Arts and Sciences, Irving K. Barber School of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Richards, Andrew Eric
Master of Science - MSc
University of British Columbia
Wine grape production is an important economic asset in many nations, however a significant proportion of vines succumb to soil-borne pathogens, reducing yields and causing economic losses. Young vine decline (YVD) occurs when grapevines experience stunted growth, reduced yield, delayed fruiting, and root necrosis, often leading to dieback in vineyards worldwide. These symptoms are largely due to soil-borne fungal pathogens including Ilyonectria species which may be present in the soil or brought to the area by contaminated nursery plants. Restrictions in fungicide use and high-density planting regimes have exacerbated the incidence of YVD in vineyards. Public demand for low-impact sustainable agriculture has encouraged researchers to test alternative approaches to combat soil-borne diseases. Cover crops are plants that are grown alongside main crops or during non-production seasons in order to maintain and enhance soil composition. These plants provide several well known benefits and also may serve as a line of defense against soil-borne fungal pathogens by producing volatile root exudates and/or harbouring suppressive microbes. I tested whether cover crop diversity and provenance affect the persistence and infectivity of YVD pathogens in agricultural soil. In a greenhouse experiment, I inoculated soil with a 106 conidia spore suspension of Ilyonectria liriodendri and then conditioned soil with cover crops for several months to investigate changes in pathogen abundance and fungal communities via Illumina sequencing. After removal of cover crops, Chardonnay cuttings were introduced and grown for a short period to analyse growth responses and disease symptoms. When grown alone, white mustard was the only cover crop associated with reductions in necrotic root damage and abundance of Ilyonectria in Chardonnay roots. The suppressive effects of white mustard largely disappeared when paired with other plants or when grown with all other plants in the high diversity treatment. Soil fungal communities under white mustard were similar to buckwheat and wheat but not clover. These results highlight the suppressive potential of white mustard and its value as a biofumigant cover crop in vineyards.
USING COVER CROPS TO MITIGATE VINE DECLINE AND REDUCE PATHOGENS IN VINEYARD SOILS by Andrew Eric Richards B.Sc., Dalhousie University, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) May 2020 ©Andrew Eric Richards, 2020 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: USING COVER CROPS TO MITIGATE VINE DECLINE AND REDUCE PATHOGENS IN VINEYARD SOILS submitted by Andrew Richards in partial fulfillment of the requirements of the degree of Master of Science . Miranda Hart, Irving K. Barber School of Arts and Sciences Supervisor José Ramón Úrbez-Torres, Adjunct Professor, Summerland Research and Development Centre Supervisory Committee Member Patricia Bowen, Adjunct Professor, Summerland Research and Development Centre Supervisory Committee Member Thomas Lowery, Adjunct Professor, Summerland Research and Development Centre Supervisory Committee Member Ian Walker, Biology, Earth, Environmental and Geographic Sciences University Examiner Sylvie Desjardins, Department of Computer Science, Mathematics, Physics and Statistics External Examiner iii Abstract Wine grape production is an important economic asset in many nations, however a significant proportion of vines succumb to soil-borne pathogens, reducing yields and causing economic losses. Young vine decline (YVD) occurs when grapevines experience stunted growth, reduced yield, delayed fruiting, and root necrosis, often leading to dieback in vineyards worldwide. These symptoms are largely due to soil-borne fungal pathogens including Ilyonectria species which may be present in the soil or brought to the area by contaminated nursery plants. Restrictions in fungicide use and high-density planting regimes have exacerbated the incidence of YVD in vineyards. Public demand for low-impact sustainable agriculture has encouraged researchers to test alternative approaches to combat soil-borne diseases. Cover crops are plants that are grown alongside main crops or during non-production seasons in order to maintain and enhance soil composition. These plants provide several well-known benefits and also may serve as a line of defense against soil-borne fungal pathogens by producing volatile root exudates and/or harbouring suppressive microbes. I tested whether cover crop diversity and provenance affect the persistence and infectivity of YVD pathogens in agricultural soil. In a greenhouse experiment, I inoculated soil with a 106 conidia spore suspension of Ilyonectria liriodendri and then conditioned soil with cover crops for several months to investigate changes in pathogen abundance and fungal communities via Illumina sequencing. After removal of cover crops, Chardonnay cuttings were introduced and grown for a short period to analyse growth responses and disease symptoms. When grown alone, white mustard was the only cover crop associated with reductions in necrotic root damage and abundance of Ilyonectria in Chardonnay roots. The suppressive effects of white mustard largely disappeared when paired iv with other plants or when grown with all other plants in the high diversity treatment. Soil fungal communities under white mustard were similar to buckwheat and wheat but not clover. These results highlight the suppressive potential of white mustard and its value as a biofumigant cover crop in vineyards. v Lay summary Wine grape production is limited by microorganisms in the soil that attack and feed on the plant’s roots, eventually leading to death after five to seven years. Cover crops are plants that are grown alongside main crops or during non-production seasons to maintain soil structure and increase nutrients. To test whether cover crops could reduce disease symptoms in grapevines, I grew eight different cover crops in soil that contained disease-causing microorganisms and then planted young grapevines in the same soil. I found that white mustard reduced disease symptoms in grapevines after four months of growth. These results are consistent with other studies using white mustard due to its production of chemicals that inhibit fungal activity. vi Preface This project was developed by my supervisor Dr. Miranda Hart, committee member Dr. José Ramón Úrbez-Torres, and I in 2017. I received assistance from Dr. Eric Vukicevich (PhD, UBC Okanagan) and Dr. Vasilis Kokkoris (PhD, UBC Okanagan) with the experimental design and methodology, including pathogen management and digital PCR assays. I was responsible for experimental setup, sourcing materials, sampling, and processing with assistance from laboratory technicians from the Summerland Research and Development Centre and students at the University of British Columbia Okanagan. I completed my statistical analyses with assistance from Dr. Vasilis Kokkoris and bioinformatics from Dr. Mehrbod Estaki (PhD, UBC Okanagan). My thesis was written with guidance from Dr. Miranda Hart and committee members Dr. Pat Bowen, Dr. Tom Lowery, and Dr. José Ramón Úrbez-Torres. vii Table of Contents Abstract………………………………………….....…………………………………………...iii Lay Summary…………………………………………………………………………………….v Preface……………………………………………………………………………………………vi Table of Contents……………………………………………………………………………….vii List of Tables……………………………………………………………………………….…...xii List of Figures…………………………………………………………………………..…...…..xv List of Abbreviations………………………………………………………………….……….xxi Acknowledgements…………………………………………………………………………....xxii 1. Introduction……………………………………………………………………………………1 2. Literature Review……………………………………………………………………………..3 2.1 Suppression of pathogens in natural ecosystems…………………………………..3 2.2 Potential of cover crops to suppress pathogens…………………………………….4 2.3 The link between plant diversity and soil borne disease…………………………..5 2.3.1 Density dependence…………………………………………………………4 2.3.2 Direct mechanisms of pathogen suppression………………………………..7 2.3.2.1 Dilution effect……………………………………………………..7 2.3.2.2 Functional diversity and root exudate variation…………………...7 2.3.2.3 Plant litter………………………………………………………….9 2.3.3 Indirect mechanisms of pathogen suppression…………………………….11 2.3.3.1 Arbuscular mycorrhizal fungi…………………………………....11 2.3.3.2 Plant growth promoting rhizobacteria…………….……………..11 2.3.3.3 Induced systemic resistance……………………………………...13 viii 2.4 Plant provenance and suppression of soil-borne pathogens……………………..14 2.4.1 Plant-soil feedback and plant growth promoting rhizobacteria....................14 2.4.2 Native plants and environmental conditions……………………………….15 2.4.3 Local adaptation of plants and soil microbiota…………………………….15 2.4.4 Future directions…………………………………………………………...16 3. Mitigation of young vine decline with cover cropping…………………………………….17 3.1 Background…………………………………………………………………………17 3.1.1 Young vine decline………………………………………………………...17 3.1.2 Mode of infection…………………………………………………………..18 3.1.3 Current approaches………………………………………………………...18 3.1.4 Cover crops………………………………………………………………...19 3.1.5 Cover crops and soil-borne disease in perennial systems………………….19 3.1.6 Plant diversity and ecosystem services…………………………………….20 3.1.7 Plant provenance…………………………………………………………...20 3.1.8 Objectives……………………………………………………………….....21 3.2 Materials and Methods……………………………………………………………..22 3.2.1 Establishment of experiments……………………………………………...22 3.2.2 Experimental design………………………………………………………..23 3.2.2.1 Cover crop diversity and grapevine growth responses…………..23 3.2.2.2 Cover crop treatments……………………………………………24 3.2.2.3 Location and greenhouse conditions……………………………..25 3.2.2.4 Soil……………………………………………………………….25 3.2.2.5 Pathogen incubation and inoculation…………………………….25 ix 3.2.3 Germination and growth of plants…………………………………………26 3.2.3.1 Cover crops………………………………………………………26 3.2.3.2 Vine propagation and growth…………………………………….27 3.2.4 Dependent variables………………………………………………………..28 3.2.4.1 Incidence of disease……………………………………………...28 3.2.4.2 Grapevine physiology……………………………………………28 3.2.4.2.1 Leaf greenness………….……………………………...28 3.2.4.2.2 Root and shoot biomass………………………………..28 3.2.4.2.3 Leaf area………………………………………………..29 3.2.4.2.4 Available phosphorus………………………………..…29 3.2.5 Molecular data……………………………………………………………..29 3.2.6 Isolation of DNA…………………………………………………………...30 3.2.6.1 DNA extraction from soil………………………………………..30 3.2.6.2 DNA extraction from roots………………………………………30 3.2.7 Droplet digital assay……………………………………………………….31 3.2.8 Illumina sequencing and bioinformatics…………………………………...32 3.2.8.1 Filtering, denoising, and chimera removal………………………32 3.2.8.2 Fungal community analysis……………………………………...33 3.2.9 Statistical analyses…………………………………………………..……..33 3.2.9.1 Incidence of disease……………………………………………...34 3.2.9.2 Grapevine physiology……………………………………………35 3.2.9.3 Abundance of Ilyonectria in the soil……………………………..35 3.2.9.4 Abundance of Ilyonectria in roots……………………………….35 x 3.2.9.5 Fungal diversity and community analyses……………………….36 3.3 Results……………………………………………………………………………….37 3.3.1 Effects of cover crop diversity on symptoms and abundance of Ilyonectria pathogens in soil and roots……………………………………………………….37 3.3.1.1 Native study……………………………………………………...37 3.3.1.2 Cultivar study…………………………………………………….38 3.3.2 Effect of cover crop diversity on fungal alpha and beta diversity………....39 3.3.2.1 Alpha diversity in native and cultivar studies……………………39 3.3.2.2 Beta fungal diversity in native and cultivar studies……….……..41 3.3.3 Effect of cover crop provenance on incidence of disease in vines and abundance of Ilyonectria…………………………………………………………42 3.3.3.1 Necrotic tissue damage of native vines…………………………..42 3.3.3.2 Necrotic tissue damage of cultivar vines………………………...44 3.3.4 Grapevine physiology……………………………………………………...45 3.3.4.1 Native study……………………………………………………...45 3.3.4.2 Cultivar study…………………………………………………….46 3.3.5 Recovery of Ilyonectria from soil………………………………………….47 3.3.5.1 Native study……………………………………………………...47 3.3.5.2 Cultivar study…………………………………………………….49 3.3.6 Recovery of Ilyonectria from roots………………………………………...51 3.3.6.1 Native study……………………………………………………...51 3.3.6.2 Cultivar study…………………………………………………….53 3.3.7 Sequence results……………………………………………………………55 3.3.8 Effect of cover crop provenance on fungal diversity………………………56 xi 3.3.8.1 Alpha diversity in native and cultivar studies……………………56 3.3.8.2 Beta diversity in native and cultivar studies……………………..57 3.4 Discussion……………………………………………………………………….…..61 3.4.1 Effect of cover crop diversity on root necrosis…………………………….61 3.4.2 Effect of plant provenance on root necrosis……………………………….63 3.4.3 Grapevine physiology and growth responses……………………………...64 3.4.4 Recovery of Ilyonectria DNA from soil…………………………………...64 3.4.5 Recovery of Ilyonectria DNA from roots………………………………….65 3.4.6 Effect of cover crops on fungal diversity…………………………………..65 3.4.7 Effect of cover crops on community composition…………………………67 4. Conclusion………………………………………………………………………………...….69 4.1 Does cover crop diversity reduce disease symptoms in young vines and abundance of Ilyonectria?...............................................................................................69 4.2 Does cover crop provenance affect the severity of disease symptoms and abundance of Ilyonectria?...............................................................................................70 4.3 Does cover crop diversity increase fungal diversity and is fungal diversity associated with disease symptoms?................................................................................70 4.4 Concluding remarks………………………………………………….…………….71 Bibliography…………………………………………………………………………………….73 Appendix – Supporting material for Chapter 2………………………………………...…….91 A.1 Cover crop growth…………………………………………………...…………….91 A.2 Figures and tables………………………………………………………………….92 xii List of Tables Table 2.1 Proposed mechanisms for pathogen suppression from growing cover crops in a vineyard. Mechanisms may be direct or indirect and diverse cover crop assemblages may activate more than one mechanism at a given time ………………………………………………6 Table 3.1 Selected cover crops for native study and cultivar study greenhouse experiments. Native plants were sourced in the Okanagan Valley while seeds of cultivar plants were ordered online …………………………………………………………………………………………….24 Table 3.2 Cover crop combinations and their corresponding diversity level for native study and cultivar study experiments. Initially, each study contained four monocultures, six mixtures, and one treatment in which all cover crops were grown together ……………….……….………….24 Table 3.3 PERMANOVA summary of Native Study responses (left column). Mean square (MS), Pseudo-F statistic (F), and P values (P) at a significance level of 0.05 are provided for each interaction …………………………………………………….....................................................38 Table 3.4 PERMANOVA summaries of Cultivar Study response variables (left column). Mean square (MS), Pseudo-F statistic (F), and P values (P) are provided for each interaction ....…….39 Table A1 Average biomass (grams) for each cover crop treatment in native and cultivar studies. Cover crops are bluebunch wheatgrass “bluebunch”, Silky lupine “lupine”, Holboell’s rockcress “rockcress”, white yarrow “yarrow”, buckwheat “buckwheat”, Crimson clover “clover”, white mustard “mustard”, and wheatgrass “wheat”. Letters to the right of values indicate significance at p ≤ 0.05 ……………………………………………………………………………………...101 Table A2 Fertilizer application for vinifera cuttings in native and cultivar studies. Miracle-Gro© fertilizer was used to prepare solutions of varying concentrations. Enough fertilizer was applied to cover the soil and allowed to soak through ………………………………………………….101 Table A3 Number of reads retained at each step in the DADA2 pipeline. Samples are shown with block number first followed by treatment number (Table 2.2) separated by a hyphen. From left to right are initial read counts (input), reads after filtering (filtered), forward reads after denoising (denoisedF), reverse reads after denoising (denoisedR), number of reads after merging (merged), and number of reads after chimera removal (nonchim) …………………………….102 Table A4 Pairwise comparison (diversity level) results for fungal alpha diversity. Treatments are monocultures (1 cover crop), mixtures (2 cover crops) and all native/cultivar (all crops grown together). P values and adjected q values are shown at a significance level of 0.05 …………………………………………………………………………………………………..105 xiii Table A5 Pairwise comparison results for fungal alpha diversity under individual cover crops. Native study treatments are all species together “all native”, bluebunch wheatgrass “bluebunch”, Silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Cultivar study treatments are all plants grown together “all cultivar”, buckwheat, crimson clover “clover”, fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat” ………………………….105 Table A6 Pairwise comparison results for fungal alpha diversity under cover crop mixtures. Native study treatments are mixture of all plants “all native”, bluebunch wheatgrass with silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Cultivar study treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, fallow “Fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass with crimson clover “wheat-clover” ………………………………………………………………………………....106 Table A7 Pairwise PERMANOVA results of fungal community composition under native and cultivar cover crops. Pairwise comparisons represent cover crops by diversity level. Treatments are monocultures (1 cover crop), mixtures (2 cover crops) and all native/cultivar (all crops grown together). P values and adjected q values are shown at a significance level of 0.05 …………..107 Table A8 Pairwise comparison results for fungal community composition under individual cover crops. Native study treatments are all species together “all native”, bluebunch wheatgrass “bluebunch”, Silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Cultivar study treatments are all plants grown together “all cultivar”, buckwheat, crimson clover “clover”, fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat” …………………………………………………………………………………………………..107 Table A9 Pairwise comparison results for fungal community composition under cover crop mixtures. Native study treatments are mixture of all plants “all native”, bluebunch wheatgrass with silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Cultivar study treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, fallow “Fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass with crimson clover “wheat-clover” ………………………………………………………………………….108 Table A10 Dispersion of fungal communities for native and cultivar cover crops. Pairwise comparisons represent cover crops by diversity level. Treatments are monocultures (1 cover crop), mixtures (2 cover crops) and all native/cultivar (all crops grown together). P values and adjected q values are shown at a significance level of 0.05 ……………………………………109 xiv Table A11 Dispersion of fungal communities in individual cover crops. Native study treatments are all species together “all native”, bluebunch wheatgrass “bluebunch”, Silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Cultivar study treatments are all plants grown together “all cultivar”, buckwheat, crimson clover “clover”, fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat” …………………………………………………..110 Table A12 Dispersion of fungal communities under cover crop mixtures. Native study treatments are mixture of all plants “all native”, bluebunch wheatgrass with silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Cultivar study treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, fallow “Fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass with crimson clover “wheat-clover” ………………………………………………………………………………………….110 xv List of Figures Figure 2.1 Cover crops increase plant diversity in a perennial agroecosystem which can lead to suppression of soil-borne pathogens. A: Antifungal exudates from certain plants directly inhibit pathogens by preventing spore germination or hyphal growth. B: Cover crops host PGPR in their rhizosphere which are capable of suppressing fungal pathogens. C: Cover crops stimulate PGPR that are capable of initiating an immune response in the host plant, reducing disease. D: Dilution theory. Cover crop roots mask the host plant or act as partial barriers reducing infectivity of pathogens………..…………………………………………………………….…..10 Figure 3.1 Shannon diversity index of fungi from soil under native cover crops. Treatments are monocultures (1 species), mixtures (2 species), and all plants growth together (3 species). Diversity of native cover crops was positively correlated with fungal diversity (P = 0.044)…………………………………………………………………………………………….40 Figure 3.2 Shannon diversity index of fungi from cultivar cover crop soil. Treatments are un-inoculated bare soil (fallow), 1 cover crop (monocultures), mixtures of 2 cover crops (mixtures), and all 4 plants grown together (All cultivar). There was no significant correlation between plant diversity and below-ground fungal diversity (P = 0.437)…………….…………………...……..40 Figure 3.3 Principal coordinate analysis (PCoA) of fungal communities under native cover crops. Diversity levels are visualized using Weighted UniFrac distance. Treatments are monocultures (spheres), mixtures of two crops (diamonds), and all crops together (triangles). Overall PERMANOVA revealed no differences between diversity levels (P = 0.093), however variation of fungal communities under monocultures (spheres) and mixtures (diamonds) is evident (P = 0.023, q = 0.069)………………………………………………………………...