The concentrations of 7.5% and 11% sucrose were used to achieve osmotic balance between the strawberries and the impregnation solutions.Isochoric chambers , constructed from aluminum- 7075 with a type-II anodized coating, with a total volume capacity of 1.5 L and pressure rated up to 210 MPa, was used for the isochoric cold storage . Initially, a steel nut, serving as an ice nucleating agent, was placed at the base of the isochoric chamber to ensure that ice formation occurred away from the sample bags. Subsequently, a plastic spacer apparatus was inserted into the chamber, and the samples were transferred onto the plastic apparatus, with three bags placed in each chamber. The chambers were then sealed after being filled with water. The isochoric chambers were subsequently stored in chest freezers at −2 ◦C for 7 days. The freezing temperature of −2 ◦C was chosen based on preliminary work to avoid high pressures that would result in cell damage. In an isochoric environment, the temperature and the volume of unfrozen liquid in the chamber are correlated. At the triple point , about 45% of the initial volume remains unfrozen. As the temperature rises, this percentage increases, reaching 90% of the initial volume at −2 ◦C, 20 MPa. By carefully controlling temperature and pressure, strawberries can be kept within the unfrozen region of the system, ensuring they remain preserved without ice formation. Following the ICS, the chambers were relocated to a fridge at 5 ◦C and left overnight to allow the ice within the chamber to melt.The addition of sucrose and calcium chloride can influence microbial populations on strawberries. Sucrose, as a fermentable sugar, grow bags garden may promote the growth of certain microorganisms, including yeasts and molds, which utilize sugars as a primary energy source .

The reduction of microorganisms in strawberries treated with CaCl2 may be attributed to the ability of calcium salts to lower intracellular pH or reduce water activity.Visible signs of decay mark the point when strawberries are no longer acceptable for consumption. While fungicide applications during the growing cycle are the traditional method for controlling postharvest decay, their use is increasingly questioned due to sustainability and safety concerns, with bans in many countries. Alternatives like pulsed light, hypobaric treatment , or ultrasound help to slow decay but do not fully prevent spoilage. In contrast, the findings of this study demonstrate that the pressure applied during ICS successfully inactivated fungal growth on strawberries after a week. Furthermore, fungal growth remained undetectable during the subsequent 3 weeks of refrigerated storage at 4 ◦C. The classification of fruit decay as simply “rotten” and “non-rotten” is a limitation in our study. A more detailed decay assessment method will provide a more comprehensive understanding of the decay process and enable a better evaluation of treatment effectiveness.The changes in strawberry weight over the storage period are depicted in Figure 3. After 1 week of storage at 4 ◦C, RF and RF + C strawberries lost weight, while RF + S strawberries showed weight gain. As shown in Figure 3, the ICS strawberries in both impregnation solutions experienced weight gain after 1 week due to the solution penetrating the porous tissue and intercellular spaces through a hydrodynamic mechanism consisting of capillary action and pressure gradients. Throughout storage, the refrigerated strawberries experienced significant weight loss, reaching up to 80% by week 4. This was primarily due to moisture loss from evaporation between the fruit tissue and the surrounding environment as well as the respiration processes. Given their extremely thin skin, strawberry fruits are highly prone to rapid water loss. However, strawberries subjected to isochoric impregnation had limited weight loss compared to the refrigerated samples. This can be attributed to the mechanisms of isochoric impregnation, which involve pressure-induced mass transfer, allowing the liquid solution to penetrate the fruit’s porous structure.

This process not only enhances the retention of moisture within the fruit but also creates a barrier to water loss, effectively mitigating dehydration.The moisture content, pH, titratable acidity , and total soluble solid parameters of the strawberries are presented in Table 2. The moisture content of all the strawberries in solution either slightly increased in value or remained stable after 1 week, which could be due to an infusion of the solutions. However, the RF + S and RF + C samples had significantly lower moisture contents by weeks 3 and 4, albeit not as low as those of the RF sample. ICS impregnated strawberries exhibited less moisture loss than refrigerated strawberries , regardless of the solution used. The pH values of the RF + C and ICS + C samples were significantly lower than the others due to acidic nature of ascorbic acid. All samples showed a decrease in pH value throughout storage. The titratable acidity of fresh strawberries was 1.8%. All the strawberry samples showed an increase in TA during storage, in agreement with Vicente, Martínez, Civello, and Chaves. In addition, the TSS of all the strawberry samples significantly increased in value during storage. This was attributed to moisture loss, the breakdown of complex sugars into simpler sugars, the degradation of cell walls, and the overall decay of the fruit. After 4 weeks, the RF + S and RF + C strawberries became too small and shriveled, making it impossible to perform pH, TA, and TSS analyses.The hardness values of the strawberries are presented in Table 4. The RF strawberries showed a reduction in hardness during the refrigerated storage, especially between week 1 and week 2. This decrease in firmness is mainly related to biochemical alterations at the cell wall, middle lamella, and membrane levels due to the activity of pectin methylesterase, an enzyme that hydrolyzes pectin, leading to structural breakdown. However, the firmness of the RF sample during storage increased to a higher level than that of the fresh sample, which had been attributed to an increase in pectin viscosity.

Additionally, water loss from the outer layer can increase fruit density, lower gas permeability, reduce oxygen levels, and elevate internal carbon dioxide concentrations, which may contribute to the firmer texture. The refrigerated strawberries also showed an increase in hardness throughout the storage period, likely attributed to a significant loss of internal water content in the cells. However, the samples had high standard deviations since certain strawberries remained quite firm while others were very soft. The breakdown in texture may have been caused by the increase in aerobic microbial counts, which could have led to the production of pectinolytic enzymes responsible for tissue softening. The ICS strawberries were harder than the RF strawberries for the same solution. Also, the strawberries impregnated with the solution containing sucrose, CaCl2, and AA were harder than the strawberries impregnated only with the sucrose solution. The increase in hardness was attributed to the addition of exogenous calcium ions to the strawberry fruit via impregnation with pressure. Calcium ions are essential for maintaining fruit quality by inhibiting the activity of polygalacturonase , an enzyme responsible for breaking down cell wall components such as pectins. Furthermore, Ca2+ binds with demethylesterified pectin backbones to form a pectin–Ca2+ network, which strengthens the mechanical properties of the cell wall and helps preserve fruit texture. Koushesh Saba and Sogvar reported that Ca2+ helped maintain and enhance the integrity and mechanical properties of the cell wall, effectively preventing the softening of fruits.Figure 5 shows the total anthocyanin content of the strawberries during storage. The total anthocyanin content of the fresh strawberries was 20.2 ± 4.4 mg/g dry matter . The strawberries stored in sucrose solution for one week did not show a significant change in the total anthocyanin content , whereas the strawberries in the sucrose + CaCl2 + AA solution showed a significant decrease in the total anthocyanin content . The high anthocyanin content in the RF + S samples can be attributed to the sucrose treatment, grow bag for tomato which stimulates anthocyanin accumulation by upregulating the expression of genes involved in anthocyanin biosynthesis. The CaCl2 treatment positively influenced the retention of monomeric anthocyanins during storage by facilitating pectin–anthocyanin binding. The presence of ascorbic acid accelerated anthocyanin degradation and led to a loss of color, indicating a direct interaction between the two molecules. The lower pH in the strawberries impregnated with the AA solution could also contribute to the reduced anthocyanin content, since the stability of anthocyanins is influenced by pH.The total anthocyanin content of the refrigerated samples significantly decreased during storage . The anthocyanin degradation might be associated with water loss during storage, leading to physiological stress and accelerating fruit senescence.

Water loss led to membrane disintegration and leakage of cellular contents, both of which contributed to the decrease in anthocyanin concentration. In addition, the increase in enzyme activity, such as polyphenol oxidase, may also have contributed to the reduction in anthocyanin content in the strawberries during storage. The ICS samples showed significantly lower anthocyanin contents after one week compared to the refrigerated samples. This initial decrease can likely be attributed to the effects of pressure and impregnation during isochoric treatment, which may introduce physical stress and promote anthocyanin degradation. At week 4, the ICS samples exhibited significantly higher total anthocyanin contents compared to the RF, RF + S, and RF + C samples , becoming redder and darker over time due to the synthesis of anthocyanins, the pigments responsible for the red color in strawberries.This study examined the effects of isochoric cold storage at −2 ◦C/48 MPa in combination with isochoric impregnation with sucrose solution or sucrose solution containing calcium chloride and ascorbic acid on the quality of strawberries. The refrigerated strawberries at 4 ◦C experienced growth of mesophilic aerobic bacteria, yeasts, and molds over the 4-week storage period, whereas isochoric impregnation effectively inhibited the growth of these microorganisms over the same period. After 4 weeks, refrigeration at 4 ◦C resulted in significant weight loss in the strawberries, with reductions of 79% in the CaCl2 solution and 82% in the sucrose solution. In contrast, ICS helped minimize weight loss, with reductions of 68% in the CaCl2 solution and 60% in the sucrose solution during refrigerated storage. Also, ICS strawberries in the presence of CaCl2 and ascorbic acid showed better mechanical properties, color stability, and higher nutrient content than those in the sucrose solution or under refrigeration. Overall, ICS with sucrose, CaCl2, and ascorbic acid impregnation proved to be a highly promising postharvest technology for extending the shelf life of strawberries for up to 4 weeks. This study highlights the potential of ICS not only for improving the storage stability of strawberries but also as a sustainable alternative to conventional methods. Future research should focus on scaling up this technology and evaluating its feasibility for commercial applications, offering a pathway to reduce postharvest losses and meet the growing demand for longer-lasting, high-quality fresh produce.Whole genome duplications , also known as polyploidy, are an important recurrent process over evolutionary time that have contributed to the origin of novel phenotypes and driven species diversification across eukaryotes . Polyploids are species that contain three or more complete sets of chromosomes in each nucleus, ranging from triploid to dodecaploid. For example, two rounds of whole genome duplication, termed 1 R and 2 R events, are unique to vertebrates. 1 R preceded the origin of crown vertebrates, while 2 R occurred in the lineage leading to bony vertebrates after the divergence of the cyclostome lineage. Many retained duplicated genes from these two ancient polyploidy events have functionally diverged and are associated with the evolution of several novel structures including the neural crest, cartilage, bones and/or adipose tissue. Similar patterns have also been reported following ancient polyploidy events in yeast and plants. Polyploids often evolve novel phenotypes and show greater phenotypic plasticity, which may explain certain polyploid lineages surviving mass-extinction events and exhibiting subsequent shifts in net diversification rates. There are two main categories of polyploids; autopolyploids and allopolyploids. Autopolyploids are formed from genome doublinginvolving a single diploid progenitor species, while the formation of allopolyploids involves genome doubling after hybridization of two or more diploid progenitor species. Newly formed allopolyploid genomes may experience instability, as the previously separate genomes of each diploid progenitor species, known as subgenomes, have evolved independently and now coexist in a single nucleus.

Consequently, recombination between the causal and marker loci will occur during the breeding process and as allele frequencies change with each selection cycle, LD shifts impacting the accuracy of predictive models over time. GS models therefore requires regular updating and as such, model training becomes an important component of a modern breeding system. With the cost of genotyping rapidly decreasing and the recent release of multiple chromosome-scale, haplotype-phased genome assemblies for alfalfa , genomics is becoming a viable option for many smaller breeding programs. Recently the use of GS has been investigated by alfalfa breeders for biomass yield , forage quality and salinity tolerance . However, these studies were based on phenotypic and/or genotypic data at the individual plant level. Although useful, alfalfa is often evaluated at the family level using half- or fullsib families and then marketed as a synthetic population. Andrade et al. proposed GS may be better incorporated into an alfalfa breeding program by genotyping pooled families to obtain allele frequency marker data rather than individual genotyping calls. One major takeaway from much of the work in GS is the size of the training population plays a key role in the predictive ability of the final model . However, for lesser funded breeding programs, genotyping and phenotyping can quickly become prohibitively expensive with the inclusion of more material. Pooled genotyping is one method of lowering the cost to breeders. Another is incorporating remote sensing and high throughput phenotyping to reduce the expensive labour component of phenotyping the training population.Plant phenotyping is a core foundation of plant breeding and has evolved through the years. Accurate and rapid measurement of phenotypic data is essential to understanding the genetic basis for plant traits and for the subsequent generation of improved germplasm.

