However, the long-term effects of non-native plant species on pollinator populations are not well known. Invasion, which leads to plant species declines and losses in resource heterogeneity, may negatively impact forager biodiversity, as seen in other systems . Overall, these studies suggest that non-native species play varying roles in pollinator networks, depending on their ability to provide foraging resources and their impact on the native plant community.Comparing interaction networks before and after an event can tell us more about the maintenance of pollination services than typical biodiversity studies can. Unfortunately, though empirical research on the spatial and temporal variation of plant–pollinator networks is badly needed, the lack of historic data and the intensive sampling effort required to identify multiple empirically gathered networks has limited research in this area . Only a few empirical network studies have specifically examined how habitat alteration impacts network architecture. Forup and Memmott compared pollinator networks for old intact hay meadows and restored hay meadows , and found no significant difference between the two in terms of plant or insect species richness or abundance, but did find that old meadows had a slightly higher proportion of potential links between plants and pollinators. In a second study, Forup et al. examined ancient and restored heath lands and found that, while the plant and pollinator communities were similar, the interaction networks were significantly less complex, in terms of connectance values, in the restored heathlands. These results suggest that even in ‘restored’ human-altered landscapes supporting similar levels of species diversity, flower harvest buckets the complexity of plant– pollinator interactions may not be easily recreated, and this may ultimately limit the long-term persistence of plant and pollinator communities.

In communities with high degrees of network complexity, such as the species-rich plant and pollinator communities of the tropics , network recovery post human-alteration may be less likely. Most remaining studies have examined plant–pollinator interactions over time within the same sites, and these have largely focused on intra- and inter-annual variations in network dynamics . Studies comparing networks within a single year have often found substantial species turnover in composition, emphasizing the need to consider plant–pollinator networks for shorter and more biologically relevant time periods . One study that examined plant and pollinator interactions on a daily basis, also found pronounced species turnover, and found that the most connected species, and thus perhaps the most resilient species, were those with the longest flowering– foraging periods . Studies that have examined variation in pollinator networks across multiple years have also found a large degree of turnover in species composition, but have surprisingly found that the number of plant and pollinator species, connectance, degree of nestedness, and modularity were conserved over the years . Overall, these studies indicate that plant–pollinator systems are dynamic, but that pollinators are flexible in resource use, potentially making networks more resilient to climate change. Furthermore, they indicate that high levels of connectance and nestedness allow for functional redundancies in the network, and greater potential resilience to climate change-induced biodiversity loss. However, research on pollinator networks over multiple years is sorely needed , specifically research which examines how habitat alteration and environmental change impacts complex and spatially explicit pollinator network architectures . These future studies will greatly improve our understanding of environmental change impacts on pollinator community dynamics.As mentioned earlier in the chapter, plants and pollinators provide a number of critical ecosystem services. Throughout this chapter, we have discussed research indicating that alterations in local and regional climate can disrupt plant and pollinator phenology, potentially leading to population and community alteration. In our discussion of pollinator networks, we have further shown that simulated alteration of plant and pollinator phenology can lead to marked changes in community-level interactions.

The consequences of these population-level and community-level alterations on ecosystem services could be various, and include potential changes in the quantity, quality, spatial availability, and temporal availability of ecosystem services. Unfortunately, research that directly examines the impact of the various dimensions of local climate change on pollination service acquisition is rare to nonexistent. In the following paragraphs, we discuss how potential outcomes of warming or warming and drying scenarios, specifically reduction in the abundance and diversity of pollinators, may impact ecosystem services provided by wild plants and native pollinators.The impact of pollination disruption on wild plant communities and the ecosystem services they provide is potentially wide-ranging, but largely understudied . Though more than 75% of wild plant species are dependent on insect pollination for reproduction , the impacts of this dependency on community or population level ecosystem services are not clear. Most existing studies have focused on single-species analyses of wild plant reproductive success across varying habitat treatments . A recent meta-analysis of these studies has found that self-incompatible pollinator dependent plant species exhibited greater declines in fragmented habitats than self-compatible plant species , and cross studies, the effects of fragmentation on pollinators were highly correlated with the effects on plant reproduction. Both of these findings suggest that pollination limitation could be a key driver for wild plant population decline. Of the wild plant species studied, 62–73% show pollination limitation , and though the long-term consequences of pollen limitation on population growth are uncertain , simultaneous declines in native plant and pollinator populations suggest a link between these two patterns . Thus, wild plants may face declines if their pollinators exhibit climate-induced spatial or temporal change, or general population decay. Biodiversity loss in wild plant communities can have devastating effects on ecosystem services because wild plants are critical for ecosystem processes in both natural and humanaltered landscapes.

Aside from providing humans with food, medicines, fuel, and construction materials, wild plants also support important processes in agricultural, rural, and urban landscapes, such as pest-predation , nitrogen fixation , erosion control , water filtration and storage , and carbon sequestration . Lastly, wild plants provide habitat needed for the migration of important seed dispersers and serve as plant propagule reservoirs for the recolonization of disturbed habitats . Thus, wild plants are critical for the function and regenerative capacity of natural and human-altered landscapes, and their decline would undoubtedly reduce the depth and range of ecosystem services they currently provide.As discussed in the introduction of the chapter, animal pollination is important for crop production and contributes to the stability of food prices, food security, food diversity, and human nutrition . An estimated 15–30% of the American diet depends on insect pollination and globally, the cultivation of pollinator-dependent crops is growing . Thus the loss of pollinators, without strategic market response, could translate into a production deficit of an estimated 40% for fruits and 16% for vegetables . These studies all suggest that climate-induced pollinator declines or disruptions to crop pollination could result in the alteration or reduction of food quantity, quality, diversity, availability, and nutritional content, potentially compromising global food security.A number of options exist for improving conditions for pollinators and buffering disruption of pollination interactions and general biodiversity loss. Unfortunately, very little research on pollinator restoration has been conducted specifically in the context of climate. In the following paragraphs, we present mitigation strategies that have been developed with respect to other types of environmental change, round flower buckets as they serve as key starting points for climate-specific restoration strategies. Though many of the practices for pollination restoration are similar, restoration projects can vary in their specific objectives and thus may have different concepts of restoration success . In particular, we focus on local and regional habitat mitigation strategies that are aimed at increasing the abundance and diversity of native pollinators, but also briefly discuss the challenges and opportunities for better developing pollinator restoration practices in the context of climate. Generally, the best insurance for protecting pollination services in the face of any alteration in local and regional climate involves maintaining or restoring high abundances and diversities of wild pollinators, their food plants, and their nesting resources throughout their current and predicted geographical ranges.Research on local habitat restoration strategies is the most well studied area of pollinator conservation and includes a wide range of on-site practices, such as the sowing flowering strips and installation of hedgerows. Pollinators are dependent on both flowering and nesting resources . Thus, it is essential to consider pollinator nesting and floral resource needs while deciding on the location, size, configuration, and longevity of the restoration. When considering the selection of plants to include in the local restoration, it is also critical to consider nectar and pollen needs of the target pollinator community across their foraging periods .