…41 Figure 3.4 Robust Aitchison visualization of fungal communities under cultivar cover crops. Treatments are monocultures (spheres), mixtures of two crops (diamonds), all crops together (triangles), and uninoculated fallow (squares). Fungal communities from Fallow soil are different than those under monocultures (P = 0.003, q = 0.014), mixtures (P = 0.005, q = 0.014), and all cultivar soils (P = 0.007, q = 0.014)…………………………...……………………...…………42 Figure 3.5 Percent necrotic tissue of grapevines grown in soil conditioned by native cover crops. The extent of necrosis is measured from the basal end up. Treatments are all plants grown together “all native”, bluebunch wheatgrass “bluebunch”, silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). Data was normalized by taking the square root of the reciprocal (100.975 – percent necrosis. There was no significant difference between cover crop treatments (P = 0.407)……………………………….……………………..43 xvi Figure 3.6 Progression of necrotic damage in grapevines grown in soil conditioned by native cover crops. The extent of necrosis is measured from the basal end. Treatments are all plants grown together “all native”, bluebunch wheatgrass “bluebunch”, silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). Data was normalized by taking the natural logarithm plus 1 (log1p) of necrotic progression values. There is no significant difference between cover crop treatments……………………………………………………….44 Figure 3.7 Percent necrotic tissue of grapevines growing in soil conditioned by cultivar cover crops. Treatments include mixture of all plants “all cultivar”, buckwheat “buckwheat”, crimson clover “clover”, uninoculated fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). Letters represent statistical significance at P < 0.05……………………….….45 Figure 3.8 Principal coordinate analysis of native study grapevine growth responses based on leaf greenness (SPAD), leaf area, dry root biomass, dry shoot biomass, and total leaf phosphorus. Symbols represent different cover crop treatments including white yarrow “yarrow”, Holboell’s rockcress “rockcress”, silky lupine “lupine”, bluebunch wheatgrass “bluebunch”, and all plants grown together “all native”. There was no significance detected between treatments (P = 0.664)…………………………………………………………………………………...………..46 Figure 3.9 Principal coordinate analysis of cultivar study grapevine growth responses based on leaf greenness (SPAD), leaf area, dry root biomass, dry shoot biomass, and total leaf phosphorus. Symbols represent different cover crop treatments including white mustard “mustard”, buckwheat “buckwheat”, wheatgrass “wheat”, crimson clover “clover”, mixture of all plants “all cultivar”, and uninoculated fallow “fallow”. There was no significance detected between treatments (P = 0.816)………………………………………………………...……………..…..47 Figure 3.10 Recovery of I. liriodendri DNA from soil after conditioning by native cover crops. Treatments are all plants grown together “all native”, bluebunch wheatgrass “bluebunch”, silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Copy number did not differ significantly among treatments (P = 0.731)………………………………………...…48 Figure 3.11 Recovery of I. liriodendri DNA from soil after conditioning by native cover crop mixtures. Treatments are all plants grown together “all native”, bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Copy number did not differ significantly between cover crop treatments (P = 0.903)……….....49 Figure 3.12 Recovery of I. liriodendri DNA from soil conditioned by cultivar cover crops. Treatments are mixture of all plants “all cultivar”, buckwheat “buckwheat”, crimson clover “clover”, uninoculated fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Letters above boxplots represent statistical significance at P < 0.05…………………..………..50 xvii Figure 3.13 Recovery of I. liriodendri DNA from soil conditioned by cover crop mixtures. Treatments include mixture of all plants “all cultivar”, crimson clover and buckwheat “buckwheat-clover”, uninoculated fallow “fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass and crimson clover “wheat-clover”. Letters above boxplots represent statistical significance at P < 0.05……………………………………………………………………………………………….51 Figure 3.14 Recovery of I. liriodendri DNA from native study Chardonnay roots. Treatments are all plants grown together “all native”, bluebunch wheatgrass “bluebunch”, silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Data was normalized by taking the square root of the copy number and then removing three outliers using Tukey’s inter quartile range. Copy number did not differ significantly between cover crop treatments (P = 0.109)………………………………………………………………………………………….....52 Figure 3.15 Recovery of I. liriodendri DNA from native study Chardonnay roots. Treatments are all plants grown together “all native”, bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Data was normalized via log transformation. There were no significant differences between cover crop treatments (P = 0.584)…………………………………………………………………………………………….53 Figure 3.16 Abundance of I. liriodendri DNA from cultivar study Chardonnay roots. The cube root of copy number was taken to normalise data. Treatments are mixture of all plants “all cultivar”, buckwheat “buckwheat”, crimson clover “clover”, uninoculated fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Letters above boxplots represent significant differences at P < 0.05……………………………………………………………………….......54 Figure 3.17 Abundance of I. liriodendri DNA from cultivar study Chardonnay roots. Data was normalized via log transformation. Treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “buckwheat-clover”, uninoculated fallow “fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass and crimson clover “wheat-clover”. Letters above boxplots represent significant differences at P < 0.05……………………………………...……………………………..……..55 Figure 3.18 Shannon diversity of fungi in native study soils. Monoculture treatments are bluebunch wheatgrass “bluebunch”, silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow” (A). Mixtures are bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress” (B). Overall group significance was observed in monocultures (P = 0.047) but not mixtures (P = 0.098)………………..……..57 xviii Figure 3.19 PCoA of fungal communities from native study cover crops visualized by Weighted UniFrac distance. Treatments are all species together “All native”, bluebunch wheatgrass “Bluebunch”, Silky lupine “Lupine”, Holboell’s rockcress “Rockcress”, and white yarrow “Yarrow”. Fungal communities show no significant clustering overall (P = 0.051), however bluebunch wheatgrass (squares) and white yarrow (stars) reveal differences in beta diversity (P = 0.036, q = 0.280)……………………………………………………………………...………….58 Figure 3.20 PCoA of fungal communities from native cover crops visualized by Weighted Unifrac distance. Treatments are mixture of all plants “All native”, bluebunch wheatgrass with silky lupine “Bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “Rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “Yarrow-bluebunch”, white yarrow and silky lupine “Yarrow-lupine”, and white yarrow and Holboell’s rockcress “Yarrow-rockcress” (P = 0.099)…………………………………………………………………………………………..59 Figure 3.21 PCA of fungal communities from cultivar cover crops visualized by Robust Aitchison distance. Treatments are all plants grown together “All cultivar”, buckwheat, crimson clover “Clover”, uninoculated fallow “Fallow”, white mustard “mustard”, and wheatgrass “wheat” with the following significant pairwise interactions: All cultivar and Fallow (P = 0.005, q = 0.045), All cultivar and wheatgrass (P = 0.017, q = 0.084), Clover and Fallow (P = 0.006, q = 0.045), clover and mustard (P = 0.025, q = 0.084), clover and wheatgrass (P = 0.037, q = 0.092)………………………………………………………………………………………..….60 Figure 3.22 Fungal communities from cultivar study cover crops visualized by Robust Aitchison distance. Treatments are mixture of all plants “All cultivar”, crimson clover and buckwheat “Clover-buckwheat”, uninoculated fallow “Fallow”, white mustard and buckwheat “Mustard-buckwheat”, white mustard and crimson clover “Mustard-clover”, white mustard and wheatgrass “Mustard-wheat”, wheatgrass and buckwheat “Wheat-buckwheat”, and wheatgrass with crimson clover “Wheat-clover” (P = 0.001)…………………………………………………..…………61 Figure A1 Percent necrotic tissue of native study grapevines grown in soil conditioned by cover crop pairs. The extent of necrosis is measured from the basal end up. Treatments are all plants grown together “all native”, bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). Data were normalized by taking the square root of the reciprocal (100.267 – values). There was no significant difference between cover crop treatments (P = 0.283)……………………………………………………...92 xix Figure A2 Progression of necrotic damage in native study grapevines grown in soil conditioned by cover crop pairs. The extent of necrosis is measured from the basal end up. Treatments are all plants grown together “all native”, bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress” Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). Data was normalized by taking the natural logarithm plus 1 (log1p) of necrotic progression values. There is no significant difference between cover crop treatments (P = 0.214)……..……………………………..……..93 Figure A3 Percent necrotic tissue of cultivar study grapevines growing in soil conditioned by cover crop pairs. The percentage of necrotic tissue from basal cross sections is shown. Treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “buckwheat-clover”, uninoculated fallow “fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass and crimson clover “wheat-clover”. Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). There were no statistical differences observed between treatments (P = 0.727)………………………………………………..…………………………94 Figure A4 Principal coordinate analysis of native study grapevine growth responses based on leaf greenness (SPAD), leaf area, dry root biomass, dry shoot biomass, and total leaf phosphorus. Symbols represent different cover crop treatments including: all plants grown together “all native”, bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress” There was no significance detected between treatments (P = 0.645)………………………………………………..………..………………………...………..95 Figure A5 Principal coordinate analysis of cultivar study grapevine growth responses based on leaf greenness (SPAD), leaf area, dry root biomass, dry shoot biomass, and total leaf phosphorus. Treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, uninoculated fallow “fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass and crimson clover “wheat-clover”. No significance between cover crop treatments was detected (P = 0.916)………………………………………………..…………………………………….……..96 Figure A6 Relative abundance of phyla across native study samples (A) and cultivar study samples (B) after initial denoising and filtering. Samples are shown with block number followed by treatment number (see Table 2.2). Phyla are Ascomycota (brown), Basidiomycota (pink), Mortierellomycota (yellow), Chytridiomycota (orange), Glomeromycota (purple), and unidentified (blue and green)……………………………………………………………….…....98 xx Figure A7 Shannon diversity of fungi in soil before and after conditioning by cover crops. Treatments are soil before seeding with cover crops “Baseline”, soil conditioned by cultivar cover crops for three months “Cultivar”, and soil conditioned by native cover crops for four months “Native” Letters above boxplots represent significant differences at P < 0.05………....99 Figure A8 Shannon diversity of fungi in cultivar study soil. Monoculture treatments are all plants “all cultivar”, buckwheat “buckwheat”, crimson clover “clover”, uninoculated fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Fungal diversity did not vary among treatments (P = 0.531) (A). Mixtures are all plants grown together “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, uninoculated fallow “fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass and crimson clover “wheat-clover” Shannon diversity of fungi did not vary in cover crop mixtures (P = 0.580) (B)………………………………..…………………………………100 xxi List of Abbreviations AM Arbuscular mycorrhizal ANOVA Analysis of variance ASV amplicon sequence variant DAPG diacetylphloroglucinol ddPCR droplet digital polymerase chain reaction GTD grapevine trunk disease ISR induced systemic resistance ITS internal transcribed spacer PCA principal component analysis PCO principal coordinate analysis (PRIMER7) PCoA principal coordinates analysis (QIIME2) PCR polymerase chain reaction PDA potato dextrose agar PERMANOVA permutational analysis of variance PGPR plant growth promoting rhizobacteria PRN pyrrolnitrin PSF plant-soil feedback SPAD soil-plant analyses development SuRDC Summerland Research and Development Centre YVD young vine decline xxii Acknowledgements First and foremost, I would like to thank my supervisor Dr. Miranda Hart for sharing her research knowledge, patience, and extremely prompt feedback throughout the duration of my master’s degree. I would like to thank Dr. José Ramón Úrbez-Torres for his expertise in grapevine trunk disease pathogens, Dr. Tom Lowery for his knowledge of cover crops, and Dr. Pat Bowen for her knowledge of viticultural practices. I have learned a wealth of information from all of you and am very grateful for your help from start to finish. My stress levels would have been much higher had I not been supported by my friends and fellow students Vasilis Kokkoris, Eric Vukicevich, Taylor Holland, and Mehrbod Estaki. I am thankful for your countless suggestions and advice on experimental design, statistical analyses, and methodology and cannot thank you enough. I would like to extend my gratitude to the entire Hart lab at UBCO and lab technicians in José’s pathology lab in Summerland for helping with experimental setup and sample collection. A special thank you to Xeriscape Endemic Nursery and SeedsCo Community Conservation for supplying native seeds for the experiment. Finally, I would like to thank my parents for their continued support throughout my degree and Julia Million for being there during times of stress and doubt. 1 1. Introduction Wine grapes are an important economic commodity in Canada, especially in British Columbia where the industry has grown rapidly in recent decades (BC Wine Grape Council 2010; Statistics Canada 2019). Wine grape yield is limited by a variety of biotic stressors, many of which are fungal pathogens that cause a variety of grapevine trunk diseases (GTDs) (Gramaje, Úrbez-Torres, and Sosnowski 2017). Young vineyards are especially susceptible to young vine decline, (YVD), a disorder that is caused primarily by two grapevine trunk diseases known as Petri and Black foot disease. These disease complexes cause the growth and yield of grapevines to decline in the first five to seven years after planting (Úrbez-Torres, Haag, Bowen, and O’Gorman 2013). Upon inspection, infected vines typically have severe necrotic damage to the xylem and phloem in the roots and basal section of the trunk. Young vine decline is difficult to manage because the causal pathogens are diverse (Gramaje and Armengol 2011) and they can easily spread though infected material from nurseries, tools and equipment, and/or through open pruning wounds (Cardoso et al. 2013). Chemical treatment of soil is not ideal due its inability to remove all pathogens and/or reduce populations of beneficial microbes (Cabrera et al. 2015). Restrictions on chemical products and public demand for more sustainable approaches has led to the research of cover cropping as a solution to mitigate trunk diseases in vineyards. Cover crops can help reduce fungal pathogens by promoting beneficial and antagonistic microorganisms, releasing antifungal compounds into the soil, or by concealing the hosts making it difficult for pathogens to infect them. However, most studies highlight the effects of single cover crop species. My thesis aims to determine the effect of cover crop diversity, i.e. the additive effects of multiple cover crops on grapevine trunk pathogens. In this thesis I incorporate 2 both native plants and cultivated species as potential candidates to reduce disease symptoms and the abundance of fungal pathogens in the soil. 3 2. Literature Review Soil-borne pathogens significantly reduce crop yields worldwide, leading to economic hardships and food security risks in many nations (Macedo et al. 2017). Although rotation of annual crops has proven to be a robust strategy against pathogen buildup in soils, this method cannot be applied to perennial agroecosystems because crops can persist in the soil for many years and in some cases over a century (Huang et al. 2015). Most crops are prone particularly to negative plant-soil feedback (PSF) (Bennett and Klironomos 2018; Mariotte et al. 2018) which occurs when plants condition the soil in a way that makes it less beneficial for their own growth over time (Maron et al. 2016). The main culprit of negative PSF is the buildup of specialist pathogenic microorganisms that are specific to crops (in ’t Zandt et al. 2019; Kardol et al. 2015). Traditional cultivation approaches such as pesticide use and fallowing are likely to exacerbate negative PSF because they disrupt beneficial microbes (Helander et al. 2018; Vukicevich et al. 2016), and increase the risk of infection by soil-borne pathogens (Wiggins and Kinkel 2005a). 2.1 Suppression of pathogens in natural ecosystems Natural ecosystems often show increased resistance to soil-borne pathogens or disease outbreaks compared to agroecosystems (Van Bruggen et al. 2006). In natural grasslands for example, Garbeva et al. (2006) found a positive correlation between plant species richness and pathogen suppression while Mommer et al. (2018) observed decreases in fungal pathogenic diversity with increasing plant species richness. Mechanisms linking plant diversity to suppression are likely density dependence (fewer susceptible hosts for plant specific pathogens) (Lekberg et al. 2018) and maintenance of beneficial/antagonistic microorganisms via the creation of various niches from differential root exudation, plant litter and colonizable substrates (Garbeva et al. 2006). 4 Plant species richness is known to influence microbial community composition (Lange et al. 2015) and the removal of plant functional groups results in reduced microbial abundance (Chen et al. 2016) along with reductions in antagonistic plant growth promoting rhizobacteria (PGPR) (Latz et al. 2012). Increasing evidence suggests that soil microbiota can suppress soil-borne plant diseases in natural systems (Latz et al. 2015; Vukicevich et al. 2016) as demonstrated by pasteurized soils that lose their suppressive properties (Mendes et al. 2011). In light of the extensive literature describing the antagonistic effects of PGPR on soil-borne pathogens (Bhattacharyya and Jha 2012; Latz et al. 2016; Mauchline and Malone 2017), cultural practices should aim to increase these types of microbes. Recent studies linking the activity of beneficial microbes to plant identity provide incentive to diversify agroecosystems in an attempt to harbour beneficial microbes and create disease suppressive conditions (Bakker et al. 2010; Eisenhauer 2012; Latz et al. 2012). 2.2 Potential of cover crops to suppress pathogens Despite long cropping cycles, perennial agriculture which encompasses tree fruit production, viticulture and other arboricultural practices can increase plant species richness through cover cropping. Cover crops are plants that are grown with main crops during the regular season or between crops during off seasons. Cover crops have been used to improve soil quality, prevent erosion, increase nutrient supply, and reduce weeds in various cropping systems (Clark 2008). Cover cropping is typically implemented by seeding one or more plant functional groups such as grasses, legumes and/or brassicas in order to obtain multiple benefits (Messiga, Sharifi, and Munroe 2016a). Over the years, research has been dedicated toward uncovering companion plant combinations (Pickett et al. 2010) as well as cover crop systems that maximize nutrient 5 benefit and yield (Snapp et al. 2005), however their role in pathogen suppression is less understood. Cover crops may be an important tool to mitigate soil-borne disease in perennial cropping systems if they establish well, create unfavourable abiotic conditions for pathogens, and increase plant diversity in such a way that promotes beneficial and/or antagonistic microbial communities. Despite abundant literature linking plant diversity to pathogen suppression (Dignam et al. 2016; Garbeva et al. 2006; Van Elsas, Garbeva, and Salles 2002), manipulating cover crops to control soil-borne pathogens in perennial agriculture remains largely unexplored (Mazzola et al. 2002; Mazzola and Manici 2012). Findings from cover crop research in annual agriculture highlight the suppressive capabilities of cover crops (Wen et al. 2017) and provide insight into species richness, plant functional diversity, and plant identity as tools for suppressing soil-borne pathogens in perennial agroecosystems. 