Forestry, Faculty of
DSpace
UBCV
Reich, Jonathan
Doctor of Philosophy - PhD
University of British Columbia
Many economically important agricultural plant pathogens are spread on air currents; as a result, monitoring the air for fungal spores could help predict epidemics and therefore prevent losses due to disease. This thesis describes the coupling of air sampling and molecular identification and quantification methods to both (i) characterize the air fungal microbiome, with an emphasis on regionally important plant pathogens, and (ii) describe and predict the occurrence of a specific pathogen and disease in southern Alberta: the fungus Sclerotinia sclerotiorum, causal agent of white mould in dry bean. For the first objective, Illumina amplicon sequencing of the internal transcribed spacer (ITS1) region was performed on air samples collected across Alberta. This approach was able to simultaneously detect many airborne plant pathogens, in contrast to currently employed methods that only detect a single pathogen. Furthermore, there was some evidence that read abundance was reflective of biological abundance, since certain fungal genera were found in greater levels in samples collected in their host crops compared to nonhost crops. For the second objective, quantitative PCR (qPCR) was used to quantify ascospores of S. sclerotiorum from spore samplers placed in commercial dry bean fields in Alberta, Manitoba, and Ontario over four growing seasons. In-field weather stations collected a range of variable data, and disease surveys were performed to assess white mould levels. Disease incidence was most significantly associated with dry bean cultivar, with Pinto beans having, on average, the greatest disease levels compared to Great Northern, Yellow, Red, and Black. Due to the wide range of disease intensity observed, disease was not associated with ascospore levels or most environmental variables. Daily ascospore counts were not correlated strongly with most environmental variables but were strongly correlated with the numbers of ascospores released the previous day. This research lays the groundwork for future work in multi-pathogen identification using new DNA sequencing technologies. Furthermore, it provides insights into the dynamics underlying ascospore dispersal and the development of white mould epidemics in dry bean crops.
COMBINING AIR SAMPLING AND MOLECULAR METHODS TO MONITOR AIRBORNE FUNGAL PLANT PATHOGENS by Jonathan Reich B.A.Sc., Quest University Canada, 2013 M.Sc., University of Lethbridge, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2023 © Jonathan Reich, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Combining air sampling and molecular methods to monitor airborne fungal plant pathogens submitted by Jonathan Reich in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Forestry Examining Committee: Richard Hamelin, Professor, Forest Conservation Sciences, UBC Co-supervisor Syama Chatterton, Research Scientist, Agriculture and Agri-Food Canada (Lethbridge) Co-supervisor William Mohn, Professor, Microbiology and Immunology, UBC Supervisory Committee Member Jim Kronstad, Professor, Microbiology and Immunology, UBC University Examiner Guus Bakkeren, Research Scientist, Agriculture and Agri-Food Canada (Summerland) University Examiner iii Abstract Many economically important agricultural plant pathogens are spread on air currents; as a result, monitoring the air for fungal spores could help predict epidemics and therefore prevent losses due to disease. This thesis describes the coupling of air sampling and molecular identification and quantification methods to both (i) characterize the air fungal microbiome, with an emphasis on regionally important plant pathogens, and (ii) describe and predict the occurrence of a specific pathogen and disease in southern Alberta: the fungus Sclerotinia sclerotiorum, causal agent of white mould in dry bean. For the first objective, Illumina amplicon sequencing of the internal transcribed spacer (ITS1) region was performed on air samples collected across Alberta. This approach was able to simultaneously detect many airborne plant pathogens, in contrast to currently employed methods that only detect a single pathogen. Furthermore, there was some evidence that read abundance was reflective of biological abundance, since certain fungal genera were found in greater levels in samples collected in their host crops compared to nonhost crops. For the second objective, quantitative PCR (qPCR) was used to quantify ascospores of S. sclerotiorum from spore samplers placed in commercial dry bean fields in Alberta, Manitoba, and Ontario over four growing seasons. In-field weather stations collected a range of variable data, and disease surveys were performed to assess white mould levels. Disease incidence was most significantly associated with dry bean cultivar, with Pinto beans having, on average, the greatest disease levels compared to Great Northern, Yellow, Red, and Black. Due to the wide range of disease intensity observed, disease was not associated with ascospore levels or most environmental variables. Daily ascospore counts were not correlated strongly with most environmental variables but were strongly correlated with the numbers of ascospores released the previous day. This research lays the groundwork for future work in multi-pathogen identification iv using new DNA sequencing technologies. Furthermore, it provides insights into the dynamics underlying ascospore dispersal and the development of white mould epidemics in dry bean crops. v Lay Summary Many economically important agricultural fungal plant pathogens are spread on air currents; as a result, monitoring the air for fungal spores could help predict epidemics and therefore prevent losses due to disease. We collected daily aerosols over the course of four growing seasons across Alberta, Manitoba, and Ontario. We found that using new DNA sequencing technologies enabled the simultaneous detection of many fungal plant pathogens in a single sample – a substantial improvement over current practices of detecting a single pathogen at a time. When we analyzed the samples to quantity a specific fungus of interest, Sclerotinia sclerotiorum, the causal agent of white mould in dry bean, we found that, surprisingly, airborne spore levels of S. sclerotiorum were not clearly associated with white mould levels in dry bean fields. This research lays the groundwork for (i) future work of real-time analysis of airborne spores in the field and (ii) robust models to predict white mould epidemics in dry bean fields. vi Preface This research was carried out in both Dr. S. Chatterton’s lab at the Lethbridge Research and Development Centre (Lethbridge, Alberta) and in Dr. R. Hamelin’s lab at the Faculty of Forestry of the University of British Columbia (Vancouver, British Columbia). The COVID-19 pandemic greatly attenuated the proposed research projects. Massive shutdowns across Canada started in February and March of 2020 and continued for most of 2021. The diminished access to lab spaces and cancellation of most field work necessarily resulted in a greatly reduced research program for these two seasons. Nevertheless, through a combination of help from collaborators, tenacity, and luck, we were able to salvage data from each of these seasons, albeit in a reduced capacity. Chapter 2 was conceptualized by Dr. Hamelin, Dr. Chatterton, and Jonathan Reich (me). Samples from Burkard devices were collected by Dr. Chatterton’s and Dr. Dmitry Yevtushenko’s group, and samples from rotorod devices were collected by Dr. Kelly Turkington’s group. I performed all DNA extractions and prepared the samples for sequencing. Under the supervision of Dr. Wen Chen, I also performed all bioinformatics and statistical analysis of the Illumina reads. Chapters 3 and 4 are based on the white mould project proposed by Dr. Chatterton. With help from students and technicians, I performed the lab work, field surveys, and all statistical analyses for data resulting from our Alberta sites. Collaborators in Manitoba (Dr. D. McLaren and Dr. Y. M. Kim) and Ontario (Dr. O. Wally) collected samples and performed molecular analysis before sending me the data for analysis. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables .............................................................................................................................. xiv List of Figures ........................................................................................................................... xviii List of Supplementary Material .............................................................................................. xxii List of Symbols ......................................................................................................................... xxiii List of Abbreviations ............................................................................................................... xxiv Acknowledgements .................................................................................................................. xxvi Dedication ............................................................................................................................... xxviii Chapter 1: Review of the Literature ............................................................................................1 1.1 Introduction ..................................................................................................................... 1 1.2 Importance of airborne microbial diversity .................................................................... 2 1.3 Diversity of airborne microorganisms ............................................................................ 6 1.3.1 How to measure diversity and community (dis)similarity ...................................... 7 1.3.2 Effect of samplers ................................................................................................... 8 1.3.3 Effect of spatial scale. ........................................................................................... 10 1.3.4 Duration of sampling ............................................................................................ 12 1.3.5 Weather ................................................................................................................. 13 1.3.6 Temporal effects (diurnal, seasonal, annual) ........................................................ 15 viii 1.4 Identifying airborne biological fungi: ........................................................................... 16 1.4.1 Culture-based methods .......................................................................................... 16 1.4.2 Microscopy ........................................................................................................... 17 1.4.3 Molecular methods ................................................................................................ 17 1.4.4 High-throughput sequencing ................................................................................. 18 1.4.5 Immunoassays ....................................................................................................... 19 1.4.6 Biosensors ............................................................................................................. 20 1.4.7 Comparing identification methods ........................................................................ 20 1.5 Generalizations to draw from aerobiology research to date ......................................... 21 1.6 High throughput sequencing: pitfalls and biases .......................................................... 23 1.6.1 Appropriate controls ............................................................................................. 23 1.6.2 DNA extraction ..................................................................................................... 24 1.6.3 PCR primers and PCR .......................................................................................... 24 1.6.4 Sequencing platform and post-sequence data processing ..................................... 25 1.7 Microbiome vs individual species research .................................................................. 26 1.8 Case study: Sclerotinia sclerotiorum ............................................................................ 27 1.8.1 Life cycle of Sclerotinia sclerotiorum ................................................................... 28 1.8.1.1 Sclerotia ............................................................................................................ 30 1.8.1.2 Germination: mycelial and carpogenic ............................................................. 31 1.8.1.3 Ascospores and their dispersal .......................................................................... 33 1.8.1.4 Infection and disease development ................................................................... 35 1.8.1.5 Population biology ............................................................................................ 37 1.8.2 Epidemiology of S. sclerotiorum .......................................................................... 37 ix 1.8.2.1 Distribution of apothecia, ascospores, and disease in space and time .............. 38 1.8.2.2 Association of apothecia and ascospores with disease ..................................... 39 1.8.2.3 Influence of the environment on disease progression ....................................... 41 1.8.2.3.1 Precipitation ................................................................................................ 42 1.8.2.3.2 Wind ............................................................................................................ 43 1.8.2.3.3 Air temperature ........................................................................................... 43 1.8.2.3.4 Relative humidity ........................................................................................ 44 1.8.2.3.5 Host species and cultural practices ............................................................. 44 1.8.3 Management of Sclerotinia diseases ..................................................................... 45 1.8.3.1 Breeding ............................................................................................................ 46 1.8.3.2 Cultural controls ................................................................................................ 46 1.8.3.3 Chemical controls ............................................................................................. 47 1.8.3.4 Decision support tools ....................................................................................... 48 1.8.3.5 Other (biocontrol, RNAi, etc.) .......................................................................... 48 1.9 Objectives/Knowledge Gaps ......................................................................................... 49 Chapter 2: Combining air sampling and DNA metabarcoding to monitor plant pathogens........................................................................................................................................................51 2.1 Introduction ................................................................................................................... 51 2.2 Materials and Methods .................................................................................................. 52 2.2.1 Survey design and air sampling ............................................................................ 52 2.2.2 DNA extraction ..................................................................................................... 55 2.2.3 Mock community and negative controls ............................................................... 57 2.2.4 Amplicon-based high-throughput sequencing ...................................................... 57 x 2.2.5 qPCR ..................................................................................................................... 58 2.2.6 Bioinformatics and processing of the metabarcodes ............................................ 58 2.2.7 Statistical analysis ................................................................................................. 59 2.2.8 Plant pathogens ..................................................................................................... 60 2.2.9 Disease surveys ..................................................................................................... 60 2.3 Results ........................................................................................................................... 61 2.3.1 Summary of reads ................................................................................................. 61 2.3.2 Common and core taxa ......................................................................................... 63 2.3.3 Important plant pathogens ..................................................................................... 65 2.3.4 Aero-mycobiota diversity ..................................................................................... 67 2.3.4.1 Geography hypothesis (rotorod samplers) ........................................................ 67 2.3.4.2 Crop type hypothesis (Burkard samplers) ......................................................... 72 2.3.5 Comparison of sampler types ................................................................................ 75 2.3.6 Comparison of LeRDC Burkard samplers. ........................................................... 76 2.3.7 HTS read counts vs disease levels ........................................................................ 77 2.3.8 HTS read counts vs qPCR..................................................................................... 77 2.3.9 Positive and negative controls .............................................................................. 78 2.4 Discussion ..................................................................................................................... 81 Chapter 3: Occurrence of white mould caused by Sclerotinia sclerotiorum in dry bean fields in Alberta and comparison of ascospore release to other dry bean growing regions in Canada ..........................................................................................................................................87 3.1 Introduction ................................................................................................................... 87 3.2 Materials and Methods .................................................................................................. 89 xi 3.2.1 Disease surveys ..................................................................................................... 89 3.2.2 Effect of fungicide application on disease ............................................................ 90 3.2.3 Weather data for Alberta ....................................................................................... 90 3.2.4 Spore collection in Alberta ................................................................................... 91 3.2.5 Spore and weather data collection in Manitoba and Ontario ................................ 92 3.2.6 Comparison of spore samplers and sampler types for the collection of S. sclerotiorum ascospores ........................................................................................................ 92 3.2.7 Flower infection .................................................................................................... 93 3.2.8 DNA extraction ..................................................................................................... 94 3.2.9 Generation of standard curve and qPCR analysis ................................................. 94 3.2.10 Statistical analysis ................................................................................................. 95 3.3 Results ........................................................................................................................... 99 3.3.1 Weather ................................................................................................................. 99 3.3.2 Disease surveys ................................................................................................... 101 3.3.3 Effect of fungicides ............................................................................................. 103 3.3.4 Ascospore collection ........................................................................................... 103 3.3.5 Correlations of ascospores among samplers and sampler types ......................... 107 3.3.6 Flower infection .................................................................................................. 108 3.3.7 Models of disease incidence ............................................................................... 108 3.4 Discussion ................................................................................................................... 110 Chapter 4: Predicting airborne ascospores of Sclerotinia sclerotiorum through machine learning and statistical methods ...............................................................................................117 4.1 Introduction ................................................................................................................. 117 xii 4.2 Methods....................................................................................................................... 119 4.2.1 Spore sampling .................................................................................................... 119 4.2.2 Data quality control ............................................................................................. 120 4.2.3 Weather data ....................................................................................................... 120 4.2.4 Predictor variables .............................................................................................. 120 4.2.5 Data preparation for modelling ........................................................................... 121 4.2.6 Variable (feature) selection ................................................................................. 122 4.2.7 Modeling approaches .......................................................................................... 122 4.2.8 Machine learning models .................................................................................... 123 4.2.9 Statistical models ................................................................................................ 124 4.2.10 Evaluation of models .......................................................................................... 124 4.3 Results ......................................................................................................................... 125 4.3.1 Summary of ascospores across provinces, weeks, months ................................. 125 4.3.2 Correlations ......................................................................................................... 126 4.3.3 Machine learning models .................................................................................... 129 4.3.4 Statistical models ................................................................................................ 133 4.3.5 Comparison of ML and statistical approaches .................................................... 139 4.4 Discussion ................................................................................................................... 139 Chapter 5: Conclusion ...............................................................................................................144 References ...................................................................................................................................152 Appendices ..................................................................................................................................201 Appendix A ............................................................................................................................. 201 Appendix B ............................................................................................................................. 207 xiii Appendix C ............................................................................................................................. 209 xiv List of Tables Table 2.1: Summary of average and total number of fungal taxa recovered from air samples based on ITS1 metabarcodes. The top panel of the table is a summary of all reads in the data set whereas the bottom section includes only those reads classified as plant pathogens according to FUNGuild. Rotorod samplers collected samples for the geography hypothesis and Burkard samplers collected samples for the crop type hypothesis. ............................................................ 62 Table 2.2: Proportion (%) of reads in top 10 taxa recovered by rotorod or Burkard samplers from 2019 and 2021 sampling years through metabarcoding of the ITS1 region. The genera are arranged in descending order in the rotorod 2019 samples. Note that the top ten most abundant taxa were not the exact same for each data set, and so this table contains fifteen of the taxa that were commonest across the whole data set. .................................................................................. 64 Table 2.3: Means of diversity metrics and environmental variables for the geography hypothesis (rotorod) data set at each of the four locations sampled in Alberta in 2019. Environmental variables are averages of air temperature and relative humidity, and seasonal totals of precipitation. ................................................................................................................................. 69 Table 2.4: Summary table of the most parsimonious (lowest AIC and residual deviance) generalized linear model for Shannon diversity as determined by forwards, backwards, and stepwise selection for the rotorod sampler data set. ..................................................................... 71 Table 2.5: PermANOVA table for the 2019 rotorod data set, testing the effect of location and environmental factors on the microbial community composition for the rotorod sampler data set........................................................................................................................................................ 71 xv Table 2.6: PermANOVA table for the 2019 and 2021 Burkard sampler data sets, testing the effect of crop type and year on the Bray-Curtis dissimilarity index (microbial community composition). ................................................................................................................................ 75 Table 3.1: Historical weather (values represent ranges among several stations) in southern Alberta, accessed from provincial weather records from several regional weather stations (ACIS 2022). The in-field column represents summaries of the weather stations placed within bean fields as part of this research. ........................................................................................................ 99 Table 3.2: Comparison of in-field weather stations to regional (provincial) weather stations. Station comparisons were performed with a t-test and Bonferroni p-value adjustment. Year comparisons were performed with an ANOVA and means were separated following Tukey’s HSD. Below, the Person correlation coefficients and p-value are given for all data points comparing in-field weather stations to the nearest provincial weather station. .......................... 100 Table 3.3: White mould incidence (%) in southern Alberta commercial dry bean fields from 2018 to 2021. .............................................................................................................................. 101 Table 3.4: Severity of white mould epidemics in southern Alberta commercial dry bean fields from 2018 to 2021. The severity rating goes from 1 (totally healthy) to 4 (severe infection or dead); see Methods for details. ................................................................................................... 102 Table 3.5: Summary of white mould incidence and severity in southern Alberta by market class over 2020 and 2021 seasons combined. ...................................................................................... 103 Table 3.6: Summary of average daily ascospore collections from spore samplers placed side-by-side, one above the canopy and one below, in three commercial dry bean fields in Alberta in 2018. Mean and standard deviation calculations were performed on the log10(number of xvi ascospores per day + 1), and the data presented here have been back-transformed to represent the number of ascospores per day. .................................................................................................... 107 Table 3.7: Coefficients of the final generalized linear model as assessed by stepwise selection. The model predicted the proportion of white mould incidence based on environmental variables as measured by in-field weather stations. Weather variables in this model represent values from the month of July (i.e. meanT is the mean air temperature in July). .......................................... 109 Table 4.1: Top 10 Pearson correlations of daily log(number of ascospores + 1) with environmental variables for combined data from 2018-2021 in Alberta. ................................... 127 Table 4.2: Top 10 Pearson correlations of daily log(number of ascospores + 1) with environmental variables for combined data from 2019-2021 in Manitoba. ............................... 128 Table 4.3: Top 10 Pearson correlations of daily log(number of ascospores + 1) with environmental variables for combined data from 2019-2021 in Ontario. .................................. 129 Table 4.4: Evaluation metrics for the GLM linear regression models and ML random forest regression model. Deviance and AIC are those for the training data set in the GLM model development and R2 and MSE are diagnostics on the test data set. ........................................... 130 Table 4.5: Model diagnostics for three ML classification algorithms (RF = random forests, LR = logistic regression, SVM = support vector machines) and four daily threshold values (100, 200, 500, and 1,000) of ascospores. These diagnostics are for models developed with the VIF-selected variables. ..................................................................................................................................... 133 Table 4.6: Coefficients for variables included the best regression models as determined by backward, forward, and stepwise procedures using generalized linear models in R. The predicted variable was the daily log(no. ascospores + 1). Model diagnostics are given in Table 4.4. Refer to Tables 4.1 and 4.2 for a description of the variable names. ........................................................ 135 xvii Table 4.7: Coefficients for variables included the best logistic regression models as determined by backward, forward, and stepwise procedures in R. Successes were defined as 1 = the number of ascospores per day were greater than the shown threshold (100, 200, 500, or 1,000 ascospores per day) and 0 otherwise. Model diagnostics are given in Table 4.8. Refer to Tables 4.1 and 4.2 for a description of the variable names. ...................................................................................... 136 Table 4.8: Model diagnostics for three model selection methods (backward, forward, and stepwise) and four daily threshold values of ascospores. Models were developed using generalized linear models and a logit-link, where successes were defined as 1 = the number of ascospores per day were greater than the shown threshold (100, 200, 500, or 1,000 ascospores per day) and 0 otherwise. Deviance and AIC are those for the training set and other columns represent diagnostics on the test set. Refer to Table 4.7 for model specificiations. ................... 138 xviii List of Figures Figure 1.1: Number of articles published each year on the topic of aerobiology. Web of Science search query was “TI = ‘aerobioloy’ OR ‘air diversity’ OR ‘air microbiome’”. ........................... 4 Figure 1.2: The life cycle of Sclerotinia sclerotiorum. Most of the life of the fungus is spent in melanized hyphal aggregates called sclerotia (1), which survive long-term in the soil. Depending on environmental cues, sclerotia can either germinate myceliogenically (2) or carpogenically (3). Myceliogenic germination involves growth of hyphae from the sclerotium that can directly penetrate plant tissue, although this mode of infection is poorly studied. Carpogenic germination is considered the dominant mode of infection and involves the sclerotium germinating to form a mushroom-like structures called apothecia. Carpogenic germination of sclerotia requires moist soil with moderate temperatures (10 to 20 °C). Apothecia release ascospores (4) that are transported on the wind and deposited on a plant surface. Under moist conditions, the ascospores germinate to form hyphae that can infect the plant either directly through a wound or through the creation of an infection peg. Because S. sclerotiorum is considered a weak pathogen, it requires an exogenous nutrient source like senesced flower petals to develop an infection peg and infect the host (5). The fungus spreads within the plant and symptoms develop with prolonged cool and moist environments (6). As the S. sclerotiorum depletes the nutrient base of the host, the white fluffy mycelia begin to form sclerotia (7), which will fall to the soil to survive until the next season. ........................................................................................................................................... 29 Figure 2.1: Map of Alberta, Canada, showing the sampling locations (large grey circles) in the present study. ................................................................................................................................ 55 Figure 2.2: Venn diagram summarizing the shared genera among all data sets in this study. ..... 64 xix Figure 2.3: The seasonal dynamics of three common plant pathogenic genera revealed by rotorod samplers at each of the four locations used to test whether location influences aerial fungal communities (top panel), and by the Burkard samplers at LeRDC in 2021 to test whether crop type influences aerial fungal communities (bottom panel). .......................................................... 66 Figure 2.4: Shannon diversity of the ITS1 ASVs. Data presented are post quality control, thus some controls (e.g. water blanks) have been removed entirely from the visualization. Refer to the Results section for a summary of statistical tests performed. Comparisons made by Tukey HSD (but not on controls), and only significant differences are shown. Significant levels are denoated at *, alpha = 0.05; **, alpha = 0.01; ***, alpha = 0.001; ****, alpha < 0.0001. .......................... 68 Figure 2.5: Weekly averages of Shannon diversity of the ITS1 ASVs (± standard deviation) at each of the locations recovered by rotorod samplers. Week 26 represents the week starting June 24 and Week 34 is the week starting August 19. For all locations combined, Shannon diversity did not vary by week (repeated measures ANOVA, p = 0.96), indicating that diversity across the province is stable throughout the growing season. ....................................................................... 70 Figure 2.6: Biweekly averages of Shannon diversity of the ITS1 amplicon (± standard deviation) for each of the crop types sampled in 2019 and 2021 in the Burkard data set. Week interpretation is as for Figure 5. For all crop types combined, Shannon diversity indices remained stable over the course of the season for both 2019 (repeated measures ANOVA: p = 0.23) and 2021 (p = 0.15). ............................................................................................................................................. 74 Figure 2.7: Comparison of the ITS1 amplicons from Burkard samplers at LeRDC in 2019 in each of three experimental plots. All samplers were located within 180 m of each other. PCoA of samples based on the Hellinger-transformed read count and Bray-Curtis dissimilarity of ASVs. Lines/polygons connect samples collected on the same day from each of the samplers. ............. 