For biomass yield in alfalfa this traditionally required the destructive sampling, plastic pot drying and weighing of hundreds to thousands of experimental units multiple times over the 2-4 year lifespan of alfalfa breeding trials.This process is labor-intensive, time-consuming, and costly. Recently, improvements in camera technology, aerial photography, and data processing have resulted in the broad adoption of remote sensing and high throughput phenotyping in agriculture which can significantly reduce the high labor cost of phenotyping. Remote sensing allows for the accurate, efficient, and non-destructive estimate of biomass and has been shown to be useful for high throughput phenotyping in breeding applications , including the prediction of biomass yield in large alfalfa breeding plots . What is not yet clear however, is whether the same predictive ability transfers to the variety of other plot types used in alfalfa breeding; family rows and minisward plots. This is of particular interest for training a genomic selection model where upwards of 1000 families need to be evaluated for optimal predictive ability . Firstly, it will enable trial sizes to increase, benefitting not only training populations for genomic selection, allowing greater prediction accuracies, but will be useful to upscale traditional evaluation trials. Secondly, non-destructive biomass measurement will allow the tracking of growth rates throughout the season and other temporal traits, something that is not currently feasible with traditional destructive harvest methods.Alfalfa is one of the most widely grown perennial forage legumes in temperate and Mediterranean-climate regions worldwide , owing to its exceptional yield, high nutrition, broad adaptability, nitrogen fixation, and host of beneficial ecosystem services . Alfalfa hay grown in California predominantly supports the largest dairy sector in the USA, but also provides forage for sheep, beef, and horse production as well as a growing export market . Alfalfa is an allogamous autotetraploid and is characterized by severe inbreeding depression .

Alfalfa is highly heterozygous, and cultivars are synthetic populations that exhibit high variability . Most breeding programs currently utilize recurrent phenotypic selection, where the best genotypes are recombined following evaluation trials that typically last 2-4 years . Despite the numerous benefits of alfalfa, the economic viability of alfalfa is under threat from an increasing yield gap relative to major cereal crops and other potential substitutes in the dairy ration . This yield gap has developed due to low rates of genetic gain for forage yield in alfalfa, particularly over the last 30 years where progress has stalled completely . This lack of yield improvement can be ascribed to a range of factors common in outcrossing perennial forages, namely long selection cycles, multiple harvests per year,small breeding investment, the inability to develop hybrids, the harvesting of all above ground biomass , the need to maintain forage nutritive value, and significant genotype by environment interaction . However, yield improvement has occurred in other perennial forages such as perennial ryegrass  and white clover ; therefore, progress should be possible in alfalfa. Lamb et al. suggested that the lack of yield improvement in alfalfa is because less breeding focus has been placed on yield, instead there has been a focus on improving tolerance to biotic and abiotic stresses. Although this enables alfalfa to reach its yield potential, it is not increasing yield per se in populations under improvement. Furthermore, alfalfa yield is often selected indirectly based on evaluation of vigor on spaced plants or on family rows , which has been shown to be a poor proxy for forage yield in the dense swards used in commercial alfalfa production . Marker-assisted selection is a useful tool for plant breeding programs and may be one way to improve the rate of genetic gain. Early research enabled breeders to identify molecular markers strongly linked to quantitative trait loci for a variety of important traits in alfalfa . However, MAS is primarily effective for traits controlled by relatively few genes with large effects. Complex traits, including yield, are usually controlled by many loci with small effects . In this case, genomic selection offers a compelling alternative to MAS by using a model that includes the effect of all markers in computing a genomic estimated breeding value for each individual in the population. Genomic selection can address one of the largest impediments to faster genetic gain in alfalfa – the need for multi-year evaluations that extend the length of each selection cycle. Selection can be made on genotypic information alone without the need for phenotypic evaluation, reducing the cycle time length from3-5 years to less than 6 months. With the cost of high-throughput sequencing decreasing and the recent publication of multiple chromosome-scale, haplotype phased genome assemblies for tetraploid alfalfa , the prospect of a robust genomic selection program is now possible for many alfalfa breeding programs. Various studies have investigated the use of GS in alfalfa breeding for a range of traits including biomass yield, forage quality and salinity tolerance. Moderate prediction accuracies were obtained for biomass yield, stem digestible neutral detergent fiber , and leaf protein content, ranging from 0.3-0.4 . The vigor of alfalfa under salt stress has also been assessed and a predictive model developed with a prediction accuracy of 0.793 . Although the results of these studies suggest GS could be used to increase the rate of genetic gain for a rage of traits in alfalfa, no empirical demonstration of GS has been published to date.

We hypothesized that using genomic selection for high yield based on a model developed from the phenotypic evaluation of clonally replicated genotypes would result in higher yield than a population selected by genomic selection for low yield or than phenotypic selection. The objective of this study was to empirically test populations developed from a genomic selection model for forage dry matter yield in densely sown sward plots of alfalfa.The germplasm used for genomic selection derived from a population created by Dr. Don Viands at Cornell Univ. in the 1990s called NY0358. We previously described this population, grow bag the NE-1010 clonal selection population, in an experiment using SSR markers for association analysis . Briefly, NY0358 was formed by intercrossing three elite, semi-dormant cultivars and recombining the resulting population twice. The NY0358 population underwent two cycles of selection for biomass yield using clonal evaluations at multiple locations . About 200 individual plants were included at the beginning of each cycle. These plants were clonally propagated using stem cuttings, and three replications were planted to the field at each location. In each replication, three clones were included in a plot; thus, each individual genotype was replicated nine times at each location in each cycle. Yield data were collected across multiple harvests and multiple years on individual plots, bulking the biomass of the three clones within the plot. In the first cycle, data were obtained from Ithaca, New York; Ste.-Foy, Québec; and Ames, Iowa. The top yielding 10% of genotypes selected based on an across location analysis of total annual yield were recombined to form NY0847. A second cycle of phenotypic selection was conducted using NY0847; genotypes were clonally propagated as for Cycle 1 and yield data collected from Ithaca, NY and Ste.-Foy, Québec. The best 10% of genotypes from NY0847 based on a statistical analysis of total annual biomass yield from NY only were intercrossed to form NY1221 .For genomic selection, we used a model developed from the initial clonal evaluation cycle total annual yield measured in NY only, because these data were more robust than those from the other locations . We based the model on total annual yield, which is a more important trait than yield of any individual harvest. We applied the model to seedlings from the population NY0847 and subsequently conducted a second cycle of genomic selection. We grew 19 or 20 individual seedlings from each of the 20 maternal families composited to create NY0847, for a total of 384 individual seedlings genotyped using GBS, as described previously , multiplexing 100 genotypes in a single lane of a HiSeq 2000 DNA sequencer. We aligned sequences with previously determined sequence tags to only analyze SNP that had been part of the model . Following SNP scoring and imputation, we computed GEBVs for each individual plant. Based on GEBVs, we selected the top 20 genotypes, restricting selections to no more than four individuals from any given maternal half-sib family to maintain variation in the population. These 20 individuals were intermated in the greenhouse by hand without emasculation to form the GSC1H population. An analogous population, GSC1L, based on the lowest 20 GEBVs was also formed. In addition, a random selection of 20 plants from the 400 plant population was intermated as a control population, GSC1R. For the second cycle of selection, seeds of each maternal half-sib family used to form GSC1H were germinated and DNA from 19 or 20 plants from each of the 20 families was isolated for a total of 384 plants analyzed with GBS markers. We again selected the top and bottom 20 individuals based on GEBVs as done for Cycle 1 and intercrossed them separately in the greenhouse to create GSC2H and GSC2L, respectively.Experiments were established in April 2017 at two locations in the United States each consisting of ten replications laid out in a randomized complete block design. The two locations were the Cornell University Research Farm in Ithaca, NY on a Niagara silt loam and 973 mm average annual rainfall); and Tulelake, CA , on a Tulebasin mucky silty clay loam . The sowing rate was 20 kg ha-1 with plots measuring approximately 1.5m × 5m. Each plot contained 8 rows spaced 17cm apart. An alfalfa border was sown around the entire experimental plot area. Soil tests were conducted at each location and fertilizer applied to maintain P and K at recommended levels for high yielding perennial forages . Trials were monitored for weeds, insects, and mammalian pests, with control measures conducted accordingly. Forage yields were estimated by mowing a swath through each plot leaving a residual of 7 cm. Prior to harvest, alleyways between plots were mown to remove edge effects and ensure plots were of uniform length. The target maturity for harvest was bud to early flowering stage. In Ithaca the harvest area was 1- by 4m. There was a total of nine harvests, three in each of 2018, 2019 and 2020, with no data collected in the establishment year. In Tulelake the harvest area was 0.9- by 4-m. There was a total of 12 harvests, three in 2017, four in 2018 and 2019, and a single harvest in 2020. Hand grab samples were collected and weighed from 20% of the plots at each harvest. They were dried in a forced-air drying oven at 55°C for seven days, reweighed and used to calculate dry matter percentage.

The main goal for designers of bee landscapes should be to create highly functional pollinator landscapes. In other words, designers should build landscapes which are appealing for bees to feed, reproduce, and live in. Good habitat design must be local area specific and consider the site’s context. For example, plants should be suitable for predicted California’s future climactic conditions, in many cases, be less dependent on water and more resilient to drought stress . Designers should aim to compensate for land cover which is not conducive to making suitable habitat. Thus, designers need to look at ways to reduce the footprint of impervious surfaces, such as roads, roofs, building sides, among many others. There is a need to convert wasted landscape space into habitat for bee habitat resiliency in the face of climate change in human-dominated landscapes. The Earth is undergoing a new epoch, influenced mainly by human activity, coined the Anthropocene . Similarly, the vast majority of Earth’s landscapes have been shaped by humans, coined the Anthroscape . Furthermore, creating good habitat is only one part of the solution for creating effective native bee designs. Showcasing bee educational information, such as scientific findings is necessary to increase public awareness, interest and conservation. This work begins by investigating the degree to which urban human dominated bee habitats represent ecological bee refugia in California. Then, landscape designs are conceptualized on various improvements of human landscape types. Planning for native bee conservation under the ecological threat of climate change requires examining ecosystems from a target bee’s perspective. Solving novel ecosystem issues requires holistic study, based on scientific data to meet the needs of native bees. Moreover, black flower bucket bees represent a large number of different organisms, therefore, it is impossible to design for all bees at once.

Instead, focal bee species will be selected based on the data and results from Dissertation Chapter 1 and Chapter 2 and with interest from student ecological designers. Designs will focus around the biological needs of focal bee genera, with an emphasis on those which seem to have potential for maintaining ecosystem services in urban ecosystems. Ideally, focal bee genera conservation will act as an umbrella, also helping to support bees which have narrower habitat requirements. Selection of plants for bees was an important element of bee habitat design, seen in Chapters 1 and 2, but this research goes further, aiming to teach site visitors about bees. Design is utilized to captivate, inspire and educate humans about the fantastic pollinators that we depend on. Ecologically functional faunal bee urban landscapes, built upon the principles of resiliency, will help to guarantee pollination ecosystem services in the future.This research concludes by presenting strategically determined design concepts as examples of how to implement the best possible bee habitat plans. Designs focus on providing the best possible plant selection for temporal continuity, spatial habitat continuity, creativity, public education and artistic themes. Examples of design intervention will be made to demonstrate how different degrees of designing for bees could be achieved with varying results in the real world. Design holds a key role not just in providing habitat, but also in promoting education and communication in memorable ways. Designers have the power to alter the transparency of their landscapes’ functions , and bee landscapes must be thought of in this way to help protect against the uncertainties of climate change. Through striving to create high quality habitat and increase landscape literacy this research aims to promote pollination ecosystem services into the future.The University of California Davis Arboretum and Public Garden was the study site for all fieldwork and analysis completed in Dissertation Chapters 1 and 2.

This chapter deals with the Arboretum site too, but also examines case studies of the ecological role of bees over the greater landscape extent in the Californian cities including: Mill Valley, Glendora, andSan Luis Obispo . While the Arboretum study was in the order of a couple miles, the subsequent urban studies were in the order of tens of miles.Target bee species were identified for conservation based on the results in Chapters 1 and 2. Bee genera which were found capable of utilizing urban ecosystem landscapes have been emphasized in conservation efforts for this research, including: Andrena, Apis mellifera, Bombus, Megachile, Osmia, and Xylocopa. All of these bees are listed as common bee genera found throughout the state of California . These bees provide pollination ecosystem services despite the unique qualities of urban bee habitat. We believe that prioritizing pollination is most important when facing the extreme influences and danger of climate change today. While it would be more ideal to plan for conservation of all bees, that is likely not possible or conducive to conserving pollinator landscape functionality. Since so many ecosystems and portions of them are dependent on pollination occurring, it is absolutely essential to conserve the pollination functionality above all other goals. Bees which are exceedingly prone to habitat fragmentation and exhibit highly specialized feeding behaviors are likely not good candidates for human-dominated ecosystem services conservation efforts. For example, obligate vernal pool bees, consisting of Andrena species: A. blennospermatis, A. submoesta, and A. puthua; A. limnanthis, A. duboisi, A. lativentris . While the most specialized bees may seem be good focal bee candidates because of their extreme geographic limitations and obligate feeding nature we argue that this would not be a good strategy. Designing for specialist bees, such as vernal pool bees would be inappropriate for most other bees and not focus on the goal of functional pollination in urban areas. Instead, extreme specialists, such as vernal pool bees should have their own conservation areas and strategies, aside from the urban bee communities and habitats.