2.3 The link between plant diversity and soil-borne disease 2.3.1 Density dependence Density dependent infection of hosts is a well-illustrated mechanism for the spread of soil-borne pathogens in agroecosystems (Termorshuizen 2016). In monoculture systems, host detection and pathogen dispersal is easier (Mordecai 2011) and the Janzen-Connell hypothesis predicts that the likelihood of disease increases with the proximity of one host to another. In perennial agriculture the root systems of these crops often overlap (Jackson 2008), promoting infection of neighbouring plants. Root to root transmission may be important in vineyards, especially those with nematode infestations as root lesions can act as entry points (Zunke 1990). It is not unusual to observe symptoms in clusters of plants (Úrbez-Torres et al. 2014) making perennial systems especially prone to negative plant-soil feedback. 6 Cover crops in perennial systems may impede pathogen proliferation and incidence of disease through direct and indirect mechanisms. For example, the exudation of metabolites and/or synthesis of isothiocyanates that reduce pathogen load due to antifungal properties would be a direct mechanism of suppression. Alternatively, an indirect mechanism would be the suppression of a fungal pathogen by an antagonistic microbe that benefits from the presence of a cover crop (Table 2.1). In the case of diverse cover crop mixtures, both direct and indirect mechanisms may limit pathogen proliferation due to differential interactions between certain plant species, pathogens, and beneficial microbes (Vukicevich et al. 2016). For example, in a field seeded with a grass, a legume, and a brassica, the grass and legume may stimulate antagonistic bacteria, creating an indirect mechanism (Latz et al. 2015), while the mustard may delay pathogenic activity from the release of antifungal chemicals (Runno-Paurson et al. 2019). Mechanisms of suppression likely act on a temporal scale (Kasuya et al. 2006) and direct mechanisms may serve dual functions in which their effects also trigger an indirect function that confers suppression. Table 2.1 Proposed mechanisms for pathogen suppression from growing cover crops in a vineyard. Mechanisms may be direct or indirect and diverse cover crop assemblages may activate more than one mechanism at a given time. Mechanism Type Example Reference Dilution effect Direct Host camouflage by cover crops chemical signaling interference (Tomaškin, 2007) (Berlanas, López-Manzanares, & Gramaje, 2017) Root exudates Direct Growth suppression (Fahey, Zalcmann, & Talalay, 2001) Plant litter Indirect Promotes decomposer communities Inhibits nematodes (Bardgett & van der Putten, 2014) (Walker, 1997) Antibiotic metabolites Indirect Suppression via antagonistic microbes (Bhattacharyya & Jha, 2012) Arbuscular mycorrhizal fungi Indirect Competition for root space (Veresoglou & Rillig, 2012) Induced systemic resistance Indirect Immune response via beneficial microbes (Kloepper, Ryu, & Zhang, 2004) 7 2.3.2 Direct mechanisms of pathogen suppression 2.3.2.1 Dilution effect The dilution effect hypothesis (Figure 2.1 D) predicts that disease incidence will decrease with increasing plant diversity (Gilbert and Parker 2016). That is, as plant diversity increases, pathogen host density decreases. In addition to classic mammal-pathogen interactions, evidence for the dilution effect in plant-pathogen interactions is also present in the literature (Civitello et al. 2015). For example, Hantsch et al. (2014) observed a reduction in foliar fungal infection with increasing tree species diversity. Similarly, Termorshuizen (2016) found that total infected leaf area was much higher in monoculture fields compared to mixed species grasslands. Below ground, Maron et al. (2011) found that fungicide application altered the diversity-plant productivity relationship such that the biomass of monoculture stands increased compared to the non-fungicide control, eliminating the positive correlation between plant diversity and productivity. Just as non-host plants provide camouflage above ground by intercepting airborne pathogen propagules (Termorshuizen 2016), a similar mechanism could be at play in the soil. The root systems of non-host vegetation may provide a physical barrier (Tomaškin 2007) preventing pathogens from reaching the roots of susceptible hosts. Likewise, cover crops may interfere with host-pathogen chemical signaling or act as asymptomatic hosts (Berlanas et al. 2017) preventing pathogenic chemical signals from reaching the host. 2.3.2.2 Functional diversity and root exudate variation Increased plant functional diversity of cover crops may result in higher variation of root exudates (Figure 2.1 A) and potential suppression of soil-borne pathogens (Jimenez-Lopez et al. 2016; Kirkegaard et al. 2000). Exudates from legume seeds and roots have been shown to alter fungal pathogen loads (Lupwayi and Kennedy 2007) and legume cover crops have traditionally 8 been used to break non-legume pest cycles (Lupwayi and Kennedy 2007). Legumes produce a variety of secondary metabolites, including saponins, flavonoids and peroxidases, some of which have antimicrobial properties (Dakora and Phillips 1996). Legume peroxidases have been shown to inhibit growth of Fusarium oxysporum (Ye and Ng 2002) and other antifungal metabolites of legumes have shown suppressive capability against Rhizoctonia solani (Ye, Wang, and Ng 2000). Mixtures containing brassica species may also inhibit pathogens through root exudation (Rahman and Somers 2005). Chemical diversity among brassica exudates means that mixtures of these cover crops have the potential to limit the activity of disease complexes offering greater suppression (Mazzola et al. 2007; Yim et al. 2016). Plants from this functional group are known for their production of glucosinolates which are highly diverse, sulphuric compounds formed upon cell damage of roots (Olivier et al. 1999; Rosa and Rodrigues 1999). Glucosinolates form isothiocyanates upon hydrolyzation and have been studied for their growth inhibition as well as anti-fungal characteristics (White and Weil 2010). While brassicaceous plants have been used in annual cropping systems as “pathogen break crops” (Kirkegaard et al. 2000), research in perennial agriculture has shown that these plants also have potential to suppress fungal pathogens (Yim et al. 2016) and nematodes (Walker 1997). Isothiocyanate residues from brassicaceous crops are an effective way to manage apple root rot pathogens such as Pythium spp. and R. solani (Cohen and Mazzola 2006). In addition to legumes and brassicaceous plants, other cover crops may help reduce soil-borne diseases. When applied as green manure, buckwheat (Fagopyrum esculentum, Polygonaceae) decreased the activity of Verticillium dahliae, the causal agent of Verticillium wilt of potatoes (Wiggins and Kinkel 2005a) and increased antagonistic Streptomyces in soils 9 suffering from Alfalfa root rot (Wiggins and Kinkel 2005b). Streptomyces activity likely increased due to nutrients from green manures (Kinkel et al. 2012). In apple replant soils, Mazzola et al. (2002) found that wheat (Triticum spp.) cultivation increased apple tree growth and altered Pseudomonas populations. During decomposition wheat roots release toxins that can suppress nematodes such as Pratylenchus penetrans. Other species such as Gaillardia pulchella (Asteraceae) may be effective against nematode infestations in many fruit production systems due to root exudates that reduce egg viability at certain concentrations (Tsay, Wu, and Lin 2004). A combination of cover crops representing different plant functional groups could create an array of exudates with antifungal properties leading to effective, broad spectrum suppression of fungal root pathogens in perennial cropping systems (Merwin, Wilcox, and Stiles 1992). However, differences in plant species, pathogen species, and management strategies make it difficult to create general guidelines for selecting cover crops to mitigate soil-borne diseases. 2.3.2.3 Plant litter Plant litter from cover crops may reduce soil-borne pathogens indirectly by stimulating competitive saprophytic communities that are known to suppress soil-borne pathogens (Alabouvette, Lemanceau, and Steinberg 1993; Fravel, Olivain, and Alabouvette 2003). Many soil-borne fungal pathogens act as saprobes, decomposing already dead plant material (Bonanomi et al. 2019). Plant litter from cover crops could increase the abundance of competitive saprobes and increase intraspecific competition for pathogens (Lodge 1997). Many strains of Trichoderma are active mycoparasites, however they are also found in plant litter (Jaklitsch 2009), which could stimulate Trichoderma leading to enhanced suppression of soil-borne pathogens. 10 Plant litter from specific cover crops could act directly to suppress pathogens. The addition of brassica residues for example, has been shown to reduce damage from nematodes by altering male-female sex ratio (Walker 1997), and in vineyards the addition of chopped leaves from several brassica species reduced nematode abundance (Mcleod and Steel 1999). In fact, chopped brassica leaves and/or stems have conferred pathogen suppression in a number of different agroecosystems (Dutta, Khan, and Phani 2019) making plant litter a valuable mechanism for suppressing soil-borne pathogens. Figure 2.1 Cover crops increase plant diversity in a perennial agroecosystem which can lead to suppression of soil-borne pathogens. A: Antifungal exudates from certain plants directly inhibit pathogens by preventing spore germination or hyphal growth. B: Cover crops host PGPR in their rhizosphere which are capable of suppressing fungal pathogens. C: Cover crops stimulate PGPR capable of initiating an immune response in the host plant, reducing disease. D: Dilution theory. Cover crop roots mask the host plant or act as partial barriers reducing infectivity of pathogens. 11 2.3.3 Indirect mechanisms of pathogen suppression In perennial systems, cover crops can influence microbes indirectly in the rhizosphere as a result of normal plant processes such as root exudation and litter deposition (Steinauer, Chatzinotas, and Eisenhauer 2016). For example, cover crop inputs may increase the activity of antifungal metabolite producing microbes, leading to pathogen suppression (Eisenhauer, Reich, and Scheu 2012), or positively impact arbuscular mycorrhizal (AM) fungal communities which are known to increase nutrient availability for crops and compete with pathogens (Eke et al. 2016). Likewise, PGPR communities harboured by non-crop vegetation could provide induced systemic resistance (ISR) for main crops as an alternate form of pathogen suppression (Magnin-Robert et al. 2013). 2.3.3.1 Arbuscular mycorrhizal fungi While the literature about AM fungi inhibiting host pathogens is mixed (Holland et al. 2019) there is some evidence that these fungi may suppress nematodes and fungal pathogens under certain conditions (Veresoglou and Rillig 2012). For example, certain cover crops may host more AM fungi that trigger production of secondary metabolites like phenols and flavonoids; products that may be involved in plant defense (Mechri et al. 2015). Since AM fungi are known to colonize more than one host (Smith and Read 2008), colonization of cover crops could indirectly aid in the colonization of perennial rootstocks by AM fungi, providing nutritional benefit and defence against soil-borne pathogens. 2.3.3.2 Plant Growth Promoting Rhizobacteria PGPR are known to produce inhibitory compounds that target fungal pathogens (Verma et al. 2013), increase plant growth (Niu et al. 2012), increase yield (Zaccardelli et al. 2011), and create suppressive soils. For example, perennial ryegrass was associated with the gene 12 expression of PGPR antifungal metabolites throughout a plant diversity gradient (Latz et al. 2012). PGPR isolates capable of suppressing mycelial growth have been isolated from the rhizosphere of corn (Bjelić et al. 2018; Djuric et al. 2011). Increasing cover crop diversity in agroecosystems could stimulate PGPR activity via enhanced carbon inputs and root exudation creating suppressive soils (Campos et al. 2016; Garbeva et al. 2006). If plants harbour distinct PGPR strains, cover crop diversity could be crucial to pathogen suppression if they produce different antifungal compounds (Figure 2.1 B). Two potent compounds, 2,4-diacetylphloroglucinol (DAPG) and pyrrolnitrin (PRN), are produced by many PGPR such as Pseudomonas, Serratia and Bacillus (Raaijmakers, Vlami, and De Souza 2002). In an in-vitro study, Boruah and Kumar (2002a) demonstrated antifungal activity of Pseudomonas fluorescens derived DAPG against Fusarium solani and Fusarium oxysporum, that are associated with disease complexes (Chebil et al. 2017). DAPG can also affect other soil-borne pathogens such as plant parasitic nematodes by reducing hatchling mobility (Cronin et al. 1997). Pyrrolnitrin is an effective antifungal metabolite due to its close resemblance to fungicides Fenpiclonil and Fludioxonil, as well as its antagonistic activity toward Fusarium spp. and other pathogenic fungi (Ligon et al. 2000; Pillonel et al. 2003). Pyrrolnitrin is reported to remain in the soil for up to 30 days and its gradual release from lysing cells contributes to inhibition of fungal pathogenic activity (Dwivedi and Johri 2003). Cover crops may further suppress pathogens by harbouring PGPR that produce enzymes and/or siderophores which make nutrients inaccessible to pathogens (Kloepper et al. 1980) Chitinase is an enzyme produced by strains of Bacillus, Pseudomonas and Streptomyces that hydrolyses fungal cell walls (Hjort et al. 2010; Mauchline and Malone 2017). In vitro experiments showed that Pseudomonas-derived chitinase inhibited growth of Cylindrocarpon 13 destructans, a major cause of root rot in ginseng (Han, Park, and Kim 2017; Junfan et al. 2012). Iron-chelating siderophores produced by PGPR limit nutrient uptake by soil-borne fungal pathogens (Vida, Cazorla, and de Vicente 2017) by forming iron complexes in the soil, making nutrients unavailable to the pathogen (Kloepper et al. 1980; Pal and Gardener 2006). Recent studies have shown that siderophore extracts from Pseudomonas syringae reduce F. oxysporum spore germination in vitro (Yu et al. 2017). Since PGPR genotypes and communities in the rhizosphere vary with plant species (De La Fuente, Landa, and Weller 2006; Landa et al. 2006), incorporation of cover crops could create suppressive conditions by harbouring a diverse PGPR community that creates multiple secondary metabolites that target soil-borne pathogens. 2.3.3.3 Induced systemic resistance In addition to supporting PGPR that produce volatile metabolites that harm pathogens, cover crops may also attract PGPR species that provide suppression through ISR (Figure 2.1 C). This type of suppression occurs when an immune response from the plant is triggered by a pathogen or in this case, by beneficial rhizobacterium such as Bacillus or Pseudomonas spp. through chemical signaling (Beneduzi, Ambrosini, and Passaglia 2012). These bacteria have shown suppressive capacity against both foliar and root diseases of many annual crops (Kloepper et al. 2004), and split-pot/soil separation experiments which separate PGPR from pathogens demonstrate that these bacteria initiate an immune response of the plant via chemical signaling without direct contact from the pathogen itself (Ahmad et al. 2013; Larkin, Hopkins, and Martin 1996). PGPR such as Burkholderia have induced immune responses in grapevines in the presence of temperature stresses (Choudhary et al. 2016) and other species, including Bacillus subtilis and Pseudomonas fluorescens, are known to induce a response in the presence of grey 14 mould pathogens, decreasing leaf and fruit decay (Aziz et al. 2016; Magnin-Robert et al. 2013). The molecular processes involved in ISR are diverse which could indicate the specificity of the host-symbiont-pathogen system in question (Drogue et al. 2012; Lakkis et al. 2019). Nevertheless, the aforementioned PGPR that are specific to grapevines were isolated from the rhizosphere of “healthy” vineyards (Trotel-Aziz et al. 2008). Management strategy can shape bacterial communities (Burns et al. 2016) and possibly the cultivation and persistence of grapevine specific PGPR from season to season. 2.4 Plant provenance and suppression of soil-borne pathogens The origin or provenance of cover crops may be a vital factor regarding suppression of soil-borne pathogens in perennial cropping systems. Native and introduced plants have evolved different traits and vary in their life history strategies, which may impact plant-soil feedback. For example, indigenous plants may limit pathogen propagation by attracting native, beneficial fungi and bacteria through efficient coevolutionary mechanisms (Revillini, Gehring, and Johnson 2016). Native plants might reduce competition in perennial systems due to different nutrient and water requirements, especially in drier climates (Costello 2010). The use of native plants in perennial agroecosystems is largely unexplored (Shields et al. 2016; Vukicevich et al. 2019). If resident soil microbes (PGPR, AM fungi, and other beneficial fungi) colonize native plant rhizospheres more efficiently compared to introduced plants, this could lead to heightened suppression of pathogens. 2.4.1 Plant-soil feedback with resident PGPR PGPR are often found in the soil of natural ecosystems that are adjacent to agricultural plots (Schisler and Linderman 1984; Van Elsas, Garbeva, and Salles 2002). Many studies have demonstrated suppression of soil-borne diseases when PGPR from natural ecosystems are 15 amended to agricultural soils (Goudjal et al. 2014; Ridout and Newcombe 2016; Schisler and Linderman 1984; Van Elsas et al. 2002). Ridout and Newcombe (2016) speculated that agricultural soils may still contain remnants of PGPR communities from natural ecosystems which could be restored given the appropriate conditions. The lower abundance of PGPR found in disturbed forest sites compared to undisturbed sites (Cordero et al. 2017) provides support for this speculation. Establishment of native plants in cropping systems may encourage the proliferation of PGPR communities and help suppress soil-borne diseases (Kinkel, Bakker, and Schlatter 2011). 2.4.2 Native plants and environmental conditions It is well known that moist soil conditions generally favour soil-borne fungal pathogens (Duncan, Graham, and Timmer 1993) which is an inconvenience for the management of perennial crops requiring irrigation (Merwin et al. 1992). Grapevines and fruit trees use water as a nutrient solvent, meaning nutrient uptake occurs when the soil is moist. In arid climates, growers could accelerate soil desiccation after irrigation by establishing native plants within rows as opposed to commercial cover crops less adapted to xeric conditions. Using only drip-irrigation to introduce water directly into the rhizosphere of the crop would conserve water and lower moisture content within the rows, possibly reducing the spread of soil-borne fungal pathogens. 2.4.3 Local adaptation of plants and soil microbiota Native plants and beneficial microbes may provide better suppression compared to introduced species due to local adaptation of their environment. For example, plants that are adapted to dry, hot climates will be more tolerant of these conditions and better able to maintain normal plant functions. Conversely, some commercial plants adapted to cool or moist climates 16 could become stressed, leading to reductions in metabolic activities and overall contribution to the rhizosphere (Song et al. 2017). Likewise, resident PGPR may perform better than commercial varieties, some of which have failed to persist in agricultural soils (Bashan, Holguin, and De-Bashan 2004; Cummings 2009; Jacoud et al. 1998). Incorporation of native plants as cover crops in agricultural soils may be more effective than commercially available species because native plants have the potential to stimulate resident beneficial microbes (Holland et al. 2016; Schisler and Linderman 1984) , increasing opportunity for mutualism with crops. 2.4.4 Future directions It is clear that cover crops can benefit agricultural ecosystems by increasing nutrient availability, adding organic matter, and maintaining soil structure (Clark 2008; Vukicevich et al. 2016). Although single cover crop species have shown potential to suppress soil-borne crop diseases, it is unclear whether cover crop assemblages may confer pathogen suppression more effectively than any single species on their own. While it is known that some cover crops have biofumigant effects and others have been shown to increase beneficial microbes, whether multiple cover crop species when sown together will confer suppression through the mechanisms described above remains to be determined. This thesis aims to answer that question. 17 3 Mitigation of young vine decline with cover cropping 3.1 Background Grapevines (Vitis vinifera L.) are one of the most widely grown crops worldwide and an important economic commodity, especially in British Columbia where vineyards account for a total of 3,737 hectares (Statistics Canada 2016). Grapevines experience multiple challenges, including competition with weeds (Klodd et al. 2016), nutrient leaching (Messiga et al. 2016a), drainage (Costantini et al. Barbetti 2006), topography (Arnó et al. 2009) root lesion nematodes (Rahman and Somers 2005), viral infections, and especially fungal diseases, that reduce profit for growers (Gramaje, Úrbez-Torres, and Sosnowski 2018). Although historic reports of GTDs exist (Maluta and Larignon 1991), they gained a considerable amount of attention in the late 1990s (Úrbez-Torres et al. 2013) likely due to the phase out of effective fungicides in many countries. During this time, wine grape production increased in Australia, Canada, United States, and South Africa among other countries (Anderson 2005; Sumner and Bombrun 2001; Townsend, Kirsten, and Vink 1998), and GTD research was gaining popularity. 