76 xx Figure 2.8: Correlations (and linear regression line) of the log(number of ascospores + 1) of Sclerotinia sclerotiorum ascospores as quantified by qPCR vs the Hellinger-transformed read count for Sclerotiniaceae_sp. as determined in the same samples. ............................................... 78 Figure 2.9: Top panel: The top 10 most common taxa found in the negative controls for the ITS1 amplicon. Data represent the proportion of reads in each sample. Bottom panel: The top 10 most common taxa found in the mock community for the ITS1 amplicon. Mock communities with different numbers were communities that were constructed and had DNA extracted independently; communities with different letters following the same number indicate technical replicates of DNA sent for sequencing. Data represent the proportion of reads in each sample. ‘Theoretical’ values are based on the number of spores (and not ITS copy number) included in the mock community. The ‘Diluted’ sample is a 1:10 dilution of the Mock 1 community. ......... 80 Figure 3.1: Daily ascospore numbers in each field surveyed in Alberta over the four years of data collection. The grey lines represent the number of daily ascospores for each individual field in Alberta included in this study. The blue line represents the (geometric) mean of all fields for each day. ..................................................................................................................................... 105 Figure 3.2: Boxplots of the log(no. ascospores + 1) by month for each province and year in the present study. Statistical analysis is described in the results section. The boxes represent the first and third quartile, and the horizontal line is median. Vertical lines do not exceed 1.5*(interquartile range); dots outside of these lines represent outliers. .................................... 106 Figure 4.1: Boxplots of the daily log(no. ascospores +1) collected during the study period, stratified by year and province. Week refers to the week of the year, so Week 30 corresponds to approximately the last week of July (23 to 29). .......................................................................... 126 xxi Figure 4.2: Importance of the top 10 variables included in the machine learning models implemented in this study. The top panel represents variable importances in the RF_Ref model, and the bottom three panels represent importances in the classification approaches (RF_Class, LR, and SVM) for only the threshold of 500 ascospores per day. Results for all thresholds were qualitatively similar. ................................................................................................................... 131 xxii List of Supplementary Material Table C.1: Annual breakdown of correlations of environmental variables with daily ascospore counts for each province (.xlsx digital file). xxiii List of Symbols °C – degrees Celsius µ – micro xxiv List of Abbreviations ANOVA – analysis of variance ASV – amplicon sequence variant BHQ – black hole quencher BLAST – basic local alignment search tool bp – base pairs Ct – cycle threshold DNA deoxyribonucleic acid EDTA – ethylenediaminetetraacetic acid ELISA – enzyme-linked immunosorbent assay FAM – fluorescein amidite g – gram GLM – generalised linear model h – hours HEX – hexachloro-fluorescein HSD – honest significant difference HTS – high-throughput sequencing IGS – intergenic spacer ITS – internal transcribed spacer L – liter LeRDC – Lethbridge Research and Development Centre M – molarity m – meter xxv mL – milliliter mm – millimeter OA – oxalic acid PCA – principal components analysis PCR – polymerase chain reaction PDA – potato dextrose agar PGA – Potato Growers of Alberta QIIME – Quantitative Insights Into Microbial Ecology qPCR – quantitative (real-time) polymerase chain reaction RH – relative humidity RNA – ribonucleic acid s – seconds SD – standard deviation SE – standard error of the mean sp. – species (plural = spp.) SSM – Sclerotinia semi-selective media VPD – vapour pressure deficit xxvi Acknowledgements This thesis would not have been possible without the immense support of many students, faculty, staff, technicians, and supervisors. My colleagues and fellow students in both Dr. S. Chatterton’s and Dr. R. Hamelin’s labs have been enormously kind and helpful. In particular, I extend my gratitude to the technicians and lab managers Anthony Erickson, Eric Amundsen, Christine Vucurevich, Padmini Herath, and Hesther Yueh for their help in field and lab work and ordering supplies. A special thank you to Christine for being a constant emotional support. I was fortunate to work with an incredible group of students during the summers of field work. I am especially indebted to Uriel Karerwa and Natasha Tetzlaff for bringing smiles, laughter, and support, and for their unparalleled caliber of work. I am also very grateful for the help of Jason Patey and Brooke Groenenboom. My supervisors and committee were immensely supportive of all aspects of my life and research. I thank Dr. R. Hamelin for his incorrigible optimism, which I try to remember and emulate in my more difficult times. I also thank him for his incisive thinking, ambitious ideas, and for pushing me to apply for a Genome Alberta grant. I owe special gratitude to Dr. S. Chatterton, with whom I completed my MSc studies and who graciously asked me to return for my PhD research. Her unrelenting support of my psychological wellbeing above the research project taught me that it is possible to balance compassion and intellect, and still produce excellent research. I thank Dr. W. Mohn for his expertise, constant support, and thoughtful suggestions to improve my research. I offer my enduring gratitude to my parents and siblings who have been supportive and proud of my dreams of being an eternal student in my pursuit of a Ph.D. xxvii Finally, I would like to thank collaborators and the financial support of funders. I acknowledge the help of collaborating technicians Tom Henderson, Waldo Penner, and Ashley Wragg, and the help of ProMax Agronomy and Viterra employees for the maintenance of spore samplers and collection of samples. I am deeply grateful for the many sources of financial support that contributed to this research: the Natural Sciences and Engineering Research Council of Canada; the Canadian Agriculture Partnership Pulse Cluster program, through Agriculture and Agri-food Canada; the Alberta Pulse Growers; Manitoba Pulse and Soybean Growers; and the Ontario Bean Growers. xxviii Dedication This thesis is dedicated to my mother, a woman who taught me how to be open to new ideas, to question what I’ve been told, and how to hold space for conflicting ideas – all with compassion and humility. 1 Chapter 1: Review of the Literature 1.1 Introduction Few people think of the air as having a rich ecology of its own, yet it hosts and transports a wide diversity of organisms including bacteria, fungi, viruses, plant pollen, and microalgae. Some microorganisms influence regional weather patterns (Huffman et al., 2013), some are transported on storm fronts to establish on different continents (Brown & Hovmøller, 2002; D. J. Smith et al., 2013), and others cause animal (including human) and plant diseases (Fausto et al., 2019; Fisher et al., 2020). While the density of airborne microorganisms in air is much lower than in soil (~103-104 spores m-3 in air (Elbert et al., 2007) vs more than ~108 cells gram-1 of soil (Raynaud & Nunan, 2014)), the overall diversity may be comparable to plant and soil microbiomes (Frohlich-Nowoisky et al. 2009, 2012; Behzad et al. 2015). However, comparatively little research has been performed to understand aerial microbial diversity (Banchi et al. 2020). Understanding the impact of airborne microorganisms on our environment has been a pursuit of humans for millennia. In a review of airborne plant pathogens, Mahaffee and Stoll (2016) describe ancient Sumerian, Egyptian, and Roman beliefs that associate wind-traveling demons with plant disease. Darwin collected air samples to culture for microorganisms while on-board the Beagle and Amelia Aerhardt attached an air sampling device to her airplane as she circumnavigated the globe (Womack et al., 2010). The systematic and quantitative study of airborne microbes began in earnest with the development of more sophisticated air sampling technology in the 1950s. In 1952, Hirst published plans for a sampler that could collect aerosols at a known rate, enabling calculations using the volume of air sampled for the first time (Hirst, 2 1952). Fungal and pollen samples collected in this manner were examined by microscopy and taxa were identified based on morphology, which provided the first estimates of airborne concentrations. Research carried out at the Rothamsted Experimental Station (England) by Hirst and associates in the 1950s remains some of the most informative works in aerobiology to date (e.g. Gregory and Hirst (1957), Lacey (1962)). 1.2 Importance of airborne microbial diversity Many of the most aggressive plant pathogens disperse on the air and are major contributors to global food insecurity (Savary et al., 2019). Despite its obvious importance to food production, recent aerobiological research has tended to instead focus on human health (e.g. Haig et al. (2016), Haddrell and Thomas (2017), Grewling et al. (2019), Nunez et al. (2019). For human health, extensive networks of publicly funded air samplers deployed across the globe track levels of chemical components in the air (eg. carbon dioxide, nitrous oxide) that pose threats to human respiration (Buters et al., 2018), while other networks monitor pollen and spore loads for people with allergies (Abrego et al., 2018). Monitoring networks for airborne plant pathogens, however, are smaller and not publicly funded (Buters et al., 2018). Managing airborne plant pathogens is uniquely challenging, since many of the measures currently utilized to manage the spread of plant pathogens (e.g. trade restrictions, border controls) may not be effective for airborne pathogens. It is well understood that air currents provide the primary method of plant pathogens establishing in a new environment or spreading within an existing one. In cases like wheat rust, spores from Puccinia graminis ‘migrate’ from Mexico to Canada each season on air currents, termed ‘Puccinia pathways’ (Aboukhaddour et 3 al., 2020; Brown & Hovmøller, 2002). Thus, knowing what is in the air and when can provide critical information for the management of important plant diseases. Fortunately, general interest in the biology of the air has increased over the last 25 years. A search in Web of Science for the topic “aerobioloy OR ‘air diversity’ OR ‘air microbiome’” shows a steady increase in the number of publications since the 1990s (Figure 1.1). The average annual rate of increase over those years is 15%, substantially more than the estimated 2-5% annual increase across all disciplines (Johnson et al., 2018; Larsen & von Ins, 2010). As a testament to this increased interest in aerobiology, the Global Spore Sampling Project was recently initiated to characterize airborne fungal diversity across the globe (Ovaskainen et al., 2020). While impressive in scope, this research is nevertheless a decade behind similar efforts for the soil and aquatic microbiomes (Gilbert et al., 2018). In fact, in a Nature Reviews article on the mycobiome, the air was not mentioned as an environment in which to find fungal diversity (Peay et al., 2016). 4 Figure 1.1: Number of articles published each year on the topic of aerobiology. Web of Science search query was “TI = ‘aerobioloy’ OR ‘air diversity’ OR ‘air microbiome’”. Understanding the importance of aerial microbial diversity – or ‘communities’ of airborne organisms – is a complex question, and due to its relative infancy as a discipline, basic methodological and ecological questions in aerobiology are still being investigated: What are the best sampling protocols? How should DNA be extracted? Which organisms are where and when? By answering some of these questions, we can change focus from monitoring and predicting single organisms that cause a plant disease to groups of organisms that contribute to disease. This change in focus represents a paradigm shift towards the ‘pathobiome’ (Alverdy & Krezalek, 2017; Sweet & Bulling, 2017; Vayssier-Taussat et al., 2014). Traditionally, the guiding framework in plant pathology has been the ‘disease triangle’ which describes the three pillars that will result in a plant disease: a susceptible host, a conducive environment, and a pathogen. However, an increasing body of research suggests that the third pillar should encompass more than a single pathogen, as the presence of other organisms could 02004006008001995 2000 2005 2010 2015 2020Year publishedNumber of articles 5 enhance or inhibit disease expression in plants. For instance, acute oak decline is caused by synergistic interactions of several bacterial species (Denman et al., 2018), and unique fungal communities were associated with healthy and diseased trees when infected with the olive knot pathogen (Gomes et al., 2019). One question raised by the above findings is whether unique groups of airborne microorganisms could moderate or enhance the disease progression of major airborne plant pathogens. As one example, Sclerotinia sclerotiorum Lib. (de Bary), is a widespread fungal plant pathogen that causes white mould and stem rots in crops around the world. Its ascospores, the primary dispersal and infective units, are airborne. Work done in Ontario in the 1980’s revealed that naturally co-occurring airborne fungal species including Drechslera sp., Alternaria alternata, Epicoccum purparescens, Fusarium spp., and Trichoderma viride, when co-inoculated on plants with S. sclerotiorum, could reduce S. sclerotiorum infections by 40 to 100% (Boland & Inglis, 1989; Mercier & Reeleder, 1987). Subsequent field experiments confirmed these findings (Inglis & Boland, 1992). No hyperparasitism of S. sclerotiorum was observed in these instances, so the presumptive cause of reduced infection was from competition among the fungi. In light of these findings, one hypothesis is that if airborne ascospores of S. sclerotiorum occur at the same time as spores of antagonistic fungi, white mould expression could be greatly reduced. This hypothesis raises the possibility that ‘healthy’ or ‘diseased’ air microbial communities exist, similar to other diseases in which unique microbial communities can predict a healthy or diseased status of individuals (Sharma & Tripathi, 2019; Sweet & Bulling, 2017). By expanding our understanding of how plant diseases are influenced by groups of microorganisms rather than caused by a single species in isolations, we might also be better able to manage epidemics. 6 1.3 Diversity of airborne microorganisms Determining how many organisms are transported through the air began in earnest when Hirst (1952) published plans for the first volumetric spore trap in the 1950s. In 1952, Gregory and Hirst (1957) performed the first extensive surveys of daily pollen and fungal spore loads over a five-month period at the Rothamsted Experimental Station in England. They found noticeable seasonal patterns of spore release: some spores only occurred early in the season, others peaked when there were rainfall events, and others had a clearly diurnal release pattern. A subsequent study in the same area found that surrounding vegetation played a large role in the types and quantities of spore and pollen that were collected (Lacey, 1962). With recent developments in high throughput DNA sequencing (HTS) methods and genetic databases, molecular identification of airborne microorganisms facilitated a vastly more comprehensive and nuanced analysis of air samples. Interestingly, much of the subsequent and recent research has broadly corroborated these earlier findings. As with other ecological surveys, getting an answer to “How diverse are air microbes?” is immediately confounded by both theoretical and practical issues that must be addressed when performing such a survey. How ‘diversity’ is defined and calculated will influence both within and between sample or study comparisons (Bálint et al., 2016). The type of air sampler will determine the size of airborne particles collected and may preferentially collect certain taxa (W. Chen, Hambleton, et al., 2018; Woo et al., 2018). Spatially, both the height above ground and the local ecology in which the air sampler is placed can affect the microbial communities that are found (Gregory & Hirst, 1957; Lacey, 1962). Temporally, airborne diversity can change due to diurnal fluctuations in fungal spore release, due to weather events, or due to the season (Burch & Levetin, 2002; Crandall & Gilbert, 2017). Finally, the methods used to identify the microbial 7 community - microscopy vs plate culture vs HTS - can influence the diversity of microbial communities (Castaño et al., 2017; Schweigkofler et al., 2004). Thus, varying each of the factors may result in dramatically different estimates of airborne microbial composition. Due to these complexities, the review that follows will focus on fungi, since different taxa, e.g. bacteria, responds differently than fungi to each of the above factors (e.g. Fierer et al. (2008), Bowers (2013), Mbareche et al. (2018), Nunez et al. (2019)). 1.3.1 How to measure diversity and community (dis)similarity Theoretically sound and/or useful definitions of a ‘species’ have remained a constant debate in biology for almost a century (Fišer et al., 2018; Freudenstein et al., 2016; Hey, 2006), but is the fundamental starting point for measuring diversity. For HTS studies of microbes, the species concept was replaced with the operational taxonomic unit (OTU), which groups organisms as ‘the same’ at some arbitrary genetic similarity level, often 97%. Problems with this approach are myriad (Bruns & Taylor, 2016; He, 2018) and recently a shift toward amplicon sequence variants (ASVs) – similar to OTUs clustered at 100% similarity – based on genetic barcodes has gained traction (Callahan et al., 2017). Even if delineating species was not an issue, dozens of diversity metrics have been developed for community analysis which all highlight different aspects of diversity, making the choice of diversity metric a difficult task (Bálint et al., 2016; Hughes et al., 2001; Leinster & Cobbold, 2012; Lozupone et al., 2007; Thukral, 2017; Tuomisto, 2010). Studies in the aerobiological literature often report different richness and diversity indices, so it is not always clear if reported differences are artefacts of the different indices used or real biological variation. Furthermore, conclusions on similarity between communities may be 8 biased by thresholds at which researchers considers two things ‘the same’ or ‘different’. For instance, a Finnish study concluded that an air sample is representative of a region within a 10 km radius of where the sample was collected (in other words, microbial composition of other samples collected within that 10 km radius would be the same as the first), but that an air sample collected further than 100 km from the first sample would have a different microbial community (Abrego et al., 2018). In contrast, a study that collected samples in the United Kingdom (UK), the Netherlands, and Denmark concluded that samples collected 700 km apart were highly similar (Nicolaisen et al., 2017). Much of the literature suggests that, in fact, both are true: most air samples, regardless of location, are dominated by a handful of common taxa; however, the diversity and contribution of less common taxa may be influenced by location effects like surrounding vegetation (Dietzel et al., 2019; Lacey, 1962; Lymperopoulou et al., 2016; Núñez et al., 2017). Thus, if one’s interest is the dominant taxa, one may conclude that air samples are the same, whereas if one’s interest is the rarer taxa, one may conclude that the samples are different. 1.3.2 Effect of samplers Broadly, there are two types of air samplers that may be used in studies of aerobiological monitoring: passive and active. Passive samplers include agar plates or adhesive surfaces (e.g. a microscope slide covered with Vaseline or rain collectors) which collect particles that land on them. Passive samplers are often placed so the surface is facing into the wind, which increases the number of airborne particles deposited on its surface. If the sampler is not placed facing the wind, deposition may only happen in very calm environments (Parker et al., 2014a; West & Kimber, 2015). While this is the simplest method of air sampling (and cheaper than active samplers), the main drawback is that an unknown quantity of air is sampled. Because the volume 9 of air collected is related to the microbial species richness (Abrego et al., 2018), sampling an unknown quantity of air confounds comparisons between sampling times or studies. However, passive samplers may be better at collecting samples during rainfall events, which is an important feature as groups of taxa associated with rainfall events are different from those collected when no rain is present (W. Chen, Hambleton, et al., 2018). For fungi, this means a shift from Ascomycete-dominated assemblages on most days to Basidiomycete-dominated assemblages on rainy days (W. Chen, Hambleton, et al., 2018; Woo et al., 2018). Active (volumetric) samplers have the advantage that the volume of air sampled is known, and so comparisons of spore density between sampling times can be made. In most active samplers, a vacuum intakes air at a predetermined rate, ranging from 2 to 900 L min-1 (West & Kimber, 2015) and deposits bioaerosols on a filter paper, an adhesive surface, or in a collection vial. In other common active samplers, a small, rapidly rotating armature collects particles that impact on an adhesive surface. Given the surface area of the collecting surface and the rate at which it rotates, the volume of air sampled can be calculated (Aylor, 1993). In addition to rate of collection, the collection efficiency of a sampler determines the size of particles that will be collected from the air: the smallest size of particle collected will vary by sampler, and ranges from 0.08 µm, which will collect most bacteria, to 20 µm, which excludes most bacteria and some fungi (West & Kimber, 2015). Clearly, the volume of air sampled and the size of organisms collected will influence any findings related to aerial diversity, but few studies have investigated this directly. In a study comparing five different passive samplers, Aguayo et al. (2018) found significant differences in community composition and diversity between the samplers. A field study comparing two different passive spore samplers had similar results (Castaño et al., 2017). In contrast, in a study 10 comparing three different active samplers, PCA revealed that airborne communities of fungi clustered by location of sampling rather than sampler type, indicating that the samplers had only a minor influence on community diversity (Mbareche et al., 2018). This trend was not the same for bacterial communities, which exhibited significant sampler effects. Finally, a study comparing three active and passive samplers revealed significantly different groups of organisms in each – in large part due to rain events which were more efficiently sampled by passive samplers (W. Chen, Hambleton, et al., 2018). 1.3.3 Effect of spatial scale. Air currents act at scales from the plant-level to the continent-level, so the location of air samplers - both vertically (above ground) and horizontally (distance from a source of interest) - will influence the types of microbes collected (Aylor, 1986; Schmale & Ross, 2015). Vertical and horizontal factors interact with complex air currents at different spatial scales: air currents higher in the atmosphere tending to have fewer particles but move them further distances (Aylor, 2003; Aylor et al., 2001; Mahaffee & Stoll, 2016; Schmale & Ross, 2015). Samples collected closer to the ground (e.g. 1 m above ground) tend to contain more microbes and are more representative of the surrounding vegetation, while samples collected further above the ground (e.g. 20 to 2000 m above ground) are more representative of larger, regional scales (Burch & Levetin, 2002; Gregory & Hirst, 1957; Hirst et al., 1967; Khattab & Levetin, 2008; Maki et al., 2019). For these reasons, studies that have purported to obtain a representative air sample of a region (rather than, say, a single field), the sampling tends to be performed ~15-25 m above ground, with samplers located on tops of buildings (Banchi et al., 2018; Bowers et al., 2012; Nicolaisen et al., 2017). 11 Regarding horizontal variation in microbial diversity, most fungi have steep spore dispersal gradients, with the vast majority of spores released being deposited within several (~10) to several hundred (~400) metres from the point source. This finding appears to be fairly robust, as similar trends have been observed for species as diverse as Mycosphaerella fijiensis in Cameroonian banana plantations (Rieux et al., 2014), Hymenoscyphus fraxineus in European forests (Chandelier et al., 2021), Puccinia striiformis in French wheat fields (Soubeyrand et al., 2007), and Sclerotinia sclerotiorum in New Zealand pastures (Bourdôt et al., 2001). In light of these steep dispersal gradients, one might expect that substantial variation exists between airborne communities within small sampling areas, but few aerobiological studies account for small-scale spatial variation. Nuñez et al. (2017) placed two volumetric spore samplers 2 m apart to collect biological replicates. They concluded that each sampler collected comparable fungal communities at each sampling period, although this conclusion may primarily reflect that the dominant taxa were similar between each sampler. Surprisingly, only 40-60% of the OTUs between the samplers were the same, meaning that samplers just 2 m apart also collected 40-60% unique OTUs. Similarly, samples collected in the same region of Spain showed large variation in diversity over relatively small distances, although the size of the study area was not reported (Castaño et al., 2017). One other study that incorporated biological replication in the experimental design did not present data on inter-sample variability (Mbareche et al., 2018). With these considerations in mind, the appropriate location for sampling will depend on the purposes that sampling is undertaken. For monitoring in plant disease contexts, samplers should be placed in fields, closest to potential sources of inoculum, rather than, for instance, on the roof of a building in a nearby city. Since vegetation has a large influence on the microbial composition of the air (Lacey, 1962; Lymperopoulou et al., 2016) and spore concentrations are 12 dramatically reduced as they move further from their source (Aylor, 1986), investigations aimed at field-level surveillance should be as close to the source as possible. For biosurveillance purposes, detecting novel or rare taxa may require mobile spore traps that can cover a much larger area and collect a greater volume of air (Jackson & Bayliss, 2011; Savage et al., 2012). 1.3.4 Duration of sampling The sampling intensity is the product of sampling duration (time) and sampling rate (volume per unit time), but few studies have investigated how the duration of sampling influences the community of organisms collected. This factor is particularly important for aerobiological studies because the density of airborne particles is so low, and, for molecular methods, a minimum amount of biological material is required for successful DNA extraction and polymerase chain reaction (PCR). To ensure an adequate mass of aerosol is collect, most HTS studies have collected samples over the period of a week (W. Chen, Hambleton, et al., 2018; Fröhlich-Nowoisky et al., 2009; Nicolaisen et al., 2017; Núñez et al., 2019). At a finer temporal resolution, others have collected samples over 24-h periods (Banchi et al., 2018; Bowers et al., 2013). Only one study appears to have actually tested the effect of sampling duration on the community composition (Abrego et al., 2018). In forests of Finland, samples were collected at durations ranging from 1 min to 4 hours at a rate of 16.5 L min-1 (~1m3 per hour). Increased sampling duration was associated with increased fungal diversity of the samples; however, the variance of species richness between samples increased drastically with sampling duration: at 1 min durations, richness ranged from ~10 to 40 species per sample, and at 4 hours richness ranged from ~10 to 120 species per sample. 13 Because most recent aerobiology studies employ HTS techniques to study microbial composition, one of the main considerations for sampling duration is simply having enough DNA to analyze. Surprisingly, in the Finnish study mentioned above, enough DNA was collected to enable metabarcoding (described in more detail below) of the sample after just 1 min of sampling. Few studies report the DNA concentrations of their samples, but HTS sequencing services like Genome Quebec recommends a minimum DNA concentration of 10 ng/ul for metabarcoding analyses. This minimum requirement may be difficult to reach with air samples due to low biomass concentration. Núñez et al. (2017) sampled for a continuous week (~100 m3 of air) and found DNA concentration ranged from 0.003 to 0.2 ng m-3 of air sampled. Roughly, this would translate to 0.3 to 20 ng of DNA extracted per sample, with most samples being below the recommended 10 ng per sample. Other studies have performed HTS on samples with far smaller sampling volumes (< 1 m3), although the influence of low DNA concentrations on downstream analysis is not typically discussed (Fierer et al., 2008; Maki et al., 2019; Mbareche et al., 2018). 1.3.5 Weather The influence of weather events on airborne microbial composition remains poorly understood and studies that have attempted to associate individual fungal taxa with weather events find that different taxa respond differently to weather events (W. Chen, Hambleton, et al., 2018; A. M. Jones & Harrison, 2004). In general, changes in quantities and compositions of airborne fungi are primarily driven by rain and moisture (Núñez et al., 2019). When examining the quantities of airborne fungal spores, rain events are associated with an increase in aerial spore loads by a factor of ten or more (Burch & Levetin, 2002; Hernández Trejo et al., 2012; Huffman 14 et al., 2013; Núñez et al., 2019). These so-called ‘spore plumes’ are associated with sudden changes in weather and may be caused by mechanical disturbance of spores by raindrops, forcible ejection of spores by species that have evolved to release spores in moist conditions, or hygroscopic forces (Huffman et al., 2013). As mentioned above, rain also tends to ‘scrub’ certain organisms from the atmosphere that may not otherwise be collected (Barnes et al., 2009; West & Kimber, 2015). Thus, groups of organisms collected during rain events are typically noticeably different from those collected on dry days. For instance, Basidiomycetes tend to be more highly represented in rain samples, compared to Ascomycetes on dry days (W. Chen, Hambleton, et al., 2018; Woo et al., 2018). While there are strong theoretical foundations for hypothesizing that wind is a major driver of spore dispersal and airborne microbial composition (Aylor, 1986), only very weak empirical observations support this hypothesis (W. Chen, Hambleton, et al., 2018; Grinn-Gofroń et al., 2018; Hernández Trejo et al., 2012; Lagomarsino Oneto et al., 2020; Rutter et al., 1998). These findings could be a result of the complexities involved with air currents at all scales, particularly at the micro scale which determines spore liberation (Andrade et al., 2009; Aylor, 1986; Aylor & Taylor, 1983). If this is the case, spore dispersal may be more highly related to wind gusts than average, or even maximum, wind speeds (Aylor, 1990). When statistically significant relationships with wind speed or direction and fungal spore levels are found, these tend to be weak (Chandelier et al., 2021; Priyamvada et al., 2017). Interestingly, in one study, wind direction rather than intensity was associated with allergenic fungal spores: the majority of fungal spores that arrived on the dominant winds were only mildly allergenic, whereas the fungal spores of the same species that arrived on winds from the opposite direction were associated with much more aggressive allergenic characteristics (Grewling et al., 2019). At larger scales, 15 back-trajectory analysis has been used to associate spores arriving from distant sources by modeling history of the air parcel (Grinn-Gofroń et al., 2016; Sadyś et al., 2014, p.). At the country or continent scale, these methods could be useful for tracking certain species of interest. 1.3.6 Temporal effects (diurnal, seasonal, annual) Temporal effects tend to be a result of weather and climatic events, although many studies investigate only the change of abundance or diversity over time (day, season, year) rather than relating it to the weather explicitly. It is well documented that airborne fungal spore loads frequently exhibit high variation throughout the course of a day (i.e. diurnal patterns) or between days, seasons, and years (Crandall & Gilbert, 2017; Fierer et al., 2008; Gregory & Hirst, 1957; J. Reich et al., 2017). Weather events may even be a larger driver of airborne fungal composition than geographical ones (Crandall & Gilbert, 2017), and determining what drives this variation is an ongoing area of research for many economically important fungi. In one of the earliest studies that involved HTS of airborne biological particles, samples were collected for four hours every day for five days. The authors concluded that the variation in taxa between those five days was larger than the variation between sites thousands of kilometres away when compared to datasets reported in other studies (Fierer et al., 2008). In contrast, a later study collecting 24-hour samples determined that fungal communities within sites were relatively stable over the course of two weeks, and that the main sources of variation were due to location (Banchi et al., 2018). Again, these conclusions may be influenced, in part, by how different studies define and measure diversity and community dissimilarity. At broader time scales, there are clear shifts in airborne community composition between seasons, with many finding that, at a very coarse scale, Ascomycete composition tends to be 16 higher in the spring whereas Basidiomycete composition tends to be higher in the fall (Abrego et al., 2018; Castaño et al., 2017; Fröhlich-Nowoisky et al., 2009; Kraaijeveld et al., 2015; Nicolaisen et al., 2017). This pattern could be caused by changing plant communities, increased moisture, or increased decaying matter in fall time. The seasonal and annual shifts in airborne microbial diversities are a common finding among studies, although little work has been done in assessing the importance of this pattern (Bowers et al., 2013; W. Chen, Hambleton, et al., 2018; Núñez et al., 2019). 1.4 Identifying airborne biological fungi: Many methods for identifying airborne organic materials have been used, and a good review of these methods is provided in Heard and West (2014). A brief summary of some of these methods is presented below: 1.4.1 Culture-based methods Culture-based methods typically involve leaving Petri dishes containing a growing medium (e.g. PDA) exposed to ambient air for a period of time, then sealing the dishes and allowing colonies to grow over a period of days. Identification of the organisms is then performed based on culture morphology or using molecular methods. This method is typically used if a particular organism of interest is being studied and it can grow in vitro (Bourdôt et al., 2001; Gutierrez & Shew, 1998; Parker et al., 2014a). The main disadvantages of this method are that the volume of air sampled cannot be quantified, it selects for only fungi that can be grown in vitro, there is a significant delay (several days) in obtaining results, and high degree of fungal morphology expertise is required to identify organisms. 