Some studies suggest the strategic planting of ‘framework’ and ‘bridging’ plants, which respectively, provide resources necessary for supporting large pollinator numbers and provide resources during resource poor time periods . Bridging plants may become even more important, if there is a mid-summer decline in floral resource availability associated with warmer conditions . Furthermore, it is important to consider the facilitative and competitive interactions between the plants within the restoration in order to select a mix that optimizes resource availability for pollinators, as well as, reproductive capacity for the plants themselves . For the restoration location, field margins are the most commonly utilized areas within agricultural areas , because they are usually not planted with crop plants and are often long and linear, easing the process of sowing, planting, and weeding. Within crop fields, field margins and adjacent lands, flowering strips, especially those that include non-legume forbs , are a low cost method to provide pollinators with floral resources. These flowering strips have been shown to increase the abundance and diversity of native bees and butterflies for at least a single season, often more . If a longer term restoration is preferred, hedgerows that include woody perennial plants can potentially provide both nesting and floral resources .Regional habitat restoration strategies for pollinator conservation include the preservation of unmanaged natural habitat and modifications of existing practices on human-managed lands. A number of studies have shown that the preservation of natural habitat within agricultural areas can lead to higher pollinator abundances, richness, and pollination services for adjacent crops . Furthermore, the presence of remnant habitats can be critical for the colonization of recently restored habitats . Human-altered regional habitat can also be used to support pollinator populations, if managed appropriately. Minimizing grazing and cutting of grasslands can increase regional floral resource availability and insect nest site availability . Pasture that is infrequently grazed can provide bee populations with important floral and nesting resources , and the reduction of fertilizer application, in conjunction with reduced grazing, has been shown to provide improved habitat for a number of butterfly species . Whether natural habitat is preserved or human-managed landscapes are modified for pollinator conservation, it is essential to consider the role of habitat restoration in supporting essential regional pollinator dispersal and migration processes, which may vary depending on pollinator community . A number of spatial simulation models of pollinator restoration have shown that the best habitat restoration design for pollinator persistence and pollination service was strongly influenced by the foraging behavior of the target pollinator species . Thus, restoration should keep in mind the dispersal capacities of target pollinator species . For example, for highly mobile species, the restoration processes can consider creating a ‘stepping stone’ habitat , whereas dispersal limited species may need more contiguous linear corridors of high-quality habitat to facilitate movement through inhospitable matrices. In fact, within agricultural settings, plant populations connected by corridors or highly biodiverse matrices have been shown to participate in extensive pollen transfer. Thus, habitat restoration that facilitates pollinator movement has the potential to support improved pollination services across natural and human-altered landscapes, particularly in light of current and plausible future changes in local and regional weather patterns and climate.The habitat-restoration strategies discussed in this chapter provide only indirect options for buffering global climate change; however, the act of increasing pollinator abundance and species richness in a community, at the least, increases the probability that a community or population can persist in altered conditions. Increased population densities and gene flow levels usually lead to populations with greater adaptive genetic diversity . These genetically diverse populations are more likely to be comprised of individuals genetically more suited to altered habitat conditions.

Numerous studies on Arabidopsis and cereal crops have advanced our understanding of starch biosynthesis in leaf and endosperm, and this knowledge has been applied to starch quality improvement in agronomical crops. On the contrary, the functions of starch in diverse horticultural crops are poorly understood, but it may play an essential role in their postharvest quality. SBEs largely determine starch composition and function , and there are three major classes of SBEs across cereal and horticultural crops . Compared to the well-studied SBE1 and SBE2, the function of the emerging SBE3 isoform in horticultural crops remains unknown . Although SBE3 has less invariant catalytic residues compared to SBE1 and SBE2 , the gene structure of the SBE3 is highly conserved and as is the protein secondary structure, including the critical CBM48 module . A unique coiled-coil region may provide SBE3 with a distinctive role in starch metabolism as an ‘accessory protein’ through forming protein complexes with core starch biosynthetic enzymes. SBEs in leafy greens, tubers and roots, and fruits show divergent transcriptional patterns during organdevelopment . The activity of SBEs may influence postharvest quality of these crops, influencing starch digestibility to sugars and hence its ability to serve as an energy source during storage, thereby affecting shelf-life. The proportion of sugars affects tissue osmotic properties, and if sugars levels are optimal at the crucial stage of postharvest life, this may reduce wilting, thereby boosting the visual appeal of leafy greens. Upon consumption, the proportion of sugars available in fruit vs. that used for respiration, black plastic plant pots bulk or that remaining as starch, could influence taste, i.e., sweetness and nutritional attributes.

Therefore, modulation of SBEs in major edible organs of these produces could test these hypotheses, and broaden our understanding of tissue- and species-specific starch metabolism, and potentially improve the postharvest attributes of several horticultural crops.Highbush blueberries , native to the northeastern United States, are important commercial fruit and are the most planted blueberry species in the world . In the United States, blueberries traditionally have been grown in cooler northern regions; however, the development of new southern cultivars with low chilling-hour requirements has made possible the expansion of blueberry production to the southern United States and California .Blueberry production in California was estimated in 2007 at around 4,500 acres and is rapidly increasing. Common southern cultivars grown include ‘Misty’ and ‘O’Neal’, but other improved southern highbush cultivars are now being grown from Fresno southward, such as ‘Emerald’, ‘Jewel’ and ‘Star’ . Southern highbush “low-chill” cultivars are notable for their productivity, fruit quality and adaptation , and require only 150 to 600 chillhours, making them promising cultivars for the San Joaquin Valley’s mild winters . Since 1998, we have conducted long-term productivity and performance evaluations of these cultivars at the University of California’s KearneyAgricultural Center in Parlier . North American production of highbush blueberry has been increasing since 1975, due to expansion of harvested area and yields through improvements in cultivars and production systems. In 2005, North America represented 69% of the world’s acreage of highbush blueberries, with 74,589 acres producing 306.4 million pounds . Acreage and production increased 11% and 32%, respectively, from 2003 to 2005. The U.S. West, South and Midwest experienced the highest increases in acreage. In 2005, 63% of the world’s production of highbush blueberries went to the fresh market. North America accounts for a large part of global highbush blueberry production, representing 67% of the fresh and 94% of the processed markets .

As a result, fresh blueberries are becoming a profitable specialty crop, especially in early production areas such as the San Joaquin Valley . In general, a consumer’s first purchase is dictated by fruit appearance and firmness . However, subsequent purchases are dependent on the consumer’s satisfaction with flavor and quality, which are related to fruit soluble solids , titratable acidity , the ratio of soluble solids to titratable acidity, flesh firmness and antioxidant activity . Vaccinium species differ in chemical composition, such as sugars and organic acids. The sugars of the larger highbush blueberry cultivars that are grown in California are fructose, glucose and traces of sucrose. Lowbush blueberries — which are wild, smaller and grow mostly in Maine — lack sucrose. The composition of organic acids is a distinguishing characteristic among species. In highbush cultivars, the predomi- – nant organic acid is usually citric , while the percentages of succinic, malic and quinic acids are 11%, 2% and 5%, respectively. However, in “rabbiteye” blueberries the predominant organic acids are succinic and malic, with percentages of 50% and 34%, respectively, while citric acid accounts for only about 10% . These different proportions of organic acids affect sensory quality; the combination of citric and malic acids gives a sour taste, while succinic acid gives a bitter taste . In addition to flavor, consumers also value the nutritional quality of fresh fruits and their content of energy, vitamins, minerals, dietary fiber and many bioactive compounds that are beneficial for human health . Fruits, nuts and vegetables are of great importance for human nutrition, supplying vitamins, minerals and dietary fiber. For example, they provide 91% of vitamin C, 48% of vitamin A, 27% of vitamin B6, 17% of thiamine and 15% of niacin consumed in the United States . The daily consumption of fruits, nuts and vegetables has also been related to reductions in heart disease, some forms of cancer, stroke and other chronic diseases. Blueberries, like other berries, provide an abundant supply of bioactive compounds with antioxidant activity, such as flavanoids and phenolic acids . For example, a study performed in rats showed that when they were fed diets supplemented with 2% blueberry extracts, age-related losses of behavior and signal transduction were delayed or even reversed, and radiation-induced losses of spatial learning and memory were reduced .