A balanced approach, therefore, would employ a coarse and fine filter conservation strategy for preserving pollination ecosystem services. In other words, ecological design strategies should be employed that conserves a variety of bees, both generalists and specialists. Conservation planning exclusively for specialists such as obligate Andrena vernal pool bees should be detrimental for most other bee types, as they have a high degree of specialization. Instead, the focal bee must be chosen with landscapes in mind. Special conservation areas, such as vernal pools, would have their own conservation plans, while anthrocentric, human-dominated, landscapes would focus on focal bees capable of providing pollination to provide resiliency against the harsh environmental conditions facing us in the Anthropocene . Human dominated landscapes, also known as the anthroscape , will require pollination conservation plans which would encompass bees capable of pollination across the greater landscape. Functional pollination would help to ensure that ecological biodiversity remains stable, along with all the other plants and animals that depend on pollination. Therefore, by choosing strategically which bees to focus on, designers can help to build resilient bee pollinated landscapes now, and for the future.Target bee species were identified for conservation based on the results in Chapters 1 and 2. Bee genera which were found capable of utilizing urban ecosystem landscapes have been emphasized in conservation efforts for this research, including: Andrena, Apis mellifera, Bombus, Megachile, Osmia, and Xylocopa. All of these bees are listed as common bee genera found throughout the state of California . Furthermore, author KC studied the interest level with which student designers were attracted to work with. There were obvious trends among landscape architecture undergraduate college students for particular bee physical and lifestyle traits. The above mentioned commonly found native bee genera were also quite popular with students as subjects for design projects. Thus, while some bees, such as Halictus, were excellent foragers in earlier chapters, square black flower bucket they were not popular with student designers, most likely due to their relatively hairless bodies and subterranean nesting style. Other less popular bee genera included: very small Lasioglossum, especially the tiny Dialictus subgenus types and/or small and hairless Hylaeus , Cuckoo bees were vastly unpopular, and never utilized for a project at all. Overall, students were instead, drawn to bees with special attributes, such as: hairy, colorful, robustly bodied bee types . Notably students were commonly drawn to working with European honey bees, Apis mellifera, instead of with native bees. This is likely because most people think that European honey bees are quintessentially conventional. We all know the patterns of honeycomb and images of hives, and bee keeping. So, even students who were educated about the importance of bees native to California, still clung to knowledge about these naturalized bees at times, despite their biology and ecology differing from the local abundance of bee varieties.

This could also be due in part to the media attention given to Apis mellifera, to “save the bees”. There are countless social media trends on this theme, many, possibly most, of which present misinformation. It is clear that people are not aware of the diversity of native bees in our own landscapes in California. Even designers are not aware of biological and ecological differences and needing education and scientific themed projects. We must work hard to work beyond the “seductive” honeycomb patterns, black and yellow bee cartoon images, social hives, and posts about dandelions “saving the bees”. After all, which bees are we trying to save? How can we do it? Landscape researchers are calling for action on better landscape architecture education for college students . Part of that climate education should be about how bee pollination network resiliency is necessary. Habitat mapping for any animals often begins with mapping landscape vegetation and then assessing how the landscape’s vegetative form and properties meet the needs of the animal species models. Furthermore, Chapter 2 aimed for scientifically made, high resolution maps in the order of miles. However, in Chapter 3, these same mapping techniques and testing were completed in Chapter 2 could not be done over a larger spatial extent with the technology available today. Therefore, Chapter 3 aims to analyze more spatially extensive areas, but in doing so, with lower floristic resolution. Additionally, this paper focuses on recommending design solutions to existing urban bee habitat shortcomings. This research analyzes the patterns of urban vegetation in relation to native bee habitat area. It is accepted that urban areas have different bee community compositions than wild land areas . Next an extrapolation of the ecological function of gardens found in Chapters 1 and 2 to the larger landscape scale can be made to help gain understanding about how the bee meta population functions. Students were advised to assign the following habitat quality characteristics for landscape cover types: wild, agricultural, and urban. Each are described below. Wild land: Excellent habitat for native bees, probably honey bees as well. Most plants peak blooming earlier in the season . Wild habitat patches are often located very far from other wild patches. Often made of hilly areas unsuitable for agricultural crop growing and/or grazing. Native plants, but interspersed commonly with non-native invasive plants as well. Fire in these areas encourages native pollinator population boosts . Agricultural land: Infrequent use for most native bees. Vast, monotonous plantings, hostile management and habitat matrix quickly changing , no place to overwinter for bees, may provide foraging habitat with juxtaposition to bee source habitat. Scale often represents a major obstacle for bees to travel through at the landscape scale, i.e., mono-cropping acts as a sink, too vast to traverse. Irrigation could allow for blooms and therefore foraging habitat while wildlands would be desiccated. Management of crops and the surrounding areas can drastically sway the quality of bee habitat ; Wilson et al., 2017; Shackelford et al., 2019. Further spatial bee habitat studies should shed light onto how urban areas function as native bee genera habitat. A focus on urban areas which are adjacent to agricultural lands seem to hold promise, as that land presents potential for urban bees to subsidize pollination ecosystem services within agricultural lands, with benefits such that hedgerows provide. Urban land: Urban land is an ecological refugia for many native bees and naturalized honey bees. The landscape matrix is highly dissected and plant palettes vary greatly, some of which provide excellent habitat for particular bee genera. It also provides a longer foraging season than wildlands, partly due to plant choices, but also use of irrigation. These planting combinations are often unique combinations and function as novel ecosystems. This land cover acts as source habitat for many bee species, helping them maintain their populations despite the juxtaposition to less hospitable landscape types.

A more precise, ecologically-based way of describing the trends of bee foraging preference is the term ‘association,’ which has previously been used , though not yet adopted as a standard term. We propose adopting the term ‘bee-to-plant association’ as the standard to describe the ecological trends of attraction by particular bees to certain plant’s flowers. This research uses bee-to-plant associations either as a binary value or as relative attraction associations . It is imperative that the relationship between bees and their foraging plants be proved and supported by scientific research so that designers can maximize bee habitat design effectivity.Characterized as “listmania,” Garbuzov and Ratnieks , reviewed 15 lists of ‘plants for bees’ in North America and Britain and found minimal overlap between the recommendations between the lists for similar geographic regions. The authors argue that the efficacy of how these plant lists function ecologically needs further study . Within the existing literature there are numerous conflicts and inconsistencies between plant lists to determine which plants are best .However, in contrast to Garbuzov and Ratnieks , we believe that habitat solutions are likely to reflect localized climates and specializations among various geographic bee populations and, as Garbuzov and Ratnieks found, lists of forage plants for bees will likely not have much overlap between world-wide geographically distant locations. This theory is part of a broader ecological theory stating that differences in sites and years may show different geographical mosaics of coevolution . Identifying inadequacies in current bee plant lists is an essential first step in understanding how to improve bee habitats. There is a need for better empirical data on bee’s use of plant resources, plastic flower bucket including the issues of locality, but also appropriateness of plantings for bees . This study utilizes plant list datasets which were derived from empirical data, published by Frankie and Xerces .

We test the strength of these Central Valley California geographically pertinent datasets on-site, to see how well they perform for bees, both naturalized and native, in California. At the time of fieldwork for this study these qualitatively tested datasets were both available to the public, designers included, and both reflect the climate locality of the Davis, California study site. In essence, this study explored the merits and limitations of pollinator plant lists which were available at the time. As Garbuzov and Ratnieks points out, the strength of a model is only as good as the dataset from which it is built. For example, if both data sets are stated to be the best for bees- why would their plant species differ? Designers must have the best possible quantified plant lists to maximize pollinator habitat effectively.Targeted, strategic habitat analysis and modifications could help to boost both habitat connectivity and native bee populations , and in doing so, protect pollination networks and services . Ultimately, conservation and stabilization of bee populations is vital for human resiliency . Due to the diversity and complexity of native bees and their habitat needs, it is vital to understand that protecting bee ecosystem services means conserving an entire suite of insects and considering their various feeding preferences in the process . For example, of the approximately 20,000 species of the world’s bees, about 4,000 of them live in North America, of which nearly 2,000 are in California . According to renowned bee entomologist Robbin Thorp there are 21-26 bee genera in Davis, California, with 58-72 species . In contrast, Frankie estimates that 17 genera and 46 species are commonly found in California. Effective conservation needs better basic information for guidance. A variety of bees should be studied in a site’s location and management should strive to simultaneously meet the needs of the most important bees to maintain pollination ecosystem services .A major autecological framework for conducting habitat analysis is the application of wildlife habitat relationships modeling .

A WHR model for any species typically consists of three life requisites defined by plant communities: feeding habitat, cover habitat, and reproductive habitat. Another component of WHR models is identifying essential ‘habitat elements’ which can beliving or non-living . Since plant communities tend to change over ecoregional spatial extents, WHR models can vary regionally. For instance, California has a well-developed WHR modelling system and Oregon and Washington have a different system . Historically, WHR models were created for predicting vertebrate animal occurrences, however, this study tests whether a WHR modeling approach, based on foraging data could be applied to California native and naturalized bees. WHR models have successfully been used for vertebrate animal conservation for many decades, but this approach has not yet been applied to study bees or other insects, to the authors’ knowledge. We believe it is an important step to approach bee conservation from this point of view to identify critical ecological shortcomings and to maximize conservation efforts using habitat models to guide best management practices.Located in California’s Central Valley, the UC Davis Arboretum and Public Garden is a unique environment to study bee-to-plant associations. Situated in a Mediterranean climate, 35 distinctly themed gardens compose the linear Arboretum landscape, which spans approximately 2.4 km in length . Garden themes and names range from geographic , to ecological , to special plant type . Some are more eclectic in planting theme; they are simply named after neighboring buildings . Importantly, each garden has a geographically defined border and is mapped to the plant species, subspecies, or cultivar level . The high-resolution Arboretum plant collection maps and ancillary aerial photography make spatial accuracy possible within two meters.Building a matrix of bee life history was the first step in creating a WHR model. Literature was searched to collect and compile existing information on bee-to-plant relationship lists for foraging associations, predominant nesting styles , and foraging distances . In this study we concentrated on developing the foraging component of the model and did not test reproductive needs .

We compiled a comprehensive matrix of bee foraging association data from four studies including: Frankie and Xerces . Most plants In the Arboretum collections are horticultural plantings, but there are also some remnant native heritage trees which are long established and contribute strongly to plant community structure. We also included any associations to food crops, since ensuring pollination of agricultural crops has extreme importance and has received much attention in recent years . It was unlikely that we would find crop plants in the Arboretum; however, plants of the same genus as food crops may be found. Quantifying bee-to-plant observations for crops and their close relatives should be a priority in future studies due to the gravity of importance. Meanwhile, with geographic juxtaposition, urban areas could help to support or subsidize pollination of crop plantings . Moreover, urban pollinators could contribute to the greater ecology and food webs of their place, helping more than with human needs. Table 1 shows the completed presence-only bee-to-plant foraging matrix, derived from literature-based observational, quantified data . All of the Frankie and Xerces datasets were compiled by observing the relative attraction of bee-to-plant associations. Both studies tried to determine which plants are best for bees based on site observations by counting which plants received the most visits by bees. As a baseline for our study, Table 1 reports the sum total number of plants utilized for each native and naturalized bee genera and the sum total of the number of plants per bee genera. Next, construction of the bee-to-plant foraging relationship models was done by first obtaining the Arboretum’s plant collection geodatabase , which has every planting mapped with geographic coordinates and supplementing those data with the CalFlora bloom time database . This was done for all Arboretum plant species and was added to the geodatabase using a table join function in ArcGIS . Approximately half of the Arboretum’s plant list was supplemented with CalFlora’s researched bloom times . As the remaining half of the list’s bloom times required further research, flower buckets wholesale they were determined on a case-by-case basis from reliable literature sources . In cases where bloom data were not available, approximations were made based on other ancillary data from scientific papers on each plant genus and/or species as needed; however, this was uncommon. Upon completion, Arboretum plants could be queried in the database by plant name, garden location and/or bloom month.Bee plant association data were collected on a weekly time interval for one calendar year . This frequency of sampling was chosen because previous trial runs with classic monthly and two-week sampling resolution was not sufficient to track rapid phenological changes of plants in this environment. Data collection was done primarily through classical non-lethal entomological field netting and foraging observation methods as described in Pardikes et al. .

