Science, Faculty of
Zoology, Department of
DSpace
UBCV
Tuttle, Sasha
Master of Science - MSc
University of British Columbia
Insects are in decline worldwide, with Hymenoptera being especially affected. Pollinator decline is driven by conventional farming practices such as intensive insecticide and herbicide use that negatively affect pollinator health and limit the availability of floral resources. With agricultural intensification and habitat loss further driving pollinator decline, growers have begun to rely on managed honey bees (Apis mellifera) to meet the pollination needs of their crops. Honey bees, however, cannot pollinate certain crops as effectively as wild pollinators. Highbush blueberry (Vaccinium corymbosum), a widely grown crop in British Columbia (BC), Canada, is one such crop that benefits from pollination by wild Bombus species. BC blueberry growers have previously been found to face limited crop yields and revenue due to insufficient pollination, but it is unknown how pollination limitation is affected by blueberry varieties and management practices. Research on other methods for growers to maximize yield, such as inter-varietal block design is also limited. I conducted a two year study on highbush blueberry fields with varying management practices within the Lower Mainland of BC. I ran pollen limitation experiments along transects set at varying distances from the field’s edge and surveyed pollinators visiting blueberry flowers on these transects to determine which taxa contributed the most to pollination. I also ran a pilot study in which I evaluated the effects of inter-varietal pollination on two different blueberry varieties. Results indicated fruit set and weight deficits are strongly related to bumble bee visitation rather than honey bee visitation and that growers who used clover as a cover crop experienced higher visitation than growers who did not. It was also found that growers may further increase yield by considering inter-varietal pollination during block design, but the level of yield increase depends on the blueberry varieties involved. These findings indicate that establishing floral resources beyond those offered by crops can encourage pollination services from wild Bombus sp., adding to a growing body of evidence that diversification of agricultural land can promote ecosystem services while contributing to conservation.
Pollination Services from Wild Bombus sp. and Inter-varietal Pollination Can Help Highbush Blueberry (Vaccinium corymbosum) Growers Achieve Maximal Yield by Sasha Tuttle B.Sc., The University of the Fraser Valley, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Zoology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2022 © Sasha Tuttle, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Pollination Services from Wild Bombus sp. and Inter-varietal Pollination Can Help Highbush Blueberry (Vaccinium corymbosum) Growers Achieve Maximal Yield submitted by Sasha Tuttle in partial fulfillment of the requirements for the degree of Master of Science in Zoology Examining Committee: Dr. Claire Kremen, Professor, Institute for Resources Environment and Sustainability, and Department of Zoology, University of British Columbia Supervisor Dr. Amy Angert, Professor, Department of Botany, and Department of Zoology, University of British Columbia Supervisory Committee Member Dr. Risa Sargent, Professor, Faculty of Land and Food Systems, University of British Columbia Supervisory Committee Member Dr. Wayne Maddison, Professor, Department of Botany, and Department of Zoology, University of British Columbia Additional Examiner iii Abstract Insects are in decline worldwide, with Hymenoptera being especially affected. Pollinator decline is driven by conventional farming practices such as intensive insecticide and herbicide use that negatively affect pollinator health and limit the availability of floral resources. With agricultural intensification and habitat loss further driving pollinator decline, growers have begun to rely on managed honey bees (Apis mellifera) to meet the pollination needs of their crops. Honey bees, however, cannot pollinate certain crops as effectively as wild pollinators. Highbush blueberry (Vaccinium corymbosum), a widely grown crop in British Columbia (BC), Canada, is one such crop that benefits from pollination by wild Bombus species. BC blueberry growers have previously been found to face limited crop yields and revenue due to insufficient pollination, but it is unknown how pollination limitation is affected by blueberry varieties and management practices. Research on other methods for growers to maximize yield, such as inter-varietal block design is also limited. I conducted a two year study on highbush blueberry fields with varying management practices within the Lower Mainland of BC. I ran pollen limitation experiments along transects set at varying distances from the field’s edge and surveyed pollinators visiting blueberry flowers on these transects to determine which taxa contributed the most to pollination. I also ran a pilot study in which I evaluated the effects of inter-varietal pollination on two different blueberry varieties. Results indicated fruit set and weight deficits are strongly related to bumble bee visitation rather than honey bee visitation and that growers who used clover as a cover crop experienced higher visitation than growers who did not. It was also found that growers may further increase yield by considering inter-varietal pollination during block design, but the level of yield increase depends on the blueberry varieties involved. These findings indicate that establishing floral resources beyond those offered by crops can encourage pollination services from wild Bombus sp., adding to a growing body of evidence that diversification of agricultural land can promote ecosystem services while contributing to conservation. iv Lay Summary Insect pollinators are in severe decline as a result of conventional farming practices that limit their access to pollen, nectar and potential nesting sites. Wild bumble bees are especially important for blueberry growers as they are more effective pollinators compared to domestic honey bees. Previous studies have recorded limited yields in blueberry fields due to insufficient pollination. Growers have since been encouraged to increase the diversity of flowers available on their farms to attract wild pollinators and increase visitation to crops. I found that certain blueberry varieties experienced more severe pollen limitation than others and that bumble bees are better at reducing pollen limitation in blueberry fields than honey bees. I also found that farmers can maximize yields by planting white clover between rows, thus encouraging bumble bee visitation to crops during flowering, and by planting multiple varieties of blueberries, so long as they are compatible. v Preface While Dr. Claire Kremen and Dr. Juli Carrillo initiated the research idea with support from the Delta Farmland &Wildlife Trust, further additions to the study design were carried out by Sasha Tuttle, under the guidance of Dr. Claire Kremen. Carly McGregor helped develop the seed mix intended to be used in the study’s original before-after design, provided feedback on the project methodology and direct assistance in the field. Other individuals who assisted with fieldwork in 2021 include: Kahsennaró:roks Deom , Julia Bizon, Duncan MacNaughton, Rebecca Pain, Tyler Kelly, Jenna Melanson and Claire Kremen. Individuals who assisted with field work in 2022 include: Carly McGregor, Tyler Kelly, Tatum Narode, Isabelle Factor and Claire Kremen. Duncan MacNaughton, Kahsennaró:roks Deom and Tatum Narode assisted with pollinator and vegetation sampling, insect processing and storage, and data entry. Tyler Kelly assisted with sample processing and storage, data management and programming in the R statistical software. Dr. Simone Castellarin and Dr. Michael Dossett provided guidance on what metrics to use to assess blueberry quality and Yifan Yan provided training on how to determine the TA, TSS, pH and sugar/ acid ratio of the blueberries. Dr. Michael Dossett and Dr. Eric Gerbrandt also provided advice on the design of the pilot study on the effects of cross-pollination on highbush blueberry quality. All data visualizations and analysis were carried out by Sasha Tuttle with advice from Dr. Claire Kremen, Dr. Rassim Khelifa and Dr. Josephine Gantois. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures ............................................................................................................................... xi List of Abbreviations ................................................................................................................. xiv Acknowledgements ......................................................................................................................xv Dedication .................................................................................................................................. xvii Chapter 1: Introduction ................................................................................................................1 1.1 Pollinators are in decline worldwide ........................................................................................... 1 1.2 Bees are key contributors to pollination of wild plants and agricultural crops ........................... 1 1.3 Diverse communities of wild pollinators are necessary for maximal crop pollination ............... 2 1.4 A lack of wild pollinators is limiting highbush blueberry production ......................................... 3 1.5 Floral diversity on agricultural land is vital for promoting species diversity and pollination services …………………………………………………………………………………………………..3 1.6 Chapter overview & research objectives ..................................................................................... 4 1.6.1 Chapter 2: Highbush blueberry growers must encourage visitation from wild pollinators to reduce pollination limitation and maximize revenue ....................................................................................................... 4 1.6.2 Chapter 3: Inter-varietal pollination increases blueberry quality ........................................................... 5 1.6.3 Chapter 4: General conclusions ............................................................................................................. 6 Chapter 2: Highbush blueberry growers must encourage visitation from wild pollinators to reduce pollination limitation and maximize revenue .................................................................7 2.1 Introduction ................................................................................................................................. 7 2.2 Methods ..................................................................................................................................... 11 2.2.1 Study sites ............................................................................................................................................ 11 2.2.2 Pollination limitation experiment ........................................................................................................ 13 2.2.3 Fruit harvest ......................................................................................................................................... 14 2.2.4 Seed count ............................................................................................................................................ 15 2.2.5 Estimated yield and revenue under each pollination condition ............................................................ 16 vii 2.2.6 Blueberry flower visitor surveys .......................................................................................................... 17 2.2.7 Pollinator processing & identification ................................................................................................. 18 2.2.8 Data analysis and statistical models ..................................................................................................... 18 2.3 Results ....................................................................................................................................... 23 2.3.1 Pollination limitation ........................................................................................................................... 23 2.3.2 Harvest date ......................................................................................................................................... 26 2.3.3 Estimated yield and revenue under each pollination condition ............................................................ 28 2.3.4 Top contributing pollinators to highbush blueberry ............................................................................. 28 2.3.5 The effects of distance on pollinator abundance and diversity ............................................................ 30 2.3.6 The effects of management of space between blueberry rows on crop visitation and pollinator diversity ………………………………………………………………………………………………………...32 2.4 Discussion .................................................................................................................................. 34 2.4.1 Pollination deficits across varieties and management practices ........................................................... 34 2.4.2 Reducing pollination limitation may also decrease development time ................................................ 36 2.4.3 Top contributing pollinators to highbush blueberry ............................................................................. 36 2.4.4 Distance from the field’s edge does not influence wild pollinator abundance or diversity ................. 37 2.4.5 The use of white clover (Trifolium repens) and grass as a cover crop attracts wild pollinators .......... 38 2.5 Conclusion ...................................................................................................................... 39 Chapter 3: Inter-varietal pollination increases blueberry quality ..........................................40 3.1 Introduction ............................................................................................................................... 40 3.2 Methods ..................................................................................................................................... 43 3.2.1 Pollination treatments .......................................................................................................................... 43 3.2.2 Fruit harvest ......................................................................................................................................... 44 3.2.3 Blueberry quality assessment ............................................................................................................... 44 3.2.4 Data analysis ........................................................................................................................................ 45 3.3 Results ....................................................................................................................................... 47 3.3.1 Blueberry quality across pollination treatments in the field study ....................................................... 47 3.3.2 Fruit set (%) across all pollen donor-recipient combinations .............................................................. 49 3.3.3 Blueberry weight (g) across all pollen donor-recipient combinations ................................................. 50 3.3.4 pH across all pollen donor-recipient combinations .............................................................................. 51 3.3.5 Titratable acidity (%) across all pollen donor-recipient combinations ................................................ 53 3.3.6 Total soluble solids across all pollen donor-recipient combinations ................................................... 54 3.3.7 Sugar/ acid ratio across all pollen donor-recipient combinations ........................................................ 55 3.4 Discussion .................................................................................................................................. 57 3.4.1 Additional pollen does not influence highbush blueberry quality ....................................................... 57 viii 3.4.2 Inter-varietal pollination can increase fruit set and weight .................................................................. 59 3.4.3 Inter-varietal pollination can increase blueberry quality ...................................................................... 60 3.5 Conclusion ...................................................................................................................... 61 Chapter 4: General conclusions ..................................................................................................63 4.1 Research outcomes .................................................................................................................... 63 4.2 Study limitations and future research ........................................................................................ 63 Bibliography .................................................................................................................................66 Appendix A: Supplementary information for Chapter 2 .........................................................75 A.1 Figures ............................................................................................................................................. 75 A.2 Tables ............................................................................................................................................... 76 Appendix B: Supplementary information for Chapter 3 .........................................................77 B.1 Figures ............................................................................................................................................. 77 B.2 Tables ............................................................................................................................................... 78 ix List of Tables Table 2.1: Results of linear mixed effect models investigating the effects of each pollination treatment, variety, and distance from the field’s edge on proportion fruit set (# fruit/# flowers) and fruit weight (g). Significant effects at P < 0.05 are in bold. Only data from 2022 was used for the model explaining fruit set whereas data from 2021 and 2022 was used for the fruit weight. A random effect of site was included in both models and a random effect of year was only included in the model explaining fruit weight. ............................................................................................ 23 Table 2.2: Results of linear mixed effect model investigating the effects of each pollination treatment and variety on the harvest date of the fruit. The proportion of ripe berries was also included as a covariate since clusters were harvested once 50% or more of the fruit was ripe. A random effect of site was also included to account for differences in location and management. Only data from 2022 was used for this model. Significant effects at P < 0.05 are in bold. ......... 28 Table 2.3: Model selection for log(fruit set + 1) and log(fruit weight + 1) deficits (supplemental - ambient) as predicted by pollinator visits and blueberry variety. Visits were categorized as total visits (bumble bee + honey bee visits), bumble bee visits alone, and honey bee visits alone. The models are organized in order from most to least supported with the model with the smallest AIC value at the top. ............................................................................................................................. 30 Table 2.4: Results of generalized linear mixed effects model with negative binomial distribution investigating the effects of distance and variety on the total bees seen per survey. Average wind speed (m/s) and temperature (°C) during surveys was also included as covariates. Site and the date of the survey were as random effects in both models but year was only included as a random effect in the bumble bee visitation model. Significant effects at P < 0.05 are in bold. ................ 31 Table 2.5: Results of a linear mixed effects models investigating the effects of transect location (i.e., distance from the field’s edge) and blueberry variety on log(species richness + 1) of pollinators observed visiting highbush blueberry flowers. The random effects of study site was included. ........................................................................................................................................ 