17 1.4.2 Microscopy Microscopy continues to be one of the most used methods of organism identification in aerobiological surveys. The first studies by Hirst employed a volumetric sampler in which aerosols were deposited on an adhesive surface (Gregory & Hirst, 1957). In studies following this protocol, the adhesive surface is then scanned under a microscope and spores and pollen are identified based on morphology. Many allergy-monitoring networks employ this method, and aerobiology studies continue to be published using this method (Crandall & Gilbert, 2017). The advantage of this method is that sample collection is relatively simple. The drawbacks of this method are that a high level of expertise is required to identify pollen and spore types, it is difficult or impossible to assess the viability of the pollen or spores, and taxon resolution is quite coarse (often maximally down to the genus level). 1.4.3 Molecular methods DNA-based methods have been used extensively for identifying and quantifying airborne microorganisms over the past 20 years, and a review of their utilization can be found in Rogers (2009). Molecular methods overcome many of the limitations of microscopy but come with their own set of limitations. Sample preparation for molecular analyses involves DNA extraction and PCR, and each of these steps may be expensive or difficult to optimize. For instance, rust spores are notoriously difficult to extract DNA from (Zambino, 2002), and downstream analysis will be impaired if the DNA extraction procedure fails. Another drawback is the cost, time, and labour requirements for analysing a single sample: DNA extraction kits, pipettes and sterile Eppendorf 18 tubes, primer and master mix storage, other reagents, and thermocyclers all require money and energy resources, making these tools mostly inaccessible to any single person. To overcome some of these challenges, other chemistries employ isothermal amplification, thus obviating the need for an expensive and time-consuming thermocycling step. These chemistries include loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification. One of the main advantages of these technologies is that they can be used in the field for real-time identification and quantification of organisms (De Boer & López, 2012). PCR and quantitative PCR (qPCR) methods have the advantage that organisms can be identified to species or strain level – if the primers exist – and that very small quantities of organisms can be detected or quantified. It is possible to identify or quantify several species in a single reaction, although the upper limit is around 4 to 6. However, technologies like microarrays, panels which bind DNA of specific sequences and emit fluorescence when binding occurs, allow for hundreds or thousands organisms of interest to be detected at once (De Boer & López, 2012). DNA multiscan (https://www.dnamultiscan.com) is a commercial example of an array technology specific to plant pathogens. 1.4.4 High-throughput sequencing Recent advances in sequencing technology and data analysis tools have made high-throughput DNA sequencing methods accessible to a wide range of researchers. These methods are becoming increasingly popular in the aerobiology community and there have been several recent reviews on this topic (Banchi et al., 2020; Behzad et al., 2015; Haddrell & Thomas, 2017). HTS methods might be broadly divided into two categories: metagenomics and metabarcoding. Metagenomics involves extracting DNA from a sample, shearing the DNA, sequencing all the 19 fragments of DNA, and then rebuilding the original genomes from overlapping fragments (Quince et al., 2017). Metabarcoding involves extracting DNA from a sample, amplifying a region of the DNA in PCR using ‘universal primers’ for the taxon of interest, sequencing these PCR fragments, and aligning the sequences to reference genomes (Nilsson, Anslan, et al., 2019). Due to the low DNA concentrations in air samples, the lower cost of metabarcoding, and the development of pipelines to facilitate data analysis, most HTS aerobiology studies currently employ metabarcoding. The main advantage of HTS methods is that, theoretically, all DNA in a sample can be sequenced, which allows for the identification of all known taxa (when compared to a reference database) and the sequencing of unknown taxa. Thus, for plant diseases, the main pathogen of interest will be identified, but so will many other microbes that are involved with the pathobiome - and potentially even novel pathogens (De Boer & López, 2012). However, the potential pitfalls of this method are myriad and are discussed in more detail in a section below. 1.4.5 Immunoassays The enzyme-linked immunosorbent assay (ELISA) has been used for several decades to identify species. ELISAs involve binding antigens from a sample to an inert surface followed by washing the surface with a matching enzyme-linked antibody. In the presence of antigens from the species of interest, the enzyme will catalyze its substrate which results in a colour change. A similar theory is employed in lateral flow devices, a common example of which is the pregnancy test. For plant pathogens, Pocket Diagnostic (https://www.pocketdiagnostic.com/) offers commercially available immunoassays. These tools tend to be simple and portable, although their results are usually qualitative and deployment of these tools in research contexts has yielded mixed results (Bom & Boland, 2000a). 20 1.4.6 Biosensors Biosensors have been deployed in a wide range of disciplines but remain curiously uncommon in plant pathology. At a high level, biosensors take a biological sample and translate it into an electrical signal, which is interpreted on a digital screen. The translation of biological samples to electrical signals may take almost any form, including ELISA-, DNA-, or laser-based assays, among many others (Mehrotra, 2016). Thus, lateral flow devices, like those described above, could also be considered a type of biosensor (Khater et al., 2017). Another common example of a biosensor is a blood glucose monitor, which takes in a drop of blood and displays the concentration of glucose on a screen. The advantages of this technology are that it is rapid and portable. Some work in this area has been done to monitor airborne fungal spores (Pham et al., 2019), but this area remains poorly explored for continual, real-time analysis of air samples. One notable and promising example is the use of lasers to identify and quantify airborne pollen spores in real time (Kawashima et al., 2017). The field monitoring device relays the data in real-time to a user’s computer, and this approach could be extended to monitoring airborne fungal spores. Other recent technological advances for real-time monitoring of bioaerosols may make this area of research more common (Berg et al., 2017; Kaliszewski et al., 2016). 1.4.7 Comparing identification methods Studies that compare various identification techniques invariably point to the advantages of molecular methods over culture-based or microscopic methods. qPCR assays can differentiate between pathogenic or non-pathogenic strains of microbes that would otherwise be grouped together (Grewling et al., 2019). qPCR methods are currently more sensitive and specific than 21 HTS methods, with the trade-off that qPCR methods are limited to a handful of taxa of interest whereas HTS methods cast a much wider net (Aguayo et al., 2018). HTS studies consistently identify an order of magnitude greater diversity than microscopic methods (Banchi et al., 2018). Even though HTS techniques have many potential pitfalls and biases, high correlations between taxa observed through microscopy or qPCR and read counts from HTS analyses have been observed (Castaño et al., 2017; Dannemiller et al., 2014; Kraaijeveld et al., 2015; Núñez et al., 2017); however, these correlations do not always hold (Kraaijeveld et al., 2015; Palmer et al., 2018). Studies that relate HTS from air samples to field surveys for fungal species have determined that air samples are highly representative of the species fruiting in the region, showing that HTS approaches could replace or supplement current survey methods (Abrego et al., 2018; Castaño et al., 2017). One drawback of HTS methods is the time required to process the samples and obtain results. These delays may be overcome by new devices such as the Oxford Nanopore MinION, which enables metagenomic sequencing and analysis of a DNA sample in real-time from a laptop (Jain et al., 2016). These technologies may soon surpass the ease and time investment of earlier microscopic methods. 1.5 Generalizations to draw from aerobiology research to date Despite the confounding factors described above, a handful of consistent patterns in fungal diversity have emerged from the aerobiological research to date. In general, the aerobiome, as with the terrestrial biome (Tedersoo et al., 2014), seems to be dominated by Ascomycota and Basidiomycota, with more than ~97% of reads or spore counts typically assigned to these phyla (Abrego et al., 2018; W. Chen, Hambleton, et al., 2018; Fierer et al., 22 2008; Fröhlich-Nowoisky et al., 2009, 2012; Gregory & Hirst, 1957; Huffman et al., 2013; Lymperopoulou et al., 2016; Nicolaisen et al., 2017; Womack et al., 2015). Within these phyla, the majority of air samples are dominated by high concentrations of common genera, regardless of location: Cladosporium, Penicillium, Epicoccum, and Alternaria (Banchi et al., 2018; W. Chen, Hambleton, et al., 2018; Nicolaisen et al., 2017). Interestingly, this pattern is consistent with findings from global surveys of the soil mycobiome: the majority of fungal taxa in soils around the world belong to Ascomycetes which are, incidentally, dispersed on air currents (Egidi et al., 2019). Due to the ubiquity of a handful of taxa regionally and globally, some authors have suggested that a ‘core’ air biota exists (cf. Sweet & Bulling (2017)). In Canada, 23% of taxa were shared across all sampling sites and times, even though samples were collected over several months and locations were separated from each other by ~3,500 km (W. Chen, Hambleton, et al., 2018). All samples were dominated by a handful of taxa: 20-50 taxa (<1% of all taxa detected) comprised 45% of the reads across all samples. In Italy, 62 shared genera (~25% of all genera described) comprised 99% of all reads in the data set across 4 sites 100 to 500 km apart (Banchi et al., 2018). Similar results were found in California (Lymperopoulou et al., 2016), Spain (Núñez et al., 2019), and across Europe (Nicolaisen et al., 2017). Finally, even though air parcels may be dominated by ‘core’ microflora, there are enough unique taxa to distinguish air communities by sample location or geography (Banchi et al., 2018; Mbareche et al., 2018). One group devised a method to determine the geographic origin – to within two hundred kilometres in the continental USA – of airborne dust samples based on the unique fungal community they contain (Grantham et al., 2015). 23 1.6 High throughput sequencing: pitfalls and biases HTS technologies have become the dominant method to analyze microbial communities, and many reviews have been published which address the numerous pitfalls, biases, and confounding factors associated with HTS of biological samples (Behzad et al., 2015; Gohl et al., 2016; Knight et al., 2018; Murray et al., 2015; Nguyen et al., 2015; Nilsson, Anslan, et al., 2019; Yilmaz et al., 2011). Below is a list of a handful of important factors to consider when performing a HTS study. These factors focus on the process after sample collection, although appropriate experimental design in sample collection is paramount to quality studies and many experimental design considerations were discussed in the ‘Diversity of airborne microorganisms’ section. 1.6.1 Appropriate controls Surprisingly few studies include positive or negative controls in their HTS workflow, and some that do may only state that ‘appropriate controls were used’ without giving any detail as to how they interpreted contamination (Hornung et al., 2019). As with any other kind of experiment, appropriate negative and positive controls are imperative for the interpretation of results, particularly as the risk of contamination and sequencing errors in HTS methods can be high. Negative controls can be incorporated at various stages of the workflow to account for contamination during the sample collection, DNA extraction, PCR, or sequencing. How analysis is performed in light of these negative controls - whether sequences are deleted, samples removed, samples normalized, etc. - is a matter of debate (Hornung et al., 2019; Nguyen et al., 2015). Positive controls, also called mock communities, are less commonly used in sequencing studies, but are critically important for interpreting the final results (Nguyen et al., 2015). A 24 mock community is a mixture of DNA from known organisms at known quantities, and can help determine at whether and where processing methods may be biased towards certain taxa. Thus, a mock community can also help account for false negatives. 1.6.2 DNA extraction DNA extraction can be an issue in any sort of molecular biology research, but biases associated with DNA extraction are often not discussed in HTS publications. Historically, DNA extraction methods were often optimized to target a taxon of interest; however, in a microbiome context, all taxa are of interest, and so any given method will be biased towards or away from certain taxa. Compared to more complex substrates like soil or stool, extracting DNA from the air is relatively easy but still has unique challenges of its own. Low DNA quantities in air samples requires extraction methods that are highly sensitive that are also free from contamination (Eisenhofer et al., 2019; Minich et al., 2018). In addition, many airborne fungal spores have thick cells walls that are resistant to lysing during DNA extraction protocols (Kumar & Mugunthan, 2018), which may result in downstream biases. These potential sources of bias reinforce the importance of appropriate controls. 1.6.3 PCR primers and PCR Biases associated with primers and PCR have been extensively documented (Amend et al., 2010; De Filippis et al., n.d.; Gonzalez et al., 2012). Even ‘universal’ barcode primers fail to amplify many taxa or may not be informative below a family or species level (Schoch et al., 2012; Silverman et al., 2021). Therefore, primer pairs must be chosen with the particular environmental taxa of interest in mind (W. Chen et al., 2021). PCR bias refers to many factors, 25 including primer mismatches, primer efficiencies, homopolymer runs in the DNA, DNA GC content, PCR cycle number, starting DNA concentration, and chimeric reads (Egan et al., 2018; Gohl et al., 2016). An outcome of this bias is that even with inputs of equimolar DNA sequences, the output of PCR products can have orders of magnitude of difference among species, making the relationship between read count and taxon quantification very difficult (Egan et al., 2018). Related to both issues is that the main barcodes for fungal identification (ITS1 and ITS2) have variable copy numbers among species, and possibly even within the same species (Lofgren et al., 2019; Zhou et al., 2019). 1.6.4 Sequencing platform and post-sequence data processing Many aspects of microbiome sample and data analysis are under rapid change, and so it can be difficult to summarize all the potential pitfalls associated with these methods. Sequencing platforms impose limits on the length of amplicon sequenced and therefore the options for region to sequence. Sequencing error rates are also variable among sequencing platforms (Fouhy et al., 2016; Piper et al., 2019). Numerous platforms and pipelines exist for the analysis of HTS data, and the choice of which one to use can significantly impact the final results of a study based on algorithms and assumptions employed by each method (Anslan et al., 2018; Reynolds et al., 2022). Importantly, these methods rely on databases to identify sequences and these databases are incomplete and may contain errors in the sequences or taxonomy of the reads (Khomich et al., 2018). Finally, numerous new statistical methods have been developed to address some of the unique issues that come from HTS and compositional data. However, the statistical soundness and practical importance of many of these methods is still debated, and results are largely 26 influenced by steps taken prior to statistical analysis (Hawinkel et al., 2019; Nearing et al., 2022; Weiss et al., 2017). 1.7 Microbiome vs individual species research Understanding airborne microbial diversity may help shed light into the complex interactions of organisms involved in the pathobiome; however, due to the biases and limitations described above, these methods are not always conducive to studying individual species. In plant pathology contexts, it is often important to know the precise amount of pathogen in an environment to facilitate, for example, modeling and predicting epidemics. Currently, HTS methods are considered semi-quantitative, since exact quantification by these methods is hampered by PCR bias, compositionality of these data sets, and issues with sequence and database taxonomic resolution (Lamb et al., 2019). Different species of airborne fungi also respond differently to environmental cues (W. Chen, Hambleton, et al., 2018), so what is true of a group of organisms may not be relevant to a particular organism. In cases where a specific pathogen is known to be the causal agent of a disease, it is important to isolate the effect of that pathogen using well-established methods. Often, this will include molecular identification and quantification using primers specific for that taxon. These more precise methods can also help benchmark HTS methods for future use. Practically, a comprehensive understanding of plant disease progression may involve studying both the groups of organisms present and the specific taxa/taxon causing the disease. Below, a case study involving a single pathogenic species of fungus is described. This fungus disperses its spores on air currents, and so air monitoring can provide important information into the pathogen’s presence in a crop. This case study highlights the complexities associated with 27 tracking just one pathogen (let alone hundreds of airborne organisms) and provides some important considerations when addressing the, at times tenuous, relationship between HTS and species-specific quantification methods. 1.8 Case study: Sclerotinia sclerotiorum Sclerotinia sclerotiorum Lib. (de Bary), a ubiquitous, aggressive, and well-studied fungal plant pathogen, is a useful case study for addressing some of the complexities that arise when applying air monitoring to a specific pathogen. Its main dispersal strategy is through airborne ascospores, which, upon landing on a susceptible host, can germinate and infect. Many aspects of this pathogen have been intensively studied, but ascospore dispersal at the population level remains poorly understood. Understanding these population dynamics more deeply could play an important role in managing the diseases caused by S. sclerotiorum in agricultural crops. S. sclerotiorum was first described in 1837 by Libert (Purdy, 1979), and has therefore been studied by the scientific community for almost 200 years. Its continued importance in research is a result of its wide host range (over 400 species of plants; Boland & Hall (1994)) and aggressiveness: the fungus causes economic impacts on important crops worldwide, including carrot (Foster et al., 2011; Parker et al., 2014b), canola (Turkington & Morrall, 1993; Twengström, Köpmans, et al., 1998), dry bean (Abawi & Grogan, 1975; del Río et al., 2004), lettuce (Clarkson et al., 2014; Young et al., 2004), soybean (Lehner et al., 2017; Willbur, Fall, Bloomingdale, et al., 2018), and sunflower (McCartney & Lacey, 1991). Despite its rich history of scientific research, epidemics caused by S. sclerotiorum remain difficult to manage, largely due to their apparent sporadic nature (J. D. Reich & Chatterton, 2022). In a study of crop-rotations, a 5-year continuous crop of soybean began with very high 28 numbers of apothecia m-2 in the soil in the first year, but in the fifth year almost no apothecia were observed (Gracia–Garza et al., 2002). In this case, bad disease management practices (i.e. a susceptible host planted in the same location for many seasons) should have led to increased disease levels, yet, surprisingly, almost no disease developed. In other cases, the opposite was true: good management practices (crop rotations, fungicide) were associated with high levels of disease (Morrall & Dueck, 1982). These difficulties – found in many S. sclerotiorum pathosystems – have inspired continued research into methods for effectively managing epidemics caused by this pathogen. 1.8.1 Life cycle of Sclerotinia sclerotiorum S. sclerotiorum is a sexual, homothallic, and monocyclic fungus, with a relatively simple life cycle (Figure 1.2). S. sclerotiorum survives in the soil long-term as a hardened mycelial mass called a sclerotium. In temperate climates, sclerotia will germinate in the spring and either produce mycelia or, because they are self-compatible, produce sexual fruiting bodies called apothecia (termed carpogenic germination). Apothecia produce and release airborne ascospores, so carpogenic germination is often considered the primary mode of infection by S. sclerotiorum. Myceliogenic germination can also result in substantial infection rates, although the environmental cues for what causes myceliogenic vs carpogenic germination are not clearly understood (D. Lane et al., 2019; D. W. Lane et al., 2018). Ascospores, produced in asci in the apothecia, disperse on air currents and, if they land on a susceptible host in appropriate conditions, will germinate and infect. As the fungus grows through its host, the white, fluffy mycelium often becomes visible – the basis for one of its common names, ‘white mould’. Sclerotia begin to form in or on the host once the fungus depletes the nutrients from its host. 29 Eventually, the sclerotia fall to the soil where they can survive over winter. Each of these stages in S. sclerotiorum’s life cycle is described in more detail below. Figure 1.2: The life cycle of Sclerotinia sclerotiorum. Most of the life of the fungus is spent in melanized hyphal aggregates called sclerotia (1), which survive long-term in the soil. Depending on environmental cues, sclerotia can either germinate myceliogenically (2) or carpogenically (3). Myceliogenic germination involves growth of hyphae from the sclerotium that can directly penetrate plant tissue, although this mode of infection is poorly studied. Carpogenic germination is considered the dominant mode of infection and involves the sclerotium germinating to form a mushroom-like structures called apothecia. Carpogenic germination of sclerotia requires moist soil with moderate temperatures (10 to 20 °C). Apothecia release ascospores (4) that are transported on the wind and deposited on a plant surface. Under moist conditions, the ascospores germinate to form hyphae that can infect the plant either directly through a wound or through the creation of an infection peg. Because S. sclerotiorum is considered a weak pathogen, it requires an exogenous nutrient source like senesced flower petals to develop an infection peg and infect the host (5). The fungus spreads within the plant and symptoms develop with prolonged cool and moist environments (6). As the S. sclerotiorum depletes the nutrient base of the host, the white fluffy mycelia begin to form sclerotia (7), which will fall to the soil to survive until the next season. 30 1.8.1.1 Sclerotia The fungus is named after its characteristic hardened mycelial masses called sclerotia (from Greek sklēros, meaning ‘hard’). Sclerotia consist of three layers – medulla, cortex, and melanized rind – that are made up of a dense mycelium (Willetts & Wong, 1980). Sclerotia act as long-term survival structures because they are resistant to extreme temperatures and microbial degradation in the soil (Bolton et al., 2006; H. C. Huang, 1985). The size of sclerotia is typically 2 to 8 mm in diameter and the weight ranges from 5 to 15 mg, with the variability resulting from both the S. sclerotiorum isolate and the host plant on which sclerotia are formed (Taylor et al., 2018). The number of sclerotia produced is highly dependent on the host, with certain plants like canola showing a 5 to 10-fold increase in the numbers of sclerotia compared to bean plants (Taylor et al., 2018). The duration of sclerotia survival is influenced by size of the sclerotia (larger sclerotia survive longer; Harvey et al. (1995); Taylor et al. (2018)), parasitism rates (Bourdôt et al., 2001; E. E. Jones et al., 2011), burial depth (sclerotia buried deeper survive longer; Cook et al. (1975); Morrall et al. (1977)), and environmental factors such as soil moisture or soil texture (prolonged soil moisture may reduce sclerotial viability; Morrall (1977)). Despite these potential threats to survival, sclerotia are known to remain viable for more than three years after burial (Ben-Yephet et al., 1993; Cook et al., 1975). The longevity of sclerotia would suggest that inoculum load will increase if susceptible crops are planted in sequence, but a study of dry bean found no evidence of this occurring in field conditions over the course of 3 years of continual bean crops (Schwartz & Steadman, 1978). These results were replicated in soybean over a 5-year continuous crop (Gracia–Garza et al., 2002), and supported by field surveys of blossom blight in seed alfalfa (J. Reich et al., 2017) and other studies (Haas & Bolwyn, 1972). This finding, while surprising, could be due, in part, to 31 the fact that different hosts induce different levels of sclerotial production. As mentioned above, when S. sclerotiorum formed sclerotia on lettuce, canola, potato, carrot, and bean, there was a significant effect of plant type on the amount and sizes of sclerotia produced on each host (Taylor et al., 2018). The sclerotia formed on lettuce and canola were larger and had a higher germination rate than other plants. Therefore, continuous planting may only increase white mould epidemics for some plants but not others. 1.8.1.2 Germination: mycelial and carpogenic Following water imbibition in the spring, sclerotia may germinate to form hyphae (myceliogenic germination) or apothecia (carpogenic germination). Because the majority of plant infections are considered to be a result of carpogenic germination, relatively little attention has been given to myceliogenic germination, although myceliogenic germination can also result in plant infection (Willetts & Wong, 1980). It remains unclear what exactly causes a sclerotium to germinate myceliogenically vs carpogenically, but myceliogenic germination has been associated with rich growing media (Willetts & Wong, 1980) or exposure to extreme hot (37°C) or cold (-20°C) temperatures and/or sclerotial desiccation (D. Lane et al., 2019; D. W. Lane et al., 2018). The vast literature on carpogenic germination in S. sclerotiorium presents conflicting results on specific requirements for the process to occur. The research agrees on the general requirements for apothecial formation but presents contradictory findings on the specific requirements, as is evidenced by the dozens of methods reported for inducing apothecia formation in vitro (Dillard et al., 1995; Garg, Sivasithamparam, et al., 2010; Pethybridge et al., 2015; Wu & Subbarao, 2008). In general, rates of apothecia production are governed by 32 temperature and duration of sclerotial conditioning, soil moisture, soil temperature, and fungal isolate, the specifics of which are discussed below. Conditioning refers to a period of cold temperatures sclerotia must experience to produce a fruiting body called an apothecium. Under natural conditions in temperate environments, the conditioning period is achieved during the winter months (H. C. Huang & Kozub, 1991). In vitro, conditioning is generally achieved by placing sclerotia at 4 to 10 °C for 4 or more weeks (Clarkson et al., 2007; H. C. Huang & Kozub, 1991; Sun & Yang, 2000). Apothecia formation rate is proportional to the duration of the conditioning phase and inversely proportional to the temperature of the conditioning phase, so a colder and longer conditioning phase will result in apothecia being formed earlier (Clarkson et al., 2007). Conditioning is not always required, since carpogenic germination is also dependent on the geographic and climatic origin of the S. sclerotiorum isolate. Isolates from tropical regions do not require cold conditioning like those from temperate climates (H. C. Huang & Kozub, 1991). Carpogenic germination by sclerotia requires high levels of soil moisture, ranging from -100 to 0 kPa (where 0 kPa is saturated soil), for approximately 2-4 weeks (Clarkson et al., 2007; Hao et al., 2003; Sun & Yang, 2000). Given some of the difficulties with accurately measuring soil moisture, Clarkson et al. (2007) concluded that for modeling purposes, using a lower limit of -10 kPa gave a more accurate representation of field observations. Under field conditions, carpogenic germination requires 25-30 days of continual moisture, although some apothecia may still form under more intermittent moisture regimes (Twengström, Köpmans, et al., 1998). These prolonged moist periods tend to coincide with cropping events like canopy closure, which helps maintain high soil moisture levels (Fall, Willbur, et al., 2018; Kora et al., 2005). The optimal temperature for apothecia formation ranges from 10 to 20 °C, but tends to be 15 °C (Clarkson et 33 al., 2007; Hao et al., 2003; Sun & Yang, 2000; Wu & Subbarao, 2008). Sclerotia in optimal moisture and temperature conditions form apothecia faster and form more apothecia per sclerotiorum (Hao et al., 2003). Under ideal conditions, a single sclerotium can produce over ten apothecia (Hao et al., 2003). Substantial variation in carpogenic germination exists between isolates of S. sclerotiorum. Isolates belonging to different mycelial compatibility groups (MCGs) exhibit dramatically different rates of apothecia formation (Wu & Subbarao, 2008). Furthermore, because sclerotia size and number varies between isolates (Taylor et al., 2018), and size of sclerotia is highly correlated with the rate of germination and the number of apothecia produced (Ben-Yephet et al., 1993; Hao et al., 2003; Taylor et al., 2018), some isolates have substantially greater potential for spreading ascospores. 1.8.1.3 Ascospores and their dispersal The ‘cup’ portion of an apothecium contains thousands of asci, each holding eight ascospores, which range in size from 10-14 um in length and 6-8 um in breadth (Kohn, 1979; Willetts & Wong, 1980). Estimates of the amount of ascospores a single apothecium can produce range between 1.9 x 104 to 2.6x105 per day (Bourdôt et al., 2001; Clarkson et al., 2003; Schwartz & Steadman, 1978). In the field, apothecia have been observed to be viable for up to 70 days, although the majority were around ten days (Bourdôt et al., 2001). In the lab, the longest period documented was 20 days (Clarkson et al., 2003). Thus, a single apothecium is capable of releasing millions of spores over its life. Relatively few studies examine the factors driving the production and release of ascospores in vitro compared to other aspects of the S. sclerotiorum life cycle, and there is little 34 consensus among field-based studies about the driving factors of spore release. Most studies that investigate spore release in the field find that ascospores are released primarily between 08:00 and 14:00 (Gutierrez & Shew, 1998; Hudyncia et al., 2000; Qandah & del Río Mendoza, 2011), although a handful of studies have found that most ascospores are released at nighttime (Archer & Yuan, 2004; Qandah & del Río Mendoza, 2011). Light is thought to be an influencing factor on apothecia development (Sun & Yang, 2000), but not necessarily on ascospore release (Clarkson et al., 2003). In one of the only lab studies investigating ascospore release, Clarkson et al. (2003) found that ascospore release is continuous throughout light and dark periods. Thus, the diurnal ascospore release pattern is probably influenced by changes in temperature, relative humidity, soil moisture, or wind rather than light. The influence of these environmental factors is discussed in more detail in the ‘Epidemiology of S. sclerotiorum’ section. Ascospores are forcibly ejected from each ascus, and may occur in a continual discharge or a sudden spore plume (Clarkson et al., 2003; Roper et al., 2010). Once liberated, ascospores are deposited within ~100 m of their source (Wegulo et al., 2000). Much further distances have been noted for ascospore release, but these are unlikely to play significant roles in the developments of epidemics (Bourdôt et al., 2001; Williams & Stelfox, 1980). In contrast to rust spores which have pigmented cell walls that may reduce damage from UV radiation (E. Wang et al., 2019), ascospores of S. sclerotiorum lack many of the features associated with spore longevity. Nevertheless, under lab conditions, ascospores can remain viable for several days, and up to several months under dry (RH < 70%) and cool (15°C) conditions, but viability rapidly decreases as relative humidity increases (Abawi & Grogan, 1975; Clarkson et al., 2003). Under field conditions, ascospores are expected to live on average six days (Caesar & Pearson, 1983). 35 Thus, spores released under dry conditions may remain dormant for several days until moisture levels enable germination and infection of the host. 1.8.1.4 Infection and disease development Infection of a host may occur directly by mycelia, but the primary route of infection studied is via ascospores. Ascospores are surrounded by a sticky membrane which helps in surface adhesion, and ascospores can remain intact on a plant surface until sufficient moisture conditions trigger germination (Bolton et al., 2006). Typically, ascospores require a nutrient source in order to infect healthy plant parts (Abawi et al., 1975; Sedun & Brown, 1987). In natural conditions, this is often achieved through senescent tissue like flower petals (Abawi et al., 1975; Newton & Sequeira, 1972), but infection also occurs directly through damaged tissue (Abawi & Grogan, 1975; Sedun & Brown, 1987). The timeline for ascospore germination and hyphal penetration of a plant tissue is remarkably consistent across studies. In greenhouse experiments, when plants were inoculated with ascospores and placed in a dew chamber for 24 h, moving the inoculated plants to an area of low RH for more than 4 hours stopped infection entirely (Sedun & Brown, 1987). In a similar experiment in the same study, inoculated plants were moved from the dew chamber at 0, 1, 2, 4, 12, 24, 48, or 72 h post inoculation to a low relative humidity (RH) chamber for 24 h before returning them to the original dew chamber. Interestingly, infection progressed the same if plants were removed at 0, 1, 2, or 72 h after inoculation but halted almost entirely if plants were removed between 4 and 48h after inoculation. These data suggest that ascospores require more than 2 h to germinate on a host, and once germinated, are highly susceptible to dry environments until they have penetrated the host, approximately 72 h later. This timeline is validated by 36 microscopic studies of S. sclerotiorum infection of canola performed twenty years later: by 2 hours post inoculation (hpi), ascospores had just started to germinate and by 4 hpi, almost all ascospores had germinated; by 48 hpi, simple appressoria had formed and by 72 hpi, complex appressoria had formed and the mycelium had penetrated the host (Garg, Li, et al., 2010). Nevertheless, as with other aspects of its lifecycle, the optimal temperature for infection varies by isolate but tends to be optimal around 20°C (Uloth et al., 2015). Infection of a host tissue by S. sclerotiorum involves the development of appressoria, deployment of cell wall degrading enzymes, secretion of oxalic acid (OA), and a complex host-pathogen cascade of reactions (Bolton et al., 2006). OA is an essential part of the infection process by S. sclerotiorum and is hypothesized to facilitate pathogenicity by a wide range of methods (Hegedus & Rimmer, 2005). OA levels have been correlated with virulence in some isolates of the fungus, and mutants that were unable to produce OA were unable to colonize host tissue (Godoy et al., 1990). Indeed, resistance to S. sclerotiorum by hosts has been attributed to the ability to degrade OA (Kolkman & Kelly, 2000). A plant’s hypersensitive response that leads to programmed cell death of infected cells works in favour of a necrotrophic pathogen like S. sclerotiorum: the dead cells serve as a nutrient base for the pathogen and thus help further infection (Hegedus & Rimmer, 2005). Mutant plants unable to initiate programmed cell death were more resistant to colonization by necrotrophic pathogens (Hegedus & Rimmer, 2005). Following host cell death, S. sclerotiorum releases a suite of enzymes, which break down plant cells and result in the characteristic sunken, water-logged looking lesions of early S. sclerotiorum infections (L. Huang et al., 2008; Jamaux et al., 1995; Liang & Rollins, 2018). As the fungus spreads through the plant and depletes its nutrient base, it begins to form compact, hardened, and melanized hyphae that will become sclerotia, but 37 the mechanisms that trigger this transformation are not entirely clear (Bolton et al., 2006). Sclerotia typically form in spaces between plant tissues, in stems or fruit cavities, and fall to the soil when the plant dies or is harvested. 