Some studies have shown that the effects of consuming whole foods are more beneficial than consuming compounds isolated from the food, such as dietary supplements and nutraceuticals. Because fruit consumption is mainly related to visual appearance, flavor and antioxidant properties, we decided to evaluate fruit quality attributes, antioxidant capacity and consumer acceptance of the early-season blueberry cultivars currently being grown in California. We characterized the quality parameters of six southern highbush blueberry cultivars grown in the San Joaquin Valley for three seasons , and evaluated their acceptance by consumers who eat fresh blueberries.Field plots. For the quality evaluations at UC Kearney Agricultural Center, we used three patented southern highbush blueberry cultivars — ‘Emerald’ , ‘Jewel’ and ‘Star’ , and three non-patented cultivars — ‘Reveille’, ‘O’Neal’ and ‘Misty’. The plants were started from tissue culture and then grown for two seasons by Fall Creek Farm and Nursery in Lowell, Ore. Before planting these cultivars in 2001, the trial plot was fumigated to kill nut grass . Because blueberries require acidic conditions, the plot’s soil was acidified with sulfuric acid, which was incorporated to a depth of 10 to 12 inches with flood irrigation, resulting in a pH ranging from 5.0 to 5.5. A complete granular fertilizer was broadcast-applied at a rate of 400 pounds per acre . The plants were mulched with 4 to 6 inches of pine mulch and irrigated with two drip lines on the surface of the mulch, one on each side of the plant row. Irrigation frequency was two to three times per week in the spring and daily during June and July. The emitter spacing was 18 inches , procona system with each delivering 0.53 gallon per hour of water acidified with urea sulfuric acid fertilizer to a pH of 5.0. The plot received an application of nitrogen in the first season, as well as in subsequent growing seasons. The rate was 80 pounds nitrogen per acre at planting, 60 pounds the second year, 90 pounds the third year and 120 pounds the fourth year. Annual pest control was limited to one application of Pristine fungicide in February for botrytis management, and two or three herbicide treatments of paraquat . In year three, the plants received one insecticide treatement of spinosad for thrips management. Twenty-eight plants per cultivar were planted in a randomized block design using seven plants per block as an experimental unit, replicated in four rows. Rows were spaced 11 feet apart, with the plants in the rows spaced 3 feet apart, with a space of 4 feet between plots. Fruit was harvested at times when it would have been commercially viable if it had been in a commercial field. Fruit from each of the seven plant blocks was harvested and a composite sample of 80 random berries per each replication was used for quality evaluations. Quality measurements. Berries were randomly selected from each replication for quality evaluation at the first harvest time for each respective season . During the 2007 season, in addition to the initial quality evaluations, harvested berries were stored at 32°F in plastic clam shells, and measured for firmness 15 days after harvest and for antioxidant capacity 5, 10 and 15 days after harvest. Three replications per cultivar were measured for each quality parameter. The initial firmness of 10 individual berries per replication was measured with a Fruit Texture Analyzer  . Each berry was compressed on the cheek with a 1-inch flat tip at a speed of 0.2 inch per second to a depth of 0.16 inch and the maximum value of force was expressed in pounds force . Sixty berries per replication were then wrapped together in two layers of cheesecloth and squeezed with a hand press to obtain a composite juice sample.

The juice was used to determine soluble solids concentration with a temperature-compensated handheld refractometer and expressed as a percentage. Twenty-one hundredths of an ounce of the same juice sample was used to determine titratable acidity with an automatic titrator and reported as a percentage of citric acid. Some samples that had a high viscosity were centrifuged with a superspeed centri-fuge at 15,000 rpm for 5 minutes, in order to get liquid juice for soluble solids concentration and titratable acidity measurements . The ratio of soluble solids concentration to titratable acidity was calculated. Antioxidant analysis. Antioxidant capacity was measured in the 2005 and 2007 seasons. Eighteen hundredths of an ounce of berries per replication was used to determine the level of antioxidants by the DPPH free-radical method . Samples were extracted in methanol to assure a good phenolic representation, homogenized using a polytron and centrifuged for 25 minutes. The supernatant was analyzed against the standard, Trolox, a water-soluble vitamin E analogue, and reported in micromoles Trolox equivalents per gram of fresh tissue . Consumer tests. An in-store consumer test was conducted on ‘Jewel’, ‘O’Neal’ and ‘Star’ blueberry cultivars in 2006, and on the six blueberry cultivars studied in 2007, using methods described previously . The fruit samples were held for 2 days after harvest at 32°F prior to tasting. One hundred consumers who eat fresh blueberries, representing a diverse combination of ages, ethnic groups and genders, were surveyed in a major supermarket in Fresno County. Each consumer was presented with a sample of each blueberry cultivar in random order at room temperature, 68°F . A sample consisted of three fresh whole blueberries presented in a 1-ounce soufflé cup labeled with a three-digit code. At the supermarket, the samples were prepared in the produce room out of sight from the testing area. For each sample, the consumer was asked to taste it, and then asked to indicate which statement best described how they felt about the sample on a 9-point hedonic scale . Consumers were instructed to sip bottled water between samples to cleanse their palates. Consumer acceptance was measured as both degree of liking and percentage acceptance, which was calculated as the number of consumers liking the sample divided by the total number of consumers within that sample . In a similar manner, the percentage of consumers disliking and neither liking nor disliking the sample was calculated. Statistical analysis. Quality values and data on degree of liking were analyzed with analysis of variance and LSD mean separation with the SAS program. Blueberry cultivar performance Production. Among the studied cultivars, ‘Emerald’ and ‘Jewel’ had the highest productivity for 2005 to 2007 . However, ‘Star’ had an unexpectedly high productivity in 2007. Yield increases for all varieties were due to the maturity of the plants. At planting, the tissue-culture plants were 2 years old; as they matured, they all produced larger yields.

The xylem waters sampled in this study provided a series of snap-shots of plant water over the course of the growing season at five northern experimental catchments. This resulted in an unusually rich comparative data set allowing a meta-analysis of inter- and intra-site similarities. Some clear findings emerged from this inter-comparison, though there remain many unanswered questions. The close link to soil water at each site was apparent from the similar positions of xylem water when plotted in dual isotope space . However, for most sites, much of the xylem water tracked towards lower δ2 H and δ18O plotting below the meteoric water line and below the soil water samples. The swexcess was shown to be a helpful metric to describe the dynamics of the deuterium offset of xylem waters compared to soil water. For some sites, there was much less or no overlap for gymnosperms or some angiosperms . The results also showed seasonal variations in xylem composition at most sites, although this differed . The plotting positions of xylem water from angiosperms and gymnosperms were quite distinct at some sites, despite some overlap. Apart from Dry Creek, gymnosperms at most sites were more offset from both the LMWL and soil waters compared to the angiosperms.The operationally-defined boundary polygon analysis provided an objective way of comparing the distribution of the soil and xylem data from the five sites . It is notable that the sites with greatest general overlap between all sampled angiosperm xylem waters and soil waters are characterised by smaller shrubs and trees . That said, larger trees at Dorset also showed quite a high degree of overlap, especially for more depleted, plastic growers pots potentially snowmelt-recharged water sources earlier in the growing season. In contrast, Vaccinium at Krycklan showed little overlap.

However, the physiology of smaller plants, with shorter rooting systems, lower internal storage and more rapid water throughput rates may at least partly explain the greater coherence between xylem water and soil water. Indeed, previous ecohydrological modelling experiments at Bruntland Burn by Kuppel et al. and calibrated only on hydrometric data, found quite good agreement between simulated and observed soil water and xylem δ2 H values in angiosperm using the spatial distributed EcH2O-iso model. Conversely, the same model failed to simulate the xylem isotopes in gymnosperms . The polygon analysis at most sites also seemed to indicate that overlaps between soil and xylem waters reflected integrating effects of water sources across the rooting zone, which at most sites was relatively shallow . This is consistent with the conclusions of Amin et al. for northern sites in their global meta-analysis that found isotopic evidence that cold region plant water was sourced from shallower depths compared to more temperate and arid regions. Given the groundwater isotope data available at all sites apart from Dorset, there is little evidence that deeper water sources can help explain the xylem samples not potentially related to soil water sources . Furthermore, at Dorset the thin soil cover overlies what seems to be relatively unfractured bedrock. It is possible that some trees have roots that are tapping water held in fractures, but given the geology it is unlikely that there is sufficient storage to sustain a significant fraction of evapotranspiration.It is clear that some of the observed changes in xylem water throughout the growing season are related to phenological changes . This temporal correspondence partly reflects the “switching on” of plants in the spring as photosynthesis and transpiration increase as well as the availability and isotopic composition of soil water.

Previous work by Sprenger et al. showed that variations in soil water isotopic composition at the study sites were mainly driven by precipitation and snowmelt over the preceding weeks, although there was also an effect of evaporation on kineticfractionation of isotope ratios during summer. These dependencies highlight the importance of precipitation frequency and intensity, infiltration, soil wetness and the mixing interactions that govern soil water residence time distributions . The way in which processes and interactions relate to plant demand highlights the importance of the temporal integration of root uptake and water transport into the main plant stems. The non-stationary travel times from uptake to transpiration may average many months , with tailing in the travel time distribution potentially a result of plant-stored water contributing to transpiration under dry conditions and possible mixing of xylem water with other plant water . The temporal trajectory of the xylem waters varied relative to soil water through the growing season, but this differed between angiosperms and gymnosperms. Also, inter-site contrasts between the angiosperm and gymnosperm differences were apparent: For Bruntland Burn, soil and xylem water signals were most similar in spring, deviated more strongly in summer and then returned to greater overlap in autumn for angiosperms. However, this was not the case for gymnosperms which showed dissimilarity throughout the year. For angiosperms at Dorset, there was a degree of overlap to start with, but depletion increased through summer and then closed again in autumn. In contrast, gymnosperm xylem waters became more 2 H- and 18O-enriched. At Dry Creek, there was a large difference through the autumn and winter for both angiosperms and gymnosperms until spring, but compositions became increasingly similar in summer. At Krycklan, angiosperms were most similar in the spring and early summer, but became increasingly different as the summer progressed. At Wolf Creek, there was an offset at the beginning of spring but samples then increasingly converged.