31 Table 3.1: Pollen donor-recipient pairs used in the pilot study. Data from ‘Reka’ as the pollen recipient was omitted since the bushes showed signs of poor health and failed to produce fruit regardless of pollen donor. The sample size of each treatment is indicated by n. ........................ 43 Table 3.2: Results of four linear mixed effects models investigating the effects of pollination treatment (i.e., supplemental vs ambient) and variety on fruit pH, log(TA), TSS and log(sugar/ acid ratio). Site is included as a random effect. Significant effect at P-values < 0.05 are in bold........................................................................................................................................................ 48 Table A.1: Results of linear model investigating the effects of study site and year on log(fruit weight deficit + 1) Fruit weight deficit was calculated for each bush as the fruit weight of the ambient pollination treatment subtracted from the supplemental pollination treatment. Negative x values for fruit weight deficits were turned to zero and attributed to human error. Significant effect at P < 0.05 are in bold. ……………………………………………………………………76 Table A.2: Results of linear mixed effects model investigating the effects of distance from the field’s edge and variety on average bee visitation per 15 minutes. The number of bees observed per transect were summed and divided by the total number of surveys to give the average visitation rate per 15 minutes. Average bee visitation was modelled separately for honey bees and bumble bees. Farm site and the study year were attempted to be included as random effects in both models but year was only included as a random effect in the honey bee visitation model……………………………………………………………………………………………..76 Table A.3: Results of linear mixed effects model investigating the effects of pollination treatment, distance from the field’s edge, and blueberry variety on log(fruitset + 1). Farm site was included as a random effect. Significant effects at P < 0.05 are in bold……………………76 Table B.1: Results of multiple linear mixed effect models with percent fruit set (# berries/ # flowers x 100) and log(fruit weight) as dependent variables as a function of pollination treatment (i.e., inter- or intra-varietal pollination) and the blueberry variety of the pollen recipient. Data used for these models is derived from the pilot greenhouse study. Significant effect at P-values < 0.05 are in bold…………………………………………………………………………………..78 Table B.2: Results of multiple linear models with pH, TA, TSS and log(sugar/ acid ratio) as dependent variables as a function of pollination treatment (i.e., inter- or intra-varietal pollination) and the blueberry variety of the pollen recipient. Data used for these models is derived from the pilot greenhouse study. Significant effect at P-values < 0.05 are in bold……………..................78 xi List of Figures Figure 2.1: Farm area (ha) occupied by each major crop produced in the Lower Mainland of British Columbia, Canada. Major crops were arbitrarily defined as those that make up 10% or greater of the total production area. This figure was created using census data from Statistics Canada for the years 2001, 2006, 2011, 2016, and 2021. The blueberry icon indicates the year in which Button & Elle (2014) carried out their research on pollination deficits. .............................. 9 Figure 2.2: Map of 8 study sites across the Lower Mainland of British Columbia, Canada in 2021 and 2022. Squares represent farms with one study edge and circles represent farms with two study edges. The two ‘Bluecrop’ sites were only included in 2021. ............................................ 13 Figure 2.3: Median fruit set and fruit weight by pollination treatment (Supplement – flowers supplemented by hand-pollination and Ambient – flowers left accessible to pollinators), variety and year. Boxplots represent the first quartile, median and third quartile, whiskers denote standard errors and black points represent outliers. Fruit set data from 2021 was excluded as marketability failed to be considered, which would have severely underestimated fruit set deficits. The effects of year on fruit weight deficit was not significant so fruit weight data from 2021 was included......................................................................................................................... 26 Figure 2.4: The difference in harvest date between the supplement and ambient pollination treatments (ambient – supplement) per bush in 2022, organized by variety. Statistical analyses are in Table 2.2. Greater delay in harvest of the ambient-pollination treatment is on the right side of 0 and lesser delay of ambient-pollination treatment is on the left side of 0. ............................ 27 Figure 2.5: Average rate of visitation by managed honey bees (Apis mellifera) and bumble bees (Bombus sp.) per 15 minutes. Whiskers denote standard errors. .................................................. 29 Figure 2.6: Total bumble bee visits to highbush blueberry flowers summed across all surveys and organized by year and management type. Management type refers to how growers utilized space between blueberry rows (clover - clover and grass cover crop and mulch - bare ground or mulch). .......................................................................................................................................... 32 Figure 3.1: pH (A), log(TA) (B), TSS (C) and log(sugar/ acid ratio) (D) across all treatments and varieties. Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. Grey points represent individual pH, log-transformed TA, TSS and log-transformed sugar/acid ratio values. The grey lines indicate optimal ranges for each quality metric as described by Beaudry (1992). ....................................................................................... 48 xii Figure 3.2: Fruit set (%) across pollen donor-recipient pairs. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. ............................................................................................................................... 50 Figure 3.3: Average blueberry weight across all pollen donor-recipient pairs. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. ......................................................................................................................... 51 Figure 3.4: pH across all pollen donor-recipient pairs. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. The grey line indicates the lower bound of the optimal range for pH of V. corymbosum according to Beaudry (1992). ............................................................................................................................ 52 Figure 3.5: Titratable acidity (%) of the fruit across all pollen donor-recipient pairs. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. The grey line indicates the lower range for the optimal TA for V. corymbosum according to Beaudry (1992). .................................................................................. 54 Figure 3.6: TSS across all pollen donor-recipient pairs. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. The grey line indicates the lower bound of the optimal range for TSS of V. corymbosum according to Beaudry (1992). ............................................................................................................................ 55 Figure 3.7: Sugar/ acid ratio of the fruit across all pollen donor-recipient pairs per variety. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. The grey lines indicate the lower bound and upper bound of the optimal range for the sugar/ acid ratio of V. corymbosum according to Beaudry (1992). ...... 57 Figure A.1: Positive linear relationship between fruit weight (g) and the number of seeds (df = 1, p = 2.2e-16).…………………………………………………………………………………...…75 Figure B.1: Boxplot of median stem diameter (cm) of the branches used in the pilot greenhouse study testing the effects of inter-varietal pollination. Data was subset into when ‘Bluecrop’ acted xiii as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Linear models were fit with stem diameter as the dependent variable and the pollen donor set as the independent variable to test if there was a significant difference in branch width amongst treatments. Results from ANOVA tests done on these models are indicated in brackets. Significant effects at P < 0.05 are in bold…………………………………………………………………………………..77 xiv List of Abbreviations AIC: first order Akaike Information Criterion BC: British Columbia HP: supplement-pollination treatment NHB: Northern highbush blueberry O: ambient-pollination treatment SHB: Southern highbush blueberry TA: titratable acidity TSS: total soluble solids xv Acknowledgements I am eternally grateful to the friends, family, faculty and fellow students who supported me throughout this project. Without your continuous support and patience, I would not be where I am today. The sharing of your knowledge and passion for conservation are what drove my curiosity and ambitions to pursue a career in this field. Special thanks are also owed to the Semá:th community, who supported me throughout my post-secondary journey, both emotionally and financially. I thank Dr. Claire Kremen for helping me believe in myself and see my true potential. I thank you for remaining patient through my endless barrage of questions, and for opening my eyes to the importance of wild pollinators. I am incredibly honored that you entrusted me to lead this project. I thank Dr. Juli Carrillo for her continuous advice and for making me feel welcome at UBC. Thank you to my supervisory committee members Dr. Amy Angert and Dr. Risa Sargent for the sharing of your expertise and valuable time. Thanks are also owed to the DF&WT team for helping us access our invaluable study sites and maintaining relationships with the farmers involved. I thank Tyler Kelly for not only sharing his deep and broad knowledge of pollinators but his love for them. Moving forward, I will always struggle to walk past flowers without first checking for bees. I would truly be at a loss without your guidance and support during this project. For every time you helped me grapple with imposter syndrome, for helping me celebrate every small achievement and for your honest advice, I thank you. I thank Carly McGregor not only for developing the floral seed mix intended to be tested during this study, but for helping me understand the pre-determined aspects of this study’s design, build on those methods and carry them out in the field. Your work ethic and willingness to help motivated me throughout this project. Special thanks are also owed to Dr. Rassim Khelifa and xvi Dr. Josephine Gantois for their advice on my statistical analysis, coding and feedback on my writing. It was a pleasure to work with all of you. I thank Duncan MacNaughton for being the most laidback and diligent field assistant possible. For driving long hours to gather other field assistants and for your incredible work ethic, I thank you and look forward to what you will accomplish as a graduate student. Thanks are also owed to Kahsennaró:roks Deom for her assistance, both in the field and in the lab, and to my last field assistant, Tatum Narode, for her tireless efforts in the field and can-do attitude. Thank you to Isaac Heida, Rebecca Pain, Martina Clausen and Jonathan Tingley for your assistance during the most hectic of field days. Thank you to the members of the WoRCS labs for your feedback during the design stage of this project and direct assistance in the field. I am incredibly grateful for your contributions to this project. I am also grateful for the financial support from the UBC Zoology department. This study was funded in combination by UBC’s Aboriginal Grant Fellowship, an Accelerate Internship from Mitacs, and a UBC Faculty of Science Graduate Award. Finally, I would like to thank my husband, Chandler, for his continuous love and support. Thank you for helping me keep my head above water, especially when I felt the most overwhelmed. Thank you for keeping me fed and motivated when I was on the brink of burn out. And thank you for encouraging me to take time off work to actually sit and enjoy life. Your support during the start, middle and end of this project is what kept me going. xvii Dedication This thesis is dedicated to my grandmother, Mona Sepass, for her endless love and support. To a truly remarkable woman who not only survived the horrors of residential school but found the courage to reclaim our language by pursuing a Language Proficiency Certificate in Halq'eméylem at the Fraser Valley College and Simon Fraser University. As one of many traditional knowledge keepers in our family, you are truly an inspiration. Thank you for your guidance on how to honor the pollinators sacrificed for this research and for always believing in me. 1 Chapter 1: Introduction 1.1 Pollinators are in decline worldwide Wild pollinators are in decline worldwide with species at varying risk of extinction (Potts et al., 2010; Williams & Osborne, 2009; Winfree, 2010). Between 2007 and 2009, several historically abundant North American bumble bee species were found to have declined by as much as 96% (Cameron et al., 2011). In British Columbia (BC), once abundant species like Bombus occidentalis are now considered at-risk whereas numbers of non-native species like Bombus impatiens are on the rise (Colla & Ratti, 2010; BC Conservation Data Centre, n.d.). Despite the vital importance of pollinators for the pollination of wild plants and agricultural crops, they are continuously threatened by habitat degradation and loss, introduced diseases and parasites from commercially used pollinators, and widespread pesticide use (Cameron et al., 2011; Frankie et al., 1997; Kevan, 1977; Kremen et al., 2002; Steffan-Dewenter & Tscharntke, 1999; Winfree, 2010). These trends are major causes for concern for the future of plant-pollinator networks and food security (Allen-Wardell et al., 1998; Potts et al., 2010). 1.2 Bees are key contributors to pollination of wild plants and agricultural crops Roughly 88% of all flowering plants depend on animal-pollination (Ollerton et al., 2011). Agricultural crops are no exception as 87 of the leading global food crops experience reduced production in the absence of pollinators (Klein et al., 2007). In addition, crops that are fully or partially dependent on animal pollination contain many of the vitamins and nutrients vital to a balanced human diet including vitamin C, vitamin A, antioxidants, and calcium (Eilers et al., 2011). Many taxa contribute to pollination including, but not limited to, flies, beetles, wasps, birds, and even some mammals, but none compare to the sheer number of flowers visited by bees (Allen-Wardell et al., 1998; Winfree, 2010). Given the relative importance of bees as pollinators, ongoing hymenopteran insect decline may not only disrupt the pollination of wild plants and crops but further limit our ability to maintain a nutritionally adequate diet (Eilers et al., 2011; Potts et al., 2010). 2 1.3 Diverse communities of wild pollinators are necessary for maximal crop pollination Modern agriculture depends heavily on managed honey bees (Apis mellifera) for the production of animal-pollinated crops (Benjamin & Winfree, 2014). Honey bees, however, are less effective pollinators for many crops including highbush blueberry (Vaccinium corymbosum), lowbush blueberry (Vaccinium angustifolium), watermelon (Citrullus lanatus) and tomato (Solanum lycopersicum) (Bobiwash et al., 2017; Greenleaf & Kremen, 2006; Javorek et al., 2002; Kremen et al., 2002; Winfree et al., 2007). Honey bees, unlike bumble bees, cannot buzz-pollinate (a behavior in which the muscles of hind tibia shiver, vibrating the anthers of a flower to release pollen), potentially explaining the difference in their pollination abilities for select crops such as blueberry and tomato (Abrol, 2012; Buchmann & Hurley, 1978; Javorek et al., 2002). Floral morphology has also been shown to influence the foraging behavior of honey bees, encouraging them to rob nectar from certain varieties of highbush blueberry rather than pollinate (Courcelles et al., 2013). In order for growers to achieve optimal crop pollination sustainably, a high diversity of pollinators is necessary (Garibaldi et al., 2016; Mallinger & Gratton, 2015; Rogers et al., 2014). Wild pollinators not only act as biological insurance against ongoing honey bee losses but are better suited to pollinate certain crops, and can reduce fruit set and weight deficits that honey bees alone fail to alleviate (Blitzer et al., 2016; Winfree et al., 2007). Pollination services provided by wild bees have also been shown to produce heavier and higher quality fruit in strawberries, tomatoes, apples and even an allegedly-pollinator-independent variety of almond (Klatt et al., 2014; Mallinger & Gratton, 2015; Sáez et al., 2020; H. Zhang et al., 2022). Large-bodied bees, like bumble bees, may also compliment honey bee pollination by providing pollination even during poor weather, when honey bees are less active, or by altering honey bee foraging behavior and encouraging more inter-varietal pollination amongst different crop varieties (Brittain et al., 2013; Tuell & Isaacs, 2010). Given the ongoing decline of pollinators worldwide, more research is needed to develop strategies for wild pollinator conservation and to increase pollinator diversity on agricultural land to support natural pollination services. 3 1.4 A lack of wild pollinators is limiting highbush blueberry production In 2021, British Columbia’s (BC) highbush blueberry production made up 48% of Canada’s total blueberry production at a farm gate value (i.e., the price of blueberries sold directly from the grower) of nearly $1.6 million (Statistics Canada, 2022a). Despite the fact that this economically important crop benefits greatly from buzz-pollination, many farmers still depend on honey bees for crop pollination (Benjamin & Winfree, 2014; Blaauw & Isaacs, 2014; Van Westendorp & McCutcheon, 2001). Honey bees primarily forage on blueberry flowers for nectar but may still inadvertently pollinate when moving anthers to the side to access nectaries (Dogterom & Winston, 1999). Honey bees have also been observed foraging for pollen by tapping flowers with their forelegs but they are still far less efficient blueberry pollinators relative to wild bumble bees (Javorek et al., 2002). A honey bee would have to visit a flower four times over to deposit the same amount of pollen that a Bombus sp. queen deposits in a single visit (Dogterom & Winston, 1999; Javorek et al., 2002). Moreover, there is evidence that highbush blueberry production is limited even in the presence of managed A. mellifera hives, with potential for yield increases up to 23% for ‘Bluecrop’ growers and potential revenue increases up to $18,400/ha (Benjamin & Winfree, 2014; Button & Elle, 2014; Gibbs et al., 2016). While increased honey bee stocking rates may not be able to alleviate these fruit set and weight deficits, additional bumble bee visits or a combination of additional bumble bee and honey bee visits may be able to (Button & Elle, 2014). 1.5 Floral diversity on agricultural land is vital for promoting species diversity and pollination services In an agricultural landscape, homogeneity of the land is associated with lower on farm biodiversity (Marshall et al., 2006). Monoculture crop plantings and intensively farmed land limit floral resources and potential nesting sites, thus reducing pollinator diversity and grower’s access to pollination services (Blaauw & Isaacs, 2014; Potts et al., 2010). Recently developed strategies that could diversify agricultural land include the establishment of grass margin strips at the edges of fields (Marshall et al., 2006), establishment of wildflowers (Venturini et al., 2017) 4 and intercropping with flowering plant species (Gowton et al., 2021); However, evidence for the success of these strategies is mixed (Albrecht et al., 2020; Nicholson et al., 2020). More work is needed to find cost-effective methods of diversifying farmland that promote both pollinator diversity and pollination services. 1.6 Chapter overview & research objectives Over the course of this project, I documented the top contributing pollinators of highbush blueberry within the Lower Mainland of British Columbia and compared present day pollination limitation (i.e., the difference in fruit set and weight under supplemental and ambient pollination) to values recorded 10 years ago by Button and Elle (2014) (Chapter 2). I explored potential explanations behind these differences by discussing changes in pesticide use, farm management practices and crop diversity since Button and Elle’s work in 2011 – 2012 (Chapter 2). I also explored whether there were quality differences in blueberry fruit produced from supplementally-pollinated flowers relative to ambient-pollinated flowers by assessing quality metrics associated with blueberry flavor (Chapter 3). In Chapter 3, these field-scale results were combined with those of a pilot greenhouse study that investigated the effects of inter-varietal pollination on blueberry quality. 