1.8.1.5 Population biology The population biology of S. sclerotiorum is an ongoing area of research, and our understanding is largely shaped by developments in molecular tools employed to study its population. Attanayak et al. (2019) describe many of the historical developments and contradictions in this area of research. Because S. sclerotiorum is homothallic, it does not require mating with another partner to reproduce sexually. Earlier population studies used mycelial compatibility groups (MCGs) or restriction fragment length polymorphisms (RFLPs) to evaluate population diversity (e.g. Kohli et al. (1995)), and concluded that, as expected, the populations of S. sclerotiorum were predominantly clonal. However, developments with simple sequence repeat (SSR, or microsatellite) markers revealed much more diversity in S. sclerotiorum populations than previously thought, suggesting outcrossing (Attanayake et al., 2014). While it remains unclear how or under what conditions S. sclerotiorum may undergo outcrossing, some hypothesize that two isolates co-infecting a host could be in close enough proximity to produce a heterokaryon (Attanayake et al., 2019). 1.8.2 Epidemiology of S. sclerotiorum The aphorism that “what is true of the part is not necessarily true of the whole” is particularly relevant when extending the study of single isolates of S. sclerotiorum under lab 38 conditions to field populations under field conditions. With some notable exceptions, diseases caused by S. sclerotiorum remain difficult to manage across the globe. 1.8.2.1 Distribution of apothecia, ascospores, and disease in space and time Apothecia of S. sclerotiorum in field crops are predominantly aggregated in space and may even be relatively stable across years (Boland & Hall, 1988a, 1988b). Aggregation also holds true of disease in the field (Boland & Hall, 1988a, 1988b), which is consistent with the steep dispersal gradient (i.e. short travel distances) of most ascospores mentioned above. In pasture lands of New Zealand, >99% of ascospores were deposited within 10 m of the inoculum source (Bourdôt et al., 2001). Similarly, the majority of S. sclerotiorum-infected soybean plants were found within 15 m of the inoculum source, although some plants up to 45 m away were also infected (Wegulo et al., 2000). Long distance transport of ascospores has been reported frequently (Bourdôt et al., 2001; Gugel & Morrall, 1986; Leyronas et al., 2018), but this mode of transport does not play a significant role in the development of severe epidemics. For most field crops, the majority of ascospores are likely to remain trapped below the canopy following liberation from an apothecium, making long distance transport a possibility for relatively few ascospores, at least under calm conditions (Aylor, 1990; Aylor & Taylor, 1983). Because S. sclerotiorum exhibits a monocyclic lifestyle, one might expect that ascospore release is synchronized once per season; however, this pattern has rarely been observed in the field. Instead, in many cases, a near-constant level of ascospores are released in the air, punctuated by some days of much greater spore release (Archer & Yuan, 2004; Gutierrez & Shew, 1998; Hartill, 1980; Leyronas et al., 2018; J. Reich et al., 2017). This pattern (or lack thereof) could be a result of microclimatic variation within surveyed regions, variability among 39 isolates, and differential distance of ascospore travel on air currents. Within a day, ascospore release generally follows a diurnal pattern where the majority of spores are released midday (10:00h to 15:00h) (Hartill, 1980; McCartney & Lacey, 1991; Qandah & del Río Mendoza, 2011). This pattern of spore release may help the fungus spread further distances, since winds are typically highest around midday (Oneto et al., 2020). 1.8.2.2 Association of apothecia and ascospores with disease The literature on this topic is reviewed in detail in Reich & Chatterton (2022). While there are strong theoretical reasons to hypothesize that apothecia numbers, ascospore numbers, and disease intensity should all be correlated, establishing firm associations between any of these variables in field contexts has been surprisingly difficult. Apothecia density was correlated with concentrations of ascospores in some situations (McCartney & Lacey, 1991; Willbur, Fall, Bloomingdale, et al., 2018), but this association has not been commonly studied. Nevertheless, some circumstantial evidence suggests that often these correlations do not always hold (Parker et al., 2014b; J. Reich et al., 2017). The handful of studies that have attempted to relate airborne ascospore concentrations to final disease incidence follow a similar pattern: some studies have indicated a moderate or strong association between the two (Gutierrez & Shew, 1998; McCartney & Lacey, 1991; Qandah & Del Rio Mendoza, 2012), while others found little or no association (Fall, Willbur, et al., 2018; Parker et al., 2014b; J. Reich et al., 2017). These conflicting findings could be a result of experimental design (the former studies used experimental plots or were performed inside greenhouses), overall disease intensity (the latter studies had relatively low disease incidence compared to the former), or sampling intensity (the former studies had more data points). 40 Associations between apothecia density and final disease incidence has been more promising, although the findings are still somewhat conflicting. Several reports indicate that low to no correlations exist between these two factors (Bom & Boland, 2000b; Gugel, 1985; Morrall & Dueck, 1982; Schwartz & Steadman, 1978), while others show substantial correlations (Boland & Hall, 1988a, 1988b; Holley & Nelson, 1986). In the latter cases, correlations were only found in large (100 m2) survey plots and only moderate correlations in small (1.4 m2) plots. A recent and very successful model developed for soybean in the USA relies on the correlation of apothecia with disease (Willbur, Fall, Bloomingdale, et al., 2018). This model incorporates information from irrigation, row spacing, and 30-day averages of daily maximum air temperature, maximum relative humidity, and wind speed to predict the probability of apothecia presence. Subsequent work established thresholds of outputs from this model to a disease severity index (Willbur, Fall, Byrne, et al., 2018). For many crops in Canada, apothecia are not commonly found, even in the presence of disease (van der Heyden, personal communication; (Foster et al., 2011; J. Reich et al., 2017)), so using apothecia presence as a risk predictor may not be as useful in this context. Finally, several attempts at establishing relationships between infected flowers and final disease have been made, again with mixed results, and a review of these methods is provided in McLaren et al. (2004). In seed alfalfa, consistently high levels of floret infection were often associated with negligible disease levels, although this could be due, in part, to confounding factors associated with growers’ management practices (J. Reich et al., 2017). Several others found no correlations between petal infection and disease levels (Gugel, 1985; Kader et al., 2018). Almquist and Wallenhamer (2015)(2015) found a statistically significant correlation between petal infection and disease incidence in the field, however the correlation was very low 41 (r = 0.15). Others have reported greater associations, although the ranges of correlations are often large or involve unintuitive data transformations like the arcsine transformation (Bom & Boland, 2000a; Gugel & Morrall, 1986; Turkington & Morrall, 1993). 1.8.2.3 Influence of the environment on disease progression This topic is again described more systematically in Reich & Chatterton (2022), but a summary is presented here. A diverse set of environmental drivers have been established by models attempting to predict diseases caused by S. sclerotiorum or the presence of the pathogen itself. It can be difficult to draw generalizations from these models since they often report conflicting information on the importance of environmental variables. This lack of consistency may be a result of different crops, different environmental variables measured, and/or different survey and modeling methods. A review of currently developed models for S. sclerotiorum management is presented in Rothman & McLaren (2018). Below, these and other models are discussed and organized by the environmental drivers found to be associated with disease. Soil moisture and temperature. S. sclerotiorum spends most of its lifecycle as sclerotia in the soil, so an obvious environmental cue to measure is soil moisture or soil temperature. Because of this close connection, it is surprising that one of the more successful models for predicting apothecia presence in soybean made no attempt at measuring these variables (Willbur, Fall, Bloomingdale, et al., 2018). Studies by this same group showed that soil temperatures between 21 and 25°C were associated with high apothecia densities, and outside of these ranges almost no apothecia were detected (Fall, Willbur, et al., 2018). In the same study, ascospore counts, while correlated with apothecial counts, were not correlated with soil temperature. Bom & Boland (2000b) found that soil moisture was associated with severe (>20%) disease incidence 42 of canola, but no final model was evaluated or validated. A model developed for carrot included both soil moisture and soil temperature (Foster et al., 2011). Once the carrot canopy closed, risk of disease increased as soil moisture increased and as soil temperature decreased: risk was greatest when average soil moisture was above -20 kPa and average soil temperature was below 19°C. 1.8.2.3.1 Precipitation Rain (or irrigation) influences soil moisture, soil temperature, and canopy relative humidity and temperature, so could be an important environmental consideration in the progress of S. sclerotiorum diseases. As expected, high levels of irrigation resulted in increased white mould incidence in dry bean plot experiments (Kader et al., 2018). At equal amounts of total rain, five times more apothecia were produced when precipitation occurred at smaller volumes but greater frequency than at larger volumes but lesser frequency in canola crops (Twengström, Köpmans, et al., 1998). However, in the development and validation of models, mixed outcomes are reported. In one model, the amount of precipitation in the previous two weeks was associated with an increased risk of sclerotinia stem rot in canola (Twengström, Sigvald, et al., 1998); in another, the amount of precipitation in July increased the prevalence of stem rot (Mila et al., 2003). However, other well-established models did not find that precipitation was an important predictor for sclerotinia diseases: none of the 31 potential models for apothecia emergence proposed by Willbur et al. (2018) included a precipitation parameter, although they did propose different models for crops that were irrigated vs nonirrigated. The SkleroPro model developed in Germany utilizes precipitation to model canopy climate, but is not directly related to disease (Koch et al., 2007). Other studies report no effect of precipitation on disease incidence (Ficke et 43 al., 2018) or ascospore release (Leyronas et al., 2019). Another study found a negative association with ascospore release and precipitation from the previous day (Reich et al., 2017). 1.8.2.3.2 Wind Wind is often not measured in studies on sclerotinia diseases, perhaps because circumstantial evidence from earlier studies found no associations (e.g. Hartill (1980); McCartney & Lacey (1991)). In the model for sclerotinia stem rot of soybean mentioned above, apothecia emergence was negatively associated with 30-day averages of wind speed in nonirrigated fields; wind had no effect in irrigated fields (Willbur, Fall, Bloomingdale, et al., 2018). For ascospores, average wind speeds were positively correlated with spore concentrations (Leyronas et al., 2019). 1.8.2.3.3 Air temperature Air temperature is often negatively correlated with apothecia and disease, but positively correlated with ascospore release. In nonirrigated fields, apothecia were negatively associated with 30-day averages of maximum air temperature, but for irrigated fields the association was positive (Willbur, Fall, Bloomingdale, et al., 2018). The researchers speculate that irrigation can substantially reduce the canopy temperature, so the positive association is a result of the increased air temperature moving the canopy environment towards the optimal temperature for apothecial growth. Modeling the prevalence of S. sclerotiorum in soybean, Mila et al. (2003) found that average air temperatures in July and August were negatively associated with disease. In the SkleroPro model, Koch et al. (2007) found that ‘infection hours’ - a composite index of relative humidity and air temperature - was positively correlated with disease in canola fields. 44 The infection hours index is a highly nonlinear function, so the exact association of disease with air temperature is unclear, but tends to increase faster if air temperatures are above 18°C. Ascospore release is associated with hot and dry conditions: in France, ascospore levels were positively correlated with minimum air temperature (but not mean or maximum) (Leyronas et al., 2019), and in Canada, ascospore levels were positively correlated with maximum daily air temperature (J. Reich et al., 2017). 1.8.2.3.4 Relative humidity Germination of ascospores requires high levels of relative humidity (RH) and/or leaf wetness, so RH could be associated with S. sclerotiorum diseases in the field. Within irrigated soybean fields, 30-day averages of maximum RH were positively associated with apothecia, but there was no association in nonirrigated fields (Willbur, Fall, Bloomingdale, et al., 2018). In a similar study, RH showed variable (positive, negative, and/or not statistically significant) associations with apothecia and ascospores (Fall, Willbur, et al., 2018). Leyronas et al. (2019) found that airborne ascospores were positively correlated with minimum, mean, and maximum RH values on the day-of or day-before ascospores were collected. In contrast, Reich et al. (2017) found that large and sudden changes in RH were associated with ascospore release and that overall ascospore release was negatively correlated with maximum RH. Similar findings were reported by Archer & Yuan (2004). 1.8.2.3.5 Host species and cultural practices A wide range of host and cultural factors on diseases caused by S. sclerotiorum have been investigated. Canopy closure was associated with apothecia presence and disease incidence, 45 although this may differ by crop: in soybean, apothecia emerged when the canopy was 60-80% closed (Fall, Willbur, et al., 2018), whereas in carrot apothecia emerged when the canopy was >95% closed (Foster et al., 2011; Kora et al., 2005). The effects of crop rotation are mixed: some models have found decreased disease associated with crop rotations (Kurle et al., 2001; Twengström, Sigvald, et al., 1998) while others report no effect (Haas & Bolwyn, 1972). Planting density is positively associated with disease occurrence (Twengström, Sigvald, et al., 1998; Willbur, Fall, Bloomingdale, et al., 2018). Fertilizer and no-till practices were associated with decreases in prevalence of sclerotinia stem rot of canola, whereas herbicide and liquid manure applications were associated with increased prevalence (Mila et al., 2003). 1.8.3 Management of Sclerotinia diseases Due to its economic importance in a wide range of crops, many strategies have been developed to manage diseases caused by S. sclerotiorum, and reviews are provided in Derbyshire & Denton-Giles (2016) and Willbur et al. (2019). It is worth noting that there is not always a clear relationship between disease incidence and yields or economic losses (Fall, Boyse, et al., 2018; Foster et al., 2011; Haas & Bolwyn, 1972). In the study by Mila et al. (2003), factors like increased precipitation or liquid manure application would both increase disease prevalence and yields. In other studies that have quantified yield losses caused by disease, the variance in yields between fields is often still very high (Lehner et al., 2017). Nevertheless, in situations where economic losses have been quantified, estimates of loss are ~0.3 to ~1.0% yield loss (7 to 26 kg ha-1) for every percentage increase in disease incidence in dry bean and soybean (del Río et al., 2004, 2007; Lehner et al., 2017). Compounded over large areas typical of many field crops, these losses can amount to millions of dollars. For dry bean in Alberta alone, a 10% disease incidence 46 of white mould could result in financial losses of $2 to $6.6 million a year for the industry. For soybean in Brazil, the losses are estimated at over US $1 billion (Lehner et al., 2017). Thus, managing this disease is of significant economic importance. 1.8.3.1 Breeding Breeding resistance to S. sclerotiorum in any plant host has remained elusive and is part of what makes this generalist pathogen so difficult to manage. Depending on the crop, several quantitative trait loci (QTL) associated with disease resistance have been identified (Derbyshire & Denton-Giles, 2016; Kandel et al., 2018; Schwartz & Singh, 2013). In dry bean, for instance, breeding has shown some modest improvements against white mould, although progress is often related to changes in the growth habit of plants (e.g. canopy porosity, distance between nodes, time of flowering) rather than physiological resistance per se (Balasubramanian et al., 2014; Schwartz & Singh, 2013). Differences in growth habit could explain, in part, why some varieties tend to be more resistant to white mould than others (del Río et al., 2004). Further, issues in consistently evaluating germplasm remain, and are related to issues discussed previously: evaluating resistance against a large range of S. sclerotiorum isolates and, for field trials, requires having a consistently high level of disease each season to perform screening, which is not always feasible. 1.8.3.2 Cultural controls As mentioned above, there is some weak evidence to suggest that tillage and/or crop rotations can reduce apothecia formation, although these associations are highly context-dependent (Gracia–Garza et al., 2002; Schwartz & Steadman, 1978; Twengström, Sigvald, et al., 47 1998). Tillage buries sclerotia deep in the soil, preventing them from producing apothecia (Abawi & Grogan, 1979). However, deep burial also extends the life of sclerotia, and yearly tilling could bring sclerotia back to the surface. Utility of crop rotations depends on the crop rotation and the presence of wild or volunteer alternate hosts (Derbyshire & Denton-Giles, 2016). For some crops, like carrot, simply cutting back the foliage was shown to dramatically reduce white mould incidence by reducing RH and increasing air temperature (Kora et al., 2005). However, this approach requires new machinery to be developed and is impractical for crops in which the above-ground portion of the plant is harvested. 1.8.3.3 Chemical controls Due to the seemingly sporadic nature of white mould epidemics, many growers of susceptible crops apply fungicides as a preventative measure, once or twice, or sometimes more, at the flowering stage (McCaghey et al., 2019). Potential drawbacks for this method of management are that the fungicides are unnecessary in some years, and so is economically inefficient for the grower, and the potential for fungicide resistance to develop in Sclerotinia populations. A remarkably robust rule of thumb across studies is that the cost of fungicide application is only economical once disease incidence reaches ~20-25% (del Río et al., 2007; Koch et al., 2007; Twengström, Sigvald, et al., 1998; Wallenhammar et al., 2007). Thus, in German canola crops, 53 to 67% of fungicides were deemed uneconomic (Dunker & von Tiedemann, 2004; Koch et al., 2007). Fungicide resistance is another reason to be cautious with relying too heavily on fungicides to manage S. sclerotiorum diseases, and resistance has been documented for several fungicides (Gossen et al., 2001; Ma et al., 2009). However, because S. sclerotiorum is monocyclic and homothallic and does not seem to benefit from sexual 48 recombination, it is hypothesized that resistance to most fungicides will not occur rapidly (Derbyshire & Denton-Giles, 2016). 1.8.3.4 Decision support tools Related to fungicide use are decision support tools - models that help growers estimate disease risk in their crops. Decision support tools have been developed for a wide range of crops and diseases, and some have had success for S. sclerotiorum. For carrot, following recommendations for fungicide timing based on a model reduced total fungicide applications from five (the conventional calendar application regime) to one or two (depending on which model was used) (Foster et al., 2011). A recent model for S. sclerotiorum in soybean has shown to be useful at predicting disease risk, although the authors do not estimate how many fungicide applications could be reduced by following model recommendations (Willbur, Fall, Byrne, et al., 2018). One of the main drawbacks of decision support tools is the difficulty of getting growers to actually use it, even if it is a highly reliable model (Gent et al., 2013). Adoption rates and economic impact of a model could be a result, in part, of the type of cropping system under consideration: growers of more intensive, horticultural crops may be more likely to adopt decision support tools than growers of commodity crops, where there is generally less use of fungicides (Shtienberg, 2013). 1.8.3.5 Other (biocontrol, RNAi, etc.) Biocontrol agents - both fungi and bacteria - have shown great promise in published studies but, even though there are registered biocontrol agents, they do not have widespread use. Fungal biocontrol agents, like Trichoderma harzianum and Coniothyrium minitans, are applied 49 to the soil where they parasitize the sclerotia, whereas bacterial biocontrol agents, like Bacillus spp., Pseudomonas spp., and Streptomyces spp., are applied foliarly and parasitize the ascospores or hyphae (Derbyshire & Denton-Giles, 2016). In a field study comparing registered biocontrol agents T. harzianum, C. minitans, and B. subtilis, all were able to significantly reduce the amount of sclerotia produced on the plants by up to 95% at harvest and performed equally as well as fungicides (Zeng et al., 2012). New developments in biocontrol methods involve viral particles, which have been shown to be selective to fungal hosts and able to survive for at least 10 days when applied to plant surfaces (Yu et al., 2013). Finally, ribonucleic acid interference (RNAi), another new area of study, involves applying double stranded RNA (dsRNA) exogenously to a pathosystem. The dsRNA, designed to be complementary to a specific messenger RNA (mRNA) strand in the target, are taken into the cells of its target and prevent transcription. Thus, this method can be highly host-specific. RNAi has been demonstrated to control S. sclerotiorum control in lab settings and is currently being scaled up for field application (McLoughlin et al., 2018). 1.9 Objectives/Knowledge Gaps In light of the information presented above, substantial knowledge gaps remain in the discipline of aerobiology. Few, if any, high quality, finely resolved spatial- and temporal-scale studies have been published in this area. Without these studies, important fundamental questions remain unanswered: Over what spatial and temporal scales do airborne microbial diversity change and by how much? What are the environmental or geographic drivers of these changes? What is the relationship between airborne microbial diversity, individual pathogens of interest, 50 and disease expression in plants? For pathogens of interest, what are the environmental drivers behind spore release? How is their inoculum related to disease intensity under field conditions? In addition to providing insights on the fundamental ecologies of airborne microbes, answering the above questions has practical applications. If airborne microbes interact to determine disease expression in agricultural crops, finding ‘healthy’ groups of airborne microbes could provide management options for disease (e.g. promote beneficial microbes). Methods used to study air diversity can also identify many existing and novel pathogens, and so by monitoring what is already in the air, growers could be more readily prepared for new diseases. By studying the lives of specific pathogens more closely, models could inform growers when their crops are at high risk of disease and could help reduce fungicide use when disease risk is low. Thus, the overall objectives of this thesis are as follows: 1. Describe the fine-scale (daily) temporal and spatial changes of airborne microbial diversity over the growing season in the agricultural landscape of Alberta. 2. Assess the influence of location and crop type on Objective 1. 3. Assess the utility of HTS methods for multipathogen detection and quantification. 4. Describe and predict the occurrence of white mould epidemics in dry bean fields of southern Alberta. 5. Develop predictive models for the occurrence of Sclerotinia sclerotiorum ascospores in the field and their relationship with disease in dry bean fields. 51 Chapter 2: Combining air sampling and DNA metabarcoding to monitor plant pathogens 2.1 Introduction Many economically important or novel plant pathogens are dispersed on air currents (Brown & Hovmøller, 2002; W. Chen, Hambleton, et al., 2018) and monitoring the air for pathogen inocula has become an important component of many plant disease management programs (Lees et al., 2019; Meyer et al., 2017; Pan et al., 2010; Van der Heyden et al., 2021). In Alberta, Canada, there are currently several commodity organizations independently investigating monitoring programs for airborne plant pathogens including Phytophthora infestans (causal agent of late blight in potato), Puccinia spp. (rusts in cereal crops, Araujo et al. (2021)), and Sclerotinia sclerotiorum (white mould in dry bean and sclerotinia stem rot in canola). Airborne spores of these organisms are identified either through molecular assays, typically quantitative PCR (qPCR), or microscopy. Advances in DNA sequencing technologies and concomitant reduction of costs has led to an interest in employing high-throughput sequencing (HTS) techniques such as metabarcoding to characterize microbiota from environmental samples, including aerosols (Aguayo et al., 2018, 2021; Banchi et al., 2020; Clare et al., 2021). While there is still much research remaining to optimize HTS methods and ensure their results are robust for complex environmental samples (Gonzalez et al., 2012; M. R. McLaren et al., 2019; O’Sullivan et al., 2021; Porath-Krause et al., 2022; Yeh et al., 2018), these technologies can play an important role in the understanding of plant disease epidemics. In particular, by characterizing a microbial community in a semi-quantitative way, HTS methods provide the potential to identify many plant pathogens 52 simultaneously along with antagonistic or beneficial plant microbes (W. Chen, Hambleton, et al., 2018; Vayssier-Taussat et al., 2014). Using HTS methods, previous work has shown that substantial regional differences exist in airborne microbial composition (Banchi et al., 2018; Grantham et al., 2015; Mbareche et al., 2018) and that, at a smaller scale, vegetation can influence airborne microbial composition (Lymperopoulou et al., 2016). These findings have fundamental importance to theories of biogeography and practical importance for the management of economically important plant diseases. Recent calls to focus on the ‘pathobiome’ – the suite of microbes that accompanies disease (or lack of disease) – holds the promise of shedding insight into how symbioses among microorganisms could affect disease development in plants (Bass et al., 2019; Vayssier-Taussat et al., 2014). The air microbiome in Alberta has never been analyzed before, so the goals of this research project are threefold: first, to determine the geographical differences in airborne microbial composition; second, to assess the effects of different crop types on airborne microbial composition; and third, to evaluate the use of amplicon sequencing for the simultaneous monitoring of regionally important agricultural plant pathogens. By monitoring for multiple fungal organisms at the same time, a provincial air monitoring program could help inform all commodity groups of the seasonal risks of their economically important pathogens. 2.2 Materials and Methods 2.2.1 Survey design and air sampling Air sampling surveys were designed to test each of the first two objectives. For the first objective, to assess how the air microbiome differs over spatial scales, four sites were selected 53 across Alberta. The sampling sites were part of the Sclerotinia stem rot of canola trials conducted at agriculture research centres in, from southernmost to northernmost, Lethbridge, Brooks, Lacombe, and Beaverlodge, Alberta (Figure 2.1). The distance between the furthest sites in the study, Beaverlodge and Lethbridge, is approximately 750 km; the distance between the closest sites in the study, Lethbridge and Brooks, is approximately 120 km. GRIPST-2009 rotation impaction samplers (hereafter ‘rotorod samplers’; Aerobiology Research Laboratories, Ottawa, ON) were set up in each of these locations and collected samples from 0800 h to 1600 h Monday, Wednesday, and Friday. The samplers were set up such that the rods sampled at approximately 1.5 m above the ground, which was also above the canopy of the canola crop. In Lethbridge, samples were also collected on Tuesday and Thursday. To avoid oversaturation of the rods’ impaction surfaces by dust and/or spores, the samplers were active for 1 min out of every 10 mins. Upon collection from the field, rods were stored in sterile 1.5 mL Eppendorf tubes at -20°C until DNA extraction. Cubic metres of air sampled was calculated using the user manual accompanying the rotation impaction samplers, and approximately 2 m3 of air was sampled each day at each site. For the second objective, to assess how the air microbiome differs across crop types, air samples were collected by Burkard 7-day volumetric cyclone samplers (Burkard Manufacturing Co. Ltd., Hertfordshire, England) in bean, canola, wheat, and potato fields in southern Alberta. For all samplers, the intake orifice is approximately 1.2 m above the ground, which was above the canopy of all crops. For the bean fields, one sampler was placed in the white mould disease nursery at the Lethbridge Research and Development Centre (LeRDC) and the other was placed in a nearby commercial field. For the canola fields, one sampler was placed in a canola research trial at LeRDC directly beside the rotation impaction sampler above. To collect wheat samples, 54 one sampler was placed in a wheat research trial at LeRDC, and another was placed in a nearby commercial wheat field. To collect air samples of the potato fields, the samples were obtained from an existing network of Burkard spore samplers maintained by the Potato Growers of Alberta (PGA). Except for samplers in the potato fields, all Burkard cyclone samplers operated continuously for 24 hours a day. Samplers in the potato fields were operated for 3 hours per day, from 1300h to 1600h, based on the protocol set up by the PGA. The same sampling strategy was carried out in 2021 in one research plot each of beans, canola, and wheat at LeRDC. Burkard samplers collect air at a rate of approximately 1 m3 hr-1, so for samplers in bean, canola, and wheat fields, 24 m3 of air were sampled each day and for samplers near potato fields, 3 m3 of air were sampled each day. For non-potato air samples, seven vials were collected per week (one tube per day for seven days) by field cooperators and shipped to LeRDC for storage at -20°C until DNA extraction. For potato air samples, samples were collected by the PGA group and sent to the university of Lethbridge for microscopic examination of spores. Samples were stored at -20°C until they were transferred to LeRDC for DNA extraction. 55 Figure 2.1: Map of Alberta, Canada, showing the sampling locations (large grey circles) in the present study. 2.2.2 DNA extraction The DNA extraction methods were optimized on Sclerotinia sclerotiorum ascospores to facilitate future potential for rapid, in-field extraction of DNA for use in-field, real-time instruments, although these methods were not employed in this project. DNA from the rotorod samplers was extracted as described by Carisse et al. (2009). In brief, each rod was placed in a 2 mL screw cap tube with 100 mg of acid-washed 425-600 µm glass beads (Sigma-Aldrich Canada Co., Oakville, Canada). Eighty microlitres of 100% isopropanol were added to each tube and shaken on a FastPrep homogenizer (MP Biomedicals, Irvine, CA) for 20 s at 4.0 m s-1. Isopropanol was 56 evaporated from the tube on a Speed Vac at 60 °C and 300 µL of DNA isolation solution (comprising 10 ng µL-1 salmon sperm (Invitrogen, Ottawa, Canada), 5% w:v Chelex (Chelex 100, 50-100 mesh, Sigma-Aldrich), 100 mg L-1 bovine serum albumin (BSA, New England Biolabs Ltd., Whitby, Canada), and nuclease free water) was added to each vial. Vials were vortexed for 5 s and then incubated for 20 min at 105 °C on a block heater. Vials were then centrifuged at 13,200 RPM for 5 min at 4 °C. Supernatant from each vial was pipetted into new, labeled 1.5 mL tubes and DNA was stored at -20°C until it was sent for amplicon Illumina sequencing and qPCR. DNA from the Burkard samplers was extracted using a modified protocol of Ferencova et al. (2017). In brief, DNA was extracted directly from the 1.5 mL Eppendorf tubes once they were retrieved from the samplers as follows: 50 mg of acid-washed 425-600 µm glass beads (Sigma-Aldrich Canada Co., Oakville, Canada), 100 µL molecular grade water with 100 mg L-1 BSA were added to each vial. Vials were then placed in a TissueLyserII (Qiagen, Germantown, USA) at 30 Hz for 30 s to dislodge particles from the sides of the vials. Following bead beating, 100 µL of a 10% (w:v) Chelex:molecular grade water mixture was added to each vial. Vials were then vortexed for 10 s and placed in a block heater at 95 °C for 20 min. At 5, 10, 15, and 20 min, samples were vortexed for 3 s. Following the final vortex, samples were centrifuged at 13,200 RPM for 1 min. Supernatant from each vial was pipetted into new, labeled 1.5 mL tubes and DNA was stored at -20°C until it was sent for amplicon Illumina sequencing and qPCR. For the rotorod and Burkard samples, DNA quantification was performed on a Qubit fluorometer (Invitrogen, Burlington, ON, Canada). 57 2.2.3 Mock community and negative controls A series of positive and negative controls were included to account for biases and contamination in sample collection, DNA extraction, and sequencing protocols. A mock community was created in-house to act as a positive control. The mock community comprised eight Ascomycota species (Blumeria graminis, Botrytis cinerea, Chaetomium globosum, Cladosporium herbarum, Fusarium avenaceum, Penicillium expansum, Sclerotinia sclerotiorum, Sordaria sp.,), and one Basidiomycota species (Puccinia triticina), each with 104 spores (for detailed information refer to Appendix Table A.1). Several sets of negative controls were set up as follows: for the rotorod sampler data set, DNA was extracted from rods that were stored but had not been used for sampling. For the Burkard sampler data set, DNA was extracted from Eppendorf tubes that had been placed in the samplers in the fields but had not been actively sampled into. A further sets of negative controls included DNA extracted from the same water used in the DNA extractions, DNA extracted from an empty vial, and water blanks. 