This post-winter offset, also evident at Dry Creek, may relate to desiccation and/or diffusion within the plant during the biologically inactive period .Inclusion of longer antecedent periods for soil isotope data generally improved overlaps within the boundary polygons for most sites, especially for angiosperms. The “sampling window” over which soil water may have been a source for plant uptake and contributed to xylem water in the trunk at breast height is unknown, and is likely to be non-stationary given seasonal variations in soil moisture and plant physiology. However, the greater overlaps for the longer antecedent period would support the hypothesis that xylem water at any point in time represents an integrated sample of soil water accumulating over preceding months, rather than soil water on the sampling day which will be most influenced by the most recent rainfall. In this sense, the results are similar to those of Allen et al. who demonstrated that trees throughout Switzerland predominantly use soil water derived from winter precipitation for summer transpiration. In our study, however, findings across sites and plant species were not consistent. Regardless, results from both studies suggest that caution should be used when constructing conceptual models of how plants access soil water based on synoptic, space-based sampling. Our phenologically-timed sampling strategy, particularly at such high latitude sites, is novel. However, more frequent sampling would likely be advantageous providing more nuanced insights into the phenological controls and short-term dynamics of xylem isotopes, particularly in relation to short term soil moisture dynamics and periods of higher atmospheric moisture demand . Nevertheless, higher-frequency sampling will still likely show that the xylem samples indicate stronger fractionation which has been widely shown for many vegetation types around the world . This focuses attention on potentially fractionating processes linked to small-scale interactions at the root-soil pore interface, especially close to the soil surface where most fine roots are present and where labile nutrients are also highest inacidic, organic soils. However, blueberry in pot methodological issues may at least partly explain some of the difference. These are discussed in the following section.Dry Creek stands out as an anomalous site in many results, most of which can be explained by its warm, dry conditions and high seasonality. Wolf Creek, however, the coldest site, shares similar results. The two sites obscure an otherwise clear relationship between plotting position along the GMWL and the mean annual temperature , they show the most overlap between xylem and soil water isotopes in bulk and at various depths , and they have the highest negative lc-excess values for both xylem and soil water . They also have the lowest May-August relative humidity at 38% and 63%, as well as precipitation at 19mm and 44mm, for Dry Creek and Wolf Creek, respectively . The relatively dry conditions shared by both sites expose soil waters to sustained evaporative environments, which may cause hydro-patterning of roots . Roots grow where water is available, which tends to be in less conductive pores where water has longer residence times and likely more isotopic fractionation due to evaporation. This evaporatively-enriched soil water also has limited potential for mixing with isotopically-different incoming precipitation that would alter its isotopic composition, partly because the growing-season precipitation at these sites is low.

Accordingly, plant roots in dry environments have fewer soil water source options, so xylem water and bulk soil water will trend towards similar isotopic compositions.Recent research shows that various complex bio-physical processes in the soilplant-atmosphere continuum may help explain why xylem water at the VeWa sites cannot be fully explained by soil water sources . As noted above, one possibility is that exchange between the soil liquid and vapour phase is complex and may affect root water uptake. This may be either through roots being able to access a fractionated vapour phase and/or condensation onto soil surfaces from the soil atmosphere increasing the likelihood that plants take up water depleted in heavier isotopes, especially deuterium. Both recent field and modelling studies have highlighted the plausibility of such mechanisms, but mechanistic studies to test such a hypothesis are limited and urgently needed. Similarly, the complex interactions in the symbiotic relationship between mycorrhiza and plant roots cause uptake of more 2 H- and 18O-depleted water compared to bulk soil water. In particular, widespread arbuscular mycorrhizal fungi which penetrate the cortical cells in the roots of vascular plants may be an effective mechanism that can facilitate fractionation of root water uptake . This occurs as part of the complex symbiosis of nutrient exchange that also affects plant-water relationships and is focused in the upper soil horizons. Such mycorrhizal interactions are particularly important in nutrientpoor minerogenic northern soils, and may have strong effects at sites like Bruntland Burn, Dorset and Krycklan. Again, more specific process-based studies are required to test this hypothesis in contrasting soil-plant systems. Finally, diffusion and evaporation through bark may be important biophysical processes, especially during winter when there is no transpiration . This is potentially a factor in northern regions where winter conditions preclude transpiration but can expose vegetation to arid conditions with high wind speeds and low humidity at sites like Dry Creek and Wolf Creek . Isotope transport through bark may explain why the gymnosperms at Dry Creek showed much greater overlap with the isotopic composition of soil water sampled over a range of antecedent intervalsin spring compared with Bruntland Burn, Dorset, and Krycklan where there was very little overlap. However, this inter-site difference was less pronounced for angiosperms .Extraction of vegetation and soil water: We do not fully know what kind of vegetation water is mobilized by the cryogenic extraction, although it is usually assumed to characterise xylem water. However, it is likely that some of the water that gets extracted is part of live cells subject to potentially fractionating biophysical processes that are independent of the hydrological cycle. Zhao et al. saw large differences between xylem sap, extracted with a syringe, and twig water extracted via cryogenic extraction with the former being more enriched in 2 H compared to the latter. In such cases, differences in the ratio of cell water to xylem water, which would depend on soil wetness, could have an effect on the differences between the isotopic composition of plant water and cryogenically extracted water . Barbeta et al. support this interpretation and call for more specific characterisation of what is assumed to be extracted xylem water. Very recent experimental work by Chen et al. showed that cryogenic extraction can enhance deuterium exchange with organically bound water and contribute to the deuterium depletion. Moreover, they showed the effect can be greatest under more moisture-limited conditions which may explain the tendency for more negative swexcess values as sites become drier. Physiological and biochemical differences between angiosperms and gymnosperms may also contribute to differences in extraction effects . As with vegetation water extraction, differences from contrasting soil extraction techniques may explain some of the mis-match between observed xylem water and soil sources. For example, the similarities between soil and xylem water at Dry Creek involved cryogenic extraction of soils, whereas all other sites used equilibration.

Does S. carpocapsae prefer or naturally navigate towards milkweed roots or milkweed-feeding insects by using CGs or other chemicals as cues? Drosophila was previously shown to be susceptible to all three of these EPN species . Larvae were fed a non-toxic diet or a diet containing the purified CG ouabain . We chose the hydrophilic CG ouabain since we could deliver millimolar levels of this CG into this non-CG-sequestering insect via its diet to mimic the high CG levels that can be found in the hemolymph of monarch caterpillars without needing levels of a solvent such as DMSO that would have adverse effects on insects and nematodes . Fly food containing ouabain was created by preparing Nutri-Fly food packets in a flask. Once cooled, 15 mM of ouabain was added to the flask. Fly food was poured into vials and allowed time to cool, then stored at 4 °C. Non-toxic fly food, prepared as described above without the addition of ouabain, served as the control. Six to eight adult males and six to eight adult females were placed into each vial and allowed to mate for 3–4 days. Adults were removed and larvae were left to hatch. Twelve-well plates were prepared with filter paper or NGM agar and one second-instar fly larva in each well. Twenty EPNs in 10 µL of water were then added to the wells with fly larvae that were either feeding on nontoxic or ouabain-containing food. Plates were covered with parafilm. Larval mortality was recorded at 2, 12, 24, and 48 h post infection to assess whether CGs influence the ability of EPNs to kill their insect hosts. The experiment was performed in triplicate.Asclepias curassavica seeds were germinated in seedling trays containing organic planting mix . Seedlings were maintained in a growth chamber at 26 °C with a 16 h light / 8 h dark phase at a light intensity of 200 µM m–2 s–1 until the first true leaf was observed.

