Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Mahovlic, April
Master of Science - MSc
University of British Columbia
Grapevine leafroll-associated virus 3 (GLRaV-3) and Grapevine red blotch virus (GRBV) are economically important viruses in British Columbia that decrease grapevine health and fruit quality. This study aimed to investigate the use of crop thinning to mitigate negative impacts of GLRaV-3 and GRBV on vine health and fruit quality of ‘Merlot’ and ‘Cabernet Sauvignon’ vines in the Okanagan Valley, British Columbia, as well as understand the role of virus titer in disease severity. A commercial vineyard site with adjacent ‘Merlot’ and ‘Cabernet Sauvignon’ blocks was selected in a complete-randomized trial conducted in 2021 and 2022 growing seasons. GLRaV-3 and GRBV vines were paired 1:1 with healthy vines, and each pair was designated a cropping treatment, applied at pea-sized berries stage, of either 1.5 clusters per shoot (1.5 C/S) or 1.0 cluster per shoot (1.0 C/S). Vine health parameters and virus titer measurements were taken four times in the season and fruit quality measurements were taken once at harvest. GLRaV-3 infection increased crop load and titratable acidity (TA), and decreased Brix. Crop thinning decreased TA and anthocyanins and increased pH of healthy vines. 1.0 C/S GLRaV-3 vines had significantly higher pH than 1.5 C/S GLRaV-3 vines. However, all these impacts were year and cultivar dependent, suggesting that crop thinning is not sufficient to mitigate GLRaV-3 impacts in ‘Merlot’ and ‘Cabernet Sauvignon’. GRBV had more consistent impacts on vine health and fruit quality, as infection decreased leaf greenness (SPAD), increased berry weight, cluster weight, pruning weight, crop load, TA and pH, and decreased Brix, yeast assimilable nitrogen (YAN). Crop thinning did not significantly impact GRBV infected vines, thus was not sufficient to mitigate virus effects. GLRaV-3 and GRBV titer was highest at pea-sized berries and harvest, and may follow a seasonal pattern corresponding to temperature changes. Viral titer was negatively correlated with photosynthesis (Anet), stomatal conductance (gs), SPAD, berry weight, cluster weight, yield, TA and YAN. However results differed by virus, cultivar and year, and weren’t indicative of negative impacts to vine health or fruit quality.
Mitigating the effects of Grapevine leafroll-associated virus 3 and Grapevine red blotch virus in ‘Merlot’ and ‘Cabernet Sauvignon’ using crop thinning, and investigating the role of virus titer in disease severity in the Okanagan Valley, British Columbia by April Mahovlic B.Sc. Hons, University of British Columbia Okanagan, 2019 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) July 2023 © April Mahovlic, 2023 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Mitigating the effects of Grapevine leafroll-associated virus 3 and Grapevine red blotch virus in ‘Merlot’ and ‘Cabernet Sauvignon’ using crop thinning, and investigating the role of virus titer in disease severity in the Okanagan Valley, British Columbia submitted by April Mahovlic in partial fulfillment of the requirements of the degree of Master of Science . Dr. Miranda Hart, Faculty of Science, University of British Columbia Okanagan Supervisor Dr. José Ramón Úrbez-Torres, Summerland Research and Development Centre, Agriculture and Agri-Food Canada Co-Supervisor Dr. Kevin Usher, Summerland Research and Development Centre, Agriculture and Agri-Food Canada Supervisory Committee Member Dr. Michael Deyholos, Faculty of Science, University of British Columbia, Okanagan Supervisory Committee Member Dr. Simone Castellarin, Faculty of Land and Food Systems, University of British Columbia University Examiner iii Abstract Grapevine leafroll-associated virus 3 (GLRaV-3) and Grapevine red blotch virus (GRBV) are economically important viruses in British Columbia that decrease grapevine health and fruit quality. This study aimed to investigate the use of crop thinning to mitigate negative impacts of GLRaV-3 and GRBV on vine health and fruit quality of ‘Merlot’ and ‘Cabernet Sauvignon’ vines in the Okanagan Valley, British Columbia, as well as understand the role of virus titer in disease severity. A commercial vineyard site with adjacent ‘Merlot’ and ‘Cabernet Sauvignon’ blocks was selected in a complete-randomized trial conducted in 2021 and 2022 growing seasons. GLRaV-3 and GRBV vines were paired 1:1 with healthy vines, and each pair was designated a cropping treatment, applied at pea-sized berries stage, of either 1.5 clusters per shoot (1.5 C/S) or 1.0 cluster per shoot (1.0 C/S). Vine health parameters and virus titer measurements were taken four times in the season and fruit quality measurements were taken once at harvest. GLRaV-3 infection increased crop load and titratable acidity (TA), and decreased Brix. Crop thinning decreased TA and anthocyanins and increased pH of healthy vines. 1.0 C/S GLRaV-3 vines had significantly higher pH than 1.5 C/S GLRaV-3 vines. However, all these impacts were year and cultivar dependent, suggesting that crop thinning is not sufficient to mitigate GLRaV-3 impacts in ‘Merlot’ and ‘Cabernet Sauvignon’. GRBV had more consistent impacts on vine health and fruit quality, as infection decreased leaf greenness (SPAD), increased berry weight, cluster weight, pruning weight, crop load, TA and pH, and decreased Brix, yeast assimilable nitrogen (YAN). Crop thinning did not significantly impact GRBV infected vines, thus was not sufficient to mitigate virus effects. GLRaV-3 and GRBV titer was highest at pea-sized berries and harvest, and may follow a seasonal pattern corresponding to temperature changes. Viral titer was negatively correlated with photosynthesis (Anet), stomatal conductance (gs), SPAD, berry weight, cluster weight, yield, TA and YAN. However results differed by virus, cultivar and year, and weren’t indicative of negative impacts to vine health or fruit quality. iv Lay Summary Grapevine leafroll-associated virus 3 (GLRaV-3) and Grapevine red blotch virus (GRBV) are two viruses that infect grapevines in British Columbia and damage a vines’ ability to produce quality fruit. This study aimed to test whether removing clusters (crop thinning) could protect against GLRaV-3 and GRBV harmful effects. We also wanted to understand whether the amount of virus particles an infected vine contains is related to the amount of damage caused by GLRaV-3 or GRBV. We determined that GLRaV-3 caused minimal damage, and that GRBV was very damaging to grapevines in our study. Unfortunately, crop thinning did not improve the vine’s ability to fight off virus effects. We also learned that the amount of virus particles in an infected grapevine changes throughout the season, but can’t be sure if it is related to the amount of damage to the grapevine from GLRaV-3 or GRBV. v Preface The origination of this project came from a meeting between Dr. José Ramón Úrbez-Torres and April Mahovlic along with members of the BC grape and wine industry; determining a need to explore potential techniques to mitigate virus impacts in BC vineyards. The study site was provided by a commercial producer in the South Okanagan, BC. Experimental design and methodology for this study was developed with collaboration from Dr. José Ramón Úrbez-Torres, April Mahovlic and Dr. Kevin Usher, along with revision and approval from Dr. Miranda Hart and Dr. Michael Deyholos. Experimentation and data collection was carried out primarily by April Mahovlic, with support from co-op students, graduate students, and technical staff at Agriculture and Agri-Food Canada, Summerland Research and Development Centre as outlined in the Acknowledgements. Data analysis was performed by April Mahovlic. Experimentation and data analyses were guided and revised by Dr. José Ramón Úrbez-Torres and Dr. Miranda Hart. The thesis was written by April Mahovlic, with editorial support from Dr. José Ramón Úrbez-Torres and Dr. Miranda Hart, and review from Dr. Kevin Usher and Dr. Michael Deyholos. vi Table of Contents Abstract .................................................................................................................................... iii Lay Summary ........................................................................................................................... iv Preface ....................................................................................................................................... v Table of Contents ..................................................................................................................... vi List of Tables ............................................................................................................................. x List of Figures ......................................................................................................................... xii Acknowledgements ............................................................................................................... xiv Dedication ............................................................................................................................... xv Chapter 1: Introduction ............................................................................................................ 1 1.1 Significance of the grape and wine industry in Canada ............................................. 1 1.2 Grapevine viral diseases........................................................................................... 2 1.2.1 Impacts on plant health, fruit and wine quality ..................................................... 3 1.2.2 Grapevine leafroll disease (GLD) ........................................................................ 3 1.2.2.1 Causal agents ............................................................................................. 4 1.2.2.2 Primary and secondary source of infection .................................................. 5 1.2.2.3 Impacts on plant health, fruit and wine quality ............................................. 6 1.2.2.4 Economic significance ................................................................................. 7 1.2.3 Grapevine red blotch disease (GRBD) ................................................................ 7 1.2.3.1 Causal agents ............................................................................................. 8 1.2.3.2 Primary and secondary source of infection .................................................. 8 1.2.3.3 Impacts on plant health, fruit and wine quality ............................................. 9 1.2.3.4 Economic significance ................................................................................. 9 1.2.4 Control strategies ...............................................................................................10 1.2.5 Status of GLD and GRBD in British Columbia ....................................................12 vii 1.3 Research Objectives ................................................................................................14 Chapter 2: Mitigating the effects of Grapevine leafroll-associated virus 3 in ‘Merlot’ and ‘Cabernet Sauvignon’ using crop thinning, and investigating the role of virus titer in disease severity in the Okanagan Valley, British Columbia ................................................ 16 2.1 Introduction .................................................................................................................16 2.2 Materials and Methods ............................................................................................20 2.2.1 Site selection, experimental design and weather data ........................................20 2.2.2 Shoot and crop thinning .....................................................................................21 2.2.3 Plant health measurements ...............................................................................22 2.2.3.1 Leaf greenness and leaf gas exchange ......................................................23 2.2.3.2 Berry and cluster weight, yield, pruning weight and crop load ........................23 2.2.4 Fruit quality measurements ................................................................................24 2.2.4.1 Soluble solids (Brix)....................................................................................25 2.2.4.2 Titratable acidity (TA) .................................................................................25 2.2.4.3 pH ..............................................................................................................26 2.2.4.4 Yeast assimilable nitrogen (YAN) ...............................................................26 2.2.4.5 Phenolics ...................................................................................................27 2.2.4.5.1 Anthocyanins and total phenolics ............................................................28 2.2.4.5.2 Tannins ...................................................................................................31 2.2.5 Quantification of GLRaV-3 titer ..........................................................................32 2.2.6 Statistical analyses ............................................................................................35 2.3 Results ....................................................................................................................37 2.3.1 Weather data .....................................................................................................37 2.3.2 Shoot and crop thinning .....................................................................................38 2.3.3 Plant health measurements ..............................................................................39 viii 2.3.3.1 Leaf greenness and leaf gas exchange ......................................................39 2.3.3.2 Berry and cluster weight, yield, pruning weight and crop load ........................44 2.3.4 Fruit quality measurements ................................................................................47 2.3.5 GLRaV-3 titer .....................................................................................................51 2.4 Discussion ...............................................................................................................55 2.4.1 Weather data .....................................................................................................55 2.4.2 Shoot and crop thinning .....................................................................................55 2.4.3 Plant health measurements ..............................................................................57 2.4.3.1 Leaf greenness and leaf gas exchange ......................................................57 2.4.3.2 Berry and cluster weight, yield, pruning weight and crop load ........................59 2.4.4 Fruit quality measurements ................................................................................61 2.4.5 GLRaV-3 titer .....................................................................................................67 Chapter 3: Mitigating the effects of Grapevine red blotch virus in ‘Cabernet Sauvignon’ using crop thinning, and investigating the role of virus titer in disease severity in the Okanagan Valley, British Columbia ....................................................................................... 70 3.1 Introduction ..............................................................................................................70 3.2 Materials and methods ............................................................................................74 3.2.1 Site selection and experimental design ..............................................................74 3.2.2 Shoot and crop thinning .....................................................................................74 3.2.3 Plant health measurements ...............................................................................75 3.2.4 Fruit quality measurements ................................................................................75 3.2.5 Quantification of GRBV titer ...............................................................................75 3.2.6 Statistical analyses ............................................................................................77 3.3 Results ....................................................................................................................77 3.3.1 Weather data .....................................................................................................77 ix 3.3.2 Shoot and crop thinning .....................................................................................77 3.3.3.1 Leaf greenness and leaf gas exchange ......................................................78 3.3.3.2 Berry and cluster weight, yield, pruning weight and crop load ....................81 3.3.4 Fruit quality measurements ................................................................................83 3.3.5 GRBV titer..........................................................................................................85 3.4 Discussion ...............................................................................................................87 3.4.1 Plant health measurements ..............................................................................87 3.4.1.1 Leaf greenness and leaf gas exchange ......................................................87 3.4.1.2 Berry and cluster weight, yield, pruning weight and crop load ....................88 3.4.2 Fruit quality measurements ................................................................................90 3.4.3 GRBV titer..........................................................................................................95 Chapter 4: Conclusion ............................................................................................................ 98 4.1 Mitigating the effects of Grapevine leafroll-associated virus 3 in ‘Merlot’ and ‘Cabernet Sauvignon’ using crop thinning, and investigating the role of virus titer in disease severity in the Okanagan Valley, British Columbia.............................................................98 4.1.1 Mitigating the effects of GLRaV-3 using crop thinning ........................................98 4.1.2 Investigating the role of GLRaV-3 titer in disease severity .................................99 4.2 Mitigating the effects of Grapevine red blotch virus in ‘Merlot’ and ‘Cabernet Sauvignon’ using crop thinning, and investigating the role of virus titer in disease severity in the Okanagan Valley, British Columbia ........................................................................... 100 4.2.1 Mitigating the effects of GRBV using crop thinning ........................................... 100 4.2.2 Investigating the role of GRBV titer in disease severity .................................... 101 4.3 Future Directions ................................................................................................... 102 Bibliography .......................................................................................................................... 103 x List of Tables Table 1. Economically important Grapevine Viral Diseases* ...................................................... 2 Table 2. Blocks design for GLRaV-3 experiment in Merlot and Cabernet Sauvignon and GRBV in Cabernet sauvignon demonstrating the division of study vines into treatment groups. ..........21 Table 3. Observation dates of key phenological stages for the 2021 and 2022 growing seasons in ‘Merlot’ and ‘Cabernet Sauvignon’ study vineyards ...............................................................23 Table 4. Average clusters at harvest, shoots at pruning, and cluster/shoot ratio of ‘Merlot’ and ‘Cabernet Sauvignon’ study vines in 1.5 C/S and 1.0 C/S treatment groups in 2021 and ..........39 Table 5. Impacts of GLRaV-3 infection, crop thinning, and their interaction, on Anet, gs, T, and SPAD values at phenological stages: bloom, pea-sized berries, veraison and harvest of ‘Merlot’ vines in 2021 and 2022. ............................................................................................................40 Table 6. Impacts of GLRaV-3 infection, crop thinning, and their interaction, on Anet, gs, T, and SPAD values at phenological stages: bloom, pea-sized berries, veraison and harvest of ‘Cabernet Sauvignon’ vines in 2021 and 2022. .........................................................................42 Table 7. Impacts of GLRaV-3 infection, crop thinning, and their interaction on average berry and cluster weight, yield, pruning weight and crop load of ‘Merlot’ vines for 2021 and 2022. ...........45 Table 8. Impacts of GLRaV-3 infection, crop thinning, and their interaction on average berry and cluster weight, yield, pruning weight and crop load of ‘Cabernet Sauvignon’ vines for 2021 and 2022. .........................................................................................................................................46 Table 9. Impacts of GLRaV-3 infection, crop thinning, and their interaction on fruit quality parameters of ‘Merlot’ in 2021 and 2022. ..................................................................................49 Table 10. Impacts of GLRaV-3 infection, crop thinning, and their interaction on fruit quality parameters of ‘Cabernet Sauvignon’ in 2021 and 2022. ............................................................50 Table 11. Correlations between GLRaV-3 titer, leaf greenness and leaf gas exchange for ‘Merlot’ vines in 2021 and 2022. ................................................................................................53 xi Table 12. Correlations between GLRaV-3 titer, leaf greenness and leaf gas exchange for ‘Cabernet Sauvignon’ in 2021 and 2022....................................................................................53 Table 13. Correlations between GLRaV-3 viral titer, measured at harvest, on yield related vine health parameters, and fruit quality parameters in ‘Merlot’ and ‘Cabernet Sauvignon’ in 2021 and 2022 study seasons. ..........................................................................................................54 Table 14. Blocks design demonstrating the division of study vines into treatment groups. ........74 Table 15. Average clusters at harvest, shoots at pruning, and cluster/shoot ratio of ‘Cabernet Sauvignon’ study vines in 2021 and 2022 .................................................................................78 Table 16. Impacts of GRBV infection, crop thinning, and their interaction, on Anet, gs T, and SPAD value at phenological stages: bloom, pea-sized berries, veraison and harvest of ‘Cabernet Sauvignon’ study vines in 2021 and 2022. ................................................................79 Table 17. Impacts of GRBV infection, crop thinning, and their interaction on yield-based vine health parameters of ‘Cabernet Sauvignon’ study vines in 2021 and 2022. ...............................82 Table 18. Impacts of GRBV infection, crop thinning, and their interaction on fruit quality parameters from ‘Cabernet Sauvignon’ study vines for 2021 and 2022. ....................................84 Table 19. Correlations of viral titer with leaf-based vine health parameters of GRBV infected ‘Cabernet Sauvignon’ vines in 2021 and 2022. .........................................................................86 Table 20. Correlation of GRBV titer and yield based vine health, as well as fruit quality parameters of ‘Cabernet Sauvignon’ study vines in 2021 and 2022. .........................................86 xii List of Figures Figure 1. Symptoms of GLD in white-fruited (a and b) and red-fruited (c and d) cultivars of Vitis vinifera L. ................................................................................................................................... 4 Figure 2. Line plot showing changes in average daily temperature at the study site from May to October in 2021 and 2022. ........................................................................................................38 Figure 3. Boxplots depicting changes in viral titer in GLRaV-3 infected ‘Merlot’ vines in 2021 (top left) and 2022 (top right) growing seasons. Phenological stage of measurement is indicated on the x-axis and boxes are color coded for phenological stage. GLRaV-3 titer is indicated on the left-hand Y axis. Lower line plots indicate mean daily temperatures (y axis) for 2021 (bottom left) and 2022 (bottom right) with horizontal lines color coded to indicate the mean daily temperature on the date titer measurements were taken for the corresponding stage. Each box encloses the 25th percentile of the data (bottom) a bold line at the median value and the 75th percentile (top). Whiskers indicate standard deviation, and dots outside of the whiskers are outliers less than 3 standard deviations away from the mean. ...................................................51 Figure 4. Boxplots depicting changes in viral titer in GLRaV-3 infected ‘Cabernet Sauvignon’ vines in 2021 (top left) and 2022 (top right) growing seasons. Phenological stage of measurement is indicated on the x-axis and boxes are color coded for phenological stage. GLRaV-3 titer is indicated on the left-hand Y axis. Lower line plots indicate mean daily temperatures (y axis) for 2021 (bottom left) and 2022 (bottom right) with horizontal lines color coded to indicate the mean daily temperature on the date titer measurements were taken for the corresponding stage. Each box encloses the 25th percentile of the data (bottom) a bold line at the median value and the 75th percentile (top). Whiskers indicate standard deviation, and dots outside of the whiskers are outliers less than 3 standard deviations away from the mean. ........52 Figure 5. Boxplots depicting changes in viral titer in GRBV infected ‘Cabernet Sauvignon’ vines in 2021 (top left) and 2022 (top right) growing seasons. Phenological stage of measurement is xiii indicated on the x-axis and boxes are color coded for phenological stage. GRBV titer is indicated on the left-hand Y axis. Lower line plots indicate mean daily temperatures (y axis) for 2021 (bottom left) and 2022 (bottom right) with horizontal lines color coded to indicate the mean daily temperature on the date titer measurements were taken for the corresponding stage. Each box encloses the 25th percentile of the data (bottom) a bold line at the median value and the 75th percentile (top). Whiskers indicate standard deviation, and dots outside of the whiskers are outliers less than 3 standard deviations away from the mean. ...................................................85 xiv Acknowledgements I would like to acknowledge all the technical, writing, and emotional support provided by student, technical, and scientific staff at AAFC SuRDC, as well as from my advisory committee. Student support was provided by Mia Alexander, Brett Pfliger, Madison McIntosh, Jared Hrycan and Isidora Silva-Valderrama who assisted in lab and field data collection. Varying levels of technical support were provided Jane Theilmann, Melanie Walker, Julie Boule, Cassandra Bruce, Dieter Kahl, Steve Marsh, Brad Estergaard, Steve Orban, John Drover, Huogen Xiao, Brenda Lannard, Andrea Brauner, David Weir and Naomi DeLury. I would also like to thank the Scientists Dan O’Gorman, Carl Bogdanoff, Dr. Patricia Bowen, Dr. Ben-Min Chang, Dr. Kelly Ross and Dr. D. Thomas Lowery for granting me permission to borrow equipment, share lab space, and answer questions that coached me on how to produce good science. Lastly, I would like to acknowledge the unwavering dedication of Dr. José Ramón Úrbez-Torres and Dr. Miranda Hart. Both offered countless academic and personal support. Lastly, to my committee members Dr. Kevin Usher and Dr. Michael Deyholos, who gave me constructive feedback, encouragement, support, and mentorship which resulted in me becoming a better student, both in and out of the lab. xv Dedication To my late uncle, Kevin Wanjoff. You changed my life forever. If only you were here, to experience how beautiful it has become. 