2.2.4 Amplicon-based high-throughput sequencing Following DNA extraction, 10 µL aliquots of DNA were sent to Centre d’expertise et de services Génome Québec (Montreal, Canada) for Illumina amplicon sequencing. Due to low biomass in these samples, the DNA concentrations were typically below 100 ng µL-1. Fungi were targeted by amplifying the internal transcribed spacer (ITS) region using the primer set ITS1-f_KYO1 and ITS2_KYO2 (Toju et al., 2012). Including field and control samples, 542 samples were sequenced in 2019 on Illumina NovaSeq 6000 SP flow cells (generating 250 bp paired-end sequences) and 112 samples from 2021 were sequenced on Illumina MiSeq PE 250. 58 2.2.5 qPCR To assess the degree to which Illumina sequencing is quantitative, aliquots of DNA samples that were sequenced on the Illumina platform were also analyzed for DNA of Sclerotinia sclerotiorum using a quantitative PCR (qPCR) TaqMan assay (J. Reich et al., 2017). The qPCR assay targets the intergenic spacer (IGS) region and was tested against closely related Sclerotinia and Botrytis species to ensure specificity. The assay was performed in technical triplicates and a standard curve was created using DNA extracted from serial dilutions of S. sclerotiorum ascospores. Therefore, the qPCR results were summarized as number of ascospores per day. 2.2.6 Bioinformatics and processing of the metabarcodes Sequencing reads from the rotation impaction samplers and from the Burkard spore samplers were analyzed in parallel for each of the regions amplified. General bioinformatics protocols followed the QIIME2 (v. 2020-6) pipeline for amplicon sequencing analysis, with variations described below (Bolyen et al., 2019). The ITS1 region of the raw paired-end fastq files was extracted using the ITSxpress plugin (Rivers et al., 2018) following by denoising using DADA2 (Callahan et al., 2016) with default settings, except that --p-min-fold-parent-over-abundance was specified as 2.0. Taxonomic assignment was performed with the RDP classifier (Q. Wang et al., 2007) as implemented in mothur (Schloss et al., 2009) and the classify.seqs() function, specifying the UNITE fungal ITS database as the reference (Nilsson, Larsson, et al., 2019). Reads that were not assigned to the kingdom Fungi were removed prior to statistical analysis. 59 2.2.7 Statistical analysis All statistical analyses were performed in R environment (v.4.0.3, R Core Team (2020)). The Amplicon Sequence Variant (ASV) tables generated by the QIIME2 pipeline were imported into R using qiime2R (v.0.99, Bisanz (2018)). For alpha diversity analysis, five low read count samples were removed from the data set and the remaining samples were rarefied to the lowest sample depth of 15,588 for subsequent analysis (Nearing et al., 2022; Weiss et al., 2017). Shannon diversity index was calculated with the estimate_richness() function in phyloseq (v.1.32.0, McMurdie and Holmes (2013)). The differences in Shannon diversity index were tested using ANOVA and using Location as the main factor for the geography hypothesis and Crop Type as the main factor for the crop type hypothesis. To assess whether there were any seasonal shifts in alpha diversity, a repeated measures ANOVA was performed and Location or Crop Type were used as replicates, as appropriate. If the ANOVA was significant, pairwise comparison of the means was performed using Tukey’s HSD. ANOVA and Tukey’s HSD tests were performed using anova_test() and tukey_hsd() commands in the rstatix package (Kassambara, 2020). Generalized linear models using a Gaussian link function were constructed to test the effects of location and environment conditions on Shannon diversity in the rotorod data set. Models from forward, backward, and stepwise were compared based on Akaike’s information criterion (AIC) and residual deviance. For beta diversity, the Bray-Curtis dissimilarity was calculated on Hellinger-transformed read counts of the non-rarefied data set. Permutational ANOVA was performed with the adonis() command and dispersion was evaluated with the betadisper() command from the vegan package (v.2.5.6, Oksanen et al., (2019)). In situations with a statistically significant (p < 0.05) difference, pairwise comparisons were made and p-values were adjusted for multiple comparisons using the 60 Benjamini-Hochberg method and p.adjust() from the stats package. For taxonomic composition analysis, the RAM package (v.1.2.1.7, Chen et al. (2018)) was used to fill in missing taxonomic information with the lowest level available. Venn diagrams were made with the VennDiagram package (v.1.6.20, Chen (2018)) and all other plots were made with ggplot2 (v.3.3.2, Wickham (2016)). 2.2.8 Plant pathogens FUNGuild (Nguyen et al., 2016) was used to assign identified fungal taxa to ecological guilds, and the ones designated as plant pathogens were examined. In particular, causal agents of important grain and pulse diseases in Alberta were examined graphically and statistically (ANOVA) for differential relative abundance of reads based on location or crop type. Fungi of interest included: Alternaria spp., Ascochyta spp., Blumeria graminis, Cochliobolus sativus, Fusarium spp., Leptosphaeria spp., Puccinia spp., Ramularia spp., and Sclerotinia sclerotiorum. An exhaustive list is given in Appendix Table A.2. 2.2.9 Disease surveys For the 2019 rotorod data set, Sclerotinia stem rot disease surveys were performed at the end of the season in each of the canola trial plots where rotorod samplers were sampling. For the 2019 and 2021 Burkard data set, disease surveys were performed at the end of the season in the wheat and bean fields and trial plots, but not the potato fields. In all cases, disease incidence and severity were recorded. 61 2.3 Results 2.3.1 Summary of reads Between 92 and 95% of reads were retained following the DADA2 pipeline for each of the three data sets (rotorod 2019, Burkard 2019, and Burkard 2021; Appendix Table A.3). The total number of unique phyla collected by the rotorod samplers was much greater compared to the Burkard samplers in 2019 or 2021 (10 vs 5 or 4, respectively; Table 2.1). However, from each sample, the Burkard samplers in 2019 collected 117 ± 93 (mean ± SD) genera compared to only 86 ± 41 with the rotorod samplers in 2019 and 68 with the Burkard samplers in 2021 (p < 0.001). Correspondingly, we recovered more fungal genera in total by Burkard samplers in 2019 (908 genera) compared to both the rotorod sampler in 2019 (769) and the Burkard sampler in 2021 (521). When plant pathogens as identified by FUNGuild were extracted from each of the data sets, the trends were slightly different (Table 2.1). Both the rotorod 2019 and Burkard 2019 data contained more plant pathogenic genera per sample (34 ± 23 and 37 ± 22, respectively) than the Burkard data set in 2021 (22 ± 7, p < 0.001). In total, the rotorod 2019 and Burkard 2019 data sets had a greater number of unique plant pathogenic genera overall (152 and 138 vs 76, respectively). 62 Table 2.1: Summary of average and total number of fungal taxa recovered from air samples based on ITS1 metabarcodes. The top panel of the table is a summary of all reads in the data set whereas the bottom section includes only those reads classified as plant pathogens according to FUNGuild. Rotorod samplers collected samples for the geography hypothesis and Burkard samplers collected samples for the crop type hypothesis. ASVs Phyla Families Genera Meana SD Unique Mean SD Unique Mean SD Unique Mean SD Unique All reads Rotorod 2019 351 a 197 16956 2 0.7 10 57 b 24 295 86 b 41 769 Burkard 2019 362 a 322 14903 2 0.7 5 71 a 47 314 117 a 93 908 Burkard 2021 113 b 43 3760 2 0.6 4 49 b 16 230 69 b 25 521 Plant pathogens Rotorod 2019 91 a 32 2074 2 0.0 2 21 a 6 59 34 a 23 152 Burkard 2019 82 a 57 1263 2 0.2 2 22 a 12 58 37 a 22 138 Burkard 2021 37 b 13 288 2 0.0 2 15 b 5 40 22 b 7 76 a Rows within the same column and section (all reads vs reads assigned to potential plant pathogens) with different letters are statistically different from each other at alpha = 0.05 following Tukey HSD means separation 63 2.3.2 Common and core taxa Out of 1,065 fungal genera identified in this study, 410 (38%) were collected by both type of samplers in both 2019 and 2021 (Figure 2.2). Reads from these genera comprised 99% of all reads in this study. In general, the commonest genera were similar across data sets. For all data sets, Ramularia spp. were most dominant and prevalent (rotorod 2019: 40% in abundance, Burkard 2019: 27%, and Burkard 2021: 57%; Table 2.2). Alternaria was the second commonest in most of these data sets at 10, 16, and 19% of reads, respectively. For the 2019 data sets, Epicoccum was the third commonest taxa collected, with 9% of rotorod 2019 reads and 16% of Burkard 2019 reads being assigned to this genus. In contrast, Epicoccum made up a very small proportion (0.14%) of reads in the Burkard 2021 data set. Subsequent ordering of fungal taxa varied by sampler type and amplicon, although all data sets were similar in their top ten most dominant fungal taxa. We defined the “core” taxa as the fungal genera shared by locations or crop types (Appendix Figures A.1 and A.2). In 2019, 318 of the 799 (40%) fungal genera were recovered by all Burkard samplers at LeRDC, which comprised over 98% of total abundance. Similarly, in 2021, out of the 521 fungal genera recovered by the same set of Burkard samplers, 226 (43% in diversity, 99% in abundance) were found in all samplers. The remaining fungal genera recovered by these samplers (~60%) accounted for less than 2% in abundance and were collected by individual samplers, therefore, represent the rare biosphere in the environment. 64 Figure 2.2: Venn diagram summarizing the shared genera among all data sets in this study. Table 2.2: Proportion (%) of reads in top 10 taxa recovered by rotorod or Burkard samplers from 2019 and 2021 sampling years through metabarcoding of the ITS1 region. The genera are arranged in descending order in the rotorod 2019 samples. Note that the top ten most abundant taxa were not the exact same for each data set, and so this table contains fifteen of the taxa that were commonest across the whole data set. Genus (or lowest rank) Rotorod 2019 Burkard 2019 Burkard 2021 Ramularia 40.14 26.56 56.67 Alternaria 10.28 16.12 19.48 Epicoccum 9.45 16.37 0.14 Parastagonospora 3.19 1.51 1.41 Blumeria 2.32 2.72 0.66 Vishniacozyma 2.18 2.79 1.53 Stemphylium 2.07 0.05 0.15 Itersonilia 1.95 0.06 0.06 Pleosporaceae_sp. 1.57 3.57 2.38 Cladosporium 1.31 0.90 0.96 Drechslera 0.68 2.83 0.03 Sporobolomyces 0.30 2.01 0.80 Aspergillus 0.20 0.94 1.18 Entylomatales_sp. 0.12 0.77 1.62 Exobasidium 0.03 1.33 1.26 65 2.3.3 Important plant pathogens Seasonal dynamics of specific fungi varied by location or crop type. Here, only a handful of regionally important fungi are discussed. For example, Alternaria spp. were more abundant in Lethbridge (15.8 ± 7.9% (mean ± SD)) and Brooks (13.5 ± 8.5%) than in Beaverlodge (1.6 ± 1.8%) and Lacombe (4.9 ± 4.5%; pairwise Wilcoxon test, p < 0.001). Blumeria was, on average, more abundant in Lethbridge (5.9 ± 11.8%) than any other location (averages ranging from 0.04 to 0.2% of reads; p < 0.001, Figure 2.3). In contrast, Beaverlodge had a much greater abundance of Sclerotiniaceae spp. (1.1 ± 0.8%) than any of the other locations (averages ranging from 0.06 to 0.32% of reads; p < 0.001, Figure 2.3). Fusarium spp. were at similar levels across all sites surveyed (proportion of reads 0.3 to 1.2% of reads; p > 0.05). Abundance of fungi among crop types in the experimental plots at LeRDC also varied. Here, only results from 2021 are discussed, since the data set from 2019 was relatively small, although trends were similar for 2019. Blumeria, causal agent of powdery mildew on wheat, comprised a much greater proportion of reads in the wheat experimental plot (1.7 ± 1.9%) than in the dry bean (0.2 ± 0.3%) or canola (0.4 ± 0.5%) plots (p < 0.001, Figure 2.3). However, reads of Fusarium (0.3 to 0.5% of reads) and Sclerotiniaceae_sp. (< 0.001% of reads) were similar across all crop types (p > 0.05, Figure 2.3) 66 Figure 2.3: The seasonal dynamics of three common plant pathogenic genera revealed by rotorod samplers at each of the four locations used to test whether location influences aerial fungal communities (top panel), and by the Burkard samplers at LeRDC in 2021 to test whether crop type influences aerial fungal communities (bottom panel). 67 2.3.4 Aero-mycobiota diversity 2.3.4.1 Geography hypothesis (rotorod samplers) Shannon diversity index was significant for Location effects (ANOVA, p < 0.0001), and following Tukey’s HSD means separation, Beaverlodge had significantly greater Shannon diversity indices than those at other locations (Figure 2.4, Table 2.3). The alpha-diversity remained relatively stable throughout the season at each location, as there was no overall effect of sampling week on Shannon diversity indices (repeated measures ANOVA, p = 0.96; Figure 2.5). Generalized linear models of Shannon diversity revealed that minimum and mean relative humidity and daily precipitation, in addition to geographic location, were the most important associations with airborne mycobiota diversity (Table 2.4). 68 Figure 2.4: Shannon diversity of the ITS1 ASVs. Data presented are post quality control, thus some controls (e.g. water blanks) have been removed entirely from the visualization. Refer to the Results section for a summary of statistical tests performed. Comparisons made by Tukey HSD (but not on controls), and only significant differences are shown. Significant levels are denoated at *, alpha = 0.05; **, alpha = 0.01; ***, alpha = 0.001; ****, alpha < 0.0001. 69 Table 2.3: Means of diversity metrics and environmental variables for the geography hypothesis (rotorod) data set at each of the four locations sampled in Alberta in 2019. Environmental variables are averages of air temperature and relative humidity, and seasonal totals of precipitation. Diversity Environment Shannon Chao1 Air Temp RH Precip Lethbridge 2.54 a1 256 a 18.2 a 60.5 a 97.0 Brooks 2.52 a 215 a 17.1 a 68.0 b 284.8 Lacombe 2.84 a 281 ab 14.3 b 81.2 c 181.9 Beaverlodge 3.45 b 390 b 13.9 b 77.9 c 204.6 1Values in the same column followed by different letters are statistically different following ANOVA and Tukey’s HSD means separation at alpha = 0.05. 70 Figure 2.5: Weekly averages of Shannon diversity of the ITS1 ASVs (± standard deviation) at each of the locations recovered by rotorod samplers. Week 26 represents the week starting June 24 and Week 34 is the week starting August 19. For all locations combined, Shannon diversity did not vary by week (repeated measures ANOVA, p = 0.96), indicating that diversity across the province is stable throughout the growing season. 71 Table 2.4: Summary table of the most parsimonious (lowest AIC and residual deviance) generalized linear model for Shannon diversity as determined by forwards, backwards, and stepwise selection for the rotorod sampler data set. Estimate SE t p Intercept 4.253 0.502 8.47 <0.0001 *** Location Brooks -0.067 0.229 -0.29 0.771 Lacombe 0.532 0.219 2.43 0.017 * Beaverlodge 0.898 0.214 4.19 <0.0001 *** Precip 0.048 0.025 1.94 0.056 MinRH 0.034 0.010 3.57 <0.001 *** MeanRH -0.049 0.013 -3.88 <0.001 *** The PermANOVA test showed that community compositional structure based on Bray-Curtis dissimilarity was statistically significant between sampling locations (p < 0.0001, Table 2.5). All pairwise comparisons between locations were statistically different (Benjamini-Hochberg adjusted p < 0.05), indicating that each location had its own unique community composition (Appendix Figure A.3). However, the dispersion tests were also significant (p < 0.0001), and so the differences in community composition by location could be due, in part, to unequal dispersions among groups. Table 2.5: PermANOVA table for the 2019 rotorod data set, testing the effect of location and environmental factors on the microbial community composition for the rotorod sampler data set. Df SS MS F R2 p Location 3 3.58 1.19 4.30 0.118 0.0001 Precip 1 0.50 0.50 1.80 0.017 0.0018 MeanT 1 0.60 0.60 2.17 0.020 0.0004 MeanRH 1 0.64 0.64 2.29 0.021 0.0005 Residuals 90 24.95 0.28 0.82 Total 96 30.26 1.00 72 Meteorological conditions varied substantially among the locations surveyed: Lethbridge received half as much precipitation (97 mm) as Lacombe (182 mm) and Beaverlodge (205 mm), and one-third as much precipitation as Brooks (285 mm; Table 2.3). On average, Lacombe and Beaverlodge had similar air temperature, relative humidity, and precipitation over the course of the growing seasons. Both locations had substantially significantly cooler air temperatures (13.9 to 14.3 °C) than Lethbridge and Brooks (18.2 and 17.1 °C, respectively), and greater relative humidity (78 to 81%) than Lethbridge and Brooks (61 and 68%, respectively). These environmental variables were all statistically significant in the PermANOVA, however, collectively, they explained only ~6% of the variation in community dissimilarity of the samples (Table 2.5). 2.3.4.2 Crop type hypothesis (Burkard samplers) ANOVA of the Shannon diversity index revealed that alpha-diversity was similar among crop types within both years surveyed (2019: p = 0.12; 2021: p = 0.06; Figure 2.4) but means separation using Tukey’s HSD revealed that in 2021, average Shannon diversity index was statistically greater in canola than bean (p = 0.04). As with the geography hypothesis, Shannon diversity indices remained relatively stable throughout the season in each of the crops surveyed, with variation within biweekly time periods similar to, or greater than, variation among biweekly time periods (Figure 2.6). For both years, the PermANOVA test showed high yearly effect (explained 24% of community heterogeneity, p < 0.001, Table 2.6) and a relatively small effect from crop type (10%, p < 0.001) on aero-mycobiota compositional shift. Within year, all crops differed in their community composition from one another except bean and canola plots in 2019 (all other 73 pairwise comparisons significant at p < 0.01 following the Benjamini-Hochberg adjustment for multiple comparisons; Appendix Figure A.4). The tests for dispersion were only significant in 2019 (p = 0.048), which indicates that these differences are truly due to crop effects rather than dispersion of the data in 2021. We did not detect interaction effects of sampling year and crop type (p = 0.07), which suggests that vegetation-associated air mycobiota remained relatively stable over the two sampling seasons (Table 2.6). 74 Figure 2.6: Biweekly averages of Shannon diversity of the ITS1 amplicon (± standard deviation) for each of the crop types sampled in 2019 and 2021 in the Burkard data set. Week interpretation is as for Figure 5. For all crop types combined, Shannon diversity indices remained stable over the course of the season for both 2019 (repeated measures ANOVA: p = 0.23) and 2021 (p = 0.15). 75 Table 2.6: PermANOVA table for the 2019 and 2021 Burkard sampler data sets, testing the effect of crop type and year on the Bray-Curtis dissimilarity index (microbial community composition). Df SS MS F R2 p Crop type 3 7.38 2.46 10.92 0.104 0.0001 Year 1 17.23 17.23 76.56 0.242 0.0001 Crop type:Year 2 0.67 0.34 1.50 0.009 0.0727 Residuals 204 45.92 0.23 0.645 Total 210 71.21 1 2.3.5 Comparison of sampler types There were only four overlapping sampling days available to make comparisons between the rotating impaction sampler and the Burkard sampler in 2019. Nevertheless, from these four days of common sampling, it was clear that very different microbial communities were collected by the two sampler types. Even though samplers were placed beside each other and at the same height, Ramularia comprised, on average, 75% of reads from the rotorod samples but only 17% of reads in the Burkard samples. In contrast, Alternaria comprised only 8% of reads in the rotorod samples but 19% of reads in the Burkard samples. Epicoccum, the third most common genus collected by both samplers, comprised 3% of reads in the rotorod samples but 25% of reads in the Burkard data set. For these four days, the Burkard samplers collected a greater diversity of taxa than did the rotating impaction samplers (382 vs 167 genera, respectively). When ordinations of these samples were performed, the samples tended to more closely cluster by sampler type than by day of sampling, indicating the presence of a sampler effect on community composition (Appendix Figure A.5). 76 2.3.6 Comparison of LeRDC Burkard samplers. Three samplers were placed in experimental plots of dry bean, canola, and wheat at LeRDC in 2019 and 2021, and distances between samplers ranged from 60 to 180m. Airborne fungal communities from these samplers were compared to assess how representative a Burkard sampler is over a small region. Ordinations of the daily airborne communities revealed that samples tended to cluster by date, indicating that similar communities were collected by each sampler on each day (Figure 2.7). Samples also often clustered by nearby dates, suggesting that community profiles are somewhat stable from day to day. In 2021, ordinations were difficult to interpret due to many more data points, but similar trends were observed as for 2019 (data not shown). When ordinations of samples from 2019 and 2021 were performed, samples clearly separated by year, showing that fungal airborne communities vary dramatically from year to year (PermANOVA: p < 0.0001; Table 2.6 and Appendix Figure A.6). Figure 2.7: Comparison of the ITS1 amplicons from Burkard samplers at LeRDC in 2019 in each of three experimental plots. All samplers were located within 180 m of each other. PCoA of samples based on the 77 Hellinger-transformed read count and Bray-Curtis dissimilarity of ASVs. Lines/polygons connect samples collected on the same day from each of the samplers. 2.3.7 HTS read counts vs disease levels 2019 was one of the driest years in the last 10 years in southern Alberta, and as a result only trace levels of disease were observed in the bean, canola, potato, and wheat fields. Similarly, in canola trials in Lethbridge, Brooks, and Lacombe, where the rotorod samplers were sampling, trace levels of Sclerotinia stem rot (SSR) were observed. In Beaverlodge, however, mean SSR incidence was 32%. Notably, Beaverlodge also had between 4 and 17 times higher proportion of Sclerotiniaceae spp. reads as compared to other locations (1.09% of reads compared to 0.06 to 0.20% of reads at other sites, p < 0.0001). 2.3.8 HTS read counts vs qPCR. All sequenced samples were also quantified for Sclerotinia sclerotiorum ascospores using a qPCR assay. Most metabarcodes assigned to Sclerotiniaceae were not resolved at the genus level, with some assigned to Botrytis and all others assigned to Sclerotiniaceae. For correlations, reads assigned to Sclerotiniaceae were used. Various transformations of Illumina read counts were correlated against the log(no. of ascospores + 1), and the strongest correlations were obtained with Hellinger-transformed read counts (r = 0.32, p < 0.0001 in 2019; r = 0.14, p = 0.18 in 2021; Figure 2.8). Notably, 2019 had, on average, a much greater proportion of Sclerotiniaceae (0.2% of total reads) than 2021 (0.005%). In many cases, large amounts of ascospores were quantified with the qPCR assay when there were no read counts of Sclerotiniaceae in the samples, suggesting that qPCR is a more sensitive detection and quantification method than amplicon sequencing. 78 Figure 2.8: Correlations (and linear regression line) of the log(number of ascospores + 1) of Sclerotinia sclerotiorum ascospores as quantified by qPCR vs the Hellinger-transformed read count for Sclerotiniaceae_sp. as determined in the same samples. 2.3.9 Positive and negative controls Most of the water controls did not form a band on the gel following PCR and were therefore not sequenced. However, one water control and a few of the DNA extraction blanks (‘lab blanks’) were sequenced (Figure 2.9). The dominant taxa in these samples were Ramularia and Blumeria, also two of the commonest fungi in the whole data set. Negative controls intended to assess the background noise or contamination of equipment (‘rod blanks’ and ‘field blanks’) were similar to the extraction blanks in that they tended to be dominated by Ramularia, Alternaria, and Blumeria but the patterns of contamination are not clear. Examining reads from the mock community revealed that DNA extraction methods and Illumina sequencing were highly consistent between samples but that notable biases exist in the workflow (Figure 2.9). Across all samples, replicates, and amplicons, 6 of the 9 expected fungal taxa were recovered (Blumeria, Botrytis, Chaetomium, Cladosporium, Fusarium, and Penicillium) and no reads of Sclerotinia, Sordaria, or Puccinia were detected. However, Cladosporium and Penicillium were absent from 4 and 2 replicates, respectively, of the mock 79 community. Most reads for all replicates were assigned to Botrytis and no reads were assigned to Sclerotinia, but a small proportion (< 1% per sample, typically) were assigned to Sclerotiniaceae. However, when the mock communities were quantified for DNA of Sclerotinia sclerotiorum with a qPCR assay, results showed DNA from the expected number of ascospores (104 ascospores) were present in the samples. Furthermore, a sample of pure Sclerotinia sclerotiorum ascospores was sequenced and returned 99.7% of reads assigned to Sclerotiniaceae and no reads assigned to Botrytis. Taken together, these findings suggest that, even though the ITS1 region of the Botrytis and Sclerotinia species are genetically extremely similar (Amselem et al., 2011), amplicon sequencing of the ITS1 region can still differentiate them. 80 Figure 2.9: Top panel: The top 10 most common taxa found in the negative controls for the ITS1 amplicon. Data represent the proportion of reads in each sample. Bottom panel: The top 10 most common taxa found in the mock community for the ITS1 amplicon. Mock communities with different numbers were communities that were constructed and had DNA extracted independently; communities with different letters following the same number indicate technical replicates of DNA sent for sequencing. Data represent the proportion of reads in each sample. ‘Theoretical’ values are based on the number of spores (and not ITS copy number) included in the mock community. The ‘Diluted’ sample is a 1:10 dilution of the Mock 1 community. 81 2.4 Discussion This study was designed to test the hypotheses that airborne fungal communities are influenced by location and vegetation and to assess the utility of HTS for the simultaneous detection of regionally important agricultural plant pathogens. The geography and crop type hypotheses were generally supported by our findings: airborne fungal communities were statistically different at each location (Lethbridge, Brooks, Lacombe, and Beaverlodge) and in each type of crop field (bean, canola, potato, and wheat), although effect sizes were small (R2 = 0.12 for the geography hypothesis and R2 = 0.10 for the crop type hypothesis). Location of sample collection within the province had an impact on airborne mycobiota diversity. Beaverlodge was the furthest from all other sites in this study and also had the highest Shannon and Chao1 diversity of all sites surveyed (Table 2.3, Figure 2.4). Differences in diversity may not be explained by weather conditions alone since Beaverlodge and Lacombe had similar climatological conditions for the 2019 season (Tables 2.3 and 2.4). Beaverlodge is an agricultural landscape surrounded by Boreal forest, and so this higher diversity could be a result of mixing ecosystems and/or land use types (Bowers et al. 2011). The biggest differences among crop types were seen in the samples from the potato fields, which tended to cluster further from the samples from dry bean, canola, and wheat. This finding may be an artifact of the different sampling regime employed in the potato samplers. Potato air samples were received from an existing spore monitoring network for late blight in potato fields which actively sampled for 3 hours in the afternoon, compared to the 24 hours a day for all other Burkard samplers in this study. Thus, samples from the potato air sampling network samples would not contain fungi that tend to be released in the morning or at night. Still, ordinations revealed considerable overlap in the community profiles of fungi collected in all 82 these crops (Figure Appendix A.4). This could indicate that nighttime fungi do not contribute dramatically to different community compositions in this region, in contrast to findings reported by several others on the diurnal impact of spore liberation (Hirst, 1953; Oneto et al., 2020). Data from the three samplers at LeRDC have some important implications for air monitoring programs. First, these samplers, all within 180 m of each other (representing an area of ~0.14 km2), collected similar community profiles, as indicated by sampling day clustering together in ordination plots (Figure 2.7), suggesting that, at least at a regional scale, aero-mycobiota collected by a single sampler may be representative of that area. This finding is reinforced by the results showing that, in both years, 40% of the identified genera were shared between all samplers and comprised over 98% of reads, indicating that a single sampler can collect the vast majority of taxa in that area. Second, in both years of sampling at LeRDC, certain fungi were more prevalent than others. Blumeria spp., causal agent of powdery mildew on grasses, were much more abundant in the wheat plots than in the canola and bean plots in both years surveyed (Figure 2.3). Previous research in Blumeria has shown that few spores are collected above the wheat canopy compared to within the canopy, indicating that the spores of this fungus have a steep dispersal gradient (Cao et al. 2012). Similarly, communities of airborne fungi collected within a patch of vegetation were shown to be different from those communities collected upwind or downwind from that patch of vegetation (Lymperopoulou et al. 2016). These findings suggest that, in cases where specific pathogens are of interest, the samplers should be placed within that host crop. Third, there were substantial day-to-day changes in relative abundance of many taxa in each of the three samplers at LeRDC, highlighting the importance of the fine-scale temporal sampling for fungi of interest (Figures 2.3 and 2.7). We are continuing carrying out nationwide 83 sampling campaign to identify the environmental drivers of these changes. Fourth, there was a clear yearly effect on the community profiles which shows the importance of sampling every year to address annual changes in airborne fungal communities (Table 2.6; Appendix Figure A.6). Annual variation may be a reflection of different meteorological conditions or land use changes (e.g. crop rotations, urban development, deforestation, etc). Finally, in the plot where both the rotorod and Burkard sampler were placed side-by-side, fungal communities tended to cluster by sampler type, which points to biases with a chosen sampling method. The effect of sampler types on community profiles has been documented elsewhere (W. Chen, Hambleton, et al., 2018; Mbareche et al., 2018). Results presented here should be interpreted in light of the controls sequenced. The water controls typically showed little contamination from lab procedures and were removed from analysis during quality control, but the controls designed to assess background contamination in the rods and vials had high read counts of several common taxa: Ramularia, Stemphylium, Botrytis, Blumeria, and others (Figure 2.9). The contamination was not systematic across samples and was therefore difficult to account for prior to analysis of the field samples. Analysis of the mock communities (Figure 2.9) revealed a few important trends: first, there was complete loss of some taxa; second, there were taxa that appeared that were not included in the mock community; and third, the relative abundance of read counts are not strongly correlated with starting quantity of fungal biomass, a conclusion that is corroborated by the qPCR results (Figure 2.8). Because most microbiome studies do not include positive controls in the form of mock communities (Hornung et al., 2019), it is difficult to assess how surprising these results are. Conversely, studies that do analyze mock communities tend to focus only on the mock communities and do not incorporate analysis of environmental samples (Bakker, 2018; 84 Egan et al., 2018; O’Sullivan et al., 2021). Complete loss of some taxa, dramatic deviations from the expected read count proportions, and appearance of taxa that should not be in the mock community are all common results from mock community studies (Egan et al., 2018; Pawluczyk et al., 2015; Yeh et al., 2018). The magnitude of the difference between expected and observed read counts varies from taxon to taxon and by laboratory and computation methods used, and can therefore be difficult to generalize across all taxa in a mock community. Therefore, the low correlation between read counts of Sclerotinia from Illumina amplicon sequencing and the amount of ascospores quantified by qPCR should not be surprising, and is in-line with a recent review of this subject (Lamb et al., 2019). Importantly, while our mock community analysis shows substantial biases in the methods we used here, mock community results were highly consistent among replicates which indicates that the biases are consistent across treatments. Sources of bias that could contribute to the poor mock community results are discussed in detail by Nguyen et al. (2015) and Hornung et al. (2019), so here we will focus on specifics of the mock community analyzed in the present research. Puccinia spores are notoriously difficult to extract DNA from, so it is likely that our DNA extraction method, which was optimized for S. sclerotiorum ascospores, was inadequate to crack the hard Puccinia spore wall and could explain why this taxon was absent from the mock community. In addition, primer bias is a well-documented source of bias in amplicon sequencing studies (Schoch et al., 2012; Tedersoo et al., 2015), and the primers used in this study when tested in silico were not able to amplify rust fungi (W. Chen et al., 2021). Because qPCR of the mock community showed the expected quantity of S. sclerotiorum DNA and the taxonomic assignment of DNA from S. sclerotiorum was Sclerotiniaceae sp., one explanation that this taxon was mostly absent from the mock community could be bias in PCR amplification rather than wrong taxonomic assignment to the closely 85 related Botrytis genus (Amselem et al., 2011). Taken together, results from our mock community analysis add support to the call for including mock communities in all sequencing runs (Yeh et al., 2018). All samplers collected a wide range of plant pathogens of interest, contributing to the growing body of knowledge that this form of surveillance could be useful in the management of plant disease epidemics (Abrego et al., 2018; Aguayo et al., 2021; W. Chen, Hambleton, et al., 2018; Nicolaisen et al., 2017). In addition, samplers collected a wide range of antagonistic organisms to plant pathogens. Epicoccum, Fusarium, and Alternaria all exhibited antagonism towards diseases caused by Sclerotinia sclerotiorum in field trials (Boland & Inglis, 1989; Hannusch & Boland, 1996; Hu et al., 2016; Inglis & Boland, 1992; Mercier & Reeleder, 1987), and these taxa were all commonly recovered by samplers. Therefore, in the spirit of elucidating the pathobiome, future research might determine whether the presence of these taxa could influence disease development in the field. However, as noted above, it remains unclear to what relative abundance of read counts correlates with actual organism abundance in the field. Results from Illumina amplicon sequencing take several weeks or months from sample collection to data interpretation, so the utility of this approach would rely on extensive sampling and surveying to build predictive models. In contrast, technologies like the Oxford Nanopore MinION could provide near real-time results. The rapid DNA extraction protocols we describe here might be employed in a field workflow for portable technologies like the MinION, which could both avoid biases associated with the PCR steps from barcoding methods and sequence longer regions of DNA which could provide more reliable taxonomic assignments (Mafune et al., 2019). Preliminary trials with the MinION were attempted as part of this research project, but results were inconsistent and so this technology is under consideration for future projects. For an 86 individual sample, MinION sequencing is much more expensive than Illumina sequencing; however, kits that enable multiplexing multiplex samples can greatly reduce per sample costs for the MinION. Again, more research would be required to interpret the read counts to actionable interventions for plant disease management. Another challenge in practical implementation of some of these microbiome insights is sample size: monitoring enough fields with a range of disease expression over the course of a season. Fortunately, efforts to create national (W. Chen, Hambleton, et al., 2018) and global (Buters et al., 2018) air monitoring networks could help provide important insights into understanding pathogen dissemination and disease forecasting and management. 