Seedling roots were then thoroughly rinsed with water to eliminate soil particles. Subsequently, plastic planters bulk the roots were flash-frozen in liquid nitrogen and pulverized into a fine powder using a mortar and pestle. The resulting powdered root tissues were then dissolved in three volumes of 5% methanol solution and centrifuged at 10,000 g for 10 min. The resulting supernatant was carefully removed, and the pellet was suspended in H2O before being stored at 4 °C until further use .Chemotaxis plates were set up according to a previously published protocol . Chemotaxis agar media was poured into small Petri dishes . Then, using a pipette, a small crater-like shape was created in the middle, forming a higher level of agar referred to as the volcano deck. IJs were exposed to wax worm host cuticle for a duration of 20 min to allow host stimulation, which allows for higher participation rates of EPNs . A quantity of 20 µL of root extract was placed onto the volcano deck, followed by the addition of 4 µL of the paralytic agent sodium azide at 0.5 M. The NaN3 solution was made by adding 500 µL of 1-M stock to 500 µL of milliQ water. The paralytic agent was used to visualize the EPNs’ initial directional movement. Subsequently, a suspension containing 100–200 IJs in 20 µL of H2O was carefully dispensed around the perimeter of the deck slope. Plates were then stacked into groups of three in opposite orientations, placed in a box with a lid on a vibration-resistant platform and stored in the dark for 24 h. After 24 h, the numbers ofIJs on and below the deck as well as the number of IJs that displayed a coiling phenotype were recorded. The experiment was performed in triplicate.Sand was autoclaved and then washed repeatedly with tap water followed by DI water. The assays were performed in olfactometers , the setup of which has been described previously .

The measurements of the pipes and glass were as follows: each tube measured 9 cm in diameter and 5 cm in length, the pipe was 7 cm in length and 6 cm in height, and the entire set-up was 15 cm in length. Sand was dried at 60 °C overnight and moistened to 12% with tap water. Filter paper was spotted with 20 µL of tap water as a control or with 20 µL of test solution, and then placed at the end of the tubes. A consistent weight of 28 g of sand per tube was used for each replicate and trial. Root extracts were protected from light. Prenol is a known repellent for EPNs and served as a negative control test solution . A 2-M solution of prenol was made by adding 203 µL of prenol to 797 µL of DI water. Asclepias curassavica milkweed root extracts were prepared using the method described above. The region near the middle of the olfactometer consisted of sand moistened to 12% with tap water, which was the same as the conditions on the control side of the olfactometer to ensure that no biases were introduced. One thousand IJs in 100 µL of H2O were carefully dispensed into the center of the olfactometer. IJs were collected from fresh white traps followed by host stimulation prior to each experiment. For host stimulation, three wax worms were placed on Petri dishes with filter paper and the IJs were allowed to interact with host cuticle for 15–20 min before being collected and used . Each olfactometer was placed horizontally on a foam pad to suppress any vibrations. These were then placed in the dark in random orientations to avoid any potential directionality biases. Each assay ran for 24 h before the caps were removed from each side of the olfactometer separately. Nematodes were collected using the Baermann funnel technique followed by counting their numbers. Each replicate had three biological replicates for each condition and each EPN species. Choice percentages were calculated by counting the number of nematodes in the control area or the test area, dividing these by the total number of IJs inoculated, and multiplying the result by 100.

The experiment was performed in triplicate within each of three biological replicates. The figures were graphed using means across biological replicates, and a chi-squared analysis was conducted on the average of each replicate, with the number in the control treatment serving as the expected value and the number in the test treatment serving as the observed value.Chemotaxis plates were prepared as described previously : 17 g agar was dissolved in 1,000 mL dH2O and autoclaved for 30 min; this was followed by the addition of 5 mL filThered potassium phosphate buffer, 1 mL filThered MgSO₄, and 1 mL filThered CaCl₂. Plates were left at room temperature for 12 h before the experiment. EPNs were collected from fresh white traps to ensure healthy IJs were used in assays. IJs were collected and washed with DI water before 500 µL of IJ suspension at a density of one IJ per µL was placed onto a wax worm. IJs were given 20 min to have contact with the host cuticle for host stimulation . They were then collected and left at a density of eight IJs per µL, collection pot ready to be used for chemotaxis assays. A 2-M solution of the EPN repellent prenol was made by adding 203 µL of prenol to 797 µL of DI water. A tetrahydrofuran solution was made by adding 14.4 µL of THF to 985.6 µL of DI water. THF is a known attractant for S. carpocapsae and S. feltiae . A 0.5-M solution of the paralytic NaN3 was made by adding 500 µL of 1-M stock to 500 µL of milliQ water. Ouabain solutions of 15 mM or 100 µM were made by dissolving ouabain in DI water with 0.3% DMSO. Templates for chemotaxis assays were printed and placed under each chemotaxis plate. On the test side, 5 µL of chemical solution was placed in the test circle. On the control side, 5 µL of DI water with 0.3% DMSO was placed in the control circle. Chemicals were given 15–20 min to diffuse. Then, 2 µL of 0.5 M NaN3 was placed in each scoring circle. A 15-µL suspension of IJs at a density of 5 IJs per µL was placed in the center of the plate, containing a total of 70–170 nematodes. Plates were then stacked into groups of three in opposite orientations, placed in a box with a lid on a vibration-resistant platform and stored in the dark. The assay was run for two hours, after which data was taken on where nematodes were found: the test side, the control side or in the middle. Choice percentages were calculated by counting the number of nematodes in the control area or the test area, dividing these by the total number of IJs inoculated, and multiplying the result by 100. The figures were graphed using means across biological replicates, and a chi-squared analysis was conducted on the average of each replicate, with the number in the control area serving as the expected value and the number in the test area serving as the observed value.Plant functional traits have proved useful in identifying life history strategies for predicting plant community assembly and for assessing the impact of vegetation composition and diversity on ecosystem functioning . Consequently, vegetation models including coupled climate–vegetation models benefit from a better representation of plant trait variation to adequately analyse terrestrial biosphere dynamics under global change .

Today, in combination with advanced gap-filling techniques , databases of plant traits have sufficient coverage to allow quantitative analyses of plant form and function at the global scale . Analysing six fundamental traits, Díaz and colleagues revealed that essential patterns of form and function across the plant kingdom can be captured by two main axes. The first reflects the size spectrum of whole plants and plant organs. The second axis corresponds to the ‘leaf economics spectrum’ emerging from the necessity for plants to balance leaf persistence against plant growth potential. The concept of a global spectrum of plant form and function has since been investigated from various perspectives . It has been shown, for instance, that orthogonal axes of variation in size and economics traits emerge even in the extreme tundra biome or at the scale of plant communities . However, it remains unclear whether the two axes remain dominant for extended sets of traits or when differentiating among growth forms. A particular knowledge gap is what environmental controls determine these two axes of plant form and function. There is ample evidence that large-scale variation of individual plant traits is related to environmental gradients. Early plant biogeographers suggested that climate and soils together shape plant form and function but could not propose a more precise theoretical framework describing these fundamental relationships. Over the last decades, examples have thus accumulated without an overall framework in which to place them . For instance, tree height depends on water availability while leaf economics traits depend on soil properties, especially soil nutrient supply, as well as on climatic conditions reflected in precipitation . Leaf size, leaf dark respiration rate, specific leaf area , leaf N and P concentration, seed size and wood density, all show broad-scale correlations with climate or soil . It has also been reported that many of these traits show latitudinal patterns . Generalizing such insights is, however, not trivial, as soil properties partly mirror climate gradients, as a consequence of long-term soil formation through weathering, leaching and accumulation of organic matter—processes related to temperature and precipitation ; however,climate-independent features reflecting geology and surface morphology also contribute to soil fertility . Soil may furthermore buffer climate stresses; for example, by alleviating water deficit in periods of low precipitation . Combining the insights suggests that the global spectrum of plant traits reveals two internally correlated orthogonal groups and that many plant traits are individually linked to environmental gradients, we expect that both trait groups should closely follow gradients of climate and soil properties. Here, we investigate to what extent the major dimensions underpinning the global spectrum of plant form and function can be attributed to global gradients of climate and soil conditions; and to what extent these factors can jointly or independently explain the global spectrum of form and function.