Additionally, global positioning system technology was used to enhance traditional netting and observation methods with spatial location data. To study bees at the landscape scale, entomological on-site methods were adapted to meet the needs of this study extent . In particular, net collection was utilized due to its ability to reflect correlations of plant species richness,particularly in sites 100 m in diameter or less . Pan traps were not used due to concern of biased collection results, but also because they do not help to understand bee foraging patterns . In accordance with accepted methods in bee biology fieldwork, data surveys were completed on days with best weather for that week . Ideally, best weather is defined as calm wind , clear/sunny skies, and warm temperatures which are all preferred by bees . The weather application Weather Underground was used for daily climate data such as temperature and wind speed . In summer months with peak abundance of bee activity, a single survey took up to three days to complete due to the volume of data collected. Bee foraging surveys consisted of a weekly walk via the circular path loop throughout each of the 35 gardens in the UC Davis Arboretum and Public Garden in Davis. For each survey author KC randomized the starting point of this sequential circular sampling transect. Construction activity in a small portion of the gardens occurred from January through October 2017 at the east end of the Arboretum which limited site access times to those areas, but did not seem to affect bee behavior in those gardens. Due to varied start points for each weekly walk, the different gardens were visited at a variety of times of day throughout the year to avoid observational bias. This helped to ensure no garden would be favored by warmer afternoon temperatures or changes in sun and shadow.Bee foraging observations were done one garden at a time, by identifying each bee genus foraging on plant genera within the garden. A modern system for recording written notes and images with corresponding geographic coordinate data was devised for this task. A digital DSLR Canon T1i camera equipped with a high-quality Sigma macro lens captured a representative image of each foraging association. At each flowering plant genus per garden, author KC motionlessly observed for insect activity. If insect movement was detected author KC visually focused on the insect’s physical attributes, behavior, and movement patterns, such as has been shown to work with Citizen Scientists for bees . Using a single same observer throughout the study avoided the potential for observational bias of multiple observers. Netting was essential in collecting new specimens, both for ideal on-site as well as in-lab identification. Unique or unidentified bee specimens were collected and frozen, then thawed, pinned and identified with a dissection microscope—a standard protocol for bee identification . Due to practicalities of identification of both plants and bees in the field, and because the study collected bee foraging data across a relatively large site, we settled on genus levels of phylogeny. This was done to reconcile the micro-site scales at which bees forage versus the miles wide spatial extent of the Arboretum gardens and plantings. Importantly, bee-to-plant foraging associations were recorded per garden. In this way, entomological methods could be adapted to look for bee-to-plant associations across a large study site, rather than classical insect surveys.Data processing occurred post circular transect walk using a personal computer. Handwritten notes were transcribed to a data collection spreadsheet . One representative JPEG image with coordinates for each unique floral visitation per garden was then loaded into ArcGIS , using the ‘Photos to Points’ tool.

This classification follows from a stability analysis of the susceptible free and resistance-free equilibria. Since we are working in discrete time, an equilibrium is stable if the magnitude of the largest Eigenvalue of the Jacobian matrix evaluated at the equilibrium is less than unity. If neither equilibrium is stable then both susceptible and resistant plants are able to invade a population consisting almost exclusively of the other when rare, and so the genotypes are predicted to coexist. If only the susceptible-free equilibrium is stable, then resistance dominates. If only the resistance-free equilibrium is stable, then susceptibility dominates. But if both equilibria are stable, then the long term outcome depends on the initial densities of each genotype. Extensive numerical simulations of the model were performed to verify that local stability analyses could be used to infer the long-term outcome for all initial conditions. In particular we tested 10,000 combinations of parameters and initial conditions . In all cases the outcome after 10,000 generations of the model matched that predicted by the stability analysis described above. We also performed a number of individual tests for pairs of sets of parameters chosen to cross stability boundaries: the stability analysis predicted behaviour in full simulations of the model in the large number of cases we tested.CMV-Fny accumulates to a higher titer than CMVΔ2b in systemically-infected tomato leaves. Semiquantitative reverse transcription-polymerase chain reaction analysis of viral RNA accumulation leaves of tomato plants systemically infected with CMV-Fny or CMVΔ2b. CMV RNA accumulation was determined by RT-PCR after 30 cycles of PCR and compared to the levels of the elongation factor 1 alpha transcript .

The CMV-specific PCR products from CMV-infected leaves accumulated to higher levels than those from CMVΔ2b infected leaves. RT-quantitative PCR of CMV accumulation relative to CMVΔ2b. Graph shows the mean accumulation of viral RNA in systemically-infected tissues of plants inoculated with CMV-Fny or CMVΔ2b at 10 and 18 dpi. Mean accumulation of virus-specific PCR products is shown for CMV and CMVΔ2b and error bars represent standard errors around the mean for n = 4 samples for CMVΔ2b at 10dpi and n = 3 and 2, respectively, for CMV at 10 and 18dpi. The housekeeping transcript control was EF1α and levels are shown relative to CMVΔ2b, which is designated as ‘1’. . Pollen yield from mock-inoculated and virus infected flowers is similar. Fully open flowers from 12 mock-inoculated and nine CMV-PV0187-infected plants were excised into microfuge tubes containing 300μl of water and vortexed for 40 seconds. Using a microscope, plastic pot manufacturers released pollen grains were counted in technical triplicates using a cell-counting chamber. The mean number of pollen grains released by flowers is shown. Error bars indicate standard error around the mean. The viability of pollen from mock-inoculated and CMV-infected flowers is similar. Pollen was harvested into microfuge tubes from flowers by manual buzzing with an electrical toothbrush and stained with fluorescein diacetate. Data are from nine mock-inoculated and nine CMV-PV0187 infected plants. Esterase activity in viable pollen grains releases fluorescein that fluoresces under blue light. The percentage of pollen grains fluorescing is indicated with error bars indicating standard error around the mean. Typical microscopic fields of view for pollen grains extracted from flowers of mock-inoculated and CMV-PV0187-infected plants viewed under blue light and bright field with an epi-fluorescent microscope connected to a digital camera . Upper panels were viewed with blue light illumination under bright field optics enabling viable and non-viable pollen grains to be counted. Lower panels show pollen grains viewed with epi-fluorescent optics only. Scale bar = 100μm.The three genomic RNAs of CMV-PV0187 were sequenced.

The RNA sequences were compared to those of CMV-Fny and other CMV strains and isolates. Phylogenetic analysis using the RNA sequences of CMV-PV0187 RNAs 1, 2, and 3, with corresponding sequences of other CMV strains and isolates. Phylogenetic analysis using the neighbour-joining method under the Kimura-2 parameter was conducted in MEGA software . The bootstrap consensus tree was carried out with 1000 replications. Panels show the phylogenetic analysis of RNAs1, 2 and 3. The CMV-PV0187 sequence data used in this analysis is available at NCBIunder GenBank accession numbers KP165580, KP165581 and KP165582 corresponding to RNA1, RNA2, and RNA3, respectively. PV0187-CMV groups closely with CMV-Fny , with which it has an overall 99% RNA sequence identity. The predicted 110 residue amino acid sequences of the 2b proteins of CMV-Fny and CMV-PV0187 are identical. The amino acid sequences are a virtual translation of the 2b open reading frames of the two CMV strains. The numbers 60, 61, and 110 indicate amino acid residue positions.The growth and morphology of leaves, flowers and fruit were compared between tomato plants that had been mock-inoculated or infected with CMV-PV0187. Plants or plant organs were photographed and typical images are shown in panels A-E. Tomato plants inoculated with CMV-PV0187 at the seedling stage show marked stunting compared to mock-inoculated plants . Mature, expanded leaves of infected and mock-inoculated plants. Young, upper leaves of infected and mock-inoculated plants. Flowers from mock-inoculated and CMV-PV0187 infected plants are similar in appearance and show no gross differences in morphology. Tomato fruits from mock-inoculated plants are larger than those from CMV-PV0187 infected plants. Scale bars = 3 cm.Growth rate of resistant mutants in the vicinity of the equilibrium at which only susceptible plants are present. The panel shows a series of full two-way sensitivity analyses of the model, showing effects on the growth rate of rare mutant resistant plants in the vicinity of the equilibrium at which only susceptible plants are present, caused by independently changing pairs of parameters . All pair-wise combinations of two parameters are shown: dots on each axis show default values of each parameter. In all cases, the magnitude of the largest Eigenvalue of the Jacobian matrix at the model equilibrium–which is equivalent to the initial discrete time rate of exponential growth over successive seasons of rare mutant resistant plants -is shown by color. Note that Fig 8 in the main text characterises long-term evolutionary outcomes by distinguishing regions in which growth rates of each type of mutant are larger than or smaller than one, and so in which the equilibria can be invaded : these results therefore provide additional numerical detail in support of that figure. Growth rate of susceptible mutant plants in the vicinity of the equilibrium at which only homozygous resistant plants are present .Design of free choice bee-pollination experiment. A large flight arena was constructed out of nylon netting with three zipped doors to allow full access. Within this flight arena a bumblebee colony was attached by a tube to a small flight arena containing a microtiter plate filled with 30% sucrose to allow the bumblebees to feed freely. Sliding gates on the side of the small arena permitted one bee to be released into the larger arena containing three mock-inoculated and three cucumber mosaic virus -infected flowering tomato plants. Cartoon demonstrating the arrangement of mock-inoculated and CMV-infected plants within the larger flight arena. The plant microbiota, defined here as the community of bacteria, fungi, archaea, viruses, and other microscopic organisms that live on or in plant tissues , confer many services as well as disservices to their hosts, including disease development and defense , protection against herbivory , tolerance of abiotic stress , and aid in nutrient uptake . These microbial communities associate with all plant tissues , including seeds . Seeds play a major role in plant communities as agents of dispersal, genetic diversity,and regeneration , and they have significant economic and social value through agriculture . Seeds also are a major bottleneck in natural plant populations, as they face heightened mortality from abiotic stressors, pests, pathogens, and predators . As the initial source of inoculum in a plant’s life cycle, seed microbes are can be transmitted across plant generations and have lifelong impacts .

Consequently, understanding how seeds acquire and interact with their microbiota, for example, via priority effects or according to the Primary Symbiont Hypothesis , has implications for improving seed health, seedling establishment, and plant community structure. Previous work on seed microbiota has primarily taken a pattern-based approach to studying assembly processes . Such an approach uses culturing and/or next-generation sequencing to compare, contrast, and correlate patterns in microbial community composition, diversity, and species co-occurrences. Typically, however, these community data provide limited insights into processes such as dispersal, microbe-plant interactions, and microbemicrobe interactions. Given that seed microbial communities are highly variable across individual plants, plant species, and locations , such pattern-based data cannot always be used to predict assembly outcomes. Moreover, such studies often consider how these assembly processes occur at a single spatial scale . We hypothesize that a mechanistic, black plastic plant pots wholesale multi-scale approach would provide a more complete understanding of how microbial communities assemble in seeds, with the field of meta community ecology providing a theoretical framework for such an approach. Metacommunity theory accounts for the interaction between ecological processes and habitat heterogeneity across spatiotemporal scales to impact community patterns . This emphasis on multiple scales and heterogeneity can help explain the main drivers of community assembly and patterns of biodiversity and co-occurrence . Plant-associated microbial communities vary widely across environmental gradients and host genetics from the levels of tissues to populations . As such, treating individual plants as heterogeneous habitats for microorganisms that are embedded in a larger, heterogeneous landscape of multiple plants representing different species provides a new approach to observing, testing, and modeling drivers of microbial community variation . However, the study of microbiota through a meta community lens is still relatively new, both for animals and plants , and the plant seed represents a relatively understudied microbiome in this context. In this review, we address how mechanisms of seed microbial community assembly have been studied at different spatial micro-, meso-, and macro-scales , and advocate for a meta community-based approach to seed microbiology in future work. For this review, we use the definition of community assembly from Fukami : “the construction and maintenance of local communities through sequential, repeated immigration of species from the regional species pool.” Additionally, most studies that we cover in our review will be focused on fungi and bacteria . We acknowledge that archaea, viruses, and protists are frequent members of plant-associated microbial communities , many plant viruses are seed transmitted , and viruses can play a major role in the diversity and function of soil microbial communities . However, the ecological roles of these microbes in plant microbial communities, including those of seeds, are still largely unknown. As such, we cannot speak on their contributions to seed microbiota assembly here and recommend new research on these microbes in seeds. We will first summarize the modes of microbial acquisition into seeds, and how meta community ecology frames this assembly process. We then discuss studies of seed microbiome assembly which examine the processes of filtering, species interactions, dispersal, and ecological drift. We specifically highlight studies that address assembly processes during seed development and maturation, as these stages are understudied compared to seed dormancy and germination, and they are likely the source of microbes that persist between plant generations . Lastly, we suggest future lines of research to gain a more mechanistic, scale-explicit understanding of seed microbiome assembly.Plant seeds are generally composed of three tissues: a seed coat which provides physical protection , an embryo which is the precursor to the seedling and is made up of an immature root, a stem, and one or more embryonic leaves , and an endosperm which typically consists of carbohydrates and proteins and provides nutrition for the embryo during germination and growth before photosynthesis can occur . Seed development involves three stages . Following fertilization by pollen, the egg cells divide and differentiate into the embryo and endosperm tissues, in a process called histodifferentiation . Next, the cells expand and mature with reduced division, and seed mass increases during this filling stage, as nutrient reserves are deposited into the endosperm . After this, nutrient accumulation declines, and the seed goes into maturation drying and loses about 10%–15% moisture content before it is ready to be dispersed .During seed development, microbes may enter the seed tissues via three distinct routes of transmission: vertical, floral, and horizontal . Vertical transmission involves microbes traveling from other organs of the mother plant to the developing embryo.