1 Chapter 1: Introduction 1.1 Significance of the grape and wine industry in Canada With more than 6.7 million ha planted and over 73.5 million tonnes harvested in 2021, grapes (Vitis vinifera L. and Vitis spp.), are one of the largest fruit crops cultivated worldwide (FAO 2023). The first records of winemaking in Canada date back to the end of the XVI century when European settlers used the native grape species V. labrusca and V. riparia found throughout the St. Lawrence riverbanks for the making of primarily sacramental wine. The first commercial vineyards and wineries were established in the mid 1800’s in Ontario (ON) and British Columbia (BC) and mostly included native Vitis species and French-American hybrids, much more adapted and tolerant than V. vinifera to the cold temperatures experienced in the Canadian winter (Bell 2019). Signing of the North American Free Trade Agreement (NAFTA) in 1988 resulted in more than a 50% reduction in acreage and production, however opening of the Canadian market to imports resulted in a shift from production of mostly low quality and low yielding V. labrusca and French-American hybrids to higher value domesticated V. vinifera cultivars. In addition, establishment of the Vintners Quality Alliance (VQA) in the 1980’s helped ensure quality standards for Canadian wines, boosting reputations of wine industries in both ON and BC (Statistics Canada, 2009). Currently, over 13,000 ha of grapes are cultivated across four provinces, including ON, BC, Québec (QC), and Nova Scotia (NS). Although Canadian production represents only about 0.2% of the worldwide total, the Canadian grape and wine industry is a significant contributor to the economy, reported at CAD $9.04 billion in 2015 (Rimerman and Eyler 2017). The sector creates over 37,000 full-time jobs primarily in agriculture and tourism and is steadily growing (Rimerman and Eyler 2017). Despite the success of a strong industry, a significant threat to wine production in Canada looms in the form of grapevine virus diseases. It can be hypothesized that virus diseases evolved with early cultivation of vines, however their detrimental effects were only 2 realized after the use of pathogen resistant rootstocks were popularized in the 1960’s (Reynolds 2017). Through worldwide import and export of planting material to maintain and/or introduce V. vinifera cultivars into new areas, grapevine viruses have become a worldwide issue (Reynolds 2017). Understanding the impact these viruses have on vine health and fruit quality is essential for the continued success of wine industries worldwide, and in Canada. 1.2 Grapevine viral diseases To date, 86 viruses have been identified from grapevines - the most viruses ever found in a cultivated crop (Martelli 2014; Fuchs 2020). However, not all grapevine viruses are detrimental to the health of the vine. Viruses known to cause significant damage to grapevines are reported to cause vine health decline, decreased yield and reduced fruit and wine quality in the cultivars they infect (Martelli 2014). The most economically important viruses are summarized in Table 1. Table 1. Economically important Grapevine Viral Diseases* Family Genus Species Genome Disease Betaflexiviridae Foveavirus Grapevine rupestris stem pitting-associated virus (GRSPaV) (+) ssRNA Rugose wood Trichovirus Grapevine Pinot gris virus (GPGV) (+) ssRNA Leaf mottling/deformation Vitivirus Grapevine virus A (GVA) Grapevine virus B (GVB) (+) ssRNA Rugose wood Caulimoviridae Badnavirus Grapevine vein clearing virus (GVCV) dsDNA Vein clearing Closteroviridae Closterovirus Grapevine leafroll-associated virus 2 (GLRaV-2) (+) ssRNA Leafroll/graft incompatibility Ampelovirus Grapevine leafroll-associated virus 1 (GLRaV-1) Grapevine leafroll-associated virus 3 (GLRaV-3) (+) ssRNA Leafroll (GLD) Geminiviridae Grablovirus Grapevine red blotch virus (GRBV) ssDNA Red Blotch (GRBD) Secoviridae Nepovirus Grapevine fanleaf virus (GFLV) (+) ssRNA Degeneration *Table adapted from Fuchs 2020 3 1.2.1 Impacts on plant health, fruit and wine quality It is widely accepted that some grapevine viruses have damaging effects to their hosts (Martelli 2014; Mannini and Digiaro 2017; Fuchs 2020). These effects include decreased yield and poor fruit quality, commonly manifested as lower total soluble solids (Brix) levels at harvest. These negative effects can have important economic consequences due to less fruit available for production and lower quality wines. For this study, the effects that Grapevine leafroll disease (GLD) and Grapevine red blotch disease (GRBD) have will be the focus, as they are the most widespread and economically relevant to both the Canadian and BC grape and wine industry (Poojari et al. 2017a,b; Poojari et al. 2020; Xiao et al. 2018). 1.2.2 Grapevine leafroll disease (GLD) Grapevine leafroll disease (GLD) is one of the oldest known infections in grapevines. First coined as “rougeau” in France, visual accounts of GLD have been recorded since the mid 1800’s, however the viral nature of the disease was not discovered until documented by Scheu in 1936 (Hoefert and Gifford 1967). GLD symptoms can be cultivar specific but are generally characterized by reddening of inter-venial leaf area in red-fruited cultivars, and chlorosis of inter-veinal leaf areas in white cultivars (Figure 1), in addition to downward rolling of leaf margins in most red and white cultivars, hence the name “leafroll” (Naidu et al. 2015). 4 Figure 1. Symptoms of GLD in white-fruited (a and b) and red-fruited (c and d) cultivars of Vitis vinifera L. Due to the rapid growth and expansion of the grape and wine industry, GLD is now considered the most economically important, impactful, and widespread grapevine virus disease worldwide (Naidu et al. 2015). A recent survey of GLD in BC found it to be widespread in the province, with the highest concentration in the South Okanagan grape growing region (Poojari et al. 2017a). Due to the economic importance of GLD, it is imperative to understand its effects on grapevines in the Okanagan Valley as well as management strategies that can be implemented to control its spread. 1.2.2.1 Causal agents There are six etiological agents of GLD, and they are named following the order they were discovered, including Grapevine leafroll-associated virus 1 (GLRaV-1), GLRaV-2, GLRaV-3, GLRaV-4, GLRaV-7, and GLRaV-13 (Fuchs 2020). All GLRaVs are positive sense single-stranded RNA viruses from the family Closteroviridae. Though morphological similarities exist 5 between the different virus species, they are genetically distinct, and belong to different genera (Naidu et al. 2015). GLRaV-3 is generally accepted as the main etiological agent of GLD and the one that causes the most negative impact on grapevine health (Burger et al. 2017). GLRaVs are secluded to the vine’s phloem tissue, and cause damage to phloem-associated cells, (Naidu et al. 2015). This results in disrupted phloem transport, leading to nutrient buildup in the leaves and subsequent deficiencies of these nutrients in the grape berry. This source to sink disruption results in decreased photosynthesis, impacting vine vigor and yield (Song et al. 2021). 1.2.2.2 Primary and secondary source of infection The primary source of infection for GLRaVs is from infected grapevine propagated material, either in the form of infected rootstock or scion entering in the nursery propagation process. Infected material can originate from budwood collected from commercial vineyards or from rootstock and scion nursery mother blocks. Since the mid-twentieth century, it has been recognized that establishing rigorous clean plant programs with proven methods of virus detection and plant material sanitation, including virus elimination are essential to minimizing the introduction of viruses into worldwide viticulture regions and are the first line of defense in infection prevention (Golino et al. 2017). Secondary spread of some GLRaVs happens in the vineyard by way of insect vectors. GLRaVs -1, -3, and 4 are spready by several mealybug (Hemiptera: Pseudococcidae) and soft scale (Hemiptera: Coccidae) insects feeding on the phloem of grapevine tissue, and spreading viruses contained within from one vine to the next in a semi-persistent manner, while GLRaVs -2 and -7 do not have any known insect vectors (Naidu et al. 2015; Herrbach et al. 2017). Insect vector management is an important control measure in vineyards to prevent secondary spread; agrochemicals and beneficial insects are often used to control vector populations in vineyards where viruses are a threat (Fuchs 2020). 6 1.2.2.3 Impacts on plant health, fruit and wine quality The effects of GLRaVs on plant health and fruit quality have been widely studied, and their effects in grapevine have been highlighted in a few review articles (Maree et al. 2013; Naidu et al. 2015; Burger et al. 2017; Song et al. 2021). GLRaVs are reported to decrease vine vigor shown as lower pruning weights, decrease total yield, reduce number of clusters and berry weight, resulting in crop losses of up to 40% (Maree et al. 2013; Naidu et al. 2015; Burger et al. 2017). A reduction in photosynthetic capacity in the leaves of infected vines is also commonly reported, with metrics such as stomatal conductance and CO2 assimilation being the most effected (Maree et al. 2013; Burger et al. 2017; Song et al. 2021). The most reported and most significant effect to growers is a decrease in total soluble solids (Brix) and increase in fruit acidity from grapevines infected with GLRaV-3 (Maree et al. 2013; Naidu et al. 2015; Burger et al. 2017; Song et al. 2021). These alterations in berry chemistry have negative impacts on wines made from GLRaV-3 positive vines, such as decreased color and phenolics, and lower alcohol (Alabi et al. 2016; Bowen et al. 2018; Maree et al. 2013). The effect of GLRaV-3 on sugar accumulation in berries is hypothesized to be due to altered source-sink relationships because of impaired sugar translocation in virus infected phloem tissue (Alabi et al. 2016). Though many groups have studied the effects of GLRaVs on plant health and fruit quality, most of these studies have been done in red-fruited cultivars and involve co-infections of multiple GLRaVs, or GLRaVs and other grapevine viruses. Therefore elucidating the true effects of individual GLRaVs on their grapevine host, as well as describing the pathobiology of these infections has been difficult (Song et al. 2021). It is also worth noting that the combination of different cultivars and rootstocks, as well as growing region and environmental conditions can impact the development of grapevines, and thus the impacts that GLRaVs have on their physiology. Lastly, it remains to be understood whether the viral load of the vine has any impact on the virus’ negative effects to plant health and fruit quality. 7 1.2.2.4 Economic significance Due to the impact of GLD on yield and fruit quality of infected grapevines, significant economic losses have been estimated. Atallah et al. (2012) used a net present value (NPV) model to estimate that the economic impact of GLRaVs may range from USD $20,000 to $40,000 per hectare in the absence of control measures, in the Finger Lakes region of New York. These costs can be reduced to a range of USD $3,000 to $23,000 with appropriate intervention, such as roguing programs (Atallah et al. 2012). Similarly, Ricketts et al. (2015) used survey data from California wine growers to estimate the impact of GLRaVs to be anywhere from USD $29,902 to $226,405 per hectare. The researchers also suggest that economic losses can be reduced through roguing programs, encouraging a full vineyard replacement if infection rates are higher than 25% (Ricketts et al. 2015). 1.2.3 Grapevine red blotch disease (GRBD) Grapevine red blotch disease (GRBD) is an emerging disease in grapevine. First reported in 2011 as a potential “new virus causing red leaf symptoms” at the Oakville research experimental station vineyard in Napa Valley, California (CA) (Calvi 2011), it was first characterized in 2012 by Krenz et al. as “Grapevine Cabernet Franc-associated Virus”. In contrast to GLRD, GRBD is concentrated mostly in North America, and is an emerging threat to the grape and wine industry (Calvi 2011; Cieniewicz et al. 2020). Disease symptoms are similar to GLRD, characterized by red blotches on interveinal areas in leaves of red cultivars, and chlorosis in white cultivars, but lacks the characteristic “leaf-rolling” phenotype caused by GLRD. Due to these subtle differences, it is often difficult to visually discern between GRBD and GLRD in the vineyard. GRBD is responsible for significant yield losses and reduction in fruit quality, mostly reported in red cultivars in the United States (Cieniewicz et al. 2020). Poojari et al. (2017b) described the spread and epidemiology of GRBD in the Okanagan Valley in 2017 8 and suggested to be most-likely introduced with planting material imported from the USA. GRBD is currently considered an emerging threat. A recently published paper by Bowen et al. (2020) demonstrated significant yield losses and impacts to fruit quality because of GRBV infection in an Okanagan Valley ‘Cabernet Franc’ vineyard, underlining the importance of management of the disease in BC. 1.2.3.1 Causal agents The causal agent of GRBD is Grapevine red blotch virus (GRBV), a single stranded DNA virus of the genera Geminivirus (Krenz et al. 2012). The virus particle is a monopartite circular single-stranded DNA of 3,206 nucleotides with six open reading frames (ORFs) (Cieniewicz et al. 2017). There are two strains of GRBV known as clade one and clade two with the majority belonging to clade 2. However, a biological difference between the clades has yet to be elucidated (Cieniewicz et al. 2020). Since its discovery in North America, GRBV has been disseminated to Switzerland, India and South Korea, from infected cuttings originating in the USA (Cieniewicz et al. 2020). 1.2.3.2 Primary and secondary source of infection GRBV enters vineyards through infected planting material (Sudarshana et al. 2015) and spread within vineyards was recently confirmed to occur by the three-cornered alfalfa treehopper Spissistilus festinus (Hoyle et al. 2022). The alfalfa treehopper is not determined to be a pest of grapevines, and instead prefers to host on legumes, however, will feed and oviposit on grapevines (Cieniewicz et al. 2017). In addition to S. festinus, two other species of tree hopper, Entylia carinata and Enchenopa binotata were found to spread GRBV from infected to virus-free grapevines in a greenhouse experiment in Missouri, USA, however E. carinata is the most likely vector due to its abundance it the study region (LaFond et al. 2022). 9 1.2.3.3 Impacts on plant health, fruit and wine quality The effects of GRBV on vine health and fruit quality are like those of GLRaVs, however tend to be more severe. GRBV causes significant reductions in total soluble solids, up to 4 Brix lower in some cases, as well as decreased anthocyanin content in berry skins, leading to lighter coloured fruit (Cieniewicz et al. 2017). A recent and comprehensive study on the effects of GRBV on ‘Cabernet Franc’ in the Okanagan Valley concluded that, in addition to an average decrease of 4.1 Brix, infected vines had significantly higher titratable acidity (TA), lower pH, and decreased anthocyanins and tannins (Bowen et al. 2020). These alterations translated to negative effects on wine quality including decreased YAN, anthocyanin, and alcohol in addition to lighter colored wines that were higher in acidity and vegetal aromas and flavours. These significant changes were observable with as low as 20% of fruit in the wine being GRBV infected. (Bowen et al. 2020). In addition to poor fruit and wine quality, vines infected with GRBV were less vigorous, as demonstrated by decreased pruning mass, and yield decreases on average of 42% were reported because of less clusters per vine and fewer berries per cluster (Bowen et al. 2020). Similar studies with other cultivars have reported GRBV infection resulting in decreased Brix and anthocyanins, though report conflicting findings with respect to effects on yield parameters, and acidity (Calvi 2011; Blanco-Ulate et al. 2017; Reynard et al. 2018; Cauduro Girardello et al. 2019a, b; Martínez-Lüscher et al. 2019; Levin and KC 2020). Despite this, all studies echo the need for management and control measures to prevent spread of this devastating disease in vineyards. 1.2.3.4 Economic significance Few studies have demonstrated the true economic impact of GRBD as knowledge of the virus impact on crop yield and rate of spread is still in its infancy (Cieniewicz et al. 2017). 10 Notwithstanding, Ricketts et al. (2017) estimated that the economic impact of GRBD may range from USD $2,213 to $68,548 in vineyards located in Washington state and Napa Valley (CA), respectively. The lower end of the range is an example of lower disease prevalence, and low penalty for poor quality fruit, whereas the higher end responds to higher disease prevalence and a higher penalty for poor quality fruit. 1.2.4 Control strategies Due to the significant economic burden posed by both GLD and GRBD, prevention and control of these viruses is paramount. The first line of defense in virus prevention is the use of clean plant nursery stock certified by clean plant programs. Techniques such as heat treatment and micro shoot tip culture ensure that propagative material used to establish vineyards is free of known viruses (Fuchs 2020). If a vineyard is found to contain virus, the next steps focus on the selective removal of infected vines, a practice referred to as roguing. This practice is often used alongside insect vector management techniques such as the use of agrochemicals or cover cropping, to reduce insect populations and minimize spread of virus within the vineyard (Fuchs 2020). Unfortunately, removing infected vines is often insufficient and if disease incidence is high (over 25% for GLD and over 30% for GRBD) a whole vineyard replacement is recommended (Ricketts et al. 2015; Ricketts et al. 2017). For that reason, studies are investigating the potential use of common viticultural practices to mitigate the harmful effects of GLD and GRBD. It has been hypothesized that decreasing water availability of virus infected grapevines could mitigate detrimental effects to plant health. However, water deficit treatments have not been found to improve physiology (El Aou-ouad et al. 2016), respiratory and metabolic processes (El Aou-ouad et al. 2018), water use efficiency (WUE) or hydraulic conductance (El Aou-ouad et al. 2017) in ‘Malvasia de Banyalbufar’ and ‘Giró Ros’ cultivars infected with 11 GLRaV-3. In addition, water deficits did not mitigate plant health and or fruit quality of ‘Pinot Noir’ grapevines infected with GRBD (Levin et al 2020). Instead, it has been shown that supplemental irrigation or well-watering may mitigate some of the harmful effects of GRBD. Increased water regimes were shown to improve photosynthesis and yields as well as increase total soluble solids and TA in a ‘Pinot noir’ vineyard in Oregon (Copp and Levin 2021). The use of vine and canopy management techniques such as leaf removal to mitigate virus effects have also been studied. Leaf removal in the area surrounding clusters at or around veraison was shown to improve must quality, most notably by decreasing titratable acidity and increasing total soluble solids in GLRaV-3 infected ‘Albariño’ (Pereira-Crespo et al. 2012). Leaf removal caused a 1.5 Brix increase, along with improvements to anthocyanins and phenols of ‘Cabernet Sauvignon’ infected with GLRaV-3 (Hébert-Haché 2015). These results are promising as most growers use leaf removal as a technique to promote airflow and aid with berry ripening (BCWGC 2010). Crop thinning has also been investigated as a strategy to minimize the effects of grapevine viruses. It is a simple technique in which growers reduce the number of clusters on a vine, allowing the plant to re-allocate resources and bring it into balance, improving fruit ripening (BCWGC 2010). In one of the first studies investigating this topic, crop thinning improved total soluble solids, proline, and arginine in GLD vines such that their performance was closer to non-infected, non-thinned vines (Kliewer and Lider 1976). Crop thinning also impacted ‘Cabernet Sauvignon’ infected with “red leaf disease” (suspected GRBD), with pre-symptom crop thinning mitigating delayed ripening and sugar accumulation when compared to infected vines without crop adjustment (Calvi 2011). However, Hébert-Haché (2015) did not observe cluster thinning to improve fruit quality in GLRaV-3 infected ‘Cabernet Sauvignon’ vines but noted that these findings could not be accurately evaluated without crop load information. Overall, these studies 12 show some support for crop thinning as a strategy against virus infection in grapevine, however they are either outdated or not peer-reviewed. 1.2.5 Status of GLD and GRBD in British Columbia Recent studies have been conducted to determine the status of GLRaVs and GRBV in British Columbia Vineyards. For GLRaVs, the largest incidence is that of GLRaV-3, where 16.7% of 3,056 samples tested were positive for the virus, making it the most prevalent and rapidly spreading in the region (Poojari et al. 2017a). GLRaVs were prevalent all across BC, with the largest concentration recorded in vineyards located in the South Okanagan region (Poojari et al. 2017a). The grape mealybug (Pseudococcus maritimus) was determined to be the main vector of GLRaVs in BC, and scale insects were also found across the valley. These insects have the potential to increase the already rapid rate of spread of these viruses across BC (Poojari et al. 2017a). GRBV was found to be at a much lower prevalence, with only 1.6% of 2000 composite samples testing positive (Poojari et al. 2017b). Though the alfalfa tree-hopper is not commonly found in BC, studies conducted by Kahl et al. (2021a) showed a close relative, the buffalo tree-hopper (Stictocephala bisonia and Stictocephala basalis) to be a potential vector in BC vineyards and could contribute to spread of the virus. Despite the low incidence, the impacts of GRBV infection remain severe, and growers are highly concerned with the possible presence of GRBV in their vineyards. Despite current available and successful management strategies to mitigate disease impacts, including roguing and vector control, the time commitment and cost of these types of programs are significant and require an area-wide approach (Bell et al. 2018, 2021). With less than 30 years of history, the threat of grapevine viruses is relatively new to the BC wine industry, and it may take time to prepare to implement such management strategies. Though 13 GLD and GRBD are harmful to plant health, it remains to be understood whether alternative strategies can alleviate the stress of grapevine viruses on the vines and the growers who tend them. Given the threat that GLD and GRBD pose to the BC grape and wine industry, it is imperative that an integrated and holistic approach be explored. Due to limited availability of insecticides, management of insect vectors is a challenge for growers in BC. This can be of particular concern if a vineyard owner decides to undergo a roguing program. The grower may be able to remove infected vines and source virus-free material, but the incidence of virus in the surrounding vineyards may be high, and without appropriate vector control measures, the vineyard is likely to get re-infected. In addition, a full vineyard replacement is usually recommended if infection rates are above 25% in vineyards (Atallah et al. 2012; Ricketts et al. 2015, 2017), and this is the case for many vineyards located in the South Okanagan growing region of BC. This process is extremely costly, and growers may find themselves ripping out old vines that once produced high quality wines. There is a desire for more sustainable grapevine production in BC and more vineyards are making the switch to organic and low-intervention viticulture. Cluster thinning is a practice that is already being used by grape growers in the Okanagan as a way to alter resource allocation through source-sink relationships and improve fruit ripening (BCWGC 2010). It is hypothesized that GLRaV-3 infection interferes with ripening related processes, and sugars tend to accumulate in the leaves rather than in berries (Alabi et al. 2016; Song et al. 2021). In GRBV infected vines, these processes may be ‘desynchronized’ due to impaired sugar transport (Martínez-Lüscher et al. 2019). Altering the source-sink relations in the vine through crop thinning may lend the opportunity for increased sugar accumulation in fruit due to increased carbohydrate availability (source) to the reduced number of clusters (sink). Limited information is available regarding the effectiveness of using crop thinning to mitigate the effects of grapevine viruses, and the relationship between viral load and the severity of these effects is 14 unknown. Therefore this research is important to give local grape growers a better understanding of how these viruses affect their vines, and more options to help them manage disease in their vineyards. 1.3 Research Objectives Due to the lack of information currently available on the use of crop thinning to mitigate the effects of grapevine viruses in their hosts, and the role that viral load has in the severity of these effects, this research aims to answer the following questions. 