8 26.1 26.1 26.1 26.3 26.1 26.1 26.3 26.1 26.3 26.3 93 93 93 93 93 93 93 93 93 93 93 93 93 93 93 93 0.357 0.929 -0.049 1.963 -4.902 2.089 -0.998 1.055 -5.951 1.189 2.054 -4.953 2.181 -7.007 0.141 7.100 1.0000 0.9824 1.0000 0.5120 0.0001 0.4298 0.9736 0.9642 <0.0001 0.9330 0.4523 0.0001 0.3727 <0.0001 1.0000 <0.0001 486 Table C16. Group 3 2021, by rate and by strain, mean total area of growth (mm2) of Pss strains 22, 26, and 37, belonging to phylogroup (PG) 2b and 2d, ± SE, in co-inoculations of biological control agent Blossom Protect™ (BP) or water (H2O) at three rates (0.5x, 1.0x, 1.5x), with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each rate and strain, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG Rate Pss Mean total area (mm2) SE df lower C.L. upper C.L. 2021 3 22 2b 26 2d 37 2d 0.5x 1.5x 1.0x 135.2 174.8 132.1 9.9 8.7 9.3 126 126 126 115.7 157.6 113.7 154.8 192.0 150.5 a b a Year Group Strain/PG contrast estimate SE df t-ratio p-value 2021 3 22 2b 26 2d 37 2d 0.5x v. 1.5x 0.5x v. 1.0x 1.5x v. 1.0x -39.6 3.2 42.7 13.2 126 13.6 126 12.7 126 -3.007 0.233 3.354 0.0089 0.9705 0.0030 Year Group Strain PG 2021 3 22 26 37 2b 2d 2d Mean total area (mm2) 116.8 151.1 174.1 Year Group contrast estimate SE df lower C.L. upper C.L. 9.9 9.0 9.1 SE 126 126 126 97.3 133.4 156.2 136.4 168.9 192.0 a b b df t-ratio p-value 2021 3 strain 22 v. strain 26 strain 22 v. strain 37 strain 26 v. strain 37 -34.3 -57.3 -22.9 13.3 126 13.4 126 12.7 126 -2.574 -4.276 -1.801 0.0300 0.0001 0.1733 487 Table C17. Group 4 2021, mean total area of growth (mm2) of Pss strains 9 and 27, belonging to phylogroup (PG) 2d, ± SE, in co- inoculations of biological control agent Blossom Protect™ (BP) or water (H2O) at three rates (0.5x, 1.0x, 1.5x), with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each media*rate, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG media Rate 2021 4 9 2d 27 2d KB PDA KB PDA KB PDA 0.5x 0.5x 1.5x 1.5x 1.0x 1.0x Pss Mean total area (mm2) 156.7 233.4 248.5 191.9 247.0 198.7 Year Group Strain/PG contrast estimate 2021 4 9 2d 27 2d KB 0.5x - PDA 0.5x KB 0.5x - KB 1.5x KB 0.5x - PDA 1.5x KB 0.5x - KB 1x KB 0.5x - PDA 1x PDA 0.5x - KB 1.5x PDA 0.5x - PDA 1.5x PDA 0.5x - KB 1x PDA 0.5x - PDA 1x KB 1.5x - PDA 1.5x KB 1.5x - KB 1x KB 1.5X - PDA 1X PDA 1.5x - KB 1x PDA 1.5x - PDA 1x KB 1X - PDA 1X -76.7 -91.7 -35.2 -90.3 -42.0 -15.0 41.5 -13.6 34.7 56.6 1.4 49.8 -55.1 -6.8 48.3 488 SE 17.4 16.5 17.4 16.9 17.4 16.9 SE 24.0 24.6 24.2 24.6 24.2 24.0 23.6 24.0 23.6 24.2 24.6 24.2 24.2 23.8 24.2 df 94 94 94 94 94 94 df 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 lower C.L. upper C.L. 122.3 200.7 214.0 158.4 212.6 165.3 191.2 266.2 283.0 225.4 281.5 232.2 a b b ab b ab t-ratio p-value -3.202 -3.735 -1.453 -3.676 -1.735 -0.628 1.762 -0.568 1.472 2.338 0.059 2.056 -2.278 -0.286 1.996 0.0223 0.0042 0.6946 0.0051 0.5125 0.9887 0.4953 0.9929 0.6826 0.1896 1.0000 0.3195 0.2133 0.9997 0.3522 Table C18. Group 4 2021, mean total area of growth (mm2) of Pss strains 9 and 27, belonging to phylogroup (PG) 2d, ± SE, in co- inoculations of biological control agent Blossom Protect™ (BP) or water (H2O) at three rates (0.5x, 1.0x, 1.5x), with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each rate*strain, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Rate strain 2021 4 Year Group 2021 4 0.5x 1.5x 1.0x 0.5x 1.5x 1.0x 27 27 27 9 9 9 contrast 0.5x strain 27 v. 1.5x strain 27 0.5x strain 27 v. 1x strain 27 0.5x strain 27 v. 0.5x strain 9 0.5x strain 27 v. 1.5x strain 9 0.5x strain 27 v. 1x strain 9 1.5x strain 27 v. 1x strain 27 1.5x strain 27 v. 0.5x strain 9 1.5x strain 27 v. 1.5x strain 9 1.5x strain 27 v. 1x strain 9 1x strain 27 v. 0.5x strain 9 1x strain 27 v. 1.5x strain 9 1x strain 27 v. 1x strain 9 0.5x strain 9 v. 1.5x strain 9 0.5x strain 9 v. 1x strain 9 1.5x strain 9 v. 1x strain 9 Pss Mean total area (mm2) 175.1 231.7 266.0 215.1 208.7 179.8 estimate -56.7 -90.9 -40.1 -33.6 -4.7 -34.3 16.6 23.1 51.9 50.9 57.3 86.2 6.5 35.4 28.9 SE df lower C.L. upper C.L. 94 94 94 94 94 94 df 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 140.6 198.3 232.5 182.4 174.2 145.3 t-ratio -2.341 -3.758 -1.672 -1.368 -0.192 -1.439 0.704 0.952 2.146 2.158 2.369 3.563 0.269 1.476 1.176 209.5 265.2 299.5 247.9 243.2 214.3 p-value a ab b ab ab a 0.1884 0.0039 0.5534 0.7459 1.0000 0.7035 0.9811 0.9315 0.2730 0.2676 0.1779 0.0074 0.9998 0.6804 0.8470 17.4 16.9 16.9 16.5 17.4 17.4 SE 24.2 24.2 24.0 24.6 24.6 23.8 23.6 24.2 24.2 23.6 24.2 24.2 24.0 24.0 24.6 489 Table C19. Group 4 2021, mean total area of growth (mm2) of Pss strains 9 and 27, belonging to phylogroup (PG) 2d, ± SE, in co- inoculations of biological control agent Blossom Protect™ (BP) or water (H2O) at three rates (0.5x, 1.0x, 1.5x), with confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each media*treatment nested within rate, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Strain/PG Rate media treatment Pss Mean total area (mm2) SE df lower C.L. upper C.L. 2021 4 9 2d 27 2d 0.5x 1.5x 1.0x KB PDA KB PDA KB PDA KB PDA KB PDA KB PDA BP BP H2O H2O BP BP H2O H2O BP BP H2O H2O 153.4 239.8 160.0 227.1 253.5 214.7 243.5 169.1 227.1 237.7 267.0 159.8 23.8 23.8 25.3 22.8 23.8 23.8 25.3 23.8 25.3 23.8 23.8 23.8 Year Group Strain/PG Rate contrast estimate SE 2021 4 9 2d 27 2d 0.5x KB BP v. PDA BP KB BP v. KB H2O KB BP v. PDA H2O PDA BP v. KB H2O PDA BP v. PDA H2O KB H2O v. PDA H2O 1.5x KB BP v. PDA BP 33.7 34.7 33.0 34.7 33.0 34.1 33.7 -86.4 -6.6 -73.6 79.8 12.7 -67.0 38.8 490 94 94 94 94 94 94 94 94 94 94 94 94 df 94 94 94 94 94 94 94 106.1 192.5 109.9 181.8 206.2 167.4 193.3 121.7 176.9 190.4 219.7 112.4 200.8 287.1 210.2 272.4 300.8 262.1 293.6 216.4 277.2 285.0 314.3 207.1 a a a a a a a a ab ab a b t-ratio p-value -2.563 -0.190 -2.232 2.296 0.386 -1.969 0.0570 0.9976 0.1222 0.1062 0.9803 0.2072 1.150 0.6597 Table C19.