Nevertheless, the limited evidence suggest that genetic factors may be important for the interindividual variability, in particular, genetic polymorphisms of genes involved in phase I and phase II metabolism, such as COMT or CYP7A1, and others, such as the APOE genotype or cholesterol transporters. The gut microbiota is an emerging key player explaining variability, as evidenced by the differences in biological response observed between equol and non–equol producers, but also in the differential effects observed in relation to ellagitannin metabolism. Finally, health and metabolic status seem to be other factors playing a role, with some evidence suggesting that “at risk” participants or patients may be more likely to gain benefits from increased plant bio-active compound intake than healthy individuals may be. Although some variability according to age and sex has been shown, the current evidence is not strong enough to make any conclusion. From this review, it clearly appeared that current published studies reporting inter individual variability were not initially designed to study between-subject variation in the response. In most of these studies, the inter individual variability was observed post hoc and without adequate a priori definition of subgroups, planning, and power calculation that result in low numbers of subjects in subgroups and inadequate study power for statistical analysis. Therefore, there is a need for additional controlled-intervention studies specifically designed to identify the factors affecting the variability in the response to plant-food bio-active compounds. Future intervention studies should be suitably powered and randomized based on the factor of variability of interest .

Furthermore, it would be important to avoid as much as possible the use of complex foods as sources of bio-active compounds; indeed, raspberry grow in pots because of the difficulty of having well-matched controls, the attribution of the observed effects to the bio-active compounds of interest is questionable. In these studies, it will also be crucial to systematically measure both biomarkers of effects and bioavailability variables, including the concentration and nature of circulating metabolites whose biological potential may be variable. In the long run, this knowledge will guide the provision of evidence-based, targeted dietary recommendations.There are multiple ways in which removal of infected hostplant tissue can be employed as an element of disease management. These include removal of reservoir hosts to limit pathogen spillover onto a focal host , roguing of infected focal hosts to limit secondary spread , and removal of localized infections within hosts to limit further infection or to retrain an unproductive plant . Studies of bacterial pathogens in perennial crops have evaluated the utility of pruning as a disease management tool, with mixed results . The removal of infected plant tissues is analogous to measures used for management of trunk diseases, often referred to as “remedial surgery,” as an alternative to replacing infected plants . In this study, we investigated whether severe pruning of Xylella fastidiosa-infected grapevines in commercial vineyards could clear vines of existing infections. Pierce’s disease is a lethal vector-borne disease of grapevines caused by the bacterium X. fastidiosa . After susceptible plants are inoculated by X. fastidiosa, pathogen populations multiply and move through the xylem network, leading to symptoms of reduced water flow , including leaf scorch, cluster desiccation, vine dieback, and eventually death. There is no cure for grapevines infected with this bacterium; current strategies for management of PD in California vineyards involve limiting pathogen spread to uninfected vines by controlling vector populations, disrupting transmission opportunities, and eliminating pathogen sources in the surrounding landscape .

PD is notable for the numerous sources of variability in infection levels and symptom severity in plants. X. fastidiosa infection levels vary among plant species , grapevine cultivars , seasons , and as a function of temperature . Like other bacterial plant pathogens , X. fastidiosa is often irregularly distributed within individual hosts. For example, X. fastidiosa infection levels in grapevines may vary by more than 10-fold between grapevine petioles and stems ; in other hosts, infection levels may vary by more than 100-fold between basal and apical sections of shoots . This within-host heterogeneity may be epidemiologically significant if it affects pathogenacquisition efficiency . Moreover, if such variation is associated with protracted localized infection near inoculation points, such heterogeneity may facilitate other disease management tactics. In addition to grapevines, other plant species that are susceptible to X. fastidiosa infection include citrus in South America . Management of the resulting disease in C. sinensis relies on clean nursery stock, vector control, and pruning infected plant tissue from established trees or roguing young plants . The concept of pruning of infected plant material is based on the fact that, in established trees , tissue with early symptoms of infection can be pruned ~1 m proximal to the most symptomatic basal leaf, effectively eliminating infections, as the remaining tissue is free of X. fastidiosa . However, pruning is not adequate for young trees or for removing bacterial infections if any symptoms are present in fruit . X. fastidiosa multiplies and spreads through the xylem vessels, reaching the roots of perennial hosts such as citrus , peach , alfalfa , and blueberry . Nonetheless, under field conditions, chronic infection of grapevines is temperature and season dependent. In regions with freezing winter temperatures, infected plants can recover in winter, curing previously infected and symptomatic grapevines . Infections that occur during spring lead to chronic disease ; however, infections that occur during late summer and fall may cause disease symptoms in the current year, but a high proportion of vines lack symptoms of X. fastidiosa infection in the following year .

Nonetheless, models that incorporate low temperatures have substantial explanatory power in predicting rates of winter curing of X. fastidiosa infections in grapevine . Infections that occur early in the season may have a longer period during which X. fastidiosa can colonize and reach high infection levels, which may increase the likelihood of the disease surviving over the winter. Following this rationale, if most late-season infections remain in the distal ends of shoots and have lower infection levels, removing the symptomatic portion of the vine might eliminate X. fastidiosa. In other words, the efficacy of pruning infected grapevine tissue could depend on both the time of year in which the plant was infected and on winter temperature. A potential benefit of severe pruning versus replanting is that pruning leaves a mature rootstock in place, which is likely to support more vigorous regrowth compared to the developing rootstock of a young transplant . Recent attempts to increase vine productivity by planting vines with more well-developed root systems are based on this presumption. However, even if severe pruning can clear vines of infection, it removes a substantial portion of the above ground biomass of the vine. Thus, a method for encouraging rapid regrowth of the scion after aggressive pruning is needed. We studied the efficacy of pruning infected vines immediately above the rootstock graft union—the most aggressive pruning method—for clearing grapevines of infection by X. fastidiosa. We reasoned that if such severe pruning was ineffective at clearing vines of infection, less severe pruning would not be warranted; if severe pruning showed promise, less severe pruning could then be tested. We use the term “severe pruning” to refer to a special case of strategic pruning for disease management, analogous to the use of “remedial surgery” for trunk diseases . To test the efficacy of clearing vines of X. fastidiosa infection, we followed the disease status of severely pruned versus conventionally pruned vines over multiple years, characterized the reliability of using visual symptoms of PD to diagnose infection, and compared two methods of restoring growth of severely pruned vines.Study design. Pruning trials were established in Napa Valley, CA in commercial vineyards where symptoms of PD were evident in autumn of 1998. The vineyards used for these trials varied in vine age, cultivar, 30 planter pot and initial disease prevalence . All study vines were cordon-trained and spur-pruned. We mapped the portions of the six vineyards selected for study according to evaluation of vines for disease symptoms.

The overall severity of PD symptoms for each vine was recorded as follows: 0 = no symptoms, apparently healthy; 1 = marginal leaf scorch on up to four scattered leaves total; 2 = foliar symptoms on one shoot or on fewer than half of the leaves on two shoots on one cordon, no extensive shoot dieback, and minimal shriveling of fruit clusters; and 3 = foliar symptoms on two or more shoots occurring in the canopy on both cordons; dead spurs possibly evident along with shriveled clusters. To test the reliability of the visual diagnosis of PD, petiole samples were collected from the six vineyard plots when symptom severity was evaluated for vines in each symptom category; these samples were assayed using polymerase chain reaction . Petioles were collected from symptomatic leaves on 25, 56, and 30 vines in categories 1, 2, and 3, respectively.Next, severe pruning was performed between October 1998 and February 1999 in the six vineyard plots by removing trunks of symptomatic vines ~10 cm above the graft union. Cuts were made with saws or loppers, depending upon the trunk diameter. During a vineyard survey, severe pruning was conducted on 50% of vines in each symptom category; the other 50% of vines served as conventionally pruned controls. Sample sizes for control and severely pruned vines in each disease category ranged between six and 62 vines depending on the plot, with at least 38 total vines per plot in each control or pruned treatment. In spring 1999, multiple shoots emerged from the remaining section of scion wood above the graft union on severely pruned vines. When one or more shoots were ~15 to 25 cm long, a single shoot was selected and tied to the stake to retrain a new trunk and cordons, and all other shoots were removed at this time. We evaluated the potential of severe pruning to clear vines of infection, by reinspecting both control and severely pruned vines in all six plots for the presence or absence of PD symptoms in autumn 1999 and 2000. In all plots, category 3 vines were inspected in a third year ; in plot 6, vines were inspected an additional two years . Finally, in plot 6 we investigated chip-bud grafting as an alternate means of ensuring the development of a strong replacement shoot for retraining. To do this, 78 category 3 vines were selected for severe pruning, 39 of which were subsequently chip-bud grafted in May 1999. An experienced field grafter chip budded a dormant bud of Vitis vinifera cv. Merlot onto the rootstock below the original graft union, and the trunk and graft union were removed. The single shoot that emerged from this bud was trained up the stake and used to establish the new vine. The other 39 vines were severely pruned above the graft union and retrained in the same manner as vines in plots 1 to 5. Development of vines in plot 6, with and without chip-bud grafting, was evaluated in August 1999 using the following rating scale: 1) “no growth”: bud failed to grow, no new shoot growth; 2) “weak”: multiple weak shoots emerging with no strong leader; 3) “developing”: selected shoot extending up the stake, not yet topped; and 4) “strong”: new trunk established, topped, and laterals developing. Statistical analysis. All analyses were conducted using R version 3.4.1 . We used a generalized linear model with binomial error to compare the relative frequency of X. fastidiosa-positive samples from vines in the different initial disease severity categories . Next, we analyzed the effectiveness of chip budding versus training of existing shoots as a means for restoring vines after severe pruning. This analysis used multinomial logistic regression that compared the frequency of four vine growth outcomes the following season: strong, developing, weak, or no growth. This main test was followed by pairwise Fisher exact tests of the frequency of each of the individual outcomes between chip budded-trained and trained vines . We analyzed the effect of severe pruning on subsequent development of PD symptoms using two complementary analyses. First, we compared symptom return between severely pruned and control vines in the three symptom severity categories for two years after pruning.

The above values were calculated for each inoculated microbe individually and for all five species collectively. For comparison with real nectars and to test our inoculum in artificial solutions, we also added 1μL of inoculum to 10μL of 30% m/m sucrose and an artificial nectar containing sugars and peptone in strip tubes. Tubes were sealed and incubated at 25°C for 24h, then processed identically to actual nectar samples.We estimated concentrations of hydrogen peroxide , a known antimicrobial reactive oxygen species found in some nectars , in the nectar of separate, noninoculated flowers of most sampled plant species . Peroxide values from noninoculated nectar represent initial conditions which would be experienced by microbes arriving in flowers. To assess the contribution of floral morphology, we scored floral phenotypes of all plant species on the basis of 28 binary traits used in past studies to represent pollination syndromes in multivariate space using Bray–Curtis dissimilarity. We determined trait states through a combination of observation and reference with the Jepson eFlora . We also encoded other traits of particular interest such as inflorescence density and corolla fusion.We conducted analyses in R . Using package lme4 , we constructed linear mixed effect models with nectar volume, total and by-species CFU density and CFU Shannon–Wiener diversity index as dependent variables. As independent variables, we included nectar volume and temperature extrema, blueberry production and plant species as a random intercept effect. We obtained type III sums of squares, F– and p-values and Kenward-Roger degrees of freedom using function ‘Anova’ in package car . We inspected model residuals for normality and variance inflation factors to assess multicollinearity. We also created separate linear models with either plant species or nectar peroxide concentration as a fixed effect, as peroxide data were not collected for three species .

For linear models in which we included a quadratic predictor, we conducted a likelihood ratio test comparing the goodness of fit of the models with and without the quadratic term. To test if microbial community composition differed by plant species and temperature extrema, we used function ‘adonis’ in package vegan to perform a permutational multivariate analysis of variance. We used function ‘betadisper’ to examine multivariate homogeneity of dispersions across plant species. Community composition was visualised using nonmetric multidimensional scaling ordination, and we tested for significant microbe species vectors using function ‘envfit’. As above, a separate analysis was conducted with peroxide concentration as a predictor variable. To test for co-occurrence between microbe species, we generated Pearson correlation matrices on CFU densities, for both our entire dataset and for each plant species individually, and visualised matrices using package ‘corrplot’ . To estimate plant phylogenetic relationships among sampled plant species, we used the function ‘phylo.maker’ in package V.PhyloMaker2 using the reference plant phylogeny GBOTB.extended.TPL. Using this tree, we tested for a phylogenetic signal of nectar volume, CFU densities and Shannon diversity using function ‘multiPhylosignal’ in package picante with 10,000 simulations. To test for relationships between plant phylogenetic relatedness and multivariate microbe community composition, we created a pairwise distance matrix of plant phylogenetic relatedness using function ‘cophenetic.phylo’ in package ape . We compared this distance matrix to a Bray– Curtis dissimilarity matrix of the mean CFU densities of each microbe by plant species using a Mantel test via function ‘mantel’ in package vegan, calculating Spearman’s ρ with 10,000 permutations. We also created a Bray–Curtis dissimilarity matrix of plant species based on floral trait data and compared this to the two aforementioned matrices.