Host 1 samples also exhibited some variation in the presence Prevotella OTU006, whereas Host 2 and Host 3 did not. Concern over the validity of PCA on relative abundance data prompted us to perform PCA again on the transforms of the relative abundance data. For this part of the analysis, we used both the centered-log-ratio transformation and the isometric log-ratio transformation. Figure 31 shows the PCA results on the CLR transform of the non-rarefied relative abundances. Compared to the clustering before CLR transformation , samples Hosts 2 and 3 spread out much more while samples from Host 1 clustered together more tightly . Color-coding samples by preservation conditions revealed no apparent trend , as was the case without CLR transformation . The Veillonella OTU still contributed greatly to the separation of Host 1 samples from Host 2 and Host 3 samples. However, after CLR transformation, Prevotella OTU006 appeared much more influential in accounting for the differences between Host 1 and Hosts 2 and 3. PCA on the ILR transformation of relative abundances yielded similar groupings, albeit with different scaling and different directions for the individual components . In the PCA results of the ILR transform, two components helped account for the differences observed between Host 1 samples from Host 2 and Host 3 samples while other components accounted for most of what was left of the sample variation. In the PCA results of both the ILR and CLR transformations, the first two principal components together accounted for 57% of the total sample variation.

As an additional quantitation of the extent of the influence exerted on the sample compositions by different preservation conditions and different hosts, blueberry containers we conducted ANOSIM significance tests with Bray-Curtis distance measures. We found that the relative abundance differences were not greatly influenced by preservation condition, as evidenced by the low correlation coefficients of 0.05505 and 0.1287 for non-rarefied and rarefied relative abundances, respectively. The compositional differences, as we had already observed in PCoA and PCA, were apparently influenced to a much greater extent by host differences, with correlation coefficients of 0.3365 and 0.3147 for non-rarefied and rarefied relative abundances, respectively. Hence, we numerically confirmed that host-based variation contributed the most to the observed differences across all samples.In the last two decades, more and more effort has been devoted to researching the effect of various preservation methods on complex microbiome samples. Particularly close attention has been paid to the gut microbiota, as the therapeutic potential of faecal microbiota transplantation has increased. Many ways of preserving both natural and artificial human gut microbiota have been investigated, including cryopreservation, lyophilization, and long-term storage in commercial storage media. Unlike the gut microbiome, however, the preservation of the oral microbiome does not seem to have received nearly as much attention despite our increasing understanding of its crucial role in human health and disease. Until five years ago, not much has been explored regarding the effects of storage methods on the stability of native human oral communities, let alone the stability of in vitro models.

It was one of our goals, in this set of experiments, to begin probing the effects of refrigeration and glycerol-assisted cryopreservation on communities derived from healthy hosts and generated in an in vitro environment. For this set of experiments, we chose an incubation time of 72 hours based on the results from the temporal experiments, where we observed a transition from the dominance by Streptococcus OTU genus to the dominance by the Veillonella genus. This incubation time seemed a good middle ground for capturing as many core members of the native oral bacterial community as possible without excessive internal contamination. Unlike in the temporal experiments, however, the relative abundances of the 72-hour cultures were not inclined toward Veillonella OTUs except for Host 1 . In fact, the Streptococcus OTU remained dominant in Hosts 2 and 3 throughout the preservation and propagation processes while dominance in Host 1 samples oscillated between Streptococcus and Veillonella OTUs except for one set of propagated samples that contained much higher proportions of Prevotella than others . It is not entirely clear whether preservation monotonically decreased or increased the relative abundance of any singleOTU. What is clear is that the combination of culturing and preservation procedures seemed to drive the community toward what we termed an “attractor” composition unless a substantial presence of the Veillonella genus already existed. In cultures with visible Veillonella presence, the relative abundances after preservation and propagation varied quite greatly, even within the same host and same preservation conditions. In terms of the effect of preservation on community composition, we observed that preservation alone did not lead to drastic changes in the relative abundances of the initial culture for any host. Members of the Streptococcus genus seemed to respond particularly well to glycerol-assisted cryopreservation as well as refrigeration, evidenced by the relatively minor changes in the abundances before and after preservation .

The Veillonella OTUs seemed to respond less well, as their relative abundances decreased upon propagation . Perhaps members of the Veillonella genus are less robust toward environmental changes, and the consequent decrease in the absolute biomass of the Veillonella OTUs in these experiments helped emphasize the increase in the relative abundances of Prevotella and Streptococcus OTUs. In any case, we clearly see in Figure 29 that in all hosts, community compositions pre- and post-preservation were remarkably similar. The preservation conditions we chose helped retain a substantial quantity of how OTUs were distributed in each sample. Thus, these conditions would be valuable for assessing community compositions in experiments where immediate processing may not be possible. On the other hand, the propagation of preserved cells seemed to preferentially select for Streptococcus OTUs, perhaps because this genus already occupied somewhat high proportions of initial cultures. Furthermore, it seemed that at least in Hosts 2 and 3, the Veillonella OTU did not respond as robustly to preservation as the Streptococcus OTU, hence contributing to the increase in relative abundance of at least one Streptococcus taxon. Another very plausible explanation for the shift to the Streptococcus genus is thatthe propagation was simply not long enough – we chose to incubate the preserved samples for 48 hours instead of 72 hours like the incubation for the initial cultures, and the difference of 24 hours might have allowed us to observe a rise in the relative abundance of Veillonella in the propagated cultures. However, we cannot conclude that increasing incubation time would indubitably allow us to see such a shift, especially in light of the observation that the relative abundances of the Veillonella OTUs had already begun to increase noticeably by the 48-hour mark in the temporal experiments for all three hosts . By contrast, only the propagated cultures in Host 1 showed observable presence of Veillonella OTUs, and only one pellet from the 1.5-week cryopreservation retained the presence of this genus. The differences between the relative abundances of the initial/preserved cultures on one hand and the propagated cultures on the other imply that different taxa respond differently to preservation, i.e. the number of viable cells after preservation differs for different OTUs, even if the cells were still intact and their DNA could be extracted. It is also possible that a few procedural changes contributed to the absence of Veillonella, including the step aspiration of approximately 1.5mL liquid from the wells during incubation, which we only introduced into the preservation experiments and not into the temporal experiments. This step, an attempt to minimize internal contamination, could have essentially removed a means by which the sedimented culture was re-inoculated during incubation. These factors and more may be worth investigating in future experiments should we aim to produce propagated communities with compositions that would be similar to those of the initial and preserved communities.