1.6.1 Chapter 2: Highbush blueberry growers must encourage visitation from wild pollinators to reduce pollination limitation and maximize revenue In Chapter 2, I wanted to determine if supplementally-pollinated flowers developed fruit faster and produced greater yields than ambient-pollinated flowers. I hypothesized that all fields would experience a fruit set and fruit weight deficit (i.e., that all fields would experience actual fruit set and weights smaller than the potential fruit set and weight under supplemental pollination) but that these deficits would be the largest for the ‘Liberty’ grower and the smallest for the ‘Duke’ growers. This hypothesis was driven by the fact that among these varieties ‘Liberty’ has the smallest corolla throat diameter and ‘Duke’ has the greatest throat diameter, and flowers with a smaller throat diameter encourage nectar robbing behavior in honey bees rather than pollination 5 (Courcelles et al., 2013). I then compared pollination limitation (i.e., the difference between potential and actual pollination) and estimated revenue increases from maximum pollination with the values reported by Button & Elle (2014) and examine changes in crop diversity and management practices that may have affected pollination limitation in the Lower Mainland. Other factors, such as climate change, that may have affected our results were not accounted for due to either a lack of data or logistical challenges. I also tested if flower visitation, fruit set or fruit weight decrease with increasing distance from the field’s edge. I hypothesized that visitation and yield would be greatest towards the field’s edge, where floral resources from weeds were expected to be most abundant. I tested this hypothesis by setting transects for visual flower visitor surveys and pollination limitation experiments at three different distances from the field’s edge (0, 50 and 100 m). Lastly, I determined if bumble bees better explain variance in fruit set and fruit weight deficits by comparing linear mixed effect models with total pollinator visits, honey bee visits, or bumble bee visits as the explanatory variable, using AIC criterion (Burnham & Anderson, 2004). 1.6.2 Chapter 3: Inter-varietal pollination increases blueberry quality In Chapter 3, I wanted to determine if supplementally-pollinated flowers yield better quality fruit (i.e., sweeter, less sour tasting) than ambient-pollinated flowers. I predicted that supplementally-pollinated flowers would produce fruit with a lower titratable acidity (%), and a higher total soluble solids (%), pH and sugar/ acid ratio than ambient-pollinated flowers. This hypothesis was driven by evidence that blueberry weight increases with seed set, suggesting that blueberry bushes may invest more resources into berries produced by better pollinated flowers (Button & Elle, 2014). I tested this hypothesis by preserving berries harvested from the pollination limitation experiment in the field-scale study (Chapter 2) and later blending these samples so that I could use a digital refractometer to measure TSS and a pH meter to record pH and perform acid-base titrations. In Chapter 2, I also tested if inter-varietal pollination produces better quality fruit than intra-varietal pollination. I hypothesized that blueberry bushes would invest more sugars into fruit produced from inter-varietal pollination relative to fruit produced from intra-varietal pollination due to the increased genetic variability. Thus, I predicted that inter-varietal 6 pollination would produce fruit with a higher TSS and a lower TA (i.e., fruit that is sweeter tasting) than fruit produced from intra-varietal pollination. I tested this hypothesis by performing a pilot greenhouse study in which I pollinated ‘Duke’ and ‘Bluecrop’ blueberry bushes by hand with either pollen from the same variety or pollen from another variety and recorded the quality metrics of the resulting fruit. In combining the results from the field-scale study and the pilot greenhouse study, I was able to parse out which factors most affect blueberry quality. 1.6.3 Chapter 4: General conclusions In Chapter 4, the findings from Chapters 2 and 3 were combined to make recommendations for highbush blueberry growers in the Lower Mainland of BC on how to maximize yields and revenue sustainably by utilizing pollination services from wild Bombus species and encouraging inter-varietal pollination. I also outlined the limitations of this study and directions for future research in this chapter. 7 Chapter 2: Highbush blueberry growers must encourage visitation from wild pollinators to reduce pollination limitation and maximize revenue 2.1 Introduction Recommendations from researchers for highbush blueberry (Vaccinium corymbosum) growers in the Fraser Valley of British Columbia, Canada to use managed honey bee (Apis mellifera) hives for crop pollination dates back to the 1980s (Winston & Graf, 1982). At this point in time, researchers suggested that wild pollinators were no longer abundant enough to meet the pollination needs of crops and growers who were reliant on these pollination services were bound to experience a decrease in yield (Winston & Graf, 1982). The ongoing decline of wild pollinators has pushed blueberry growers to shift management strategies and pay for honey bee hive rentals instead of taking advantage of natural pollination services (DeGrandi-Hoffman, 2011). The strong reliance on honey bees, however, is an unsustainable practice as managed honey bee stocks have also recently begun to decline from infection by parasitic mites and diseases (Allen-Wardell et al., 1998; Potts et al., 2010; Winfree et al., 2007). In addition, managed honey bees are not as effective at pollinating highbush blueberry as wild pollinators like bumble bees (Bombus sp.) (Bobiwash et al., 2017). Highbush blueberry flowers have poricidal dehiscent anthers that require sonication (i.e., buzz-pollination) but honey bees cannot sonicate blueberry flowers like bumble bees can (Javorek et al., 2002). Honey bees also primarily forage for nectar on highbush blueberry and have been known to exhibit nectar robbing behavior (i.e., where bees access nectar through holes in the corolla, avoiding contact with the anthers or stigma) in blueberry varieties with long, narrow flowers (Courcelles et al., 2013; Dogterom & Winston, 1999). Bumble bees, however, generally have longer tongues than honey bees and are far less likely to exhibit nectar robbing behavior (Courcelles et al., 2013). Lastly, bumble bees are known to pollinate more frequently than honey bees during poor weather, likely due to larger-bodied bees being more able to thermoregulate (Peat et al., 2005; Tuell & Isaacs, 2010). There may be concern over the idea of relying on wild bumble bees for pollination, since bumble bees prefer to forage for pollen with high protein, and blueberry pollen is known to have 8 a low crude protein content (Toshack & Elle, 2019). Wild pollinators may be most attracted to the edges of fields where weedy flowering species and potential nesting sites exist (Levin & Anderson, 1970). Button & Elle (2014), however, found that there was only a small decrease in bumble bee visits as distance from the field’s most natural edge increased and that distance from the field’s edge did not affect fruit set or fruit weight. In addition, Button & Elle (2014) determined that ‘Bluecrop’ fields with managed honey bee hives still had a potential for a 23% increase in yield under supplemental-pollination conditions. The estimated yield was not as high for ‘Duke’ growers but the estimated revenue increase under supplemental-pollination was still estimated at $7,800/ha (Button & Elle, 2014). Button & Elle (2014) suggest that pollination limitation could be reduced by either a combination of honey bee and wild bumble bee visits or wild bumble bee visits alone but not with honey bee visits alone. I add to Button & Elle’s work by investigating the fruit set and fruit weight deficits (i.e., the difference in fruit set/fruit weight under supplemental-pollination and ambient pollination) in highbush blueberry varieties not included in their study such as ‘Draper’ and ‘Liberty’ while also comparing current ‘Duke’ pollination limitation to that calculated in 2011. I explore possible explanations for changes in pollination limitation including changes in crop diversity, management practices and pesticide use. I also look at other potential benefits of supplemental-pollination to growers and consumers, such as the influence of supplemental-pollination on harvest date. The transect layout used in this study also permitted me to test if the frequency of pollination changes with distance from the field’s edge. While Button & Elle (2014) chose to study the field’s most natural edges, field edges in this study were selected based on their suitability for floral strip establishment (see Chapter 2.2.1) or as a control (i.e., a representation of a standard field edge). I hypothesized that a diverse array of weedy flowering species would still be prevalent on these field edges, resulting in decreasing pollination services with increasing distance from the edge. I also hypothesized that the diversity of pollinators visiting blueberry would decrease as distance from the field’s edge increased as a result of decreasing diversity of flowering plants. Landscape simplification can drive pollinator decline if a less diverse plant community fails to provide a continuous and diverse supply of floral resources (Goulson et al., 2008; Raderschall et al., 2021). In 2006, highbush blueberry production in the Lower Mainland spiked, 9 five years prior to when Button & Elle carried out their research (Button & Elle, 2014). In 2011, the farm area occupied by blueberry was nearly 1,200 hectares less compared to the previous spike in production (Figure 2.1) but blueberry was still the most abundant fruit crop, taking up 62.7% of all agricultural land within the Lower Mainland. The area occupied by blueberry in 2021 has increased by nearly 30% relative to the land occupied by blueberry during Button & Elle’s study. The increasing dominance of blueberry in the Lower Mainland poses a risk to wild pollinators and could potentially restrict growers’ access to pollination services, further driving pollination limitation. Figure 2.1: Farm area (ha) occupied by each major crop produced in the Lower Mainland of British Columbia, Canada. Major crops were arbitrarily defined as those that make up 10% or greater of the total production area. This figure was created using census data from Statistics Canada for the years 2001, 2006, 2011, 2016, and 2021. The blueberry icon indicates the year in which Button & Elle (2014) carried out their research on pollination deficits. In 2009, spotted wing drosophila (Drosophila suzukii), a major fruit pest, was first detected in British Columbia (Lee et al., 2011), leading to a gradual intensification of pesticide use to control the pest from that point until present. Unlike other Drosophila, spotted wing 10 drosophila have a serrated ovipositor, allowing them to damage fruit before harvest (Walsh et al., 2011). Without some form of prevention, D. suzukii will infest ripe blueberries, rendering the fruit unmarketable (Walsh et al., 2011). At the time of detection in 2009, the immediate management response primarily involved monitoring for adult activity with apple cider vinegar traps and monitoring fruit for larval infestations (Lee et al., 2011; Walsh et al., 2011). In 2010, research shifted to assess potential insecticides that could provide growers with immediate chemical management options (Lee et al., 2011). By 2012, control of this pest relied heavily on insecticide, despite their lack of effectiveness at the time (Cini et al., 2012). The presence of D. suzukii has since pressured farmers to use harmful broad-spray insecticides such as malathion (a spray known to be extremely toxic to bees) to avoid crop infestation (Kumar & Singh, 2012; Lee et al., 2011; Van Timmeren & Isaacs, 2013). Button & Elle (2014) did not find the year in which observations were made to significantly affect the number of bumble bees seen, so management practices were likely consistent enough between years to avoid affecting visitation. Given how soon their observations were made after the first detection of D. suzukii in BC, it is possible that insecticide use for this pest was still limited and did not significantly affect Button & Elle’s results. The research presented in this thesis, however, was carried out more than 10 years after the introduction of D. suzukii and insecticide use for the control of this pest was frequently observed amongst our field sites. Thus, increased insecticide use is likely to be another factor influencing bumble bee populations and potentially increasing pollination limitation. The presence of weeds on farmland can provide wild pollinators with continuous access to floral resources outside of crop flowering (Carvalheiro et al., 2011). However, conventionally managed farms limit weed presence in and between blueberry rows to avoid competition with flowering crops (Abrol, 2012). There is growing evidence that intercropping with other flowering plants can actually attract wild pollinators, increasing pollination services (Gowton et al., 2021; Venturini et al., 2017). Management of spaces between blueberry rows is another factor that differed between the current study and Button & Elle’s study. While in Button & Elle’s study, the between-row spaces were practically unmanaged and primarily consisted of grass and weeds such as Taraxacum sp. (E. Elle, personal communications, October 4, 2022), in this study growers either managed this space by covering it with mulch, tilling it so the ground remained bare or by planting white clover (Trifolium repens) and grass. I hypothesized that more 11 wild pollinators would be observed visiting blueberry flowers on sites where growers planted grass and clover between rows relative to sites that used mulch or bare ground alone. I also hypothesized that the sites with the grass and clover cover crop system would experience less severe pollination limitation relative to the other sites. Considering the changes in land use, pesticide use and management practices, I hypothesized that all sites would see a decrease in visitation from wild pollinators (principally bumble bees) and an increase in fruit set and weight deficits compared to the values reported by Button & Elle (2014), possibly as a result of decreased crop diversity in the Lower Mainland and potentially increased insecticide use. Quantifiable data was not obtainable for changes in pesticide use or management practices, so the changes in the factors discussed above are only explored as potential explanations behind expected changes in pollination limitation and not directly tested in this study. I further expected that sites utilizing clover and grass between blueberry rows would be less affected by the changes in conditions mentioned above than sites using mulch or bare ground alone. The effects of pollination on blueberry development time may also be of interest to growers as fruit harvested earlier in the season can be sold at higher prices before market demand is saturated and is less likely to be damaged by spotted wing drosophila (Hampton et al., 2014; Klatt et al., 2014; Sobekova et al., 2013). I compared the harvest dates of fruit produced under supplemental-pollination and fruit produced from ambient-pollination, hypothesizing that supplementally-pollinated flowers would develop fruit faster than ambiently-pollinated flowers. This hypothesis was driven by the fact that ovules release hormones that trigger fruit development and supplementally-pollinated flowers should have more successfully fertilized ovules than open-pollinated flowers (Primack, 1987). 2.2 Methods 2.2.1 Study sites The Lower Mainland of British Columbia is located on the unceded territory of the Musqueam First Nation, Stó:lō First Nation, Tsawwassen First Nation and other Indigenous peoples of the 12 Coast Salish language group. This area encompasses the Fraser Valley, Greater Vancouver, and the southern portion of the Sunshine Coast (Statistics Canada, 2022b). The Lower Mainland acts as a major contributor to the province’s economy and food security as over 200 commodities are produced here including cranberries, raspberries, blueberries, dairy products, poultry and eggs (Government of BC, n.d.). The region’s cooler, wet climate and fertile soils make it especially suitable for high-value crop production like highbush blueberry (Government of BC, n.d.). All of the study sites used were located within the Lower Mainland of BC with the majority of our sites located in Delta, BC, one of the most productive agricultural regions of the province (Corporation of Delta, 2011), and two sites located in Langley and Richmond. While Delta largely consisted of vegetable and forage farms in 2011, more land has been converted for greenhouse-grown produce, cranberries and blueberries since then (Corporation of Delta, 2011; Delta Farmland & Wildlife Trust, 2022). This study was conducted over two consecutive field seasons from April 18th to September 20th in 2021 and from May 2nd to August 25th in 2022 on highbush blueberry (Vaccinium corymbosum) farms within the Lower Mainland of BC. Sites were originally selected based on a grower’s willingness to have a flower strip established during the second year of the study and the size of the fields. Sites were originally divided into pure control sites with a single study edge and no floral strip, sites with both a floral strip and a control edge (to account for spillover effects), and a single site with a floral strip and no control. Intensive competition from invasive plant species and flooding on field sites prevented floral strips from establishing, barring us from testing our original hypotheses. The 8 sites studied consisted of several different blueberry varieties, including 3 fields of ‘Duke’, 2 fields of ‘Bluecrop’, 2 fields of ‘Draper’ and 1 ‘Liberty’ field (Figure 2.2). The two ‘Bluecrop’ growers and one ‘Duke’ grower used conventional methods to manage space between blueberry rows (i.e., used mulch and herbicides to suppress weed growth) whereas the single ‘Liberty’ site, two of the ‘Duke’ sites and one of the ‘Draper’ sites used planted grass and clover between blueberry rows. The two ‘Bluecrop’ variety farms were not included in 2022. There was also a single ‘Draper’ grower that tilled the space between blueberry rows to keep the ground bare. 13 Figure 2.2: Map of 8 study sites across the Lower Mainland of British Columbia, Canada in 2021 and 2022. Squares represent farms with one study edge and circles represent farms with two study edges. The two ‘Bluecrop’ sites were only included in 2021. 2.2.2 Pollination limitation experiment The number of study edges per site were originally selected based on the availability of space for a floral strip to be established (see Ch 2.2.1). Sites were designed to have either a floral strip and a control edge, just a control edge or just a floral strip. On each study edge, three 100 m long transects were marked at three distances from the edge (0, 50 and 100 m) using wooden stakes and flagging tape. If a site had space for both a flower strip and a control edge, at least 100 m of space was kept between them. On each transect, eight bushes were selected for the pollination deficit experiment. The first bush was chosen as the first healthy bush 10 m from the start of the 14 transect while the remaining bushes were spaced roughly 10 m apart and only chosen if they appeared healthy. On each bush, two canes with similar exposure to sunlight, number of flowers, and similar stages of floral development were selected for the experiment. Experiments were restricted to 50 – 60 terminal flowers per cane, henceforth referred to as a “cluster”. Each cluster was marked with masking tape and twist ties and randomly designated as either a supplementally-pollinated treatment (received supplemental pollination on top of ambient pollination) or an ambient-pollination treatment (left open to receive ambient pollination). Pollen was collected from adjacent bushes in the transects, of the same variety, using a Be the Bee Pollinator TM (aerogarden.com/be-the-bee-pollinator). After pollen was collected in a plastic bead jar, it was liberally applied to the stigma using a camelhair paint brush. Supplemental-pollinations occurred every 3-5 days per site in the absence of rain and with newly collected pollen each time, to assure pollination of each flower in the cluster (Blitzer et al., 2016; Button & Elle, 2014). In 2021, supplemental-pollination took place between April 21st and May 31st for a maximum of 11 supplemental-pollinations per site (range, 6 – 11). In 2022, an unusually wet and cool spring restricted supplemental-pollination to occur between May 6th and June 11th for a maximum of 10 supplemental-pollinations per site (range, 8 – 10). The fruit set data from 2021 was not utilized in this study because some unmarketable fruits (i.e., including some tiny fruit lacking seeds) were mistakenly counted towards fruit set, which would have severely underestimated pollination limitation. Average berry weight was found to not be as severely affected when compared to the fruit weight data from 2022, in which marketability was considered. As a result, average fruit weight data from 2021 was used with caution. 