We controlled for the effect of plant phylogenetic distance on pollination syndrome using a partial Mantel test via function ‘mantel.partial’. We generated correlograms for all Mantel tests using the function ‘mgram’ in package ecodist . Figures were created using package ggplot2 and tree plots using ggtree and custom function ‘ggtreeplot’ .In this study, we observed shifts in the composition of a synthetic microbe community inoculated into the floral nectar of 31 flowering plant species, mainly predicted by plant species and temperature. Host species-dependency of plant microbiomes is consistent with previous observational studies of nectar , pollen , phyllospheres and roots . Our manipulative study complements this work by leveraging a phylogenetically diverse array of plant taxa, highlighting the role of plant host identity as a driver of microbe community assembly outside of dispersal and priority effects. Specifically, we provide experimental evidence that nectar microbiomes become distinct across plants even when initial community composition is the same.Several factors can impact nectar microbe community assembly even when controlling for dispersal , including filtering by host plants and interactions among microbe species . Our study shows support for both processes. In certain plant species , few microbe taxa established and were at low densities. Furthermore, community assembly in certain plant species was more stochastic than in others ; it may be that environmental stress generates stronger selection and more uniform communities . Interestingly, the highest uniformity in community composition we documented was in our two experimental control solutions . This suggests community stochasticity is higher in actual flowers, likely due to variation in nectar properties across plants. Previous work in a single plant species suggests dispersal positively contributes to nectar microbe beta diversity via priority effects. Our study suggests that even in the absence of dispersal, nectar microbe beta diversity may be more constrained in some plant species than in others.

Plant-level mediation of microbe community assembly outside of pollinator vectoring is consistent with the hypothesis that physical or chemical properties of flowers and nectars differentially inhibit microbegrowth. This phenomenon may serve as an adaptive defence against nectar spoilage , but it is also possible that some nectars could facilitate the growth of particular microbes. Several plants we inoculated belong to genera containing species known to produce antimicrobial nectar metabolites: for example, alkaloids, phenolics, and terpenoids . We suspect that the occurrence of secondary metabolites or other nectar constituents might explain the distinct differences in community structure we observed across plant species. This is supported by our finding that nectar peroxide concentration, which is regulated via nectarin proteins in Nicotiana , was negatively associated with total microbe density across plant species. The effects of peroxide concentration differed depending on microbe species, mirroring trends from in vitro assays , perhaps due to differences in microbial detoxification mechanisms. However, mean peroxide concentration on its own explained very little variation in the dataset. Future work incorporating a much broader diversity of nectar chemicals and compounds in a similarly diverse array of plant species is needed to determine if such a predictive framework exists. Similarity in nectar chemistry among species can be associated with phylogenetic relatedness in certain plant clades . We found plant relatedness was weakly positively associated with similarity in microbe community composition, but not with the densities of any individual microbes. Plant relatedness alone was not sufficient to explain the similarity in microbe community assembly however as this relationship was not monotonic. Within major plant clades, plant species in our study hosted similarly composed microbe communities , but several exceptions are clear. Hierarchical clustering analysis reflected this pattern as some, but not all, plant species of major clades clustered together and congeneric plant species did not necessarily cluster closely. In other plant microbiomes, host plant phylogeny can be a predictor of microbial communities , vary between bacteria versus fungi or show little predictive power . In the latter cases, microbe communities were better predicted by plant traits, implying a weak relationship between plant phylogeny and traits . We found that floral morphological traits were correlated with plant relatedness, but were not predictive of nectar microbe communities , blueberry in container suggesting that key host traits mediating microbial growth were not measured in the current experiment. Floral trait similarity, here approximating pollination syndromes , not predicting variation in microbial composition is contrary to predictions based on floral surveys of open flowers in which pollinator identity or pollination syndrome is a key predictor of nectar microbial communities .

Nevertheless, the microbes used here are common in most geographical regions sampled to date, and we expect that pollinator movement will homogenise microbial populations to some extent within coflowering communities.Interactions among microbes likely influenced community assembly within flowers, and we detected signatures of both facilitation and competition depending on analytical approach. All five species in our synthetic community were capable of coexisting after 24 h at varying densities in artificial nectar in vitro. We detected only positive or neutral correlations between microbe species pairs, similar to Francis, Mueller, and Vannette , in both our pooled dataset and separately within each plant species. In the pooled dataset, Neokomagataea was the only species showing no positive correlations with any other microbe, perhaps due to unknown specificities in its nutrient requirements. At first, this all seems to suggest facilitation among some species pairs , or that competition between microbes at 24 h was insignificant. However, we also observed a unimodal, ‘hump-shaped’ relationship between CFU Shannon diversity and increasing total CFU density across plant species. Shannon diversity increased with CFU density until roughly 102CFU μL−1, after which diversity declined as density increased. Similar unimodal relationships between microbe diversity and productivity have been documented in both artificial and natural aquatic environments . Several underlying mechanisms have been proposed for this relationship, including a shift from abiotic to biotic pressures along the gradient of increasing productivity . We suggest that extreme resource limitation or antimicrobial conditions in some nectars may limit the growth of all microbes in some nectars, whereas the availability of pollen or other nutrients may enable dominance of specific microbes in other nectars. Co-occurrence networks reflect the combined influence of biotic interactions and the environment and may under represent negative, nontrophic interactions relative to empirically observed interactions . Additionally, only 24h post inoculation may represent an early to intermediate time point in community progression, perhaps preceding manifestation of antagonistic interactions . Conversely, the unimodal relationship across plant species suggests that the growth of specific microbes in highly productive environments can effectively reduce community diversity, resulting in competitive exclusion of other microbes. We hypothesise that such competitive dynamics will be more apparent in longer persisting or senescing flowers , but interaction outcomes can also depend on microbes’ phylogenetic relatedness or their local adaptations to flower environments .Consistent with our expectations, microbial growth was correlated with seasonal temperature shifts. Maximum and minimum ambient temperatures over 24h of growth were differentially associated with components of community assembly and species individual densities, further supporting that nectar microbe species differ in their temperature ranges for optimal growth . Notably, increases in daily minimum temperature increased densities of the nectar yeast Metschnikowia and bacterium Acinetobacter, suggesting that their population densities are limited by growth rate. In contrast, high maximum daily temperatures decreased Lactobacillus and Neokomagatea densities, suggesting temperature thresholds for these microbes. These patterns are consistent with previous observations that nectar yeast prevalence was found to be positively correlated with temperature , while high temperatures can negatively impact nectar bacterial diversity . Although we did not detect a significant effect of temperature on nectar volume in the current study , open flowers likely experience increased evaporation affecting nectar composition and secretion . In any case, our observations indicate, similar to other plant– microbe systems , that shifts in temperature extrema over time may alter baseline effects of plant host filtering on nectar microbial communities in predictable ways, such as favouring certain microbe species over others or limiting maximum achievable levels of diversity. The implications of these shifts for plant–pollinator interactions deserve further attention. We also emphasise that in our study, different plant species were necessarily sampled at different times of year due to flowering phenology, so temperature was confounded with other variables like plant host identity, humidity and solar radiation, all of which affect microbial assembly in flowers . Nevertheless, our results suggest that differential response to temperature minima and maxima mediates microbial growth and interactions. We show that plant species host consistent microbial communities, suggesting plant populations could potentially adapt to the presence of specific microbes . Insect populations could also adapt to plant-specific microbial growth, such as perceived volatile cues or acquired microbes . For example, Apilactobacillus is thought to benefit pollinators , thus increasing high temperatures may inhibit the growth of this beneficial microbe.

Bee foraging activity can also be affected by preferences for particular weather conditions , temperatures , or preferences for floral phenology leading to temporal complementarity. Interspecific interactions between bee species can also increase honey bee efficiency . In almonds, wild bee presence increases the likelihood that honey bees will move between different rows, which leads to higher pollen tube initiation and subsequent fruit set . Both niche complementarity and interspecific interactions likely underlie the positive relationship we detected between richness and seed set . In agreement with past findings , we detected an interactive effect between wild bee and honey bee visitation on sunflower seed set. We did not, however, detect any main effects of wild bee and honey bee visitation, despite strong evidence that wild bees positively increase seed set regardless of honey bee abundance . In order to evaluate the direct contribution of wild bees, other studies have estimated the contribution of wild and honey bee visitation to seed set separately . We were unable to do this because of our study design, which did not examine seed set from single bee visits. Nevertheless, this is the first sunflower seed set study to detect an interspecific interactive effect at the community-level rather than at the individual-level. However, despite the importance of these interactive effects on sunflower yield, company was the factor that most strongly influenced seed set. Although there was little variation in head size between sunflower companies , using company as a classification may mask other differences, such as genetic differences between varieties and variation in field management techniques. By pairing control and hedgerow sites by company, variety and landscape context, we sought to minimize these potential differences, and the few differences in management practice were noted between companies. It is hypothesized that the effectiveness of field-edge vegetation re-diversification is maximized in landscapes that retain a small percentage of natural areas that can facilitate recolonization of restored habitats . The added benefits of diversification efforts may be minimal in complex landscapes with high proportions of natural habitat since ecosystem service providers are often already supported.

Diversification efforts may not support ecosystem providers in highly intensified landscapes with no remaining natural habitat, big plastic pots either because there are no source areas to colonize the new habitats or because the new habitats alone cannot support populations of ecosystem service providers . Although the landscape where we conducted our study constitutes a “cleared” landscape, and we did not detect landscape effects, other studies in the same location have found that hedgerows increase wild bee abundance, richness and population persistence and promote rare and/or more specialized species . Nevertheless we did not find evidence that these biodiversity benefits translated into higher rates of pollination services in adjacent sunflower crop fields. Although both wild bee richness and abundance were important factors contributing to sunflower seed set, these contributions may be attributable to factors other than hedgerows. For example, wild bee visitors to sunflower were predominately sunflower specialists; the amount of sunflower maintained in the landscape over time could therefore influence sunflower pollinator populations more strongly than hedgerow plantings that do not contain floral resources suitable for the specialists’ dietary requirements , as we found was true in the independent dataset. While conservation and ecosystem service outcomes can be synergistic, win–win scenarios are challenging to achieve . Hedgerows augment pollinator populations, which can be important for achieving wild bee conservation goals ; however, they may not be a “silver bullet” strategy for increasing crop pollination. Both the scale of the re-diversification effort relative to the farming system and the adjacent crop type could limit the effectiveness of hedgerow plantings. Hedgerows occupy <1% of our study landscape and contain 175 times less area than a typical average crop field in our study area.

The intensity of bloom in hedgerows is also minimal in comparison to the hundreds of thousands of blooms in a single MFC field . Increasing the size of hedgerows relative to fields or introducing a suite of diversification techniques could increase the effectiveness of re-diversification efforts . Patch size may influence a habitat’s capacity to host different densities of pollinators . Alternately, the configuration of habitat could impact pollinator populations. For example, when Morandin and Winston examined the optimal spatial distribution of a MFC, canola , they found that both profits and pollination services would be maximized if a central field was left fallow or allowed to revert to semi-natural habitat. The size, configuration and quality of habitat may all interact to influence pollinator communities . The benefits of field-edge diversifications may also differ based on crop identity and landscape context . For example, sunflower has easily accessible florets that attract both generalist and specialist pollinators. However, in systems where flowers have specific requirements, such as highbush blueberry that requires buzz-pollination, the identity of pollinator species may be of more importance . Further, species-specific responses to habitat features may differ. Carvell et al. found bumble bees had differential responses to wildflower patch size and landscape heterogeneity, indicating that local and landscape habitat factors can also interact with one another, and with crop-specific attributes, to affect crop pollination. In a tropical region, Carvalheiro et al. found that wildflower plantings worked in concert with natural habitat to heighten mango production. There are a paucity of studies on the ecosystem service benefits from field-edge plantings, therefore the complex range of factors, including farming type, crop system, landscape context, and region , influencing their performance is still relatively unknown .Xylella fastidiosa is a Gram-negative bacterium in the Xanthomonadaceae family that colonizes the xylem vessels of its plant hosts and is exclusively vectored by xylem sapfeeding hemipteran insects. This bacterium causes several crop diseases, such as Pierce’s disease of grapevine, citrus variegated chlorosis, coffee leaf scorch, plum leaf scald, and olive quick decline syndrome.While X. fastidiosa has also been associated with diseases in many other plant species, the bacterium behaves as a commensal endophyte in a variety of its plant hosts. A range of pathogenicity and virulence factors has been identified in X. fastidiosa that potentially enable the bacterium to overcome host defenses and successfully establish itself in the xylem tissue. X. fastidiosa cells form biofilm-like structures that are crucial for successful acquisition and transmission by the insect vectors as well as for plant host colonization and pathogenesis. Progression of the disease symptoms is associated with X. fastidiosa systemic spread through the xylem vessel network which requires dispersal of bacterial cells from the biofilms as well as twitching motility and degradation of pit membranes by bacterial cell wall–degrading enzymes. Moreover, the severity of symptoms is exacerbated by host-derived xylem occlusions elicited by X. fastidiosa colonization of grapevine. Indeed, the symptoms caused by X. fastidiosa infection are suggestive of hydric stress and vary in intensity depending on pathogen genotype, plant host species/genotype, plant age, cultivation practices, and environmental conditions. Originally confined to the Americas, X. fastidiosa has spread to various plant species in a number of European countries, possibly through the importation of infected plant material.