As for the composition of the attractor community and its relationship with different OTUs, two Streptococcus OTUs and one Veillonella OTU seemed to sit at the center of the attractor. Interestingly, the Prevotella and Alloscardovia taxa persisted through both preservation and propagation in Host 1, implicating their roles in a different attractor composition, perhaps one that is more developed than the attractor observed in Host2 and Host 3. The presence of these two taxa may hold special significance for human health given that members of both taxa have been linked to diseased states in the oral cavity. Perhaps the composition of the attractor community changes as the environment is primed for later colonization, potentially by pathogenic species. There has certainly been evidence that organisms of the Prevotella genus may be dependent on other organisms such as those in the Fusobacterium genus, which have also been shown to coaggregate with Veillonella and implicated in oral diseases. Whether the taxa unique to Host 1 cultures would truly compose part of a developed/separate attractor community would need to be investigated further in future experiments. As for the principal components that contribute to the total variation in the data set, best indoor plant pots neither of the log-ratio transformations eliminated underlying biological correlations or averaged out real biological differences. What the transformations did was mitigating some of the positive bias seen in the PCA results of untransformed relative abundance data. The log-ratio transformations yielded PCA results similar in kind to those from PCA of the untransformed relative abundance data. The separation of Host 1 samples from Hosts 2 and 3 persisted across both transformations, and the major components that contributed to host-based differences – the Streptococcus, Veillonella, and Prevotella OTUs – remained mostly the same before and after transformation. However, the degree of separation was much diminished post-transformation, and contributions from smaller but still important components, such as the Alloscardovia OTU and an additional Streptococcus OTU, surfaced upon transformation . Furthermore, the CLR-transformed PCA clearly showed that preservation conditions did not fundamentally influence compositional differences, whereas this lack of influence was not entirely evident in the untransformed PCA. Rather than preservation conditions, it may be the differential organismal responses to these conditions that exerted the greatest influence on sample variation, and the differential responses may well be connected to microbial interactions – perhaps ones similar to those between Veillonella and Strepto-coccus species or between Streptococcus and Actinomyces species – that affect the robustness of an organism toward low-temperature, desiccation, or nutrient depletion stresses, such as those occurring during preservation. The interaction-related responses would then fundamentally depend on the community composition before preservation, just as salivary Veillonella species depend on a specific strain of Streptococcus for coaggregation. We will attempt to investigate composition-based differential responses to preservation in the next phase of this project. Returning to a point made in a previous section, we observed in the relative abundances of the mock microbial community that the DNA extraction process seemed to generally favor Gram-negative bacteria , particularly E. coli and S. enterica, while the sequencing process seemed to favor B. subtilis at the cost of P. aeruginosa. These results underscore the importance of choosing proper bacterial strain should we ever revisit bacterial cell spike-ins for quantitation purposes. Ideally, the spikein organism would be non-oral, Gram-positive, and related to neither B. subtilis nor P. aeruginosa. Furthermore, even if we do not use a spike-in, we should strive to understand the biases in the extraction, amplification, and sequencing steps for different oral microbes. We may need to start the process by examining the extraction efficiencies of single-cell cultures, or in the absence of such a possibility, of co-cultures with known or easily characterizable strains. The results of these efficiencies could then be used to mathematically correct for relative abundance data, though ensuring the validity of this approach requires extensive proof of repeatability from one extraction-amplification sequencing trial to the next. The current dearth of research regarding the preservation of oral microbes may have originated from a perceived lack of need. Since facile identification of oral microbes has been difficult until high-throughput sequencing became viable, storing complex microbial communities for model-building and future study would not have been a reproducible or efficient approach. One of the few examples of examining the effect of preservation on oralbacteria compared both storage and transportation methods for the human supragingival dental plaque. The results showed that freezing dental samples in transport media without cryopreservation reagents led to no substantial differences between 48- and 72- hour storage for either S. sanguinis or S. mutans, though the survival rates of viable bacteria in frozen samples were predictably much lower than the those in samples stored at temperatures above freezing.

Cultures cluster close to one another while liquid samples show large inter-sample variation both before rarefaction and after . While rarefaction changed both the total and coordinate-specific amounts of variation, it did not do so to a remarkable extent – 97.2% to 97.5% and 89.4% to 88.5% for total variation in spiked and unspiked, respectively, and below 5% in all cases for individual coordinate axes in all cases. On the other hand, removing the spike-in OTU from the read counts did substantially change the clustering as well as the x/y positioning of both the liquids and cultures. Even a cursory comparison of the left column in Figure 15 to the right column in the same figure shows that the E. coli OTU affected the apparent similarities of the samples shown by PCoA. A few finer points should be made here about removing this process. First, removing this OTU pulled the liquids and cultures together; whereas the first coordinate spanned from -0.5 to 0.6 before removal, it spans from about -0.3 to 0.6 afterwards, indicating a tighter grouping for all samples. Second, as might be expected, removing this OTU allowed the compositional variations in the liquid samples to surface , whereas the liquids were clustering according to whether they had received the spike in before removal . The liquid samples were clearly more compositionally diverse than the cultures, evidence by the wider horizontal spread of the triangles in Figures 15 b and d. Third, removing the E. coli reads did not greatly affect the grouping of the cultures. Culture samples still fall within 0.2 units of one another in both coordinate axes, with one exception of a culture spiked by 100µL of E. coli. Fourth, after removal of the E. coli OTU, blueberries in pots neither liquids nor cultures cluster with visibly discernible patterns that group with spike volumes anymore, whereas the grouping of samples with spike volumes was apparent before removal .

Overall, these clusters and their disappearances fall well within expectation, considering that the spike-ins were much higher in biomass than the liquids but much lower than the cultures. The difference in biomass between the liquids and cultures inevitably led to the increased propensity of the liquids to similarity/dissimilarity influences from the spike-in. For both liquids and cultures in the preliminary experiments, it seems that as expected, no distinct groupings based on inherent compositional dissimilarities can be observed. It is also not clear whether the variations in the liquid samples come, in large part, from the low numbers of read counts after E. coli removal, as even a few reads in a low-read-count sample could lead to seemingly large inter-sample differences. In any case, the total percentage of variation accounted by PCoA here falls between 89% and 98%, indicating that two axes were sufficient for this set of samples. Interestingly, removing the E. coli OTU decreased the total percentage of variation accounted for by more than 8%, once again underscoring the sway that the spike-in had on liquid samples. From the compositional analyses, we see that cultures in the preliminary experiments yielded sufficient biomass and contained dental plaque bacteria, without exhibiting unexpected similarities or dissimilarity with themselves or with the liquids above the sedimented cultures. As to what the principal coordinates represent, i.e. what underlying biological differences may have led to two coordinates being sufficient, we would need to adopt a different analytical approach, which we do in the next stage of the project.In these preliminary experiments, we established a culturing procedure that minimizes external contamination while producing high numbers of viable cells from the human oral/dental bacterial community. Compositional analysis of the cultures showed that OTUs with the highest relative abundances belong to the Neisseria, Streptococcus, and Veillonella genera, two of which have been shown to be early and middle colonizers of the oral microbiome and all three of which have been shown as core genera in the supragingival plaque community.

The prevalence of OTUs from commonly occurring oral bacterial genera confirms that the culturing conditions support the growth and proliferation of anaerobic oral microbes without resorting to traditional, closed-form anaerobic culturing techniques such as anaerobic agar. Not many members from the group of later colonizers were present in the cultures in the preliminary experiments, though an Eikenella OTU was cultivated in the in vitro oral community to the extent of having a visible relative abundance value . Previous research has show that members of this OTU belong to groups of later colonizers that also include Actinomyces spp., Capnocytophaga ochracea, Propionibacterium acnes, and Haemophilus parainfluenzae. In this case, the absence or low abundance of later colonizers is not surprising, given that the cultures were incubated for less than 24 hours and not replenished with fresh host plaque. The short incubation time and lack of re-inoculation are part of the widely known scientific truth that in vitro conditions frequently select for organisms that can survive without the rich and complex environment of the original host. For bacteria that come from humans, this truth holds even more weight because it is unfeasible to replicate the human oral cavity. The complexity of host-microbe interactions simply defies reproduction in the lab. Another aspect to consider regarding the lack of later colonizers in these cultures is that membership of the oral bacterial community can vary greatly across different hosts. Kolenbrander and coworkers presented a larger picture of all the organisms that generally colonize earlier or later, with results that implicated trends, in other words, approximate orders of succession of oral/dental bacteria instead of definite lines of succession, and their work is far from the only instance for which human microbiome compositions have shown such great inter-host variations. The oral microbiome is no exception to such variation, but there exists a core community of major genera, and our methods have captured members of these major genera .

However, the factors already mentioned as having possibly detracted from organismal diversity in the in vitro cultures can be mitigated in future experiments by periodic re-inoculation of the cultures, longer incubation times, and/or variable nutrient sources and concentrations. These changes may help meet nutrient and signaling requirements of more fastidious bacteria, as well as increase the density of cells from certain OTUs to beyond their threshold values, such that proliferation becomes possible. Some of the culturing conditions that seemed appropriate for a proof-of-concept, such as this set of experiments was intended to be, including surface hydrophobicity of the scaffold, sampling with consideration of the growth phase of the bacteria, and bacterial attachment. The results seemed to indicate a promising protocol to establish an in vitro plaque community. An aspect that deserves some special, detailed consideration is the formulation of the culturing medium, SHI, based on the work from Tian and coworkers . As we used this medium in the preliminary experiments, it provided adequate nourishment, particularly in terms of pH and ionic strength. A potential disadvantage of SHI is that it is considered an undefined culture medium because the major carbon source in SHI is porcine stomach mucin. This glycoprotein is supplied in a partially purified form, square plant pots and because the glycosyl modifi- cations on glycoproteins can varied greatly depending on the conditions in the source organism, the composition of this protein cannot be guaranteed to be entirely biochemically identical across batches. Interestingly, the undefined nature of this medium has not yet been reported to be a major obstacle. On the contrary, research has shown some evidence that this medium may outperform more defined medium. A study that com-pared the effects of two media, DMM vs. BMM , on dental plaque microcosms grown in an artificial mouth system showed that plaque growth was slower in the chemically defined DMM, which contained higher concentrations of choline, citrate, uric acid, haemin, pyridoxine, biotin, and cyanocobalamin but lower concentrations of inositol, menadione, niacin, pantothenic acid, thiamine, and riboflavin. Furthermore, enzymatic activity for DMM was lower or in some cases undetectable. The results of our preliminary experiments indirectly affirm those from the comparative media experiment – we saw fast growth with the SHI medium, which contains major components from BMM as well as supplements such as menadione. However, we did not test the enzymatic activity of the cells in the culture to ensure that it is at least detectable, and we may need to do so. Another potential improvement might be to make the medium more defined for the sake of repeatability in our lab and reproducibility in the community. There has been some evidence that an artificial saliva may substitute human saliva in the growth of streptococcal species, and the composition of this artificial saliva may be a good starting point for a defined medium that would also be nutritionally sufficient. With regards to the attempt at establishing an internal standard with a known E. coli strain, we found that it was not feasible to seek correlations between read counts and OD600 values or CFU/mL under the conditions in the preliminary experiments. Finding such correlations mathematically would require quantifying and optimizing additional steps in the sequencing process.