1. Does crop thinning mitigate the impact that Grapevine leafroll-associated virus 3 (GLRaV-3) has on plant health and fruit quality in a ‘Merlot’ and ‘Cabernet Sauvignon’ vineyard in the Okanagan Valley? H01: GLRaV-3 infected vines with 1.0 C/S will not have significantly better vine health and fruit quality compared to vines with 1.5 C/S HA1: GLRaV-3 infected vines with 1.0 C/S will have significantly better vine health and fruit quality compared to vines with 1.5 C/S 2. Does crop thinning mitigate the impact that Grapevine red blotch virus (GRBV) has on plant health and fruit quality in a ‘Cabernet Sauvignon’ vineyard in the Okanagan Valley? H02: GRBV infected vines with 1.0 C/S will not have significantly better vine health and fruit quality compared to vines with 1.5 C/S HA2: GRBV infected vines with 1.0 C/S will have significantly better vine health and fruit quality compared to vines with 1.5 C/S 3. Does Grapevine leafroll-associated virus 3 (GLRaV-3) and Grapevine red blotch virus (GRBV) viral load correlate with decreased plant health and fruit quality in ‘Merlot’ and ‘Cabernet Sauvignon’ ? 15 H03: Higher viral load will not correlate with decreased plant health and fruit quality HA3: Higher viral load will correlate with decreased plant health and fruit quality 16 Chapter 2: Mitigating the effects of Grapevine leafroll-associated virus 3 in ‘Merlot’ and ‘Cabernet Sauvignon’ using crop thinning, and investigating the role of virus titer in disease severity in the Okanagan Valley, British Columbia 2.1 Introduction Grapevines host the most viruses of any cultivated crop (Fuchs 2020). Of those viruses, the ones responsible for Grapevine leafroll disease (GLD) are among the most widespread and significant. First recognized in the 1800’s as “rougeau” due to the striking reddenning of inter-veinal leaf area of infected vines, the viral nature of GLD was only determined in 1936 by Scheu (Hoefort and Gifford 1967). Since that characterization, GLD has been identified in all wine growing regions across the globe (Naidu et al. 2015). The causative agents of GLD are grapevine leafroll-associated viruses. These positive sense single-stranded RNA molecules are members of the family Closteroviridae. Seven strains of GLRaVs have been identified so far, though not all of them are responsible for severe disease. GLRaV-1 and GLRaV-3 are the most studied due to their detrimental impacts to yield and fruit quality in infected vines (Song et al. 2021). Grapevine leafroll-associated virus 3 is often referred to as the main etiological agent of GLD, due to its strong negative influence on grapevine health and fruit quality (Burger et al. 2017). GLRaV-3 is visually recognizable in red cultivars due to inter-veinal reddening of leaf area, whereas white cultivars display varying levels of chlorosis. In addition, downward rolling of leaf margins is generally present (Burger et al. 2017). The primary source of GLRaV-3 is from infected nursery planting material. It is hypothesized that the global prevalence of GLRaV-3 is primarily due to the increased demand to maintain classic cultivars, and introduce them into new regions (Naidu et al. 2015). Secondary spread of GLRaV-3 is due to semi-persistent phloem feeding by mealybug and soft scale insect vectors (Fuchs et al 2020). This allows the virus, after being established, to spread within a vineyard site, or between sites, as insects can travel on 17 farm staff or equipment. GLRaV-3 has been widely reported to negatively impact photosynthesis, total yield, and sugar and acid content of berries from infected vines (Maree et al. 2013; Song et al 2021). However, the exact mechanism by which the virus causes these impacts is not known, and understanding the impact virus titer plays in the pathology of GLRaV-3 is an important step in better understanding plant-virus interactions in infected vines. A 2017 survey of GLRaVs in the Okanagan Valley (BC) discovered that GLRaV-3 was prevalent in the region. Vineyards from across the Okanagan Valley were surveyed by testing composite samples with a PCR based detection assay. It was concluded that, of all GLRaVs (GLRaV-1, -2, -3, and -4) surveyed, GLRaV-3 was the most widespread, and was detected in 16.7% of samples (Poojari et al. 2017). The highest incidence of GLRaV-3 was determined in vineyard blocks that had been planted between 2001 and 2010. The study also reported some presence of mealybug (Pseudococcus maritimus) and soft scale (Parthenolecanium corni) insects in vineyards where GLRaVs were detected, and suggests that spread within and between vineyards in the Okanagan is occurring as a result of vector-mediated spread (Poojari et al. 2017). A 2018 report by Bowen et al. of the impacts of GLRaV-3 on yield, fruit and wine quality of ‘Cabernet Franc’ in the Okanagan Valley concluded that virus impacts were highly dependent on the growing season, suggesting the need for additional studies on virus impacts in BC. GLRaV-3 does not have a cure; once it has established in the vine, the plant is infected for life. As a consequence of the nature of infection, few effective strategies are currently available, and they are limited to insect vector control, or vine removal (roguing) (Fuchs 2020). Control of insect vectors is done in the vineyard through insecticide sprays, cover crop management, or introduction of natural predators as biological controls. However, these strategies are only helpful if the virus is not established at that site, and if virus infection is discovered in a vineyard, vine removal is often the only solution (Fuchs 2020). A study on the 18 financial impact of GLRaV-3 in California estimates the cost of GLRaV-3 infection in a vineyard to be approximately USD $29,902 to $226,405 per hectare (Ricketts et al. 2015). Similar analyses in the Finger Lakes region of New York estimate costs of USD $20,000 to $40,000 may be reduced to USD $3,000 to $23,000 when proper roguing programs and other interventions are implemented (Atallah et al. 2012). Despite reported improvements in economic losses, vine removal is a slow and complicated process, which drives growers to often leave viruses in their vineyards, risking spread to other vines and other sites. Cluster thinning is a common viticultural practices at which berry clusters are removed from a vine at a selected stage during the grapevine growing season. It is believed that this process will improve fruit quality by forcing the vine to adjust resource allocation to a lower crop load. Specifically, cluster thinning is thought to improve sugar accumulation in berry, though the alteration of source (leaf) to sink (berry) ratios, allowing more resources to be partitioned to less fruit (BCWGC 2010). Reduced berry soluble solid content (Brix) is a commonly reported finding in studies comparing GLRaV-3 infected fruit to that from healthy vines, so we hypothesized that crop thinning may be able to mitigate the lack of sugar accumulation in GLRaV-3 infected fruit, while concurrently improving overall vine health and fruit quality. Very few studies have been conducted to answer this question. The first study was conducted in 1976 by Lider and Kleiwer in ‘Burger’ vines in California. They tested whether cluster thinning would improve fruit composition of GLRaV-3 infected vines by reducing cluster number by 50% a month before veraison. The researchers found higher Brix, as well as increased concentrations of proline and arginine in cluster-thinned GLRaV-3 vines, compared to infected vines that were not thinned, suggesting that crop thinning was sufficient to improve fruit quality. The next study on this topic was conducted 39 years later in an Undergraduate Thesis at Brock University in ON, Canada. Hébert-Haché was investigating the use of several cultural practices, including crop thinning, as strategies to mitigate the negative impacts of GLRaV-3 on 19 vine health and berry quality in ‘Cabernet Franc’. Vines were cluster thinned two weeks before veraison, and fruit quality parameters were assessed at harvest. No significant improvement was noted when GLRaV-3 vines were thinned, however the researcher notes that without pruning weight data, it was impossible to assess crop load, so it could not be concluded whether crop thinning truly had an impact in this study (Hébert-Haché 2015). Therefore, a lack of studies on the impacts of crop thinning on GLRaV-3 infected vines require us to understand whether this cultural practice may be a suitable mitigation strategy in the Okanagan Valley, BC. With all this information taken together, GLRaV-3 poses a threat to wine industries around the world, including the BC wine industry. It has been detected in local vineyards, along with insect vectors that are likely responsible for its spread. The virus has been shown to negatively impact vine health and fruit quality of red and white fruited cultivars worldwide, however these studies have not been conducted in the Okanagan Valley. In addition, the exact mechanism by which GLRaV-3 damages the vine is yet to be known, and the role that virus titer plays in infections should be elucidated. Lastly, determining whether cultural practices, specifically crop thinning, can mitigate negative impacts of the virus could be of great value to local growers that may be able to implement these strategies while undertaking established management programs. The goal of this work is to understand the unique impact GLRaV-3 might have in ‘Merlot’ and ‘Cabernet Sauvignon’ in the Okanagan Valley, BC, as well as whether crop thinning might be an effective mitigation strategy against the reported detrimental impacts of this virus. We will also report on whether viral load changes throughout the season, and if a higher viral load is more detrimental to infected vines. We hypothesize that GLRaV-3 will negatively impact vine health and fruit quality of infected vines compared to healthy controls, that crop thinning will mitigate these effects, and that vines with higher viral load will show more severe impacts 20 compared to vines with lower viral titer. We hope findings from this study will inform GLRaV-3 and other grapevine virus management strategies in the Okanagan Valley, BC. 2.2 Materials and Methods 2.2.1 Site selection, experimental design and weather data A vineyard in the South Okanagan, BC with a ‘Merlot’ and ‘Cabernet Sauvignon’ block next to each other was used for this study across two growing seasons. ‘Cabernet Sauvignon’ vines were clone 169 on ‘101-14 Mgt’ V. riparia x V. rupestris rootstock, and ‘Merlot’ clone 412 on ‘3309C’ V. riparia x V. rupestris rootstock . All vines were double-cordon trained and spur pruned in north-south orientation. Rows were 8’ wide with approximately 4’ vine spacing. Shoots were vertically trained and held upright with three trellis catch wires in a vertical shoot positioning system (VSP). Vines were drip-irrigated, fertilized, sprayed, and maintained according to regular vineyard practices for the region. Experimental vines were selected based on visual symptoms recorded during October of 2020 by Dr. Úrbez-Torres. Confirmation of GLRaV-3 and GRBV infection status from selected vines was conducted by PCR-based methods at the Grapevine Virus Testing Laboratory in the Cool Climate Viticulture and Oenology Institute (CCOVI), Brock University, St. Catharines, Ontario following the methodology described by Poojari et al. (2017a,b). All vines selected for these experiments were re-tested each year of the study to confirm the status of infection. Healthy and infected vines were paired 1:1, and each pair of vines were near each other in their respective vineyard block. All study vines were marked at the trunk using flagging tape to indicate infection status and treatment. Vines were designated a unique ID number recorded on labels attached to the fruiting wire for easy identification. The total number of vines used in each experiment, the different treatments applied, and the number of vines used per treatment for each experiment are shown in Table 2. 21 Table 2. Blocks design for GLRaV-3 experiment in Merlot and Cabernet Sauvignon and GRBV in Cabernet sauvignon demonstrating the division of study vines into treatment groups. Merlot Cabernet Sauvignon Treatment Control GLRaV-3 Control GLRaV-3 1.5 C/S 20 20 18 18 1.0 C/S 20 20 18 18 Weather data (temperature, relative humidity, and precipitation) were collected using portable HOBO® data loggers connected to a HOBO® micro station unit in the middle of each vineyard block (Onset Computer Corporation, MA, USA). Readings were collected every 30 seconds and stored on the device. Data was offloaded from weather meters every 2 weeks. Stations were set up in April of each year, and taken down after harvest in each season. Growing degree days (GDD) were calculated by taking the sum of days where the mean daily temperature was above 10°C. 2.2.2 Shoot and crop thinning For the 2021 growing season, shoot thinning was performed by commercial vineyard crews between May 24-27th in the ‘Cabernet Sauvignon’ and between May 30th and June 1st in the ‘Merlot’ to obtain an average of 18-22 shoots per vine. Shoots and clusters were counted on May 28th in ‘Cabernet Sauvignon’ and on June 2nd in ‘Merlot’. On June 3rd, additional shoot thinning was performed to ensure a maximum of 22 shoots and 40 clusters per vine. Cluster and shoot counts were recorded in a Microsoft Excel sheet used to determine the number of clusters to remove to achieve 1.5 clusters per shoot in the 1.5 C/S group, and 1.0 cluster per shoot in the 1.0 C/S group. Crop thinning was performed on July 8th, 2021 in the ‘Merlot’ and on July 8th and 9th in the ‘Cabernet Sauvignon’ at the pea sized berries stage. Based on calculations for clusters per shoot, a varying number of clusters was removed in each vine to achieve 1.0 C/S. However no clusters were removed in the 1.5 C/S treatment as prior calculations averaged those vines at 22 1.5 clusters per shoot. In crop thinned vines, only apical clusters were removed, and care was taken to ensure crop was removed evenly across the vine. For the 2022 growing season, shoot thinning was performed on June 10th in ‘Merlot’ and on June 10th and 15th in ‘Cabernet Sauvignon. Cluster thinning was performed as described above on July 7th and 8th. Apical clusters were removed in both 1.5 C/S and 1.0 C/S treatment groups to achieve the goal of 1.5 and 1.0 clusters per shoot respectively. When apical clusters could not be removed, basal clusters were selected. Care was taken to ensure crop was removed evenly across the vine. 2.2.3 Plant health measurements Leaf greenness (SPAD) and leaf gas exchange measurements were taken at four times during both growing seasons for each experiment at key phenological stages, including bloom, pea-sized, veraison, and harvest. Specific dates for these stages are outlined in Table 3. It is worth noting that measurements for the bloom stage were missed in 2022 due to the impossibility to access the study site because of spray application. Yield, total number of clusters and berry weight, were assessed at harvest for both years, and pruning weights were collected in the March following each growing season . 23 Table 3. Observation dates of key phenological stages for the 2021 and 2022 growing seasons in ‘Merlot’ and ‘Cabernet Sauvignon’ study vineyards 2021 2022 Stage Merlot Cabernet Sauvignon Merlot Cabernet Sauvignon Bloom June 7th June 10th June 23rd June 25th Pea-sized June 30th July 3rd July 15th July 18th Veraison August 9th August 13th August 24th August 27th Harvest measurements September 22nd &24th October 4th & 13th October 6th - 7th October 11th - 14th Commerical harvest October 4th October 20th October 18th October 26th & 27th 2.2.3.1 Leaf greenness and leaf gas exchange Leaf greenness was measured using a SPAD 502 Plus Chlorophyll Spectrophotometer (Spectrum Technologies Inc, Bridgend UK). Measurements were taken between 10:00 and 14:00 hr on the sun side (east facing) of the vines. Measurements were taken on fully expanded mature leaves in the middle of the canopy. Ten leaves per vine were measured to capture variability, and the average of these measurements were recorded on a data sheet in the field. Leaf gas exchange was measured using a LI-COR LI-6400-XT Portable Leaf Gas Exchange System (LI-COR Biosciences, NE, USA). Photosynthetic photon flux of the IRGA was set to 1000 µmol/m2/sec and chamber air flow to 500 µmol/sec. Measurements were taken between 10:00 and 15:00 hr on two fully expanded mature leaves in the middle of the canopy on sunny days on the sun side (East side) of the vines. Forty vines (10 per infection status/treatment combination) were measured in each experiment. These data were used to determine photosynthetic rate, stomatal conductance, and transpiration rate. 2.2.3.2 Berry and cluster weight, yield, pruning weight and crop load Berry and cluster weight, yield, pruning weight and crop load were assessed at harvest each year. Upon harvesting, clusters were counted and weighed using a field scale (Ohaus, 24 Mettler Toledo, Switzerland) and values were recorded on a data sheet in the field. Average cluster mass was calculated as the total yield divided by the number of clusters for each vine. A 70-berry and 50-berry sub-sample were collected from harvested fruit (70 berries from all vines, and 50 berries from a subset of 40 vines per experiment as described for plant health measurements). Five berries per cluster were collected from all locations (top, bottom, medium back and front) until the required number of berries was obtained. These subsamples were placed on ice for transport to the Summerland Research and Development Centre (SuRDC). Average berry weight was determined by dividing the weight of the 70 berry sample by the number of berries (70) and was recorded in grams. Fruit from the 70-berry sub-sample was used to measure Brix, TA, pH and YAN, and the 50-berry sub-sample was stored at -20°C for later use to measure phenolics. Pruning weights were collected between March 16th and 22nd 2022 for the 2021 growing season, and on March 6th 2023 for the 2022 growing season. Each vine was spur-pruned to two buds per spur, and resulting canes were weighed using a field scale (K-TRON, AZ, USA). Pruning weights were recorded in grams on a data sheet in the field. Crop load was calculated as the ratio of total yield in kilograms, to pruning weight in kilograms. 2.2.4 Fruit quality measurements Each 70-berry sample (collected as described above) was weighed, and average berry weight was calculated by dividing the total weight of the sample by the number of berries contained in the sample. Berries were then placed in a mortar lined with cheese cloth, and crushed using a pestle, taking care not to crush any seeds, and only burst the fruit. The juice from the berry sample was strained through the cheese cloth and poured into a labeled plastic dilution vial. The mortar and pestle were rinsed in between samples, and a new piece of cheese cloth and separate vial were used for each sample. 25 2.2.4.1 Soluble solids (Brix) Total soluble solids (Brix) were measured using a Palette digital refractometer (ATAGO, Japan). Before any measurements were taken, the machine was zeroed using a small amount of distilled water. After mixing the sample to ensure homogeneity, a small volume of juice was transferred from the sample vial using a plastic Pasteur pipette onto the measurement area of the refractometer. The measurement area of the instrument was cleaned between each sample using distilled water and wiped with a Kimwipe™. All measurements were recorded on a data sheet in the lab. 2.2.4.2 Titratable acidity (TA) Titratable acidity (TA) of juice was measured using an automated titrator (Metrohm, Canada). Before titration, the instrument was calibrated using pH 7.0 and 4.0 standards (ThermoFisher Scientific, MA, USA). Juice was mixed thoroughly before measurements to ensure homogeneity, then 5mL grape juice was added to 80mL of distilled water using an auto-pipette, ensuring adequate mixing of the juice with the water. This mixture was placed on the instrument and titrated with 0.1N NaOH to a pH endpoint of 8.1. Titration values in g/mL were recorded on a data sheet in the lab and final titration values calculated using Equation 1. 𝑇𝑖𝑡𝑟𝑎𝑡𝑎𝑏𝑙𝑒 𝑎𝑐𝑖𝑑𝑖𝑡𝑦 = 𝑡𝑖𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑣𝑎𝑙𝑢𝑒 × (7.5𝑚𝐿 𝑜𝑓 𝑗𝑢𝑖𝑐𝑒 𝑢𝑠𝑒𝑑 𝑖𝑛 𝑡𝑖𝑡𝑟𝑎𝑡𝑖𝑜𝑛) … (1) After basic fruit quality parameters were measured, any remaining juice was stored in the original dilution vials and stored at -20°C to be analyzed for yeast assimilable nitrogen concentration (YAN). 26 2.2.4.3 pH Juice pH was measured using a pH metre with electrode (London Scientific, ON, Canada). Before taking any measurements, the pH meter was calibrated using pH standards of 4.0 and 7.0 (ThermoFisher Scientific, MA, USA). A slope of 100% ± 2.0 was considered acceptable to begin measurements. After mixing each sample to ensure homogeneity, the pH probe was inserted into the juice sample and stirred constantly until the machine had stabilized. Measurements were recorded on a data sheet in the lab. After every 10 measurements, the 4.0 pH standard was measured again, and deviations within +/- 0.02 warranted a machine re-calibration as described above. 2.2.4.4 Yeast assimilable nitrogen (YAN) Grape juice from the 70-berry sample used to analyze basic fruit quality metrics was thawed at room temperature. One pH meter and two auto-titrators were calibrated using pH 10, 7 and 4 standards (ThermoFisher Scientific, MA, USA). A pH metre and pH electrode (ThermoFisher Scientific, MA, USA) in a fume hood were used to monitor and maintain a pH of 8.0 in of 37% formaldehyde. The two auto-titrators were used to bring up the pH of the grape juice to 8.0, both before and after addition of formaldehyde. For samples analyzed from the 2022 growing season, juice standards were prepared using thawed ‘Sauvignon Blanc’ must that was aliquoted into 50 mL conical tubes. One of the samples had 1mL juice replaced with 1 mL 0.12M NH4Cl to create a “spiked” standard. Each the regular juice standard and “spiked” juice standard were measured each day, and their titration values recorded. Grape juice samples described above were decanted from the dilution vial into a 50mL centrifuge tube and spun for 10 minutes at 10,000 x g in a centrifuge (Beckman Coulter, CA, USA). For 2021 samples, 10 mL of supernatant was transferred into a 30 mL beaker using an auto-pipette and adjusted to pH 8.0 with 0.1N NaOH using an auto-titrator (Metrohm, Canada). 27 Five mL pH-adjusted formaldehyde was added to the sample using an auto-pipette and placed on a second auto-titrator (Metrohm, Canada) allowing 15-20 seconds for the pH probe to equilibrate before titrating to an endpoint of pH 8.0 with 0.1N NaOH. The volume required to titrate each sample was recorded on a data sheet in the lab. For samples from the 2022 growing season, the volume of juice used was reduced to 2mL diluted in 4mL deionized water mixed in a 10mL beaker, and the volume of formaldehyde was reduced to 1mL . A standard curve was prepared following the protocol above for each year with the exception of replacing the volume of juice sample with the same volume of NH4Cl standards. A stock solution of 0.16M NH4Cl was prepared by dissolving 1.714 g NH4Cl in 200 mL reverse osmosis filtered and deionized water (Millipore Sigma, MA,USA) in a 50mL conical tube and inverting 40 times to mix. From this stock solution, standard solutions of 0.012, 0.008, 0.004, 0.002 and 0.001M were prepared. Initial titration of standard solutions was performed manually using solutions of 0.01 and 0.001N NaOH with pH endpoint 8.0 measured using a benchtop pH metre with an electrode (ThermoFisher Scientific, MA, USA). After the addition of formaldehyde, YAN titration was performed using an auto-titrator (Metrohm, Canada). Concentration of NH4Cl standard (g/L) was converted to concentration NH4 (mg/L) using Equation 2. [𝑁𝐻4] (𝑚𝑔𝐿) = [𝑁𝐻4𝐶𝑙](𝑔𝐿) × 0.337 × 1000 …(2) Titration volumes of the standards were graphed against their concentration of NH4 in mg/L to obtain a regression equation. This regression equation was used to calculate YAN using Equation 3. 𝑌𝐴𝑁𝐶 (𝑚𝑔𝐿𝑁) = 𝑡𝑖𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑣𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿 𝑁𝑎𝑂𝐻) × [𝑁𝐻4](𝑚𝑔𝐿) … (3) 2.2.4.5 Phenolics Grape berry tissues were hand separated into skins, seeds and pulp. The pulp was discarded. Two screw-cap glass storage vials (one for skins, and one for seeds) were weighed 28 without caps, and weights were recorded on a data sheet in the lab. After grape peeling, skins and seeds were placed in their respective vials, and vial weights were recorded again. Vials were purged with nitrogen gas, capped, and sealed with parafilm before being stored at -20°C. Skins and seeds were lyophilized using a freeze drier (VirTual, SP Scientific, PA, USA). Dry weights of lyophilized samples including vial were recorded. Seed samples were ground using a freezer mill (SPEX SamplePrep LLC, IL USA), and skins were ground in liquid nitrogen using a mortar and pestle. Ground skins and seeds were returned to their original vials, purged, capped, sealed, and stored at -20°C until extraction. Anthocyanins were determined using the Glories method (1984) and tannins were determined using a methyl cellulose precipitation (MCP) assay (adapted from Mercurio et al. 2007). Anthocyanins and tannin data in this thesis was only available for the 2021 season. Data from the 2022 season is currently being collected and will be included in future publications. 2.2.4.5.1 Anthocyanins and total phenolics Ground samples were weighed (approximately 0.250 grams for skins, and 0.750 grams for seeds, actual weights were recorded) and added to a 15 mL centrifuge tube along with 8 mL 100% acidified methanol solution, vortexed for 30 seconds at 3000 x g to suspend the sample and sonicated for 2 minutes on degas setting (Cole-Parmer, IL, USA). Samples were then centrifuged for 3 minutes at 5,000 x g (ThermoFisher Scientific, MA, USA) and the supernatant transferred to a 25mL volumetric flask. The pelleted sample was resuspended in 8 mL of 80% acidified methanol, vortexed, sonicated, centrifuged, and supernatant decanted as described above. This was then repeated for each sample pellet twice more using 5 mL of 50% acidified methanol for each extraction. The combined supernatants were brought up to volume in the 25mL flask using 73.6% acidified methanol. Extracts were transferred to 50 mL tubes and centrifuged for 10 minutes at 10,000 x g (Beckman Coulter, CA, USA). 29 Samples were processed in groups of 6, in addition to a blank for each set. Each sample, including the blank, was run in duplicate, for a total of 16 reaction tubes. Reactions were carried out in 15 mL amber screw-top vials (Supelco Inc. Milipore Sigma, MA, USA) to protect the reactions from light. To each tube 4.55 mL 2% HCl and 250 µL 95% acidified ethanol were added using an auto-pipette. Two hundred and fifty µL 73.6% acidified methanol was added to only the blank tubes. Two hundred and fifty µL seed or skin extract was added to the corresponding sample tube. All tubes were mixed by vortexing for 10 seconds at 3,000 x g and let sit at room temperature for 15 minutes. Two mL of samples and blanks were transferred into 1.5 mL disposable UV-Vis cuvettes (BrandTech Scientific Inc. CT, USA). Absorbance was measured at 280, 320, 360 and 520 nm using a UV-Visible spectrophotometer (Agilent Technologies, CA, USA) and recorded on a Microsoft Excel sheet. If absorbance readings for the duplicates were not within 10% of each other, the Glories assay was repeated for these samples. The average of duplicate values for each sample was used in determining phenolic profiles of skin and seed samples. A standard curve was prepared for each wavelength. Gallic acid was used to prepare a standard curve at 320 nm, corresponding to total phenolics. Serial dilutions of a 500 ppm stock solution were prepared to obtain solutions at 500, 250, 125, 62.5, 31.25 and 15.625 ppm. The stock was prepared by combining 0.025 g gallic acid with 73.6% acidified methanol in a 50 mL volumetric flask, sonicating for 20 seconds, and inverting 30 times to mix. Five milliliters of this stock solution were transferred to a 10 mL volumetric flask, and topped up with 73.6% acidified methanol to obtain the 250 ppm solution. Following serial dilutions were prepared in the same fashion. The Glories assay was then performed as described above, substituting the sample for the gallic acid standards. Each dilution was read on the spectrophotometer at 230 nm and values were recorded in Microsoft Excel. 