Currently, most of X. fastidiosa strains are categorized in three major subspecies, fastidiosa, pauca and multiplex, which are presumed to have originated in Central America , South America and North America. Another two subspecies native to North America have also been proposed. Furthermore, X. fastidiosa strains can be classified into sequence types based on a multilocus sequence typing scheme with seven housekeeping genes. There is a loose association of X. fastidiosa subspecies or STs with host specificity, yet some strains can infect multiple hosts. Indeed, intersubspecific homologous recombination has been associated with X. fastidiosa adaptation to novel hosts. However, the mechanisms by which the distinct X. fastidiosa strains successfully colonize specific plant hosts remain unclear. X. fastidiosa lacks the Type III secretion system, growing berries in containers a membrane-embedded nanomachine typical of Gram-negative pathogens, which delivers effector proteins directly into host cells triggering or suppressing defense mechanisms, respectively in resistant or susceptible plants. Instead, X. fastidiosa type II secretion system seems to be a relevant delivery apparatus of its virulence proteins. It has been suggested that compatibility between xylem pit membrane carbohydrate composition and X. fastidiosa T2SS-secreted cell wall degrading enzymes is necessary for disease progression. Moreover, since X. fastidiosa lipopolysaccharide long chain O-antigen effectively delays plant innate immune recognition in grapevine, the heterogeneity of O-antigen composition may be among the mechanisms underlying X. fastidiosa host range. Comparative genomics studies of X. fastidiosa strains isolated from different plant hosts and from diverse geographical regions identified shared and exclusive genes among these strains, chromosome rearrangements, indels, single nucleotide polymorphisms as well as differences in their mobile genetic elements repertoire, such as plasmids, genomic islands and prophages. While some studies suggest that strains belonging to a phylogenetic group have similar pathogenicity mechanisms and strong selection, possibly driven by host adaptation, other studies identified differences in each subspecies, such as enriched molecular functions and distinct rates and events of recombination. The availability of new whole genome sequences of X. fastidiosa strains from diverse plant hosts and distinct geographical regions fosters up-to-date comparisons to be made. Here we present a thorough comparative analysis of 94 X. fastidiosa genomes with the goal of providing insights into host specificity determinants for this phytopathogen as well as expanding the knowledge of its MGE content and of its immunity systems.Nucleotide sequences of core genome orthologous CDSs were aligned using Clustal Omega v.1.2.1 with default parameters. Then, the sequences were concatenated and homologous recombination regions were masked using Gubbins v.3.1.6. The core genome phylogenetic tree was built with a maximum-likelihood method using IQ-TREE v.1.5.4 with a model predicted by ModelFinder and an ultrafast bootstrap of 1000 replicates. Phylogenetic trees for 1605 orthologous CDSs found in more than 80 strains including the soft-core and core genomes were built with a maximum-likelihood method using IQ-TREE v.1.5.4 with an ultra fast bootstrap of 1000 replicates. Information of plant host of origin for the strains was mapped to the conserved CDSs phylogenetic trees and a Score of mapping was estimated. The overall concept behind Smap was based on consenTRAIT, a metric that estimates the clade depth where organisms share a trait. The Smap for each phylogeny was estimated using a custom Python script that uses Phylo module to find clades in a tree and to calculate the proportion of each plant hostin each clade . The highest proportions of a given host is then retrieved and summed to obtain the Smap. We calculated Smap for both ML and bootstrap trees to get the average of Smap and the percentage of the trees with the same Smap to retrieve the confidence level. Smap values close to 1 indicate a strong relationship between specific hosts and the phylogenetic tree of an orthologous CDS while lowest values are found for highly conserved CDSs unrelated to specific hosts.Mobile Genetic Elements , such as prophages, genomic islands and insertion sequences were identified in the genome assemblies by a combination of prediction tools coupled with manual curation as previously described. Prophage regions were predicted with Virsorter2 and PHASTER. Inovirus_detector software accessed in 4 November 2021 was used for identification of prophages from the Inoviridae family. GI regions were defined using SeqWord Sniffer and GIPSy software, which was used to assign one or more categories related to GI potential function. GI regions overlapping to prophage regions were not considered. IS regions were predicted using the ISEScan software. Retrieved prophage, GI, and IS nucleotide sequences were compared to explore homology relationships using BLAST all-vs-all. Results of BLAST with an identity and coverage alignment higher than 50% and 80%, respectively, were filtered, analyzed and the resulting sequence similarity network was visualized with Cytoscape 3.8 software. Taxonomic classification of intact and incomplete prophages according to PHASTER output was performed with vContact2 and with PhaGCN.The relationship of the orthologous clusters of 1605 CDSs found in more than 80 strains with their respective plant host of origin was explored by mapping the host metadata to the individual phylogenies. A Score of mapping was estimated where Smap close to 1 indicates a strong relationship between the hosts and the phylogenetic tree of each orthologous CDS.

Reduced floral fidelity translates functionally into less conspecific pollen carried by bees and transferred between individual plants of the same species, which was ultimately reflected in decreased plant seed production in our manipulated sites. Thus, our results show that the ecosystem functional contributions of bee species in our system are not fixed, but instead are dynamic and dependent on interactions with competing species. These findings highlight a new mechanism for how biodiversity can shape ecosystem services and functions. Complementarity—in which different species perform distinctfixed roles in different components of ecosystem function—is the primary accepted mechanism for biodiversity-ecosystem function patterns . In contrast, we show a role for biodiversity per se in ecosystem function, in shaping dynamic specialization and its functional consequences over short timescales. While we focused on specialization and did not assess dynamic complementarity in our experiments, complementarity and specialization are often ecologically intertwined and there is evidence for interspecific competition shaping specialization in ways that likely increase dynamic complementarity . The mechanism of interspecific competition driving dynamic specialization is widespread both taxonomically and geographically , supporting the idea that our results likely extend to other BDEF relationships more generally. If dynamic specialization and/or complementarity drive some ecosystem functions, however, planting blueberries in containers why has this result not been uncovered in the hundreds of studies on BDEF relationships? Three factors might contribute. First, the vast majority of BDEF studies have focused on plants and other sessile autotrophs , while much of the work on competition and dynamic specialization has focused on animals .

Given the rapid behavioral plasticity of animals in response to interspecific competition , dynamic specialization and/or complementarity may be especially important for animal-driven ecosystem functions. Still, there is recent evidence of dynamic specialization in plant communities driven by morphological phenotypic plasticity . Second, most BDEF studies cover relatively short time scales; but longer-timescale experiments, especially for plants, may allow for more dynamic specialization to occur, and may explain in part the result that greater biomass “overyielding” in species-rich treatments relative to monocultures often occurs only after months or years in long-term BDEF experiments . Third, the bulk of BDEF experiments are designed such that species identities and abundances are tightly controlled. While such studies are undoubtedly of great value for mechanistically untangling BDEF relationships, manipulative experiments under natural field conditions , especially in terms of interspecific competition regimes, may allow for stronger dynamic responses and thus greater impacts on ecosystem function. As we have demonstrated, losses of a single pollinator species lead to reductions in dynamic specialization, driven by interspecific interactions, that in turn drive significant negative effects on ecosystem functioning in terms of plant reproduction. Our work thus suggests that ongoing pollinator declines could already be causing negative impacts on plant populations, in contrast to the network-based simulation models that predict plant communities will be robust to pollinator species losses. To prevent disruptions of pollination and other critical ecosystem functions and services, we must move beyond assuming that the functional roles of species are static and work to understand how and under what conditions phenotypic plasticity and interspecific competition can interact to drive dynamic changes in ecosystem services and functions.We assessed the effects of our manipulations on stigmatic pollen deposition and seed production in Delphinium barbeyi , a common, long-lived perennial herbaceous wildflower in our plots that is pollinated by several species of bumble bees.

We pre-bagged racemes containing immature floral buds of D. barbeyi in each plot 48-72 hours before experiments. We opened 15 separate bags containing mature, virgin flowers in the control and the manipulation experimental periods, and re-closed the bags at the end of a standardized 4-hr period. We returned to the site 3-4 days after the treatment to harvest stigmas , and 7-15 days later to harvest fruits after they had matured. Stigmas were removed from the flower and mounted on a slide with fuschin jelly in the field to avoid any loss of pollen in transit. We counted both total number of pollen grains as well as proportion of delphinium and non-delphinium pollen grains on the stigmas. We dissected mature fruits and counted developed and undeveloped seeds in the laboratory.Data collected within a study site are not independent: bees and plants are likely to be closely related genetically, and environmental conditions, floral resources, and competitive community context are all similar. To address this lack of independence and prevent pseudoreplication, we used generalized linear mixed-effects models with site as random effect. Thus, the site represents the level of experimental replication across all of our statistical models. We included three fixed effects in all models: experimental state , Bombus species richness, and Bombus abundance, allowing us to statistically control for the changes in abundance in our manipulations, as well as the differing levels of initial Bombus species richness in each plot. We assessed full models to maintain consistency and comparability among the different analyses. Our data on floral fidelity, pollen carriage, and stigmatic pollen deposition were measured in a binomial fashion: individual bees either displayed fidelity or infidelity; foraging transitions were either to a conspecific or to aheterospecific plant; pollen loads were either “pure” or else “mixed” ; and pollen grains on stigmas were identified as either conspecific or heterospecific. We used GLMMs with binomial errors to model these response variables. For all of these outcomes, the data were overdispersed , and so we included an individual-level random effect in the model to compensate for the over-dispersion .

We used the lme4 package for the R statistical programming language to conduct binomial-errors GLMMs. Seed production is a count variable, and plants with insufficient pollination do not produce seeds, resulting in zero counts. Seed count data were both highly overdispersed and zero-inflated relative to a Poisson distribution, so we modeled seed production with a zero-inflated negative binomial distribution using the glmmADMB library for R.Most pollinators are generalist foragers that can switch between plant species within a single foraging bout . This sharing of pollinators among plant species within a community can lead to the transfer of heterospecific pollen to plant stigmas. Such heterospecific pollen deposition is highly variable in nature , and can represent a substantial percentage of total pollen on a stigma, often more than 50% of grains . Stigmatic heterospecific pollen can negatively impact plant reproductive function , but we continue to have a limited understanding of the magnitude and mechanisms of those impacts, particularly under field conditions. Most of what we know about the reproductive effects of heterospecific pollen comes from hand-pollination studies, which have primarily focused on mechanisms of reproductive disruption . Heterospecific pollen can reduce reproductive output by physically blocking conspecific pollen from adhering to the stigma ; by driving stigma closure ; by producing allelochemicals that limit subsequent pollen germination ; by interfering with pollen tube growth in the style , and by usurping ovules, especially among closely related plant species .There is reason to suspect that these hand-pollination results, which come primarily from greenhouse or potted-plant studies, may not reflect the reality of field situations. In nature, we would expect a range of proportions of heterospecific pollen deposited on stigmas, as well as a range of pollen from different plant species, with different amounts of diversity on stigmas. Most hand-pollination experiments have applied fixed heterospecific:conspecific pollen ratios to stigmas, and the results on plant reproduction are variable, with some finding detrimental effects of heterospecific pollen and others showing no impact . Most of these studies used only one heterospecific pollen species and in proportions not necessarily common in nature, making it difficult to apply inference from these results to plant reproduction in the field. Moreover, we know of only one study that assessed the impact of heterospecific pollen over a continuous range of experimental heterospecific pollen proportions—a situation that is likely common in nature—and that was a hand-pollination study that held conspecific pollen quantity constant, using a single heterospecific pollen donor species. In addition, the greenhouse or potted-plant context of most or all of these studies may not translate to the field in terms of possible interactions between resource limitation and heterospecific pollen deposition. For example, container growing raspberries heterospecific pollen may have larger impacts on seed set in plants that are water- or nutrient- stressed relative to plants that are not facing serious resource limitation. Thus, while we know from field studies that heterospecific pollen deposition is common in nature, and from hand-pollination studies understand some of the mechanisms by which it can disrupt plant reproduction, the extent to which heterospecific pollen impacts plant reproduction in the field remains poorly understood. Another gap in the literature on heterospecific pollen is assessment of possible interactions between heterospecific and conspecific pollen, i.e. if the impact of a fixed amount of heterospecific pollen has varying impacts on plant reproduction depending on conspecific pollen deposition.