3.1.1 Young vine decline Young vine decline (YVD) is a challenge that occurs in British Columbia and other major wine grape regions around the world (Gramaje and Di Marco 2015; Úrbez-Torres et al. 2013). YVD results in symptoms such as stunted growth, reduced yield, delayed fruiting, and root necrosis, and eventually death in young vineyards in the first five to seven years after planting (Úrbez-Torres et al. 2015). Young vine decline is caused by Petri and Blackfoot disease, which are described as disease complexes whereby the physical symptoms observed are a result of abiotic and biotic factors. There are over 50 fungi that are linked to YVD (Úrbez-Torres et al. 2015). Among many of them is Ilyonectria, a genus of soil-borne fungi that contains several 18 species of grapevine pathogens (Morales-Cruz et al. 2018). In addition to grapevines, these fungi are known to infect the roots of certain apple and cherry cultivars (Watson, Forge, and Nelson 2018a) and are considered opportunistic, meaning they proliferate under the appropriate environmental conditions (Holland et al. 2019). Ilyonectria is not only confined to vineyards but also is found in grapevine nurseries all over the world, which often serve as breeding grounds for the pathogen (Álvarez-Pérez et al. 2017; Cardoso et al. 2013; Gramaje and Di Marco 2015). 3.1.2 Mode of infection If GTD pathogens are present in the vineyard, grapevines may be infected by spores through pruning tools, wounds, machinery, and contaminated water (Cardoso et al. 2013; Eskalen, Feliciano, and Gubler 2007). Soil-borne propagules may invade through lesions made by nematodes or damaged roots (Zunke 1990). As many YVD pathogens are generalist species, conversion from an apple or cherry orchard without fumigation may present an increased risk of infection (Watson et al. 2018b). Even if GTD pathogens are not present in the vineyard, they may be introduced via contaminated propagation material (Langenhoven et al. 2018) and may colonise pruning material that is left on the ground between rows. GTD pathogens can form fruiting bodies on the bark from where conidia spores are released (Gubler et al. 2005). 3.1.3 Current approaches In Canada there are no commercially available fungicides or fumigants for managing grapevine trunk diseases (Health Canada 2017). Methyl bromide, a once popular soil fumigant has been phased out due to its toxicity, effects on ozone depletion (Schneider et al. 2003), impact on AM fungi and other beneficial organisms (Bendavid-Val et al. 1997). Available fumigants such as 1,3-dichloropropene and chloropicrin do not protect against the full spectrum of fungal 19 pathogens, including Phytophthora and Fusarium (Cabrera et al. 2015). Growers also rely on fungicides applied directly to plants as a liquid or powder coating, however they can enter the soil and accumulate overtime, reducing microbial diversity and activity (Baćmaga, Wyszkowska, and Kucharski 2019). Other attempts to control GTDs include hot water treatment whereby propagation material is soaked in hot water (~50°C) for a specified time (Fourie and Halleen 2004a; Gramaje et al. 2009). However, this treatment offers no long term protection (Bruez et al. 2017) and improper procedures can damage propagation material or reduce vigour (Waite and Morton 2007). 3.1.4 Cover crops Cover cropping is a potential tool to mitigate YVD in vineyards. Traditionally, cover crops have been grown to reduce soil erosion (Corti et al. 2011), increase available nitrogen for grapevines (Bair, Davenport, and Stevens 2008), control pests (Irvin, Hagler, and Hoddle 2018), and suppress weeds via allelopathy (Khanh et al. 2005). Growers also use cover crops to decrease vegetative growth in high vigour situations in order to reduce canopy cover and improve the microclimate for ripening fruit (Monteiro and Lopes 2007). Although cover crops have a long history of use in vineyards, their potential to mitigate soil-borne diseases has not been fully explored. 3.1.5 Cover crops and soil-borne disease in perennial systems Existing literature highlights the biofumigant effects of brassicaceous crops (mustards/crucifers) that have exhibited suppression of soil-borne pathogens in vineyards and nurseries (Bleach, Jones, and Jaspers 2010; Whitelaw-Weckert et al. 2014). Other cover crops such as forbs, legumes, and grasses may help reduce soil-borne diseases by harbouring beneficial and antagonistic microbes (Mazzola et al. 2002; Wiggins and Kinkel 2005b), or via host dilution 20 in which the risk of infection decreases with increasing host diversity (Ostfeld and Keesing 2012). These various mechanisms of suppression provide an incentive to implement cover crop diversity in vineyards as a management strategy for soil-borne disease. 3.1.6 Plant diversity and ecosystem services Natural systems with inherently high plant diversity often have disease suppressive soils (Boruah and Kumar 2002b) and greater biomass production, carbon sequestration, and resistance to disturbances as compared to agroecosystems in the same area (Brockerhoff et al. 2017). These ecosystem services are lost when natural habitats are disturbed by anthropogenic activity (De Leijster et al. 2019). A diverse plant community could increase soil microbial diversity (Steinauer et al. 2016), microbial biomass (Eisenhauer et al. 2010), and biological activity (Lange et al. 2015) via root exudation, rhizodeposition, and plant litter (Mariotte et al. 2018; Morgan, Bending, and White 2005) which could restore ecosystem services. Soils from long term grasslands and forests contain PGPR that can suppress pathogens when added to agricultural soil (Garbeva et al. 2006; Van Elsas et al. 2002). Implementation of cover crops in vineyards could increase the activity of PGPR (Latz et al. 2012, 2015) which are commonly found in soil (Lee et al. 2015; Shen et al. 2015). If beneficial microbes can be isolated from nearby soil and used to reduce disease symptoms in agricultural plots, it is possible that cover crops can encourage proliferation of microbes that suppress fungal pathogens. 3.1.7 Plant provenance To date, the majority of cover crop experiments use commercially available rather than native plants (Irvin, et al. 2014; Langenhoven et al. 2018). The efficacy of native cover crops has not been studied extensively in vineyards (Shields et al. 2016; Tompkins 2010; Vukicevich et al. 2018, 2019). Plant provenance may be as important as diversity due to local adaptation and 21 coevolution between native plants and their microbial counterparts (Revillini et al. 2016). This is observed in highly specific legume-rhizobia interactions (Lupwayi and Kennedy 2007; Ratcliff, Kadam, and Denison 2008) and could hold true for other plant-microbe interactions. In many “home vs. away” studies, plants perform better when grown with soil from the same region as where the plant grew naturally (Mehrabi, Bell, and Lewis 2015; Rúa et al. 2016; Smith et al. 2012). Moreover, the decomposition rate is increased when plant litter is sympatric to the soil compared to allopatric soils (Tian et al. 2018; Veen et al. 2019). Since plant-microbe interactions heavily depend on genotypic differences (Madritch, Greene, and Lindroth 2009) and resource availability (Morgan et al. 2005; Wei et al. 2013), microbial communities under native plants may differ compared to common cultivar cover crops leading to differences in ecosystem services and possibly the suppression of pathogens. Given these circumstances, native cover crops may stimulate and harbour local microbial communities through more efficient interactions based on root exudation, litter decomposition and chemical signaling that have been subject to selective forces over many generations (Lambers et al. 2009). 3.1.8 Objectives Despite the numerous studies that highlight the benefits of cover crops on grapevine growth response and pathogen protection, few studies evaluate the potential of cover crop diversity and provenance to manage grapevine trunk diseases (Langenhoven et al. 2018; Vukicevich et al. 2018, 2019). To determine whether cover crops could help mitigate YVD, specifically Blackfoot disease in vineyards, I asked the following questions: 1. Does cover crop diversity (species richness) reduce Blackfoot disease symptoms in vines and the abundance of Ilyonectria liriodendri in the soil and roots? H0: Increasing plant diversity via cover crops has no effect on disease severity in young grapevines or abundance of Ilyonectria. 22 H1: Increasing cover crop diversity will lead to pathogen suppression via density dependent mechanisms (lack of hosts/dilution effect) or augmentation of beneficial microbes thus resulting in fewer disease symptoms and a reduction in Ilyonectria abundance H2: Increasing cover crop diversity results in higher incidence of disease because cover crops act as non-target hosts, maintaining spore bank and hyphal networks. 2. Does cover crop provenance (plant origin) affect the severity of Blackfoot disease symptoms and abundance of Ilyonectria liriodendri in soil and roots? H0: Cover crop provenance does not affect the severity of symptoms. Native and cultivar cover crop treatments will result in similar disease symptoms and Ilyonectria abundance. H1: Cover crops comprised of native plants are better adapted to local soils and are more effective pathogen suppressors due to efficient stimulation of beneficial antagonistic microbes. This will result in fewer disease symptoms and suppress Ilyonectria pathogens. H2: Cover crops comprised of native perennial plants mature more slowly and take longer to condition the soil, leading to ineffective suppression of Ilyonectria. Grapevines growing in soil conditioned by native plants will have more severe symptoms. 3. Does cover crop diversity increase fungal diversity and is fungal diversity associated with disease symptoms? H0: Cover crop diversity does not increase fungal diversity and fungal diversity does not affect symptoms of disease H1: Cover crop diversity positively correlates with fungal diversity and the presence of diverse fungi reduces disease symptoms. H2: Cover crop diversity increases fungal diversity, however fungal communities do not affect disease symptoms. 3.2 Materials and Methods 3.2.1 Establishment of experiments In order to understand the effects of cover crops on Blackfoot disease symptoms, I established two separate greenhouse experiments at the Summerland Research and Development Centre (SuRDC) in Summerland, BC, Canada: 23 A. “Cultivar Study”. This experiment used four cover crops that are commonly used in vineyards (Table 3.1). Crimson clover (Trifolium incarnatum L.) and buckwheat (Fagopyrum esculentum Moench) were purchased from WestCoastSeeds© and white mustard (Sinapis alba L.) and wheatgrass (Triticum aestivum L.) were sourced from Richters©. B. “Native Study”. This experiment used four plants native to southern interior British Columbia as cover crops. I used white yarrow (Achillea millefolium L.) and silky lupine (Lupinus sericeus Pursh) which were sourced from Xeriscape Endemic Nursery in West Kelowna, BC, along with bluebunch wheatgrass (Pseudoroegneria spicata Pursh, Löve) and Holboell’s rockcress (Boechera hoellbelii Hornem, Löve) which were collected from Summerland BC and SeedsCo Community Conservation respectfully (Table 3.1). Holboell’s rockcress is likely a naturalised plant native to Greenland (Windham and Al-Shehbaz 2007). 3.2.2 Experimental design 3.2.2.1 Cover crop diversity and grapevine growth responses In order to observe the effect of cover crop diversity on incidence of Blackfoot disease, I established treatments whereby each species for the native and cultivar studies was grown on its own (monoculture), with one other species (mixture), and with all species (all native or all cultivar) totalling 11 treatments (Table 3.2). In addition to the cover crop treatments, the cultivar study had an additional “fallow” treatment included to determine the incidence of disease and grapevine growth without the addition of inoculant or cover crop. 24 Table 3.1 Selected cover crops for native study and cultivar study greenhouse experiments. Native plants were sourced in the Okanagan Valley while seeds of cultivar plants were ordered online. Common name Latin binomial Status Functional group Life history Crimson clover Trifolium incarnatum Cultivar Legume Annual Wheat Triticum aestivum Cultivar Grass Annual Buckwheat Fagopyrum esculentum Cultivar Forb Annual White mustard Sinapis alba Cultivar Brassica Annual Silky Lupine Lupinus sericeus Native Legume Perennial Bluebunch Wheatgrass Pseudoroegneria spicata Native Grass Perennial White Yarrow Achillea millefolium Native Forb Perennial Holboell’s Rockcress Boechera hoellbelii Native Brassica Perennial 3.2.2.2 Cover crop treatments Both experiments consisted of a randomized block design with 10 blocks (five blocks per greenhouse bench) to account for environmental variation within the greenhouse and the elongated dimensions of the benches. Each experiment consisted of 11 treatments and 10 replicates per treatment, totalling 120 experimental units (pots) including fallow for each experiment. Each treatment was assigned to its block via random number generation (Table 3.2). Treatments were standardized to four plants per pot regardless of diversity level. Table 3.2 Cover crop combinations and their corresponding diversity level for native study and cultivar study experiments. Initially, each study contained four monocultures, six mixtures, and one treatment in which all cover crops were grown together.A. Cover crop combinations for native study B. Cover crop combinations for cultivar study # Treatment Diversity # Treatment Diversity 1 Yarrow 1 12 White mustard (WM) 1 2 Rockcress 1 13 Buckwheat 1 3 Silky lupine 1 14 Wheatgrass 1 4 Bluebunch Wheatgrass (BBWG) 1 15 Crimson clover (CC) 1 5 Yarrow – Rockcress 2 16 WM – Buckwheat 2 6 Yarrow - Silky Lupine 2 17 WM – Wheatgrass 2 7 Yarrow – BBWG 2 18 WM – CC 2 8 Rockcress – Silky Lupine 2 19 Wheatgrass – Buckwheat 2 9 Rockcress – BBWG 2 20 Wheatgrass – CC 2 10 BBWG - Silky Lupine 2 21 Buckwheat – CC 2 11 All native 4 22 All cultivar 4 23 Fallow N/A 25 3.2.2.3 Location and greenhouse conditions Plants were grown in a greenhouse at SuRDC (49°33'57.8"N 119°38'10.0"W) from April 27th 2018 to March 4th 2019. To reduce stress during warm summer months, the room was cooled by a fog system that turned on when temperatures rose above 28ºC and shade curtains were activated from 12:30pm until sunset. During the spring and fall, daytime and nighttime temperatures were kept at 20ºC and 15ºC respectively with supplementary lights to maintain 15-hour days. 3.2.2.4 Soil Soil was collected at SuRDC on March 21st 2018 from a cherry planting. This soil is described as a Skaha loamy sand which had previously harboured apples (Braeburn grafted to M.26 rootstock) until it was replanted with sweet cherry in 2014 (Watson et al. 2018c). Fusarium, Ilyonectria, and Rhizoctonia species which are known to infect grapevine roots were isolated previously from this site (Watson et al. 2018b) increasing the likelihood of resident pathogens occurring in the soil. Soil was homogenized on a large tarp, transferred into three litre nursery pots, and placed in the greenhouse at SuRDC for the duration of the study. 3.2.2.5 Pathogen incubation and inoculation I inoculated the collected field soil with three isolates of Ilyonectria liriodendri (SuRDC 340, 60, 393) to increase the likelihood of infection. This pathogen was previously isolated from vineyards in British Columbia (Úrbez-Torres et al. 2014) and selected for their pathogenicity, ability to sporulate, and fulfillment of Koch’s postulates. The addition of inoculum also ensured the presence of Blackfoot pathogens that could infect grapevine cuttings. Single cultures of each isolate were incubated for one week (22ºC) using 5% potato dextrose agar (PDA) media autoclaved at 121ºC for 30 minutes. Cultures were propagated by cutting a 1 cm2 slice of 26 colonised agar and placing it upside down on new PDA until enough material was available for inoculation of all pots. Plates were examined under a compound light microscope to observe sporulation before inoculum preparation. A separate 106 conidia spore suspension was created for each isolate by flooding the agar plates with 1% tween solution and disturbing the surface with an inoculation loop. The liquid was then passed through double layer cheese cloth to form the stock solution. A hemocytometer was used to count conidia spores to prepare the specified concentration. Soil was inoculated on April 24th, 2018, by pouring 45 ml of inoculum on top of the soil, in a circle near the centre of the pot. 3.2.3 Germination and growth of plants 3.2.3.1 Cover crops Seeds were germinated in starter trays with an equal mixture of field soil and Sunshine® Mix #4 (Sun Gro Agawam, MA, USA) peat/perlite mix (autoclaved at 121°C, 1.5 hours) before transplantation into three litre pots on April 27th, 2018. Due to lower germination of native plants, pots were re-seeded following transplantation so that there were four plants in each pot. Pots were watered by hand with no additional fertilizer and allowed to dry before subsequent watering. During the summer months, pots were watered more frequently to prevent drought and heat stress. Cultivar study plants were grown in the greenhouse until July 30th, 2018, while native study plants were grown for an additional month until September 3rd, 2018 due to the perennial nature of the native plants. At harvest, soil was removed from roots followed by a thorough rinsing to remove as much soil as possible. Plants were bagged and taken to UBC Okanagan where they were dried and weighed. 27 3.2.3.2 Vine propagation and growth Vitis vinifera (Chardonnay) cuttings were collected from a vineyard at SuRDC on February 15th 2018 (49°33'56.2"N 119°37'46.7"W) and placed in a cold storage room at 2ºC until propagation. Cuttings were taken out of cold storage on June 5th, 2018 and cut into two nodes (~30cm) with a pruning tool. Canes with visible signs of mould on the surface were discarded and the remaining canes were put in a plastic container filled to 3cm with water then placed in the greenhouse until the appearance of roots. The rooted cuttings were transplanted on July 31st,2018 for the cultivar study and August 1st, 2018 for the native study. Vines in the cultivar study were grown for approximately four and a half months while those in the native study were grown for seven months to maximize exposure to pathogens. During the first week any vines that died were removed. Initially, grapevines were given 150 ml Miracle Gro (20N-20P-20K) soluble fertilizer on a weekly basis according to manufacturer’s instructions. Nutrients were reduced to the same volume of (15N-15P-18K) after six weeks followed by dilutions to induce stressful conditions (Table A1). Cultivar study grapevines were harvested on December 12th, 2018. At harvest, soil was removed from roots followed by a thorough rinsing with reverse osmosis water. Root samples were placed into paper bags and held at 4ºC until January 2019. Cuttings grown in soil conditioned by native cover crop treatments were left without fertilizer from December 7th, 2018 to January 6th, 2019 to further induce nutrient stress. On January 8th , 2019, leaves were removed from each vine to further stress the plants and increase susceptibility to pathogens. Grapevine cuttings were removed from the greenhouse on February 12th and put into cold storage until they were destructively harvested on March 4th, 2019. 28 3.2.4 Dependent variables 3.2.4.1 Incidence of disease To determine the extent of necrotic tissue in the Chardonnay plants, a cross section was cut 1 cm from the basal end of each and placed on a scanner (Epson Expression 1680). Images were created with Adobe Photoshop CS2 and analysed with WinRhizo Pro ©2013 (Regent Instrumentals Quebec, QC, Canada ) by defining colour classes representing necrotic and healthy tissue. Percent necrosis was determined by dividing the area of necrotic tissue by the total analysed area. For the native plant treatments, an additional measurement was performed. After image analysis, the progression of necrosis from the basal end to the top was determined by cutting the cane into 1 cm sections and looking for signs of necrotic tissue under a dissecting microscope (VWR Bioimager BRC-1600). Disease progression was rounded to the nearest centimetre. 1 cm cane fragments were then plated on agar (described above) to re-isolate the inoculum from the cane. 3.2.4.2 Grapevine physiology Data pertaining to chloroplast concentration, shoot and root biomass, total leaf area, and available phosphorus were collected as indicators to evaluate changes in vine performance: 3.2.4.2.1 Leaf greenness Leaf greenness was measured using a chlorophyll meter (Minolta SPAD-502) by randomly measuring five leaves per replicate and averaging those five measurements. Readings for the cultivar study were taken on December 7th 2018 and SPAD measurements from native study treatments were recorded on January 9th 2019. 3.2.4.2.2 Root and shoot biomass 29 Root material was washed with a 1% tween solution, surface sterilized in 10% sodium hypochlorite for one minute, and rinsed with RO water. Roots from the cultivar study were separated from shoots by pruning just above the emergence of the highest root on the cane. Roots from plants in the native study were separated from shoots by trimming all roots from the base of the cane. Roots were dried at 60ºC for 36 hours then weighed. 