30 Caffeic acid standards were used to measure tartaric esters at 280nm, with solutions of 1000, 500, 250, 125, 62.5, 31.25 and 15.625 ppm. The stock solution was prepared by combining 0.025 g caffeic acid with 73.6% acidified methanol in a 25mL volumetric flask and inverting 30 times to mix. Preparation and analysis of serial dilutions was performed as described for gallic acid, with absorbances for all standards read at 280nm. To measure hydroxycinammic acids, standards of Quercetin were prepared of 500, 250, 125, 62.5, 31.25 and 15.625 ppm. The 500 ppm stock solution was prepared by dissolving 0.025g quercetin un a 50mL volumetric flask with 73.6% acidified methanol, sonicating for 20 seconds and inverting 30 times to mix. Preparation and analysis of serial dilutions was performed as described for gallic acid, with absorbances for quercetin standards read at 360 nm. Lastly, malvidin chloride standards were prepared to measure total anthocyanidins. A 250 ppm stock solution of was prepared by dissolving 0.0025g malvidin chloride in 73.6% acidified methanol in a 10mL volumetric flask, sonicated, and inverted 30 times to mix. Serial dilutions were prepared at 125, 62.5, 31.25, 15.625 and 7.8125 ppm. Standards were prepared and analyzed as described for gallic acid, with readings performed at 520nm. Standard curves were performed for each standard by plotting absorbance values against the concentration of each dilution, and a regression equation calculated. This equation was used to determine the concentration of each component in mg/L of each skin and seed sample using Equation 4. 𝐶𝑜𝑛𝑐 (𝑚𝑔𝐿) = (𝑎𝑏𝑠𝑜𝑟𝑏𝑎𝑛𝑐𝑒𝑠𝑙𝑜𝑝𝑒) − 𝑏 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡…(4) The mass of each sample was calculated using Equation 5 with the dilution factor set at 20.2, and the liters of extract solution at 0.025. 𝑚𝑎𝑠𝑠 (𝑚𝑔) = 𝑐𝑜𝑛𝑐 (𝑚𝑔𝐿) ∗ 𝑑𝑖𝑙𝑢𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 ∗ 𝐿 𝑜𝑓 𝑒𝑥𝑡𝑟𝑎𝑐𝑡 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛…(5) 31 The final concentration of each component was calculated in mg/g by dividing the mass as calculated in Equation 3, by the actual weight of the skin or seed sample in grams. 2.2.4.5.2 Tannins Approximately 0.250 g ground skin or seed (actual weights recorded) were added to a 15 mL centrifuge tube with 3 mL 50% ethanol. Samples were placed in a sonicator (Cole-Parmer, IL, USA) with the bath set at 30°C and run on degas setting for 5 minutes. Samples were then centrifuged at 10,000 x g for 3 minutes (Beckman Coulter, CA, USA), and the supernatant decanted into a second 15mL tube. The extraction was repeated an additional two times on the remaining pellet to amass approximately 9 mL supernatant. The final extract was centrifuged at 10,000 x g for 3 minutes (Beckman Coulter, CA, USA) before decanting into a 10mL volumetric flask, and made up to volume with 50% ethanol. The extracts were mixed and then transferred to a new 15 mL tube and vortexed for 10 seconds at 3,000 x g to be analyzed immediately. Six samples were analyzed at a time, prepared with methyl cellulose (treatment) and without (control), with each sample being run in duplicate for each treatment for a total of 12 tubes per run. To a 15 mL centrifuge tube containing 200 µL extract, 3 mL methyl cellulose (treatment) or 2 mL ammonium sulphate (control) was added. Tubes were either mixed by inverting (treatment) or by vortex (control). Samples were topped up to 10 mL using deionized water, mixed as described above, and incubated at room temperature for 10 minutes. The tubes were centrifuged at 10,000 x g at room temperature for 5 minutes (Beckman Coulter, CA, USA) before 2 mL was transferred into UV-Vis cuvettes (BrandTech Scientific Inc. CT, USA) and absorbance measured at 280 nm on a UV-Vis spectrophotometer (Agilent Technologies, CA, USA). Values were recorded in Microsoft Excel. 32 Epicatechin standards were prepared, beginning with a stock solution of 250 mg/L made by adding 50 mg epicatechin to distilled water in a 200 mL volumetric flask. The solution was gently heated at 45°C until the epicatechin dissolved. The solution was allowed to cool at room temperature before being brought up to the full 200 mL volume with distilled water and inverted 30 times to mix. Starting from the stock solution each time, dilutions in distilled water were prepared in 25 mL volumetric flasks resulting in standard solutions of 200, 150, 100, 75 and 50 mg/L. Two milliliters of each standard solution were transferred to a UV-Vis cuvette (BrandTech Scientific Inc. CT, USA), and absorbance at 280nm was measured using a UV-Vis spectrophotometer (Agilent Technologies, CA, USA). Absorbance values of the standards were plotted against their concentrations in Microsoft Excel, and a regression equation was obtained. The absorbance of tannin in solution is calculated as the difference between the absorbance of control samples and treatment samples. Tannin concentration in mg/L of epicatechin equivalents (EE) was calculated using Equation 6 with a dilution factor of 50. 𝑡𝑎𝑛𝑛𝑖𝑛 (𝑚𝑔𝐿 𝐸𝐸) = (𝐴280𝑛𝑚 𝑡𝑎𝑛𝑛𝑖𝑛𝑠𝑙𝑜𝑝𝑒− 𝑏 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡) ∗ 𝑑𝑖𝑙𝑢𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟…(6) The concentration of the extracts in g/L was determined by dividing the recorded weight of dry skin or seed sample by the total volume of extract (10 mL) and multiplying by 1000. The extract concentration, and above calculated tannin concentration were used to calculate mg tannin per gram of dried skin or seed using Equation 7. 𝑇𝑎𝑛𝑛𝑖𝑛𝑚𝑔𝑔=𝑡𝑎𝑛𝑛𝑖𝑛 𝑚𝑔𝐿 𝐸𝐸𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑒𝑥𝑡𝑟𝑎𝑐𝑡 (𝑔𝐿) …(7) 2.2.5 Quantification of GLRaV-3 titer At the four phenological stages described previously (except for bloom in 2022), five mature basal leaves were collected from each vine, immediately bagged, placed on ice, and transported to SuRDC for processing. In the lab, petioles were removed from the leaves for each 33 sample and immediately ground in a mortar and pestle using liquid nitrogen. Each ground sample was transferred using a sterile scoopula into two 1.5 mL microtubes to minimize freeze-thaws while processing. Tubes containing ground sample were stored in liquid nitrogen until all samples were processed, then stored at -80°C until nucleic acid extraction. Mortars and pestles were cleaned in between experiments by first washing in Liquinox® detergent (Alconox, NY, USA), rinsed with distilled water, then soaked overnight in 10% bleach solution. Metal scoopulas were only soaked for 10 minutes to prevent rusting. All implements were removed from the bleach solution and triple rinsed with distilled water before air drying for next use. RNA extraction was performed using the Spectrum™ total plant RNA kit (Milipore Sigma, MA, USA) modified by addition of 2.5% w/v PVP-40 to the lysis solution as described in Xiao et al. (2018). After addition of the lysis buffer, sample containing epi-tubes were incubated in a dry-block for 5 minutes at 56°C. The kit specified “Protocol A” was followed by addition of 750 µL Binding Buffer. In addition, a DNase digest step was added using an On-Column DNase 1 digestion kit (Milipore Sigma, MA, USA) following manufacturers specifications. Purified RNA was eluted in 50 µL Elution Solution. RNA concentrations were quantified using a Qubit™ fluorometer and the RNA BR (Broad-Range) assay kit (ThermoFisher Scientific, MA, USA) following kit instructions. RNA quality was determined using a NanoDrop™ spectrophotometer (ThermoFisher Scientific, MA, USA). Select RNA extracts were also run on a 1% agarose gel to ensure integrity of the RNA. Amplification and quantification were carried out using Droplet Digital PCR™ (ddPCR) with the Bio-Rad® One-Step RT-ddPCR Kit for probes with target and reference in a duplex assay (Bio-Rad, CA, USA). Quantitative primers targeting GLRaV-3 ORF1a were used with forward sequence 5’–TGTGTAGGGCTCAGAAGTC-3’ and reverse sequence 5’–GCGCGTAGGAATAGATCCT- 3’ and a FAM labeled TaqMan hydrolysis probe with sequence 5’-34 CCTGTTGTAGCTGGTTCTAGT-3’. Primers were previously developed by Dieter Kahl and Sudarsana Poojari (personal communication) using Primer-BLAST (National Centre for Biotechnology Information (NCBI), MA, USA) and stability confirmed by Net Primer (Premeir Biosoft, CA, USA). The probe was developed based on the existing primer set using PrimerQuest™ software (Integrated DNA Technologies, IA, USA). The SAND family protein was used as a reference gene, with forward sequence 5’–CAACATCCTTTACCCATTGACAGA-3’ and reverse sequence 5’–GCATTTGATCCACTTGCAGATAAG-3’ as described in Reid et al. (2006), referenced by Tashiro et al. (2016). A HEX labeled TaqMan hydrolysis probe with sequence 5’-TTGCACGTCCGTATCGCCAAGG-3’ was developed for SAND with existing primer set using PrimerQuest™ software (Integrated DNA Technologies, IA, USA). Each sample was diluted 1:1,000 before analysis by serially diluting 2 mL RNA extract in 18 mL PCR-grade water (ThermoFisher Scientific, MA, USA) in a 96-well plate. The plate was vortexed for 10 seconds and centrifuged in a plate-spinner for 1 minute before 2 mL of this dilution was then transferred to another 96-well plate containing 198 mL PCR-grade water, vortexed and centrifuged as described above. This final dilution was used as the sample in the PCR reaction. PCR master mix was prepared (per reaction) using 5.5 µL Supermix, 2.2 µL RT Enzyme, 1.1 µL DTT, 0.99 µL of 20 µM primers, 0.275 µL 20 µM probes and 3.19 µL PCR-grade water. All ddPCR reagents were vortexed for 30 seconds and centrifuged before preparation of the mix, and the final master mix was also vortexed for 30 seconds and centrifuged before aliquoting into a 96-well plate. Each plate was prepared with at least two positive controls, with RNA extracted as described above from confirmed GLRaV-3 positive greenhouse material maintained at the SuRDC, and at least two non-template controls containing confirmed negative GLRaV-3 greenhouse material. After addition of samples and controls, the final 96-well place was sealed using sealing foil and a PCR plate sealer (Bio-Rad, California USA) then vortexed for 10 seconds, centrifuged for one minute, then let stand for 10 minutes before droplet generation. Droplets were 35 generated using a QX200 Automatic Droplet Generator™ (Bio-Rad, California USA) and PCR was be carried out on a C1000 Touch Thermal Cycler (Bio-Rad, California USA) using the following thermocycling parameters all set at a ramp rate of 2°C/second: 1 cycle of reverse transcription at 50°C for 60 minutes, 1 cycle at 90°C for 10 minutes, 40 cycles at 95°C for 30 seconds followed by 58°C for 1 minute, 1 cycle of 98°C for 10 minutes, then held at 4°C. To analyze the droplets, the plate was placed on a QX200 Droplet Reader™ (Bio-Rad, California USA), and analyzed using QX Manager Software™ with direct quantification in channel 1:FAM and channel 2:HEX (Bio-Rad, California USA). The average number of accepted droplets for all samples was 14,043 (range 7,648 – 17,787), however samples containing under 10,000 droplets were re-run. The range of amplitude for positive GLRaV-3 droplets was generally between 1500 - 8000 and for negatives between 700 - 1200. For SAND, positive droplets ranged between 5,000 – 6,000, and negatives from 1,000 – 2,000. Threshold values varied between plates but were always set just above the negative cluster. Equation 8 was used to determine virus titer (Kahl et al. 2021b). 𝑣𝑖𝑟𝑢𝑠 𝑡𝑖𝑡𝑒𝑟 (𝑐𝑜𝑝𝑖𝑒𝑠𝑛𝑔) = 𝑑𝑑𝑃𝐶𝑅 𝑐𝑜𝑛𝑐.(𝑐𝑜𝑝𝑖𝑒𝑠µ𝐿)∗𝑑𝑑𝑃𝐶𝑅 𝑟𝑥𝑛 𝑣𝑜𝑙𝑢𝑚𝑒 (µ𝐿)∗𝐷𝐹𝑁𝐴 𝑣𝑜𝑙𝑢𝑚𝑒 𝑖𝑛 𝑟𝑥𝑛 (µ𝐿)∗𝑄𝑢𝑏𝑖𝑡 𝑐𝑜𝑛𝑐.(𝑛𝑔µ𝐿)… (8) The Qubit™ concentration and ddPCR concentration were specific to each sample analyzed, with the ratio of reference gene to target gene used as the ddPCR concentration to normalize samples. Whereas the dilution factor was set to 10000, the ddPCR reaction volume 22 µL, and the nucleic acid (RNA) volume in the ddPCR reaction was 5.5 µL. 2.2.6 Statistical analyses All statistical analyses were performed using the R programming language (v. 4.2.1; R Core Team 2022) in R Studio (v. 2022.12.0.353; Posit team 2022). Data for each cultivar was analyzed separately by year, with a unique model fit for the effect of infection status, treatment, 36 and their interaction term on each vine health or fruit quality parameter. An alpha value of 0.05 was used for all tests. Berry and cluster weight, yield, pruning weight, crop load, and all fruit quality data were analyzed using Analysis of Variance (ANOVA) with a Type III sum of squares with the ‘Anova’ function in the ‘car’ package (v.3.1-1 Fox and Weisberg 2019). Model assumptions were checked visually, by using the ‘plot’ function (R Core Team 2022) on the linear model, as well as numerically using the ‘gvlma’ function from the ‘gvlma’ package (v. 1.0.0.3; Pena and Slate 2019), Shapiro Wilk test through the Shapiro.test function from the ‘car’ package, and for equal variance (homoscedasticity) of residuals using the Breusch-Pagan test through the ‘bptest’ function from the ‘lmtest’ package (v.0.9-40; Zeileis and Hothorn 2002). Outliers identified by boxplot methods using the ‘identify_outliers’ function in the ‘car’ package were removed, and if any assumptions were violated, dependent variables were transformed. The best transformation for the data was determined using the ‘bestNormalize’ function from the ‘bestNormalize’ package (v. 1.9.0; Peterson 2021; Peterson and Cavanaugh 2020). If the assumption of heteroscedasticity could not be satisfied through variable transformation, White-adjusted p-values were calculated using the white.adjust argument within the ‘Anova’ function. Bonferroni tests were used as post-hoc pairwise comparisons with the ‘emmeans’ function from the ‘emmeans’ package (v. 1.8.4-1; Lenth 2023). Compact letter displays for figures were determined based on the estimated marginal means and 75% quartiles using the ‘cld’ function from the ‘multcomp’ package (v. 1.4-23; Hothorn et al. 2008). Repeated measurements (leaf gas exchange, SPAD, virus titer) were assessed using a linear-mixed effects model structure using the ‘lmer’ function from the ‘lme4’ package (v. 1.1-31; Bates et al. 2015). Each parameter was assessed for effects of infection status, treatment, phenological stage, and their interaction terms, with a random intercept (repeated measure) for each vine measured. For the leaf gas exchange data from 2021, there was an additional 37 random intercept term fitted for measurement unit, to remove any potential variation due to machine measurement issues. The models were then analyzed using the ‘Anova’ function (‘car’ package) with Type III sum of squares, Wald’s F test and Kenward-Roger approximation for degrees of freedom. Model assumptions for the mixed effects models were checked visually by plots using the ‘allEffects’ function in the ‘effects’ package (v. 4.2-2; Fox and Weisberg 2018, 2019) as well as numerically using functions ‘check_normality’, ‘check_heteroskedasticity’ in the ‘performance’ package (v. 0.10.3; Lüdecke et al 2021). Models that did not meet assumptions had dependent variables transformed. Multiple pairwise comparisons and compact letter displays were extracted from model outputs as described above. To assess the impacts of increased viral titer on vine health and fruit quality parameters, each individual parameter was modeled against the temporally closest viral titer measurement using simple linear regression from the native ‘lm’ function in base R (R Core Team, 2022). Yield, cluster and berry weight, pruning weight and crop load, and all fruit quality data were compared to viral titer at harvest, whereas leaf gas exchange and SPAD measurements were compared to virus titer at all phenological time points for which measurements coincided in that year. Model assumptions were checked, and necessary variables transformed using visual and numerical methods as described above for fruit quality data. All plots were prepared using the ‘ggplot2’ package (v. 3.4.1; Wickham 2016) with custom colors from the ‘wesanderson’ package (v. 0.3.6; Wickham 2018). Multiple plots in the same figure were joined using the ‘patchwork’ package (v. 1.1.2; Pedersen 2022). 2.3 Results 2.3.1 Weather data The two years of this study had hotter than normal average temperatures for the region. The average temperature for the 2021 growing season was 21.52 °C, with a maximum of 43.98 38 °C, recorded on June 30th. The average temperature for the 2022 growing season was 20.23 °C with a maximum of 43.71 °C recorded on July 28th. There was an average of 1682.67 growing degree days (GDD, base 10 °C) in 2021 and 1630.71 in 2022 between May 15th and October 4th. Average daily temperatures from May to October for both 2021 and 2022 are displayed in Figure 2. Figure 2. Line plot showing changes in average daily temperature at the study site from May to October in 2021 and 2022. 2.3.2 Shoot and crop thinning Shoot thinning resulted in approximately 20 shoots per vine for ‘Merlot’ and ‘Cabernet Sauvignon’ in 2021, and 19 for both cultivars in 2022 (Table 4). 1.5 C/S vines were not crop thinned in 2021 for either ‘Merlot’ or ‘Cabernet Sauvignon’, resulting in an average of 1.66 and 1.6 clusters per shoot respectively. 1.0 C/S vines for the same year were approximately 1.0 cluster per shoot. In 2022, for both cultivars, the ratio of clusters per shoot was close to 1.0 for 39 1.0 C/S, and 1.5 for 1.5 C/S groups (Table 4). Total number of clusters were accordingly decreased when comparing 1.5 C/S to 1.0 C/S groups in both cultivars, across both years. Table 4. Average clusters at harvest, shoots at pruning, and cluster/shoot ratio of ‘Merlot’ and ‘Cabernet Sauvignon’ study vines in 1.5 C/S and 1.0 C/S treatment groups in 2021 and 2022. 2021 2022 Cultivar Treatment Clusters Shoots Clusters/ shoot Clusters Shoots Clusters/ shoot Merlot 1.5 C/S 32.64 19.75 1.66 28.75 19.25 1.49 1.0 C/S 20.13 19.95 1.02 19.18 18.47 1.04 Cabernet Sauvignon 1.5 C/S 30.56 19.41 1.59 27.94 18.53 1.51 1.0 C/S 21.15 20.18 1.05 19.18 19.09 1.00 2.3.3 Plant health measurements 2.3.3.1 Leaf greenness and leaf gas exchange SPAD values were significantly different from each other at each phenological stage measured for both ‘Merlot’ and’ Cabernet Sauvignon’ across both growing seasons (Tables 5 and 6, p<0.001). Neither GLRaV-3 infection nor crop thinning treatment had a significant impact on SPAD values of ‘Merlot’ and ‘Cabernet Sauvignon’ vines across both years of study (Tables 5 and 6). Leaf gas exchange parameters differed throughout the growing season based on the phenological stage they were measured (Tables 5 and 6). There was no significant impact of GLRaV-3 infection, or crop thinning treatment on photosynthesis (Anet), stomatal conductance (gs) or transpiration rate (T) at either of the four phenological time points measured for both ‘Merlot’ and ‘Cabernet Sauvignon’ in either year of study (Tables 5 and 6). 40 Table 5. Impacts of GLRaV-3 infection, crop thinning, and their interaction, on Anet, gs, T, and SPAD values at phenological stages: bloom, pea-sized berries, veraison and harvest of ‘Merlot’ vines in 2021 and 2022. 2021 2022 Stage Status Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Bloom Healthy 11.39±0.18 0.18±0.01 3.67±0.12 27.92±0.24 NAa NA NA NA GLRaV-3 11.05±0.36 0.15±0.01 3.25±0.14 28.03±0.26 NA NA NA NA Effect n.s.b n.s. n.s. n.s. NA NA NA NA 1.5 C/S 11.30±0.23 0.17±0.01 3.43±0.13 27.99±0.30 NA NA NA NA 1.0 C/S 11.18±0.29 0.17±0.01 3.53±0.14 27.94±0.19 NA NA NA NA Effect n.s. n.s. n.s. n.s. NA NA NA NA Healthy * 1.5c 11.54±0.25 0.18±0.01 3.58±0.17 27.90±0.40 NA NA NA NA Healthy * 1.0d 11.25±0.26 0.18±0.01 3.75±0.16 27.93±0.28 NA NA NA NA Effect n.s. n.s. n.s. n.s. NA NA NA NA GLRaV-3 * 1.5 10.96±0.43 0.15±0.01 3.20±0.18 28.13±0.49 NA NA NA NA GLRaV-3 * 1.0 11.11±0.57 0.15±0.01 3.28±0.22 27.94±0.26 NA NA NA NA Effect n.s. n.s. n.s. n.s. NA NA NA NA Pea-sized Healthy 14.08±0.38 0.26±0.01 7.47±0.21 33.24±0.15 15.57±0.29 0.32±0.01 6.75±0.21 31.90±0.26 GLRaV-3 13.92±0.48 0.25±0.01 7.34±0.19 33.55±0.17 15.30±0.29 0.30±0.01 6.63±0.21 32.56±0.28 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. 1.5 C/S 14.12±0.38 0.25±0.01 7.28±0.21 33.26±0.15 15.72±0.29 0.32±0.01 6.80±0.23 32.11±0.28 1.0 C/S 13.90±0.46 0.26±0.01 7.54±0.20 33.48±0.17 15.21±0.28 0.30±0.01 6.60±0.20 32.27±0.27 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Healthy * 1.5c 14.44±0.48 0.26±0.01 7.52±0.28 33.00±0.13 15.80±0.42 0.32±0.02 6.78±0.35 31.99±0.30 Healthy * 1.0d 13.72±0.60 0.26±0.01 7.42±0.33 33.48±0.28 15.34±0.41 0.31±0.01 6.72±0.26 31.81±0.45 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GLRaV-3 * 1.5 13.68±0.60 0.23±0.01 6.93±0.26 33.64±0.28 15.60±0.42 0.31±0.01 6.82±0.27 32.27±0.55 GLRaV-3 * 1.0 14.10±0.73 0.26±0.01 7.66±0.24 33.48±0.21 15.07±0.41 0.30±0.01 6.47±0.31 32.78±0.25 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Values are means ± standard error (SE) of the mean aNA= no data available bn.s., not significant c1.5=1.5 C/S d1.0=1.0 C/S 41 Table 5 cont. Impacts of GLRaV-3 infection, crop thinning, and their interaction, on Anet, gs, T, and SPAD values at phenological stages: bloom, pea-sized berries, veraison and harvest of ‘Merlot’ vines in 2021 and 2022. 2021 2022 Stage Status Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Veraison Healthy 11.10±0.61 0.19±0.01 4.35±0.28 36.17±0.23 13.34±0.46 0.24±0.02 6.11±0.44 38.01±0.40 GLRaV-3 11.39±0.66 0.19±0.01 4.56±0.16 36.60±0.34 13.26±0.40 0.23±0.02 5.97±0.38 37.09±0.38 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. 1.5 C/S 11.41±0.70 0.19±0.01 4.41±0.28 36.68±0.29 13.37±0.47 0.25±0.02 6.31±0.48 37.82±0.51 1.0 C/S 11.06±0.57 0.19±0.01 4.47±0.21 36.07±0.27 13.25±0.42 0.22±0.02 5.81±0.36 37.41±0.27 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Healthy * 1.5c 11.40±0.88 0.19±0.02 4.36±0.43 36.48±0.35 13.39±0.71 0.25±0.03 6.20±0.72 38.19±0.75 Healthy * 1.0d 10.80±0.88 0.18±0.02 4.33±0.37 35.85±0.32 13.30±0.63 0.22±0.02 6.03±0.57 37.83±0.34 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GLRaV-3 * 1.5 11.43±1.22 0.19±0.02 4.49±0.31 36.96±0.52 13.34±0.57 0.24±0.03 6.48±0.63 37.29±0.70 GLRaV-3 * 1.0 11.35±0.75 0.19±0.01 4.62±0.17 36.32±0.46 13.20±0.58 0.22±0.03 5.57±0.44 36.94±0.43 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Harvest Healthy 7.83±0.64 0.13±0.01 2.77±0.22 32.31±0.34 11.50±0.40 0.22±0.01 3.81±0.15 34.76±0.39 GLRaV-3 7.64±0.72 0.14±0.02 2.82±0.23 32.23±0.37 10.43±0.29 0.19±0.01 3.46±0.15 32.87±0.38 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. 1.5 C/S 8.35±0.78 0.15±0.02 2.82±0.27 33.04±0.41 11.22±0.44 0.23±0.01 3.89±0.17 34.76±0.40 1.0 C/S 7.21±0.54 0.13±0.01 2.77±0.17 31.58±0.25 10.85±0.33 0.19±0.01 3.44±0.12 33.16±0.40 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Healthy * 1.5c 8.32±1.06 0.14±0.02 2.72±0.38 33.12±0.55 11.85±0.55 0.25±0.01 4.02±0.22 35.36±0.61 Healthy * 1.0d 7.35±0.73 0.13±0.01 2.82±0.23 31.49±0.34 11.15±0.59 0.20±0.01 3.60±0.17 34.15±0.47 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GLRaV-3 * 1.5 8.38±1.25 0.16±0.03 2.96±0.39 32.93±0.67 10.33±0.60 0.21±0.02 3.71±0.27 33.91±0.40 GLRaV-3 * 1.0 7.06±0.83 0.13±0.02 2.71±0.28 31.68±0.38 10.51±0.26 0.18±0.01 3.26±0.17 32.06±0.57 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Values are means ± standard error (SE) of the mean aNA= no data available bn.s., not significant c1.5=1.5 C/S d1.0=1.0 C/S 42 Table 6. Impacts of GLRaV-3 infection, crop thinning, and their interaction, on Anet, gs, T, and SPAD values at phenological stages: bloom, pea-sized berries, veraison and harvest of ‘Cabernet Sauvignon’ vines in 2021 and 2022. 2021 2022 Stage Status Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Bloom Healthy 16.51±0.28 0.37±0.01 7.90±0.19 32.01±0.22 NAa NA NA NA GLRaV-3 16.37±0.37 0.37±0.01 8.04±0.25 32.43±0.28 NA NA NA NA Effect n.s.b n.s. n.s. n.s. NA NA NA NA 1.5 C/S 16.33±0.40 0.36±0.01 7.90±0.22 32.13±0.29 NA NA NA NA 1.0 C/S 16.56±0.20 0.38±0.01 8.04±0.21 32.29±0.20 NA NA NA NA Effect n.s. n.s. n.s. n.s. NA NA NA NA Healthy * 1.5c 16.67±0.47 0.36±0.01 7.90±0.33 31.89±0.33 NA NA NA NA Healthy * 1.0d 16.35±0.30 0.37±0.01 7.90±0.19 32.12±0.31 NA NA NA NA Effect n.s. n.s. n.s. n.s. NA NA NA NA GLRaV-3 * 1.5 15.98±0.65 0.36±0.02 7.91±0.31 32.37±0.51 NA NA NA NA GLRaV-3 * 1.0 16.79±0.26 0.39±0.02 8.19±0.40 32.49±0.27 NA NA NA NA Effect n.s. n.s. n.s. n.s. NA NA NA NA Pea-sized Healthy 13.20±0.40 0.25±0.01 6.75±0.18 35.10±0.35 14.26±0.87 0.28±0.03 7.35±0.50 35.36±0.51 GLRaV-3 13.48±0.52 0.24±0.01 6.52±0.18 35.26±0.42 14.92±0.70 0.28±0.03 7.11±0.50 35.67±0.43 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. 1.5 C/S 13.38±0.47 0.24±0.01 6.66±0.17 35.33±0.40 14.29±0.89 0.27±0.03 7.02±0.52 35.55±0.52 1.0 C/S 13.28±0.45 0.25±0.01 6.61±0.19 35.02±0.37 14.83±0.71 0.28±0.03 7.46±0.48 35.47±0.43 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Healthy * 1.5c 13.20±0.62 0.25±0.01 6.81±0.24 35.36±0.51 13.22±1.76 0.24±0.05 6.67±0.77 35.56±0.79 Healthy * 1.0d 13.19±0.55 0.25±0.01 6.69±0.28 34.83±0.49 14.89±0.92 0.31±0.04 7.96±0.62 35.15±0.69 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GLRaV-3 * 1.5 13.57±0.74 0.23±0.01 6.52±0.23 35.30±0.64 15.09±0.85 0.31±0.03 7.37±0.73 35.53±0.71 GLRaV-3 * 1.0 13.38±0.76 0.24±0.01 6.53±0.28 35.22±0.57 14.75±1.18 0.23±0.06 6.76±0.70 35.82±0.51 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Values are means ± standard error (SE) of the mean aNA= no data available bn.s., not significant c1.5=1.5 C/S d1.0=1.0 C /S43 Table 6 cont. Impacts of GLRaV-3 infection, crop thinning, and their interaction, on Anet, gs, T, and SPAD values at phenological stages: bloom, pea-sized berries, veraison and harvest of ‘Cabernet Sauvignon’ vines in 2021 and 2022. 2021 2022 Stage Status Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Veraison Healthy 12.16±0.63 0.24±0.01 4.77±0.31 36.49±0.34 9.55±0.36 0.09±0.01 2.84±0.20 36.93±0.49 GLRaV-3 11.72±0.65 0.24±0.01 4.78±0.28 37.12±0.49 9.50±0.54 0.10±0.01 2.87±0.20 36.74±0.45 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. 