Such interactions could arise from mechanisms either driven by the heterospecific pollen or driven by the plant on which the heterospecific pollen is deposited. Mechanisms mediated by heterospecific pollen include stigma clogging, stylar clogging, allelopathic inhibition, and ovule usurpation . One common feature of these mechanisms is that heterospecific pollen is likely to have stronger impacts if it is deposited before conspecific pollen, especially if it is in more contact with the receptive stigmatic surface . In most realistic scenarios of pollen deposition, the more heterospecific pollen that is proportionally present on a stigma, the greater the chance that it arrived early, enhancing its negative impact, whereas with more conspecific pollen the chance of an early arrival and concomitant deleterious effects is reduced. Second, there is at least one documented mechanism driven by the plant receiving the heterospecific pollen, which is stigma closure, i.e. stigmatic lobes closing in response to heterospecific pollen, effectively ruling out subsequent seed production in that flower , and plants could also hypothetically drive active inhibition of pollen tube growth ; or ovule or carpel abortion, in response to heterospecific pollen. To effect such active mechanisms, flowers must be able to detect the presence of heterospecific pollen. If they can also detect quantity or proportion of heterospecific pollen grains, plants could potentially use that signal when actively disrupting pollination at various points in the process , ultimately as a means to conserve resources by not investing in flowers that may have low-quality seed production or quality. In this study, to begin to understand the impact of heterospecific pollen in natural systems, we used a field approach linking stigmatic pollen deposition to seed set in the same individual carpels in wild plants that had been naturally pollinated . In contrast to hand-pollination studies, this approach allowed us to assess stigmatic pollen loads varying greatly in conspecific pollen and heterospecific pollen quantities , while achieving a relatively large sample size and replication across space. This approach also allowed us to assess interactions between conspecific and heterospecific pollen in assessing plant reproduction. We examined how the total amount of naturally deposited heterospecific pollen co-varied with the reproductive output of Delphinium barbeyi Huth , a common subalpine flower species in the Rocky Mountains of Colorado, USA. D. barbeyi receives visits from several species of bumble bees that are known to visit many co-flowering species within a community . We asked the following specific questions: How variable is heterospecific pollen deposition in naturally occurring D. barbeyi populations? Is conspecific pollen and/or heterospecific pollen deposition related to whether or not a carpel will abort? How are the amounts of conspecific pollen and heterospecific pollen deposition related to seed production in carpels? We hypothesized that there would be a positive relationship between heterospecific pollen deposition and carpel abortion rates and a negative relationship between heterospecific pollen deposition and seed set. In particular, we predicted that the effect of heterospecific pollen would vary depending on conspecific pollen deposition, with heterospecific pollen having a larger negative impact on stigmas with lower conspecific pollen deposition.We used generalized linear mixed-effects models because of hierarchical lack of independence in our data . Similarly, flowers within a plant clearly do not represent independent samples. Thus, we included nested random effects in the model, with flower nested within plant nested within site. We assessed carpel abortion as counts of aborted vs.

In studies of the European elderflower without any additional food ingredients, linalool and linalool derivates, such as -linalool pyranoxide and cis-linalool oxide, have frequently been identified as prominent. The aroma of linalool, the main aroma compound in lavender, can be described as citrus, fruity, floral, and woody. The age of the flower when harvested as well as how the flowers are stored after harvest can greatly impact the volatile profile. As expected from the other data on inter-cultivar variation, the volatile profile is heavily influenced by the cultivar. For example, wild elderflower had twice as much rose oxide and more linalool oxide than the other 12 cultivars. While this could be a challenge for manufacturers that use elderflower in products to have a consistent aroma from batch to batch, it also allows for more selectivity to find a cultivar that matches desired organoleptic properties in the product. American elderflowers have not yet been evaluated for their volatile profile, nor have blue elderflowers.Elderberry and elderflower are becoming more common ingredients and flavoring agents in beverages and food products. However, a vast majority of the products on the market today utilize the European subspecies of this plant due its more established cultivation and a deeper understand of the composition, particularly the phenolic profile, of the fruit and flower. North American subspecies, the American elderberry S. nigra ssp. canadensis and the blue elderberry S. nigra ssp. cerulea, have some information available regarding composition, but further analyses are needed to understand how they may perform in the common applications that European elderberry and elderflower are used in today.As global warming and water scarcity issues continue to impact food systems, large plastic pots for plants fire-resilient and drought-tolerant plants will become more important for supplying nutrient-rich foods. Wildfires throughout the western United States are becoming more common and more serious as seasons are hotter and drier.

California has been experiencing unprecedented levels of wildfires, including over 1.9 million acres burned in 2018, over 4.2 million acres burned in 2020, and over 2.5 million acres in 2021. One native and fire-resilient plant is the blue elderberry , which grows wild throughout the western United States and has become a popular choice to grow in hedgerows. The blue elderberry is drought-tolerant, and the roots of the blue elderberry can survive fires to regrow the next season to continue providing valuable flowers and fruit, making it an ideal choice to plant in regions of California and American West often stricken by wildfires. While European and American elderberries have been studied for decades, there is currently little information on the subspecies native to the western region of North America, S. nigra ssp. cerulea , known as blue elderberry due to a white-colored bloom on the exterior of the berry which makes it appear blue. In California, it grows wild in riparian ecosystems near rivers and streams, but is also planted in hedgerows on farms to improve water, air, and soil quality, in addition to providing a habitat for birds, pollinators, and other beneficial insects. The plant can grow several meters tall and wide and flowers from May to August, with peak fruit ripening throughout July and August. While elderberry prefer moist soil and some hedgerows may receive some irrigation during the summer months, most are not irrigated once the hedgerow has been established, about 2-4 years. That is one of the benefits of using native and drought-tolerant plants, as they can better withstand the natural climate without excess resources. Elderberries have a long history of use by Native Americans and Europeans in foods, beverages, and herbal medicines. Research exploring links between elderberry consumption and health has increased dramatically, particularly in the past decade. Numerous in vitro and in vivo studies demonstrate that elderberries have potent antioxidant, antibacterial, and antiviral properties.

Results of two randomized, double-blind, placebo-controlled clinical trials suggest that elderberry supplements reduce the duration and severity of cold symptoms. Roscheck et al. identified two non-anthocyanin flavonoids in elderberry extract that inhibited viral ability to infect host cells when bound. While most bioactivity of elderberries is assumed to result from the phenolic compounds like anthocyanins, the high-molecular weight fraction of concentrated elderberry juice was found to contain acidic polysaccharides that had potent effects against the human influenza virus. The health-promoting properties of elderberry have led to recent increases in its use in products such as supplements, syrups, gummies, and teas, as well as wine and jams. During the COVID-19 pandemic, elderberry supplements gained wide attention because of potential anti-viral activities; however, there is no strong clinical evidence that elderberry could be beneficial in preventing or treating COVID-19. The market for elderberries is expected to continue to increase, as the sales of herbal dietary supplements was over $11 billion in 2020, a 17.3% increase from 2019. Elderberry was the topselling herbal supplement, with sales over $275 million, as consumers became more interested with supporting their immune systems. In addition to the interest in elderberry as an ingredient in functional foods, elderberry can be an excellent source of natural coloring agents for food and beverage applications due to the high content of red and purple anthocyanins 35 .Characterization of the chemical composition, functional properties, and impact of processing on the bio-active compounds in elderberry is largely limited to S. nigra ssp. nigra and, to a lesser extent, S. nigra ssp. canadensis. S. nigra ssp. nigra is commonly referred to as the European black elderberry, which has many established cultivars, such as “Haschberg” and “Samyl”, and it has an established market.

The European elderberry is the most frequently used subspecies in commercial elderberry-based products and has been extensively studied for its composition, anthocyanin stability , and health benefits in European black elderberry-based products. S. nigra ssp. canadensis is commonly referred to as the American elderberry, a subspecies native to the eastern and central regions of North America. There are several cultivars of the American elderberry, including “Johns” and “Bob Gordon”. The American elderberry, which is utilized in small-batch products, has also been evaluated for its composition and health-promoting properties. The acreage grown of this subspecies has been increasing rapidly and there is a goal to grow over 2,000 acres by 2025, according to the Midwest Elderberry Cooperative. Currently, there is no information on the chemical composition of the fruit of the blue elderberry . With the recent increase in demand for elderberry, blue elderberry grown in hedgerows may be an additional and valuable source of bio-active phenolics and natural colorants. The objective of this study was to determine the moisture content, soluble solids, pH, titratable acidity, and establish the anthocyanin and phenolic profiles of blue elderberries grown in Northern California to support the use of this robust, native crop in commercial products.Hedgerows of S. nigra ssp. cerulea were identified on five farms near Davis, California in Spring 2018 with the assistance of an experienced agronomist at The Cloverleaf Farm . Farm, hedgerow, plant pots with drainage and harvest information is presented in Table 1. Blue elderberries were determined to be ripe when the berries in a cyme were deep purple, with or without the white bloom, and had no green berries present. Ripe elderberries were harvested by hand from all four quadrants of the elderberry shrub, totaling approximately 3 kg of elderberries. The berries were placed in clear plastic bags, stored on ice, and transported to the laboratory. A subsample was separated for moisture analysis, while the rest was de-stemmed and stored at -20 °C until analyzed.Elderberries were extracted by combining 5 g frozen berries with 25 mL MeOH:formic acid in a conical tube. The contents were homogenized, placed in a shaker without water at speed 7.5 for 20 min, then centrifuged at 3,000 rpm for 7 min. The supernatant was transferred to a 15 mL plastic tube and stored at -80 °C for no more than two weeks prior to analysis. Duplicate extracts were made from each shrub. TPC was determined using the Folin-Ciocalteu method. First, elderberry phenolic extract was diluted 1:4 with water. Each extract was analyzed in duplicate and averaged. In 10 mL glass tubes, 6 mL water was combined with 100 µL sample and 500 µL Folin-Ciocalteu reagent. After mixing and incubating for 8 min at room temperature, 1.5 mL 20% aqueous sodium carbonate was added. The tubes were mixed, covered with foil to avoid light exposure, placed in a water bath at 40 °C for 40 min, then cooled at room temperature for 15 min. The samples were read by a UV visible spectrophotometer at 765 nm and quantified using an external standard curve prepared with gallic acid . TPC is expressed as mg gallic acid equivalents per 100 g FW.Five grams of frozen berries were mixed with 25 mL of in a conical tube, which was then homogenized for 1 min at 7,000 rpm . The mixture was stored at 4 °C overnight, then in the morning, centrifuged at 4,000 rpm for 7 min. The supernatant was used directly for analysis. Three pooled samples were made for each hedgerow, each consisting of even amounts of berries from three distinct shrubs. Eachpooled sample was extracted once to give 3 biological replicates, and each extract was run in duplicate . Averages concentrations for compounds were determined across the hedgerow in mg per 100 g FW. The concentration of phenolic compounds in blue elderberry followed the method by Giardello et al. with some modifications. Briefly, samples were analyzed via reversed-phase liquid chromatography on an Agilent 1200 with a diode array detector and fluorescence detector . The column used was a PLRP-S 100A 3 µm 150 x 4.6 mm at 35 °C, and the injection volume was 10.0 µl. Mobile phase A was water with 1.5 % phosphoric acid, while mobile phase B was 80%/20% acetonitrile/ mobile phase A. The gradient used was 0 min 6% B, 73 to 83 min 31% B, 90 to 105 min 6% B. The DAD was used to monitor hydroxybenzoic acids at 280 nm, hydroxycinnamic acids at 320 nm, flavonols at 360 nm, and anthocyanins at 520 nm. The FLD was used to monitor flavan-3-ols, with excitation at 230 nm and emission at 321 nm. External calibration curves were prepared using chlorogenic acid for phenolic acids, rutin for flavonols, and cyanidin-3-glucoside for anthocyanins , at the following concentrations: 200, 150, 100, 75, 50, 25, 10, 5, and 2.5 mg/L. Catechin was used to quantify flavan-3-ols and standards were run at 150, 100, 75, 50, 25, 10, 5, 2.5, and 1 mg/L. Compounds were identified based on retention time and spectral comparisons with standards. Information about the linear equations and lower limits of detection and quantitation can be found in Table S1 in the supplementary material. The LLOD was calculated as 3.3 times the standard deviation of the y-intercept of the curve divided the slope, while the LLOQ was calculated as 10 times those values.Several peaks appeared in the HPLC chromatograms that could not be identified using the above parameters. Chromatographic eluents of these peaks were collected individually and dried under vacuum. These extracts were reconstituted with mobile phase A, and 5 µL were injected into the HPLC- QTOF-MS/MS for accurate mass analysis . A Poroshell 120 EC-C18 column was used at 35 °C. Mobile phase A was 1% formic acid in distilled water, and mobile phase B was 1% formic acid in acetonitrile. The gradient used was 0 min 3% B, 30 min 50% B, 31-32 min 95% B, 33-38 min 3% B. The mass spectrometer was used in negative mode, and the mass range for MS was 100 to 1000 m/z while the range for MS/MS was 20-700 m/z. Collision energies at 10, 20, and 40 V were applied. The drying gas was set to a flow of 12 L/min at 250 °C, while the sheath gas was set to 11 L/min at 350 °C. The nebulizer was set to 40 psig, the capillary voltage was 3500 V, the nozzle was set to 500 V, and the fragmentor was set to 100 V. Data was analyzed using Agilent MassHunter Workstation Qualitative Analysis 10.0 . Tentative identification was achieved by comparing the mass to charge ratio of the precursor and fragment ions to online libraries of compounds as well as using formula generation for the peaks in the spectra.Anthocyanins are a class of phenolics that contribute red, purple, and blue hues to fruits and vegetables, act as attractants for pollinators, and are potent antioxidants.