3.2.4.2.3 Leaf area To measure total leaf area, leaves were cut at the base (excluding stem) a day before harvest and scanned using a Li-Cor 3000A leaf area meter ©1982 (Li-Cor Lincoln, NE, USA). The meter was calibrated with 25 and 50 cm2 disks before each run and belts were cleaned as required. Leaves were bagged and frozen (-20ºC) until phosphorus analysis. 3.2.4.2.4 Available phosphorus Total phosphorus was measured by randomly sampling 0.5g of leaf tissue which was placed in a crucible and dried at 550 ºC (ThermoLyne 48000) for three hours. Phosphorus content was extracted from the dried tissue and quantified according to Murphy and Riley (1962). Percent transmittance (amount of phosphorus in leaves) was measured in a double beam spectrometer (VWR UV-6300PC). 3.2.5 Molecular data To determine the effects of cover crop diversity and provenance on the abundance of Ilyonectria liriodendri, I assayed the abundance of DNA extracted from soil. Soil samples were also used to measure fungal community composition and species richness. Soil samples were taken from each nursery pot after inoculation with I. liriodendri before seeding with cover crops (starting soil) and again before removal of cover crops (conditioned soil). Root samples were collected after four and five months of growth for cultivar study and native study experiments 30 respectfully. I used a digital droplet (dd) PCR assay to observe changes in the abundance of I. liriodendri and Illumina sequencing of the internal transcribed spacer (ITS)2 region to uncover fungal community composition. 3.2.6 Isolation of DNA 3.2.6.1 DNA extraction from soil Soil samples were taken before and after soil conditioning by cover crops. On April 26th, 2018, three core samples (1cm diameter) totalling approximately 20 grams were collected from the centre of each pot at a depth of five centimetres. Another set of soil samples was collected on July 30th and September 5th, 2018 from the cultivar and native study respectfully before removal of cover crops using the same method described above. Soil cores from each pot were homogenized and prepared in the University of British Columbia Okanagan laboratories for DNA extraction and downstream analysis. Soil was dried at 60ºC for 24 hours and then 0.5 g from each sample was used for DNA isolation. DNA was extracted using the FastDNA Spin Kit for Soil (MPBio ©2018) according to manufacturer’s instructions. This resulted in approximately 90l of eluded DNA per sample with an average concentration of 30ng/l (nanodrop 1000c 2009). DNA was stored at -80ºC until PCR and Illumina sequencing. 3.2.6.2 DNA extraction from roots After surface sterilizing, one gram of grapevine root subsamples was placed in a 15 ml falcon tube and frozen at -20ºC until DNA extraction. Roots were then broken down in a mortar and ground up with liquid nitrogen until very small root fragments remained. 0.5 g of ground roots were put into lysing tubes and the rest was frozen at -20ºC in the falcon tubes. DNA extractions were done using the FastDNA Spin Kit for Soil (MPBio ©2018) with a few 31 modifications. Lysing was performed at an intensity of 6.5 m/s instead of the standard 6.0 m/s and initial centrifugation was extended to 10 minutes to promote complete separation of root tissues and nucleic acids. 3.2.7 Droplet digital assay In order to detect the Ilyonectria isolates used in the inoculum, a specific primer/probe assay that targets the beta-tubulin region was designed (Holland et al. 2019). The primer, forward 5’ – CGAGGGACATACTTGTTTCCAGAG – 3’ (Tm 61, GC 60%), reverse 5’ – TCAACGAGGTACGCGAAATC - 3’ -R (Tm 62, GC 50%), and probe TGTCAAACTCACACCACGTAGGCC amplify beta-tubulin, a highly conserved gene with 1-2 copies (Zhao et al. 2014), making it ideal for the quantification of spores and/or septate hyphae in soil and roots. Reactions consisted of 10 l of Supermix (Supermix for probes no dUTP by Bio-Rad Inc., Hercules, CA, USA), 7 l DES water, 1 l primer/probe, and 2 l of DNA for a total volume of 20l. Droplets were created using the Bio-Rad QX100 Droplet Generator by using the total reaction volume per sample and 70l of Bio-Rad Droplet Generator Oil for Probes. PCR runs were completed in the C1000 Thermal Cycler (Bio-Rad) with the following conditions: Initial heating at 95ºC for 10 min, 94 ºC for 1 min, annealing at 59ºC for 2 min x 44 cycles. Fluorescence was measured using the QX 100 Droplet Reader (Bio-Rad) and Quantalife software (version 1.7.4. Bio-Rad) by selecting FAM-HEX as the fluorescence setting. The threshold was set manually at 3000 using a pure positive as well as an environmental positive and negative control as a reference. For analysis, copy number for each sample was back-calculated to represent the number of copies per gram of soil and root using a formula described in Kokkoris et al. (2019). 32 3.2.8 Illumina sequencing and bioinformatics Illumina sequencing was completed at the Centre for Comparative Genomics and Evolutionary Bioinformatics (Dalhousie University, Halifax Nova Scotia). Amplicon sequencing of the ITS2 sub-region was performed for each treatment (n=5 for cover crops, n=10 for starting soil) using primers ITS86F 5’-GTGAATCATCGAATCTTTGAA-3’ and ITS4R 5’-TCCTCCGCTTATTGATATGC-3’. Samples were demultiplexed, barcodes were removed, and returned as individual, per-sample .fastq files from the sequencing facility. 3.2.8.1 Filtering, denoising, and chimera removal Initial quality control and amplicon filtering was performed using Divisive Amplicon Denoising Algorithm (dada2 package 1.12.1) in R statistical software (R version 3.6.1, 2019) by following the DADA2 ITS Pipeline Workflow 1.8 (Callahan 2019). Primers, their reverse orientation, and complements were removed from reads using cutadapt (version 2.3). Sequence reads were filtered and trimmed using filterAndTrim (dada2) by setting standard parameters (maxN=0, truncQ = 2, rm.phix = TRUE, and maxEE=2. Forward and reverse reads were dereplicated using derepFastq before applying the DADA algorithm (Callahan et al. 2016) Denoising was done by pooling samples (pool = TRUE). Sharing information across samples makes it easier for singletons appearing multiples times across samples to be resolved. Paired reads were merged and an amplicon sequence variant (ASV) table was created. Finally, chimeras were removed using removeBimeraDenovo (method="consensus") resulting in high quality, filtered reads. The number of reads retained at each dada2 step is shown in Table A3. 33 3.2.8.2 Fungal community analysis Beta diversity analyses were performed in QIIME2 (version 2019.10, https://qiime2.org) and completed separately for native and cultivar studies. First, a phylogenetic tree was constructed using the q2-phylogeny plugin for QIIME2 (version 2019.10). To assign taxonomy, a reference classifier from UNITE (version 8.0) was used (Nilsson et al. 2019) and applied to the representative sequences from dada2 (see above). Native study samples were analysed using the q2 diversity core-metrics-phylogenetic plugin (version 2019.10). First, the ASV table containing all samples was filtered to contain only native samples. A sampling depth of 3316 was chosen based on sample B3-3 (silky lupine) because it excluded only three samples while maximizing the sampling depth. Weighted UniFrac dissimilarity (Lozupone and Knight 2005) was used to create a distance matrix and beta diversity results were viewed via Principal Coordinates Analysis (PCoA). Due to non-normal distribution of features and appearance of horseshoe distributions with Weighted UniFrac distance, beta diversity for cultivar study samples was performed using the DEICODE plugin (version 0.1.5) for QIIME2 (Martino et al. 2019) which creates a Robust Aitchison principal component analysis (PCA) distance matrix that handles sparse and/or non-normal data sets. A new ASV table with only cultivar samples was created. A sampling depth of 3146 was chosen as it compromised sample exclusion and maximal sampling depth for beta diversity (see nonchim, Table A3). As with the native study, beta diversity results were viewed via PCoA. 3.2.9 Statistical analyses Initially, all response variables were analysed in PRIMER 7 (version 7.0.13 2017 Auckland, New Zealand) by setting cover crop diversity as a fixed factor and block as a random factor. Matrices were created using Euclidean distance for all datasets. A separate permutational 34 analysis of variance (PERMANOVA) was done for each response variable with the exception of the grapevine physiology in which data from all five growth responses was normalised and used to make the distance matrix. PERMANOVA designs used type III sum of squares, unrestricted permutation of raw data, and 9999 permutations. To gain insight into the effects of plant identity on disease symptoms and pathogen levels, cover crop diversity was broken down into its respective treatments which consisted of four monocultures, six mixtures, and one treatment of all species grown together (Table 3.2). For each response, monocultures were compared to each other and the all species treatment and the same was done for cover crop mixtures: 3.2.9.1 Incidence of disease Necrotic analysis was done using the lme4 package (1.1.21) in R statistical software (R version 3.6.1, 2019, (open source, http://www.r-project.org/). Differences in root necrosis were compared by fitting a linear mixed effects model with plant identity as the fixed factor and block as a random factor. For the native study, data was transformed by taking the square root of (k-x) where k is the maximum value for percent necrotic tissue plus 1 and x is percent necrotic tissue for each sample. Normality was assessed by checking model residuals and using the Shapiro-Wilk normality test (stats package 3.6.1). Treatments were analysed using one-way analysis of variance (stats package 3.6.1) in R. Disease progression of native study grapevines was analysed by taking the natural logarithm of 1+x where x is the vertical progression of the disease, measured in centimetres. Normality assumptions and analysis of variance (ANOVA) tests were completed as described above. For cultivar study grapevines, no transformation was necessary. Data was fitted to a linear mixed effects model and analysed as described above. Post hoc comparisons were 35 completed using Tukey’s honest significant difference test (Tukey 1949) with the emmeans package (1.4.1). 3.2.9.2 Grapevine physiology Grapevine growth and physiological characteristics were analysed using cover crop treatment as a fixed factor and block as a random factor for both native and cultivar studies. The response variables which included leaf greenness, available phosphorus, leaf area, root dry biomass, and shoot dry biomass were analysed via PERMANOVA in PRIMER 7. Data were normalized using default settings and resemblance matrices were made using Euclidean distance. Principal Coordinate Analysis (PCO) was chosen for visualization of data using Kruskal stress formula 1 and a minimum stress of 0.01. Response vectors were created via Pearson Correlation in PRIMER 7. 3.2.9.3 Abundance of Ilyonectria in the soil DNA from monoculture and pairwise soil from both native and cultivar studies were analysed in the same manner. Copy number/g soil was fitted to a linear mixed effects model in R (R version 3.6.1, 2019, open source, http://www.r-project.org/) using the lme4 package (1.1.21). Treatment was used as the fixed factor and block as the random factor. Normality was assessed using the Shapiro-Wilk normality test (stats package 3.6.1 and treatments were analysed using one-way ANOVA at a significance of P = 0.05 (stats package 3.6.1). 3.2.9.4 Abundance of Ilyonectria roots Copy number/g root from native study monocultures was square root transformed to satisfy normality which was assessed using the Shapiro-Wilk test (described above). After transformation, three outliers were removed from copy number values before modelling and subsequent statistical analyses using Tukey’s inter quartile range (IQR). By this method, values 36 that are more or less than 1.5 times the IQR are removed. Data from cover crop mixtures were normalized via log transformation then fitted to the same model. Copy number from cultivar study monocultures was normalized via cube root transformation whereas log transformation was used for pairwise treatments to meet normality assumptions. Copy number from native study and cultivar study roots were analysed by one-way ANOVA. 3.2.9.5 Fungal diversity and community analyses Alpha diversity of native study and cultivar study samples were compared with q2 diversity alpha-group-significance plugin (version 2019.10) using Shannon evenness vectors from the q2 diversity core-metrics-phylogenetic plugin. Overall and pairwise interactions were determined with the Kruskal-Wallis test by ranks (Kruskal and Wallis 1952) at a significance of 0.05. Beta diversity of native samples was determined via PERMANOVA in the q2 diversity beta-group-significance plugin (version 2019.10) using the Weighted UniFrac distance matrix created from the q2 diversity core-metrics-phylogenetic plugin as it incorporates sequence abundance and phylogeny when calculating the distance between samples. Distances for native cover crop treatments were visualized using principal coordinate analysis (PCoA) with the q2-emperor plugin (version 2019.10). For cultivar study samples, Robust Aitchison distance matrices were used from the DEICODE plugin to determine beta diversity. All PERMANOVA tests used 999 permutations and pseudo-F as the test statistic. Dispersion of native and cultivar study samples was determined with q2 diversity beta-group-significance by setting --p-method to permdisp. Dispersion tests were executed with 999 permutations and F-value as the test statistic. 37 3.3 Results 3.3.1 Effects of cover crop diversity on symptoms and abundance of Ilyonectria pathogens in soil and roots 3.3.1.1 Native study Contrary to my hypothesis, increasing cover crop diversity did not affect grapevine disease severity or disease progression in the native study (Table 3.3). Percent necrotic tissue of the grapevine cross sections averaged 73.19%, 65.66%, and 70.16% for monocultures, mixtures of two cover crops, and all three cover crops combined, respectfully. Disease progression was highest in grapevines grown in soil conditioned by all native plant species (mean 7.37 cm, S.E. 1.11) followed by mixtures of two cover crops (6.41 cm, S.E. 0.42) and monocultures (6.26 cm, S.E. 0.32). Contrary to my hypothesis, cover crop diversity was not related to the abundance of Ilyonectria in soil or roots (Table 3.3). Abundance of Ilyonectria DNA in soil was lower than in root samples. Soil samples ranged from zero to 2000 copy numbers of Ilyonectria per gram of soil whereas root samples ranged from zero to 494,000 copies of Ilyonectria per gram of root. Consistent with disease progression, the highest average copy number was observed in soil conditioned by all native cover crops (830 copies per gram of soil). Abundance of Ilyonectria DNA from root samples was highly variable with two samples containing zero DNA copies and four samples exceeding 100,000 copies per gram. Cover crop diversity was not significantly correlated with the abundance of Ilyonectria from soil or roots. While I. liriodendri was re-isolated from root samples, it was not recovered from grapevine canes, where necrosis was measured. Grapevine growth responses which consisted of dry biomass, leaf greenness, total phosphorus, and total leaf area did not vary with cover crop diversity (Table 3.3). 38 Table 3.3 PERMANOVA summary of Native Study responses (left column). Mean square (MS), Pseudo-F statistic (F), and P values (P) at a significance level of 0.05 are provided for each interaction. Response Diversity Block Diversity x block MS F P MS F P MS F P Percent necrosis 1.11 1.09 0.37 0.96 1.5 0.22 1.01 1.58 0.15 Disease progression 5.96 0.99 0.44 7.25 2.62 0.05 6.05 2.18 0.05 Soil abundance 1.03 x105 0.33 0.70 2.34 x105 0.91 0.52 3.16 x105 1.23 0.26 Root abundance 4.27 x108 0.12 0.93 2.79 x109 0.73 0.35 3.47 x109 0.92 0.29 Grapevine growth 4.49 0.54 0.66 6.4 1.07 0.38 8.31 1.39 0.10 3.3.1.2 Cultivar study Similar to the native plant study, cover crop diversity was not related to grapevine necrosis or measures of pathogen abundance (Table 3.4). Interestingly, necrotic damage to grapevines in uninoculated fallow soil was similar to inoculated soil, meaning that the pathogen load before inoculation was sufficient to infect plants. Ilyonectria abundance from root and soil samples varied significantly in some pairwise comparisons. As expected, abundance of Ilyonectria DNA from fallow soil was significantly lower than monoculture soil, mixture soil, and all cultivar soil (P < 0.01). Ilyonectria abundance from monoculture-trained soil was lower than soil conditioned by two cover crops (P = 0.03). Ilyonectria abundance from root samples followed a similar pattern in which fallow soil yielded a lower copy number than did soil planted with monocultures (P < 0.01), mixtures of two crops (P < 0.001), and all cover crops together (P < 0.01). Overall PERMANOVA results for cultivar study response variables are shown in Table 3.4. 39 Table 3.4 PERMANOVA summaries of Cultivar Study response variables (left column). Mean square (MS), Pseudo-F statistic (F), and P values (P) are provided for each interaction. Response Diversity Block Diversity x block MS F P MS F P MS F P Root necrosis 164.61 1.07 0.408 459.68 2.27 0.07 153.11 0.76 0.70 Soil abundance 3.21 x106 13.89 <0.001 3.12 x105 1.01 0.44 2.29 x105 0.74 0.83 Root abundance 1.78 x1010 1.84 0.187 7.29 x109 0.18 0.89 7.74 x109 0.19 0.99 Physiology 1.75 0.46 0.861 7.32 2.06 0.01 3.8 1.07 0.36 3.3.2 Effect of cover crop diversity on fungal alpha and beta diversity 3.3.2.1 Alpha diversity in native and cultivar studies As predicted, fungal diversity increased with cover crop diversity in the native study (Figure 3.1). In the native study, fungal diversity was lower in monocultures compared to the two species mixtures (P = 0.041) and all native (all cover crops grown together) (P = 0.021) but did not differ between mixtures and when all plants grew together. Contrary to predictions, increasing cover crop diversity did not increase fungal diversity in the cultivar study (P = 0.437, Figure 3.2). Variation in fungal diversity was greater in monoculture and fallow soil compared mixtures and when all crops grew together. Fallow soil diversity did not differ from soil planted with cover crops species (Figure 3.2 left and centre-left boxplots). 40 Figure 3.1 Shannon diversity index of fungi from soil under native cover crops. Treatments are monocultures (1 species), mixtures (2 species), and all plants growth together (3 species). Diversity of native cover crops was positively correlated with fungal diversity (P = 0.044). Figure 3.2 Shannon diversity index of fungi from cultivar cover crop soil. Treatments are un-inoculated bare soil (fallow), 1 cover crop (monocultures), mixtures of 2 cover crops (mixtures), and all 4 plants grown together (All cultivar). There was no significant correlation between plant diversity and below-ground fungal diversity (P = 0.437). b a a NS 41 3.3.2.2 Beta fungal diversity of native and cultivar studies As expected, fungal community composition associated with monocultures was distinct from that in plant mixtures (P = 0.023) in the native study (Figure 3.3). Similarly, cover crop diversity affected the compositional variation of fungal communities (P = 0.006) in the cultivar study (Figure 3.4). Communities associated with fallow soil differed from those grown in monocultures (P = 0.003), plant mixtures (P = 0.005), and all cultivar soil (P = 0.007). Fungal communities from all cultivar soil differed from those in monoculture soil (P = 0.039) and mixture soil (P = 0.044). Robust Aitchison distance accounted for 82.04% of compositional variation. Figure 3.3 Principal coordinate analysis (PCoA) of fungal communities under native cover crops. Diversity levels are visualized using Weighted UniFrac distance. Treatments are monocultures (spheres), mixtures of P = 0.093 42 two crops (diamonds), and all crops together (triangles). Overall PERMANOVA revealed no differences between diversity levels (P = 0.093), however variation of fungal communities under monocultures (spheres) and mixtures (diamonds) is evident (P = 0.023). Figure 3.4 Robust Aitchison visualization of fungal communities under cultivar cover crops. Treatments are monocultures (spheres), mixtures of two crops (diamonds), all crops together (triangles), and uninoculated fallow (squares). Fungal communities from Fallow soil are different than those under monocultures (P = 0.003), mixtures (P = 0.005), and all cultivar soils (P = 0.007). 