1.5 C/S 11.84±0.66 0.24±0.01 4.82±0.27 37.01±0.43 9.40±0.39 0.10±0.01 2.85±0.18 37.02±0.53 1.0 C/S 12.06±0.62 0.24±0.02 4.72±0.32 36.56±0.40 9.64±0.54 0.09±0.01 2.86±0.22 36.65±0.41 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Healthy * 1.5c 12.01±0.98 0.24±0.02 4.81±0.44 36.86±0.44 9.22±0.56 0.09±0.01 2.54±0.19 37.48±0.78 Healthy * 1.0d 12.31±0.83 0.25±0.02 4.72±0.46 36.11±0.51 9.87±0.46 0.09±0.01 3.19±0.33 36.37±0.61 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GLRaV-3 * 1.5 11.67±0.92 0.24±0.02 4.83±0.33 37.16±0.77 9.57±0.57 0.11±0.01 3.17±0.28 36.55±0.72 GLRaV-3 * 1.0 11.78±0.98 0.24±0.02 4.73±0.47 37.07±0.63 9.41±1.00 0.10±0.02 2.57±0.27 36.96±0.56 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Harvest Healthy NA NA NA 31.83±0.54 9.34±0.25 0.13±0.00 2.58±0.09 35.24±0.50 GLRaV-3 NA NA NA 31.32±0.54 9.51±0.22 0.13±0.01 2.60±0.11 34.36±0.55 Effect NA NA NA n.s. n.s. n.s. n.s. n.s. 1.5 C/S NA NA NA 31.87±0.53 9.39±0.27 0.13±0.01 2.58±0.11 34.99±0.57 1.0 C/S NA NA NA 31.31±0.56 9.46±0.21 0.13±0.01 2.60±0.09 34.62±0.47 Effect NA NA NA n.s. n.s. n.s. n.s. n.s. Healthy * 1.5c NA NA NA 32.25±0.79 9.38±0.41 0.13±0.01 2.57±0.07 35.42±0.79 Healthy * 1.0d NA NA NA 31.41±0.75 9.30±0.32 0.13±0.01 2.59±0.15 35.05±0.65 Effect NA NA NA n.s. n.s. n.s. n.s. n.s. GLRaV-3 * 1.5 NA NA NA 31.44±0.69 9.39±0.36 0.13±0.01 2.58±0.19 34.56±0.87 GLRaV-3 * 1.0 NA NA NA 31.20±0.86 9.64±0.26 0.14±0.01 2.61±0.11 34.13±0.70 Effect NA NA NA n.s. n.s. n.s. n.s. n.s. Values are means ± standard error (SE) of the mean aNA= no data available bn.s., not significant c1.5=1.5 C/S d1.0=1.0 C/S44 2.3.3.2 Berry and cluster weight, yield, pruning weight and crop load Average berry weight and average cluster weight were not significantly different with respect to vine infection status, or crop thinning treatment for either cultivar in 2021 nor 2022 (Tables 7 and 8). Yields consistently differed by treatment group for both cultivars across both years of study (Tables 7 and 8). No significant impact of GLRaV-3 infection, or cluster thinning was seen in pruning weights for either cultivar in either year. GLRaV-3 infected vines had a crop load of on average 13% higher than healthy vines in ‘Cabernet Sauvignon’ in the 2021 season (Table 8). There was no significant impact of GLRaV-3 infection on the crop load of ‘Merlot’ vines in either year (Table 7)45 Table 7. Impacts of GLRaV-3 infection, crop thinning, and their interaction on average berry and cluster weight, yield, pruning weight and crop load of ‘Merlot’ vines for 2021 and 2022. Values are means ± standard error (SE) of the mean an.s., not significant. b* p<0.05, ** p<0.01, *** p<0.001 as determined by ANOVA (α=0.05). c1.5=1.5 C/S d1.0=1.0 C/S 2021 2022 Status Berry weight (g) Cluster weight (g) Yield (kg/vine) Pruning weight (kg) Crop load (yield/ pruning mass) Berry weight (g) Cluster weight (g) Yield (kg/vine) Pruning weight (kg) Crop load (yield/ pruning mass) Healthy 0.95±0.01 77.36±1.75 2.03±0.09 0.48±0.03 4.62±0.31 0.95±0.01 77.36±1.74 4.15±0.21 0.60±0.03 7.31±0.45 GLRaV-3 0.94±0.01 82.40±2.31 2.10±0.11 0.50±0.03 4.41±0.30 0.94±0.01 82.40±2.31 3.63±0.20 0.61±0.04 6.34±0.44 Effect n.s. a n.s. n.s. n.s. n.s n.s. n.s n.s. n.s n.s. 1.5 C/S 0.96±0.01 76.94±1.67 2.51±0.09 0.50±0.03 5.62±0.33 0.96±0.01 76.94±1.67 4.55±0.22 0.59±0.04 8.26±0.50 1.0 C/S 0.93±0.01 82.27±2.25 1.65±0.05 0.49±0.03 3.51±0.14 0.93±0.01 82.26±2.25 3.31±0.14 0.62±0.03 5.59±0.29 Effect n.s. n.s. ***b n.s. *** n.s n.s *** n.s *** Healthy * 1.5c 0.96±0.02 75.63±2.03 2.45±0.11 0.47±0.05 5.85±0.49 0.96±0.01 75.63±2.03 2.45±0.11 0.47±0.05 5.85±0.49 Healthy * 1.0d 0.94±0.01 79.08±2.83 1.62±0.05 0.48±0.04 3.58±0.20 0.94±0.01 79.08±2.83 1.61±0.05 0.48±0.04 3.58±0.20 Effect n.s. n.s. n.s n.s. n.s n.s. n.s. n.s n.s. n.s GLRaV-3 * 1.5 0.96±0.02 78.57±2.80 2.56±0.15 0.52±0.05 5.38±0.44 0.96±0.02 78.57±2.80 2.56±0.15 0.52±0.05 5.38±0.44 GLRaV-3 * 1.0 0.93±0.02 85.80±3.46 1.68±0.08 0.49±0.04 3.43±0.21 0.93±0.02 85.80±3.46 1.68±0.08 0.49±0.04 3.43±0.21 Effect n.s. n.s. n.s n.s. n.s n.s. n.s n.s. n.s n.s 46 Table 8. Impacts of GLRaV-3 infection, crop thinning, and their interaction on average berry and cluster weight, yield, pruning weight and crop load of ‘Cabernet Sauvignon’ vines for 2021 and 2022. 2021 2022 Status Berry weight (g) Cluster weight (g) Yield (kg/vine) Pruning weight (kg) Crop load (yield/ pruning mass) Berry weight (g) Cluster weight (g) Yield (kg/vine) Pruning weight (kg) Crop load (yield/ pruning mass) Healthy 0.73±0.01 63.70±2.39 1.61±0.08 0.96±0.05 1.69±0.11 1.15±0.02 98.33±4.01 2.26±0.12 1.08±0.05 2.23±0.12 GLRaV-3 0.73±0.01 60.27±2.66 1.57±0.09 0.88±0.05 1.94±0.14 1.14±0.02 96.49±3.99 2.29±0.11 1.04±0.05 2.28±0.13 Effect n.s.a n.s. n.s. n.s. *b n.s. n.s. n.s. n.s. n.s. 1.5 C/S 0.74±0.01 60.92±2.17 1.85±0.08 0.89±0.05 2.18±0.12 1.13±0.02 96.90±3.59 2.69±0.10 1.02±0.04 2.77±0.10 1.0 C/S 0.72±0.01 63.24±2.84 1.33±0.06 0.96±0.05 1.38±0.07 1.16±0.02 98.04±4.39 1.86±0.08 1.11±0.05 1.74±0.07 Effect n.s. n.s. *** n.s. *** n.s. n.s. *** n.s. *** Healthy * 1.5c 0.75±0.01 64.09±2.80 1.89±0.10 0.95±0.07 2.05±0.15 1.16±0.02 100.24±4.66 2.70±0.14 1.04±0.05 2.73±0.13 Healthy * 1.0d 0.72±0.01 63.30±3.96 1.33±0.09 0.98±0.08 1.27±0.07 1.14±0.03 96.42±6.64 1.81±0.12 1.12±0.09 1.73±0.11 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GLRaV-3 * 1.5 0.73±0.02 57.36±3.23 1.81±0.12 0.84±0.08 2.32±0.18 1.10±0.03 93.13±5.55 2.67±0.14 0.99±0.08 2.80±0.16 GLRaV-3 * 1.0 0.73±0.02 63.17±4.21 1.34±0.09 0.93±0.05 1.50±0.12 1.18±0.02 99.85±5.79 1.91±0.11 1.10±0.05 1.76±0.09 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Values are means ± standard error (SE) of the mean an.s., not significant. b* p<0.05, ** p<0.01, *** p<0.001 as determined by ANOVA (α=0.05). c1.5=1.5 C/S d1.0=1.0 C/S47 2.3.4 Fruit quality measurements GLRaV-3 infection lead to a decrease in Brix of juice from berries of ‘Merlot’ across both growing seasons; on average 1 Brix decrease in 2021, and 0.5 Brix decrease in 2022 when comparing healthy vines to infected vines (Table 9). Cluster thinning treatment had no impact on Brix value of juice from ‘Merlot’ berries in either year. Lastly, there was no significant change in Brix value for ‘Cabernet Sauvignon’ from GLRaV-3 infection, or crop thinning treatment in either year of study (Table 10). The impacts of GLRaV-3 infection and crop thinning on TA were cultivar and year dependent. In the 2021 growing season, fruit from cluster thinned ‘Merlot’ vines had on average 5% lower TA than 1.5 C/S vines (Table 9). In 2022 GLRaV-3 infection caused a significant increase in TA (p<0.05), however cluster thinning had no impact. The TA of ‘Cabernet Sauvignon’ was not impacted by crop thinning in either year, increased as a result of GLRaV-3 infection by 4% and 6% in 2021 and 2022, respectively (Table 10). Cluster thinning significantly increased the pH of juice from ‘Merlot’ across both years of study (Table 9). GLRaV-3 infection had no effect on pH of ‘Merlot’ in either year. A significant interaction between crop thinning and GLRaV-3 infection in 2022 ‘Cabernet Sauvignon’ resulted in an increased pH in fruit from crop thinned GLRaV-3 vines compared to their 1.5 C/S counterparts (Table 10). YAN of ‘Cabernet Sauvignon’ vines increased on average 11% as a result of crop thinning in the 2022 study season (Table 10). There was no significant impact of GLRaV-3 infection on YAN of ‘Cabernet Sauvignon’, and ‘Merlot’ vines were not significantly affected by GLRaV-3 infection or crop thinning in either 2021 or 2022 (Tables 9 and 10). There was no significant impact of GLRaV-3 infection or crop thinning on the tannin profile of skin or seeds for either ‘Merlot’ or ‘Cabernet Sauvignon’ in 2021 (Tables 9 and 10). 48 Skin anthocyanins were negatively impacted by crop thinning in ‘Cabernet Sauvignon’ for 2021, such that values for “1.0 C/S vines were 16% lower than their 1.5 C/S counterparts (Table 10). 49 Table 9. Impacts of GLRaV-3 infection, crop thinning, and their interaction on fruit quality parameters of ‘Merlot’ in 2021 and 2022. 2021 2022 Status Soluble solids (Brix) Titratable acidity (g/L) pH Juice YAN (mg/L) Skin anthocyanin (mg/g) Skin tannin (mg/g) Seed Tannin (mg/g) Soluble solids (Brix) Titratable Acidity (g/L) pH Juice YAN (mg/L) Healthy 26.24±0.09 8.60±0.11 3.46±0.01 70.90±2.58 263.37±4.91 25.86±1.69 87.93±2.15 27.65±0.09 7.28±0.08 3.57±0.01 71.54±3.10 GLRaV-3 25.30±0.11 8.80±0.09 3.44±0.01 71.38±2.80 281.55±10.48 28.47±2.14 89.71±2.25 27.13±0.11 7.51±0.08 3.56±0.01 70.20±2.92 Effect ***a n.s.b n.s. n.s. n.s. n.s. n.s. *** * n.s. n.s. 1.5 C/S 25.84±0.13 8.90±0.10 3.42±0.01 69.07±2.67 276.21±10.40 29.18±1.59 86.25±1.85 25.84±0.13 8.90±0.10 3.42±0.01 69.07±2.67 1.0 C/S 25.76±0.13 8.45±0.10 3.48±0.01 73.00±2.65 267.20±4.93 25.08±2.03 90.93±2.34 25.76±0.13 8.45±0.10 3.48±0.01 72.99±2.65 Effect n.s. ** ** n.s. n.s. n.s. n.s. n.s. n.s. ** n.s. Healthy * 1.5c 26.28±0.12 8.88±0.14 3.42±0.02 68.93±3.83 260.35±7.85 26.37±2.07 86.14±2.92 27.61±0.16 7.42±0.11 3.54±0.02 71.22±3.79 Healthy * 1.0d 26.20±0.14 8.32±0.15 3.49±0.02 72.86±3.50 266.40±6.19 25.34±2.77 89.73±3.22 27.68±0.10 7.13±0.12 3.61±0.02 71.86±5.02 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GLRaV-3 * 1.5 25.31±0.16 8.93±0.15 3.42±0.02 69.26±3.72 298.87±20.57 33.20±1.59 86.41±1.96 27.29±0.11 7.48±0.14 3.55±0.02 74.50±3.45 GLRaV-3 * 1.0 25.28±0.16 8.6±0.11 3.46±0.02 73.15±4.14 268.09±8.20 24.79±3.14 92.28±3.57 26.98±0.18 7.54±0.10 3.57±0.02 66.38±4.48 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Values are means ± standard error (SE) of the mean a* p<0.05, ** p<0.01, *** p<0.001 as determined by ANOVA (α=0.05). bn.s., not significant. c1.5=1.5 C/S d1.0=1.0 C/S 50 Table 10. Impacts of GLRaV-3 infection, crop thinning, and their interaction on fruit quality parameters of ‘Cabernet Sauvignon’ in 2021 and 2022. 2021 2022 Status Soluble solids (Brix) Titratable acidity (g/L) pH Juice YAN (mg/L) Skin anthocyanin (mg/g) Skin tannin (mg/g) Seed Tannin (mg/g) Soluble solids (Brix) Titratable Acidity (g/L) pH Juice YAN (mg/L) Healthy 25.84±0.13 8.58±0.11 3.49±0.01 81.75±3.59 304.12±9.97 29.84±1.49 82.06±2.08 25.51±0.15 7.47±0.10 3.51±0.01 82.56±4.35 GLRaV-3 25.51±0.14 8.98±0.15 3.48±0.02 79.35±3.61 288.96±10.72 26.10±1.57 85.91±3.71 25.24±0.17 7.93±0.11 3.48±0.02 74.59±4.49 Effect n.s.a *b n.s. n.s. n.s. n.s. n.s. n.s. ** n.s. n.s. 1.5 C/S 25.59±0.11 8.74±0.15 3.47±0.02 77.06±3.50 321.16±9.47 28.41±1.38 82.15±3.36 25.41±0.16 7.61±0.12 3.46±0.01 74.13±4.36 1.0 C/S 25.78±0.16 8.79±0.13 3.50±0.02 83.90±3.59 271.03±7.78 27.61±1.79 85.61±2.42 25.35±0.17 7.76±0.10 3.52±0.01 83.26±4.44 Effect n.s. n.s. n.s. n.s. *** n.s. n.s. n.s. n.s. ** * Healthy * 1.5c 25.66±0.11 8.48±0.18 3.49±0.02 77.14±4.46 332.90±11.51 30.96±1.96 79.83±3.68 25.48±0.16 7.30±0.15 3.50±0.02 79.57±5.99 Healthy * 1.0d 26.02±0.23 8.67±0.14 3.50±0.02 86.08±5.48 275.34±10.17 28.72±2.30 84.28±1.91 25.53±0.25 7.63±0.13 3.52±0.02 85.54±6.41 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GLRaV-3 * 1.5 25.51±0.21 9.04±0.22 3.45±0.02 76.97±5.62 309.42±14.69 25.86±1.68 84.72±6.03 25.34±0.28 7.95±0.16 3.43±0.02 68.34±6.22 GLRaV-3 * 1.0 25.51±0.20 8.91±0.22 3.51±0.03 81.58±4.70 266.24±12.38 26.37±2.87 87.09±4.76 25.14±0.21 7.91±0.16 3.53±0.02 80.84±6.29 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. * n.s. Values are means ± standard error (SE) of the mean an.s., not significant. b* p<0.05, ** p<0.01, *** p<0.001 as determined by ANOVA (α=0.05). c1.5=1.5 C/S d1.0=1.0C/S51 2.3.5 GLRaV-3 titer Virus titer followed a seasonal pattern in both cultivars across both years of study (Figures 3 and 4). For ‘Merlot’, the lowest titer was observed at bloom (data not available for 2022) and the highest titer at pea-sized berries (Figure 3). Trends in virus titer for ‘Cabernet Sauvignon’ in 2022 (data for bloom not available) are similar to those observed for ‘Merlot’, however in 2021, infected ‘Cabernet Sauvignon’ vines did not show a significant change in virus titer until harvest (Figure 4). Figure 3. Boxplots depicting changes in viral titer in GLRaV-3 infected ‘Merlot’ vines in 2021 (top left) and 2022 (top right) growing seasons. Phenological stage of measurement is indicated on the x-axis and boxes are color coded for phenological stage. GLRaV-3 titer is indicated on the left-hand Y axis. Lower line plots indicate mean daily temperatures (y axis) for 2021 (bottom left) and 2022 (bottom right) with horizontal lines color coded to indicate the mean daily temperature on the date titer measurements were taken for the corresponding stage. Each box encloses the 25th percentile of the data (bottom) a bold line at the median value and the 75th percentile (top). Whiskers indicate standard deviation, and dots outside of the whiskers are outliers less than 3 standard deviations away from the mean. 52 Figure 4. Boxplots depicting changes in viral titer in GLRaV-3 infected ‘Cabernet Sauvignon’ vines in 2021 (top left) and 2022 (top right) growing seasons. Phenological stage of measurement is indicated on the x-axis and boxes are color coded for phenological stage. GLRaV-3 titer is indicated on the left-hand Y axis. Lower line plots indicate mean daily temperatures (y axis) for 2021 (bottom left) and 2022 (bottom right) with horizontal lines color coded to indicate the mean daily temperature on the date titer measurements were taken for the corresponding stage. Each box encloses the 25th percentile of the data (bottom) a bold line at the median value and the 75th percentile (top). Whiskers indicate standard deviation, and dots outside of the whiskers are outliers less than 3 standard deviations away from the mean. There was a negative correlation between virus titer and stomatal conductance in leaves of ‘Merlot’ vines at veraison in 2021 (R=-0.53) as well as their SPAD values (R=-0.35) (Table 11). SPAD values from ‘Cabernet Sauvignon’ leaves showed significant negative correlations to virus titer at bloom in 2021 (R=-0.36), harvest in 2021 (R=-0.63), and harvest in 2022 (R=-0.48) (Table 12). 53 Table 11. Correlations between GLRaV-3 titer, leaf greenness and leaf gas exchange for ‘Merlot’ vines in 2021 and 2022. 2021 2022 Stage Anet µmol CO2/m2/sec gs mol/m2/ sec T mmol/m2/sec SPAD Anet µmol CO2/m2/sec gs mol/m2/ sec T mmol/m2/ sec SPAD Bloom n.s.a n.s. n.s. n.s. n.s. n.s. n.s. n.s. Pea-sized n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Veraison n.s. *b R=-0.53 n.s. * R=-0.35 n.s. n.s. n.s. n.s. Harvest n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. an.s., not significant b*p<0.05, as determined by simple linear regression (a=0.05) Table 12. Correlations between GLRaV-3 titer, leaf greenness and leaf gas exchange for ‘Cabernet Sauvignon’ in 2021 and 2022. 2021 2022 Stage Anet µmol CO2/m2/sec gs mol/m2/ sec T mmol/m2/sec SPAD Anet µmol CO2/m2 /sec gs mol/m2/ sec T mmol/m2/sec SPAD Bloom n.s.a n.s. n.s. *b R=-0.36 N/Ac N/A N/A N/A Pea-sized n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Veraison n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Harvest N/A N/A N/A *** R=-0.63 n.s. n.s. n.s. ** R=-0.48 an.s., not significant b*p<0.05, **p<0.001 as determined by simple linear regression (a=0.05) cNA = no data available There were no significant correlations between GLRaV-3 titer and fruit quality parameters of ‘Merlot’ in either year of study (Table 13). There was a significant negative correlation between GLRaV-3 titer and YAN of ‘Cabernet Sauvignon’ in 2021 (R=-0.38). Negative correlations were also present with average cluster weight (R=-0.38) and average berry weight (R=-0.4) in 2022 ‘Cabernet Sauvignon’ (Table 13). 54 Table 13. Correlations between GLRaV-3 viral titer, measured at harvest, on yield related vine health parameters, and fruit quality parameters in ‘Merlot’ and ‘Cabernet Sauvignon’ in 2021 and 2022 study seasons. Merlot Cabernet Sauvignon Parameter 2021 2022 2021 2022 Cluster weight (g) n.s.a n.s. n.s. *b R=-0.38 Yield (kg) n.s. n.s. n.s. n.s. Pruning weight (kg) n.s. n.s. n.s. n.s. Crop load (yield/pruning weight) n.s. n.s. n.s. n.s. Average berry weight (g) n.s. n.s. n.s. * R=-0.40 Soluble solids (Brix) n.s. n.s. n.s. n.s. Titratable acidity (g/L) n.s. n.s. n.s. n.s. pH n.s. n.s. n.s. n.s. Yeast assimilable nitrogen (mg/L) n.s. n.s. ** R=-0.38 n.s. Skin anthocyanin (mg/g) n.s. NAc n.s. NA Skin tannin (mg/g) n.s. NA n.s. NA Seed tannin (mg/g) n.s. NA n.s. NA an.s., not significant b*p<0.05, **p<0.001 as determined by simple linear regression (a=0.05) cNA = no data available 55 2.4 Discussion 2.4.1 Weather data Temperatures during both years of this study were abnormal for the region, and each had their own historical weather events. The 2021 growing season was characterized by a “heat dome” from June 25th to July 1st during which average temperatures ranged between 27.5 to 32.5 °C ,and a maximum temperature of 43.98 °C . Temperatures remained quite high after the heat dome, which encouraged quick development and ripening of berries, leading to an early harvest for many varieties. In addition to weather events, GLRaV-3 symptom development was also earlier than expected in 2021, with some vines showing symptoms shortly after bloom. This is unusual as visual symptom development is rarely seen before veraison (Naidu et al. 2015). In 2022, bloom and berry set were characterized by cooler temperatures than the previous season, resulting in delayed ripening of the fruit. However, higher than normal average temperatures were reported in October. The average daily temperature for the month was 17.5 °C, this is 1.5°C higher than the historical average daily high in October of 16°C (The Weather Network, 2023), allowed late ripening varieties– like ‘Cabernet Sauvignon’, to fully mature before harvest. 2.4.2 Shoot and crop thinning In order to fulfill requirements for our two treatment groups, crop thinning was performed, to an extent, on all vines in this study, resulting in 1.5 clusters per shoot for our 1.5 C/S treatment, and 1.0 clusters per shoot for our 1.0 C/S treatment”. These levels were chosen as they are common cropping levels used in our study region. It is uncommon for a grower in the Okanagan Valley to leave all the fruit on their vines, in favour of removing slow-ripening, or secondary clusters during veraison. It is believed that 56 diverting energy away from this delayed fruit, the vine will have more resources to allocate to fuller, ripe clusters for the remainder of the season, however recent publications suggest this influence is inconsistent, and could be cultivar dependent (Mawdsley et al. 2018; Wang et al. 2018). This was the reasoning for the designation of 1.5 clusters per shoot in the 1.5 C/S group. After counting clusters in 2021, ‘Merlot’ vines averaged 1.66 clusters per shoot, and ‘Cabernet Sauvignon’ averaged 1.6 clusters per shoot - close to the target 1.5 clusters per shoot, therefore no additional cluster thinning was performed in that season (Table 4). However, the 2022 growing season saw more vigorous vine growth, resulting in higher initial cluster counts, so cluster thinning of 1.5 C/S treatment vines in that season brought the resulting total at harvest to 1.49 clusters per shoot in ‘Merlot’, and 1.51 in ‘Cabernet Sauvignon’. Maintaining consistent cluster to shoot ratios is often difficult throughout the season, as grapevines may produce clusters later in the season from secondary buds that aren’t accounted for in initial assessments. Some growers in the Okanagan valley will further thin older grapevines in blocks designated for higher value wines (tier one vineyards) to approximately 1 cluster per shoot, in hopes that this will significantly improve the fruit quality compared to other vines that are not crop thinned. This was our reasoning for designating the 1.0 C/S treatment at 1.0 cluster per shoot. Cluster thinning was performed in both 2021 and 2022 seasons in the 1.0 C/S treatment groups to reach 1.0 clusters per shoot in those vines (Table 4). Cluster thinning can be performed at different stages during the growing season, though in the Okanagan valley it is usually performed during or after veraison, when underripe clusters are visually distinguishable. In this study, all cluster thinning was performed at pea-sized berries stage. According to the Eichhorn-Lorenz (E-L) phenological stage chart this stage (E-L 31) is reached after setting, when green berries are pea-sized, and clusters have trended vertically (Lorenz et al. 1995). Cluster thinning was performed at this stage so as not to disrupt 57 fruit-set, but before veraison to allow maximal time for resource re-allocation to the remaining clusters. 2.4.3 Plant health measurements 2.4.3.1 Leaf greenness and leaf gas exchange SPAD values were unaffected by GLRaV-3 infection in either cultivar across 2021 and 2022 (Table 5, Table 6). Though not a direct measurement of chlorophyll content, SPAD values are a close approximation, and are often used to measure nitrogen status and chlorophyll content of leaves (Konica Minolta 2009). Given this caveat, our finding that SPAD values do not change in response to GLRaV-3 infection is contrary to findings in other red and white-fruited cultivars (‘Merlot’, ‘Lagrein’, ‘Albariño’, ‘Cabernet Franc’, ‘Nebbiolo’, ‘Sauvignon Blanc’, ‘Malvasia de Banyalbufar’, ‘Touriga Nacional’, ‘Malmsey’) that suggest GLRaV-3 infection results in decreased leaf chlorophyll levels (Alabi et al. 2016; Bertamini et al. 2004; Cabaleiro et al. 1999; Endeshaw et al. 2014; Mannini et al. 2012; Montero et al. 2016; Moutinho-Pereira et al. 2012; Sampol et al 2003). The decrease in chlorophyll content is thought to be due to breakdown of chloroplasts following decreased photosynthetic output after veraison, as GLRaV-3 symptoms develop (Song et al. 2021). These same studies correlated decreases in chlorophyll content with negative impacts on leaf gas exchange in GLRaV-3 infected vines. In contrast, our study found no significant impact of GLRaV-3 infection on leaf gas exchange parameters photosynthesis (Anet), stomatal conductance (gs), or leaf transpiration (T) in both cultivars. The lack of negative impact of GLRaV-3 on SPAD and leaf gas exchange observed in our study could be a consequence of relatively mild symptom development in both seasons. During both years of study, typical manifestations of GLRaV-3 symptoms (leaf reddening, rolling of margins) were late in their onset, not observed in all infected study vines, and infected vines 58 that did have symptoms displayed them to varying levels of severity. It has been demonstrated in ‘Cabernet Franc’ vines infected with GRBV that gas exchange varies in leaves from asymptomatic plants, compared to those in symptomatic vines where both the green and red areas of the leaf are measured (Bowen et al. 2020). However, Montero et al. (2016) concluded that CO2 assimilation was reduced in asymptomatic ‘Cabernet Sauvignon’ vines infected with GLRaV-3. Given the current body of research, perhaps visual symptom development is not indicative of viral load, nor disease severity, and further research should focus on this area to help understand the complex pathosystem of GLRaV-3 (Song et al. 2021). Crop thinning also had no significant impact on SPAD or leaf gas exchange of either ‘Merlot’ or ‘Cabernet Sauvignon’ throughout this study. Wang et al. (2018) found a drop in photosynthesis at pea-sized berries following early cluster thinning. This is supported by other studies, who state the plant reduces photosynthesis to acclimate to a lower crop load (Iacono et al. 1995; Naor et al. 1997 cited in Wang et al. 2018). Data from this study indicates no difference between SPAD values in either cultivar, for either year, except at harvest, where SPAD values for 1.5 C/S vines tended to be higher than 1.0 C/S vines (Tables 5 and 6) however this difference was not statistically significant. It could be hypothesized that perhaps the interaction between GLRaV-3 infection and crop thinning might restrict the vines from adjusting their gas exchange back to homeostatic levels, but there was no noticeable or significant trend in SPAD nor gas exchange values when comparing values between healthy and infected 1.5 C/S or 1.0 C/S vines (Tables 5 and 6). These findings are novel, and unique to this work, as the only available studies on crop thinning in GLRaV-3 vines did not measure vine chlorophyll or leaf gas exchange parameters (Kleiwer and Lider 1976; Hébert-Haché 2015). 59 2.4.3.2 Berry and cluster weight, yield, pruning weight and crop load GLRaV-3 infection had no impact on berry or cluster weight of ‘Merlot’ or ‘Cabernet Sauvignon’ compared to healthy vines in either year of this study (Tables 7 and 8). There is no current consensus in the literature on the impact of GLRaV-3 on berry weight. Some studies conclude the same finding as this work (Bowen et al. 2018; Martínez et al. 2016; Montero et al. 2015; Santini et al. 2001; Wolpert et al. 1992), while others report increases (Cabaleiro et al. 1999; Singh Brar et al. 2008; Lee et al. 2009) or decreases (Akbaş et al. 2009; Alabi et al 2016; Kovacs et al. 2001) in berry weight as a result of GLRaV-3 infection. It could be hypothesized that these differences are due to the studies being conducted in different geographical locations, with different cultivars, rootstocks and climactic conditions. Following no impact on berry or cluster weight, there was also no significant impact on yield of vines infected with GLRaV-3 when compared to healthy vines of ‘Merlot’ and ‘Cabernet Sauvignon’ (Tables 7 and 8). This is supported by similar studies who concluded no change in yield in GLRaV infected vines (Bowen et al. 2018; Cabaleiro et al. 1999; Giribaldi et al. 2011; Martínez et al. 2016; Montero et al. 2015; Wolpert et al. 1992). However, some studies also report decreased yields in response to GLRaV-3 infection (Akbaş et al. 2009; Alabi et al. 2016; Endeshaw et al. 2014) contradicting the findings to ours and other studies. Their findings can be justified by a lower berry weight resulting in lower yields of the vine overall. However, it has been reported that cultivar and rootstock can have differential impacts on how GLRaV-3 infection impacts gene expression in infected vines (Vondras et al. 2021). Delayed or desynchronized ripening events could explain some of the differences in berry weight and yield when comparing studies as they are from different cultivar and rootstock combinations. Pruning weights were neither impacted by GLRaV-3 in our study (Tables 7 and 8). This is in contrast to other studies that reported decreased pruning weights as a result of GLRaV-3 infection in ‘Merlot’ and ‘Cabernet Franc’ (Alabi et al. 2016; Endeshaw et al. 2014). It can be 60 hypothesized that due to a lack of significant impacts on berry weight, cluster weight, yield, and pruning weights in this study, crop loads would also be unaffected by GLRaV-3. However, this was not true in all cases. Crop loads in GLRaV-3 infected vines in ‘Cabernet Sauvignon’ were on average 13% higher when compared to healthy vines in 2021 (Table 8). Crop load assessment is an important tool in viticulture as it is a good indication of vine balance, and how capable the vine is to properly ripen its fruit (Kleiwer and Dokoozlian, 2005). Both yield and pruning weight were lower in GLRaV-3 infected vines that year, so perhaps it is just the result of a difference in these values, rather than of biological significance. It is interesting to note the discrepancies between values for crop load between ‘Merlot’ and ‘Cabernet Sauvignon’. Crop loads for ‘Merlot’ were on average 4.5 and 6.8 in 2021 and 2022, respectively. While in ‘Cabernet Sauvignon’, these values were just 1.8 and 2.3. A report from Kliewer and Dokoozlian (2005) state that a vine in balance should have a crop load around 4, which will be indicative of it’s ability to produce high quality fruit. ‘Cabernet Sauvignon’ vines in this study are far from that value, which may compound any impacts of the virus as these vines are already out of balance. Average berry weight and average cluster weight were not impacted by crop thinning in either year (Tables 7 and 8). This is contrary to a 1984 study in ‘Carignane’ grapevines, in which yield differences from crop thinning were offset by a compensatory increase in berry weight (Bravdo et al. 1984). This was not the case in our study, we determined that yield and crop load were significantly decreased in response to crop thinning across both years in both cultivars. Without an impact of berry weight, it can be hypothesized that this difference is solely due to a reduced number of clusters in 1.0 C/S vines when compared to 1.5 C/S vines, which is evident by the cluster counts and cluster to shoot ratios from Table 1. Lastly, there was no significant interaction between crop thinning and GLRaV-3 infection. This is a novel finding, as other 61 studies investigating the role of crop thinning to mitigate GLRaV-3 infection did not report crop load information (Kliewer and Lider 1976; Hébert-Haché 2015). Taken together, our results show few impacts of GLRaV-3 on vine health parameters of both ‘Merlot’ and ‘Cabernet Sauvignon’ vines across both study years. Besides decreased yields due to fewer clusters, crop thinning did not have a significant positive impact on any of the vine health parameters studied. We failed to support the hypothesis that crop thinning can mitigate the impacts of GLRaV-3 on vine health in ‘Merlot’ and ‘Cabernet Sauvignon’. However, this conclusion is not due to the insufficiencies of crop thinning as a cultural treatment, rather due to the extremely mild nature of GLRaV-3 infections in this study. 