(cont’d) KB BP v. KB H2O KB BP v. PDA H2O PDA BP v. KB H2O PDA BP v. PDA H2O KB H2O v. PDA H2O 1.0x KB BP v. PDA BP KB BP v. KB H2O KB BP v. PDA H2O PDA BP v. KB H2O PDA BP v. PDA H2O KB H2O v. PDA H2O 10.0 84.4 -28.7 45.7 74.4 -10.6 -40.0 67.3 -29.3 77.9 107.3 34.7 33.7 34.7 33.7 34.7 34.7 34.7 34.7 33.7 33.7 33.7 94 94 94 94 94 94 94 94 94 94 94 0.289 2.505 -0.827 1.355 2.142 -0.306 -1.151 1.937 -0.870 2.313 3.183 0.9916 0.0656 0.8416 0.5303 0.1476 0.9900 0.6593 0.2196 0.8202 0.1024 0.0105 491 A B C Figure C4. The 2022 quantile-quantile plots (qqplot) of the residuals for graphical visualization of the distribution of the total area of growth (mm2) of Pss strains 9, 25 (PG 2d virulent) and 23,33 (PG2b moderate virulence) grown next to the strains 14, 18 (PG 2c avirulent), and 23,33 (PG2b moderate virulence) or water (H2O) in Petri plates with KB media, group corresponds to the rate combination at which the PG2d and PG2b strains were inoculated next to the PG2c and PG2b strains or H2O, where A) group 1 all strains were inoculated at 108 cfu/ml, B) group 2 strains were inoculated at 106 v. 108 cfu/ml, and C) group 3 strains were inoculated at 104 v. 108 cfu/ml. 492 Table C20. The 2022 co-inoculations of Pss strains 9, 25 (PG 2d virulent) and 23,33 (PG2b moderate virulence) grown next to the strains 14, 18 (PG 2c avirulent), and 23,33 (PG2b moderate virulence) or water (H2O) in Petri plates with KB media, group corresponds to the rate combination at which the PG2d and PG2b strains were inoculated next to the PG2c and PG2b strains or H2O, for group 1 all strains were inoculated at 108 cfu/ml. With confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each strain, with letters indicating statistical differences with significance determined by p-value ≤0.05. SE df lower C.L. upper C.L. 41 41 41 41 df 41 41 41 41 41 41 118.2 242.9 227.0 320.5 196.6 313.5 306.1 388.5 a bc b c t-ratio p-value -5.240 <0.0001 0.0016 -3.961 -7.152 <0.0001 0.9772 0.405 0.0511 -2.668 0.0018 -3.927 Year Group Strain/PG 2022 1 23 2b 25 2d 33 2b 9 2d Pss Mean total area (mm2) 157.4 278.2 266.6 354.5 19.4 17.5 19.6 16.8 Year Group contrast estimate SE 2022 1 23 v. 25 23 v. 33 23 v. 9 25 v. 33 25 v. 9 33 v. 9 -120.8 -109.2 -197.1 11.6 -76.4 -88.0 23.0 27.6 27.6 28.6 28.6 22.4 493 Table C21. The 2022 co-inoculations of Pss strains 9, 25 (PG 2d virulent) and 23,33 (PG2b moderate virulence) grown next to the strains 14, 18 (PG 2c avirulent), and 23,33 (PG2b moderate virulence) or water (H2O) in Petri plates with KB media, group corresponds to the rate combination at which the PG2d and PG2b strains were inoculated next to the PG2c and PG2b strains or H2O, for group 2 all strains were inoculated at 106 v. 108 cfu/ml. With confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each strain and treatment, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group contrast estimate SE Year Group Strain/PG 2022 2 23 25 33 9 Pss Mean total area (mm2) 158.5 282.0 212.6 306.7 2022 2 23 v. 25 23 v. 33 23 v. 9 25 v. 33 25 v. 9 33 v. 9 Year Group Treatment 2022 2 14 18 23 33 H2O -123.5 -54.1 -148.1 69.4 -24.7 -94.1 Pss Mean total area (mm2) 224.4 249.2 286.5 195.4 244.3 17.0 14.6 17.0 14.6 19.7 24.1 24.1 24.1 24.1 19.7 17.0 17.0 24.1 24.1 9.8 Year Group contrast estimate SE 2022 2 14 v. 18 14 v. 23 14 v. 33 14 v. H2O -24.8 -62.1 29.0 -20.0 27.8 26.0 32.6 19.7 494 SE df lower C.L. upper C.L. 42 42 42 42 df 42 42 42 42 42 42 124.2 252.6 178.2 277.3 192.9 311.4 246.9 336.1 a b a b t-ratio p-value -6.282 <0.0001 0.1277 -2.246 -6.154 <0.0001 0.0303 2.883 0.7358 -1.025 0.0001 -4.787 SE df lower C.L. upper C.L. 42 42 42 42 42 df 42 42 42 42 190.0 214.8 237.9 146.8 224.5 258.7 283.5 335.0 243.9 264.2 a a a a a t-ratio p-value -0.892 -2.388 0.890 -1.015 0.8981 0.1387 0.8989 0.8470 Table C21. (cont’d) 18 v. 23 18 v. 33 18 v. H2O 23 v. 33 23 v. H2O 33 v. H2O -37.3 53.8 4.9 91.1 42.1 -49.0 32.6 26.0 19.7 36.8 26.0 26.0 42 42 42 42 42 42 -1.144 2.070 0.247 2.478 1.621 -1.883 0.7825 0.2519 0.9991 0.1153 0.4931 0.3418 495 Table C22. The 2022 co-inoculations of Pss strains 9, 25 (PG 2d virulent) and 23,33 (PG2b moderate virulence) grown next to the strains 14, 18 (PG 2c avirulent), and 23,33 (PG2b moderate virulence) or water (H2O) in Petri plates with KB media, group corresponds to the rate combination at which the PG2d and PG2b strains were inoculated next to the PG2c and PG2b strains or H2O, for group 3 all strains were inoculated at 104 v. 108 cfu/ml. With confidence limits (lower and upper C.L.) and the p-value for the pairwise comparisons of the estimated marginal means for each strain and treatment, with letters indicating statistical differences with significance determined by p-value ≤0.05. Year Group Treatment 2022 3 14 18 23 33 H2O Pss Mean total area (mm2) 161.2 193.8 149.8 150.5 225.1 24.6 24.6 34.8 34.8 14.2 Year Group contrast estimate SE 2022 3 14 - 18 14 - 23 14 - 33 14 - H2O 18 - 23 18 - 33 18 - H2O 23 - 33 23 - H2O 33 - H2O -32.6 11.4 10.7 -63.9 44.0 43.3 -31.3 -0.7 -75.3 -74.6 40.2 37.6 47.2 28.5 47.2 37.6 28.5 53.2 37.6 37.6 496 SE df lower C.L. upper C.L. 42 42 42 42 42 df 42 42 42 42 42 42 42 42 42 42 111.5 144.1 79.4 80.1 196.4 210.9 243.5 220.1 220.8 253.8 a a a a a t-ratio p-value -0.811 0.303 0.227 -2.247 0.933 1.151 -1.100 -0.013 -2.002 -1.983 0.9258 0.9981 0.9994 0.1827 0.8823 0.7783 0.8054 1.0000 0.2829 0.2918 Figure C5. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 vs. BP on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 497 Figure C6. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 vs. SO on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 498 Figure C7. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 499 Figure C8. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 vs. BP on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 500 Figure C9. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 vs. SO on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 501 Figure C10. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 502 Figure C11. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 37 vs. BP on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 503 Figure C12. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 37 vs. SO on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 504 Figure C13. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 37 vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 505 Figure C14. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 38 vs. BP on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 506 Figure C15. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 38 vs. SO on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 507 Figure C16. 2019 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 38 vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 508 Figure C17. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BB vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 509 Figure C18. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BB vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 510 Figure C19. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BB vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 511 Figure C20. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BB vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 512 Figure C21. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BB vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 513 Figure C22. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BB vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 514 Figure C23. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BB vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 515 Figure C24. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BB vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 516 Figure C25. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BB vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 517 Figure C26. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BB vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 518 Figure C27. 12021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BLT vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 519 Figure C28. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BLT vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 520 Figure C29. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BLT vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 521 Figure C30. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BLT vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 522 Figure C31. 22021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BLT vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 523 Figure C32. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BLT vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 524 Figure C33. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BLT vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 525 Figure C34. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BLT vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 526 Figure C35. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BLT vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 527 Figure C36. 32021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BLT vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 528 Figure C37. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 529 Figure C38. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 530 Figure C39. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 531 Figure C40. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 532 Figure C41. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 533 Figure C42. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 534 Figure C43. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 535 Figure C44. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 536 Figure C45. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 537 Figure C46. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 538 Figure C47. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x DN vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 539 Figure C48. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x DN vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 540 Figure C49. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x DN vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 541 Figure C50. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x DN vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 542 Figure C51. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x DN vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 543 Figure C52. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x DN vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 544 Figure C53. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x DN vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 545 Figure C54. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x DN vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 546 Figure C55. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x DN vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 547 Figure C56. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x DN vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 548 Figure C57. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x SO vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 549 Figure C58. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x SO vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 550 Figure C59. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x SO vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 551 Figure C60. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x SO vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 552 Figure C61. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x SO vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 553 Figure C62. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x SO vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 554 Figure C63. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x SO vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 555 Figure C64. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x SO vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 556 Figure C65. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x SO vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 557 Figure C66. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x SO vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 558 Figure C67. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 9 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 559 Figure C68. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 14 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 560 Figure C69. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 22 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 561 Figure C70. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 23 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 562 Figure C71. 42021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 25 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 563 Figure C72. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 26 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 564 Figure C73. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 27 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 565 Figure C74. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 34 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 566 Figure C75. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 37 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 567 Figure C76. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 0.5x BP vs. Pss 38 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 568 Figure C77. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BB vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 569 Figure C78. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BB vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 570 Figure C79. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BB vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 571 Figure C80. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BB vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 572 Figure C81. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BB vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 573 Figure C82. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BB vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 574 Figure C83. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BB vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 575 Figure C84. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BB vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 576 Figure C85. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BB vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 577 Figure C86. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BB vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 578 Figure C87. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BLT vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 579 Figure C88. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BLT vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 580 Figure C89. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BLT vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 581 Figure C90. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BLT vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 582 Figure C91. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BLT vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 583 Figure C92. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BLT vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 584 Figure C93. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BLT vs. Pss 27 on KB media. Replications 1-5. No measurements were taken as all plates were overgrown. 585 Figure C94. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BLT vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 586 Figure C95. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BLT vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 587 Figure C96. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BLT vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 588 Figure C97. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 589 Figure C98. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 590 Figure C99. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 591 Figure C100. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 592 Figure C101. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 593 Figure C102. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 594 Figure C103. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 595 Figure C104. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 596 Figure C105. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 597 Figure C106. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 598 Figure C107. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x DN vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 599 Figure C108. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x DN vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 600 Figure C109. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x DN vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 601 Figure C110. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x DN vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 602 Figure C111. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x DN vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 603 Figure C112. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x DN vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 604 Figure C113. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x DN vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 605 Figure C114. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x DN vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 606 Figure C115. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x DN vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 607 Figure C116. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x DN vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 608 Figure C117. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x SO vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 609 Figure C118. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x SO vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 610 Figure C119. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x SO vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 611 Figure C120. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x SO vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 612 Figure C121. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x SO vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 613 Figure C122. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x SO vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 614 Figure C123. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x SO vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 615 Figure C124. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x SO vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 616 Figure C125. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x SO vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 617 Figure C126. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x SO vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 618 Figure C127. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 9 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 619 Figure C128. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 14 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 620 Figure C129. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 22 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 621 Figure C130. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 23 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 622 Figure C131. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 25 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 623 Figure C132. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 26 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 624 Figure C133. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 27 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 625 Figure C134. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 34 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 626 Figure C135. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 37 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 627 Figure C136. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1x BP vs. Pss 38 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4,C4: Replication 4; A5, B5, C5: Replication 5. 628 Figure C137. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BB vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 629 Figure C138. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BB vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 630 Figure C139. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BB vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 631 Figure C140. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BB vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 632 Figure C141. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BB vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 633 Figure C142. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BB vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 634 Figure C143. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BB vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 635 Figure C144. 52021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BB vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 636 Figure C145. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BB vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 637 Figure C146. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BB vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 638 Figure C147. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BLT vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 639 Figure C148. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BLT vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 640 Figure C149. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BLT vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 641 Figure C150. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BLT vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 642 Figure C151. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BLT vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 643 Figure C152. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BLT vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 644 Figure C153. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BLT vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 645 Figure C154. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BLT vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 646 Figure C155. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BLT vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 647 Figure C156. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BLT vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 648 Figure C157. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 649 Figure C158. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 650 Figure C159. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 651 Figure C160. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 652 Figure C161. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 653 Figure C162. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 654 Figure C163. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 655 Figure C164. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 656 Figure C165. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 657 Figure C166. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 658 Figure C167. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x DN vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 659 Figure C168. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x DN vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 660 Figure C169. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x DN vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 661 Figure C170. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x DN vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 662 Figure C171. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x DN vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 663 Figure C172. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x DN vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 664 Figure C173. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x DN vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 665 Figure C174. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x DN vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 666 Figure C175. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x DN vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 667 Figure C176. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x DN vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 668 Figure C177. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x SO vs. Pss 9 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 669 Figure C178. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x SO vs. Pss 14 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 670 Figure C179. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x SO vs. Pss 22 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 671 Figure C180. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x SO vs. Pss 23 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 672 Figure C181. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x SO vs. Pss 25 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 673 Figure C182. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x SO vs. Pss 26 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 674 Figure C183. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x SO vs. Pss 27 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 675 Figure C184. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x SO vs. Pss 34 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 676 Figure C185. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x SO vs. Pss 37 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 677 Figure C186. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x SO vs. Pss 38 on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 678 Figure C187. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 9 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 679 Figure C188. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 14 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 680 Figure C189. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 22 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 681 Figure C190. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 23 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 682 Figure C191. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 25 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 683 Figure C192. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 26 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 684 Figure C193. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 27 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 685 Figure C194. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 34 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 686 Figure C195. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 37 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 687 Figure C196. 2021 co-inoculated Petri plates four days after inoculation. A1-A5) 1.5x BP vs. Pss 38 on PDA media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 688 Figure C197. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 at 108 cfu/ml vs. Pss 18 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 689 Figure C198. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 at 108 cfu/ml vs. Pss 33 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 690 Figure C199. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 23 at 108 cfu/ml vs. Pss 14 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 691 Figure C200. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 at 108 cfu/ml vs. Pss 14 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 692 Figure C201. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 at 108 cfu/ml vs. Pss 23 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 693 Figure C202. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 33 at 108 cfu/ml vs. Pss 18 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 694 Figure C203. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 at 106 cfu/ml vs. Pss 18 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 695 Figure C204. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 at 106 cfu/ml vs. Pss 33 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 696 Figure C205. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 23 at 106 cfu/ml vs. Pss 14 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 697 Figure C206. 62022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 at 106 cfu/ml vs. Pss 14 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 698 Figure C207. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 at 106 cfu/ml vs. Pss 23 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 699 Figure C208. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 33 at 106 cfu/ml vs. Pss 18 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 700 Figure C209. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 at 104 cfu/ml vs. Pss 18 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 701 Figure C210. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 at 104 cfu/ml vs. Pss 33 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 702 Figure C211. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 23 at 104 cfu/ml vs. Pss 14 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 703 Figure C212. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 at 104 cfu/ml vs. Pss 14 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 704 Figure C213. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 at 104 cfu/ml vs. Pss 23 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 705 Figure C214. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 33 at 104 cfu/ml vs. Pss 18 at 108 cfu/ml on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 706 Figure C215. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 at 108 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 707 Figure C216. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 23 at 108 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 708 Figure C217. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 at 108 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. Images with NA are not available. 709 Figure C218. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 33 at 108 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 710 Figure C219. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 at 106 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 711 Figure C220. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 23 at 106 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 712 Figure C221. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 at 106 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 713 Figure C222. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 33 at 106 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 714 Figure C223. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 9 at 104 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 715 Figure C224. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 23 at 104 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 716 Figure C225. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 25 at 104 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 717 Figure C226. 2022 co-inoculated Petri plates four days after inoculation. A1-A5) Pss 33 at 104 cfu/ml vs. H2O on KB media. B1-B5) Images after threshold adjustment in ImageJ. C1-C5) Image of measured area. A1, B1, C1: Replication 1; A2, B2, C2: Replication 2; A3, B3, C3: Replication 3; A4, B4, C4: Replication 4; A5, B5, C5: Replication 5. 718