3.3.3 Effect of cover crop provenance on incidence of disease in vines and abundance of Ilyonectria 3.3.3.1 Necrotic tissue damage of native vines Contrary to my hypothesis, grapevines grown in monocultures did not have higher rates of necrosis when compared to when all cover crops grew together (Figure 3.5). Likewise, cover crop pairs showed similar levels of disease severity when compared to the all species mixtures (P = 0.283), (Figure A1). Necrotic progression (evidence of necrosis from the basal to distal end) did not differ among monocultures (P = 0.057), (Figure 3.6). There were no significant differences in disease progression between cover crop pairs (P =0.214), (Figure A2). P = 0.006 43 Figure 3.5 Percent necrotic tissue of grapevines grown in soil conditioned by native cover crops. The extent of necrosis is measured from the basal end up. Treatments are all plants grown together “all native”, bluebunch wheatgrass “bluebunch”, silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). Data was normalized by taking the square root of the reciprocal (100.975 – percent necrosis. There was no significant difference between cover crop treatments (P = 0.407). NS 44 Figure 3.6 Progression of necrotic damage in grapevines grown in soil conditioned by native cover crops. The extent of necrosis is measured from the basal end. Treatments are all plants grown together “all native”, bluebunch wheatgrass “bluebunch”, silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). Data were normalized by taking the natural logarithm plus 1 (log1p) of necrotic progression values. There is no significant difference between cover crop treatments. 3.3.3.2 Necrotic tissue damage of cultivar vines Contrary to predictions, specific monocultures lowered disease severity compared to when all crops grew together. In monoculture comparisons, white mustard yielded the lowest percent necrotic tissue in the grapevines which was lower than in the fallow and crimson clover treatments (P = 0.035), (Figure 3.7). The low necrotic damage found in grapevines growing in white mustard soil did not persist when paired with buckwheat, crimson clover, and wheatgrass as shown in supplementary Figure A3 (P = 0.727). NS 45 Figure 3.7 Percent necrotic tissue of grapevines growing in soil conditioned by cultivar cover crops. Treatments include mixture of all plants “all cultivar”, buckwheat “buckwheat”, crimson clover “clover”, uninoculated fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). Letters represent statistical significance at P < 0.05. 3.3.4 Grapevine physiology 3.3.4.1 Native study Contrary to predictions, grapevine growth responses did not differ between monocultures and when all plants grew together (P = 0.664), (Figure 3.8). Likewise, there was no difference in growth responses of grapevines growing in soil conditioned by cover crop pairs (P = 0.645), (Figure A4). ab b ab a a ab 46 3.3.4.2. Cultivar study For cultivar study monocultures, grapevine growth responses did not differ significantly with those grown in soil conditioned by all species (P = 0.816), (Figure 3.9). Grapevines growing in soil conditioned by cover crop pairs showed a similar response with no noticeable differences among treatments (P = 0.916) as seen in Figure A5. Figure 3.8 Principal coordinate analysis of native study grapevine growth responses based on leaf greenness (SPAD), leaf area, dry root biomass, dry shoot biomass, and total leaf phosphorus. Symbols represent different cover crop treatments including white yarrow “yarrow”, Holboell’s rockcress “rockcress”, silky lupine “lupine”, bluebunch wheatgrass “bluebunch”, and all plants grown together “all native”. There was no significance detected between treatments (P = 0.664). P = 0.664 47 Figure 3.9 Principal coordinate analysis of cultivar study grapevine growth responses based on leaf greenness (SPAD), leaf area, dry root biomass, dry shoot biomass, and total leaf phosphorus. Symbols represent different cover crop treatments including white mustard “mustard”, buckwheat “buckwheat”, wheatgrass “wheat”, crimson clover “clover”, mixture of all plants “all cultivar”, and uninoculated fallow “fallow”. There was no significance detected between treatments (P = 0.816). 3.3.5 Recovery of Ilyonectria from soil 3.3.5.1 Native study After four months of soil conditioning by native cover crops, I. liriodendri was recovered from all treatments. However, its abundance varied highly between samples. Contrary to predictions, there was no significant difference in numbers from individual cover crops and when plants were grown together (P = 0.731) (Figure 3.10). Similar to monoculture treatments, cover crop mixtures showed high variation in copy number between samples, with the exception of white yarrow and silky lupine (Figure 3.11). Copy number between treatments did not differ significantly (P = 0.903) with most values between 500 and 1000 copies per gram (Figure 3.11). P = 0.816 48 Figure 3.10 Recovery of I. liriodendri DNA from soil after conditioning by native cover crops. Treatments are all plants grown together “all native”, bluebunch wheatgrass “bluebunch”, silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Copy number did not differ significantly among treatments (P = 0.731). NS NS 49 Figure 3.11 Recovery of I. liriodendri DNA from soil after conditioning by native cover crop mixtures. Treatments are all plants grown together “all native”, bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Copy number did not differ significantly between cover crop treatments (P = 0.903). 3.3.5.2 Cultivar study Contrary to predictions, abundance of Ilyonectria in soil did not vary significantly between cover crop treatments (Figure 3.12), other than fallow which was expected (P < 0.001). White mustard yielded an average of 1326 copies of I. liriodendri target DNA per gram of soil, the highest average copy number of all treatments which is inconsistent with percent necrotic tissue. Although some cover crop mixtures containing white mustard resulted in lower copies of I. liriodendri such as white mustard-wheatgrass soil (Figure 3.13) there were no differences between treatments (P = 0.118). 50 Figure 3.12 Recovery of I. liriodendri DNA from soil conditioned by cultivar cover crops. Treatments are mixture of all plants “all cultivar”, buckwheat “buckwheat”, crimson clover “clover”, uninoculated fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Letters above boxplots represent statistical significance at P < 0.05. a a a a b a 51 Figure 3.13 Recovery of I. liriodendri DNA from soil conditioned by cover crop mixtures. Treatments include mixture of all plants “all cultivar”, crimson clover and buckwheat “buckwheat-clover”, uninoculated fallow “fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass and crimson clover “wheat-clover”. Letters above boxplots represent statistical significance at P < 0.05. 3.3.6 Recovery of Ilyonectria from roots 3.3.6.1 Native study Contrary to predictions, I. liriodendri abundance did not change significantly across monocultures and when all crops were grown together (P = 0.109), (Figure 3.14). Ilyonectria abundance from grapevine roots was extremely variable in monocultures, with silky lupine displaying the most variability. Grapevines grown in rockcress and bluebunch wheatgrass soil showed the highest abundance of I. liriodendri followed by white yarrow and all native (Figure 3.14). Consistent with monoculture trends, roots from the rockcress-bluebunch wheatgrass a ab a b a a a a 52 mixture contained the largest amount of target DNA while yarrow-lupine contained fewer copies (P = 0.584), (Figure 3.15). Figure 3.14 Recovery of I liriodendri DNA from native study Chardonnay roots. Treatments are all plants grown together “all native”, bluebunch wheatgrass “bluebunch”, silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Data was normalized by taking the square root of the copy number and then removing three outliers using Tukey’s inter quartile range. Copy number did not differ significantly between cover crop treatments (P = 0.109). NS 53 Figure 3.15 Recovery of I liriodendri DNA from native study Chardonnay roots. Treatments are all plants grown together “all native”, bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Data was normalized via log transformation. There were no significant differences between cover crop treatments (P = 0.584). 3.3.6.2 Cultivar study Contrary to my hypothesis, abundance of I. liriodendri did not decrease when all plants were grown together (Figure 3.16). Abundance of I. liriodendri was lowest in white mustard roots which was consistent with the lower necrotic damage observed in grapevine cross sections from the same treatment. Abundance of I. liriodendri in white mustard was significantly lower compared to wheatgrass (P = 0.041), (Figure 3.16). When white mustard was paired with other cover crops, abundance of Ilyonectria did not decrease like it did in the mustard monoculture (Figure 3.17). Roots from white mustard-crimson clover soils showed lower levels of target NS 54 DNA, however they did not vary significantly from other cover crop mixtures. Consistent with digital PCR results from soil samples, roots from the uninoculated fallow treatment, had either zero or very small copy numbers of target DNA (Figure 3.16-3.17). Figure 3.16 Abundance of I liriodendri DNA from cultivar study Chardonnay roots. The cube root of copy number was taken to normalise data. Treatments are mixture of all plants “all cultivar”, buckwheat “buckwheat”, crimson clover “clover”, uninoculated fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Letters above boxplots represent significant differences at P < 0.05. ab ab ab c bc a 55 Figure 3.17 Abundance of I liriodendri DNA from cultivar study Chardonnay roots. Data was normalized via log transformation. Treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “buckwheat-clover”, uninoculated fallow “fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass and crimson clover “wheat-clover”. Letters above boxplots represent significant differences at P < 0.05. 3.3.7 Sequence results A total of 2089 amplicon sequence variants (unique DNA sequences) with a combined frequency of 875, 526 were present from the 111 soil samples after initial denoising and filtering. The minimum feature count per sample was 769 (Bluebunch wheatgrass) while the maximum was 17, 904 (soil before cover crop conditioning). The highest feature occurrence was 170, 112 across all 111 samples while eight features occurred only once (0.004% of all features). A total a a b a ab a a a 56 of 6 phyla (one unidentified), 19 classes, 40 orders, 68 families, and 76 genera were recovered from all soil samples (Figure A6). Ascomycota yielded the highest relative frequency followed by Basidiomycota, Mortierellomycota, and Chytridiomycota, which were present in all 111 samples. Glomeromycota, was present in 78 samples followed by an unidentified phylum which was observed in 94 samples. 3.3.8 Effect of cover crop provenance on fungal diversity 3.3.8.1 Alpha diversity in native and cultivar studies As predicted, fungal diversity increased with native cover crop diversity (Figure A7). Silky lupine yielded the lowest diversity followed by bluebunch wheatgrass. Fungal diversity was highest when all plants were grown together (P = 0.047), (Figure 3.18 A). The pairing of silky lupine and bluebunch wheatgrass resulted in less diverse communities, similar to monoculture soil. Interestingly, the inclusion of white yarrow and Holboell’s rockcress increased diversity to levels similar to the all native treatment (P= 0.098), (Figure 3.18 B). Contrary to predictions, fungal diversity did not change with cultivar cover crops. Fungal diversity was less diverse under crimson clover while diversity under buckwheat and wheatgrass was similar to the all cultivar treatment. As expected, fallow soil contained the lowest fungal diversity, although no significant differences were detected among treatments (P = 0.531), (Figure A8 A). Fungal diversity in white mustard-bluebunch wheatgrass soil was similar and in some cases higher than when all plants were grown together (P = 0.581), (Figure A8 B). 57 Figure 3.18 Shannon diversity of fungi in native study soils. Monoculture treatments are bluebunch wheatgrass “bluebunch”, silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow” (A). Mixtures are bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress” (B). Overall group significance was observed in monocultures (P = 0.047) but not mixtures (P= 0.098). Letters over monoculture treatments indicate pairwise differences at a significance level of 0.05. 3.3.8.2 Beta diversity in native and cultivar studies Contrary to predictions, fungal community composition was similar among most native monocultures and when all plants grew together (P = 0.051). However, community composition under bluebunch wheatgrass was distinct from white yarrow (P = 0.036), (Figure 3.19). Likewise, fungal community composition under silky lupine was different from white yarrow (P = 0.056). Dispersion of fungal communities (clustering) was similar under native monocultures (P = 0.881). a b ab a ab NS 58 Figure 3.19 PCoA of fungal communities from native study cover crops visualized by Weighted UniFrac distance. Treatments are all species together “All native”, bluebunch wheatgrass “Bluebunch”, Silky lupine “Lupine”, Holboell’s rockcress “Rockcress”, and white yarrow “Yarrow”. Fungal communities show no significant clustering overall (P = 0.051), however bluebunch wheatgrass (squares) and white yarrow (stars) reveal differences in beta diversity (P = 0.036, q = 0.280). Soil fungal communities were similar among the native study mixtures (P = 0.099) with the most notable differences between rockcress-bluebunch wheatgrass and white yarrow lupine (P = 0.021) followed by white yarrow-rockcress and yarrow-bluebunch wheatgrass (P = 0.033). Dispersion of fungal communities was significantly greater in bluebunch-lupine soil compared to all other cover crop mixtures (Figure 3.20). P = 0.051 59 Figure 3.20 PcoA of fungal communities from native cover crops visualized by Weighted Unifrac distance. Treatments are mixture of all plants “All native”, bluebunch wheatgrass with silky lupine “Bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “Rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “Yarrow-bluebunch”, white yarrow and silky lupine “Yarrow-lupine”, and white yarrow and Holboell’s rockcress “Yarrow-rockcress” (P= 0.099). In the cultivar study, cover crop diversity changed community composition only in some treatments (P = 0.001), (Figure 3.21). All cultivar communities were distinct from fallow (P = 0.005) and wheatgrass (P = 0.017) but not others. Dispersion of fungal communities from monocultures was similar (P = 0.183). P = 0.099 60 Figure 3.21 PCA of Fungal communities from cultivar cover crops visualized by Robust Aitchison distance. Treatments are All plants grown together “All cultivar”, buckwheat “buckwheat”, crimson clover “Clover”, uninoculated fallow “Fallow”, white mustard “mustard”, and wheatgrass “wheat” with the following significant pairwise interactions: All cultivar and Fallow (P = 0.005, q = 0.045), All cultivar and wheatgrass (P = 0.017, q = 0.084), Clover and Fallow (P = 0.006, q = 0.045), clover and mustard (P = 0.025, q = 0.084), clover and wheatgrass (P = 0.037, q = 0.092). Unlike native cover crop mixtures, there were significant differences in beta diversity of fungal communities under cultivar mixtures (P = 0.001), (Figure 3.22). As expected, fungal communities in fallow soil were significantly different from those under cover crops with the exception of mustard-wheatgrass (P = 0.505) and wheatgrass-buckwheat (P = 0.279). All cultivar communities differed from those in mustard-wheatgrass soil (P = 0.018). Interestingly, fungal communities in clover-buckwheat soil were different from all other treatments except for when all cover crops were grown together (P = 0.355). Overall dispersion of fungal communities was not statistically significant for cultivar mixtures (P = 0.441), however fungal communities in fallow soil showed significantly higher dispersion compared to clover-buckwheat communities P = 0.001 61 (P = 0.017). Robust Aitchison distance explained 82.04% of the total variance across cultivar samples. Figure 3.22 Fungal communities from cultivar study cover crops visualized by Robust Aitchison distance. Treatments are mixture of all plants “All cultivar”, crimson clover and buckwheat “Clover-buckwheat”, uninoculated fallow “Fallow”, white mustard and buckwheat “Mustard-buckwheat”, white mustard and crimson clover “Mustard-clover”, white mustard and wheatgrass “Mustard-wheat”, wheatgrass and buckwheat “Wheat-buckwheat”, and wheatgrass with crimson clover “Wheat-clover” (P = 0.001). 3.4 Discussion 3.4.1 Effect of cover crop diversity on root necrosis. Contrary to my hypothesis, cover crop diversity was not associated with necrotic root symptoms in self-rooted Chardonnay grapevines. This was true in both the native and cultivar studies. One possible explanation is that the biotic properties of the soil did not change enough due to the short soil conditioning phase by cover crops. In these studies, native and cultivar cover crops were grown for three and four months respectfully whereas grapevine shoots are active for roughly seven to eight months (Neilsen et al. 2006). In the cultivar study, buckwheat, white P = 0.001 62 mustard, and clover completed their lifecycle in three months while in the field grapevine roots are active for a much longer period (Field, Emery, and Holzapfel 2009). Since plant-soil feedback is not instantaneous (Hawkes et al. 2013), perhaps more time was needed for the annual cultivated and the perennial native plant species to develop beneficial and/or antagonistic microbial communities, leading to a delay in their suppressive effects. Eisenhauer et al. (2012) found that benefits from soil biota were more pronounced in long term grassland studies (four years) due to successional changes in soil microbial communities. Vogel et al. (2019) further elucidated the effect of time on plant-soil feedback by showing that microbial biomass was greater in soil with a 14-year conditioning period by a specific plant community compared to new soil conditioned by the same plant community for only one year. Another explanation could be the lack of cover crop incorporation into the soil resulting in very little competition from other saprophytic fungi. Decomposers represent a significant group among soil microbial life and contribute to multiple ecosystem services (Barrios 2007). In this experiment I did not incorporate any litter at harvest. Instead, all cover crop material was removed, including roots, which would have limited decomposer communities (Ball et al. 2009). Most importantly, litter contains symbiotic plant endophytes such as Trichoderma that are present during foliar growth and are also known to decompose litter (Cumagun, 2012; Naher et al. 2014). The presence of Trichoderma could reduce Blackfoot pathogens if they are surviving as saprophytes in plant litter. Further research is needed to determine whether stimulations of decomposer communities can reduce Blackfoot pathogens. In this study, the basal end of each cane was not callused, leaving a large area of vascular tissue exposed to the soil and pathogens. Such a large amount of exposed tissue in the soil would have facilitated infection even in the presence of antifungal exudates or antagonistic microbes, as 63 belowground wounds can serve as entry points in grapevines (Fourie and Halleen 2004b). This is especially the case in pathogen transfer above ground in which pruning wounds left uncovered act as entry points for airborne spores (Cobos et al. 2015; Gramaje et al. 2017). 3.4.2. Effect of plant provenance on root necrosis For both studies, white mustard was the only cover crop that reduced necrotic tissue damage in grapevine roots. This plant matures quickly, is a high biomass crop (Clark 2008), and is known for its production of sulphur containing glucosinolates including glucoerucin and glucoiberverin (Fahey et al. 2001). White mustard products have previously been associated with the suppression of Dactylonectria torresensis, Pythium spp. and nematodes in vineyards and orchards (Berlanas et al. 2018; Mazzola, Hewavitharana, and Strauss 2014; Wang and Mazzola 2019). The antifungal chemicals produced by white mustard are known to inhibit spore germination (Drakopoulos et al. 2019) and mycelial growth (Prasad and Kumar 2017) which may have resulted in the lower incidence of necrosis observed. In contrast, when white mustard was grown with other cover crops, necrotic damage was not reduced. As each pot was standardized to four plants, only two white mustard plants grew in the soil, which would have reduced total glucosinolate production per pot. This likely reduced the concentration of antifungal compounds in the soil, allowing pathogens to proliferate more easily. In the native study, Holboell’s rockcress was not associated with lower percentages of necrotic tissue. While the suppressive potential of Holboell’s rockcress has not been studied in an agricultural setting, the plant matures slower due to its perennial nature and has many natural predators including fungi (Roy 1993). Although rockcress did not show any signs of suppression 64 in this short-term study, perhaps its persistence over multiple growing seasons and/or its degradation after maturity may contribute to the mitigation of soil-borne pathogens in vineyards. 3.4.3 Grapevine growth response In both experiments, no measure of plant development correlated with cover crop diversity or plant identity. The grapevines used in these studies were propagated from canes of grapevines growing in a vineyard that had been planted in 2010 and had no visible signs of infection during the propagation process. Since infection of vascular tissue and development of symptoms occur with time, it is not surprising that the vines from different treatments did not vary in their development responses. In the field, grapevine decline symptoms arise in the first five years after planting (Úrbez-Torres et al. 2015) and they are difficult to diagnose while the vines are in the ground. In this study, grapevines with severe vascular tissue damage (high percent necrosis) were comparable to those with lower proportions of damaged tissue in terms of biomass and leaf greenness (related to chlorophyll concentration). Had the grapevine growth period been extended, differences in development would have likely arisen. 