2.4.4 Fruit quality measurements A significant decrease in Brix due to GLRaV-3 infection was only observed in ‘Merlot’ in this study. Brix from infected vines were on average 1.5 lower in infected vines compared to healthy controls (Table 9). These results are in agreement with previous studies, as it is the most commonly reported impact of GLRaV-3 on grapevine (Akbaş et al. 2019; Alabi et al. 2016; Blaisdell et al. 2016; Bowen et al. 2018; Cabaleiro et al. 1999; Endeshaw et al. 2014; Lee et al. 2019; Manini et al. 2012; Martínez et al. 2016; Montero et al. 2015; Moutinho-Pereira et al. 2012; Over de Linden and Chamberlain 1970; Wolpert et al. 1992). GLRaV-3 is a phloem limited virus and is thought to cause direct damage to sieve tube elements; discontinuities that result in reduced flow of sugars from leaf to berry (Naidu et al. 2015). In addition to physical damage, studies have reported downregulation of transporter and synthesis genes in the sugar biosynthetic pathway in berry tissues of GLRaV-3 infected fruit (Vega et al. 2011). Therefore, the resulting lower brix from GLRaV-3 infection in ‘Merlot’ is likely due to complex interactions in the plant. The true nature of this process should be elucidated in future studies. Surprisingly, ‘Cabernet Sauvignon’ vines infected with GLRaV-3 did not have significantly lower brix when 62 compared to healthy vines in either year of study (Table 10). This finding is supported by similar work in Idaho which showed no significant impact of GLRaV-3 infection on total sugars of ‘Cabernet Sauvignon’ grapevines across three growing seasons (Lee et al. 2023). Growers in the Okanagan Valley commonly use crop thinning in hopes to improve fruit quality, with an increase in soluble solids (Brix) believed to be the largest benefit. Though some studies report an increase in Brix as a result of crop thinning (Bates et al. 2021; Intrigliolo and Castel 2011; Kliewer and Lider 1976; Poni et al. 1994), there are also studies that report no change in Brix (Hébert-Haché 2015; Preszler 2013). Our study did not find a significant change in Brix of ‘Merlot’ or ‘Cabernet Sauvignon’ when vines were subject to crop thinning as compared to non-thinned vines. There may be a strong influence of season, cultivar, and rootstock that influence sugar accumulation in berry. Based on studies indicating decreased flow of nutrients from source to sink in GLRaV-3 infected vines, it was hypothesized that the practice of crop thinning may be an easy to implement solution for grape growers facing ripening issues caused by viruses. However, there was no significant impact of crop thinning on Brix level evident in GLRaV-3 ‘Merlot’ or ‘Cabernet Sauvignon’ for 2021 or 2022. This is supported by a similar study in ‘Cabernet Franc’ which determined no significant impact of crop thinning on Brix of study vines in Ontario (Hébert-Haché 2015) but is in contrast to a study in ‘Burger’ vines in 1976 in which soluble solids were improved in GLRaV-3 infected vines through removing 50% of clusters from vines (Kliewer and Lider 1976). Taken together, this study suggests that crop thinning is insufficient to mitigate the negative impact of GLRaV-3 on the brix of ‘Merlot’ vines. However, due to an insignificant impact of GLRaV-3 on brix of ‘Cabernet Sauvignon’, further studies should investigate the impacts of the virus in this cultivar across different growing regions, climates, and rootstock combinations. 63 Titratable acidity (TA) was consistently impacted by GLRaV-3 in this study. In 2022, GLRaV-3 infected ‘Merlot’ vines had significantly higher TA than healthy vines, and this was also found in both 2021 and 2022 for ‘Cabernet Sauvignon’ (Tables 9 and 10). This is supported by other studies that concluded GLRaV-3 causes increased TA in various cultivars and climates (Akbaş et al. 2009; Alabi et al. 2016; Cabaleiro et al. 1999; Endeshaw et al. 2014; Kovacs et al. 2001). Contrastingly, Blaisdell et al. (2011) reported lower TA in GLRaV-3 infected vines, however their study consisted of vector-mediated infections in ‘Cabernet Franc’ of less than 2 years, whereas the vines in this study have been infected for more than 2 years. Wolpert and Vilas (1992) reported no difference in the TA of ‘Zinfandel’ vines infected with GLRaV-3, when compared to healthy vines, and Maninni et al. (2012) reported a similar finding in ‘Dolcetto’. Taken together, we suggest that the impacts of GLRaV-3 on TA may be cultivar dependent, in addition to varying by season, and growing region. Crop thinning only had a significant impact on titratable acidity from juice in ‘Merlot’ vines in 2021 (Table 9). Though the same trend was present in ‘Merlot’ for 2022 it was not determined to be significant, nor did crop thinning have a significant impact on TA of juice from ‘Cabernet Sauvignon’ in either year. Other studies reporting no change in TA in crop thinned vines have been conducted in ‘Concord’ (Poni et al. 1994; Bates and Morris 2009), ‘Riesling’ (Preszler et al. 2013; Keller et al. 2005), ‘Pinot Noir’ (Reeve et al. 2018), ‘Tempranillo’ (Intrigliolo et al. 2011), ‘Cabernet Sauvignon’, and ‘Chenin Blanc’ (Keller et al 2005). Contrastingly, one study reported an increase in TA with thinning in ‘Carignane’ (Bravdo et al. 1984). In summary, all these studies conclude a stronger impact of temperature during the growing season, than thinning treatment on the TA of cultivars in their studies, and that finding is further supported by this work. Despite a significant impact of crop thinning on TA of ‘Merlot’ in 2021, this was not a year in which GLRaV-3 had an impact on this parameter. There were no years of this study 64 during which GLRaV-3 and crop thinning both had an impact of TA, therefore no significant interaction between the two can be concluded from this study. This finding is supported by a similar study in ‘Cabernet Franc’ (Hébert-Haché 2015) but in contrast to findings in the cultivar ‘Burger’, in which an increased TA was recorded when GLRaV-3 vines were thinned (Kleiwer 1976). Despite conflicting findings regarding interactions, it is clear that influences from the environment, which differ between growing seasons, are a much stronger influence on wine grape TA than crop thinning. There was no significant impact of GLRaV-3 infection on the pH of ‘Merlot’ or ‘Cabernet Sauvignon’ vines in either year of this study. Studies reporting the impacts of GLRaV-3 on pH show no consensus. Work by Endeshaw et al. (2014) concluded no impact of GLRaV-3 on the pH of ‘Cabernet Franc’, and Wolpert at al. (1992) reported similar findings in ‘Riesling’ and ‘Zinfandel’. Some studies concluded GLRaV-3 decreased pH in juice from GLRaV-3 infected fruit, however significance of this finding often depended on the year, so was not consistent (Alabi et al. 2016; Cabaleiro et al. 1999; Mannini et al. 2012). A 2016 study in ‘Tempranillo’ reported an increase in juice pH from GLRaV-3 infected vines compared to healthy vines (Martínez et al. 2016). Taken all this information together, impacts of GLRaV-3 on pH are highly variable, and likely differ with cultivar and growing season, as has been reported for other fruit quality parameters. Despite lack of an impact from GLRaV-3, pH was very sensitive to crop thinning in this study (Tables 9 and 10). The juice of ‘Merlot’ vines subjected to the 1.0 C/S treatment had significantly higher pH in both years, though there was no significant interaction between infection status and thinning treatment. Other studies on crop thinning in various cultivars do not support these indings, as they report no significant effect of crop thinning on pH (Preszler et al. 2013; Bates et al. 2009; Bravdo et al. 1984). In the 2022 growing season, crop thinned GLRaV-3 infected vines had significantly higher pH than their 1.5 C/S counterparts due to a significant 65 interaction between GLRaV-3 infection and crop thinning in juice from ‘Cabernet Sauvignon’ berries (Table 10). Despite this finding, an Ontario study attempting to mitigate GLRaV-3 infection through crop thinning in ‘Cabernet Franc’ reported no change in pH (Hébert-Haché 2015). We suggest that crop thinning may be able to mitigate the negative effects of GLRaV-3 on the pH of juice from infected vines, however, similar to other fruit quality parameters measured in this study, it appears to be year and cultivar dependent. There were no significant impacts on yeast assimilable nitrogen (YAN) from GLRaV-3 infection in either ‘Merlot’ or ‘Cabernet Sauvignon’ in this study (Tables 9 and 10). Few studies investigating GLRaV-3 impacts in wine grapes have reported the virus impact on YAN; however, our work is in agreement with these reports. A study in ‘Sauvignon Blanc’ by Montero et al. (2016) found GLRaV-3 infection did not impact YAN of resulting must. Moreover, Lee et al. (2009) indicated no difference in YAN between healthy and GLRaV-3 infected ‘Pinot Noir’ vines across two growing seasons. . Despite the lack of effect on YAN from GLRaV-3, levels of available nitrogen at harvest in both ‘Merlot’ and ‘Cabernet Sauvignon’ were significantly lower than the reported 140 mg/L minimum required for a healthy fermentation (Bely et al. 1990; cited in Lee et al. 2009). This is probably due to the sandy-loam soil type in the Okanagan Valley and it’s drainage properties, preventing nitrogen from being stored in the soil, even though growers regularly fertilize (Bowen et al. 2011). Therefore, even if there were a difference in YAN between GLRaV-3 and healthy vines in this study, Okanagan Valley winemakers would still supplement their fermentations with added yeast nutrients. Juice from 1.0 C/S vines had higher YAN than from 1.5 C/S vines in ‘Cabernet Sauvignon’, only in the 2022 growing season (Table 7). This could indicate altered nitrogen allocation due to less sink tissue. However, this was only seen in one season, and in one variety so a pattern cannot be established. None of the crop thinning studies referenced in this work showed data concerning the impacts of crop thinning on YAN. Therefore, it should be concluded 66 that, as with other fruit quality parameters in this study, that impacts of crop thinning are year dependent, and may be more strongly influenced by cultivar and growing season. The phenolic profile (anthocyanins and tannins) of skins and seeds from ‘Merlot’ and ‘Cabernet Sauvignon’ grapevines was not impacted by GLRaV-3 infection (Tables 9 and 10). Our findings are supported by Endeshaw et al. (2014) who determined GLRaV-3 infection did not significantly alter the phenolic profile of skins or seeds of ‘Cabernet Franc’ when compared to healthy controls. However, these conclusions are in contrast to the majority of studies which report decreases in skin anthocyanins (Alabi et al. 2016; Bowen et al. 2018; Lee et al. 2019; Martínez et al. 2016; Singh Brar et al. 2008; Vega et al. 2011) tannins (Alabi et al. 2016), and total phenolic content in wine (Mannini et al. 2012, Santini et al. 2011) from GLRaV-3 infected vines. Anthocyanin biosynthesis in berry is linked to sugar content, and it could be hypothesized that skin from GLRaV-3 infected berries should have lower anthocyanin if the juice has lower Brix (Table 9). However, Alabi et al. (2016) emphasize that, as harvest nears, anthocyanin levels in berry skin become independent of sugar levels, and are instead influenced by environmental factors. Therefore, as for other fruit quality parameters measured in this study, environmental influences are hypothesized to be a stronger factor in determining fruit quality of GLRaV-3 infected ‘Merlot’ and ‘Cabernet Sauvignon’ grapevines in this study. In response to crop thinning, ‘Merlot’ skin total anthocyanin was significantly decreased in 1.0 C/S vines, compared to 1.5 C/S vines. This is an undesirable result, as the color of resulting red wines is due to the skin anthocyanin content, and a deep red color is attributed to higher quality (Alabi et al. 2016). Interestingly, a similar finding was noted for a ‘Merlot’ vineyard in 2006 in the Okanagan Valley in response to crop thinning, which showed that skin anthocyanin content was significantly lower in crop thinned vines compared to non-thinned controls (Bowen et al. 2011). The researchers conclude that year to year differences in fruit quality parameters likely contribute to the changes in anthocyanins, and is a more influential 67 pressure than cropping treatment. In contrast, Guidoni et al. (2002) found anthocyanins to be more concentrated in the skin of berries from crop thinned ‘Nebbiolo’ vines when compared to controls. Reynolds et al. (1994) did not report differences in skin anthocyanin of Okanagan Valley ‘Pinot Noir’ vines that had been cluster thinned. 2.4.5 GLRaV-3 titer This is the first study to track GLRaV-3 viral titer throughout the growing season using droplet digital® PCR (ddPCR) in ‘Merlot’ and ‘Cabernet Sauvignon’. Our goal was to understand whether there is an influence of temperature on GLRaV-3 titer, and whether increases in GLRaV-3 titer influenced disease severity in infected vines. This work suggests that GLRaV-3 titer may change in response to temperature fluctuations throughout the growing season. GLRaV-3 titer followed similar seasonal trends with respect to phenological stage in both cultivars during our study seasons. (Figures 3 and 4). This bi-modal trend of peaking viral titer at pea-sized berries, and again at harvest has not been reported before for GLRaV-3. In an undergraduate thesis at Oregon State University, Wright noted a polynomial pattern in the virus titer of mature ‘Pinot Noir’ tissue infected GLRaV-3 from June to October, though this study did not report viral titer as a concentration, and rather as a fluorescent endpoint (cyle threshold) value of ΔΔCt, as viral titer was assessed by RT-qPCR (2012). Work in 2018 by Osman et al. did not find a significant difference in GLRaV-3 viral titer throughout the season, though on average, viral loads were similar to those reported in this study (Figure 3). Unfortunately, Osman and colleagues did not report the specific cultivars used to monitor viral titer in their study. However, given the current body of research, it can be speculated that changes in viral titer may be influenced by the infected cultivar, as well as the growing region, similar to the role it plays in fruit quality. 68 Another goal of this study was to determine whether a correlation between viral load and impacts on vine health and fruit quality could be determined for GLRaV-3, with it being hypothesized that vines with a higher viral load would have poor health and fruit quality compared to vines with lower viral titers. Findings from this portion of the study are unique, as this is the first report attempting to correlate virus impacts to their titer for GLRaV-3. Leaf greenness (SPAD value) and leaf gas exchange parameters were sensitive to viral titer. ‘Merlot’ vines infected with higher loads of GLRaV-3 had lower values for SPAD and stomatal conductance (gs) compared to vines with less viral titer, suggesting that increasing viral load is detrimental to these parameters. However, when comparing GLRaV-3 infected vines to healthy vines, there was no significant difference in SPAD or gs, suggesting the viral loads present in this study may not have been sufficient to manifest severe disease. Similarly, significant negative correlations were determined between GLRaV-3 titer and SPAD values for ‘Cabernet Sauvignon’ vines at bloom in 2021, and harvest for both 2021 and 2022 (Tables 11 and 12), though GLRaV-3 vines overall did not have lower SPAD values than their healthy counterparts in either year of study. Average cluster weight and average berry weight were significantly correlated in ‘Cabernet Sauvignon’ in 2022, suggesting that vines with a higher viral load have lower berry and resulting cluster weights. However, similar to leaf greenness and leaf gas exchange, there was no significant difference in berry or cluster weighs of GLRaV-3 infected vines compared to healthy vines in this study (Tables 7 and 8). Some studies of GLRaV-3 impacts on vine health reported decreased berry weights as a result of GLRaV-3 infection (Akbaş et al. 2009; Alabi et al. 2016; Kovacs et al. 2001). This could be due to changes in viral load between studies, but is more likely attributed to the common conclusion from most studies referenced in this work, which is that growing season and cultivar differences have stronger influence than GLRaV-3 infection. 69 As for fruit quality, parameters with significant negative correlations to viral titer were not significantly impacted by GLRav-3 compared to healthy vines. Yeast assimilable nitrogen (YAN) of ‘Cabernet Sauvignon’ was negatively correlated with GLRaV-3 titer, but only in 2021 (Table 13), whereas the significant impact of GLRaV-3 on YAN was found in ‘Cabernet Sauvignon’ in 2022. With all this information taken together, the fact that some parameters are negatively correlated with virus titer despite not being significantly different when compared to healthy vines findings suggest that perhaps, a threshold level exists such that, below a certain GLRaV-3 titer, vines are not significantly impacted. Alternatively, it is summarized by Tsai et al. (2022) that plant pathosystems respond uniquely to increases or decreases in temperature, and can demonstrate varying resistance or susceptibility depending on their environment, and the virus which infects them. Perhaps it is again, the influence of environment, which governs how grapevines respond to viral infections. 70 Chapter 3: Mitigating the effects of Grapevine red blotch virus in ‘Cabernet Sauvignon’ using crop thinning, and investigating the role of virus titer in disease severity in the Okanagan Valley, British Columbia 3.1 Introduction Grapevine red blotch virus (GRBV) is a significant emerging threat to vineyards in North America. It was first recognized in California by Calvi in 2011 as “red leaf disease” due to visual symptoms resembling GLD, but tested negative for GLRaVs and other grapevine viruses. The following year Krenz et al. (2012) characterized it as “Grapevine Cabernet Franc-associated virus”, a newly discovered DNA virus infecting ‘Cabernet Franc grapevines’. Since then, it has been re-named Grapevine red blotch disease (GRBD), and has been disseminated from North America to India, South Korea and Switzerland (Cieniewicz et al. 2020). GRBD is responsible for very significant negative impacts to vine health and fruit quality in infected vines for both red fruited and white fruited cultivars. Due to the magnitude of it’s impact it is imperative to develop strategies to mitigate and manage this devastating viral disease. The causal agent of GRBD has been identified as Grapevine red blotch virus (GRBV), a DNA virus with a circular single-stranded genome of the genera Geminivirus (Krenz et al. 2012). Infection is recognized in the vineyard by visual symptoms similar to GLD; reddenning of inter-veinal leaf areas in red-fruited cultivars, and chlorosis in white-fruited cultivars, but lacking the characteristic leaf-rolling of GLD (Cieniewicz et al. 2020). The primary source of GRBV is through infected planting material, and this is likely how the disease was able to spread from its origins in the USA, across North America and to other parts of the world (Sudarshana et al. 2015). The virus spreads secondarily by way of phloem-feeding insect vectors. It was recently reported that the three-cornered alfalfa treehopper (Spissistilus festinus) was the first true vector of the virus, as evidenced by transmission from infected to healthy grapevines in field trials in 71 New York (Hoyle et al. 2022). Other species of treehopper have been investigated as vectors of GRBV in Missouri (LaFond et al. 2022) and British Columbia (Kahl et al. 2021). Exists significant concern around the spread and prevalence of GRBD due to its severe detrimental impacts on vine health and fruit quality of grapevines. Yield losses are often reported as decreased in vines infected with GRBV when compared to healthy vines, sometimes up to 40%. (Bowen et al. 2020; Rumbaugh and Sudarshana 2021). The most commonly reported impact of GRBV is a significant decrease in soluble solids (Brix) in infected vines, up to 4 Brix lower has been reported when comparing infected vines to healthy vines (Blanco-Ulate et al 2019; Bowen et al. 2020; Cauduro Girardello et al. 2019 a, b; Martínez-Lüscher et al 2019; Levin and KC 2019). Most reports also concluded increased berry weights, decreased anthocyanins, and increased titratable acidity and pH as a result of the virus in both red fruited and white fruited cultivars (Rumbaugh and Sudarshana 2021). These impacts cause a significant threat to grape growers who find GRBV infections in their vineyards, and unfortunately, there is no known cure for the virus. Current management strategies are centered around roguing (vine removal) programs, and control of insect vectors (Fuchs 2020). However, due to only recently reported confirmation of an insect vector, the former is the most effective option. The economic impacts of GRBD have been reported from as low as USD $2,123 to upwards of USD $68,548 in Washington State and Napa Valley, however studies are lacking that assess the true impact of this virus in other regions of North America (Ricketts et al. 2017). In addition, despite financial benefits of implementing vine removal, programs are often lengthy and time consuming. It follows that such a detrimental disease with a significant economic impact leaves growers desiring mitigation strategies to implement while planning more permanent strategies such as vine removal. GRBV is an emerging threat in BC. A 2017 survey discovered that, out of 2000 composite samples, 1.6% were positive for the virus (Poojari et al. 2017b). The researchers 72 noted that vineyard blocks positive for the virus were established from imported planting material between 2011 and 2014, suggesting the culprit to be introduction of infected planting stock into Canada from the United States. Following this work, identification of potential insect vectors of GRBV was conducted in BC. Kahl et al. (2021a) developed a novel artificial transmission system to test whether a variety of hemipteran insets could transmit GRBV from an infected grapevine to a buffered sucrose solution. Their work resulted in identification of a potential candidate vector, the buffalo treehopper (Stictocephala bisioni and Stictocephala basalis) which has been found in BC (Kahl et al. 2021a). The negative impacts of GRBV on vine health and fruit quality are also evident in BC vineyards. Bowen et al. (2020) performed a study in which GRBV infected ‘Cabernet Franc’ grapevines were compared to healthy controls for yield, vine health and fruit quality over two growing seasons in a commercial vineyard in the Okanagan Valley. The researchers concluded that GRBV decreased yields by up to 42%, and hypothesized this was due to decreases in photosynthesis, stomatal conductance, and bud hardiness as evidenced by significant decreases in those metrics when comparing GRBV infected vines to healthy vines. In addition to stark decreases in yield, vines infected with GRBV had on average 4 Brix lower than healthy vines, indicating a significant negative impact to the ability of GRBV infected grapevines to adequately ripen fruit (Bowen et al. 2020). In addition, increases in pH, TA, and decrease in YAN, anthocyanin and tannin, resulted in significant differences in sensory properties of wines made from as low as 20% GRBV infected fruit. Kahl and colleagues (2021b) tracked the viral load (titer) of GRBV infected Petit Verdot vines in a commercial vineyard in the Okanagan Valley, BC and noticed that viral load increased as the season progressed from bloom to harvest. However, it is unclear whether the increase in viral load corresponds with symptom development, or disease severity. 