87 Chapter 3: Occurrence of white mould caused by Sclerotinia sclerotiorum in dry bean fields in Alberta and comparison of ascospore release to other dry bean growing regions in Canada 3.1 Introduction Pulses, like dry bean (Phaseolus vulgaris L.), have many nutritional and environmental benefits, and are an integral part of sustainable agriculture (Stagnari et al., 2017). In Canada, dry bean is a high value crop whose main biological constraint to production is white mould caused by the fungal pathogen Sclerotinia sclerotiorum (Lib.) de Bary. White mould affects the pods and/or stems of bean plants and, in extreme epidemics, can infect every plant in a field, resulting in significant yield losses. Yield loss estimates range from 7 to 26 kg ha-1 for every percent increase of infected plants, depending on cultivar (del Río et al., 2004). White mould has been common in Alberta, Manitoba, and Ontario for many decades, although intensity of the disease pressure often varies by year (Harding et al., 2017; H. C. Huang et al., 1988; Kim et al., 2017; D. L. McLaren et al., 2019). In southern Alberta, where dry bean fields are exclusively grown under irrigation, the risk of disease is high in every year. From informal surveys of growers in southern Alberta, respondents reported using a variety of control methods to manage white mould in their crops including: crop rotation, irrigation management (amount and frequency, especially at the flowering stage), and fungicide application. Most growers apply fungicides twice per season, once at the early flowering stage and another ten to fourteen days later. In some instances, growers will apply a third application, although the strategy that growers use to determine number of fungicide applications needed is not clear. Several growers reported making the decision to apply fungicide based on weather forecasts; for 88 example, if the weather was predicted to be hot and dry, they may not apply fungicide. No growers we spoke with reported leaving a non-fungicide-treated check strip to assess the effectiveness of their control methods. Fungicide trials have reported up to a doubling of yield in fungicide-treated plots compared to control plots (del Río et al., 2004; Vieira et al., 2010), however these trials often use susceptible cultivars and/or employ artificial inoculations and may not be representative of commercial growing conditions. Disease forecasting is one approach that could help growers manage white mould in their fields with reduced fungicide use. Forecasting can inform fungicide use either through recommending no fungicide application when conditions indicate disease is unlikely to develop or by recommending a more optimal timing of fungicide application to improve fungicide effectiveness. To-date, predictive models for white mould have met with limited success in a range of crops, and few statistical models have been deployed for dry bean (McDonald & Boland, 2004). Indeed, epidemiological studies of white mould based on environmental conditions or agronomic factors often find conflicting results even within the same crop (J. Reich & Chatterton, 2023). Many disease forecasting models have been based solely on environmental and/or agronomic factors, but a growing body of research suggests that monitoring for airborne inoculum can increase the reliability of disease forecasting models (Rogers et al., 2009; Van der Heyden et al., 2021). Disease prediction has been improved by incorporating air monitoring of inoculum for botrytis leaf blight in onion (Carisse et al., 2009), gray mold in strawberry (Xu et al., 2000), and downy mildew in lettuce (Dhar et al., 2020) agroecosystems. Specifically, these studies have coupled aerosol collection with molecular methods to quantify DNA levels of 89 pathogens of interest, which is an approach that could help in the forecasting of white mould in dry bean. Therefore, the goals of this research project were to characterize the annual occurrence of white mould epidemics in Alberta and relate white mould levels to environmental variables, levels of ascospores in the air, and agronomic characteristics. Due to the unique growing environment of dry beans in Alberta, preliminary assessments were made to compare how these conditions affect ascospore levels in Manitoba and Ontario. Assessing the relative contributions of each of these variables (environment, location, pathogen load) to white mould epidemics is an important first step to establishing a regional disease forecasting system for dry bean producers. 3.2 Materials and Methods 3.2.1 Disease surveys In Alberta in 2018 and 2019, fifteen commercial fields, including those with spore samplers, were surveyed approximately every 7-10 days beginning at the R1 stage. Surveys lasted until the R9 stage or until the crop was harvested, so surveys were typically performed from the end of July until the end of August. In 2020 and 2021, 41 and 45 commercial fields, respectively were surveyed once at the R5 to R7 stage (mid-August). For all surveys, in each field, ten sites were visited in a U-shaped pattern and at each site ten plants were rated for disease incidence and severity. Incidence was recorded as the percentage of plants showing infection, and severity was rated for each plant on a four-point scale: 1 = healthy plant, 2 = a single stem is infected, 3 = multiple stems are infected, 4 = the lower part of the main stem is infected or the plant is dead (Balasubramanian et al., 2014). All sites in the field were 20 m away from each other and at least 20 m away from the edge of the field. In each year, a disease nursery 90 was established at the Lethbridge Research and Development Centre (LeRDC) in which susceptible bean cultivars were planted and irrigated to favour white mould development. For surveys in the disease nursery, the survey protocol was modified such that all plants in the middle rows of susceptible cultivars were rated on the disease scale. 3.2.2 Effect of fungicide application on disease In 2020 and 2021, six growers per year agreed to leave a non-fungicide check strip in their bean fields. For these fields, disease surveys were performed in both the treated and non-treated check strip. 3.2.3 Weather data for Alberta A WatchDog Mini (model 2450) weather station (Spectrum Technologies Inc., Aurora, USA) was installed in each field that had a Burkard spore sampler. Temperature, relative humidity, and precipitation sensors were mounted at the same height as the spore sampler and were therefore collecting environmental information above the canopy. The weather stations recorded hourly air temperature, relative humidity, precipitation, soil moisture, and soil temperature. Vapor pressure deficit was calculated from these variables according to Xu et al. (2000). All variables were summarized for daily minimum, maximum, mean. Furthermore, daily data from seven provincial weather stations in southern Alberta were downloaded (ACIS, 2022), and each field that was surveyed was matched to data from the nearest provincial weather station. All commercial fields were between 1 and 10 km away from their nearest provincial weather station. 91 3.2.4 Spore collection in Alberta Daily aerosol sampling was performed by Burkard volumetric multi-vial cyclone samplers (Burkard Manufacturing Co. Ltd, Hertfordshire UK). These samplers intake air at a rate of 16.5 L min-1 (~1m3 hr-1) per hour and deposit aerosols in 1.5 mL microcentrifuge tubes. The samplers were programmed to change vials every day for the period of a week, and vials were collected and replaced weekly. In 2018, three commercial bean fields were sampled from June to August to assess the within-field distribution of ascospore dispersal. These fields were located near Cranford, Vauxhall, and Bow Island, and three samplers were placed in each field: one sampler with the intake orifice 1 m above the ground and above the canopy, one sampler beside the first one with the intake orifice 0.15 m above the ground and below the canopy, and one sampler 40 m from the first one perpendicular to the predominant direction of wind and with the intake orifice also 1 m above ground. Samples were collected weekly and stored at -20 °C until DNA extraction, described below. In 2019 to 2021, six to eight spore samplers were deployed from July to August in five to seven commercial bean fields near Vauxhall and Rolling Hills, and an additional sampler was deployed in the white mould nursery at LeRDC. One of the commercial bean fields had two samplers, set up 40 m away from each other in the same manner as the previous year. To assess the potential for expanding this service in a regional manner, in 2019 to 2021, agronomists at Viterra Inc. (Taber, AB) were trained on how to operate the Burkard spore samplers. Vials were collected by agronomists twice a week and mailed to the Lethbridge Research and Development Centre for analysis. To assess the utility of existing spore trapping networks in Alberta to monitor for S. sclerotiorum spores, air samples collected by the Potato Growers Association of southern 92 Alberta in 2019 were analyzed. This group currently maintains a spore monitoring network for Phytophthora infestans under the direction of Dr. Yevtushenko at the University of Lethbridge. The network comprises Burkard samplers deployed in or near commercial potato fields and are active for three hours every afternoon. A subset of samples from this network were selected based on the proximity of the samplers to bean fields that also had spore samplers. DNA extraction and spore quantification were performed as described below for samples from bean fields. 3.2.5 Spore and weather data collection in Manitoba and Ontario In 2019, 2020, and 2021, research sites in the provinces of Manitoba and Ontario were established. In Manitoba, two sites were located at disease nurseries at the Carman Research Farm and the Morden Research and Development Centre. In Ontario, one site was in a disease nursery at the Harrow Research and Development Centre, and two sites were located in nearby commercial bean fields. All disease nurseries were irrigated. One spore sampler per field was deployed in the manner described above for Alberta 2019 to 2021 sites. Weather stations were deployed in the same manner as described for research sites in Alberta. 3.2.6 Comparison of spore samplers and sampler types for the collection of S. sclerotiorum ascospores To determine how representative a single Burkard sampler was of the ascospore levels in the field, Pearson correlations of ascospore counts between samplers within fields were performed for all fields that had more than one Burkard sampler. To assess the relative collection abilities of S. sclerotiorum ascospores by different methods, in 2019 a GRIPST-2009 rotation 93 impaction sampler (Aerobiology Research Laboratories, Ottawa, ON) was set up directly beside a Burkard cyclone sampler, with the rotating arm at the same height as the intake of the Burkard sampler. The samplers were placed in a Sclerotinia stem rot of canola research plot at the Lethbridge Research and Development Centre. The GRIPST-2009 sampler operated at 10% sampling capacity (sampled for 1 min every 10 mins) and sampled from about 0800h to 1600h Monday to Friday and samples were collected daily. The Burkard sampler collected for 24 hours every day and samples were collected biweekly. To facilitate comparison of spore counts between samplers, data were transformed to ascospores per m-3 day-1, as described in the Statistical Analysis section below. 3.2.7 Flower infection Petal infection has been positively associated with final disease levels in Sclerotinia stem rot of canola (Turkington & Morrall, 1993) and flower infection has been closely related to airborne spores in related pathogens (Xu et al., 2000). In 2019, flowers from the white mould nursery at LeRDC were collected to relate S. sclerotiorum infection to ascospore loads based on the sampler in the same plot. Starting at the R1 stage, 25 flowers were collected each day from Monday to Friday and stored at 4 °C until surface disinfested. Initial trials identifying S. sclerotiorum infested flowers on Sclerotinia semi-selective media (SSM; Steadman et al. (1994)) revealed that identification could be hindered by the growth of other fungi, so prior to plating surface disinfestation was performed as described by Reich et al. (2017). Five flowers were plated on each of five plates of SSM and incubated at room temperature in the dark. Approximately five days later, the number of flowers infected with S. sclerotiorum, indicated by a bright yellow halo around the flower and characteristic hyphal growth, were counted. 94 3.2.8 DNA extraction DNA was extracted from all aerosol samples using the protocol described by Ferencova et al. (2017). DNA was extracted directly from the 1.5 mL microcentrifuge tubes once they were retrieved from the samplers as follows: 50 mg of acid-washed 425-600 µm glass beads (Sigma-Aldrich Canada Co., Oakville, Canada), 100 µl sterile distilled water, and 10 µl of 0.1 ng mL-1 synthetic tuna DNA (Li et al., 2015) were added to each vial. The tuna DNA was included as an exogenous internal control and amplified in a multiplex qPCR assay with S. sclerotiorum DNA. Therefore, it acted as a ctonrol for false negatives: if the tuna DNA did not amplify, the sample could be removed from analysis. Samples were then placed in a TissueLyserII (Qiagen, Germantown, USA) for 30 s to dislodge particles from the sides of the vials. Following bead beating, 100 µl of a 10% (w:v) Chelex:TE mixture (Chelex 100, 50-100 mesh, Sigma-Aldrich; 10 mM Tris, pH 8.0, 0.1 mM EDTA, IDT Coralville, IA) was added to each sample. Samples were then vortexed for 10s and placed in a block heater at 95 °C for 20 min. At 5, 10, 15, and 20 min, samples were vortexed for 3 s. Following the final vortex, samples were centrifuged at 16,000 g for 1 min. Supernatant was collected and stored in new 1.5 mL tubes at -20 °C. 3.2.9 Generation of standard curve and qPCR analysis Ascospores of S. sclerotiorum were obtained from InnoTech (Edmonton, Canada), and the standard curve was developed by extracting DNA from five-fold serial dilutions of ascospores diluted in 10 ng ml-1 sheared salmon sperm (Invitrogen, Burnaby, BC), as described in Reich et al. (2017). The use of salmon sperm in the dilutions was intended to mimic the complex environmental background present in field samples. The primers and probes for the tuna 95 DNA were described by Li et al. (2015), and the primers and probes for S. sclerotiorum were described by Reich et al. (2016). The S. sclerotiorum assay was designed for use in environmental samples and reliably identifies S. sclerotiorum from closely related fungi like Botrytis cinerea and S. trifoliorum. Reaction conditions for the multiplex qPCR assay followed those described by Reich et al. (2017). A standard curve was included for every batch of samples analyzed. All samples were run in technical triplicates, and samples were excluded from analysis if there was no amplification of the internal control (tuna DNA). 3.2.10 Statistical analysis All statistical analysis was performed in R environment (v.4.0.3, R Core Team (2020)). For data involving ascospore counts from qPCR, preliminary data cleaning was performed. Histograms of Ct values for the exogenous internal control were examined and generally followed a normal distribution. Samples for which the internal control did not amplify, or for which the Ct value was outside the normal distribution was removed from subsequent analysis. For fields that had more than one Burkard sampler, ascospore counts were averaged to get a single value per day. To assess how representative each spore sampler was of the field in which it was sampling, Pearson correlations on the log(daily number of ascospores + 1) were performed with rcorr() from the ‘Hmisc’ package (Harrell Jr, 2020) for each pair of samplers for fields where there was more than one sampler in the same field. The log transformation was performed to normalize the ascospore data and minimize the weight of the few large outliers (i.e. the days with huge amounts of ascospores) in analysis. For analysis involving the means of ascospores, means were calculated using the log-transformed values, and therefore represent geometric means rather 96 than arithmetic means. To compare the GRIPST-2009 rotation impaction sampler ascospore counts with the Burkard ascospore counts, all values were transformed to ascospores per cubic metre. The formula for calculating the volume of air sampled for the GRIPST-2009 samplers was determined by multiplying the number of minutes each sampler was active by the sampling volume per minute (0.047 m3 min-1, according to the GRIPST-2009 user manual). Similarly, for samples collected from Burkard samplers (including those from the Potato Growers Association network), sampling volumes were calculated as 0.99 m3 hr-1 (or 16 L min-1 as per manufacturer specifications) of sampling. Total number of ascospores per day were then divided by the volume of air sampled per day to result in the number of ascospores m-3 day-1. To investigate the relationship between airborne ascospore levels and infected flowers, Pearson correlations were performed between the daily percentage of infected flowers and either the daily counts of ascospores or the weekly sums (rolling sums) of ascospores prior to flower collection. Models for infected flowers were developed in a similar manner described below for disease incidence (forward, backward, and stepwise model selection procedures), using the proportion of infected flowers per day as the response variable and either (i) ascospores alone, (ii) weather variables alone, or (iii) both ascospores and weather variables as predictor variables. To compare whether airborne ascospore levels varied by province, year within province, or month within province-year, a series of one-way ANOVAs were performed using the log(number of ascospores + 1) as the response variable. ANOVAs were performed with aov()and potshoc means separation was performed using Tukey’s HSD with TukeyHSD(), both implemented in the ‘stats’ package. To determine whether white mould incidence varied by market class, disease survey results from 2020 and 2021 were combined and a Welch’s ANOVA was performed with the 97 oneway.test() from the ‘stats’ package and specifying unequal variances. Significant differences among market classes were separated with the Games-Howell method and a Bonferroni adjustment for p-values as implemented in the posthocTGH() function in the ‘userfriendlyscience’ package (Peters, 2018). To test differences in severity by market class, a similar analysis was performed using a Kruskall-Wallis rank test with the kruskal.test() from the ‘stats’ package. Signicant differences among market classes were separated with Dunn’s method and a Holm’s p-value adjustment as implemented in dunn_test() in the ‘rstatix’ package (Kassambara, 2020). To assess the influence of fungicide use on disease incidence, a paired-sample Wilcoxon test was performed with the wilcox.test() function in the ‘stats’ package to compare disease incidence in treated and non-treated survey areas of commercial fields for each year (2020 and 2021) separately. To compare how data from in-field weather stations related to provincial weather stations, Pearson correlations were performed for the daily environmental variables that were available for each: mean air temperature, mean RH, and precipitation. T-tests with Bonferroni p-value adjustment as implemented in the ‘rstatix’ package were used to compare the daily values of each in-field weather station to the nearest provincial weather station. One-way ANOVAs with Tukey’s HSD means separation were performed as described above to assess differences in environmental variables across years for each of the in-field and provincial weather data sets. Two modeling approaches were used to relate final white mould incidence to environmental variables and/or ascospore levels; only data from commercial fields were used in the analysis since data from the disease nursery were outliers in environmental conditions and disease levels. First, for commercial fields in which both weather data and ascospore data were collected (n = 18), models were built to predict final disease incidence based on environmental 98 variables and ascospore counts. Second, all surveyed commercial fields (n = 115) were matched to the nearest provincial weather station, and environmental variables collected from these weather stations were used to build disease models. Available environmental variables were different based on whether the data were collected from in-field or provincial weather stations. In the case of in-field weather stations, mean, minimum, and maximum values were calculated for each day. Variables available from in-field weather stations were: air temperature, RH, precipitation, soil moisture, and soil temperature. Vapor pressure deficit was calculated from these variables as described above. Daily ascospore counts were also summarized and included in the in-field data set. Variables available from provincial weather stations were: daily minimum, maximum, and mean air temperature; daily mean RH; daily precipitation; daily wind speed at 2 m and 10 m above ground; and daily growing degree days. In modeling approaches (in-field weather station and provincial weather station), the final disease incidence (proportion) at the field level was modeled as a function of environment and/or ascospores using a generalized linear model with a logit link function using the glm() function from the ‘stats’ package . For modeling purposes, environmental variables were summarised in two ways: in the first, mean values were calculated for all variables for the month of July in each year; in the second, mean values were calculated for the flowering period, defined as July 20 to August 10 for each year. For each approach, models were selected using forward, backward, and stepwise methods. Models were assessed based on significance of terms and were evaluated based on the Akaike information criterion (AIC) and changes in deviance. 99 3.3 Results 3.3.1 Weather In Alberta, 2018, 2019, and 2021 were three of the driest years in the last 14 years (Table 3.1). However, irrigation in dry bean fields meant that precipitation levels within these fields were consistently 4.5 to 7.5 times greater than the regional precipitation levels (Table 3.2). Within fields, mean air temperature and mean RH were, on average, not statistically different from the regional weather stations, except for mean RH in 2018 which was significantly higher than the regional weather stations (70 vs 60%; Table 3.2). Corroborating these analyses, correlations of in-field and regional weather stations showed a high level of similarity between daily mean air temperature (r = 0.91, p < 0.0001) and mean RH (r = 0.72, p < 0.0001), but a much lower level (r = 0.36, p < 0.0001) of similarity between daily precipitation (Table 3.2). Furthermore, mean air temperature and mean RH varied significantly by year, especially in in-field weather stations, but mean precipitation did not for either in-field or provincial weather stations. Although we tried to collect weather data for both Manitoba and Ontario, technical difficulties resulted in an incomplete data set. As a result, weather summaries are not presented here for these provinces, and ascospore data from these provinces were only used to compare ascospore levels among provinces and were not included in the white mould modeling. Table 3.1: Historical weather (values represent ranges among several stations) in southern Alberta, accessed from provincial weather records from several regional weather stations (ACIS 2022). The in-field column represents summaries of the weather stations placed within bean fields as part of this research. Precipitation Air temperature Relative humidity Year Provincial In-field Provincial In-field Provincial In-field 2008 158.2 - 201.6 - 16.0 - 17.2 - 58.8 - 66.1 - 2009 146.0 - 217.1 - 15.6 - 16.7 - 61.1 - 66.2 - 2010 136.2 - 237.2 - 15.6 - 16.7 - 62.8 - 71.4 - 100 2011 102.1 - 162.5 - 16.4 - 17.7 - 59.7 - 67.9 - 2012 126.5 - 222.8 - 17.5 - 18.5 - 60.8 - 66.4 - 2013 79.0 - 241.7 - 17.3 - 18.2 - 65.4 - 69.0 - 2014 126.1 - 238.4 - 17.0 - 17.8 - 67.0 - 70.2 - 2015 60.3 - 112.9 - 17.9 - 19.0 - 56.1 - 63.3 - 2016 142.0 - 196.6 - 17.2 - 18.0 - 61.4 - 66.9 - 2017 65.3 - 104.4 - 18.5 - 19.5 - 51.0 - 57.2 - 2018 61.4 - 109.7 236.8 - 299.4 17.5 - 18.6 17.7 - 18.0 55.6 - 62.2 66.8 - 73.6 2019 69.2 - 127.3 200.4 - 296.7 17.1 - 18.1 18.2 - 18.5 58.0 - 65.7 58.3 - 67.9 2020 111.7 - 153.9 166.7 - 247.6 17.3 - 18.4 18.3 - 19.2 57.1 - 66.0 58.7 - 67.4 2021 46.5 - 80.3 114.8 - 381.4 19.1 - 20.2 18.7 - 20.1 54.1 - 63.2 58.4 - 66.3 Table 3.2: Comparison of in-field weather stations to regional (provincial) weather stations. Station comparisons were performed with a t-test and Bonferroni p-value adjustment. Year comparisons were performed with an ANOVA and means were separated following Tukey’s HSD. Below, the Person correlation coefficients and p-value are given for all data points comparing in-field weather stations to the nearest provincial weather station. Mean air Ta Mean RH Mean precip Year In-field Provincial In-field Provincial In-field Provincial 2018 17.87 a 18.36 a 70.39 A a 60.58 B a 3.48 A 0.76 B 2019 18.36 ab 18.16 a 64.00 b 63.94 b 4.68 A 1.01 B 2020 19.01 c 18.87 a 61.06 c 60.44 a 3.6 A 0.47 B 2021 19.60 d 19.40 b 60.58 d 59.94 a 4.02 A 0.70 B r: 0.908 0.715 0.361 p: < 0.0001 < 0.0001 < 0.0001 a Within environmental variable, rows (in-field vs provincial) followed by different capital letters are significantly different at alpha = 0.05, and columns (comparison of years) followed by different lower-case letters are significantly different at alpha = 0.05. 101 3.3.2 Disease surveys In Alberta, white mould prevalence, incidence, and severity differed by year. In the final disease surveys, 100, 87, 69, and 96% of fields showed symptoms of white mould in 2018, 2019, 2020, and 2021, respectively. 2019 had significantly less disease incidence (mean 11.9%) than other years (means from 20.4 to 26.7%, p < 0.01; Table 3.3). When disease surveys were performed throughout the growing season, disease incidence peaked by the R7 stage, approximately mid-August. These annual differences and seasonal trends were similar for severity (Table 3.4). When disease incidence and severity were examined by market class, Pinto beans had a significantly greater disease incidence (mean 32.6%) than Great Northern (15.0%), Black (10.0%), Red (6.3%), or Yellow 4.6%; Table 3.5). Table 3.3: White mould incidence (%) in southern Alberta commercial dry bean fields from 2018 to 2021. Incidence (%) Survey 1 (R2)a,b Survey 2 (R5) Survey 3 (R7)c Survey 4 (R8) Year n Mean Range Mean Range Mean Range Mean Range 2018 15 6.8 0 - 37 22.2 0 - 65 26.7 b 2 - 85 24.0 2 - 51 2019 15 1.1 0 - 9 7.7 0 - 28 11.9 a 0 - 60 12.8 0 - 57 2020 41 - - - - 20.4 b 0 - 100 - - 2021 45 - - - - 21.4 b 0 - 69 - - a Approximate dates of surveys were: Survey 1: July 26-31; Survey 2: Aug 7-9; Survey 3: Aug 9-17; and Survey 4: Aug 23-26. b Growth stages of dry bean: R2: Beginning pod (pods 1.25 cm long at the first flower position), R5: Beginning seed (pods 8.78 to 10 cm long and seeds discernable), R7: Full seed (oldest pods with fully developed seeds), and R8: Beginning maturity (leaves yellowing over half of plant and small pods ripening). c Mean values followed by different letters in Survey 3 are statistically different at p < 0.01, following Games-Howell separation and a Bonferroni correction. 102 Table 3.4: Severity of white mould epidemics in southern Alberta commercial dry bean fields from 2018 to 2021. The severity rating goes from 1 (totally healthy) to 4 (severe infection or dead); see Methods for details. Severity Survey 1 (R2)a,b Survey 2 (R5) Survey 3 (R7)c Survey 4 (R8) Year n Mean Range Mean Range Mean Range Mean Range 2018 15 1.11 1.00 - 1.64 1.42 1.00 - 2.43 1.59 b 1.06 - 2.95 1.58 1.04 - 2.28 2019 15 1.02 1.00 - 1.18 1.11 1.00 - 1.36 1.23 a 1.00 - 2.11 1.27 1.00 - 2.43 2020 41 - - - - 1.38 b 1.00 - 3.85 - - 2021 45 - - - - 1.38 b 1.00 - 2.56 - - a Approximate dates of surveys were: Survey 1: July 26-31; Survey 2: Aug 7-9; Survey 3: Aug 9-17; and Survey 4: Aug 23-26. b Growth stages of dry bean: R2: Beginning pod (pods 1.25 cm long at the first flower position), R5: Beginning seed (pods 8.78 to 10 cm long and seeds discernable), R7: Full seed (oldest pods with fully developed seeds), and R8: Beginning maturity (leaves yellowing over half of plant and small pods ripening). c Mean values followed by different letters in Survey 3 are statistically different at p < 0.01, following Games-Howell separation and a Bonferroni correction. 103 Table 3.5: Summary of white mould incidence and severity in southern Alberta by market class over 2020 and 2021 seasons combined. Incidence Severity Market class n Meana Range Meana Range Yellow 12 4.6 a 0 - 23 1.06 a 1 - 1.27 Red 6 6.3 a 0 - 17 1.09 a 1 - 1.29 Black 3 10.0 ab 0 - 24 1.16 ab 1 - 1.38 Great Northern 26 15.0 b 0 - 62 1.26 b 1 - 2.35 Pinto 39 32.6 c 0 - 100 1.62 c 1 - 3.85 a Differences among means were tested with a Welch’s ANOVA and assuming unequal variances. Mean values in the same column followed by different letters are significant at p < 0.01 following the Games-Howell means separation and Bonferroni p-value adjustment. 3.3.3 Effect of fungicides The effect of fungicide on white mould varied by year. In 2020, the six fields in which the non-fungicide check strips were located had an overall low level of disease and there was no significant effect of fungicide on white mould incidence (no fungicide: 4.8 ± 9.8%; fungicide: 6.2 ± 14.7%; mean ± sd; p = 0.84). However, in 2021, the overall disease level in the fields with non-fungicide check strips was much greater, and the plots treated with fungicide had nearly 50% fewer plants infected than the untreated strips (no fungicide: 56.2 ± 29.0%; fungicide: 29.9 ± 27.2%; mean ± sd; p < 0.0001). 3.3.4 Ascospore collection In all field-years, ascospores were continuously released over the course of the season and levels of ascospores varied by field (Figure 1). Ascospores levels for a single day peaked at 38,199 in Alberta on August 18, 2020. Mean numbers of daily ascospores differed significantly by province, with Manitoba having the greatest numbers (396), followed by Alberta (262) and 104 Ontario (25; values back-transformed since statistical analysis was performed on log-transformed variables; all pairwise comparisons significant at p < 0.01). In each province, differences in average ascospore levels were also observed among most years sampled, except 2018 and 2021 in Alberta and 2019 and 2020 in Manitoba (all other pairwise comparisons significant at p < 0.01; Figure 2). In all years in Alberta except 2019, mean ascospore levels varied by month, and August always had the greatest average ascospore levels. In Manitoba and Ontario the results were more varied, with fewer significant differences among months and mean maximum ascospore levels occurring in different months (Figure 2). 105 Figure 3.1: Daily ascospore numbers in each field surveyed in Alberta over the four years of data collection. The grey lines represent the number of daily ascospores for each individual field in Alberta included in this study. The blue line represents the (geometric) mean of all fields for each day. 106 Figure 3.2: Boxplots of the log(no. ascospores + 1) by month for each province and year in the present study. Statistical analysis is described in the results section. The boxes represent the first and third quartile, and the horizontal line is median. Vertical lines do not exceed 1.5*(interquartile range); dots outside of these lines represent outliers. 107 3.3.5 Correlations of ascospores among samplers and sampler types In 2018, three commercial fields in Alberta each had three Burkard samplers. Pearson correlations of log(ascospores + 1) among samplers within fields were all less than 0.50 except for one comparison with r = 0.81 (p < 0.0001). For other comparisons, the r-values ranged from 0.15 to 0.46, although not all comparisons were statistically significant. Samplers placed beside each other, with one sampler above the canopy and another sampler below the canopy, did not have stronger correlations with each other compared to correlations with samplers 40 m away (data not shown). In 2018, in two of the fields surveyed, the samplers below the canopy collected, on geometric average, 2 to 10 times the number of ascospores as those above the canopy, but in the third field the sampler below collected, on geometric average, half of the ascospores as the sampler above the canopy (Table 3.6). Table 3.6: Summary of average daily ascospore collections from spore samplers placed side-by-side, one above the canopy and one below, in three commercial dry bean fields in Alberta in 2018. Mean and standard deviation calculations were performed on the log10(number of ascospores per day + 1), and the data presented here have been back-transformed to represent the number of ascospores per day. Above Below Location mean sd mean sd Cranford 39.7 4.5 369.5 9.7 Taber 39.9 2.7 22.1 3.8 Vauxhall 85.6 5.6 157.6 5.4 In 2019, there were no significant correlations between two Burkard samplers in the same commercial field approximately 40 m apart or between the three Burkard samplers at LeRDC all within approximately 100 m of each other. In the research plot where a rotorod and Burkard sampler were placed beside each other, there was no correlation of ascospore counts (r = 0.02, p 108 = 0.9). Furthermore, there were no significant correlations between ascospore counts from samples collected by the Potato Growers Association network and any of the nearest dry bean fields. In 2020 and 2021, correlations among samplers were similarly low or inconsistent (data not shown). 3.3.6 Flower infection In 2019, flowers were collected on 28 days during the flowering period from July 16 to August 22 in the disease nursery at LeRDC. Percentage of petal infection ranged from 0 to 20%, and infection levels tended to increase over the course of the sampling period (Appendix Figure B.1). The relationship between the percentage of flowers infected and the number of ascospores per day was examined graphically, and there were no clear associations (Appendix Figure B.1). Statistically, there were no significant correlations between percentage of flowers infected and the number of ascospores per day or the log(number of ascospores per day + 1) or between percentage of flowers infected and rolling sums of ascospores from the seven previous days (data not shown). The final white mould incidence of plants in this plot was 3%. 