2.2.3 Fruit harvest To protect fruit from agricultural pests such as birds and Drosophila suzukii, paint strainer bags were placed over the supplementally-pollinated and ambient-pollinated treatment clusters and secured with a twist-tie. Bushes that showed signs of viral infection following cluster selection were not harvested. Supplementally-pollinated clusters would be bagged once each of the flowers had been supplementally-pollinated at least once whereas ambient-pollinated clusters 15 were bagged once the last supplementally -pollinated treatment on that edge was bagged, giving them ample time to be visited by pollinating insects. If ambient-pollinated clusters dropped all of their flowers before this point and began to form fruit, they were bagged at that point. All fruit was harvested, ripe or otherwise, once at least 50% of the berries in the cluster were fully ripe. This harvest method provides similar fruit weight data compared to harvesting each berry as it ripens and prevents the loss of overripe berries while being far less time consuming (Tuell & Isaacs, 2010). Harvested fruit was then placed in plastic freezer bags labelled with information relevant to harvest (i.e., site, edge, transect, bush, treatment and date) and stored in a cooler until they could be processed. All berries were counted the same day as harvest, but only ripe berries were used in fruit set and fruit weight determination. Ripe berries were weighed before being stored in a -80 °C freezer for later processing. Unripe berries were excluded in the fruit set and weight measurements as to avoid skewing results with berries that were unlikely to reach marketable size and to ensure our results were comparable with Button & Elle’s work (2014). In 2022, only marketable berries, defined as ripe berries weighting 0.9 g or heavier (based on the guidance of Dr. Michael Dossett and Dr. Eric Gerbrandt, from the BC Blueberry Council), were used in fruit set and fruit weight determination. 2.2.4 Seed count One to two berries were chosen per cluster for seed counts. To keep the selection of berries random, harvested fruit would be spilled onto a numbered sheet and the first two berries would be chosen for the seed count. Seed counts gave us a direct measure of pollination and allowed us to test if fruit weight is a good proxy for the number of seeds. Frozen berries were thawed, peeled and squished between the bottom and the inverted lid of a glass petri dish. The petri dish was marked with lines using permanent markers to facilitate seed counting ease and accuracy. The dish was then placed under a dissecting microscope and true seeds were counted. True seeds were identified based on the guidelines of Desjardins and Oliveira (2006), meaning that only seeds with 1mm or greater in length with filled shapes and wrinkled surfaces were counted. 16 2.2.5 Estimated yield and revenue under each pollination condition Total fruit production per hectare was estimated using bushes from one edge per site, using the equation: Fruitsha = Rows100 m × Bushes100 m row × CanesBush × ClustersCane × FruitCluster The number of bushes per 100 m row was an average taken across our 0, 50 and 100 m transects. Canes per bush was averaged across the eight experimental bushes per transect. Clusters per cane was recorded from one haphazardly chosen cane per experimental bush, and fruit per cluster was averaged from two haphazardly chosen clusters per experimental bush. Yield was estimated as fruit production in kg/ha under ambient conditions using the equation: Yo = �Fruitha × FWo� Where FWo refers to the fruit weight (g) of the flower clusters left open for insect pollination (i.e., the ambient-pollination treatment), taken as the average across all ambient-pollination treatments within that field. Yield under supplemental-pollination conditions was estimated using the equation: Yhp = �Fruitha + �Fruitha × �FShp − FSO��� × FWhp Where FShp refers to the fruit set of the flowers pollinated by hand (i.e., the supplementally-pollinated treatments) averaged across all of the transects in a field, FSo refers to the fruit set of the flower clusters left open for insect pollination (i.e., the ambient-pollination treatment) averaged across all of the transects in a field and FWhp refers to the fruit weight of the supplementally-pollinated treatment averaged across all of the transects in a field. The formula therefore accounts for differences in both fruit set and fruit weight between supplement and ambient pollination treatments. In 2021, the mean farm gate value for blueberries produced in BC was $2.29/kg (Statistics Canada, 2022a). These values were converted and multiplied by the yield estimates under each treatment to estimate the potential fruit value under each condition. Fruit set data from 2021 was omitted, due to the presence of unmarketable fruit skewing our fruit set data, so yield and revenue estimates were calculated only using data collected in 2022. 17 2.2.6 Blueberry flower visitor surveys Blueberry flower pollinators were surveyed on the same transects used for the pollination limitation experiment from April 23rd to May 31st in 2021 and from May 10th to May 31st in 2022. The observational surveys took place while an observer slowly walked along each of these transects for 15 minutes each. Visiting (defined as insects making direct contact with the stigma through the corolla opening) honey bees (Apis mellifera) and bumble bees (Bombus sp.) were identified to species on the wing, but other insects were captured, euthanized and stored in coolers until they could be processed at the lab and identified to species at a later date. Any time an insect needed to be captured, the timer for the survey would be paused until the insect was collected and stored in a falcon tube with an ethyl acetate-soaked cellulose sheet. The objective of these surveys was to measure the rates at which honey bees and bumble bees visit highbush blueberry flowers, to identify the top contributing wild pollinators and determine if flower visitation decreased as distance from the field’s edge increased. At each edge, 15-minute pollinator diversity surveys were also performed in which observers would walk up and down blueberry rows anywhere between the 0 and 50 m transects, counting and identifying pollinators seen or capturing them if unable to identify to species on the wing. Insects were euthanized and temporarily stored using the same methods as in the surveys described before. These surveys were meant to supplement the species lists from the other surveys and allow us to record blueberry-visiting species that may have been visiting outside of our 0, 50 and 100 m transects. Data from these surveys was only used in one model evaluating the effects of management practices and blueberry variety on species richness (see Chapter 2.3.7). Surveys were divided into those conducted under optimal weather condition, defined as full to partial sun, temperatures greater than 14°C, and wind speed no greater than 2.5 m/s, or those conducted under marginal weather conditions (Button & Elle, 2014). Marginal weather surveys were defined as surveys that occurred when no more than two of the three weather conditions were met. Flower visitor surveys at the 0, 50 and 100 m transects always occurred in rapid succession; thus weather data was recorded at the start of the 0 m survey and again at the end of the 0 m survey. Weather data for the additional flower visitor diversity surveys took place 18 at the start and end of the survey. All surveys took place between the hours of 09:00 and 17:30. In both 2021 and 2022, each field was surveyed 3-4 times total. 2.2.7 Pollinator processing & identification The processing and identification of all insects captured during the blueberry flower visitor surveys was carried out by Tyler Kelly (Kremen lab manager). Flower-visiting insects captured during surveys were pinned, labeled and barcoded including information relevant to the sampling (i.e., site name, edge, transect, and date). Specimens were then sorted into easily visible taxonomic groupings before using a dissecting microscope to identify specimens to species. Collected specimen were identified down to species when possible using keys relevant to our region (Bouseman & Laberge, 1978; Gibbs, 2010; Hardy, 1945; Kimsey & Carpenter, 2012; McGinely, 1986; Triplehorn & Johnson, 2005; Vockeroth, 1992) . 2.2.8 Data analysis and statistical models For models that include fruit set as a dependent variable, only data from 2022 was used. The fruit set data from 2021 was not used because some unmarketable fruits (i.e., including some tiny fruit lacking seeds) were mistakenly counted towards fruit set, which would have severely underestimated pollination limitation. Marketable fruit was defined in 2022, based on industry standards, as fruit weighing greater than 0.9g and having firm, unwrinkled skin. In contrast to fruit set data which had known issues in 2021, fruit weight deficits were compared between 2021 and 2022 and not found to be significantly different (Table A.1). We therefore decided to include the 2021 data in models using fruit weight as a dependent variable, even though inclusion of unmarketable fruits may have reduced fruit weight slightly. As a result, models including fruit weight as a dependent variable used data from both 2021 and 2022. To determine if supplementally-pollinated flowers had a greater fruit set than ambient-pollinated flowers and if the fruit set decreased as distance from the field’s edge increased, a linear mixed effect model with proportion fruit set (number of berries/ number of flowers) as the dependent variable was fit as a function of pollination treatment, transect location (i.e., 0, 50 or 19 100m away from the field’s edge) and variety. While the variable fruit set was bound between 0 and 1, data was found to be normally distributed and did not violate normality or homoscedasticity assumptions. Another model was fit with log(fruitset + 1) as a function of pollination treatment, transect location and blueberry variety, but this model was discarded since there was no difference in the variables found to significantly affect fruit set (Table A.2) and the model with the untransformed variable was easier to interpret. Variety was also included as a covariate and site was included as a random effect to account for variation caused by differences in location and management practices. To determine if fruit weight differed between pollination treatments or set distances from the field’s edge, a linear mixed effects model with the average fruit weight per cluster as the dependent variable was fit as a function of pollination treatment, blueberry variety and transect location. Site and year were also included as random effects. Interaction effects were only included when we had specific hypotheses about an interaction (e.g. the hypothesis that the difference in fruit set and/or fruit weight between pollination treatments would become greater as distance from the field’s edge increased); in all cases, these interactions had small coefficients that were not significant and were therefore removed. To determine if the fruit produced by supplementally-pollinated flowers was ready for harvest sooner than the fruit of the ambient-pollinated flowers, a linear mixed effects model with the date of harvest (formatted as a Julian date) was fit as a function of the pollination treatment and variety. A covariate of the proportion of ripe fruit (number of ripe fruit/ number of total fruit collected) was included since there was variation in the number of ripe fruits over the threshold of 50%. Interaction between variety and treatment was tested but found not to have a significant effect on harvest date; therefore it was removed from the final model. A random effect of site was included but a random effect of bush ID could not be included as the variance explained was practically zero. To test for a positive linear relationship between berry weight (g) and the number of seeds in highbush blueberry, a linear model with log(berry weight) was fit as a function of number of seeds. The model was run with log transformed berry weight data to eliminate non-normality of the residuals. To determine if bee visitation rates decreased as distance from the field’s edge increased, the visitation data was modelled separately for bumble bee (Bombus sp.) visits and honey bee 20 visits (Apis mellifera) as honey bees exhibit different foraging behavior and range compared to bumble bees (Beekman & Ratnieks, 2000; Bobiwash et al., 2017; Dramstad, 1996; Toshack & Elle, 2019). The total number of honey bees and bumble bees visits were summed per survey. For the model used to explain honey bee visitation, a generalized linear mixed effects model with a negative binomial distribution and the total honey bee visits per survey as the dependent variable was fit as a function of distance from the field’s edge (i.e., the transect location) and the average temperature (°C) and wind speed (m/s) during each survey. Average temperature and wind speed were included as independent variables to account for the fact that surveys took place during both marginal and optimal conditions. Interaction effects between independent variables could not be included without causing convergence issues and were ultimately dropped from the model. Variety could not be included as an independent variable for the same reason. The site, date of the survey and year were also included as random effects. Survey date was included as a random effect to account for the fact that multiple sites were sometimes sampled on the same day. The model used to explain bumble bee visitation was essentially the same as for honey bees, except that year was not included as a random effect due to variation being essentially zero. To determine if the diversity of pollinators changed with distance from the field’s edge, the total number of species observed visiting blueberry was first determined for each site and transect. Honey bees were excluded from the species richness calculations as the species is introduced to the region and intentionally managed for crop pollination (Van Westendorp & McCutcheon, 2001). A linear mixed effects model with the log(species richness + 1) as the dependent variable was fit as a function of transect location (i.e., distance from the field’s edge), blueberry variety and an interaction between transect location and variety. The response variable, species richness, was transformed to meet homoscedasticity and normality assumptions. Blueberry variety was included as a potential covariate and site was included as a random effect. Fruit set deficit was calculated for each bush as the proportion fruit set of the supplemental-pollination treatment (acting as an estimate for maximal pollination) minus the proportion fruit set of the ambient-pollination treatment, just as Button & Elle (2014) calculated these deficits. If the deficit was negative, it was attributed to human error and counted as no difference (zero), which is standard practice (Larson & Barrett, 2000). I compared the strength of different pollinator groups as predictors of fruit set deficits by fitting three separate linear mixed 21 effect models with log(fruit set deficit + 1) as the dependent variable as a function of either total pollinator visits (honey bee + bumble bee visits), bumble bee visits or honey bee visits and calculating AIC values for each model. Blueberry variety was also included as a covariate in each model. Total pollinator visits were summed across all of the surveys done on a given transect. For each model, we first fit a random effect of transect nested within site but if the variance explained was practically zero, a random effect of site alone would be included instead. Only the model with total honey bee visits as an independent variable was able to be fitted with a random effect of transect nested within site. Models were compared using AIC to determine which models had the most support. The log likelihood and Akaike Information Criterion (AIC) were calculated for each model using the logLik and AIC function from the MuMin package whereas the difference in AIC values and the Akaike weights were calculated by hand (Bartón, 2022). The best model was then selected as the one with the lowest AIC score (Burnham & Anderson, 2004). Models within 4 AIC values were still acknowledged to have some support, albeit considerably less than the model with the lowest AIC score (Burnham & Anderson, 2004). To determine which pollinator groups best predicted fruit weight deficits, the same steps were carried out as with the fruit set deficit models except using log(fruit weight deficit + 1) as the dependent variable and data from both 2021 and 2022. The fruit weight deficit models were first fit with year as a random effect but the variable was removed since the explained variance was essentially zero. All of the fruit weight deficit models were fit with a random effect of transect nested within site except for the model with total bumble bee visits as an independent variable. To determine if growers utilizing clover and grass as a cover crop between rows experienced more wild bumble bee visits than those using mulch or bare ground between rows, a generalized linear model with a negative binomial distribution with the total bumble bee visits per transect as the dependent variable was fit as a function of management type. The single ‘Draper’ site that kept the ground between rows bare was grouped with sites using mulch due to the effects on weed abundance being essentially the same. The mean temperature (°C) and windspeed (m/s) during each survey were also included as covariates because surveys were conducted during both optimal and marginal conditions. Interaction effects could not be included without causing convergence issues and were ultimately dropped from the model. Random 22 effects of year, site and the date of the survey were also included. To determine if growers utilizing clover and grass as a cover crop also experienced more honey bee visits to crops relative to mulch users, a similar model to the bumble bee visitor model described above was used, except the total number of honey bees was fit as the dependent variable and a random effect of year was not included due to the variance explained being practically zero. Interaction effects between the independent variables could not be included without causing convergence issues and were ultimately dropped from the model. To determine if growers utilizing clover and grass as a cover crop between rows had a greater diversity of pollinators visiting their crop, a linear mixed effects model was initially attempted to be fit with log(species richness + 1) as a function of management and variety and an interaction between management and variety but this model was discarded because of unequal variance and rank deficiency issues (i.e., the model could not estimate this many parameters with the existing number of data points). Instead, a generalized linear mixed effects model with a Poisson distribution and species richness as the dependent variable was fit as a function of management practices and blueberry variety. An interaction effect was attempted to be fitted but caused rank deficiency issues and was ultimately dropped. Overdispersion was checked for using the check_overdispersion function from the performance package (Lüdecke et al., 2021) but was not found to occur within this data. The total number of pollinator species were summed per survey type (i.e., surveys done at the 0, 50 or 100 m transect or the continuous surveys done between 0 and 50 m transects) and date. The model was first fit with a random effect of transect type nested in site but was removed due to the variance explained being near zero. Instead, a random effect of site was included. All statistical analyses were carried out in R (version 4.2.0) (R Core Team, 2022). The lmer function from the lme4 package was used to build the linear mixed effects models described above (Bates et al., 2014). The glmer.nb function from the lme4 package was used to build generalized linear mixed effects models with negative binomial distributions (Bates et al., 