73 Given that GRBV has been identified in BC, and has been shown to negatively impact infected vines, the goal of this study was to investigate potential mitigation strategies for growers to implement in infected vineyards. Crop thinning is a very common viticultural practice in which clusters are removed from mature grapevines at certain time points in the year to alter vine balance by increasing the ratio of source tissue (leaves) to sink tissue (grapes). Growers in British Columbia commonly use crop thinning as a strategy to improve fruit quality (BCWGC 2010). Since the most commonly reported impacts from GRBV are decreased Brix and other fruit quality parameters, the goal of this study was to determine if crop thinning allowed infected vines the ability to better ripen fruit. There have been two studies to date testing crop thinning as a potential strategy to mitigate the negative impacts of GRBV. The first study was performed in Napa Valley California, in which cluster thinning significantly improved Brix of ‘Cabernet Franc’ vines infected with what was then termed “Red-leaf disease” before the true etiological agent of GRBD was elucidated (Calvi 2011). Despite the improvement, the author noted that infected vines still had consistently lower Brix compared to healthy controls, and no other fruit quality parameters measured were statistically different. More recent work by Copp et al. (2022) in Oregon demonstrated that crop thinning was not sufficient to improve fruit quality parameters of ‘Pinot Noir’ in their study. Taken together, these results suggest there could be an influence of cultivar as well as growing region on the response of GRBV infected vines to crop thinning, thus it is imperative to understand if this practice could have a positive impact in BC. In summary, GRBV is an emerging threat to the North American Wine industry, and has been recently identified in vineyards in the Okanagan Valley (Poojari et al 2017b). Potential insect vectors have been identified in the region that could be responsible for virus spread, and titer of the virus has been tracked throughout the season, along with optimal diagnostic strategies identified (Kahl et al. 2021a, b). In addition, the detrimental impacts of this virus have 74 been reported in ‘Cabernet Franc’ vines in BC. This study aims to investigate the potential utility of crop thinning to improve vine health and fruit quality of GRBV infected ‘Cabernet Sauvignon’ vines in the Okanagan Valley, BC. We hypothesize that crop thinning (1.0 C/S) GRBV infected vines will improve the overall vine health and fruit quality, compared to 1.5 C/S infected vines. Results from this study will be useful to inform GRBV mitigation and management strategies in British Columbia Vineyards. 3.2 Materials and methods 3.2.1 Site selection and experimental design The study site and experimental design are the same as used in section 2.2.1. The total number of vines used per treatment for this experiment are shown in Table 14. Table 14. Blocks design demonstrating the division of study vines into treatment groups. Treatment Control GRBV 1.5 C/S 19 19 1.0 C/S 19 19 3.2.2 Shoot and crop thinning Methodology for shoot and crop thinning follows that of section 2.2.2. For the 2021 growing season, shoot thinning was performed by commercial vineyard crews between May 24-27th to obtain an average of 18-22 shoots per vine. Shoots and clusters were counted on May 28th. On June 3rd, additional shoot thinning was performed to ensure a maximum of 22 shoots and 40 clusters per vine. Crop thinning was performed on July 8th and 9th at the pea sized berries stage. For the 2022 growing season, shoot thinning was performed on June 10th and 15th. Cluster thinning was performed as described above on July 7th and 8th. 75 3.2.3 Plant health measurements Details on methodology for measuring SPAD and leaf gas exchange follow section 2.2.3.1, and for average berry weight, yield, pruning weight and crop load in section 2.2.3.2. Specific dates for phenological stages observed in this experiment are the same as those indicated for ‘Cabernet Sauvignon’ in Table 3 of section 2.2.3. 3.2.4 Fruit quality measurements Methodology for measuring soluble solids (Brix), titratable acidity (TA), pH, yeast assimilable nitrogen (YAN), anthocyanins and tannins follow what is described in respective sub-sections of section 2.2.4. 3.2.5 Quantification of GRBV titer The protocol for determining GRBV virus titre was adapted from Kahl et al. (2021b). DNA extraction was performed using the Qiagen DNeasy® Plant Pro Kit (Qiagen Sciences Inc, Maryland USA), with modifications to the manufacturer’s protocol. As recommended for difficult tissues, 50 µL per sample of solution PS was added to the lysis buffer, and approximately 100 mg sample was added and affixed to a vortex adapter for 10 minutes on high. In addition 250 µL solution CD2 was used to remove additional particulate and contaminants from the solution. All wash steps allowed the buffer to sit on-column for 2 minutes before centrifugation, and wash steps were repeated for a total of four column washes in the protocol. Removal of residual wash buffers was accomplished by centrifugation at 16,000 rcf for 5 minutes, and DNA was eluted in 50 µL of buffer EB. DNA concentrations were quantified using a Qubit™ fluorometer and the ssDNA Assay kit (ThermoFisher Scientific, Massachusetts USA) and DNA quality was assessed by NanoDrop™ spectrophotometer (ThermoFisher Scientific, MA, USA). 76 Amplification and quantification were carried out using Droplet Digital PCR™ (ddPCR) with the Bio-Rad EvaGreen® primer assay (Bio-Rad, California USA). Quantitative primers targeting GRBV open reding frame (Kahl et al. 2021b) were used with forward sequence 5’–CGATCTGAGCGCGGAGAGGTGA-3’ and reverse sequence 5’– CCTAGCTCCAGGTCCAGGTCCAGACGTC-3’. DNA extracts were diluted 1:10,000 before analysis by serially diluting 2 mL RNA extract in 198 mL PCR-grade water (ThermoFisher Scientific, MA, USA) in a 96-well plate. The plate was sealed and vortexed for 10 seconds and spun down for 1 minute before 2 mL of this dilution was then transferred to another 96-well plate containing 198 mL PCR-grade water, sealed, vortexed and centrifuged as described above. This final dilution was used as the DNA sample in the PCR reaction. PCR master mix was prepared (per reaction) using 12.5 µL EvaGreen® Supermix and 1.25 µL of 2 µM primers. All ddPCR reagents and prepared master mix were vortexed and centrifuged before preparation of the mix, and aliquoting into a 96-well plate. Each plate was prepared with at least two positive controls, with DNA extracted as described above from confirmed GRBV positive greenhouse material, and at least two non-template controls containing confirmed negative GRBV greenhouse material. Fifteen µL of master mix was aliquoted into each well of the plate, followed by 10 µL 1:10,000 diluted sample, and sealed using sealing foil in a plate sealer (Bio-Rad, California USA). Droplets were generated using a QX200 Automatic Droplet Generator (Bio-Rad, California USA) and PCR was carried out on a C1000 Touch Thermal Cycler (Bio-Rad, California USA) using the following thermocycling parameters: 1 cycle at 95°C for 5 minutes, 40 cycles at 95°C for 30 seconds followed by 58°C for 45 seconds, 1 cycle at 4°C for 5 minutes, 1 cycle of 90°C for 5 minutes, then held at 4°C. To analyze the droplets, the plate was placed on a QX200 Droplet Reader™ (Bio-Rad, California USA), and analyzed using QX Manager Software™ with direct quantification in channel 1:EvaGreen (Bio-Rad, California USA). The average number of accepted droplets for all samples 77 was 12,893 (range 10,144 – 15,065). Positive droplets generally fell between amplitudes of 12,000 – 25,000, and negatives fell between approximately 4,500 – 7,500. Threshold values varied between plates but were always set just above the negative cluster. Virus titre was calculated using Equation 8 where the Qubit™ concentration and ddPCR concentration were specific to each sample analyzed. Whereas the dilution factor was set to 10,000, the ddPCR reaction volume 25 µL, and the nucleic acid (DNA) volume in the ddPCR reaction was 10 µL. 3.2.6 Statistical analyses Methodology for statistical analysis follows that of section 2.2.6. 3.3 Results 3.3.1 Weather data Due to being conducted in the same vineyard block as the GLRaV-3 experiment, weather data for the GRBV experiment was the same as indicated in the Results section (2.3) of Chapter 2. 3.3.2 Shoot and crop thinning Shoot thinning resulted in approximately 20 shoots per vine in 2021, and 19 in 2022 (Table 15). 1.5 C/S vines were not crop thinned in 2021, but still resulted in approximately 1.5 clusters per shoot at harvest, while 1.0 C/S vines for the same year had 1.0 cluster per shoot. In 2022, the ratio of clusters per shoot was 1.0 for 1.0 C/S and 1.5 for 1.5 C/S treatments. Total number of clusters was accordingly decreased when comparing 1.5 C/S to 1.0 C/S groups, across both years (Table 15). 78 Table 15. Average clusters at harvest, shoots at pruning, and cluster/shoot ratio of ‘Cabernet Sauvignon’ study vines in 2021 and 2022 2021 2022 Treatment Clusters Shoots Clusters/ shoot Clusters Shoots Clusters/ shoot 1.5 C/S 28.66 19.71 1.46 28.74 19.18 1.50 1.0 C/S 20.25 19.58 1.03 19.31 18.25 1.06 3.3.3.1 Leaf greenness and leaf gas exchange SPAD values changed throughout the season, corresponding to the phenological stage at which they were measured (Table 16). GRBV infection significantly decreased SPAD values by on average 1 compared to healthy vines at both veraison and harvest in 2021. Crop thinning had no significant impact on SPAD values in either year (Table 16). Leaf gas exchange parameters differed throughout the growing season based on the phenological stage they were measured. There was no significant impact of GRBV infection, or crop thinning treatment on photosynthesis (Anet), stomatal conductance (gs) or transpiration rate (T) at either of the four phenological time points measured for either year of study (Table 16). 79 Table 16. Impacts of GRBV infection, crop thinning, and their interaction, on Anet, gs T, and SPAD value at phenological stages: bloom, pea-sized berries, veraison and harvest of ‘Cabernet Sauvignon’ study vines in 2021 and 2022. 2021 2022 Stage Status Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Bloom Healthy 16.29±0.24 0.38±0.01 10.17±0.27 31.55±0.14 NAa NA NA NA GRBV 16.55±0.36 0.38±0.01 10.24±0.29 31.40±0.20 NA NA NA NA Effect n.s.b n.s. n.s. n.s. NA NA NA NA 1.5 C/S 16.26±0.29 0.38±0.01 10.03±0.33 31.32±0.16 NA NA NA NA 1.0 C/S 16.58±0.32 0.38±0.01 10.39±0.21 31.64±0.18 NA NA NA NA Effect n.s. n.s. n.s. n.s. NA NA NA NA Healthy * 1.5c 16.04±0.27 0.38±0.01 9.80±0.46 31.47±0.22 NA NA NA NA Healthy * 1.0d 16.57±0.40 0.40±0.01 10.59±0.26 31.63±0.19 NA NA NA NA Effect n.s. n.s. n.s. n.s. NA NA NA NA GRBV * 1.5 16.50±0.52 0.39±0.02 10.29±0.49 31.16±0.24 NA NA NA NA GRBV * 1.0 16.59±0.52 0.37±0.02 10.20±0.34 31.64±0.31 NA NA NA NA Effect n.s. n.s. n.s. n.s. NA NA NA NA Pea-sized Healthy 9.30±0.67 0.12±0.01 3.42±0.23 34.16±0.14 13.78±0.34 0.24±0.01 6.32±0.20 31.70±0.16 GRBV 8.71±0.66 0.11±0.01 3.29±0.25 33.50±0.16 13.65±0.28 0.23±0.01 6.17±0.23 31.69±0.25 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. 1.5 C/S 8.68±0.65 0.11±0.01 3.12±0.20 33.70±0.14 13.48±0.26 0.23±0.01 6.08±0.21 31.50±0.24 1.0 C/S 9.36±0.68 0.13±0.01 3.60±0.26 33.98±0.18 13.96±0.35 0.24±0.01 6.42±0.22 31.89±0.17 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Healthy * 1.5 9.13±1.03 0.12±0.02 3.25±0.40 34.09±0.24 13.74±1.65 0.24±0.05 6.29±0.79 31.72±0.77 Healthy * 1.0 9.49±0.92 0.13±0.01 3.61±0.25 34.23±0.17 13.82±0.92 0.24±0.04 6.35±0.62 31.68±0.69 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GRBV * 1.5 8.18±0.94 0.10±0.01 2.99±0.33 33.27±0.18 13.21±1.56 0.22±0.04 5.86±0.90 31.28±0.65 GRBV * 1.0 9.23±0.95 0.13±0.01 3.60±0.36 33.73±0.26 14.09±1.52 0.25±0.05 6.48±0.86 32.10±0.48 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Values are means ± standard error (SE) of the mean aNA= no data available. bn.s., not significant. c1.5=1.5 C/S d1.0=1.0 C/S e*p<0.05 as determined by ANOVA (α=0.05). 80 Table 16 (cont). Impacts of GRBV infection, crop thinning, and their interaction, on Anet, gs T, and SPAD value at phenological stages: bloom, pea-sized berries, veraison and harvest of ‘Cabernet Sauvignon’ study vines in 2021 and 2022. 2021 2022 Stage Status Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Veraison Healthy 10.14±0.60 0.18±0.01 3.70±0.22 36.03±0.14 14.14±0.32 0.24±0.01 5.96±0.28 36.06±0.41 GRBV 9.17±0.61 0.17±0.01 3.43±0.25 35.63±0.21 13.54±0.33 0.22±0.01 5.78±0.24 35.06±0.42 Effect n.s. n.s. n.s. *e n.s. n.s. n.s. n.s. 1.5 C/S 9.65±0.66 0.18±0.02 3.56±0.28 35.62±0.16 13.46±0.32 0.22±0.01 5.67±0.29 34.76±0.45 1.0 C/S 9.69±0.55 0.17±0.01 3.57±0.19 36.06±0.18 14.22±0.32 0.24±0.01 6.07±0.23 36.36±0.35 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Healthy * 1.5 10.14±0.94 0.18±0.02 3.72±0.38 35.94±0.14 13.80±0.65 0.23±0.01 5.74±0.19 35.40±0.76 Healthy * 1.0 10.14±0.78 0.17±0.02 3.67±0.24 36.14±0.25 14.48±0.63 0.25±0.01 6.17±0.34 36.72±0.61 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GRBV * 1.5 9.11±0.96 0.16±0.02 3.40±0.42 35.28±0.29 13.12±0.57 0.21±0.01 5.59±0.28 34.12±0.67 GRBV * 1.0 9.23±0.79 0.17±0.02 3.46±0.29 35.98±0.29 13.96±0.95 0.23±0.02 5.97±0.25 36.00±0.52 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Harvest Healthy NA NA NA 28.06±0.22 9.66±0.26 0.14±0.01 1.94±0.12 31.90±0.26 GRBV NA NA NA 27.03±0.27 8.95±0.15 0.14±0.01 1.97±0.11 30.64±0.41 Effect NA NA NA * n.s. n.s. n.s. n.s. 1.5 C/S NA NA NA 27.37±0.26 9.27±0.23 0.14±0.01 1.95±0.12 31.05±0.30 1.0 C/S NA NA NA 27.73±0.26 9.34±0.23 0.14±0.01 1.96±0.10 31.49±0.41 Effect NA NA NA n.s. n.s. n.s. n.s. n.s. Healthy * 1.5 NA NA NA 27.92±0.26 9.91±0.59 0.14±0.01 1.97±0.27 31.41±0.76 Healthy * 1.0 NA NA NA 28.20±0.37 9.41±0.32 0.14±0.01 1.91±0.15 32.39±0.65 Effect NA NA NA n.s. n.s. n.s. n.s. n.s. GRBV * 1.5 NA NA NA 26.82±0.41 8.62±0.36 0.13±0.01 1.92±0.19 30.68±0.80 GRBV * 1.0 NA NA NA 27.25±0.35 9.27±0.25 0.14±0.01 2.01±0.11 30.59±0.66 Effect NA NA NA n.s. n.s. n.s. n.s. n.s. Values are means ± standard error (SE) of the mean aNA= no data available. bn.s., not significant. c1.5= 1.5 C/S d1.0= 1.0 C/S e*p<0.05 as determined by ANOVA (α=0.05)81 3.3.3.2 Berry and cluster weight, yield, pruning weight and crop load Average berry weight was significantly higher in GRBV vines compared to healthy vines in both years. Cluster thinning had no impact on average berry weight, however average cluster weight was significantly higher in the 1.0 C/S vines compared to 1.5 C/S vines in both years, up to 18% heavier in 2022. Average cluster weight was significantly higher in GRBV infected vines compared to healthy vines in 2021 (Table 17). Thinning vines from 1.5 to 1.0 clusters per shoot caused a predictable significant decrease in yield and crop load for both years. In 2021, yield, pruning weight and crop load were all significantly impacted by GRBV infection. (Table 17). An average of 8.5% increase in yield (p<0.05) and 14% decrease in pruning weight (p<0.05) resulted in approximately 17% higher crop load in GRBV infected vines compared to healthy vines in 2021 (p<0.01). These parameters were not significantly impacted by GRBV infection in 2022. 82 Table 17. Impacts of GRBV infection, crop thinning, and their interaction on yield-based vine health parameters of ‘Cabernet Sauvignon’ study vines in 2021 and 2022. 2021 2022 Status Berry weight (g) Cluster weight (g) Yield (kg/vine) Pruning weight (kg) Crop load (yield/ pruning mass) Berry weight (g) Cluster weight (g) Yield (kg/vine) Pruning weight (kg) Crop load (yield/ pruning mass) Healthy 0.72±0.01 58.71±1.60 1.45±0.06 0.89±0.04 1.76±0.13 1.07±0.03 86.02±4.77 2.07±0.12 0.94±0.04 2.33±0.20 GRBV 0.81±0.01 67.21±2.37 1.58±0.05 0.77±0.03 2.11±0.12 1.19±0.03 91.41±4.77 2.11±0.12 0.86±0.04 2.69±0.20 Effect ***a ** * * ** *** n.s.b n.s. n.s. n.s. 1.5 C/S 0.76±0.01 60.08±2.18 1.69±0.05 0.77±0.04 2.30±0.14 1.12±0.03 80.06±3.22 2.30±0.10 0.80±0.03 3.09±0.18 1.0 C/S 0.75±0.01 65.99±1.97 1.33±0.04 0.88±0.04 1.59±0.07 1.14±0.02 97.53±3.71 1.87±0.07 1.00±0.04 1.92±0.08 Effect n.s. * *** * *** n.s. *** ** *** *** Healthy * 1.5c 0.71±0.01 55.07±1.74 1.65±0.08 0.84±0.05 2.08±0.20 1.05±0.02 78.37±2.41 2.28±0.10 0.83±0.11 2.79±0.18 Healthy * 1.0d 0.72±0.01 62.56±2.46 1.23±0.05 0.95±0.07 1.41±0.12 1.09±0.02 94.09±3.27 1.86±0.08 1.06±0.08 1.83±0.10 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GRBV * 1.5 0.82±0.02 65.10±3.71 1.74±0.06 0.70±0.05 2.52±0.20 1.19±0.05 81.84±6.19 2.33±0.19 0.77±0.19 3.37±0.30 GRBV * 1.0 0.79±0.02 69.43±2.93 1.42±0.07 0.83±0.04 1.73±0.07 1.18±0.03 100.98±6.67 1.88±0.13 0.95±0.13 2.01±0.13 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Values are means ± standard error (SE) of the mean a* p<0.05, ** p<0.01, *** p<0.001 as determined by ANOVA (α=0.05). bn.s., not significant. c1.5=1.5 C/S d1.0=1.0 C/S83 3.3.4 Fruit quality measurements Brix was significantly lower in GRBV infected vines compared to healthy vines in both years of study. Juice from berries on GRBV infected vines were on average 3 brix lower in both years of study (Table 18). Crop thinning did not have a significant impact on Brix in either year of study (Table 18). GRBV infection had a significant negative impact on TA of ‘Cabernet Sauvignon’ vines in both 2021 and 2022. Values for TA were on average 11% higher in juice from infected vines across both years of study (p<0.001). TA of ‘Cabernet Sauvignon’ vines did not respond to crop thinning in either healthy, nor infected vines (Table 18). Juice from ‘Cabernet Sauvignon’ vines infected with GRBV had significantly higher pH compared to healthy vines, by an average of 5%, in both 2021 and 2022 (p<0.001). 1.0 C/S vines did not have a significantly different pH from 1.5 C/S vines in either year (Table 18). Juice YAN was significantly lower in berries from GRBV infected grapevines compared to healthy controls. In 2021, GRBV infected vines had 19% less YAN compared to healthy vines, and 27% less in 2022. Despite a rather large increase in YAN of 1.0 C/S vines compared to 1.5 C/S vines, the difference was not found to be statistically significant due to large standard deviations in the data (Table 18). No significant differences were observed in skin anthocyanin, skin or seed tannin from berries infected with GRBV, nor crop thinned, compared to the control groups (Table 18). 84 Table 18. Impacts of GRBV infection, crop thinning, and their interaction on fruit quality parameters from ‘Cabernet Sauvignon’ study vines for 2021 and 2022. 2021 2022 Status Soluble solids (Brix) Titratable acidity (g/L) pH Juice YAN (mg/L) Skin anthocyanin (mg/g) Skin tannin (mg/g) Seed Tannin (mg/g) Soluble solids (Brix) Titratable Acidity (g/L) pH Juice YAN (mg/L) Healthy 25.91±0.14 8.80±0.15 3.48±0.02 86.67±4.39 260.80±6.92 24.15±0.94 93.45±3.32 25.79±0.12 7.51±0.13 3.47±0.03 51.02±2.28 GRBV 22.94±0.19 9.93±0.13 3.66±0.02 70.24±2.82 261.76±12.95 22.85±2.05 93.62±3.03 22.96±0.12 8.48±0.13 3.60±0.03 37.47±2.28 Effect ***a *** *** *** n.s.b n.s. n.s. *** *** *** *** 1.5 C/S 24.33±0.30 9.27±0.16 3.58±0.03 74.93±3.90 270.00±11.69 25.17±1.77 91.70±3.27 24.33±0.27 7.85±0.12 3.53±0.02 42.71±2.46 1.0 C/S 24.54±0.30 9.47±0.17 3.56±0.02 82.04±3.84 252.55±8.42 21.83±1.30 95.37±3.03 24.42±0.26 8.15±0.15 3.54±0.02 45.85±1.96 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Healthy * 1.5c 25.87±0.16 8.75±0.21 3.49±0.03 81.49±6.38 270.07±12.24 24.86±1.62 94.12±4.57 25.80±0.15 7.44±0.12 3.46±0.02 52.29±2.02 Healthy * 1.0d 25.96±0.25 8.86±0.22 3.48±0.03 92.46±5.83 251.53±5.79 23.43±0.99 92.78±5.05 25.78±0.17 7.58±0.15 3.48±0.02 49.67±2.41 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. GRBV * 1.5 22.78±0.27 9.79±0.19 3.68±0.03 68.38±4.11 269.93±20.68 25.48±3.25 89.27±4.78 22.87±0.17 8.26±0.17 3.60±0.04 33.14±3.26 GRBV * 1.0 23.11±0.27 10.08±0.17 3.64±0.03 72.21±3.91 253.58±16.29 20.22±2.36 97.96±3.42 23.05±0.17 8.71±0.18 3.61±0.03 42.03±2.88 Effect n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Values are means ± standard error (SE) of the mean a* p<0.05, ** p<0.01, *** p<0.001 as determined by ANOVA (α=0.05). bn.s., not significant. c1.5=1.5 C/S d1.0=1.0 C/S85 3.3.5 GRBV titer Trends in GRBV viral titer differ between 2021 and 2022 study seasons. In 2021, titer was low at bloom, peaked at pea-sized berries, then continued to decrease until harvest. Whereas in 2022 (no data for bloom) there is a large peak at pea-sized berries, a drop in titer at veraison, and large increase at harvest time (Figure 5). Figure 5. Boxplots depicting changes in viral titer in GRBV infected ‘Cabernet Sauvignon’ vines in 2021 (top left) and 2022 (top right) growing seasons. Phenological stage of measurement is indicated on the x-axis and boxes are color coded for phenological stage. GRBV titer is indicated on the left-hand Y axis. Lower line plots indicate mean daily temperatures (y axis) for 2021 (bottom left) and 2022 (bottom right) with horizontal lines color coded to indicate the mean daily temperature on the date titer measurements were taken for the corresponding stage. Each box encloses the 25th percentile of the data (bottom) a bold line at the median value and the 75th percentile (top). Whiskers indicate standard deviation, and dots outside of the whiskers are outliers less than 3 standard deviations away from the mean. 86 Decreases in photosynthesis (Anet) (R=-0.5) and stomatal conductance (gs) (R=-0.65) at pea-sized berries stage were significantly correlated with an increase in viral titer (Table 19). There were no significant correlations between GRBV titer and vine health parameters measured in 2021 (Table 19). Table 19. Correlations of viral titer with leaf-based vine health parameters of GRBV infected ‘Cabernet Sauvignon’ vines in 2021 and 2022. 2021 2022 Stage Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Anet µmol CO2/m2/sec gs mol/m2/sec T mmol/m2/sec SPAD Bloom n.s.a n.s. n.s. n.s. NAb NA NA NA Pea-sized n.s. n.s. n.s. n.s. *c R=-0.50 *** R=-0.65 n.s. n.s. Veraison n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Harvest N/A N/A N/A n.s. n.s. n.s. n.s. n.s. an.s., not significant bNA = no data available c*p<0.05, ***p<0.001, as determined by simple linear regression (α=0.05) GRBV titer was negatively correlated with average berry weight in 2021 (R=-0.4) and 2022 (R=-0.55) (Table 120). In 2022, average cluster weight was negatively correlated with viral titer (R=-0.51), and total yield also significantly decreased as GRBV titer increased (R=-0.43). In 2021 titratable acidity was negatively correlated to viral titer (R=-0.35) (Table 20). Table 20. Correlation of GRBV titer and yield based vine health, as well as fruit quality parameters of ‘Cabernet Sauvignon’ study vines in 2021 and 2022. Parameter 2021 2022 Cluster weight (g) n.s.a **b R=-0.51 Yield (kg) n.s. * R=-0.43 Pruning weight (kg) n.s. n.s. Crop load (yield/pruning weight) n.s. n.s. Average berry weight (g) * R=-0.40 *** R=-0.55 Soluble solids (Brix) n.s. n.s. Titratable acidity (g/L) * R=-0.35 n.s. pH n.s. n.s. Yeast assimilable nitrogen (mg/L) n.s. n.s. Skin anthocyanin (mg/g) n.s. NAc Skin tannin (mg/g) n.s. NA Seed tannin (mg/g) n.s. NA an.s., not significant b*p<0.05, ***p<0.001, as determined by simple linear regression (α=0.05) cNA = no data available 87 3.4 Discussion Data for GRBV was collected from the same ‘Cabernet Sauvignon’ block as that for the GLRaV-3 study. Data for weather and shoot and crop thinning can be found in Chapter 2, Sections 2.4.1 and 2.4.2, respectively. 3.4.1 Plant health measurements 3.4.1.1 Leaf greenness and leaf gas exchange GRBV decreased SPAD at veraison and harvest in 2021, but the trend was not observed in 2022 (Table 16). Reynard et al. (2018) noted a significant decrease in leaf chlorophyll in graft-inoculated ‘Gamay’ vines, only after June, in all years of their study. Similarly, Bowen et al. (2020) reported significantly lower leaf chlorophyll concentrations from ‘Cabernet Franc’ vines infected with GRBV, compared to healthy controls. Therefore, the inconsistent nature of the impact of GRBV on SPAD values differentiates our findings from what is present in the literature. There was no significant impact of GRBV on any leaf gas exchange parameters (Anet, gs, T) in this study. This is in contrast to the majority of studies documenting GRBV impacts on leaf physiology, which conclude that GRBV decreases stomatal conductance (gs) of infected vines, therefore reducing net photosynthesis (Anet) when compared to leaves from healthy vines (Bowen et al. 2020; Lee et al. 2021; Martínez-Lüscher et al. 2019; Reynard et al. 2018; Wallis and Sudarshana 2016). Though Calvi (2011) measured gas exchange of vines infected with GRBV and reported a significant reduction in gs that did not correspond to a significant reduction in Anet. Care was taken in this study to measure only green leaf tissue regardless of infection status of vines, so there is a possibility that gas exchange differed between asymptomatic and symptomatic leaves on the same vine. Bowen et al. (2020) showed that asymptomatic GRBV 88 positive ‘Cabernet Franc’ vines have higher net photosynthesis and stomatal conductance capabilities than leaves from symptomatic infected vines. This suggests that visual symptom expression may be linked to impairment of vine physiological processes, and could be an explanation for the lack of impact of GRBV on leaf gas exchange in this study, as both seasons showed relatively mild symptoms. Levin and KC (2020) suggested that high sugar levels in symptomatic leaves results in their storage in guard cells, causing water to flow in as a result of solute gradients, closing the stomata and decreasing conductance. Though differences between growing season may also account for the variation, further research should focus on elucidating the ways in which GRBV visual symptom development impacts vine physiology. There were no significant impacts of crop thinning on SPAD values or leaf gas exchange parameters in this study (Table 16). As summarized by Wang et al. (2018), grapevines reduce their photosynthetic capacity following cluster thinning to acclimate to the crop adjustment; however, no such change was evident in healthy, or GRBV infected vines subject to crop thinning in this study. Therefore, crop adjustment was insufficient to manage the negative impacts of GRBV on SPAD in 2021 (Table 16). Our findings are supported by a similar study investigating the utility of crop thinning to mitigate impacts of GRBV in ‘Pinot Noir’ in Oregon, which found that crop adjustment lead to no significant differences in Anet or gs of GRBV infected vines (Copp et al. 2022). However, this work only measured gas exchange once, just after veraison, and due to spread of GRBV infection did not have control vines for comparison. 3.4.1.2 Berry and cluster weight, yield, pruning weight and crop load All yield parameters were impacted by GRBV in 2021, however some findings conflict with previous work on this subject. Significantly higher average berry weights were reported in both years, but only resulting in higher average cluster weights in 2022. Increased berry weight 89 as a result of GRBV infection is a common finding in other studies on GRBV impacts (Bowen et al. 2020; Cauduro Girardello et al. 2019a, b, Martínez-Lüscher et al. 2019; Reynard et al. 2018). Cauduro Girardello (2019a) explains this phenomenon is likely due to GRBV negative impact on fruit set, causing there to be fewer berries overall, so the vine compensates through weight differences. Despite most studies reporting significantly lower yields as a result of GRBV infection, we noticed a significant increase in yields when comparing GRBV infected vines to healthy vines (Table 17). Based on these conflicting results, it is unlikely that this is a true impact of the virus, and rather, since our study essentially controlled for cluster number through crop thinning treatment, it can be inferred that the increase in yield is simply due to an increase in the berry weight of GRBV clusters. We also observed significantly reduced pruning mass in vines impacted by GRBV compared to healthy controls, which is supported by work in other studies (Bowen et al. 2020; Cauduro Girardello et al. 2019a, b; Martínez-Lüscher et al. 2019; Reynard et al. 2018). However, the confounding observation of increased yield, coupled with lower pruning weights, resulted in GRBV infected vines having significantly higher crop loads compared to healthy vines, but only in 2021. It is worth noting, the crop load of ‘Cabernet Sauvignon’ vines in this study – between 1.5 and 3.5 is significantly lower than would be expected for a vine capable of producing high quality fruit – between 4 and 10 (Kleiwer and Dokoozlian, 2002). Despite these spurious findings, there were no significant impacts of GRBV on yield parameters in 2022, suggesting the growing season may have had an influence on disease severity. Crop thinning had predictable impacts on yield parameters, such that cluster numbers and crop load were reduced in 1.0 C/S groups when compared to 1.5 C/S vines (Table 17). Cluster weight was higher in 1.0 C/S vines compared to 1.5 C/S vines in both years, suggesting adjustments in resource allocation due to a decrease in sink (berry) tissue. This was a much more significant difference in 2022, but may have been due to higher yields in that season. 