3.4.4. Recovery of Ilyonectria DNA from soil In both studies, cover crop diversity and most cover crop treatments did not affect the abundance of I. liriodendri in the soil. The only exception was for the white mustard-wheatgrass combination in which copy number was similar to that found in uninoculated fallow. The initial concentration of 1 x 106 conidia per millilitre would have spread into the soil over time as pots were watered, causing spores to travel to deeper depths in the pot. In order to maintain consistency, the first five centimetres were sampled, an area containing few roots. The majority of root biomass was found in the mid to bottom levels of the pot. 65 3.4.5 Recovery of DNA from grapevine roots Consistent with percent necrotic tissue, abundance of I. liriodendri was lower in the white mustard monocultures and when white mustard was grown with clover (Figure 3.3). At the time of harvest, white mustard cover crops had gone to seed and had started to senesce, a period in which the metabolism of glucosinolates into antifungal isothiocyanates occurs. The breakdown and release of isothiocyanates from white mustard perhaps inhibited spore germination, reducing available inoculum during the grapevine growth stage. Antifungal compounds from brassicaceous crops can stay active for a period of 25-30 days (Ma et al. 2015; Prasad and Kumar, 2017) before they start to break down. Suppressive effects of white mustard may have been more pronounced had the plant been left to decompose in the soil (Mowlick et al. 2013). In this study Ilyonectria abundance in white mustard treatments was significantly lower than in wheatgrass treatments. Wheat is used in vineyards to manage soil erosion, prevent frost damage, and build organic matter (BC Wine Grape Council 2010). However, wheat and other plants growing in a vineyard may act as off target hosts allowing pathogens to persist in the soil. This was observed in South African nurseries (Langenhoven et al. 2018) and in Spanish vineyards (Agustí-Brisach et al. 2011). Indeed, there is a diverse array of fungal pathogens that infect grapevine tissues at various growth stages, which means further research is required to elucidate whether particular combinations of cover crops and GTD fungi could be problematic in vineyards and nurseries. 3.4.6 Effect of cover crops on fungal diversity Alpha diversity increased with cover crop diversity in the native study but not cultivar study. The fact that microbial diversity changed under native but not cultivar cover crops 66 perhaps implies that native plants are more dependent on resident fungi, specifically mycorrhizal fungi compared to introduced plants (Inderjit and van der Putten, 2010). Since mycorrhizal fungi, and resident bacteria can heavily influence functional traits, including nitrogen content, stress tolerance, morphology, leaf longevity and pathogen resistance (Friesen et al. 2011), the presence of native plants may have stimulated these communities more than the cultivar varieties in order to maximize their fitness. Indeed, Klironomos (2003) found that the frequency of positive responses from foreign plants was reduced when paired with resident AM fungi compared to the more even distribution of responses observed when resident AM fungi and plants were paired. Alternatively, fungal diversity in the cultivar study may have been limited because the introduced plants increased the abundance of specific fungi. This is observed in invasion studies in which the invasive plant experiences positive plant-soil feedback which allows it to outcompete native plants (Broz, Manter, and Vivanco 2007; Klironomos 2002). It is also possible that fungal diversity changed more under native cover crops due to a longer conditioning phase. Soil was conditioned by cultivar plants for three months whereas native plants were given four, allowing an additional month for fungi to respond to exudation, rhizodeposition (Broeckling et al. 2008) and root turnover (Rosenstock et al. 2019). In addition, root exudates and carbon deposits change as plants develop, and this affects microbial communities (Garbeva, van Veen, and van Elsas 2004). The fact that cultivar plants matured quickly perhaps lead to microbial turnover whilst inputs from native plants were more consistent allowing communities to develop overtime. Since many soil fungi are saprophytic, diversity may have increased if cover crops were left to decompose (Veen et al. 2019b). This likely would have resulted in compositional differences in fungal communities as decomposers are strongly affected by plant litter type 67 (Veen et al. 2019). However, despite the increase in fungal diversity in cover crops mixtures and when all plants grew together, fungal diversity was not associated with incidence of root necrosis or abundance of I. liriodendri in soil or roots. When soil under individual cover crops was analysed, alpha diversity was significantly higher under rockcress compared to bluebunch wheatgrass. Brassicaceous crops can inhibit fungal activity due to hydrolysis products of the glucosinolates they produce (Wang et al. 2014), however this is often limited to fungal pathogens (Ma et al. 2015) and is not widely observed in symbiotic fungi (Glenn, Chew, and Williams 1988; Koron, Sonjak, and Regvar 2014). The fact that fungal diversity under rockcress was comparable to that under white yarrow and all plants combined suggests that rockcress did not inhibit fungi as much as do other brassicaceous crops. However, alpha diversity under white mustard was also similar to other cover crops suggesting that factors other than glucosinolate content contributed to alpha diversity. 3.4.7 Effect of cover crops on community composition In the native study, fungal community composition varied significantly among monocultures and cover crop mixtures. The dissimilarity among fungal communities could reflect the dissimilarity of plant communities at each diversity level. For example, white yarrow, one of the monoculture treatments, compared to a rockcress-bluebunch wheatgrass mixture will likely have different fungal communities. This was the case in a study by Prober et al. (2014) in which plant community composition strongly correlated with the beta diversity of soil microbes across 25 grasslands on four continents. When individual cover crops were compared, fungal communities were dissimilar only for bluebunch wheatgrass and yarrow. Historically, white yarrow has been used as a traditional medicine in many cultures because of the phenolic compounds it produces (Vitalini et al. 2011). 68 Its extracts are known to suppress in vitro growth of pathogenic bacteria and fungi (Candan et al. 2003). On the contrary, bluebunch wheatgrass is not as widespread as yarrow (Bradley et al. 2013) and its competitiveness is more dependent on rhizosphere microbes (Callaway et al. 2004). Given the different life history strategies employed by these plants, it is not surprising that their soil microbial communities differ. In the cultivar study, fungal communities were dissimilar between monocultures and when all plants were grown together. This could be the result of some plants influencing soil properties more than others. For example, crimson clover produced highly branched root systems and although it did not yield the most below ground biomass, it had the most above ground biomass of all cover crops. Legumes are known for their mycorrhizal attributes (Hayman 1986) and have previously been associated with increases in fungal diversity (Nieva et al. 2018). This was apparent when comparing fungal communities for clover soil that were distinct from those for white mustard, wheatgrass, buckwheat, and fallow soil, but not when all plants were grown together. Legumes are known to increase abundance of AMF (Benitez, Taheri, and Lehman 2016) as well as saprophytic fungi (Sanchez, Fultz, Lofton, and Haggard 2019). White mustard on the other hand, typically reduces abundance of soil fungi relative to controls (Mazzola, Hewavitharana, and Strauss 2014), although in this experiment community composition under mustard was similar to that under the all cultivar, fallow, buckwheat and wheat treatments. However, this treatment was associated with lower incidence of root necrosis, which suggests it reduced the overall abundance of fungi associated with disease (Ma et al. 2015; Prasad and Kumar 2017). 69 4 Conclusion I asked whether cover crop diversity and provenance were important factors for the control of Blackfoot disease. Overall results showed that after a short conditioning period, cover crop diversity was not associated with incidence of disease or recovery of Ilyonectria pathogens from soil and grapevine roots. Only when diversity was qualified by subject species were differences in necrotic damage and abundance of Ilyonectria pathogens observed. 4.1 Does cover crop diversity reduce disease symptoms in young vines and abundance of Ilyonectria? Native and cultivated cover crop diversity did not reduce root necrosis symptoms, perhaps because positive effects of microbes are likely experienced after longer periods of time (Eisenhauer et al. 2012; Meyer et al. 2016) and/or when cover crop biomass is left to decompose in the soil (Bardgett and Shine 1999). In addition, culturing a suppressive microbiome may require more than cover crop implementation, such as by changes to nutrient and watering regimes, pruning time (Urbez-Torres et al. 2019), or inoculation of plants or soil with beneficial microbes (Boruah and Kumar 2002b; Ridout and Newcombe 2016). In both studies, cover crop diversity was not associated with a reduction in Ilyonectria liriodendri abundance in soil or grapevine roots. The addition of other cover crops likely reduced the biofumigant effect of white mustard as each pot was standardized to four plants. In these studies, I did not examine cover crop roots for pathogens, however it is possible that some acted as alternate hosts (Agustí-Brisach et al. 2011; Langenhoven et al. 2018). If cover crops can be colonized by I. liriodendri and/or other pathogens, this could maintain the spore bank and allow them to persist in soils, increasing the risk of infection. Although not observed in this study, cover crop diversity could play a major role in the long term, especially if more diverse plant communities support diverse microbes with suppressive properties. 70 4.2 Does cover crop provenance affect the severity of disease symptoms and abundance of Ilyonectria? I found that white mustard was associated with lower levels of necrotic damage from soil borne pathogens. Although it did not produce as much biomass as other cover crops, its production and metabolism of antifungal glucosinolates likely reduced the infection potential of I. liriodendri and other resident fungal pathogens. This biofumigant effect, common for brassicaceous plants, was not observed in Holboell’s rockcress, the brassica in the native study. Holboell’s rockcress is a smaller, perennial plant and it did not reach maturity as did white mustard, which might have influenced glucosinolate production (Sarwar and Kirkegaard 1998). Furthermore, the glucosinolate profile of this plant is different from that of white mustard (Fahey et al. 2001) and the antifungal compounds were perhaps not as effective and/or they are not produced at the same rate. Consistent with necrotic tissue damage, white mustard was associated with a lower abundance of I. liriodendri in the roots of Chardonnay cuttings. This effect vanished when white mustard was paired with other cover crops (Figure 4.1) and was not observed for any other cover crop. Since only the first five centimetres of soil were sampled, incorporation of white mustard biomass may have reduced the abundance of soil dwelling pathogens as observed in other studies (Prasad and Kumar 2017; Wen et al. 2017). 4.3 Does cover crop diversity increase fungal diversity and is fungal diversity associated with disease symptoms? Cover crop diversity increased fungal diversity only in the native study. Fungal diversity differed between cultivar cover crops and fallow soil, however there was no additive effect when all plants grew together. It is possible that native plants were more dependent on fungi than cultivar plants (Van Der Putten, Klironomos, and Wardle 2007) due to synergistic coevolution 71 (Gouda et al. 2018) resulting in fungal diversity increasing with plant species diversity. Alternatively, carbon inputs and root exudation for cultivar crops could have promoted specific fungi through positive plant-soil feedback that limited diversity (Broz et al. 2007; Klironomos 2002) while native plants produced negative feedback loops (Perkins and Nowak 2013). In terms of disease suppression, fungal diversity under white mustard was not different from other cover crops and community composition was distinct in clover only. Although not directly tested, fungal diversity was not likely a key factor in disease suppression due to the short duration of the study and lack of cover crop incorporation. 4.4 Concluding remarks In summary, cover crop identity was more important than diversity for controlling Blackfoot pathogens in grapevines. Results from the cultivar study align with the biofumigant literature for white mustard and other brassicaceous crops (Berlanas et al. 2018; Bleach et al. 2010; Mazzola et al. 2001; Mowlick et al. 2013), however the majority of these studies focused on incorporation of brassica products whereas this study evaluated white mustard as an active then removed growing cover crop. The advantage of brassica seed meal or residue incorporation is that the antifungal compounds will already have been formed. On the other hand, a seeded brassica can offer traditional benefits such as erosion control or weed suppression and then be incorporated into the soil once its life cycle has completed. These results provide evidence that disease symptoms and pathogen abundance can be reduced by growing a cover crop that produces antifungal compounds. 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Likewise, silky lupine and rockcress experienced lower biomass yields both below and above ground. When grown with other plants, biomass measurements were more similar across treatments. Rockcress-bluebunch wheatgrass yielded lower below ground biomass compared to yarrow-bluebunch wheatgrass and yarrow-lupine but not other mixtures (Table A1). Above ground, yarrow-bluebunch yielded more biomass than rockcress bluebunch but not other treatments (Table A1). Due to poor growth, the lupine-rockcress treatment was omitted from the experiment. In the cultivar study, all cover crops experienced high germination rates and grew quickly in the greenhouse. At the end of three months, buckwheat, white mustard, and crimson clover had flowered and white mustard as well as buckwheat had gone to seed. Two crimson clover pots experienced dieback in July 2018 likely due to high greenhouse temperature and/or water stress. The plants recovered after water application but did not flower. Cultivar monocultures varied in biomass. Wheat yielded the most root biomass (Table A1), crimson clover yielded the most biomass above ground (Table A1). The lowest biomass measurements were observed for 92 white mustard in which below and above ground biomass were significantly different from all other cover crop treatments. Mixtures of cultivar species also varied in biomass and were consistent with monoculture treatments. For example, the largest below ground biomass was observed when wheat was present (wheat-clover mixture, Table A1) and the largest biomass measurements were found in treatments consisting of crimson clover (buckwheat-clover and wheat-clover mixtures, Table A1). Similarly, biomass was reduced when other crops were grown with white mustard. A.2 Figures and tables Figure A1 Percent necrotic tissue of native study grapevines grown in soil conditioned by cover crop pairs. The extent of necrosis is measured from the basal end up. Treatments are all plants grown together “all native”, bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). Data was normalized by NS 93 taking the square root of the reciprocal (100.267 – values). There was no significant difference between cover crop treatments (P = 0.283). Figure A2 Progression of necrotic damage in native study grapevines grown in soil conditioned by cover crop pairs. The extent of necrosis is measured from the basal end up. Treatments are all plants grown together “all native”, bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress” Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). Data was normalized by taking the natural logarithm plus 1 (log1p) of necrotic progression values. There is no significant difference between cover crop treatments (P = 0.214). NS 94 Figure A3 Percent necrotic tissue of cultivar study grapevines growing in soil conditioned by cover crop pairs. The percentage of necrotic tissue from basal cross sections is shown. Treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “buckwheat-clover”, uninoculated fallow “fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass and crimson clover “wheat-clover”. Boxplots show the first and third quartile, median (middle line), range (whiskers), and circles (outliers). There were no statistical differences observed between treatments (P = 0.727). NS 95 Figure A4 Principal coordinate analysis of native study grapevine growth responses based on leaf greenness (SPAD), leaf area, dry root biomass, dry shoot biomass, and total leaf phosphorus. Symbols represent different cover crop treatments including: all plants grown together “all native”, bluebunch wheatgrass and silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress” There was no significance detected between treatments (P = 0.645). P = 0.645 96 Figure A5 Principal coordinate analysis of cultivar study grapevine growth responses based on leaf greenness (SPAD), leaf area, dry root biomass, dry shoot biomass, and total leaf phosphorus. Treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, uninoculated fallow “fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass and crimson clover “wheat-clover”. No significance between cover crop treatments was detected (P = 0.916). P = 0.916 97 A 98 Figure A6 Relative abundance of phyla across native study samples (A) and cultivar study samples (B) after initial denoising and filtering. Samples are shown with block number followed by treatment number (see Table 2.2). Phyla are Ascomycota (brown), Basidiomycota (pink), Mortierellomycota (yellow), Chytridiomycota (orange), Glomeromycota (purple), and unidentified (blue and green). B 99 Figure A7 Shannon diversity of fungi in soil before and after conditioning by cover crops. Treatments are soil before seeding with cover crops “Baseline”, soil conditioned by cultivar cover crops for three months “Cultivar”, and soil conditioned by native cover crops for four months “Native” Letters above boxplots represent significant differences at P < 0.05. b a c 100 Figure A8 Shannon diversity of fungi in cultivar study soil. Monoculture treatments are all plants “all cultivar”, buckwheat “buckwheat”, crimson clover “clover”, uninoculated fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Fungal diversity did not vary between treatments (P = 0.531) (A). Mixtures are all plants grown together “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, uninoculated fallow “fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass and crimson clover “wheat-clover” Shannon diversity of fungi did not vary in cover crop mixtures (P = 0.580) (B). NS NS 101 Table A1 Average biomass (grams) for each cover crop treatment in native and cultivar studies. Cover crops are bluebunch wheatgrass “bluebunch”, Silky lupine “lupine”, Holboell’s rockcress “rockcress”, white yarrow “yarrow”, buckwheat “buckwheat”, Crimson clover “clover”, white mustard “mustard”, and wheatgrass “wheat”. Letters to the right of values indicate significance at P ≤ 0.05. Experiment Cover crop treatment Below ground biomass Above ground biomass Native study monocultures All native 0.885a 2.715a Bluebunch 0.795a 3.041a Lupine 0.319b 0.730b Rockcress 0.138b 1.380b Yarrow 1.056a 2.822a Native study mixtures All native 0.885ab 2.715ab Bluebunch-lupine 0.558ab 2.584ab Rockcress-bluebunch 0.401b 1.751a Yarrow-bluebunch 1.087a 3.721b Yarrow-lupine 1.160a 2.748ab Yarrow-rockcress 0.813ab 2.536ab Cultivar study monocultures All cultivar 1.238a 6.917a Buckwheat 0.694b 4.771b Clover 0.527b 7.483a Mustard 0.121c 1.834c Wheat 2.272d 3.663d Cultivar study mixtures All cultivar 1.238a 6.917ab Buckwheat-clover 0.96ab 8.773a Mustard-buckwheat 0.554bc 4.326c Mustard-clover 0.302c 6.801b Mustard-wheat 1.228a 3.431c Wheat-buckwheat 1.218a 4.071c Wheat-clover 2.909d 8.709a Table A2 Fertilizer application for vinifera cuttings in native and cultivar studies. Miracle-Gro© fertilizer was used to prepare solutions of varying concentrations. Enough fertilizer was applied to cover the soil and allowed to soak through. Date Fertilizer type Amount applied Dilution October 11 2018 15-15-18 150ml 50% October 25 2018 15-15-18 150ml No November 1 2018 15-15-18 150ml 50% November 22 2018 