2014). All of the figures listed in the results section were created using raw data. 23 2.3 Results 2.3.1 Pollination limitation In 2022, supplementally-pollinated flowers consistently produced a greater fruit set than those experiencing ambient pollination except on the single ‘Liberty’ variety farm (Figure 2.3). The size of the fruit set deficits varied by blueberry variety. The fruit set of the supplementally-pollinated treatment was 51.5 ± 1.3% (mean ± standard error) whereas the fruit set of the ambient-pollination treatment was 39.0 ± 1.4%. On average, the fruit set of the supplementally-pollinated treatment was 12.5% greater than that of the ambient-pollination treatment. In both 2021 and 2022, supplementally-pollinated flowers produced heavier fruit (Table 2.1) than ambient-pollinated flowers. In 2021, supplementally-pollinated flowers yielded fruit that weighed 1.61 ± 0.038g. Ambient-pollinated flowers yielded fruit weighing 1.26 ± 0.040g. The fruit produced by the supplementally-pollinated flowers was 0.35g heavier, on average, than the fruit of ambient-pollinated flowers. In 2022, supplementally-pollinated flowers produced berries that weighed 1.65 ± 0.024g whereas the ambient-pollination treatment produced berries that weighed 1.51 ± 0.030g. The fruit produced by the supplementally-pollinated flowers produced berries 0.14g heavier, on average, than ambient-pollinated flowers. The decreased difference in fruit weight between the treatments in 2022 relative to the difference in weight between treatments in 2021 may have resulted from the two ‘Bluecrop’ sites not being included in 2022. Table 2.1: Results of linear mixed effect models investigating the effects of each pollination treatment, variety, and distance from the field’s edge on proportion fruit set (# fruit/# flowers) and fruit weight (g). Significant effects at P < 0.05 are in bold. Only data from 2022 was used for the model explaining fruit set whereas data from 2021 and 2022 was used for the fruit weight. A random effect of site was included in both models and a random effect of year was only included in the model explaining fruit weight. Fruit set Fruit weight (g) DF F value P value DF F value P value Treatment 1 47.13 3.99e-12 1 63.45 1.64e-15 Distance 1 3.10 0.078 1 1.34 0.25 Variety 2 1.45 0.48 3 8.22 0.042 24 Varietal differences - In 2021, ‘Bluecrop’ had the largest fruit weight deficit, on average, as supplementally-pollinated flowers produced fruit weighing 78% more than ambiently-pollinated flowers. Supplementally-pollinated ‘Bluecrop’ flowers produced fruit weighing 1.52 ± 0.056g whereas ambient-pollinated ‘Bluecrop’ flowers produced fruit weighing 0.85 ± 0.059g (Figure 2.3). ‘Duke’ had the second greatest fruit weight deficit as supplementally-pollinated flowers produced fruit weighing 24% more, on average, than ambient-pollinated flowers. Supplementally-pollinated ‘Duke’ flowers produced fruit weighing 1.43 ± 0.057g whereas ambient-pollinated ‘Duke’ flowers produced fruit weighing 1.15 ± 0.073g. Supplementally-pollinated ‘Draper’ flowers yielded fruit weighing 20% more than ambient-pollinated flowers. Supplementally-pollinated ‘Draper’ flowers produced fruit weighing 2.01 ± 0.064g whereas ambient-pollinated ‘Draper’ flowers produced fruit weighing 1.67 ± 0.063g. Our single ‘Liberty’ farm experienced practically no fruit weight deficit as supplementally-pollinated ‘Liberty’ flowers weighed 1.07 ± 0.10g but ambient-pollinated flowers produced fruit weighing 1.11 ± 0.073g. In 2022, ‘Draper’ sites had the greatest fruit weight deficit, on average, as supplementally-pollinated flowers yielded fruit weighing 47% more than ambiently-pollinated flowers. Supplementally-pollinated ‘Draper’ flowers produced fruit weighing 1.60 ± 0.039g whereas ambiently-pollinated ‘Draper’ flowers produced fruit weighing 1.36 ± 0.054g. ‘Duke’ had the second greatest fruit weight deficit as supplementally-pollinated flowers yielded fruit weighing 29% more, on average, than fruit produced by ambient pollination. Supplementally-pollinated ‘Duke’ flowers produced fruit weighing 1.67 ± 0.034g and ambient-pollinated ‘Duke’ flowers produced fruit weighing 1.58 ± 0.039g. ‘Liberty’ experienced the smallest fruit weight deficit in 2022 as the difference in average fruit weight between the supplementally- and ambient-pollinated flowers was less than 1%. Supplementally-pollinated ‘Liberty’ flowers yielded fruit weighing 1.73 ± 0.047g and ambient-pollinated flowers yielded fruit weighing 1.68 ± 0.056g. In 2022, ‘Draper’ experienced the largest fruit set deficit as supplementally-pollinated flowers had an average fruit set 16.2% greater than ambient-pollinated ‘Draper’ flowers. Supplementally-pollinated ‘Draper’ flowers yielded a fruit set of 50.5 ± 2.0% and ambiently-pollinated flowers yielded a fruit set of 34.3 ±2.4%. ‘Duke’ had the second greatest average fruit 25 set deficit of 12.1%. Supplementally-pollinated ‘Duke’ flowers yielded a fruit set of 54.0 ± 1.8% and ambiently-pollinated ‘Duke’ flowers yielded a fruit set of 41.9 ± 1.8%.‘Liberty’ experienced the smallest average fruit set deficit of 0.2%. Supplementally-pollinated ‘Liberty’ flowers yielded a fruit set of 42.4 ± 3.4% and ambiently-pollinated flowers yielded a fruit set of 42.2 ± 2.96%. Neither fruit set nor fruit weight were found to be affected by distance from the field’s edge, but fruit weight was significantly affected by variety (Table 2.1). The random effect of year explained a much smaller amount of variance (0.0021) relative to the random effect of site (0.024), further suggesting that fruit weight was not strongly affected by the failure to take marketability into account in 2021. 26 Figure 2.3: Median fruit set and fruit weight by pollination treatment (Supplement – flowers supplemented by hand-pollination and Ambient – flowers left accessible to pollinators), variety and year. Boxplots represent the first quartile, median and third quartile, whiskers denote standard errors and black points represent outliers. Fruit set data from 2021 was excluded as marketability failed to be considered, which would have severely underestimated fruit set deficits. The effects of year on fruit weight deficit was not significant so fruit weight data from 2021 was included. 2.3.2 Harvest date When averaging across all varieties, supplemental-pollination treatments were ready for harvest on July 31st ± 0.50 days (mean ± standard error) whereas ambient-pollination treatments were ready for harvest on August 3rd ± 0.62 days. On average, the supplemental-pollination treatment was ready for harvest 3 days sooner than the ambient-pollination treatment. The difference in harvest date may not substantial but the standard deviation of the difference in harvest dates varied greatly by variety (Figure 2.4). The largest standard deviation of the difference in harvest date was 6.9 days (for ‘Draper’ variety fields) and the smallest was 4.4 days (for ‘Duke’ variety fields). Varietal differences - In 2022, ‘Duke’ variety fruit was harvested between July 24th and August 8th. ‘Duke’ variety fruit from the supplemental-pollination treatment was ready for harvest by July 26th ± 0.28 days whereas the ambient-pollination treatment yielded fruit by July 27th ± 0.44 days. On average, there supplemental-pollination treatment was ready for harvest a day sooner than the ambient-pollination treatment for ‘Duke’ variety fields. ‘Draper’ variety fruit was harvested between July 29th and August 18th. ‘Draper’ variety fruit from the supplemental-pollination treatment was ready for harvest by August 2nd ± 0.50 days whereas the ambient-pollination treatment yielded fruit by August 5th ± 0.69 days. ‘Draper’ fruit from the supplementally-pollinated treatment was ready for harvest 3.4 days sooner than fruit produced by the ambient-pollination treatment on average. The slowest developing variety, ‘Liberty’ was harvested from August 13th to 25th. For the ‘Liberty’ variety, the supplemental-pollination treatment was ready for harvest by August 17th ± 0.81 days and the ambient-pollination treatment was ready for harvest by August 18th ± 0.96 days, meaning the supplemental-pollination treatment was ready for harvest a day sooner, on average, than the ambient-pollination treatment. 27 Figure 2.4: The difference in harvest date between the supplement and ambient pollination treatments (ambient – supplement) per bush in 2022, organized by variety. Statistical analyses are in Table 2.2. Greater delay in harvest of the ambient-pollination treatment is on the right side of 0 and lesser delay of ambient-pollination treatment is on the left side of 0. In 2022, the date of harvest was found to be significantly affected by both pollination treatment and blueberry variety (Table 2.2). The model estimated that fruit produced from supplementally-pollinated flowers would be ready for harvest roughly two days sooner than fruit produced by ambient-pollinated flowers. 28 Table 2.2: Results of linear mixed effect model investigating the effects of each pollination treatment and variety on the harvest date of the fruit. The proportion of ripe berries was also included as a covariate since clusters were harvested once 50% or more of the fruit was ripe. A random effect of site was also included to account for differences in location and management. Only data from 2022 was used for this model. Significant effects at P < 0.05 are in bold. Harvest date DF F value P value Treatment 1 30.71 3.00e-8 Variety 2 128.34 2.20e-16 Proportion ripe 1 2.51 0.11 2.3.3 Estimated yield and revenue under each pollination condition Yields under ambient pollination were estimated (using the equations mentioned in Ch. 2.2.5) to reach approximately 15,900 kg/ha for ‘Draper’ growers, 29,800 kg/ha for ‘Duke’ growers and 18,600 kg/ha for ‘Liberty’ growers. Yields under supplemental pollination were estimated to reach up to 26,400 kg/ha for ‘Draper’ fields, 31,900 kg/ha for ‘Duke’ fields and 19,200 kg/ha for ‘Liberty’. In 2022, the estimated revenue increase for supplemental-pollination compared to ambient-pollination was the smallest for ‘Liberty’ growers at $1,360/ha. It was greatest for ‘Draper’ at $24,000/ha, followed by $4,800/ha for ‘Duke’. The smallest estimated potential revenue increase was $150/ha for an individual ‘Draper’ farm and the largest estimated potential revenue increase was $47,900/ha, also for an individual ‘Draper’ farm. 2.3.4 Top contributing pollinators to highbush blueberry In 2021, 144 pollinator surveys were completed for a total of 2,160 minutes and 1,446 flower visits were observed. In 2022, 98 pollinator surveys were completed for a total of 1,470 minutes and 2,456 flower visits were observed. In both years, honey bees were the most frequent visitor, making up 83.2% of all visits (3,248 out of 3,902). Bumble bees made up 14.6% of all visits (568 out of 3,902) whereas other pollinators such as sweat bees (Halictidae), flies (Diptera), 29 wasps (Vespidae) and hummingbirds (Trochilidae) made up only 2.2% (86 out of 3,902) of all visits. Because animals besides honey bees and bumble bees made up so few of the total visits, they were only included when analyzing species richness. While the average rate of visitation for honey bees was generally higher than the average visitation rate of bumble bees, the degree to which honey bees dominated visitation varied depending on variety (Figure 2.5). Honey bees made up 90% of all observed visits on ‘Duke’ blueberry farms but only made up 52% of all visits on the single ‘Liberty’ farm. Honey bee visits made up the second greatest portion of visits on the ‘Draper’ farms where they accounted for 86% of all visits. On the ‘Bluecrop’ farms, honey bee visits made up 72% of all visits. Figure 2.5: Average rate of visitation by managed honey bees (Apis mellifera) and bumble bees (Bombus sp.) per 15 minutes. Whiskers denote standard errors. Across all varieties, Bombus impatiens was the most frequently observed wild pollinator visiting the highbush blueberry flowers. In 2021, B. impatiens made up nearly 61% of all bumble bee visits on ‘Bluecrop’ flowers with the remaining 39% consisting of unknown Bombus species and one B. flavifrons and one B. rufocinctus observation each. For the ‘Draper’ variety and 30 ‘Liberty’ varieties, B. mixtus and B. melanopygous were observed the most frequently after B. impatiens. For the ‘Duke’ variety, B. mixtus and B. flavifrons were observed the second-most and third-most frequently. B. vosnesenskii and B. californicus were also observed visiting but much less frequently. The most supported models for log(fruit set deficit + 1) and log(fruit weight deficit + 1) indicated that bumble bee visitation were strong predictors of fruit set and fruit weight deficits (Table 2.3). In both cases, the best model included bumble bee (Bombus) visits. Models fit with either total visits or honey bee visits and variety as predictors of log(fruit set deficit + 1) had ∆AIC values of 9 or more, indicating that they essentially had no support (Burnham & Anderson, 2004). Models fit with either total visits or honey bee visits as predictors of the log(fruit weight deficit + 1) had ∆AIC values of at least 4, indicating that they had considerably less support (Burnham & Anderson, 2004). Table 2.3: Model selection for log(fruit set + 1) and log(fruit weight + 1) deficits (supplemental - ambient) as predicted by pollinator visits and blueberry variety. Visits were categorized as total visits (bumble bee + honey bee visits), bumble bee visits alone, and honey bee visits alone. The models are organized in order from most to least supported with the model with the smallest AIC value at the top. Response Predictor variables DF logLik AIC ΔAIC Weight Log(fruit set deficit + 1) Bumble bees + variety 6 95.19 -178.38 0 0.98 Total visits + variety 7 90.52 -167.03 9.35 0.0092 Honey bees + variety 7 90.36 -166.72 9.66 0.0079 Null 2 81.55 -159.10 19.28 6.40e-5 Log(fruit weight deficit + 1) Bumble bees + variety 7 -29.41 72.82 0 0.84 Honey bees + variety 8 -30.77 77.53 4.71 0.080 Total visits + variety 8 -30.85 77.70 4.88 0.074 Null 2 -43.22 90.44 17.62 1.26e-4 2.3.5 The effects of distance on pollinator abundance and diversity Neither the total number of bumble bee visits per survey nor the total number of honey bee visits per survey were found to be affected by distance from the field’s most natural edge (Table 2.4). 31 Models with bee visits summed per transect and divided by the total number of surveys done on each transect (i.e., average bee visitation per 15 minutes) were also fitted to investigate the effects of distance from the field’s edge and variety but no significant effects were found (Table A.3). Honey bee visits were found to be affected by variety. The model predicted that ‘Duke’ has the highest number of honey bee visits per 15 minutes. A 95% confidence interval revealed that ‘Duke’ variety flowers experience between 1.79 and 3.47 honey bee visits per 15 minutes. Table 2.4: Results of generalized linear mixed effects model with negative binomial distribution investigating the effects of distance and variety on the total bees seen per survey. Average wind speed (m/s) and temperature (°C) during surveys was also included as covariates. Site and the date of the survey were as random effects in both models but year was only included as a random effect in the bumble bee visitation model. Significant effects at P < 0.05 are in bold. Response Predictor variables DF F value P value Bumble bee visits Distance 1 0.53 0.46 Variety 3 0.76 0.86 Average temperature 1 0.26 0.61 Average wind speed 1 1.09 0.30 Honey bee visits Distance 1 0.082 0.77 Variety 3 15.79 0.0013 Average temperature 1 1.82 0.18 Average wind speed 1 3.56 0.059 The species richness of pollinators visiting blueberry flowers was not found to be significantly affected by distance from the field’s most natural edge or the blueberry variety (Table 2.5). Table 2.5: Results of a linear mixed effects models investigating the effects of transect location (i.e., distance from the field’s edge) and blueberry variety on log(species richness + 1) of pollinators observed visiting highbush blueberry flowers. The random effects of study site was included. Response Predictor variables DF F value P value Log(Species richness + 1) Distance 1 1.19 0.28 Variety 3 3.10 0.38 Distance x variety 3 0.72 0.87 32 2.3.6 The effects of management of space between blueberry rows on crop visitation and pollinator diversity Management of the space between blueberry rows was found to significantly affect the total number of bumble bees observed visiting per 15 minutes (Table 2.6). The presence of the white clover (Trifolium repens) and grass cover crop was found to increase both the visitation of Bombus sp. (Figure 2.6) and the visitation of honey bees. Sites with clover/grass cover crops experienced 4.1 ± 0.5 (mean ± standard error) bumble bee visits per survey whereas sites using mulch or bare ground experienced 0.69 ± 0.1 bumble bee visits per survey. Sites with clover/grass cover crops also experienced 23.0 ± 2.3 honey bee visits per survey whereas sites with mulch or bare ground experienced 4.8 ± 0.5 honey bee visits per survey. Figure 2.6: Total bumble bee visits to highbush blueberry flowers summed across all surveys and organized by year and management type. Management type refers to how growers utilized space between blueberry rows (clover - clover and grass cover crop and mulch - bare ground or mulch). 33 The model predicted that 3.7 times more bumble bee visits to blueberry flowers would be observed on farms with the clover/grass cover crop between blueberry rows relative to farms using mulch or bare ground between rows. The model also predicted that 4.9 times more honey bee visits to blueberry flowers would be observed on farms using clover relative to farms using mulch or bare ground. Neither the mean temperature nor the mean wind speed during the surveys was found to significantly influence the number of wild bumble bees seen visiting, indicating that surveys were performed under relatively consistent conditions. Table 2.6: Results of two generalized linear mixed effects model with a negative binomial distribution investigating the effects of the management of space between blueberry rows on the total bumble bees or total honey bees seen visiting during each 15 minute survey. The mean temperature (°C) and wind speed (m/s) during the survey were included as covariates and study site and the date of the survey were included as random effects. Only the model investigating total bumble bee visits included year as a random effect. Significant effects at P < 0.05 are in bold. Response Predictor variables DF F value P value Observed bumble bee visits Management 1 7.51 0.0061 Mean temperature 1 0.082 0.78 Mean wind speed 1 1.53 0.22 Observed honey bee visits Management 1 9.88 0.0017 Mean temperature 1 1.15 0.28 Mean wind speed 1 3.02 0.082 The management of spaces between blueberry rows was also found to have a significant effect on species diversity but variety was not found to have a significant effect (Table 2.7). We observed 3.2 ± 0.2 species pollinating flowers per survey on farms using clover and 2.0 ± 0.1 species on farms using mulch or bare ground. When examining bumble bee species richness alone, a greater species richness per survey (2.6 ± 0.1 Bombus species) was still observed at farms with clover relative to farms with mulch (1.7 ± 0.1 Bombus species) but the difference was not found to be statistically significant (df = 1, p = 0.28). 