90 Despite the appearance of difference, there was no significant interaction between crop thinning and virus infection, such that crop adjustment to one cluster per shoot did not improve yield status of GRBV infected vines, when compared to vines in the 1.5 clusters per shoot group (Table 17). Copp et al. (2022) reported increased berry weights in cluster thinned ‘Pinot Noir’ infected with GRBV, compared to non-thinned GRBV positive vines during the course of their study. Based on the current consensus in the literature being that increased berry weight as a result of poor fruit set is a detrimental effect of GRBV, Copp et al. (2022) work would suggest crop thinning is a further detriment to GRBV infected vines. In addition, Calvi (2011) noted a significant decrease in berry weight of ‘Cabernet Sauvignon’ berries from crop thinned healthy vines, when compared to non-thinned controls, however this did not mitigate the impact of GRBV on berry weight. Therefore, due to conflicting findings between this work and the body of literature, further work is required to determine the impacts of crop thinning on yield parameters of GRBV infected vines. Taken together, our results fail to support the hypothesis that crop thinning can mitigate the impacts of GRBV on vine health in ‘Cabernet Sauvignon’. 3.4.2 Fruit quality measurements A significant decrease of up to 3 Brix on average each year was observed in GRBV infected ‘Cabernet Sauvignon’ vines when compared to healthy vines in this study. This is the most commonly reported, and most damaging impact of GRBV in grapevine (Blanco-Ulate et al. 2019; Bowen et al. 2020; Cauduro Girardello et al. 2019 a, b; Martínez-Lüscher et al. 2019; Reynard et al 2018). The proposed mechanisms by which sugar is reduced in GRBV infected fruit is likely due to a combination of altered source:sink relationships as a result of damaged phloem tissue as well as delayed and desynchronized ripening events due to hormonal disruptions (Rumbaugh et al. 2022; Martínez-Lüscher et al. 2019; Blanco-Ulate et al. 2019). 91 Due to evidence suggesting altered phloem translocation of solutes could be impacting sugar accumulation in GRBV infected berries, we hypothesized that cluster-thinned GRBV grapevines may have an increased capacity to allocate sugars to a decreased number of clusters, thus mitigating decreases in Brix. Unfortunately, crop thinning did not have a significant impact on the Brix levels in healthy, or GRBV infected ‘Cabernet Sauvignon’ in our study (Table 18). This is similar to findings from Copp et al. (2022) in ‘Pinot Noir’, who did not see a significant impact of cluster thinning on Brix of GRBV infected vines compared to non-thinned vines, however this study did not have any control vines for comparison. Contrastingly, early cluster thinning (before appearance of symptoms) was found to increase significantly Brix of GRBV infected ‘Cabernet Sauvignon’, such that thinned vines were 1.2 Brix higher than non-thinned vines (Calvi 2011). This study was performed in the same cultivar, with the same rootstock as our study, however was done in California, which has a different climate and growing season to the Okanagan Valley. Therefore differences in findings between Calvi (2011) and our study could be year to year variation, which is often the strongest influence on berry quality in a vineyard. Titratable acidity was also significantly increased in GRBV infected vines in both years of study, on average of 11% higher than healthy controls (Table 18). This is in line with the majority of reports on GRBV impacts on fruit quality which conclude that TA is higher in juice from infected berries (Bowen et al. 2020; Cauduro Girardello et al. 2019 b; Martínez Lüscher et al. 2019.) Martínez-Lüscher et al. (2019) reported significant increases in TA in ‘Cabernet Sauvignon’, however only when grafted onto ‘110R’ rootstock, not ‘420A’, suggesting rootstock selection may play a role in host response to the virus. In contrast to these reports, Reynard et al. (2018) observed consistent decreases in TA in GRBV infected ‘Gamay’ vines when compared to healthy vines in all three years of their study. Lastly, Cauduro Girardello et al. (2019a) reported no significant difference in TA of GRBV infected fruit compared to that from 92 healthy vines, but this was in the white-berried cultivar ‘Chardonnay’. Lee et al. (2019) concluded similar findings to Cauduro Girardello et al. (2019a) in ‘Syrah’. Grape growers and winemakers monitor both Brix and TA to track ripening progression, and physiologically, an increase in sugars is followed by a decrease in organic acids, which causes TA to decrease as berries ripen in healthy plants (Cauduro Girardello et al. 2019; Martínez-Lüscher et al. 2019). Crop thinning did not have an impact on TA in either year of study in healthy, nor GRBV infected ‘Cabernet Sauvignon’ vines. Our findings in healthy cultivars are supported by the larger body of research on cluster thinning (Bates et al. 2009; Intrigliolo et al. 2011; Keller et al 2005, Preszler et al. 2013; Poni et al 1994; Reeve et al 2018). In addition to healthy vines, the conclusion that TA of GRBV infected vines is not improved through the use of crop thinning has been reported in similar studies on ‘Pinot Noir’ (Copp et al. 2022) and ‘Cabernet Sauvignon’ (Calvi 2011). If GRBV is impacting sugar translocation, or disrupting hormonal ripening cues, and TA is physiologically linked to sugar accumulation, it is unlikely that cultural practices will be able to mitigate the negative impacts GRBV has on these fruit quality parameters. This study reported a higher pH in GRBV infected ‘Cabernet Sauvignon’ berries when compared to those from healthy vines in both 2021 and 2022 (Table 18). This is supported by similar findings in other studies (Bowen et al. 2020; Cauduro Girardello et al. 2019b; Martínez-Lüscher et al. 2019). Despite elevated organic acid levels (TA), the higher pH in berries from GRBV infected vines is likely due to a higher water content in these berries, which is also the main contributing factor to significantly higher berry weights. Crop thinning was not sufficient to change the pH in GRBV infected vines in this study (Table 18). Thinning was able to increase the pH of GRBV infected ‘Pinot Noir’ in two out of three years in an Oregon study (Copp et al. 2022). However, that finding was not supported by this study, nor by Calvi (2011) who reported no significant difference in pH of fruit from ‘Cabernet Sauvignon’ vines infected with GRBV that were cluster thinned. If an increase in berry 93 weight due to higher water content is the culprit to higher pH in GRBV infected fruit, it is logical that the increase in pH found by Copp et al. (2022) is attributed to their finding that crop thinning also significantly increased berry weight of GRBV infected vines. However, due to desynchronized ripening events being a likely mechanism for GRBV detrimental impacts in infected berry, it is unlikely that crop adjustment will be sufficient to mitigate irregularities in berry pH. This study determined GRBV significantly decreases must yeast assimilable nitrogen (YAN) in ‘Cabernet Sauvignon vines’, compared to healthy controls (Table 18). Few studies on GRBV impacts to fruit and wine quality have reported changes to YAN. A similar study to this one in the Okanagan Valley monitoring GRBV impacts in ‘Cabernet Franc’ noted significant decreases in YAN when comparing fruit from GRBV infected vines to that from healthy vines (Bowen et al. 2020). Contrastingly, Cauduro Girardello et al. (2019a) reported higher YAN in ‘Chardonnay’ must form vines infected with GRBV, but only in one year of their study (2019a). Lee et al. (2019) reported no difference in YAN of ‘Gamay’ vines infected with GRBV compared to healthy controls. However, it is worth noting that YAN values in our region are significantly lower than expected in order to have a successful fermentation, therefore winemakers would likely add additional yeast nutrients to compensate, regardless of the impact of virus in their vineyard (Bely et al. 1990; cited in Lee et al. 2009). Nonetheless, it could be that decreased YAN in GRBV infected vines is due to impaired resource partitioning as a result of phloem damage from the plant’s innate defenses against the virus. Nitrogen is transported from leaf to berry through the phloem tissue, and vines infected with GRBV have been shown to deposit callose in their phloem in hopes to slow systemic movement of virus particles (Reynard et al. 2018). The commercial vineyard in this study regularly applied nitrogen fertilizers to soil through irrigation, 94 however the sandy-loam soils in the region of this study have a high drainage capacity, rendering these fertilizers generally unavailable to grapevine roots (Bowen et al. 2011). The other studies assessing the viability of crop thinning as a mitigation strategy for GRBV did not report YAN levels. Therefore, this is the first study to conclude that crop thinning is not sufficient to mitigate GRBV decreases in YAN in ‘Cabernet Sauvignon’. There was no significant impact of crop thinning in healthy, or GRBV infected vines across both years of our study (Table 18). As mentioned earlier, this could be due to a combination of phloem damage, and a lack of available nitrogen in the soil due to drainage capacity of sandy loam soils in our growing region. There was no significant impact of GRBV infection on skin anthocyanin, nor skin or seed tannin in this study. This is contrary to other studies in GRBV who consistently report decreased skin anthocyanins and tannins as a result of GRBV infection (Bowen et al. 2020; Martínez-Lüscher et al. 2019, Reynard et al. 2018, Lee et al. 2019). Cauduro Girardello et al. (2019b) reported a significant decrease in anthocyanin content in one of the ‘Cabernet Sauvignon’ vineyards in their study, which is in contrast to our conclusion in the same cultivar. It is interesting that no significant impact to anthocyanins was observed in this study, as sugars are a precursor for anthocyanin biosynthesis, and lower Brix in GRBV infected fruit would logically slow anthocyanin biosynthesis (Martínez-Lüscher et al. 2019). Blanco-Ulate et al. (2019) emphasize that GRBV causes disruption of metabolic pathways in infected vines, resulting in suppression of ripening and color developing genes. Perhaps there is a plant response to GRBV in this study that is allowing anthocyanin accumulation despite virus impacts. It has been noted in ‘Chardonnay’ and ‘Cabernet Sauvignon’ that warmer seasons caused GRBV infected vines to have significantly higher tannin than healthy vines, suggesting the plant is producing tannins as a defense mechanism due to their antibacterial and antifungal properties (Cauduro Girardello et al. 2019a,b). Due to warmer than average temperatures in our years of study, it 95 could be hypothesized that the plant is increasing production of tannins and other protective compounds to fight off the virus. As temperatures across the world continue to rise, it will be essential to monitor plant-pathogen interactions under increased temperature stress. This study determined that crop thinning did not have a significant impact on anthocyanin or tannin concentrations in either healthy nor GRBV infected ‘Cabernet Sauvignon’ vines across 2021 and 2022 (Table 15). Copp et al. (2022) also noted no significant impact of crop thinning in ‘Pinot Noir’ anthocyanins or tannins, stating that the influence of year had a stronger impact than that of the virus or treatment. Calvi (2011) did not measure anthocyanin content in their study on ‘Cabernet Sauvignon’. Therefore, based on our findings, and those from others, it can be concluded that crop adjustment does not impact anthocyanin content of ‘Cabernet Sauvignon’. Taken together, our findings fail to support the hypothesis that crop thinning can mitigate the impacts of GRBV on fruit quality of ‘Cabernet Sauvignon’. 3.4.3 GRBV titer GRBV virus titer followed similar trends for 2021 and 2022 (Figure 5). Similarly to what was observed for GLRaV-3, we notice large spikes in viral titer at the pea-sized berries stage, which is usually preceded by a shift to increasing temperatures. In another study quantifying GRBV titer in ‘Petit Verdot’ in the Okanagan Valley, viral titer from basal leaf samples remained relatively constant during the first two sampling dates, but significantly increased at harvest (Kahl et al. 2021b). Despite this study being done in a different growing season on a different cultivar, it seems as though viral titer continues to increase as the growing season progresses, though a lack of temperature data from this study makes it difficult to draw conclusions when comparing changes in titer to weather patterns. Due to the recent discovery of GRBV, little is known about how viral titer progresses throughout the growing season. Future studies should be conducted on this topic to understand the influence of other environmental and plant 96 processes on viral titer. It may also be the case, as has been demonstrated with changes in fruit quality, that cultivar, and also rootstock, play a role in the plant response to virus infection. That is why it is crucial to monitor patterns of virus infection and their impacts on fruit quality on a region basis, in order to properly plan and implement management strategies. Therefore, it follows that our next goal in this study was to understand whether a link exists between GRBV viral load and disease severity in infected vines. Significant negative correlations were reported between photosynthesis (Anet) and stomatal conductance (gs) but only at the pea-sized berries stage in 2022 (Table 19). It has been reported that, after cluster thinning, vines may have temporarily lower photosynthesis, as they adjust to the new crop load (Iacono et al. 1995; Naor et al. 1997; cited in Wang et al. 2018). However, our cluster thinning treatment was done just after the measurements for pea-sized berries were taken, so it is unlikely that these correlations are due to crop adjustment in GRBV infected vines. Despite a correlation suggesting vines with higher GRBV load have decreased Anet and gs, there was no overall significant impact of the virus on these parameters (Table 16). It is difficult to draw conclusions without information on the relationship between GRBV load and gas exchange in other cultivars, however the role of environmental conditions in a given growing season is always a strong pressure, and could be the reason for differences between results reported for 2021 and 2022 (Table 18). GRBV titer was negatively correlated with average berry weight in 2021 and 2022, as well as average cluster weight and yield in 2022 (Table 20). These findings suggest that GRBV loads increase in the vine, that berry weight and cluster weight will decrease, resulting in decreased yields. Given that this work and others report that GRBV infection increases berry and cluster weight, it could follow that a high load of GRBV would not be required for these impacts to manifest in the fruit of infected vines. However, the finding that viral load and yields are negatively correlated confounds this conclusion, and suggests that a sicker vine would have 97 lower yields. A similarly confusing negative correlation is that of GRBV load and titratable acidity, which suggests that vines with higher GRBV load have lower TA. However, as was reported in this study and others, GRBV infection increases TA overall. Correlation may not always equal causation, and plant-virus interactions are complex. In addition, most economically important grapevine viruses have RNA genomes (Table 1), so the plant-virus interactions for GRBV, a DNA virus, are likely different. Therefore, future studies should focus on understanding the underlying mechanisms by which viral load may play a role in determining the prognosis of viral diseases in grapevine. In conclusion, our findings fail to support the hypothesis that increased viral titer causes more severe disease in GRBV infected vines. 98 Chapter 4: Conclusion The goals of this research were to understand whether crop thinning has a positive impact on the vine health and fruit quality parameters of GLRaV-3 infected ‘Merlot’ and ‘Cabernet Sauvignon’, and GRBV infected ‘Cabernet Sauvignon’ vines in the Okanagan Valley British Columbia. We also set out to understand how GLRaV-3 and GRBV titer changes throughout the season, whether this is related to seasonal temperature fluctuations, and whether increases in viral load correspond to more severe disease. 4.1 Mitigating the effects of Grapevine leafroll-associated virus 3 in ‘Merlot’ and ‘Cabernet Sauvignon’ using crop thinning, and investigating the role of virus titer in disease severity in the Okanagan Valley, British Columbia 4.1.1 Mitigating the effects of GLRaV-3 using crop thinning There was no significant impact of GLRaV-3 on any leaf greenness and leaf gas exchange measurements taken during this study. Most berry weight and yield parameters were unaffected by GLRaV-3 infection as well, with the exception of crop load of ‘Merlot’ in 2021, though this is likely due to vine-to-vine variation and not an influence of the virus. As a result, crop thinning was not required to mitigate GLRaV-3 impacts on vine health parameters, as the severity of virus infection was mild. However, due to machine issues in our first year of study, we had to reduce the number of vines measured in leaf gas exchange by 50%, such that each infection status and cropping treatment combination had an average of 10 repetitions, in contrast to the originally planned 20. Perhaps with higher repetitions, and more reliable machinery, we could have noticed more subtle differences in gas exchange parameters if they were present. GLRaV-3 impacts on fruit quality parameters were mild, and inconsistent across cultivar and year in our study. However, the commercial producer in this study harvested both cultivars 99 quite late, at an average of 25-27 Brix , so the potential for GLRaV-3 infected vines to continue ripening could eliminate any difference that crop thinning might have made earlier in the season (Bowen et al. 2018). In 2022, GLRaV-3 infected 1.0 C/S vines had significantly higher pH than 1.5 C/S GLRaV-3 infected vines, suggesting that crop thinning may be suitable to improve the pH of virus infected vines. However, with only one year of significant findings, it is hard to justify implementation of cluster thinning for only a slight improvement in one fruit quality metric. Studies have shown leaf-thinning to improve some aspects of vine health and fruit quality in GLRaV-3 infected ‘Cabernet Franc’ (Hébert-Haché 2015) and ‘Albariño’ (Pereira-Crespo et al. 2012) stating that increased canopy air-flow can help with disease pressure. Therefore, future work should investigate the utility of other easy-to-implement cultural practices against GLRaV-3 in different growing regions. In summary, the impacts of GLRaV-3 on fruit quality metrics measured in our study were cultivar and year dependent, and very mild in nature. It is common to see environmental influence play a strong role in virus pathology, and both growing seasons in our study displayed their own historical temperature events. Perhaps historically high temperatures in our region allowed for a milder disease progression, as high temperatures have been reported to lessen the severity of viral infections (Tsai et al. 2022). This will be an important area for future research as global temperatures rise due to climate change. 4.1.2 Investigating the role of GLRaV-3 titer in disease severity This is the first study to track GLRaV-3 viral titer throughout the growing season using a newly developed Droplet Digital® PCR (ddPCR) assay in ‘Merlot’ and ‘Cabernet Sauvignon’. However, we experienced difficulties during the RNA extraction process, likely due to the high levels of phenolic and other compounds that can decrease RNA yields from column-purification kits (Sigma-Aldrich 2010). Despite significant dilutions of resulting purified RNA, and robust 100 nature of ddPCR against contaminants, it should be acknowledged that exact GLRaV-3 viral loads may be underreported in this work. We showed that viral loads may respond to temperature fluctuations throughout the season, and future research should work to understand the nature of this relationship and whether it is due to increased ferocity of plant response to the pathogen under heat stress, or a viral response to higher temperatures. This work will be important as global temperatures rise as a result of climate change. Very few significant relationships were identified between increased viral titer and disease severity in GLRaV-3 infected vines. It should be emphasized that correlation does not imply causation. Due to the limited amount of data to suggest the relationship between viral load and the magnitude of plant response, it will be essential for other studies to understand the biochemical and molecular interactions between GLRaV-3 and its grapevine host. These studies can inform more effective management and mitigation tools and may lead to the possibility of developing virus-resistant cultivars. 4.2 Mitigating the effects of Grapevine red blotch virus in ‘Merlot’ and ‘Cabernet Sauvignon’ using crop thinning, and investigating the role of virus titer in disease severity in the Okanagan Valley, British Columbia 4.2.1 Mitigating the effects of GRBV using crop thinning GRBV infection had minimal impacts on leaf greenness and leaf gas exchange in this study, with the exception of significant decreases in SPAD at veraison and harvest 2021. This may be due to known differences in gas exchange properties between leaves from symptomatic and asymptomatic vines (Bowen et al. 2020). Due to relatively mild symptom development in this study, this phenomenon should be investigated in future studies. In addition, as stated for the study on GLRaV-3, leaf gas exchange machine issues leading to a decreased number of vines measured could have impacted study results. 101 Most yield parameters were negatively influenced by GRBV infection in 2021. It is worth noting that crop loads were much lower in ‘Cabernet Sauvignon’ than is recommended for grapevines capable of producing healthy fruit (Kleiwer and Dokoozllian, 2005), and this may have compounded viral impacts in our study. However, none of the parameters were significantly influenced in 2022. This suggests, as was concluded in section 4.1, that growing season has a very strong influence on GRBV impacts. Despite the negative impacts of the virus, crop thinning treatments were insufficient to mitigate decreased vine health parameters. There was a consistent and significant negative impact on fruit quality measurements as a result of GRBV infection in both years of this study. There were some discrepancies, such as no significant impact to phenolics, which is commonly reported in other studies. Due to constraints with processing times, the number of repetitions sampled for phenolics analysis was reduced by 50% (same reduction as for leaf gas exchange). It is possible that a larger sample size could have aided in determining differences. Crop thinning was not sufficient to improve fruit quality of GRBV infected ‘Cabernet Sauvignon’. Bowen et al. (2020) suggest that the severity of impacts from GRBV infection prevent crop thinning and other cultural practices from being suitable mitigation strategies. Copp and Levin (2021) concluded that supplemental irrigation practices improved vine health and fruit quality of GRBV infected ‘Pinot Noir’ grapevines in Oregon. Though the practicality of increasing water use would be difficult to implement in an arid desert climate such as the Okanagan Valley, future studies should investigate the utility of other cultural practices to mitigate GRBV impacts. 4.2.2 Investigating the role of GRBV titer in disease severity GRBV titer displayed similar patterns of change to that of GLRaV-3, which were different than reported from similar work on GRBV in ‘Petit Verdot’ in the Okanagan Valley (Kahl et al. 102 2021b). In addition, few and inconsistent correlations between viral load and vine health or fruit quality decline suggest more information is required to understand the complex pathobiology of GRBV infection in grapevine. GRBV is an emerging grapevine virus, and one of few economically important grapevine viruses with a DNA genome. Therefore it is imperative for future studies to monitor trends in viral titer, as well as understand environmental and other influences that may result in its change, especially given the severe nature of GRBV infection. 4.3 Future Directions This study concludes that crop thinning is insufficient to mitigate the negative impacts of GLRaV-3 and GRBV in ‘Merlot’ and ‘Cabernet Sauvignon’ in the Okanagan Valley, British Columbia. In the absence of scientifically proven cultural practices to mitigate disease in the vineyard, it should be the continuing goal of the British Columbia grape and wine industry to rigorously monitor planting material entering the country, and test against GLRaV-3, GRBV and other relevant and emerging viral threats. In addition, despite the season-dependent nature of virus severity, it is imperative that growers implement informed management programs to remove infected vines and manage insect populations to slow spread of GLRaV-3 and GRBV in British Columbia. 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Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hart, Miranda
Urbez-Torres, Jose Ramon
Mitigating the effects of Grapevine leafroll-associated virus 3 and Grapevine red blotch virus in ‘Merlot’ and ‘Cabernet Sauvignon’ using crop thinning, and investigating the role of virus titer in disease severity in the Okanagan Valley, British Columbia
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