15-15-18 150ml 33% November 29 2018 15-15-18 150ml 40% December 7 2018 15-15-18 150ml 40% 102 Table A3 Number of reads retained at each step in the DADA2 pipeline. Samples are shown with block number first followed by treatment number (Table 2.2) separated by a hyphen. From left to right are initial read counts (input), reads after filtering (filtered), forward reads after denoising (denoisedF), reverse reads after denoising (denoisedR), number of reads after merging (merged), and number of reads after chimera removal (nonchim). Sample input filtered denoisedF denoisedR merged nonchim B1-1 12251 6665 6644 6644 6184 6176 B1-10 14589 8155 8133 8127 7909 7907 B1-14 14193 8257 8222 8183 7307 7290 B1-15 16920 9042 9020 9013 8424 8409 B1-16 16354 8844 8808 8789 8057 8053 B1-20 21936 9842 9807 9799 9040 9028 B1-3 9439 4599 4589 4590 4276 4268 B1-4 13175 6559 6524 6520 6091 6086 B1-6 10873 6568 6514 6530 5964 5962 B1-7 10950 6140 6113 6094 5469 5451 B10-14 6011 3446 3429 3431 3150 3146 B10-16 13831 7968 7941 7918 7248 7233 B10-17 10982 4927 4898 4896 4548 4506 B10-18 10059 5622 5605 5607 5053 5029 B10-19 12876 7437 7419 7397 6604 6579 B10-22 21342 11584 11551 11564 10118 10041 B10-6 29911 15434 15381 15404 14299 14200 B10-7 8427 4811 4787 4784 4364 4352 B10-9 13366 6560 6530 6534 5964 5957 B10-fal 24131 12374 12348 12349 11735 11671 B2-1 12412 6563 6522 6512 6117 6079 B2-10 19272 11002 10973 10965 10166 10161 B2-11 8626 4766 4754 4751 4461 4400 B2-12 17980 9804 9768 9770 8801 8729 B2-13 10050 5568 5552 5539 5178 5158 B2-17 18591 10111 10080 10067 8978 8935 B2-18 6488 3754 3734 3735 3644 3640 B2-21 20710 10897 10864 10863 10170 10086 B2-22 20429 10227 10190 10178 9386 9305 B2-5 12175 7021 6998 6981 6355 6338 B2-9 15943 8738 8696 8686 8171 8105 B3-1 14605 8041 8015 8012 7281 7259 B3-13 8386 4782 4765 4758 4303 4296 B3-14 17549 7704 7666 7648 7118 7107 B3-15 13922 5942 5919 5924 5398 5398 103 Table A3 (continued) Number of reads retained at each step in the DADA2 pipeline. Samples are shown with block number first followed by treatment number (Table 2.2) separated by a hyphen. From left to right are initial read counts (input), reads after filtering (filtered), forward reads after denoising (denoisedF), reverse reads after denoising (denoisedR), number of reads after merging (merged), and number of reads after chimera removal (nonchim). Sample input filtered denoisedF denoisedR merged nonchim B3-17 7866 4491 4473 4469 4090 4069 B3-2 22018 11332 11274 11270 10318 10247 B3-20 19794 10473 10436 10427 9681 9618 B3-21 24717 12958 12908 12923 12068 11915 B3-22 19204 10218 10182 10191 9640 9581 B3-3 6693 3643 3631 3622 3322 3316 B3-7 20743 10079 10010 10007 9315 9237 B3-fal 18855 10320 10296 10251 9587 9503 B4-11 10411 6031 6007 5994 5049 5049 B4-12 14153 7003 6958 6956 6417 6403 B4-15 16611 8708 8658 8662 7915 7840 B4-16 19340 10285 10240 10241 9650 9558 B4-18 8515 5131 5124 5122 4708 4697 B4-2 25973 13688 13617 13625 12315 12203 B4-21 23882 12563 12498 12473 11638 11530 B4-5 23443 12027 11955 11961 11003 10927 B4-7 20209 10317 10270 10265 9600 9531 B5-10 12457 6726 6689 6707 6415 6378 B5-11 35399 18215 18142 18138 16671 16523 B5-12 24203 13410 13350 13349 12385 12216 B5-13 23829 12481 12429 12414 11449 11327 B5-15 29538 17066 17035 17022 16486 16416 B5-4 23032 9630 9594 9580 8953 8943 B5-6 7570 3983 3967 3965 3565 3562 B5-9 11398 4582 4558 4557 4269 4268 B6-11 11204 6595 6572 6555 6071 6052 B6-12 8155 4512 4484 4491 4028 4021 B6-13 10779 5783 5755 5744 5194 5178 B6-19 29866 16427 16367 16360 15012 14867 B6-2 17688 9582 9411 9366 8683 8677 B6-20 8519 4795 4783 4782 4331 4317 B6-21 22819 12354 12305 12295 11632 11567 B6-3 5341 2866 2855 2851 2574 2573 B6-4 6254 847 832 835 771 769 B6-5 5377 2209 2198 2187 2000 1995 104 Table A3 (continued) Number of reads retained at each step in the DADA2 pipeline. Samples are shown with block number first followed by treatment number (Table 2.2) separated by a hyphen. From left to right are initial read counts (input), reads after filtering (filtered), forward reads after denoising (denoisedF), reverse reads after denoising (denoisedR), number of reads after merging (merged), and number of reads after chimera removal (nonchim). Sample input filtered denoisedF denoisedR merged nonchim B7-1 24124 12565 12508 12504 11635 11547 B7-10 25220 13851 13789 13798 13062 12847 B7-11 9586 5557 5530 5516 5131 5111 B7-15 26469 14189 14156 14150 13580 13489 B7-17 16917 8897 8856 8842 8204 8161 B7-19 13208 8050 7998 7990 7187 7167 B7-20 10609 6169 6151 6140 5595 5589 B7-22 29654 16540 16492 16503 15391 15259 B7-3 19382 10255 10220 10216 9450 9450 B7-5 11105 5371 5353 5342 4865 4863 B7-7 12032 6861 6824 6819 6298 6291 B7-fal 24746 13168 13147 13116 12135 12111 B8-12 20947 10878 10833 10825 9907 9825 B8-13 14151 7813 7771 7781 7010 7009 B8-18 13899 6928 6888 6886 6373 6342 B8-2 26482 14400 14337 14317 13350 13228 B8-20 18913 9475 9447 9451 8770 8695 B8-21 14129 7922 7901 7893 7391 7332 B8-22 18843 9856 9801 9807 9007 8966 B8-5 20503 10688 10648 10648 10081 10023 B8-6 13991 7014 6976 6985 6394 6378 B8-fal 4603 2518 2478 2466 2362 2358 B9-10 11953 5229 5209 5202 4679 4670 B9-14 6549 3955 3938 3941 3602 3599 B9-16 11558 6775 6752 6755 6119 6111 B9-19 11146 5144 5116 5115 4804 4744 B9-3 10354 4664 4648 4644 4081 4076 B9-4 13701 6974 6945 6933 6440 6422 B9-6 9487 4444 4414 4411 4093 4089 B9-9 19158 10136 10094 10088 9287 9246 B9-fal 7037 4131 4117 4116 3850 3836 PRE-1 9227 5446 5423 5416 4963 4954 PRE-10 41280 20182 20115 20095 18033 17904 PRE-2 10046 5575 5566 5559 5085 5084 PRE-3 16164 7724 7665 7679 7081 7074 105 Table A3 (continued) Number of reads retained at each step in the DADA2 pipeline. Samples are shown with block number first followed by treatment number (Table 2.2) separated by a hyphen. From left to right are initial read counts (input), reads after filtering (filtered), forward reads after denoising (denoisedF), reverse reads after denoising (denoisedR), number of reads after merging (merged), and number of reads after chimera removal (nonchim). Sample input filtered denoisedF denoisedR merged nonchim PRE-4 25025 11935 11915 11903 11141 11055 PRE-5 21858 11157 11125 11117 10273 10187 PRE-6 31867 17222 17163 17145 15889 15758 PRE-7 29499 14842 14816 14809 13730 13649 PRE-8 17772 9465 9443 9436 8714 8689 PRE-9 22917 12095 12053 12062 11220 11125 Table A4 Pairwise comparison (diversity level) results for fungal alpha diversity. Treatments are monocultures (1 cover crop), mixtures (2 cover crops) and all native/cultivar (all crops grown together). P values and adjected q values are shown at a significance level of 0.05. Experiment Comparison p-value q-value Native study monocultures/mixtures 0.041 0.061 monocultures/all native 0.021 0.061 mixtures/all native 0.741 0.741 Cultivar study monocultures/mixtures 0.408 0.612 monocultures/all native 0.858 0.858 monocultures/fallow 0.330 0.612 mixtures/all native 0.829 0.858 mixtures/fallow 0.099 0.597 all native/fallow 0.327 0.612 Table A5 Pairwise comparison results for fungal alpha diversity under individual cover crops. Native study treatments are all species together “all native”, bluebunch wheatgrass “bluebunch”, Silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Cultivar study treatments are all plants grown together “all cultivar”, buckwheat, crimson clover “clover”, fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Experiment Comparison p-value q-value Native study all native/bluebunch 0.025 0.169 all native/lupine 0.086 0.186 all native/rockcress 0.142 0.186 all native/yarrow 0.142 0.186 bluebunch/lupine 0.289 0.320 bluebunch/rockcress 0.034 0.169 bluebunch/yarrow 0.077 0.186 lupine/rockcress 0.150 0.186 lupine/yarrow 0.149 0.186 rockcress/yarrow 0.386 0.386 Cultivar study all cultivar/buckwheat 0.754 1 all cultivar/clover 0.465 0.871 all cultivar/fallow 0.327 0.871 all cultivar/mustard 0.916 1 all cultivar/wheat 1 1 buckwheat/clover 0.174 0.871 buckwheat/fallow 0.141 0.871 106 Table A5 (continued) Pairwise comparison results for fungal alpha diversity under individual cover crops. Native study treatments are all species together “all native”, bluebunch wheatgrass “bluebunch”, Silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Cultivar study treatments are all plants grown together “all cultivar”, buckwheat, crimson clover “clover”, fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Experiment Comparison p-value q-value Cultivar study buckwheat/mustard 0.464 0.871 buckwheat/wheat 0.624 0.936 clover/fallow 1 1 clover/mustard 0.464 0.871 clover/wheat 0.624 0.936 fallow/mustard 0.462 0.871 fallow/wheat 0.386 0.871 mustard/wheat 0.806 1 Table A6 Pairwise comparison results for fungal alpha diversity under cover crop mixtures. Native study treatments are mixture of all plants “all native”, bluebunch wheatgrass with silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Cultivar study treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, fallow “Fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass with crimson clover “wheat-clover”. Experiment Comparison p-value q-value Native study all native/bluebunch-lupine 0.009 0.135 all native/rockcress-bluebunch 0.806 0.864 all native/yarrow-bluebunch 0.601 0.851 all native/yarrow-lupine 0.347 0.744 all native/yarrow-rockcress 0.806 0.864 bluebunch-lupine/rockcress-bluebunch 0.050 0.187 bluebunch-lupine/yarrow-bluebunch 0.028 0.141 bluebunch-lupine/yarrow-lupine 0.028 0.141 bluebunch-lupine/yarrow-rockcress 0.141 0.354 rockcress-bluebunch/yarrow-bluebunch 0.462 0.770 rockcress-bluebunch/yarrow-lupine 0.086 0.259 rockcress-bluebunch/yarrow-rockcress 0.772 0.864 yarrow-bluebunch/yarrow-lupine 0.916 0.916 yarrow-bluebunch/yarrow-rockcress 0.624 0.851 yarrow-lupine/yarrow-rockcress 0.462 0.770 Cultivar study all cultivar/clover-buckwheat 0.464 0.836 all cultivar/fallow 0.327 0.836 all cultivar/mustard-buckwheat 0.806 0.836 all cultivar/mustard-clover 0.806 0.836 all cultivar/mustard-wheat 0.624 0.836 all cultivar/wheat-buckwheat 0.624 0.836 all cultivar/wheat-clover 0.754 0.836 clover-buckwheat/fallow 0.327 0.836 clover-buckwheat/mustard-buckwheat 0.462 0.836 clover-buckwheat/mustard-clover 0.327 0.836 clover-buckwheat/mustard-wheat 0.462 0.836 clover-buckwheat/wheat-buckwheat 0.220 0.836 clover-buckwheat/wheat-clover 0.464 0.836 107 Table A6 (continued) Pairwise comparison results for fungal alpha diversity under cover crop mixtures. Native study treatments are mixture of all plants “all native”, bluebunch wheatgrass with silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Cultivar study treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, fallow “Fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass with crimson clover “wheat-clover”. Experiment Comparison p-value q-value fallow/mustard-buckwheat 0.248 0.836 fallow/mustard-clover 0.563 0.836 fallow/mustard-wheat 0.083 0.836 fallow/wheat-buckwheat 0.083 0.836 fallow/wheat-clover 0.220 0.836 mustard-buckwheat/mustard-clover 0.772 0.836 mustard-buckwheat/mustard-wheat 0.386 0.836 mustard-buckwheat/wheat-buckwheat 0.772 0.836 mustard-buckwheat/wheat-clover 1 1 mustard-clover/mustard-wheat 0.563 0.836 mustard-clover/wheat-buckwheat 0.386 0.836 mustard-clover/wheat-clover 0.806 0.836 mustard-wheat/wheat-buckwheat 0.772 0.836 mustard-wheat/wheat-clover 0.462 0.836 wheat-buckwheat/wheat-clover 0.624 0.836 Table A7 Pairwise PERMANOVA results of fungal community composition under native and cultivar cover crops. Pairwise comparisons represent cover crops by diversity level. Treatments are monocultures (1 cover crop), mixtures (2 cover crops) and all native/cultivar (all crops grown together). P values and adjected q values are shown at a significance level of 0.05. Experiment Comparison p value q value Native study monocultures/mixtures 0.023 0.069 monocultures/all native 0.365 0.536 mixtures/all native 0.536 0.536 Cultivar study monocultures/mixtures 0.938 0.938 monocultures/all cultivar 0.039 0.053 monocultures/fallow 0.003 0.014 mixtures/all cultivar 0.044 0.053 mixtures/fallow 0.005 0.014 all cultivar/fallow 0.007 0.014 Table A8 Pairwise comparison results for fungal community composition under individual cover crops. Native study treatments are all species together “all native”, bluebunch wheatgrass “bluebunch”, Silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Cultivar study treatments are all plants grown together “all cultivar”, buckwheat, crimson clover “clover”, fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Experiment Comparison p value q value Native study all native/bluebunch 0.165 0.330 all native/lupine 0.108 0.330 all native/rockcress 0.568 0.596 all native/yarrow 0.511 0.596 bluebunch/lupine 0.219 0.365 108 Table A8 (continued) Pairwise comparison results for fungal community composition under individual cover crops. Native study treatments are all species together “all native”, bluebunch wheatgrass “bluebunch”, Silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Cultivar study treatments are all plants grown together “all cultivar”, buckwheat, crimson clover “clover”, fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Experiment Comparison p value q value bluebunch/rockcress 0.344 0.491 bluebunch/yarrow 0.036 0.280 lupine/rockcress 0.144 0.330 lupine/yarrow 0.056 0.280 rockcress/yarrow 0.596 0.596 Cultivar study all cultivar/buckwheat 0.028 0.084 all cultivar/clover 0.062 0.097 all cultivar/fallow 0.005 0.045 all cultivar/mustard 0.065 0.097 all cultivar/wheat 0.017 0.084 buckwheat/clover 0.061 0.097 buckwheat/fallow 0.055 0.097 buckwheat/mustard 0.329 0.375 buckwheat/wheat 0.634 0.634 clover/fallow 0.006 0.045 clover/mustard 0.025 0.084 clover/wheat 0.037 0.092 fallow/mustard 0.308 0.375 fallow/wheat 0.157 0.214 mustard/wheat 0.35 0.375 Table A9 Pairwise comparison results for fungal community composition under cover crop mixtures. Native study treatments are mixture of all plants “all native”, bluebunch wheatgrass with silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Cultivar study treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, fallow “Fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass with crimson clover “wheat-clover”. Experiment Comparison p-value q-value Native study all native/bluebunch-lupine 0.408 0.465 all native/rockcress-bluebunch 0.295 0.442 all native/yarrow-bluebunch 0.651 0.651 all native/yarrow-lupine 0.167 0.343 all native/yarrow-rockcress 0.158 0.343 bluebunch-lupine/rockcress-bluebunch 0.434 0.465 bluebunch-lupine/yarrow-bluebunch 0.359 0.448 bluebunch-lupine/yarrow-lupine 0.094 0.343 bluebunch-lupine/yarrow-rockcress 0.169 0.343 rockcress-bluebunch/yarrow-bluebunch 0.327 0.445 rockcress-bluebunch/yarrow-lupine 0.021 0.247 rockcress-bluebunch/yarrow-rockcress 0.083 0.343 yarrow-bluebunch/yarrow-lupine 0.207 0.345 yarrow-bluebunch/yarrow-rockcress 0.033 0.247 yarrow-lupine/yarrow-rockcress 0.183 0.343 109 Table A9 (continued) Pairwise comparison results for fungal community composition under cover crop mixtures. Native study treatments are mixture of all plants “all native”, bluebunch wheatgrass with silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Cultivar study treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, fallow “Fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass with crimson clover “wheat-clover”. Experiment Comparison p-value q-value Cultivar study all cultivar/clover-buckwheat 0.355 0.473 all cultivar/fallow 0.014 0.063 all cultivar/mustard-buckwheat 0.014 0.063 all cultivar/mustard-clover 0.031 0.072 all cultivar/mustard-wheat 0.018 0.063 all cultivar/wheat-buckwheat 0.109 0.190 all cultivar/wheat-clover 0.061 0.113 clover-buckwheat/fallow 0.013 0.063 clover-buckwheat/mustard-buckwheat 0.018 0.063 clover-buckwheat/mustard-clover 0.022 0.068 clover-buckwheat/mustard-wheat 0.013 0.063 clover-buckwheat/wheat-buckwheat 0.009 0.063 clover-buckwheat/wheat-clover 0.046 0.092 fallow/mustard-buckwheat 0.03 0.072 fallow/mustard-clover 0.035 0.075 fallow/mustard-wheat 0.505 0.589 fallow/wheat-buckwheat 0.279 0.390 fallow/wheat-clover 0.008 0.063 mustard-buckwheat/mustard-clover 0.468 0.569 mustard-buckwheat/mustard-wheat 0.206 0.303 mustard-buckwheat/wheat-buckwheat 0.613 0.647 mustard-buckwheat/wheat-clover 0.708 0.708 mustard-clover/mustard-wheat 0.026 0.072 mustard-clover/wheat-buckwheat 0.184 0.286 mustard-clover/wheat-clover 0.61 0.647 mustard-wheat/wheat-buckwheat 0.624 0.647 mustard-wheat/wheat-clover 0.12 0.197 wheat-buckwheat/wheat-clover 0.464 0.569 Table A10 Dispersion of fungal communities for native and cultivar cover crops. Pairwise comparisons represent cover crops by diversity level. Treatments are monocultures (1 cover crop), mixtures (2 cover crops) and all native/cultivar (all crops grown together). P values and adjected q values are shown at a significance level of 0.05. Experiment Comparison p value q value Native study monocultures/mixtures 0.168 0.307 monocultures/all native 0.205 0.307 mixtures/all native 0.735 0.735 Cultivar study monocultures/mixtures 0.943 0.952 monocultures/all cultivar 0.088 0.202 monocultures/fallow 0.952 0.952 mixtures/all cultivar 0.093 0.202 mixtures/fallow 0.923 0.952 all cultivar/fallow 0.101 0.202 110 Table A11 Dispersion of fungal communities in individual cover crops. Native study treatments are all species together “all native”, bluebunch wheatgrass “bluebunch”, Silky lupine “lupine”, Holboell’s rockcress “rockcress”, and white yarrow “yarrow”. Cultivar study treatments are all plants grown together “all cultivar”, buckwheat, crimson clover “clover”, fallow “fallow”, white mustard “mustard”, and wheatgrass “wheat”. Experiment Comparison p value q value Native study all native/bluebunch 0.694 0.841 all native/lupine 0.841 0.841 all native/rockcress 0.739 0.841 all native/yarrow 0.699 0.841 bluebunch/lupine 0.456 0.841 bluebunch/rockcress 0.836 0.841 bluebunch/yarrow 0.595 0.841 lupine/rockcress 0.747 0.841 lupine/yarrow 0.341 0.841 rockcress/yarrow 0.758 0.841 Cultivar study all cultivar/buckwheat 0.461 0.628 all cultivar/clover 0.277 0.475 all cultivar/fallow 0.106 0.475 all cultivar/mustard 0.382 0.573 all cultivar/wheat 0.927 0.927 buckwheat/clover 0.511 0.6387 buckwheat/fallow 0.233 0.475 buckwheat/mustard 0.284 0.475 buckwheat/wheat 0.285 0.475 clover/fallow 0.173 0.475 clover/mustard 0.575 0.663 clover/wheat 0.103 0.475 fallow/mustard 0.91 0.927 fallow/wheat 0.078 0.475 mustard/wheat 0.144 0.475 Table A12 Dispersion of fungal communities under cover crop mixtures. Native study treatments are mixture of all plants “all native”, bluebunch wheatgrass with silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Cultivar study treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, fallow “Fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass with crimson clover “wheat-clover”. Experiment Comparison p-value q-value Native study all native/bluebunch-lupine 0.099 0.297 all native/rockcress-bluebunch 0.386 0.526 all native/yarrow-bluebunch 0.77 0.888 all native/yarrow-lupine 0.305 0.513 all native/yarrow-rockcress 0.753 0.888 bluebunch-lupine/rockcress-bluebunch 0.019 0.095 bluebunch-lupine/yarrow-bluebunch 0.019 0.095 bluebunch-lupine/yarrow-lupine 0.006 0.09 bluebunch-lupine/yarrow-rockcress 0.029 0.108 rockcress-bluebunch/yarrow-bluebunch 0.237 0.513 rockcress-bluebunch/yarrow-lupine 1 1 rockcress-bluebunch/yarrow-rockcress 0.308 0.513 111 Table A12 (continued) Dispersion of fungal communities under cover crop mixtures. Native study treatments are mixture of all plants “all native”, bluebunch wheatgrass with silky lupine “bluebunch-lupine”, Holboell’s rockcress and bluebunch wheatgrass “rockcress-bluebunch”, white yarrow and bluebunch wheatgrass “yarrow-bluebunch”, white yarrow and silky lupine “yarrow-lupine”, and white yarrow and Holboell’s rockcress “yarrow-rockcress”. Cultivar study treatments are mixture of all plants “all cultivar”, crimson clover and buckwheat “clover-buckwheat”, fallow “Fallow”, white mustard and buckwheat “mustard-buckwheat”, white mustard and crimson clover “mustard-clover”, white mustard and wheatgrass “mustard-wheat”, wheatgrass and buckwheat “wheat-buckwheat”, and wheatgrass with crimson clover “wheat-clover”. Experiment Comparison p-value q-value Native Study yarrow-bluebunch/yarrow-lupine 0.263 0.513 yarrow-bluebunch/yarrow-rockcress 0.876 0.938 yarrow-lupine/yarrow-rockcress 0.35 0.525 Cultivar study all cultivar/clover-buckwheat 0.536 0.936 all cultivar/fallow 0.114 0.936 all cultivar/mustard-buckwheat 0.858 0.956 all cultivar/mustard-clover 0.536 0.936 all cultivar/mustard-wheat 0.514 0.936 all cultivar/wheat-buckwheat 0.38 0.936 all cultivar/wheat-clover 0.403 0.936 clover-buckwheat/fallow 0.017 0.476 clover-buckwheat/mustard-buckwheat 0.674 0.936 clover-buckwheat/mustard-clover 0.303 0.936 clover-buckwheat/mustard-wheat 0.135 0.936 clover-buckwheat/wheat-buckwheat 0.246 0.936 clover-buckwheat/wheat-clover 0.07 0.936 fallow/mustard-buckwheat 0.357 0.936 fallow/mustard-clover 0.18 0.936 fallow/mustard-wheat 0.25 0.936 fallow/wheat-buckwheat 0.949 0.984 fallow/wheat-clover 0.563 0.936 mustard-buckwheat/mustard-clover 0.569 0.936 mustard-buckwheat/mustard-wheat 0.69 0.936 mustard-buckwheat/wheat-buckwheat 0.661 0.936 mustard-buckwheat/wheat-clover 0.702 0.936 mustard-clover/mustard-wheat 1 1 mustard-clover/wheat-buckwheat 0.856 0.956 mustard-clover/wheat-clover 0.842 0.956 mustard-wheat/wheat-buckwheat 0.591 0.936 mustard-wheat/wheat-clover 0.888 0.956 wheat-buckwheat/wheat-clover 0.795 0.956
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NoDerivatives 4.0 International
Graduate
Using cover crops to mitigate vine decline and reduce pathogens in vineyard soils
Text