34 Table 2.7: Results of generalized linear mixed effects model with a Poisson distribution investigating the effects of management practices and blueberry variety on pollinator species richness. Pollinator species were summed per transect type. A random effect of transect type nested in site was included as a random effect. Significant effects at P < 0.05 are in bold. Response Predictor variables DF F value P value Species richness Management 1 5.13 0.024 Variety 3 2.67 0.45 2.4 Discussion 2.4.1 Pollination deficits across varieties and management practices A significant positive linear relationship (p = 2.2e-16) was found between fruit weight and seed count (Figure A.1). The R2 was found to be 0.27, indicating that 27% of the variation in the log(berry weight) is explained by seed set. These results indicate that fruit weight is a weak proxy for seed set, with a lot of unexplained variation. Supplementally-pollinated flowers consistently had a higher fruit set and produced heavier fruit than ambient-pollinated flowers, indicating that honey bee hives were unable to completely alleviate pollination deficits at any of our sites. Button & Elle (2014) reported a potential increase in yield for ‘Duke’ growers of 2,200 kg/ha under supplemental-pollination. In this study, I reported a near identical potential increase of 2,100 kg/ha for ‘Duke’ growers. While we hypothesized that landscape level changes in pesticide use and crop diversity may have altered pollinator populations and thus their contributions to blueberry pollination, the similarity in these estimates across time rather suggests similarity in environmental conditions in the Lower Mainland across this time period. I also reported a potential increase of 10,500 kg/ha for ‘Draper’ growers and a potential increase of 600 kg/ha for ‘Liberty’ growers under supplemental-pollination. Given that ‘Liberty’ flowers have a significantly smaller corolla throat diameter relative to ‘Draper’ and ‘Duke’ and that blueberry flowers with long, narrow corollas hinder honey bee’s access to nectar (Courcelles et al., 2013), ‘Liberty’ was expected to experience the fewest honey bee visitors and greatest 35 pollination limitation. Instead ‘Liberty’ had the least pollination limitation. Our single ‘Liberty’ farm, however, was the only site to use managed Bombus vosnesenskii pollination and the only site to have nearly equal bumble bee and honey bee visitation rates. The ‘Liberty’ farm also used a clover/grass cover crop between blueberry rows, likely attracting additional wild bumble bees. The increased presence of both managed and wild Bombus sp. was likely sufficient to significantly reduce pollination limitation that would have otherwise existed in the sole presence of honey bee visitation. One of the two ‘Draper’ sites also strongly skewed the mean potential increase in revenue due to having the largest pollination limitation with a potential increase of about 20,900 kg/ha. This estimate is over three times greater than the largest average estimated revenue increase proposed by Button & Elle (2014) of 5,300 kg/ha. This site also experienced the lowest visitation rates of 2.46 honey bee visits per 15 minutes despite the presence of numerous managed honey bee hives. This site implemented conventional management of space between blueberry rows, likely making the site unsuitable for wild pollinator establishment and visitation. Overall, growers that used a clover/grass cover crop between blueberry rows experienced significantly greater wild pollinator visitation relative to growers that used mulch or bare ground. Sites with the clover/grass cover crop also received pollination from a greater diversity of pollinators compared to sites with mulch or bare ground. Our findings suggest that the use of a clover/grass cover crop between rows is an effective practice for encouraging pollination services from a diverse array of wild pollinator species. Whether the clover/grass cover crop mix attracts enough wild pollinators to provide a “safety net” should honey bees become unavailable remains to be tested. Thus we are limited to recommending the use of a clover/grass cover crop as a method to encourage supplementary pollination services, rather than a replacement for managed honey bee use. To conclude, the effects of individual management practices of farms within the Lower Mainland, including the introduction of ground cover and bumble bee hives, seem to outweigh the effects of blueberry floral morphology on visitation. 36 2.4.2 Reducing pollination limitation may also decrease development time I found that berries produced from supplementally-pollinated flowers were ready for harvest on average 2.2 days sooner than berries produced from ambient-pollinated flowers, indicating that supplementally-pollinated flowers develop fruit faster than ambiently-pollinated flowers. While a 2-3 day difference is unlikely to affect crop value (Nicholson & Ricketts, 2018) or rates of infestation by spotted wing Drosophila, which is more problematic later in the season (Hampton et al., 2014), there was a large standard deviation among varieties. The supplementally-pollinated treatment of the ‘Draper’ variety developed on average 3.4 days sooner than the ambient-pollinated treatment, but the standard deviation amongst bushes was 6.9, indicating high levels of variation. If blueberry clusters had been checked every 3-5 days and harvested as they developed, there would likely be much fewer instances of clusters being harvested on the same day. It may have been more appropriate to analyze the data as interval censored data since the exact date fruit was ready for harvest was unknown (i.e., the data was left-censored). Because the censored nature of the data was not accounted for, the standard errors presented in Chapter 2.3.2 and the model coefficients may be underestimations of their true values (Radke, 2003), meaning the difference in harvest date between treatments may not be as great as we found it to be. Regardless, these results indicate that pollination affects factors beyond fruit yield. 2.4.3 Top contributing pollinators to highbush blueberry While the visitation rate of honey bees (Apis mellifera) to highbush blueberry flowers was much greater than the visitation rate of wild pollinators, I found that wild bumble bees (Bombus sp.) contribute significantly more to reducing pollination deficits. Honey bees alone were not able to alleviate fruit set nor fruit weight deficits at any of the sites included. At best, honey bees may have a synergistic relationship with wild pollinators and be utilized as a complementary measure, but they should not be relied on solely. The most frequently observed wild pollinator across all sites was Bombus impatiens, but from 1981 to 2006, the most commonly observed Bombus sp. within blueberry fields in the Fraser Valley was Bombus mixtus (Mackenzie & Winston, 1984; Ratti, 2006; Winston & Graf, 37 1982). Other common bumble bees observed during this time included B. occidentalis, B. melanopygus, B. sitkensis, B. flavifrons, and B. terricola (Mackenzie & Winston, 1984; Ratti, 2006; Winston & Graf, 1982). Bombus impatiens, an introduced species, was not detected in Fraser Valley blueberry fields until 2006, but likely became established sometime in 2003, when B. impatiens were first recorded within strawberry fields in Vancouver, BC (Ratti & Colla, 2010). The increase in B. impatiens presence on highbush blueberry fields and decreased presence of other wild Bombus sp. is a distinctive change and suggests that B. impatiens may be out-competing native bumble bees for floral resources and/or nesting sites. While I did not directly observe competition between Bombus impatiens and other pollinators during surveys, introduced Bombus species in other parts of the world have been linked to severe decline and local extinctions of native bee species in the past (Aizen et al., 2019). In addition, Bombus impatiens prefers to nest in old rodent burrows similar to many bumble bee species native to BC (Gervais et al., 2020; McFrederick & LeBuhn, 2006). Previous research has also shown that the presence of B. impatiens colonies can have detrimental effects on bumble bee colonies, such as B. occidentalis, potentially preventing them from reaching their full population potential (Whittington & Winston, 2004). Future research is needed to investigate reasons behind the increased abundance of Bombus impatiens within blueberry fields in the Lower Mainland and the potential consequences of increased alien Bombus presence for wild pollinator conservation and pollination services. 2.4.4 Distance from the field’s edge does not influence wild pollinator abundance or diversity While I originally hypothesized that wild pollinators would be more abundant and diverse towards the field’s edge, where more naturally existing floral resources and nesting sites would likely be available (Rao & Skyrm, 2013), I did not find a significant effect of distance on bee visitation rates of highbush blueberry flowers. Bumble bees are above- and below-ground cavity nesting species, with some species showing a strong preference for abandoned rodent burrows (Kells & Goulson, 2003; Rao & Skyrm, 2013). Voles (Microtus sp.) and other rodents are common occupants of highbush blueberry fields but are largely considered pests due to damage 38 they inflict on bushes from feeding (Strik & Yarborough, 2005). Several of the fields included in this study showed signs of rodent infestation with burrow entrances visible between and within blueberry rows. It is possible that Bombus queens were using these burrows as nesting sites. The presence of potential nesting sites between blueberry rows may have driven the equal distribution of pollination services throughout the fields. Growers may benefit from tolerating a limited presence of rodents so long as the cost of damage to bushes is outweighed by the increase in yield from increased wild bumble bee visitation. Should growers need to periodically control for rodents, they may also benefit from maintaining wood piles or abandoned anthropogenic structures as potential nesting sites near fields (Blitzer et al., 2016; Kells & Goulson, 2003). Flowering weed species or white clover (Trifolium repens) were also frequently found in spaces between blueberry rows and may have provided additional floral resources to pollinators, further encouraging them to forage throughout the field. Should growers need to control for aggressive undesirable flowering species, additional floral resources could be made available through other methods such as semi-natural habitat establishment on field margins (Blaauw & Isaacs, 2014; Marshall et al., 2006) or inter-cropping with other flowering species (Gowton et al., 2021) but the evidence for the success of these practices is mixed and requires additional research (Albrecht et al., 2020). While I could not determine whether the equal rates of visitation throughout the fields could be attributed to the presence of floral resources, nesting sites or both in this study, previous work suggests that the distribution of bees is dependent on both factors (Osborne et al., 1991). 2.4.5 The use of white clover (Trifolium repens) and grass as a cover crop attracts wild pollinators Significantly more Bombus species were observed pollinating highbush blueberry flowers on farms that utilized white clover (Trifolium repens) and grass as a cover crop between blueberry rows than those that used mulch or bare ground. The species richness of wild pollinators visiting blueberry on farms utilizing clover as a cover crop was also greater compared to the diversity of pollinators visiting blueberry on conventional farms. In addition to attracting wild pollinators, T. repens also has a shallow root mass that helps prevent soil erosion and can be partially tilled for 39 nitrogen fixation (Clark, 2007). While this cover crop system has clear advantages for growers relative to using mulch, it is unclear if the benefits are equal for native and introduced Bombus species. While Bombus species are typically generalist foragers, there are differences in foraging preferences between certain species (Bobiwash et al., 2017; Osborne et al., 1991). If the goals of conserving ecosystem services are to align with those of conserving biodiversity, more research is needed to determine whether there are other suitable cover crop mixes, more preferable to native Bombus species that still encourage pollination services to highbush blueberry. The different costs of seeding these potential cover crops, how well-suited they are for field spaces (i.e., how well they manage under heavy field traffic and shade), and what other agronomic benefits or costs they create must also be considered. 2.5 Conclusion The results of this study emphasize the importance of wild Bombus species in the pollination of highbush blueberry and the potential severity of pollination deficits, even in the presence of managed honey bees. In order for growers to experience the benefits of maximum pollination, including an increase in yield and a decrease in development time, farms must be managed to encourage wild pollinator visitation. The use of white clover as a cover crop was found to be an effective strategy for encouraging crop visitation from Bombus species but more research is needed to evaluate other cover crop options. 40 Chapter 3: Inter-varietal pollination increases blueberry quality 3.1 Introduction Despite blueberry production rising on a global scale, farmers still face significant challenges maximizing yield (Brazelton, 2011). Common barriers to maximal blueberry yield in BC include diseases caused by bacteria and fungi (Nabetani et al., 2017), insect and avian pests (Moore, 1994), limited soil nutrients (Tasa et al., 2012) and poor weather (Tuell & Isaacs, 2010). However, even when not limited by soil fertility and pests, crops still fail to produce maximal yields in the absence of adequate pollination (Button & Elle, 2014; Fijen et al., 2018). Therefore, growers must manage pollination services to ensure blueberry flowers receive ample pollen. Managing the amount of pollen deposited alone will not achieve maximal yields. Highbush blueberry (Vaccinium corymbosum) has been shown to produce greater yields and heavier fruit following inter-varietal pollination, relative to yields produced from intra-varietal pollination (Ehlenfeldt, 2001; Mackenzie, 1997; Strik et al., 2014; Taber & Olmstead, 2016). This phenomena is commonly explained by a genetic load model in which reduced yields from intra-varietal pollination are attributed to inbreeding effects and embryo abortion (Ehlenfeldt, 2001; Krebs & Hancock, 1991). Intra-varietal pollination could result in higher frequencies of homozygous deleterious alleles (i.e., genetic load) which could drive embryo abortion during development, leading to fewer viable seeds per berry. Berries that lack the minimum number of seeds are aborted before harvest, reducing fruit set (Harrison et al., 1994). Further, fruit with fewer seeds will produce fewer hormones during development, limiting fruit weight (Stephenson, 1981). Thus, growers should strive for high quality pollen receipt by encouraging pollination between different blueberry varieties (Pritts et al., 1992). Most highbush blueberry growers attempt to meet the pollination needs of their crop by renting managed honey bee (Apis mellifera) hives (Benjamin & Winfree, 2014). Honey bees, however, cannot pollinate highbush blueberry with the same degree of effectiveness as wild pollinators, resulting in significant pollination deficits (Button & Elle, 2014; Javorek et al., 2002). Moreover, honey bees tend to travel down a single row when foraging, greatly increasing rates of intra-varietal pollination in blocks of single varieties, which are commonly used field 41 designs (MacInnis & Forrest, 2020). Bumble bees, however, arrive and depart at a flower in the same direction, which can encourage inter-varietal pollination so long as fields are designed with multiple blocks of different varieties (MacInnis & Forrest, 2020). Growers typically design fields with large blocks of a single variety, despite recommendations to design blueberry fields with alternating pairs of compatible varieties (Pritts et al., 1992). Reasonable production is still obtainable from these solid blocks, but this practice greatly increases intra-varietal pollination of flowers (Ehlenfeldt, 2001). In combination, the typical single variety block design of highbush blueberry fields and the tendency for honey bees to continue down a single row when foraging results in dominance of intra-varietal pollination, limiting yields (MacInnis & Forrest, 2020). To achieve optimal yields growers should consider management practices that encourage wild pollinator visitation and plan smaller block designs with inter-varietal pollination in mind. While managing fields to increase the chances of inter-varietal pollination may seem ideal, Mackenzie (1997), cautions that inter-varietal pollination is not beneficial for every variety. While inter-varietal pollination causes an increase in berry weight for highbush blueberry varieties such as ‘Patriot’ and ‘Northland’, Mackenzie (1997) saw no such benefit for the 'Bluecrop’ variety when crossed with ‘Patriot’. Hence, growers must recognize that certain blueberry varieties are more compatible than others and select optimal pairings when planning their field design. This greenhouse pilot study tests the effects of crossing novel pollen donor-recipient pairs on fruit yield and flavor, which in contrast to yield, is not well-studied. While growers are often primarily concerned with yield, consumers are most interested in blueberry flavor and texture (Gilbert et al., 2014, 2015). Consumers typically have a strong preference for sweet blueberries with strong flavors (Gilbert et al., 2014, 2015). The primary drivers of a blueberries’ sweet flavor are the sugars glucose and fructose, which can be estimated by measuring the total soluble solids (TSS) of the juice, as this value is highly correlated with sugar content (Forney et al., 2012; Gerbrandt et al., 2020). Organic acids, such as citric and malic acid, are also an important flavor component as a little bit of tartness is desirable (Beaudry, 1992; Mikulic-Petkovsek et al., 2012). The balance of sugars and acids should be considered when estimating fruit taste as a sweet berry could either have a high TSS or low levels of acid (Zhang et al., 2020). A high sugar/ acid ratio, however, is a sure indicator of a sweet-tasting fruit (Zhang et al., 2020). Factors that influence blueberry flavor, such as pH and fructose concentration, are 42 affected by both the genetics of different varieties and environmental conditions (Gilbert et al., 2015) but little work has been done to understand the effects on inter-varietal pollination on blueberry quality and flavor. In this chapter, I test whether inter-varietal pollination increases fruit yield, fruit weight, and fruit quality for two different varieties of blueberries. I hypothesized that inter-varietal pollination would lead to a greater yield and produce sweeter berries, indicated by a low TA and a high TSS, pH and sugar/ acid ratio, relative to blueberries produced from intra-varietal pollination. This hypothesis was driven by the expectation that intra-varietal pollination would lead to a greater number of aborted seeds, and thus fewer resources, including sugars, being allocated to developing fruit, relative to inter-varietal pollination. I also test whether ideal combinations of varieties exist to maximize fruit yield and quality. I further hypothesized that less closely related pollen sources would increase fruit yield and quality. To test these hypotheses, potted ‘Duke’ and ‘Bluecrop’ blueberry bushes were brought into a greenhouse and pollinated by hand with either pollen from the same variety or pollen from one of two other varieties. This pilot greenhouse study will help guide future research on variety compatibility and may eventually help blueberry growers optimize their field designs for pollination services by wild pollinators. Using fruit harvested from the field-scale study described in Chapter 2, I also compare fruit yield and quality from plants receiving supplemental-pollination relative to those experiencing ambient-pollination conditions. I did not expect inter-varietal pollination to occur in the field due to the large size of varietal blocks; therefore I hypothesized that supplementally-pollinated flowers would yield better quality (i.e., sweeter tasting) fruit relative to fruit produced from ambient-pollination. This hypothesis was driven by evidence that seeds produce hormones involved in the mobilization of resources into developing fruit (Stephenson, 1981) and the prediction that supplementally-pollinated flowers would experience greater fertilization, yielding heavier fruit with a higher seed count relative to ambient-pollinated flowers. 