3.3.7 Models of disease incidence In both cases of disease modeling (based on in-field weather data or based on provincial weather data), results were qualitatively similar whether the environmental variables were summarized over the July period or over the flowering period. Therefore, only correlations from the July weather data set are described here. Following Benjamini-Hochberg p-value adjustment for multiple tests of significance, there were no statistically significant correlations between disease incidence and any environmental variable in either the in-field or provincial weather stations 109 (Appendix Table B.1). In general, disease incidence tended to be negatively correlated with RH and soil water content and positively correlated with air and soil temperature. It should be noted that the range of these values for the data set was relatively small, and so mean air temperature ranged from 18.6 to 21.2 °C, mean soil temperature ranged from 18.4 to 20.7 °C, and mean RH ranged from 59.7 to 74.9%. Due to few data points in the in-field weather data set and high collinearity among some weather variables, only mean air temperature, mean RH, total precipitation, and mean log(number of ascospores + 1) were included as possible predictors in model selection. Models differed slightly between variables summarized over the July period or over the flowering period. When variables were summarized over the July period, all model selection methods (forward, backward, and stepwise) determined that mean air temperature was the only significant variable in this model (Table 3.7). In contrast, when variables were summarized over the flowering period, all model selection methods determined that both mean air temperature and mean RH were significant variables in the model (Table 3.8). However, once temperature was accounted for, RH contributed relatively little to the model (RH coefficient of 0.06 vs temperature coefficient of 0.99). Table 3.7: Coefficients of the final generalized linear model as assessed by stepwise selection. The model predicted the proportion of white mould incidence based on environmental variables as measured by in-field weather stations. Weather variables in this model represent values from the month of July (i.e. meanT is the mean air temperature in July). Estimate St. Error z p Intercept -14.53 1.25 -11.64 < 0.0001 meanT 0.69 0.06 10.83 < 0.0001 110 3.4 Discussion Overall, white mould of dry bean was prevalent in Alberta throughout the study period, but incidence and severity varied significantly by year, market class, and field. This finding corroborates reports that white mould is a constant and pervasive threat to dry bean production in Canada. Disease levels from the present surveys are much higher than those reported in bean in Australia (S. Jones et al., 2012) or soybean in the USA (Mila et al., 2004), but disease trends are in-line with Sclerotinia stem rot surveys of canola in Alberta over the past few years (Harding et al., 2019, 2020, 2021) and earlier surveys of white mould in dry bean in the same region (Harding et al., 2017; Kim et al., 2017). The heterogeneous spatial and temporal intensity of diseases caused by S. sclerotiorum highlights the difficulties in predicting its occurrence in a given location and year. Indeed, in our models that used data from in-field weather stations to predict the disease level of a crop, mean air temperature was the only factor that significantly added to predicting disease incidence. In contrast to others who found that air temperature was negatively associated with disease incidence (Fall, Boyse, et al., 2018; Foster et al., 2011; Mila et al., 2004), we found that air temperature was positively associated with disease incidence. One explanation for this discrepancy is that mean air temperatures in this study had a relatively small range (18.6 to 21.2 °C), which is still well within the optimal growing conditions for this pathogen (Uloth et al., 2015). Of note, the inclusion of ascospore data did not improve disease prediction models, suggesting that pathogen presence is not a limiting factor of disease in this agroecosystem. Neither environmental data collected by the provincial weather stations nor environmental data collected by the in-field weather stations were correlated to final disease 111 incidence levels. In the framework of the disease triangle, in which a pathogen, conducive environment, and susceptible host must all be present for the development of disease, these findings may have several interpretations. First, perhaps ascospore levels do affect final white mould levels in some fields, but we were unable to detect this effect because inoculum level was not a limiting factor for disease in the fields we surveyed. Including ascospore numbers into predictive models did not increase model accuracy, and therefore may not be a main driver of disease expression, at least for some fields in the region (Table 3.7). Second, there may not have been enough variation in environmental conditions to discern what the driving conditions were. Increasing the number of in-field weather stations could help address this issue. Third, these findings may indicate that we did not measure the appropriate environmental variable(s) that are relevant to white mould expression in this agroecosystem (e.g. RH in the canopy rather than above the canopy) or that we did not summarize the variables at the biologically appropriate time scale. Fourth, perhaps we did measure the correct environmental variables, but environmental conditions are not the main limiting factor for white mould development in dry bean fields of southern Alberta; instead, other factors such as cultivar, cropping history, soil type, and plant spacing could be more important contributing factors to disease. In this study, we found a strong effect of market class on white mould levels (Table 3.5), discussed below, but did not measure other agronomic traits. It is possible that each market class or cultivar has a different interaction with the pathogen and environment, and so building models that pooled all market classes together may have masked individual effects. However, we did not have enough data to build models individually for each market class. Market class was significantly associated with white mould levels, showing that plant genetics is an important tool in mitigating white mould. Pinto beans had, on average, the highest 112 white mould incidence, and the disease incidence in these fields spanned from 0 to 100% (Table 3.5). For Great Northern beans, most fields fell below a 20% incidence level, but there were three clear outliers with much greater disease incidence than the others. These outliers could be a result of the different agronomic factors among fields or different cultivars planted, although these data were not collected. Daily mean ascospore levels ranged from a low of 25 in Ontario to a high of 396 in Manitoba. While much of these data come from irrigated disease nursery plots where one might expect the occurrence of ascospores to be higher than surrounding unirrigated fields, white mould remains a persistent problem even in unirrigated commercial fields (D. L. McLaren et al., 2019). Therefore, inoculum of S. sclerotiorum may not be the most important limiting factor in disease development in most dry bean contexts. Instead, management strategies should focus on factors such as genetics and microclimate management, including irrigation (Twengström, Köpmans, et al., 1998), row spacing (Saindon et al., 1993; Vieira et al., 2010), and canopy architecture (Haas & Bolwyn, 1972; Miklas et al., 2013). Ascospores were present at consistent levels throughout the growing season every year in all three provinces, which seems contradictory to the monocyclic lifecycle of S. sclerotiorum. However, this is in-line with similar studies of S. sclerotiorum dispersal in seed alfalfa crops in the same region (J. Reich et al., 2017) and canola in England (Rogers et al., 2009). In contrast, in canola fields, others found that spore numbers increased dramatically once the canopy closed and flowering began (Qandah & del Río Mendoza, 2011). There was no clear difference in spore release levels or patterns among the provinces andwork is currently under way to determine environmental drivers of ascospore release through more refined statistical and machine learning methods. 113 Correlations of ascospores between nearby samplers were overall extremely low, highlighting several challenges that must be overcome before these methods are used routinely in disease monitoring and forecasting in this agroecosystem. Few studies have attempted to assess how representative an air sampler is of the region it is sampling, and representativeness may vary by pathogen, crop type, or sampler type. In the present study, there was no, or inconsistent, correlation of daily ascospore numbers among nearby samplers, suggesting that the spatial distribution of S. sclerotiorum ascospores is highly heterogeneous. To our knowledge, there is no other published literature to compare this finding to, but this ascospore distribution does parallel the often patchy distribution of apothecia and white mould in fields (Boland & Hall, 1988a, 1988b). There was also no correlation between the Burkard and rotorod sampler, which could reflect their preferential collection of different spore types (W. Chen, Hambleton, et al., 2018; West & Kimber, 2015). The latter reference indicates that Burkard samplers can collect particles larger than 2 µm, whereas rotorod samplers may only collect particles larger than 10–20 µm. Because the average size of an ascospore is 10–14 µm (Willetts & Wong, 1980), the rotorod samplers may be undersampling these spores. In contrast, rotation-type samplers have been useful in collecting Botrytis squamosa spores for the prediction of onion leaf blight epidemics (Carisse et al., 2009), perhaps because of the larger nature of these spores (22 µm) (Chilvers & du Toit, 2006). Given the findings of this study, sampling the air for ascospores currently seems unlikely to contribute meaningful insights into the development of white mould in dry bean fields. This conclusion may be due, in part, to the many unknowns associated with air sampling, as described above. More work needs to be done to assess the spatial scales at which a single sampler can accurately reflect the field environment. For instance, in strawberry, one sampler per field was 114 deemed enough to collect a representative number of Podosphaera aphanis spores (Van der Heyden et al., 2014). However, for a pathogen like S. sclerotiorum, which typically has a patchy distribution (Boland & Hall, 1988a, 1988b) and whose spores often have a sharp dispersal gradient (Bourdôt et al., 2001), collection of ascospores may be a matter of luck – placing it in the right area of the field that has apothecia emerging. In the present study, correlations among samplers placed within the same field were almost always low in every year. Furthermore, the appropriate height above ground for a sampler remains an unknown. In wheat, Blumeria graminis spores were found at much greater levels within the canopy (0.6 m above ground) than above the canopy (1.2 m) (Cao et al., 2012).In the present study, more ascospores were collected below the canopy in two of three fields where samplers were placed both above and below the canopy, but in the third field the effect was reversed (Table 3.6). Airborne spore levels of Botrytis cinerea have been closely linked to flower infection in strawberry production systems (Xu et al., 2000), but in the present study we did not observe any relationship between airborne ascospores of S. sclerotiorum and infection of bean flowers. This discrepancy may be due to differences in sampling methodologies, a result of surface disinfesting the flowers before plating them and therefore potentially removing ascospores that had not fully adhered to the petals, or because we could only conduct this portion of the study in 2019, which was also the year with the lowest white mould levels in the region and the disease nursery. The day-to-day variability of flowers infected by S. sclerotiorum was substantial, but this is perhaps unsurprising since infection levels have been shown to vary depending on time of day collections are done or location of the flowers within the canopy (Turkington et al., 1991; Turkington & Morrall, 1993). Nevertheless, during the main flowering period (July 20 to August 10), flower 115 infection was predominantly very low (all dates but one < 5% incidence) and the final white mould incidence in the plot was also low (3%). In fields where disease levels were high (> 30% incidence), fungicide application significantly reduced white mould by about 50%, but in fields where disease levels were low (< 20% incidence), fungicide application had no effect on white mould levels. The overall impact of this reduction on yield and economic return to the grower remains unclear and depends on the market value of beans and the cost of fertilizer application: reduced disease incidence is not always associated with significant increases in yield (Demissie et al., 2022; Kurle et al., 2001; Lehner et al., 2017; Willbur, Mitchell, et al., 2019), and yield increases alone may not result in economic profits to growers (Webster et al., 2022; Willbur, Mitchell, et al., 2019). To some extent, lack of clear associations between white mould and environmental variables may be a result of small sample sizes. In 2020 and 2021, disease surveys were more expansive, but there were still only a few in-field weather stations. And, while we collected data on market class, finer scale information on genetic lines could be an important predictor of disease. One approach that could address these shortcomings is to employ remote sensing methods to quantify disease occurrence at the field and regional levels (Zhang et al., 2019). Remote sensing could identify those fields with particularly high levels of white mould in the whole region, and subsequent surveys could focus on finding key differences between fields with high disease levels vs those with low disease levels. In addition, archival Landsat imagery (U.S. Geological Survey) could be used along with historical weather records to assess the relative importance of seasonal weather patterns on disease development at a regional scale. Predicting the intensity of white mould epidemics in a particular field and year remains an important priority for the sustainable management of dry bean crops. However, reliable 116 models remain elusive due to spatial variation within and between fields and the seemingly sporadic nature of disease development. A large-scale, provincial disease prediction tool under this scenario may be more difficult to develop and implement due to the variable microclimates produced in each field due to irrigation amounts, as was noted by the poor correlation of precipitation between in-field and provincial weather stations (Table 3.2). At the same time, ascospores were quantified throughout the study period in all provinces, indicating that S. sclerotiorum inoculum is a constant threat to disease development in dry bean fields. In this study we were constrained, in part, by the number of fields we could intensively sample for weather and ascospores, and so future work could benefit from a larger sample size. In addition, because market class was an important predictive variable in disease levels, future work could gather more data on exact genetic lines and growers’ management practices. These variables may be important determinants of white mould development in Alberta and other dry bean-growing provinces. 117 Chapter 4: Predicting airborne ascospores of Sclerotinia sclerotiorum through machine learning and statistical methods 4.1 Introduction White mould, caused by the fungal pathogen Sclerotinia sclerotiorum (Lib.) de Bary, is an important biological constraint to the production of dry bean (Phaseolus vulgaris L.) across Canada (Boland & Hall, 1987; Harding et al., 2017). The pathogen infects plants primarily through airborne ascospores that land on senescing plant tissue, germinate, and invade the plant cell walls (Bolton et al., 2006; Willbur, McCaghey, et al., 2019). In southern Alberta, dry bean fields are irrigated and so the environmental conditions for white mould development are always high. As a result, disease incidence levels can reach up to 100% in some fields (J. D. Reich et al., 2019). Predicting epidemics caused by S. sclerotiorum has remained notoriously difficult across a range of host crops (J. D. Reich & Chatterton, 2022). As a result, growers of dry bean in southern Alberta typically apply two to three fungicides every season as a preventative measure, regardless of perceived or actual disease risk. Previous approaches to predicting diseases caused by S. sclerotiorum have often focused on predicting apothecia presence as an indication of pathogen intensity (Willbur, Fall, Bloomingdale, et al., 2018) or focused on weather and agronomic factors associated with final disease outcomes (Mila et al., 2004; Twengström, Sigvald, et al., 1998). One potential drawback of these approaches is that they may not allow for real-time evaluation of the risk of disease, since by the time apothecia or disease are observed or predicted in a crop, it is too late to make a management decision (i.e. fungicide application). In this context, preventative sprays may be logical as a risk reduction strategy. 118 In some pathosystems, monitoring the air for airborne inoculum in real-time has been useful in informing efficacious disease management. In rust (Puccinia sp.) diseases of wheat and in late blight (Phytophthora infestans) of potato, early detection of airborne spores or sporangia can alert growers when the pathogen is present and could therefore prevent unnecessary early season fungicide use (Aboukhaddour et al., 2020; Araujo et al., 2021; Rosete et al., 2021). For blackleg (Leptosphaeria maculans) of canola, a regional warning system based on airborne spore levels alerts growers when the risk of disease is high (Jedryczka et al., 2008, 2013). A similar system has been developed to alert growers of several diseases of brassicas in the United Kingdom (https://www.syngenta.co.uk/brassica-alert). At finer scales, regional networks of spore collectors have been important in managing powdery mildew (Podosphaera aphanis) of strawberry (Van der Heyden et al., 2014) and botrytis leaf blight (Botrytis squamosa) of onion (Carisse et al., 2009). These systems rely on frequent collection and analysis of samples and therefore provide real-time information on the most judicious and efficacious use of fungicides based on airborne inoculum levels. A complimentary or alternative approach to frequent sample collection and analysis is to develop models that can predict airborne pathogen levels based on environmental characteristics. The benefits of this approach are reduced labor and cost, and the ability to forecast several days into the future based on weather forecasts (D. L. Smith et al., 2018). Nevertheless, model development still requires a large, robust data set and statistical methods that can uncover potentially complex or nonlinear relationships among environmental variables and the predicted outcome. Recently, in addition to more traditional linear regression methods often used in plant pathology, machine learning (ML) algorithms have gained attention as useful tools for prediction. 119 Often the best statistical or ML model depends on the characteristics of the data set and underlying assumptions of data distributions, and therefore there may be no a priori best approach to model fitting. In the case of airborne ascospores of S. sclerotiorum, complex biological and environmental factors are involved with ascospore liberation and dispersal, making this situation a good candidate for comparing several modeling techniques. Therefore, the objectives of this research were to: (1) assess environmental drivers of ascospore release in S. sclerotiorum and (2) compare statistical and machine learning methods for predicting daily ascospore levels in commercial dry bean fields. 4.2 Methods 4.2.1 Spore sampling The collection of this spore data is described in Chapter 3. Briefly, commercial dry bean fields in southern Alberta were monitored in four growing seasons (2018 to 2021). In monitored fields, a Burkard 7-day volumetric cyclone samplers (Burkard Manufacturing, UK) was used to collect daily samples (0001h to 2359h). The Burkard sampler deposits aerosols in a 1.5 mL Eppendorf vial and DNA was extracted directly from the vial using a rapid DNA extraction method as described in Chapter 3. Ascospores of S. sclerotiourum were quantified as described in Reich et al. (2017), including the use of an external endogenous control DNA that was quantified at the same time as the S. sclerotiorum DNA. This acted as a control to determine whether the PCR amplification failed or succeeded. 120 4.2.2 Data quality control For data involving ascospore counts from qPCR, preliminary data cleaning was performed. Histograms of Ct values for the exogenous internal control were examined and generally followed a normal distribution. Any sample for which the internal control did not amplify, or for which the Ct value was outside the normal distribution was removed from subsequent analysis. 4.2.3 Weather data At each spore sampling site, a WatchDog Mini (model 2450) weather station (Spectrum Technologies Inc., Aurora, USA) was installed beside the Burkard spore sampler. Weather stations recorded hourly air temperature (abbreviation: T, unit of measurement: °C), relative humidity (RH, %), precipitation (Precip, mm), soil moisture (SoilWC, % v:v), and soil temperature (SoilT, °C). Vapor pressure deficit (VPD, mmHg) and dew point (DP, °C) were calculated from the raw data. 4.2.4 Predictor variables Weather data was summarized at daily levels because that was the frequency of ascospore collection and previous work suggests that spore release may be dependent on minor fluctuations throughout the day (Archer & Yuan, 2004; J. Reich et al., 2017). As there is little consensus on the biological and environmental drivers underlying ascospore release under field conditions, the environmental variables were summarized in several ways: First, one group of predictor variables comprised daily mean, minimum, and maximum values of T, RH, VPD, Precip, DP, SoilWC, and SoilT. To account for the potential influence of the previous day on 121 spore release, another group of predictor variables comprised all variables mentioned previously at a time lag of one day. Also included in this group of predictor variables was the number of ascospores released the previous day (logSs_t1), as some airborne fungal pathogens (e.g. Botrytis cinerea) have been shown to have high temporal correlations in spore release. Finally, to account for the possibility that spore release is caused by sudden or dramatic changes in environmental conditions, a final group of predictor variables was calculated as the differenced values between one day and the day previous for each variable (e.g. the difference of the maximum temperature on day t and the maximum temperature on day t – 1). Several other predictor variables were calculated: one binary variable indicating whether it rained the day of ascospore collection, and two variables that summarized the maximum change in RH over 2 or 3 hour periods on the day of ascospore collection (J. Reich et al., 2017). Any variables that had no variation in the values were removed from the analysis (e.g. the minimum precipitation on any given day was always 0 because these values were recorded every hour and no day had continuous precipitation). Altogether, 72 predictor variables were considered for model development. 4.2.5 Data preparation for modelling To assess simple associations between the number of ascospores and environmental conditions, Pearson correlations between the log(no. ascospores + 1) and each environmental predictor were performed. Correlations revealed highly heterogeneous relationships between ascospores and environmental variables among provinces (Appendix Table C.1). This finding suggested that different provinces may require different models for ascospore prediction based 122 on their unique climate. Therefore, modeling analysis was focused on the Alberta data set which was the most complete. 4.2.6 Variable (feature) selection Variable selection methods can improve model accuracy by obviating issues associated with multicollinearity and overfitting (Cai et al., 2018). We assessed seven approaches to variable selection in the ML models to determine which methods performed the best. The variable sets that we tested were: all variables, variables selected from the LASSO algorithm, variables with a low (< 10) variance inflation factor (VIF; algorithm provided by Beck (2013)), variables with low correlations (|r| < 0.7) with all other variables, variables from same day, variables from previous day, and variables that represented differenced values. 4.2.7 Modeling approaches Both machine learning and classical statistical methods were assessed in each of regression and classification approaches. For the regression approaches, the log(number of ascospores + 1) was modeled as the response variable. For the classification approaches, several daily ascospore threshold cutoffs were modeled as the response, since there is currently little understanding on what the biologically meaningful threshold for inoculum levels to disease levels is. For the classification thresholds, a success (1) was defined as a daily ascospore level greater than each threshold of 100, 200, 500, and 1,000 ascospores per day, and 0 otherwise. For model validation, a test data set of one field per year (n = 4 field-years) was arbitrarily chosen to be removed from model development, since the purpose of these predictive models is to predict ascospore loads in new locations and years. Model training was therefore performed on the 123 remaining field-years (n = 17). In total, the training data set had 855 data points and the test data set had 191 data points. Because there were many data points with missing soil temperature and moisture readings, they were not included in the model development. 4.2.8 Machine learning models Machine learning models were developed in Python (v.3.8.5) (Python Software Foundation, n.d.) and the Scikit-learn package (Pedregosa et al., n.d.). For regression approaches, random forests (RF_reg) were used. For classification approaches, methods used were random forests (RF_class), logistic regression (LR), and support vector machines (SVM). Models were developed using a pipeline that included scaling (StandardScaler()) and imputing (IterativeImputer()) for numeric variables and onehot encoding (OneHotEncoder()) and imputing (SimpleImputer()) for categorical variables. This pipeline was performed for every iteration of the 5-fold cross validation. Hyperparameter tuning for the ML algorithms was performed via 5-fold cross-validation for each model within the RandomizedSearchCV function for RF models and with the GridSearchCV function for LR and SVM models. For RF, the hyperparameters considered were: the number of trees per forest, the depth of the forest, and (for classification only) whether the classes were balanced or not. For LR, the hyperparameters were: a regularization parameter (C) and whether the classes were balanced or not. For SVM, the hyperparameters were: C (as for LR), the number of features used (gamma), and whether the classes were balanced or not. Each model was developed to optimize the area under the receiver operator characteristic curve (AUC) which accounts for the proportion of true positive and false negative predictions. 124 4.2.9 Statistical models Statistical models were developed in R (v.4.0.3, R Core Team (2020)). The set of variables selected by the VIF algorithm consistently performed best in ML models, and so this group of variables was used for statistical models. Generalized linear models (GLM) were used for both regression and classification approaches. For regression, the log(number of ascospores + 1) was modelled directly with an identity link function. For classification, models were developed with a logit link function for each of the thresholds described above. For each model, variables were selected in forward, backward, and stepwise methods. For statistical methods, imputation was not included in the workflow and so incomplete cases were removed from analysis, which removed ~10% of data points from the analysis. 4.2.10 Evaluation of models All models were developed on the training data set and evaluated on the test data set, therefore diagnostics focus on model performance on the test set. For regression approaches, models were evaluated with r2 (calculated as the square of the Pearson correlation between actual and predicted values) and mean-squared error (MSE). For classification approaches, models were evaluated with accuracy, sensitivity (recall), specificity, precision, F1 score, and AUC. The classification metrics were calculated from a confusion matrix as follows: 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃 + 𝑇𝑁𝑇𝑃 + 𝐹𝑃 + 𝑇𝑁 + 𝐹𝑁 𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 = 𝑇𝑃𝑇𝑃 + 𝐹𝑁 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 = 𝑇𝑁𝑇𝑁 + 𝐹𝑃 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃𝑇𝑃 + 𝐹𝑃 125 𝐹! 𝑠𝑐𝑜𝑟𝑒 = 2 × 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 Where TP = number of true positives, TN = number of true negatives, FP = number of false positives, and FN = number of false negatives. 4.3 Results 4.3.1 Summary of ascospores across provinces, weeks, months Overall, ascospores were present throughout the growing season in all provinces. Ascospore levels varied by field, province, week, and year (Figure 4.1). These results are presented and discussed in further detail in Chapter 3. 126 Figure 4.1: Boxplots of the daily log(no. ascospores +1) collected during the study period, stratified by year and province. Week refers to the week of the year, so Week 30 corresponds to approximately the last week of July (23 to 29). 4.3.2 Correlations The fine scale analysis of correlations by year and province are given in Appendix Table C.1. Overall, in all years and provinces, the log(number of ascospores + 1) was positively correlated with the number of ascospores from the previous day (range of Pearson’s r: 0.15 to 0.6, depending on the year and provinces; p < 0.001 for most provinces and years). For other variables, the direction and significance of correlations differed among provinces. Within province, the direction (positive or negative) of correlation often was the same but the 127 magnitude, and sometimes statistical significance, varied by year. For example, in Alberta, daily mean, maximum, and minimum RH were negatively associated with ascospore numbers in 2018, 2019, and 2021, but in 2020 these relationships were non-significant. In Manitoba, there was no association between measures of RH and daily ascospore levels, and in Ontario the direction was positive and significant in only 2020. Similar variability was observed in all environmental correlations. When data were pooled across years and analyzed by province, some trends emerged. In Alberta (Table 4.1), ascospores were positively associated with ascospores from the previous day; max and mean T on the same day and previous day; VPD variables on the same day and previous day; and the difference in RH from the previous day. Ascospores were negatively associated with RH variables on the same day and mean RH from the previous day; Precip on the same day and previous day; DP on the same day; and SoilWC variables on the same day and previous day. Daily ascospores were not correlated with differenced variables, which represent changes in the environment. Table 4.1: Top 10 Pearson correlations of daily log(number of ascospores + 1) with environmental variables for combined data from 2018-2021 in Alberta. Variablea r p logSs_1d 0.361 < 0.001 MeanVPD 0.120 < 0.001 MeanRH -0.118 < 0.001 MaxVPD_1d 0.118 < 0.001 MaxT_1d 0.116 < 0.001 MeanVPD_1d 0.115 < 0.001 MaxVPD 0.101 < 0.001 MinRH -0.100 < 0.001 MinDP -0.098 < 0.001 MinRH_1d -0.098 < 0.001 128 a For variables, logSs_t1 = the log(number of ascospores + 1) from the previous day; VPD = vapor pressure deficit; RH = relative humidity; T = air temperature; and variables with a ‘_1d’ represents the value from the previous day. In Manitoba (Table 4.2), SoilT data was unavailable for the duration of this study. Daily ascospore levels were positively correlated with ascospores from the previous day; SoilWC variables on the same day and previous day; Precip from the same day; and minVPD on the same day and previous day. Ascospores were negatively correlated with differences in RH on the same day and from the previous day. Ascospores were not correlated with T, RH, or differenced variables. In Ontario (Table 4.3), daily ascospore counts were positively correlated with ascospores from the previous day and minVPD, although the statistical significance of the latter was marginal (p = 0.047, unadjusted p-value). Ascospores were negatively correlated with DP variables on the same day and previous day; Precip on the same day; SoilT variables on the same day and previous day; and maxRH and maxVPD on the same day and previous day. Ascospores were not correlated with T, RH, or differenced variables. Table 4.2: Top 10 Pearson correlations of daily log(number of ascospores + 1) with environmental variables for combined data from 2019-2021 in Manitoba. Variablea r p logSs_1d 0.632 < 0.001 MinWC_1d 0.224 0.001 MeanWC_1d 0.204 0.004 MaxWC_1d 0.175 0.013 MinWC 0.171 0.014 DiffRH_0d -0.155 0.004 MeanWC 0.153 0.027 DiffRH_1d -0.142 0.008 MinVPD_1d 0.130 0.014 MaxWC 0.127 0.067 129 a Variables are as for Table 1, with the addition of: WC = soil moisture (water content) and variables that have a ‘Diff’ preceding them represent differenced values (e.g. ‘DiffRH_0d’ represents the difference between min and max RH of the same day whereas ‘DiffRH_1d’ represents the difference between the min and max RH of the previous day). Table 4.3: Top 10 Pearson correlations of daily log(number of ascospores + 1) with environmental variables for combined data from 2019-2021 in Ontario. Variablea r p logSs_1d 0.304 < 0.001 MinDP_1d -0.231 < 0.001 MeanDP_1d -0.194 0.001 MinSoilTemp -0.181 0.001 MinDP -0.161 0.007 MeanSoilTemp -0.158 0.003 MeanDP -0.147 0.013 MaxSoilTemp -0.131 0.013 MaxDP_1d -0.130 0.030 MinSoilT_1d -0.130 0.014 a Variables are as for Tables 1 and 2, with the addition of: SoilTemp = soil temperature; DP = dew point. 4.3.3 Machine learning models Comparing variable selection methods revealed that selecting variables based on the VIF algorithm performed best in 7 of the 12 scenarios (3 ML methods x 4 ascospore thresholds), and, as a result, only these results are presented and discussed. However, it should be noted that variable selection methods using LASSO or variables with low correlations resulted in very similar outcomes as for the VIF method, and these three methods vastly outperformed the other variable data sets, including the data set with all predictor variables (data not shown). 130 For RF_Reg, the R2 of the predicted values on the test set was 0.29 (Table 4.4) and the most important variable in this algorithm was logSs_t1. This variable was over 25 times more important than the next most important variable in this model (Figure 4.2). All other variables included in the RF_Reg model comprised comparatively little (< 15%) of importance. Table 4.4: Evaluation metrics for the GLM linear regression models and ML random forest regression model. Deviance and AIC are those for the training data set in the GLM model development and R2 and MSE are diagnostics on the test data set. Deviance AIC R2 MSE ML (Random forest) Null - - -0.063 0.692 VIF-variable set - - 0.285 0.466 GLM Null 465.44 1784.1 0 0.742 Backward 321.23 1519.4 0.294 0.504 Forward/Stepwise 325.24 1520.8 0.294 0.503 131 Figure 4.2: Importance of the top 10 variables included in the machine learning models implemented in this study. The top panel represents variable importances in the RF_Ref model, and the bottom three panels represent importances in the classification approaches (RF_Class, LR, and SVM) for only the threshold of 500 ascospores per day. Results for all thresholds were qualitatively similar. 