43 3.2 Methods 3.2.1 Pollination treatments In March 2021, a pilot study was carried out in which 26.5 L pots containing mature highbush blueberry (V. corymbosum) of 3 varieties were brought into a greenhouse to induce flowering. The greenhouse had a set temperature of 20°C and a relative humidity of 35%. The varieties included (three bushes each) were ‘Bluecrop’, ‘Duke’, and ‘Reka’. ‘Bluecrop’ and ‘Duke’ were chosen as they were common varieties amongst our field study sites, whereas ‘Reka’ is a more recently established variety (Pritts et al., 1992). It was also important that varities with overlapping flower bloom were chosen to ensure these results would be replicable in a field-setting. Each bush had one cluster of flowers designated for intra-varietal pollination and two designated for inter-varietal pollination. The number of replicates per pollen donor-recipient pair (i.e., specific crosses between blueberry varieties in which one acts as the pollen recipient and the other acts as the pollen donor) ranged from 3 to 6 (Table 3.1). Some flower clusters failed to produce enough fruit to measure pH, TSS or TA, resulting in a smaller sample size for certain pollen donor-recipient pairs (see Table 3.1). These clusters were pollinated every 2-5 days. Pollen was collected from each bush by sonicating flowers with a Be the Bee Pollinator TM (aerogarden.com/be-the-bee-pollinator) and was liberally applied to the stigmas using camelhair paintbrushes. Donor pollen was never taken from flowers designated for inter- or intra-varietal pollination treatments and was instead harvested from flowers on adjacent branches. Table 3.1: Pollen donor-recipient pairs used in the pilot study. Data from ‘Reka’ as the pollen recipient was omitted since the bushes showed signs of poor health and failed to produce fruit regardless of pollen donor. The sample size of each treatment is indicated by n. Pollen recipient Pollen donor Bluecrop Duke Reka Bluecrop X (n = 6) X (n = 6) X (n = 6) Duke X (n = 3) X (n = 3) X (n = 5) 44 3.2.2 Fruit harvest Because the greenhouse was not completely sealed, paint strainer bags were placed on each treatment after each pollination, to avoid any outside insect pollination or damage from pests. All fruit, ripe or otherwise, was harvested once at least 50% of the berries in the cluster were fully ripe. Fruit was then placed in plastic freezer bags labelled with information relevant to harvest (i.e., bush ID, pollen recipient, pollen donor, and date) and stored in a cooler with ice until they could be processed. This harvest method provides similar fruit weight data compared to harvesting each berry as it ripens and prevents the loss of overripe berries but is much less time consuming (Tuell & Isaacs, 2010). All berries were counted the same day as harvest, but only ripe berries were weighed before being stored in a -80 °C freezer for later processing. Unripe berries were excluded from weight measurements as to avoid skewing results with berries that were not yet mature. Refer to Chapter 2.2.3 for details on when and how fruit was harvested from each field site. 3.2.3 Blueberry quality assessment From each blueberry cluster (i.e., treatment), 10 – 20 berries were selected for analyses of total soluble solids (TSS), pH and titratable acidity (TA). The measurements of the TSS and pH were repeated three times, yielding three technical replicates but the titratable acidity could only be measured once due to time restraints. Upon processing, samples were removed from the -80°C and left at room temperature for 15 minutes to thaw. Once thawed, the berries were blended into a puree using a Magic Bullet Blender® and scraped into 15 mL Falcon tubes labeled with information relevant to harvest (field study: site, edge, transect, bush, and treatment & greenhouse study: pollen recipient, bush ID, and treatment) and the date of processing. Blended samples were used either immediately after blending or within 3-5 days of blending. If samples needed to be stored short-term for processing at a later date, they were kept in a 4°C refrigerator. The blender was washed after every sample with water and 70 – 90% ethanol. 45 A RF153:digital brix refractometer (EXTECHTM) was used to measure the TSS of each sample. A scoopula was used to scrape sample from the tube onto the center of the sensor. The measurement was repeated three times for each sample with the refractometer being calibrated with deionized water before each sample reading. The average of the three technical replicates was used in the final analysis. The pH and TA were measured using a SevenExcellence pH/mV Meter (METTLER TOLEDO®) with a solids probe. The pH meter was calibrated with standard buffer (pH 4.01, 7.0, 10.01) at the start of each day, prior to measuring the pH. Each sample was then measured three times and the average would be used for analysis. To determine the TA, a pre-rinsed 50 mL burette was first filled with 0.1 N NaOH and the initial volume of NaOH was recorded. 1.49-1.51 g of sample was added to a 50 mL beaker (with the accurate weight of the sample recorded) which would be diluted with 15 mL of deionized water. The 0.1 N NaOH was added drop by drop, with the beaker constantly being swirled, until equilibrium was reached at a pH of 8.1-8.2. At this point, the final volume of NaOH was recorded. The volume of NaOH used for the acid base titration was used to calculate the TA using the following equation (Sadler & Murphy, 2017): % citric acid �wtvol� = N × (V2 − V1) × M citric acid3 H ionsm0 × 10 Where N refers to the normality of the titrant NaOH (in this case 0.1 N), V2 refers to the final volume of the NaOH, V1 refers to the initial volume of the NaOH, M citric acid refers to the molarity of citric acid (192 g/mol), and m0 refers to the sample weight. After TSS and TA were calculated for each sample, the sugar/ acid ratio was simply taken as the TSS divided by the TA. The sugar/ acid ratios presented here are rough estimates and not as accurate as those calculated using high-performance liquid chromatography (Sadler & Murphy, 2017). 3.2.4 Data analysis For the field study, to determine if pollination type (i.e., supplemental-pollination vs ambient-pollination) affected blueberry flavor, linear mixed effect models with dependent variables TA, TSS, pH, and sugar/ acid ratio were fit as a function of pollination treatment, variety and an 46 interaction between pollination and variety. Site was also included as a random effect. Site was included as a random effect to account for differences in management practices between farms. In the models where TA and the sugar/ acid ratio were fit as dependent variables, a log transformation was necessary to meet residual normality assumptions. For the greenhouse study, to determine whether inter-varietal pollination affected blueberry yields, two linear models with percent fruit set (# berries/ # flowers x 100) and log(fruit weight) as dependent variables were fit as a function of pollination treatment (i.e., inter- or intra-varietal pollination) and the blueberry variety of the pollen recipient as a covariate. Bush ID was not fit as a covariate to either model due to collinearity issues. To determine whether inter-varietal pollination affected blueberry flavor, multiple linear models with either pH-1, TA (%), TSS (%) or log(sugar/ acid ratio) as the dependent variable were fit as a function of pollination treatment and the blueberry variety of the pollen recipient. These models pooled data from both pollen recipients to increase our statistical power and to test for a general positive effect on yield and flavor from inter-varietal pollination. While the variable percent fruit set was bound between 0 and 100, data was found to be normally distributed and did not violate normality or homoscedasticity assumptions. For the greenhouse study, to determine if specific pollen donor-recipient pairs enhanced blueberry yield and/or flavor, linear models with dependent variables fruit set (%), berry weight (g), TA (%), TSS, pH and sugar/acid were fit as a function of the pollen donor-recipient pair. The data was re-classified into individual crosses rather than being grouped into inter- or intra-varietal crosses for these models. I also attempted to fit stem diameter (cm) of the branches as a possible covariate since the experimental branches used in the ‘Duke’ bushes were found to have statistically significant different widths among treatments (Figure B.1). The difference in stem thickness was concerning as thicker stems support more leaves, which donate carbohydrates and nutrients to nearby fruit (Primack, 1987; Stephenson, 1981). Thus, thicker stems could have contributed to fruit being heavier in certain treatments. The addition of this covariate to the base models, however, failed to significantly change the coefficients of the other fixed effects and was ultimately dropped. Models were verified to meet normality and homoscedasticity assumptions, even those with dependent variables with a unit of percent. Because the sample size for this pilot 47 study was so small, these variables did not truly span 0 to 100 %, therefore meeting normality and homoscedasticity assumptions. All statistical analyses were carried out in R statistical software (version 4.2.0) (R Core Team, 2022). The lmer function from the lmerTest package was used to build the linear mixed effects models described above (Kuznetsova et al., 2017) and the emmeans function from the emmeans package was used to carry out post hoc Bonferroni tests (Lenth, 2022). The r.squaredGLMM function from the MuMin package was used to obtain the R2 values (Bartoń, 2022). 3.3 Results 3.3.1 Blueberry quality across pollination treatments in the field study Neither pollination treatment nor variety were found to have a significant effect on the pH, TA, or sugar/ acid ratio (Table 3.2). Total soluble solids, however, was found to be significantly affected by both variety and treatment (Table 3.2). Across all varieties, supplementally-pollinated treatments produced fruit with a TSS of 15.3 ± 0.1% and ambient-pollinated treatments produced fruit with a TSS of 15.7 ± 0.1%. On average, supplementally-pollinated flowers produced fruit with a TSS 0.4% less than fruit produced by the ambient-pollinated treatment (Figure 3.1). TSS was highest for the single ‘Liberty’ site at 17.2 ± 0.3% and lowest across the 3 ‘Duke’ sites at 14.9 ± 0.1%. TSS was found to be 15.4 ± 0.2% for the two ‘Draper’ variety sites and 16.1 ± 0.2 % for the two ‘Bluecrop’ sites. The difference in TSS between the supplementally-pollinated and ambient-pollinated treatments differed slightly by variety but not significantly so (Table 3.2). 48 Table 3.2: Results of four linear mixed effects models investigating the effects of pollination treatment (i.e., supplemental vs ambient) and variety on fruit pH, log(TA), TSS and log(sugar/ acid ratio). Site is included as a random effect. Significant effect at P-values < 0.05 are in bold. pH Log(TA) TSS Log(Sugar acid ratio) DF F value P DF F value P DF F value P DF F value P Treatment 1 0.13 0.72 1 1.54 0.21 1 6.57 0.010 1 3.37 0.067 Variety 3 0.77 0.86 3 1.36 0.72 3 11.57 0.0090 3 2.71 0.44 Treatment x Variety 3 5.88 0.12 3 5.94 0.11 3 0.34 0.95 3 5.09 0.17 Figure 3.1: pH (A), log(TA) (B), TSS (C) and log(sugar/ acid ratio) (D) across all treatments and varieties. Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. Grey points represent individual pH, log-transformed TA, TSS and log-transformed sugar/acid ratio values. The grey lines indicate optimal ranges for each quality metric as described by Beaudry (1992). 49 3.3.2 Fruit set (%) across all pollen donor-recipient combinations The pollination treatment (i.e., inter-varietal vs intra-varietal pollination) was not found to significantly affect fruit set but the variety of the pollen recipient was found to significantly affect fruit set (Table B.1). However, the small sample size of this pilot study severely limited our statistical power and trends in the data suggest that inter-varietal pollination may increase fruit set relative to intra-varietal pollination. When ‘Bluecrop’ acted as the pollen recipient, intra-varietal crosses overall resulted in a fruit set of 53.3 ± 5.6% (mean ± standard error) whereas inter-varietal crosses resulted in a fruit set of 67.3 ± 5.0%. When ‘Duke’ acted as the pollen recipient, intra-varietal crosses overall resulted in a fruit set of 17.9 ± 5.9 % whereas inter-varietal crosses resulted in a fruit set of 25.5 ± 5.9%. These results suggest that inter-varietal pollination may have the potential to increase fruit set relative to intra-varietal pollination but the size of this increase depends on the pollen recipient. When separating the data into individual pollen donor-recipient pairs, the pollen donor-recipient pair (df = 4, p = 5.81e-6) was found to have a significant effect on the fruit set. A post-hoc Bonferroni test was carried out afterwards to compare individual pollen donor-recipient pairs with the same pollen recipient against each other but no pairwise comparisons were found to be significantly different. When ‘Duke’ flowers were pollinated with ‘Reka’ pollen, the resulting fruit set was 34.5 ± 9.8%. ‘Duke’ flowers out-crossed with ‘Reka’ yielded a fruit set 16.6 % greater than intra-pollinated ‘Duke’ flowers, on average, (Figure 3.2) but pollinating ‘Duke’ flowers with ‘Bluecrop’ pollen did not increase fruit set relative to the intra-varietal cross. When ‘Bluecrop’ acted as the pollen recipient, the greatest average fruit set (71.3 ± 6.9%.) was also produced when flowers received ‘Reka’ variety pollen. On average, the ‘Bluecrop’ cross with ‘Reka’ lead to a 18% increase in fruit set relative to the intra-varietal cross. 50 Figure 3.2: Fruit set (%) across pollen donor-recipient pairs. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. 3.3.3 Blueberry weight (g) across all pollen donor-recipient combinations Inter-varietal pollination was not found to significantly increase fruit weight relative to intra-varietal pollination (Table B.1). Variety was also not found to have a significant effect on fruit weight (Table B.1). When separating the data into individual crosses, the pollen donor-recipient pair (df = 5, p = 0.073) was not found to have a significant effect on the blueberry weight either. Regardless, when ‘Bluecrop’ flowers received intra-varietal pollination, they produced fruit weighing 0.33 ± 0.03g but when ‘Bluecrop’ is pollinated by ‘Duke’ the resulting fruit weighs 0.51 ± 0.03g. When ‘Bluecrop’ is pollinated by ‘Reka’, the resulting fruit weighs 0.54 ± 0.04g. ‘Bluecrop’ flowers crossed with ‘Duke’ yielded berries 0.18g heavier on average than the intra-varietal cross whereas ‘Bluecrop’ flowers crossed with ‘Reka’ yielded berries averaging 0.21g heavier than the intra-varietal cross (Figure 3.3). When ‘Duke’ acted as the pollen recipient, 51 intra-varietal pollination produced fruit weighing 0.43 ± 0.1g whereas intra-varietal pollination with ‘Reka’ produced fruit weighing 0.50 ± 0.08g. When ‘Duke’ was pollinated by ‘Reka’, fruit weighed 0.07g more, on average. Figure 3.3: Average blueberry weight across all pollen donor-recipient pairs. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. 3.3.4 pH across all pollen donor-recipient combinations The pH of fruit developed by inter-varietal pollination was not significantly different from the pH of fruit developed from intra-varietal pollination (Table B.2). The variety of the pollen recipient significantly affected fruit pH (Table B.2). When ‘Bluecrop’ acted as the pollen recipient, intra-varietal pollination led to a pH of 2.8 ± 0.04 whereas inter-varietal pollination lead to a pH of 3.0 ± 0.05, indicating very little difference between treatments. When ‘Duke’ acted as the pollen recipient, intra-varietal pollination led to a pH of 3.2 ± 0.2 whereas inter- ** ** 52 varietal pollination lead to a pH of 3.2 ± 0.05, also indicating practically no difference between treatments. When organizing the data into individual crosses the pollen donor-recipient pair (df = 5, p = 0.0097) was found to significantly affect the pH of the blueberries (Figure 3.4) but post-hoc Bonferroni tests indicated no significant differences between donor-recipient pairs with the same pollen recipient. When ‘Duke’ acted as the pollen recipient, fruit with the greatest pH was produced when ‘Duke’ was pollinated with ‘Reka’ variety pollen (3.2 ± 0.02) and fruit with the smallest pH was produced when ‘Duke’ flowers experienced intra-varietal pollination (3.2 ± 0.2). This difference in pH is negligible and unlikely to cause a difference in blueberry flavor between treatments. Figure 3.4: pH across all pollen donor-recipient pairs. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. The grey line indicates the lower bound of the optimal range for pH of V. corymbosum according to Beaudry (1992). 53 3.3.5 Titratable acidity (%) across all pollen donor-recipient combinations The TA of fruit developed by inter-varietal pollination was not significantly different than fruit developed from intra-varietal pollination. The variety of the pollen recipient, however, did significantly affect the TA of fruit (Table B.2). When ‘Bluecrop’ acted as the pollen recipient, inter-varietal crosses produced fruit with a TA of 1.9 ± 0.2% and intra-varietal crosses produced fruit with a TA of 2.1 ± 0.2%. When ‘Duke’ acted as the pollen recipient, inter-varietal pollination produced fruit with a TA of 1.2 ± 0.1% and intra-varietal pollination produced fruit with a TA of 1.5 ± 0.4%. On average, inter-varietal pollination produced fruit with a TA 0.2 – 0.3% less than fruit produced from intra-varietal pollination, depending on the variety of the pollen recipient. When separating the data into individual crosses, the pollen donor-recipient pair was not found to have a significant effect on the TA of the blueberries (df = 5, p = 0.089). However, when ‘Bluecrop’ acted as the pollen recipient, fruit with the highest TA (2.1 ± 0.2%) was produced from intra-varietal pollination whereas fruit with the lowest TA (1.7 ± 0.3 ) was produced when ‘Bluecrop’ received pollen from ‘Reka’. 54 Figure 3.5: Titratable acidity (%) of the fruit across all pollen donor-recipient pairs. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. The grey line indicates the lower range for the optimal TA for V. corymbosum according to Beaudry (1992). 3.3.6 Total soluble solids across all pollen donor-recipient combinations The TSS of fruit developed by inter-varietal pollination was significantly greater than fruit developed from intra-varietal pollination but the variety of the pollen recipient was not found to significantly affect fruit TSS (Table B.2). Intra-varietal pollination produced fruit with a TSS of 18.6 ± 0.4% and inter-varietal pollination produced fruit with a TSS of 20.0 ± 0.3% . On average, inter-varietal pollination produced fruit with a TSS 1.4% greater than fruit produced by intra-varietal pollination. When separating the data into individual crosses, the total soluble solids was not found to be significantly affected pollen donor-recipient pair (df = 5, p = 0.096). Regardless, ‘Duke’ flowers that received ‘Bluecrop’ pollen yielded fruit with a TSS of 20.4 ± 0.6% and ‘Duke’ flowers that received ‘Reka’ pollen yielded fruit with a TSS of 20.6 ± 0.7% 55 (Figure 3.5). On average, when ‘Duke’ acted as the pollen recipient, inter-varietal pollination led to an increase in TSS of 1.9 to 2.1%, depending on the pollen donor. When ‘Bluecrop’ acted as the pollen recipient, the difference between the inter-varietal and intra-varietal crosses were much smaller and unlikely to affect blueberry flavor. Figure 3.6: TSS across all pollen donor-recipient pairs. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. The grey line indicates the lower bound of the optimal range for TSS of V. corymbosum according to Beaudry (1992). 3.3.7 Sugar/ acid ratio across all pollen donor-recipient combinations The sugar/ acid ratio of fruit developed by inter-varietal pollination was not significantly different from the sugar/ acid ratio of fruit developed from intra-varietal pollination (Table B.2). The sugar/ acid ratio of fruit was significantly affected by the variety of the pollen recipient (Table B.2). When ‘Bluecrop’ acted as the pollen recipient, inter-varietal pollination produced 56 fruit with a sugar/ acid ratio of 12.1 ± 1.6% and intra-varietal pollination produced fruit with a sugar/ acid ratio of 9.6 ± 1.7%. When ‘Duke’ acted as the pollen recipient, inter-varietal pollination produced fruit with a sugar/ acid ratio of 17.5 ± 1.2% and intra-varietal pollination produced fruit with a sugar/ acid ratio of 16.1 ± 5.9%. On average, inter-varietal pollination produced fruit with a sugar/ acid ratio 1.4 to 2.5% greater than fruit produced by intra-varietal pollination, depending on the pollen recipient. When grouping the data into individual crosses, the pollen donor-recipient pair was found to significantly affect the sugar/ acid ratio (df = 5, p = 0.48) but the post-hoc pairwise Bonferroni tests were unable to detect a significant difference between any of the inter- or intra-varietal crosses with the same pollen recipient. Regardless, when ‘Bluecrop’ acted as the pollen recipient, fruit with the highest sugar/ acid ratio (14.0 ± 2.9) was produced when flowers received ‘Reka’ pollen and the fruit with the lowest sugar/ acid ratio (9.6 ± 1.7) was produced from intra-varietal pollination. When ‘Duke’ acted as the pollen recipient, the fruit with the greatest sugar/ acid ratio (18.3 ± 1.1) also resulted from a cross with ‘Reka’ whereas the fruit with the smallest sugar/ acid ratio (16.1 ± 5.9) resulted from intra-varietal pollination. 57 Figure 3.7: Sugar/ acid ratio of the fruit across all pollen donor-recipient pairs per variety. The data is grouped into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Boxplots indicate the first quartile, median and third quartile. Whiskers indicate the standard error. The grey lines indicate the lower bound and upper bound of the optimal range for the sugar/ acid ratio of V. corymbosum according to Beaudry (1992). 3.4 Discussion 3.4.1 Additional pollen does not influence highbush blueberry quality I hypothesized that supplementally-pollinated flowers would produce fruit with a higher pH, a higher sugar/ acid ratio, a higher TSS, and a lower TA, relative to fruit produced from ambient-pollination. This would have indicated that flowers that receive more pollen yield sweeter tasting, better quality fruit. However, my analyses show there is no statistically significant difference in any of these quality parameters between the two treatments, except for the TSS. 58 The total soluble solids were also found to differ significantly between blueberry varieties. I found ‘Bluecrop’ to have an average TSS of 16.1%, ‘Duke’ to have a TSS of 14.9%, ‘Draper’ to have a TSS of 15.4% and ‘Liberty’ to have a TSS of 17.2%. Gerbrandt et al. (2020) recorded a TSS of 12.7% for ‘Bluecrop’ and a TSS of 11.2% for ‘Duke’ whereas Strik et al. (2017) reported TSS values of 14.5% for both ‘Draper’ and ‘Liberty’, 13.5% for ‘Duke’ and 13.7% for ‘Bluecrop’. When comparing the TSS values presented here with those recorded in other studies, it is clear the TSS values presented here are consistently higher. While other studies may have prioritized harvesting fruit all at once or harvesting it as it ripened, I harvested fruit once at least 50% of the cluster was ripe and checked on the sites once every 5-7 days (Gerbrandt et al., 2020; Strik et al., 2017). Given that the sugar content of highbush blueberries increases as they ripen, it is likely I simply harvested our fruit at a later stage of development relative to the other studies (Forney et al., 2012; Moggia et al., 2016). Factors such as the farm location and year could have also affected our results (Gilbert et al., 2015; Saftner et al., 2008). The lower TSS of fruit produced under supplemental-pollination versus fruit produced from ambient-pollination could be related to a difference in crop load and fruit weight. Research on the effects of crop thinning has shown that a reduction in crop load can increase fruit weight and quality by decreasing competition for water and mineral nutrients in the remaining fruit (Jorquera-Fontena et al., 2014; Looney, 1993; Mpelasoka et al., 2001). Considering that the supplementally-pollinated treatment often had a much greater fruit set than the ambient-pollination treatment, there could have been more competition amongst the developing fruit, limiting the sugar per berry. Fruit weight was also greater in the supplementally-pollinated treatment relative to the ambient-pollination treatment, but the difference was not as stark as that of the fruit set. If plants had received additional resources during the study, competition between the fruit may have been reduced, enabling the plant to invest more sugars into the fruit developed by supplementally-pollinated flowers. But this hypothesis remains to be tested. It is unlikely that the current difference in TSS between treatments is large enough for consumers to show a preference for fruit produced from supplemental pollination, especially considering that the sugar/ acid ratio was not significantly different amongst treatments. Given that the difference in TSS between the two pollination treatments ranged from 0.3 – 0.8%, depending on the variety, and that a difference of at least 2% is required for children to show a 59 preference (Mennella et al., 2017), it is unlikely that this difference is great enough for consumers to show a preference for fruit produced from supplementally-pollinated flowers. 3.4.2 Inter-varietal pollination can increase fruit set and weight I hypothesized that inter-varietal pollination would increase fruit set and weight for both the ‘Bluecrop’ and ‘Duke’ varieties. I was unable to find a statistically significant difference between the fruit set or weight of the inter-varietal pollination treatment relative to the intra-varietal pollination (Table B.1). However, trends in the data indicate that inter-varietal pollination resulted in a fruit set 9.2% greater than intra-varietal pollination. When analyzing trends by the individual pollen donor-recipient pairs, inter-varietal pollination did not appear to affect fruit set in ‘Bluecrop’, but the ‘Duke’ variety did see an increase in fruit set when outcrossed with ‘Reka’(although not statistically significantly different), relative to the intra-varietal cross. The average berry weight also increased for ‘Bluecrop’ (although not a statistically significant increase) when out-crossed with either ‘Duke’ or ‘Reka’. While ‘Bluecrop’ has been found to benefit from inter-varietal pollination when pollinated by ‘Patriot’ (Dogterom et al., 2000), it was not affected when pollinated by ‘Spartan’, ‘Coville’ or ‘Patriot’ (Mackenzie, 1997). Regardless, in both of these studies, the effect was either consistently beneficial for both fruit set and weight or of no effect at all. The small sample size of this pilot study likely reduced our power to detect a difference in the fruit set of the ‘Bluecrop’ crosses, despite the trends visible in Figure 3.2. Because ‘Bluecrop’ acted as a paternal parent in the crossing that produced the ‘Reka’ variety, one would assume it is more closely related to ‘Reka’ than ‘Duke’ and experience a lesser increase in fruit set or weight when pollinated by ‘Reka’ relative to being pollinated by ‘Duke’ (Bian et al., 2014; Zong et al., 2019). However, Bian et al. (2014) analyzed the genetic diversity of highbush blueberry and divided the species into three subpopulations: northern highbush blueberry (NHB), southern highbush blueberry (SHB) and ‘Weymouth’. Bian et al. (2014) found ‘Reka’, a NHB to be clustered alongside SHB varieties in their phylogenetic tree, suggesting that SHB also made a significant contribution to the lineage of ‘Reka’. In addition, the ‘Duke’ variety was found to be clustered (i.e., more closely related) with other NHB varieties 60 (Bian et al., 2014). These results suggests that ‘Reka’ is actually more genetically dissimilar to ‘Bluecrop’ than ‘Duke’ and could explain why ‘Bluecrop’ produced heavier fruit when out-crossed with ‘Reka’. It has been shown that the number of seeds in blueberry fruit decreases as relatedness between parents increase, and successfully fertilized ovules are vital for fruit development as they produce hormones that trigger growth (Krebs & Hancock, 1990; Primack, 1987). The effects of out-crossing ‘Duke’ with ‘Bluecrop’ have also been previously studied and suggested that ‘Duke’ is nearly as fertile with self-pollination as it is with inter-varietal pollination (Ehlenfeldt, 2001). However, in our pilot ‘Duke’ experienced a greater fruit set and weight (although not statistically significant) when out-crossed with ‘Reka’ suggesting that ‘Duke’ simply isn’t compatible with ‘Bluecrop’. Considering ‘Duke’ appears to be more closely related to ‘Bluecrop’ than ‘Reka’, it is interesting that ‘Bluecrop’ still produced heavier fruit when pollinated by ‘Duke’ (Bian et al., 2014). When ‘Duke’ acts as the pollen recipient, it is possible there is some other factor limiting yield besides genetic load but this hypothesis remains to be tested. The trends seen in this pilot suggest that both ‘Bluecrop’ and ‘Duke’ are most compatible with ‘Reka’ and may see greater fruit yields from inter-varietal crosses with this variety, but this hypothesis also remains to be tested with a larger scale study. 3.4.3 Inter-varietal pollination can increase blueberry quality I hypothesized that inter-varietal pollination would produce higher quality fruit (i.e., fruit with a higher pH, TSS and sugar/ acid ratio and a lower TA) than intra-varietal pollination. My analyses indicate that inter-varietal pollination significantly increase the TSS of fruit relative to intra-varietal pollination but inter-varietal pollination does not significantly affect pH, TA or sugar/ acid ratio (Table B.2). While inter-varietal pollination produced fruit with a TSS only 1.4% greater than that of intra-varietal pollination, the sugar/ acid ratio of fruit developed from inter-varietal pollination was 2.5% greater (although not significantly greater) than the fruit produced by intra-varietal pollination. This difference in sugar/ acid ratio is promising but it remains to be tested if this difference is large enough for consumers to detect a difference in sweetness. 61 When analyzing the effects of specific pollen donor-recipient pairs on blueberry quality, I found the pollen donor-recipient pair to have a significant effect on blueberry pH and sugar/acid ratio, but not on the TA or TSS. These results initially appear confounding but upon examination of the trends in the data, I found that ‘Bluecrop’ consistently produced berries with the highest pH and TSS when outcrossed with ‘Reka’. The ‘Bluecrop’ and ‘Reka’ cross also resulted in the berries with the smallest TA and greatest sugar/ acid ratio. Even if I failed to find a statistically significant difference between the inter- and intra-varietal crosses when ‘Bluecrop’ acts as the pollen recipient, it doesn’t change the fact that TA and sugar/ acid ratio values were outside of the optimal range when ‘Bluecrop’ flowers experienced intra-varietal pollination. The pattern was not as strong when ‘Duke’ acted as the pollen recipient, with ‘Duke’ and ‘Bluecrop’ crosses producing fruit with near identical pH relative to the intra-varietal cross. The median sugar/ acid ratio also falls within the optimal range when ‘Duke’ acts as the pollen recipient, regardless of the pollen donor. These results suggest that while ‘Bluecrop’ may experience an overall increase in fruit quality and flavor when out-crossed with ‘Duke’ or more preferably ‘Reka’, the quality of ‘Duke’ is not as strongly affected. However, there are trends in the data that suggest that out-crossing ‘Duke’ with ‘Reka’ could also yield sweeter, better quality fruit. Previous studies assessing the effects of inter-varietal pollination on fruit yield may have written off ‘Duke’ as a self-fertile variety with no benefits to be gained from inter-varietal pollination but there may be a benefit to the fruit’s quality and flavor. It would be interesting to replicate this pilot with a larger sample size and to include participants to sample ‘Duke’ fruit produced from the different crosses to determine if this difference in sugar and acid content is detectable, even if the differences aren’t as strong as those seen in the ‘Bluecrop’ variety crosses. 3.5 Conclusion Our results indicate that while ‘Bluecrop’ and ‘Duke’ may both potentially experience an increase in fruit yield and quality from inter-varietal pollination, there is little evidence that ‘Duke’ would experience the same increase in yield or quality as ‘Bluecrop’. Should ‘Bluecrop’ be planted in alternating rows alongside ‘Reka’, farmers could achieve higher yields of not only 62 heavier berries, but sweeter and higher quality berries too. If this block design presents too many challenges in fertigation, irrigation and spray timing, smaller blocks of compatible varieties could be another option for encouraging wild pollinator visitation (Isaacs & Kirk, 2010). Future research is needed to determine whether there are other optimal pairings for the ‘Bluecrop’ and ‘Duke’ varieties and whether differences in quality metrics are detectable by consumers. It would also be of interest to see if this block design encouraged wild bee visitation to farms by slightly extending flower bloom time and wild pollinator’s access to floral resources. 63 Chapter 4: General conclusions 4.1 Research outcomes The goals of this study were to investigate pollination limitation of highbush blueberry farms within the Lower Mainland of British Columbia and to compare present day pollination limitation to values reported by Button & Elle in 2014. Another goal was to investigate potential benefits from supplemental-pollination beyond yield increases and to investigate additional methods for growers to maximize yields. Present day ‘Duke’ growers are experiencing similar pollination limitation to what was recorded 10 years ago, indicating that conditions in the Lower Mainland have likely been consistent enough to not strongly influence highbush blueberry yield. Bumble bee visitation was found to be the strongest predictor of fruit set and fruit weight deficits indicating that growers must manage farms in ways that encourage wild pollinator visitation to reduce pollination limitation. I found the use of white clover as a cover crop to be one such method for attracting a diverse array of wild pollinators. I also found potential benefits to blueberry yield, quality and flavor from inter-varietal pollination. Thus, to achieve maximal yield and optimal blueberry quality, growers must not only encourage wild pollinator visitation but consider variety compatibility. I recommend that highbush blueberry growers manage farms to increase the availability of non-crop floral resources and potential nesting sites, while also planting smaller blocks of compatible blueberry varieties. 4.2 Study limitations and future research One limitation of this study is the small number of replicates in both the pilot greenhouse study (Chapter 3) and the field-scale study (Chapter 2). In Chapter 2, only one site was included for the ‘Liberty’ variety, making it difficult to examine the effects of variety on bee visitation and pollination deficits. Unfavorable weather in 2022 also limited the number of surveys, further limiting the volume of data collected. Continuing to sample each of the sites, possibly with greater replications of each blueberry variety, would allow for increased statistical power and stronger conclusions to be made from these results, especially considering that pollinator 64 abundance can fluctuate significantly year from year, depending on weather conditions (Abrol, 2012; Tuell & Isaacs, 2010). Another limitation of this study is that pollination limitation experiments took place on select flower clusters on each bush, rather than on a whole bush scale. Many flowering plants can reallocate resources among flowers (Knight et al., 2005). Thus, the whole blueberry bush may not be able to respond to additional pollen with a higher fruit set as our supplementally-pollinated flowers did (Knight et al., 2005). Even if pollen receipt is high, the maternal plant may be limited by resource availability (Knight et al., 2005). This could have affected our fruit set and weight deficit calculations, causing us to overestimate the degree to which yields are limited by pollination. It is difficult to say how strongly our results would be affected by this limitation as highbush blueberry may not experience limited fruit set as severely as other fruit-bearing crops. There are reports of average fruit sets reaching as high as 93% for entire ‘Bluecrop’ canes under field conditions but also average fruit sets as low as 37.8% for ‘Liberty’ variety fields (Kirk & Isaacs, 2012). ‘Duke’ variety clusters have also been found to have an average fruit set of nearly 90% without any form of experimentation (Courcelles et al., 2013). It is possible that maximal fruit set differs by variety, meaning that fruit set and weight deficit estimates could have been affected by resource limitation to varying degrees, depending on the variety. Our analyses included variety as a covariate to account for varietal effects. The field-scale study (Chapter 2) was also limited by the fact that the surveying methods and site selection were originally designed to observe how pollination services change following the establishment of a floral strip. Few fields planted the same varieties, making it difficult to make concrete conclusions about how pollination limitation differs by blueberry variety. In addition, time spent surveying the edges of fields, where floral strips were planned to be established, prevented us from performing vegetation surveys between blueberry rows, which would have enabled us to see if farms using clover/grass cover crops had a greater floral diversity between rows relative to farms using mulch or bare ground. Future research should be focused on the potential of different flowering species for intercropping as many growers have defaulted to agricultural intensification to improve yields and revenue, leaving little space for the establishment of semi-natural habitat (Alvez et al., 2012; Green et al., 2005). Future research must also consider the degree to which candidate cover crops 65 benefit wild, native pollinators relative to introduced pollinator species and the relative costs of each candidate cover crop. Cost-benefit analyses are vital as growers may be unwilling to pay higher prices for flowering species that are better for native pollinators but not necessarily better for pollination services. In general, high costs of establishment are likely to be a barrier to growers willing to try these novel management practices (Alvez et al., 2012; Robinson, 2006). 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Phylogenetic relationship and genetic background of blueberry (Vaccinium spp.) based on retrotransposon-based SSAP molecular markers. Scientia Horticulturae, 247, 116–122. https://doi.org/10.1016/j.scienta.2018.11.017 75 Appendix A: Supplementary information for Chapter 2 A.1 Figures Figure A.1: Positive linear relationship between fruit weight (g) and the number of seeds (df = 1, p = 2.2e-16). 76 A.2 Tables Table A.1: Results of linear model investigating the effects of study site and year on log(fruit weight deficit + 1) Fruit weight deficit was calculated for each bush as the fruit weight of the ambient pollination treatment subtracted from the supplemental pollination treatment. Negative values for fruit weight deficits were turned to zero and attributed to human error. Significant effect at P < 0.05 are in bold. Log(fruit weight deficit + 1) DF F value P value Site 7 8.60 9.05e-10 Year 1 3.81 0.052 Site x year 5 0.41 0.85 Table A.2: Results of linear mixed effects model investigating the effects of distance from the field’s edge and variety on average bee visitation per 15 minutes. The number of bees observed per transect were summed and divided by the total number of surveys to give the average visitation rate per 15 minutes. Average bee visitation was modelled separately for honey bees and bumble bees. Farm site and the study year were attempted to be included as random effects in both models but year was only included as a random effect in the honey bee visitation model. 1(bumble bee + 1) Log(honey bee + 1) DF F value P value DF F value P value Variety 3 4.52 0.21 3 3.61 0.31 Distance 1 1.10 0.29 1 0.28 0.60 Table A.3: Results of linear mixed effects model investigating the effects of pollination treatment, distance from the field’s edge, and blueberry variety on log(fruitset + 1). Farm site was included as a random effect. Significant effects at P < 0.05 are in bold. Log(fruitset + 1) DF F value P value Treatment 1 48.23 3.79e-12 Distance 2 2.80 0.094 Variety 1 1.39 0.49 77 Appendix B: Supplementary information for Chapter 3 B.1 Figures Figure B.1: Boxplot of median stem diameter (cm) of the branches used in the pilot greenhouse study testing the effects of inter-varietal pollination. Data was subset into when ‘Bluecrop’ acted as the pollen recipient (left) and when ‘Duke’ acted as the pollen recipient (right). Linear models were fit with stem diameter as the dependent variable and the pollen donor set as the independent variable to test if there was a significant difference in branch width amongst treatments. Results from ANOVA tests done on these models are indicated in brackets. Significant effects at P < 0.05 are in bold. 78 B.2 Tables Table B.1: Results of multiple linear mixed effect models with percent fruit set (# berries/ # flowers x 100) and log(fruit weight) as dependent variables as a function of pollination treatment (i.e., inter- or intra-varietal pollination) and the variety of the pollen recipient. Data used for these models is derived from the pilot greenhouse study. Significant effect at P-values < 0.05 are in bold. Fruit set (%) Log(fruit weight) DF F value P value DF F value P value Pollination 1 3.02 0.092 1 4.07 0.052 Pollen recipient 1 46.00 1.15e-7 1 1.15 0.29 Table B.2: Results of multiple linear models with pH, TA, TSS and log(sugar/ acid ratio) as dependent variables as a function of pollination treatment (i.e., inter- or intra-varietal pollination) and the blueberry variety of the pollen recipient. Data used for these models is derived from the pilot greenhouse study. Significant effect at P-values < 0.05 are in bold. pH-1 TA TSS Log(Sugar/acid ratio) DF F value P value DF F value P value DF F value P value DF F value P value Pollination 1 1.74 0.20 1 1.39 0.25 1 6.82 0.014 1 2.02 0.17 Pollen recipient 1 13.06 0.0013 1 9.07 0.0057 1 2.14 0.62 1 9.76 0.0045
Thesis/Dissertation
eng
Zoology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Kremen, Claire
Pollination services from wild Bombus sp. and inter-varietal pollination can help highbush blueberry (Vaccinium corymbosum) growers achieve maximal yield
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