132 For classification approaches, RF_Class, LR, and SVM models had similar AUC scores across thresholds (Table 4.5). However, they varied more in their F1 scores, which is a combination of sensitivity and precision. At thresholds of 100, 200, and 500 ascospores per day, SVM had higher sensitivity and lower precision than RF_Class and LR, but these trends were reversed at a threshold of 1,000 ascospores per day. For specificity, all models differed greatly at thresholds of 100 or 200 ascospores per day but were very similar for thresholds of 500 and 1,000 ascospores per day. In all models, the most important variable was logSs_t1 from the day before, and, as with regression approaches, this variable was dramatically more important (6 to 8 times more important) than the next most important feature (Figure 4.2). 133 Table 4.5: Model diagnostics for three ML classification algorithms (RF = random forests, LR = logistic regression, SVM = support vector machines) and four daily threshold values (100, 200, 500, and 1,000) of ascospores. These diagnostics are for models developed with the VIF-selected variables. Accuracy Sensitivity Specificity Precision F1 AUC y100 Null 0.613 1.000 0.000 0.613 0.760 0.500 RF 0.649 0.607 0.716 0.772 0.679 0.703 LR 0.665 0.607 0.757 0.798 0.689 0.729 SVM 0.607 0.940 0.081 0.618 0.746 0.725 y200 Null 0.455 1.000 0.000 0.455 0.626 0.500 RF 0.675 0.632 0.712 0.647 0.640 0.761 LR 0.723 0.644 0.788 0.718 0.679 0.765 SVM 0.644 0.793 0.519 0.580 0.670 0.769 y500 Null 0.712 0.000 1.000 NaNa NaN 0.500 RF 0.754 0.436 0.882 0.600 0.505 0.772 LR 0.749 0.382 0.897 0.600 0.467 0.766 SVM 0.754 0.473 0.868 0.591 0.525 0.759 y1000 Null 0.775 0.000 1.000 NaN NaN 0.500 RF 0.796 0.535 0.872 0.548 0.541 0.740 LR 0.796 0.093 1.000 1.000 0.170 0.755 SVM 0.801 0.233 0.966 0.667 0.345 0.750 a Not possible to calculate due to division by zero. 4.3.4 Statistical models In all cases, the forward and stepwise model selection procedures converged on the same model. In general, these models tended to be more parsimonious than, and had similar deviance and AIC as, those determined by backward model selection procedures (Tables 4.5 to 4.8). For regression approaches, models were highly similar for all diagnostics, with R2 values of 0.29 and 134 MSE of 0.5 (Table 4.4). The variable with the most influence in the model was the logSs_t1, with approximately 5 times more weight in the model than the next important variable (Table 4.6). 135 Table 4.6: Coefficients for variables included the best regression models as determined by backward, forward, and stepwise procedures using generalized linear models in R. The predicted variable was the daily log(no. ascospores + 1). Model diagnostics are given in Table 4.4. Refer to Tables 4.1 and 4.2 for a description of the variable names. Intercept logSs_t1 DiffMinT DiffMeanRH MaxRH_1d DiffRH_1d TotalPrecip DiffMaxDP MaxDP_1d MaxVPD_1d DiffMaxVPD Backward 1.386 0.527 -0.018 -0.008 -0.007 0.005 -0.013 0.025 0.016 Forward/Step 1.083 0.523 -0.013 0.010 0.008 136 Table 4.7: Coefficients for variables included the best logistic regression models as determined by backward, forward, and stepwise procedures in R. Successes were defined as 1 = the number of ascospores per day were greater than the shown threshold (100, 200, 500, or 1,000 ascospores per day) and 0 otherwise. Model diagnostics are given in Table 4.8. Refer to Tables 4.1 and 4.2 for a description of the variable names. Threshold Intercept logSs_t1 DiffMinT DiffMaxVPD MaxVPD_1d TotalPrecip Precip_1d DiffMaxDP MinT_1d DiffMeanRH MaxRH_1d MaxDP_1d DiffRH_1d RainYN1 y100 Backward -2.616 1.205 -0.059 0.045 0.067 -0.037 0.019 0.047 For/Step -2.357 1.165 0.044 0.060 -0.030 y200 Backward -1.105 1.148 -0.095 0.041 -0.023 0.080 -0.083 -0.021 -0.019 0.048 For/Step -2.998 1.166 -0.061 0.047 -0.023 0.070 -0.017 y500 Backward -3.035 1.536 -0.021 -0.019 0.011 -0.342 For/Step -4.584 1.536 0.024 -0.018 -0.335 y1000 Backward -3.175 1.594 -0.056 -0.018 -0.017 -0.401 For/Step -5.212 1.603 -0.012 -0.450 137 For classification approaches, as with the linear regression, the forward and stepwise models tended to be more parsimonious than the backward selection models (Table 4.7), and, within spore threshold, all models had similar deviance and AIC values (Table 4.8). The variables included in each model varied by threshold and selection procedure, but logSs_t1 was the only variable included in all models (Table 4.7). The relative weight of this variable was up to approximately 10 to 40 times that of the next important variables in the model. The AUC values for all thresholds and models did not vary greatly (range from 0.74 to 0.78) but the F1 scores did vary substantially: at thresholds of 100 or 200 ascospores per day, the F1 values ranged from 0.69 to 0.74, but at thresholds of 500 or 1,000 ascospores per day, the F1 value ranged from 0.41 to 0.60, depending on the model and threshold (Table 4.8). These differences in F1 scores were primarily the result of greater sensitivities at lower thresholds, since precision remained relatively constant (~0.62) across all models and data sets. sensitivity and precision – both metrics of predicted positive accuracy – at higher thresholds. However, specificity (the proportion of true predicted negatives) declined at these higher thresholds, which accounts for the relatively stable AIC value across all threshold values. 138 Table 4.8: Model diagnostics for three model selection methods (backward, forward, and stepwise) and four daily threshold values of ascospores. Models were developed using generalized linear models and a logit-link, where successes were defined as 1 = the number of ascospores per day were greater than the shown threshold (100, 200, 500, or 1,000 ascospores per day) and 0 otherwise. Deviance and AIC are those for the training set and other columns represent diagnostics on the test set. Refer to Table 4.7 for model specificiations. Deviance AIC Accuracy Sensitivity Specificity Precision F1 AUC y100 Null 816.2 818.2 0.602 1.000 0.000 0.602 0.752 0.500 Backward 690.6 706.6 0.626 0.903 0.206 0.633 0.744 0.736 Forward/Step 696.2 706.2 0.602 0.903 0.147 0.616 0.732 0.743 y200 Null 997.6 999.6 0.480 1.000 0.000 0.480 0.648 0.500 Backward 857.5 877.5 0.673 0.768 0.584 0.630 0.692 0.785 Forward/Step 861.8 875.8 0.667 0.780 0.562 0.621 0.692 0.782 y500 Null 1027.3 1029.3 0.690 0.000 1.000 NAa NA 0.500 Backward 838.3 850.3 0.754 0.547 0.847 0.617 0.580 0.777 Forward/Step 839.2 849.2 0.766 0.566 0.856 0.638 0.600 0.782 y1000 Null 863.8 865.8 0.754 0.000 1.000 NA NA 0.500 Backward 702.3 714.3 0.778 0.310 0.930 0.591 0.406 0.770 Forward/Step 705.9 713.9 0.795 0.333 0.946 0.667 0.444 0.776 139 4.3.5 Comparison of ML and statistical approaches For regression, both ML and statistical models had similar R2 (~0.29) and reductions in MSE (reduced by 0.23). All models found logSs_t1 as the most important predictor, although the GLM determined by forward and stepwise methods was the most parsimonious of all models, with TotalPrecip, MaxVPD_1d, and DiffMaxVPD the three other significant variables in the model. Diagnostics like AUC and F1 were similar for classification models with both ML and statistical approaches; however, statistical approaches had greater sensitivity (a measure of true positives) and lower specificity (a measure of true negatives) than ML approaches, especially at the lower thresholds of 100 and 200 ascospores per day (Tables 4.6 and 4.8). Therefore, there may be a trade-off between these approaches depending on which is more important to predict accurately: true positives or true negatives. 4.4 Discussion Overwhelmingly, the most important factor associated with daily ascospore counts across all provinces and years, and in all models, was the number of ascospores released during the previous day. Given the strong temporal autocorrelation of spore release, time series models were considered for this data set, but were abandoned due to the amount of missing data and relatively few available points (typically < 50) for constructing time series models. Nevertheless, this approach could be a good way forward (Damialis & Gioulekas, 2006; Stephen et al., 1990; Xu et al., 2000). The addition of environmental variables in the models contributed comparatively little to the model predictions (Tables 4.5 and 4.7, Figure 4.2), suggesting that for an ascospore-prediction network, continual quantification of airborne ascospores would be 140 necessary to enable reliable predictions into the near future. Indeed, the model predictions on the test data set relied on having information on the number of ascospores released the previous day. Interestingly, this strong autocorrelation was detected in all years and all provinces, indicating that, while microclimatic factors contributing to ascospore liberation may vary by location (discussed below), the strong autocorrelation of daily ascospore quantities could be an inherent quality of this fungus. The fact that the direction, magnitude, and significance of ascospore levels varied by year and province suggests that S. sclerotiorum has different limiting environmental factors in different regions. Alberta ascospores were positively correlated with factors related to dryness (e.g. VPD) and negatively correlated with factors related to moisture (e.g. RH and Precip). In contrast, in Manitoba, ascospores were positively associated with moisture (e.g. SoilWC and constant RH) and in Ontario, ascospores were negatively associated with higher temperatures (e.g. SoilT and DP). These contrasting patterns, especially between Alberta vs Manitoba and Ontario, may be caused by the presence of irrigation in all commercial dry bean fields in southern Alberta compared to dryland fields in Manitoba and Ontario. To corroborate this hypothesis, previous work has found that irrigation modifies the microclimate in such a way that associations with other variables (e.g. air temperature) can be reversed between irrigated and non-irrigated plots (Willbur, Fall, Bloomingdale, et al., 2018). Furthermore, once released from an apothecium, ascospores can remain dormant for up to several weeks under dry, cool conditions (Abawi & Grogan, 1975; Clarkson et al., 2003); on average, ascospores survive six days under field conditions (Caesar & Pearson, 1983). Therefore, in Alberta, ascospores being released during hotter or dryer periods may make sense, given that the ascospores can travel further on air currents during these periods and then remain dormant until moisture arrives, 141 which, due to irrigation, occurs frequently. In Manitoba and Ontario where moisture may be a limiting factor, ascospores release could be more closely linked to moisture because the windows of optimal environmental conditions for infection are more limited. Ascospores were almost entirely uncorrelated with differenced variables, which was meant to assess whether ascospore counts were related to changes in environment. At the same time, differenced variables were often the ones selected by VIF and other variable selection methods, since they were uncorrelated with other variables, and were often significant predictors in the final models. This trend corroborates previous research of ascospore release in seed alfalfa fields in the same region (J. Reich et al., 2017). For the same threshold of 100 ascospores per day, the models developed here have a greater AUC (~0.70) than that reported for ascospore release in seed alfalfa (AUC ~0.60), but models from the earlier study did not include logSs_t1 as one of the potential predictor variables. Despite these attempts to predict aerial inoculum levels, the utility of this approach to disease forecasting remains unclear. First, in a study correlating daily ascospore counts among several spore samplers within the same field, correlations were very low or inconsistent among samplers, which raises the question of how representative a single sampler per field is of the airborne inoculum level (Chapter 3). Second, even if a sampler was perfectly representative of the ascospore levels within a field, there is no empirical evidence describing how ascospore levels relate to disease intensity. Results described in Chapter 3 did not detect any relationship between ascospore levels and final white mould intensity in dry bean fields of southern Alberta. Third, the relative importance of false positives vs false negatives from any model requires some consideration, and the relative importance will depend on how strong the associations between ascospore levels and final disease incidence are. For instance, if ascospore levels are highly 142 predictive of final disease, a false negative (i.e. the model recommends taking no action when the disease risk is high) may be more detrimental to a grower than a false positive (i.e. the model recommends taking action when the disease risk is low). Finally, given the strong findings in the present research that ascospore levels are highly autocorrelated in time, a spore prediction network would necessarily also need to involve consistent, and rapid, spore monitoring. Several ML and statistical modeling approaches were used to predict ascospore levels in southern Alberta, and they all performed quite similarly. This similarity may be a result of the models all being dominated by a single variable (logSs_t1) or may suggest that ascospore release in S. sclerotiorum is inherently a stochastic process and therefore difficult to predict. From a procedural standpoint, variable selection is clearly an important part of the modeling process: models that included all possible variables always performed poorly on the test data set, likely due to overfitting on the training data set, compared to models that included only a subset of variables. From a practical standpoint, the statistical models are easier to interpret and implement compared to the ML models. The coefficients from the statistical models have a straightforward interpretation, and their presence (or absence) from the model has a clear statistical basis. Furthermore, the equations presented here can be easily implemented by anyone. In contrast, the variable importances in ML models, especially the RF and SVM approaches, have an opaquer interpretation (i.e. decrease in impurity or results from variable permutation). In addition, for these models to be implemented by others, the models must be saved and made accessible to download, which may reduce accessibility. While the models presented here performed better than previously published models in similar agricultural contexts, future attempts to predict ascospore release may be improved by several additional avenues of research. In a non-peer-reviewed article, hourly ascospore levels 143 have been highly correlated with hourly environmental variables (Archer & Yuan, 2004), so a finer temporal scale analysis could reveal important associations. Additional environmental variables like wind could also be measured, although, as with other variables, wind has had relatively poor associations with S. sclerotiorum life stages (J. D. Reich & Chatterton, 2022). Finally, a more focused attempt to relate apothecia emergence to ascospore levels could be made. Scouting for apothecia in field crops is often very difficult, and so this approach could take the form of sclerotia deposits that are monitored for emergence over a range of environmental conditions and airborne ascospores collected nearby. Many attempts have been made to predict diseases caused by S. sclerotiorum in field crops, but the published work on predicting ascospore release remains scant. The work presented here contributes to knowledge about airborne ascospore levels by employing numerous modeling approaches to assess the relative importance of environmental variables on airborne spore concentrations. Across years and provinces, and in all models developed, it was clear that ascospore release was strongly temporally autocorrelated and the environmental variables we considered contributed relatively little to ascospore levels. Nevertheless, these models require more work before they can be implemented by growers as a real-time disease risk assessment tool. 144 Chapter 5: Conclusion This thesis focused on monitoring the air to detect and predict airborne fungal plant pathogens in Alberta and across Canada. The research presented here is one of the finest-scale spatial and temporal analyses in aerobiology conducted to date, and provides insights into several fundamental and applied questions in aerobiology and plant pathology. Overall, this research corroborates previous research that HTS methods can be used for monitoring many airborne plant pathogens simultaneously. We provide circumstantial evidence that read abundance may relate to observed disease levels; however, more work is required to relate read counts to biological reality. Furthermore, because the methods described here require a significant amount of time and expertise, real-time and portable DNA sequencing platforms could play a vital role in advancing applications of this area of knowledge. In examining the dynamics of a specific airborne plant pathogen, Sclerotinia sclerotiorum, the research presented here provides a significant contribution in terms of scope (a much larger data set gathered than previous publications) and analysis (more analytical methods employed than most other publications) to the understanding of ascospore release. Taken together, this research makes substantial contributions to our understanding of the real-world ecology of airborne fungal organisms. Conclusions related to the objectives outlined in the Introduction chapter are summarized below: Objective 1: Describe the fine-scale (daily) temporal and spatial changes of airborne microbial diversity over the growing season in the agricultural landscape of Alberta. In Chapter 2, we found that Shannon diversity varied by location, but that, within location, it remained relatively stable throughout the growing season. A handful of common genera 145 dominated the air communities on most days, but less common genera fluctuated in abundance greatly from day to day. In some cases, these less common genera appeared to be those with life cycles associated with specific crops, and so were present in greater amounts in those crops (e.g. Blumeria in wheat). These results lead to a somewhat paradoxical conclusion: samplers nearby collected similar communities of fungi each day, but for any particular less common fungus, the abundance was not correlated among samplers. Objective 2: Assess the influence of location and crop type on Objective 1. In addition to the conclusions described in Objective 1, we found statistically significant effects of both location and crop type on airborne fungal community profiles. However, these variables only accounted for 10-12% of the variation in communities, indicating either that communities are influenced by other factors or that airborne fungal communities are similarly heterogeneous across a provincial spatial scale. Including common weather variables in these models only accounted for an additional 6% of variation. Objective 3: Assess the utility of HTS methods for multipathogen detection and quantification. Illumina amplicon sequencing and QIIME2 sequence analysis identified hundreds of unique fungal genera in each air sample. Many of these genera contain known plant pathogens. There was some evidence to show that the abundance of certain plant pathogens was higher in air samples from diseased plots than in healthy plots, which suggests that these methods could be further developed for use in routine pathogen monitoring. However, the main constraint with amplicon sequencing is the amount of time required from sample collection to interpretable results (several weeks or months). Objective 4: Describe and predict the occurrence of white mould epidemics in dry bean fields of southern Alberta. As expected, white mould in dry bean fields was ubiquitous in 146 time (present every year) and heterogeneous in space (highly variable among fields). Across years, the incidence of white mould was not associated with airborne ascospore levels or environmental factors like precipitation, temperature, or relative humidity. However, incidence levels were strongly associated with the market class of dry bean: Pintos, the most common market class grown, had the greatest amount of disease followed by Great Northern, Black, Red, and Yellow market classes. Objective 5: Develop predictive models for the occurrence of Sclerotinia sclerotiorum ascospores in the field and their relationship with disease in dry bean fields. A range of statistical and machine learning methods were employed to predict daily levels of airborne ascospores of S. sclerotiorum. These models all performed similarly, and analysis of this data set revealed that across all provinces (Alberta, Manitoba, and Ontario) and years, ascospore levels were highly correlated with ascospore counts from the previous day. These models had a moderate predictive ability when evaluated on a test data set. However, the relationship between airborne ascospore concentration and disease levels under field conditions remains poorly understood. The strengths of this thesis are as follows: First, we were able to sample at a finer spatial scale than other studies, which provided insights into the reliability of these methods. Sampler choice clearly influenced the fungal community profile collected, and, for S. sclerotiorum, ascospore levels were not correlated among nearby samplers. The latter observation requires more investigation before these air samplers can be used in routine ascospore monitoring. Second, the data set collected here is larger and finer in spatial and temporal resolution than other previously published studies, allowing for a more nuanced analysis and understanding of these microbial dynamics. Most other air microbiome studies collected only a handful of 147 observations over a season, and, often, samples were collected for the duration of a week in one or two locations. Here, we collected daily samples over several growing seasons and in numerous locations. These intensive sampling methods are both expensive and time consuming that likely prevents their broader use. Burkard spore samplers are an expensive up-front cost and break down regularly, and rotorod samplers require a daily visitation to change out rods. As a result, extensive and complete data sets can be difficult to collect with these methods. Third, several methods of molecular and statistical analyses were employed, both of which strengthened or cast doubt onto the limitations of the research findings here or in other published studies. For instance, analyzing the mock community as part of the HTS analysis (Chapter 2) revealed substantial biases in the workflow and comparing HTS read abundance with qPCR abundance of the same genus revealed a low correlation between these methods. Few published studies address these issues as they apply to analysis of real-world data sets. With growing awareness that these limitations exist in microbiome studies, an increasing number of solutions are being offered to address them, including the inclusion of the appropriate controls in the sequencing steps (Hornung et al., 2019; Nguyen et al., 2015) or how to account for controls in subsequent data analysis (Davis et al., 2018; Minich et al., 2018). Nevertheless, this area of research is still new and developing, and there is yet no clearcut way forward. With further development, this research has several potential applications. For the multipathogen detection through air monitoring, the methods presented here describe a baseline approach but more robust and extensive sampling methods, including greater sampling volumes and more sampling locations (described in more detail below), would need to be tested and implemented to develop models for pathogen prediction. Alternatively, with portable, real-time technologies like the Oxford Nanopore MinION, real-time in-field analysis of air samples could 148 lead to informing management decisions. In either case, an important first step will be addressing the knowledge gaps of the relationships among spore thresholds, environmental drivers, and disease for each pathogen of interest. The work presented in this thesis (Chapter 3) suggests that these approaches may not be helpful for the management of white mould in dry bean, although the lack of association we found between white mould and disease levels could be a result of sampling (e.g. we did not sample in the appropriate places) or confounding factors (e.g. fungicide use in the fields). These limitations deserve to be investigated in more depth, since spore sampling has been applied effectively in other contexts (refer to Chapter 3 for examples). One promising way forward is to couple HTS methods in a pathosystem that already has a strong foundation for the use of airborne spores in disease prediction, like the botrytis leaf blight of onion pathosystem (Carisse et al., 2009). In line with this, a pan-Canadian research group utilizing air sampling and HTS technologies has been established to address some of the goals and limitations described. The research group is focusing on Botrytis diseases associated with smaller, more intensively grown crops like onion and strawberry. It is possible that these horticultural crops are a better candidate for disease surveillance systems than for larger field crops due to the former’s more frequent fungicide application, greater value, and relatively small area that makes sampling easier (Shtienberg, 2013). Another potential application of the research presented here is the creation of a business or lab that specializes in fungal microbiome controls, particularly mock communities. One noticeable gap in fungal microbiome research is the paucity of positive controls used in analysis. Currently, Zymo Research (Irvine, California) produces mock community standards for use in bacterial microbiome sequencing studies, but there is no equivalent for fungal community standards. Therefore, creating standards that are easily accessible to the broader fungal 149 microbiome community could enhance the quality of reporting in these studies and expedite research into the best ways to incorporate control information into the analysis pipeline. A final limitation of this research was that we were unable to develop a robust predictive model of white mould in dry bean for grower use. While we were unsuccessful with our approaches, more work could be performed (described below) to create better models and a phone application for growers to use, as has been done for sclerotinia stem rot in soybean (Webster et al., 2022). Many of the limitations of this work establish clear directions forward for research possibilities in the field of aerobiology and disease forecasting. First, there are an enormous number of unanswered questions about air sampling methodologies that could be evaluated. From previous studies and this thesis, sampler types can greatly influence the communities collected, yet these biases remain unquantified. Given the complexities of air currents, how representative one particular sampler (or sample) is of airborne aerosols remains poorly explored. In this thesis, similar communities were collected by nearby samplers, but for a specific fungus like S. sclerotiorum, ascospore levels were not correlated among samplers. These paradoxes could be further explored by using high efficiency samplers (e.g. Mbareche et al. (2018)) that collect much greater volumes of air and/or by deploying more samplers within a small area. Such an approach could help answer some fundamental questions about air sampling methods (duration, horizontal and vertical location, etc.) but may or may not contribute in a meaningful way to managing field diseases. Absent from virtually all air sampling studies are sample replicates, and so high efficiency samplers could also help with the replication issue. Regarding pathobiome research, a more extensive survey, but one similar to that described in this thesis, could reveal more relationships between the air mycobiota and disease in the field. For this thesis, we had few replicates for each of our crop types (up to two replicate 150 fields per crop type) and most of the fields had very low disease levels, which is still an improvement on most published studies. Surveying more fields across a broader range of environments could elucidate some clear patterns; however, this approach is difficult due to the expense and maintenance required for the air samplers. A larger data set could be combined with further statistical analysis, with some different approaches outlined in Reich and Chatterton (2023). Subsequent analysis would be required to relate read abundance, pathogen abundance, and observed disease levels. For white mould prediction, one of the main constraints in this thesis was the amount of labour required for disease surveys, which only assessed a small portion of each field. Future work could employ remote sensing to assess the disease levels in each field over a large region. With historical remote sensing images, relationships of regional disease levels with seasonal environmental factors could be evaluated. In addition, by identifying highly diseased fields, interviews could target those growers and narrow down the range of factors that potentially influence white mould levels. This approach could also be extended to other crops and/or diseases. Finally, the research conducted in this thesis suggests that the host genotype and cultural practices could play a larger role in white mould development in dry bean fields of southern Alberta in most years. As a result, future work in the area could focus on evaluating these, and other, factors through grower surveys. Understanding aerobiology is fundamental to managing many existing and emerging fungal plant diseases. With increasing recognition of its importance in agroecological contexts, the scientific community has made substantial leaps in understanding the roles of airborne fungi in the development of plant diseases. As a discipline, however, fungal aerobiology is still relatively young, and insights remain far behind those for bacterial and fungal microbiome 151 research in water, soil, and human contexts. 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Frontiers in Microbiology, 10. https://www.frontiersin.org/articles/10.3389/fmicb.2019.02686 201 Appendices Appendix A Table A.1: Summary of fungal and oomycetes isolates included in the mock community. Spores were harvested from cultures and the spore suspensions were adjusted to 105 spores mL-1. One-hundred microlitre aliquots of each spore suspension were added to an Eppendorf tube and the spores were pelleted at the bottom of the tube by centrifuging at 13,200 RPM for 5 min. The supernatant was then pipetted off, leaving 100 mL of liquid in the Eppendorf tube. DNA was extracted as described for the Burkard samples. Species Isolate Sourcea Growth medium No. of spores in mock community (x104) Fungi Ascomycetes Blumeria graminis LaRoche Lab - 3.9 Botrytis cinerea 2421 LeRDC PDA 4.2 Chaetomium globosum ARL 870 CCFC CMA 3.6 Cladosporium herbarum DAOMC 159966 CCFC MEA 4.9 Fusarium avenaceum 1306.08 Chatterton Lab PDA 2.8 Penicillium expansum DAOMC 215350 CCFC MEA 3.1 Sclerotinia sclerotiorum InnoTech -b 4.7 Sordaria sp. G-21 Chatterton Lab PDA 3.5 Basidiomycetes Puccinia triticina LaRoche Lab -b 4.5 Oomycetes Aphanomyces euteiches Ae1 Chatterton Lab CMA 1.4 Phytophthora infestans PI24 Harding Lab Rye B Agar 1.2 aLeRDC = Lethbridge Research and Development Centre collection; CCFC = Canadian Collection of Fungal Cultures; InnoTech = InnoTech Alberta bAscospores for S. sclerotiorum are produced by cultivating apothecia, and were obtained from InnoTech (Edmonton, AB, Canada); B. graminis ascospores and P. triticina urediniospores cannot be grown in vitro and were harvested from plants in a growth chamber. Table A.2: Airborne fungi of interest and their associated crops and diseases in Alberta, Canada. 202 Fungus Crop Disease Alternaria alternata field pea leaf blight Alternaria brassicae canola black spot Alternaria solani potato early blight Ascochyta spp. chickpea leaf blight field pea leaf blight lentil leaf blight Blumeria graminis wheat powdery mildew Botrytis cinerea seed alfalfa blossom blight Claviceps purpurea barley ergot Cochliobolus sativus wheat spot blotch Fusarium avenaceum barley head blight wheat head blight Fusarium graminearum barley head blight wheat head blight Fusarium poae barley head blight wheat head blight Leptosphaeria maculans canola blackleg Leptosphaeria spp. barley glume, leaf blotch wheat glume, leaf blotch Parastagonospora nodorum wheat blotch Puccinia graminis wheat stem rust Puccinia striformis wheat stripe rust Puccinia triticina wheat leaf rust Pyrenophora teres barley net form, spot form net blotch Pyrenophora tritici-repentis wheat tan spot Ramularia spp. field pea blight lentil blight Rhynchosporium secalis barley scald Sclerotinia sclerotiorum dry bean white mould canola stem rot seed alfalfa blossom blight Stemphylium spp. lentil leaf blight Table A.3: Summary of read counts from two amplicons of environmental samples (excludes controls) in two years of sampling. Samples in 2019 were sequenced on an Illumina NovaSeq 203 platform and samples in 2021 were sequenced on an Illumina MiSeq platform. Burkard samplers collected samples for the crop type hypothesis and rotorod samplers collected samples for the geography hypothesis. Figure A.1: Venn diagram of shared genera across all four sampling locations in Alberta (rotorod data set) for the ITS1 amplicon. Reads Raw data post-DADA2 Sampler n Mean StDev Mean StDev 2019 Burkard 110 597,445 197,477 560,805 185,292 Rotorod 104 784,668 138,563 721,903 125,605 2021 Burkard 101 47,518 11,274 44,963 11,029 204 Figure A.2: Venn diagram of shared genera across the three crop types sampled at LeRDC in southern Alberta (Burkard data set) for the ITS1 amplicon in 2019 (left) and 2021 (right). Figure A.3: PCoA of samples based on the Hellinger-transformed read count and Bray-Curtis dissimilarity of ASVs of the rotorod (geography hypothesis) data set. Ellipses represent the 95% confidence intervals. 205 Figure A.4: PCoA of samples based on the Hellinger-transformed read count and Bray-Curtis dissimilarity of ASVs of the Burkard (crop type hypothesis) data set for 2019 (left) and 2021 (right). Ellipses represent the 95% confidence intervals. Figure A.5: PCoA of two sampler types (Burkard and Rotorod) located in the same canola research plot for the ITS1 amplicon. The ordination plots are based on Bray-Curtis dissimilarity, and a Hellinger transformation of the ASV table. There were not enough data points to calculate 95% confidence interval ellipses. 206 Figure A.6: PCoA of Burkard samples from LeRDC in 2019 and 2021 of the ITS1 amplicon. Ordination is based off the Bray-Curtis dissimilarity of the Hellinger-transformed ASV counts. Ellipses represent 95% confidence intervals. 207 Appendix B Table B.1: Pearson correlations between white mould incidence based on environmental data collected by in-field weather stations (n = 18) or provincial weather stations (n = 115 fields paired with nearby provincial weather stations). Data were summarized at two scales: averages over the month of July or averages over the flowering period (defined by July 20 to August 10 for each year). In-field Provincial Variablea July Flowering July Flowering logSs 0.034 0.161 - - meanT 0.294 0.394 0.034 -0.034 maxT 0.105 0.128 0.056 0.034 minT 0.467 0.405 0.001 -0.056 meanRH -0.140 -0.270 0.046 0.072 minRH 0.284 -0.044 - - maxRH -0.347 -0.338 - - meanVPD 0.122 0.226 - - maxVPD -0.141 -0.029 - - minVPD 0.399 0.383 - - precip 0.062 -0.079 0.045 -0.049 meanWC -0.125 -0.158 - - maxWC -0.101 -0.148 - - minWC -0.147 -0.119 - - meansoilT 0.654 0.293 - - maxsoilT 0.276 -0.055 - - minsoilT 0.617 0.579 - - wind2m - - -0.023 -0.019 wind10m - - -0.013 -0.001 GDD - - 0.034 0.053 a logSs = the log(number of ascospores per day); T = temperature; RH = relative humidity; VPD = vapor pressure deficit; WC = soil water content; wind = wind speed; GDD = growing degree days. 208 Figure B.1: Top: the daily numbers of S. sclerotiorum ascospores collected in the disease nursery at LeRDC by a Burkard volumetric spore sampler and quantified by qPCR. Bottom: the percentage of bean flowers collected from the disease nursery plants each day that were infected by S. sclerotiorum. 209 Appendix C Table C.1: Annual breakdown of correlations of environmental variables with daily ascospore counts for each province. Refer to attached Excel file.
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hamelin, Richard C.
Chatterton, Syama
Combining air sampling and molecular methods to monitor airborne fungal plant pathogens
Text
Dataset