The key steps to optimize here would include setting a concentration of E. coli DNA to be spiked into the samples to be sequenced, rather than using cells as spike-ins; understanding the efficiency of DNA extraction and mitigating the somewhat common bias of extraction processes to preferentially yield more DNA from Gram-negative bacteria; quantifying and optimizing the efficiency of PCR for the 16S rRNA of samples, which may involve some minor primer modifications; quantifying the composition of the library to be sequenced, potentially using genus-specific primers;quantifying how the fixed sequencing depths of HTS platforms affect the read counts and apparent compositions of samples, especially when samples do not have the same biomass; and so on. The quantification and optimization of these steps, including a detailed understanding of how systematic errors mathematically affect the results and how the number of discarded low-quality sequences affect the apparent compositions, may then enable us to find empirical relationships between number of cells in the cultures and read counts from sequencing. Gaining such a great degree of control over the whole process was not feasible at the time but would be a worthwhile venture for a future project. If we can establish a facile and rapid approach to quantification and optimization, we may be able to propagate the approach to developing many such numerical, analytical protocols. The bio-informatics process used for the preliminary experiment seemed to have served its intended purposes. With this process, we were able to perform quality control on the reads and cluster reads into OTUs at a reasonable level of sequence identity . More importantly, this procedure did not produce apparent artifacts that affected the processing and interpretation of data. The results obtained from using this bio-informatics pipeline met expectations formed from existing research on the human oral microbiome, though an aspect that may merit further consideration is the standardization of sample size by rarefaction. While there is some evidence that rarefaction helps reduce false discovery rates, there is equally reasonable evidence that rarefaction omits valid data and may bias against rare OTUs. For the purposes of these preliminary experiments, we have shown that rarefaction to 20,000 reads does not produce obvious artifacts or detectably reduce features observed in non-rarefied samples. Given that one of the major goals of the bio-informatics analysis in these experiments was to establish a procedure capable of distinguishing between biologically distinct samples without introducing much bias, rarefaction was clearly a defensible part of our approach. As for PCoA, the observation that rarefaction increases the percentage of variation accounted for is an expected result because of the nature of rarefaction. Rarefaction is a standardization procedure that simultaneously equalizes sample sizes and reduces the inter-sample variation, especially for samples with high numbers of rarer OTUs. To understand this point, we need to consider the two foundational concepts of diversity: richness and evenness. In terms of richness, adding a single OTU to a sample increases richness by one, which would only change diversity greatly in samples with low numbers of OTU. As for evenness, the addition of one OTU to a sample may or may not lead to a dramatic change in diversity, depending on two major factors.

The Results section covers the performance comparison of XGBoost with CNN and the deployment system performance evaluation. Finally, we conclude our work and discuss possible future directions in the Conclusions section.The study of brain activity using electroencephalogram typically involves extracting information from signals associated with certain activities. In recent years, machine learning techniques have been applied to the classification of mTBI because it enables the extraction of complex and typically nonlinear patterns from the EEG data. Most of the work surveyed used rule-based techniques, such as k-Nearest Neighbors . Previous investigations have studied a variety of classification techniques, including classical machine learning such as SVM and deep learning such as Convolutional Neural Networks . These techniques have been shown to perform TBI classification with more than 80% accuracy. However, in most investigations we reviewed that implement machine learning for TBI detection, the primary focus was the study of classification techniques and performance of classification models rather than portable deployment. A few systems used a small, portable computer for deployment in some form. The Neuroberry platform used a Raspberry Pi 2 device to capture EEG signals but the focus was on enabling EEG signal availability on the Internet of Things domain. The Acute Ischemic Stroke Identification System utilized an Analog to Digital Converter front end with Raspberry Pi 3 to capture physical EEG signals. However, plastic plants pots this system transferred the captured data to an HPC running MATLAB for signal analysis and processing and did not focus on signal classification.

Zgallai et al. described a Raspberry Pi-based system that used deep learning to perform EEG signal classification. It was designed to identify a subject’s intended movement direction from a multichannel EEG signal to control wheel-chair movement in a closed-loop robotic system rather than as a general system for identification, analysis, and monitoring of a physiological condition such as mTBI. Bruno et al. highlighted challenges with existing medical diagnosis techniques and described a classification system from the perspective of real-time TBI diagnosis, but their work was focused on the algorithm to perform TBI diagnosis and not on the implementation of a deployment system. In our previous work, we developed and described a CNN based model to perform automated sleep stage scoring and mTBI classification. In addition, we did a limited deployment of the CNN model on a Raspberry Pi 4 system. In that work, the focus was on describing the CNN model configuration, evaluating its performance, and showcasing that deployment to RPi was feasible rather than designing a complete, portable classification system. We have reused the previously developed CNN model in the current work to provide a baseline performance comparison with a new XGBoost model developed for this work. Further, the two models enable us to demonstrate the versatility of the current system to operate with multiple types of predictive models. To the best of our knowledge, no standalone, portable system has yet been created using Raspberry Pi that can capture real-time EEG signals, detect the presence of mTBI, and classify mTBI sleep/wake epoch states.A previously published dataset as described in [3] was used to train and evaluate deployed models. This dataset was collected as part of a study involving 11 adult male mice subjects divided into two groups—mTBI and Sham. FPI procedure was used to induce mTBI in 5 subjects and the remaining 6 mice were used as Sham subjects.

To capture the EEG signal, three ball-tipped electrodes were placed in the skull of each subject, two frontal and one in the parieto-occipital region. In this work, we proposed and demonstrated an RPi based EEG acquisition, processing, and classification system for early mTBI detection. This system was implemented using a single channel EEG data obtained from mice. This system was demonstrated to operate in a portable, real-time, and standalone configuration and perform classification of real-time EEG epochs into four target classes . As shown in Table 1, the accuracy, precision, and recall results were identical across RPi and HPC. This confirmed that the predictive model behavior did not change when the training and deployment systems involved different system architectures, i.e., x64 based MacOS/Windows HPC for training vs. ARM-based RPi for deployment and prediction. Hence, it is possible to train a predictive model on a more powerful computer and deploy it to an embedded device such as RPi that has limited memory and processing resources. This is especially applicable to multilayered neural networks like CNN that typically have long training times on an HPC, and the training times would be prohibitively long on an embedded device like RPi. We calculated the epoch processing time on RPi by varying the number of epochs, as shown in Figure 4 and described in Table 2. While it was expected that the processing time would increase as the number of processed epochs is increased, the key inference was that the processing time was considerably smaller than the time required to collect the EEG epochs. At 256 Hz sampling rate and 64 s epoch size, the processing time ranged from 0.01% to 0.03% of the epoch collection time. Hence, we concluded that the system had ample time to process previously captured EEG epochs while new epochs were captured at practical EEG signal sampling rates. We employed two different approaches for supervised learning models used in this system, the CNN model developed in our previous work, and an XGBoost predictive model created in the current work. We compared classification metrics and performance of the XGBoost and CNN models on the deployment system as well as an HPC.

We observed that the XGBoost model exhibited better performance in terms of accuracy and inference time compared to the CNN based predictive model. In the case of XGBoost, the variation of inference time remained roughly within 2 µs between HPC and RPi. A low inference time was critical for the real-time operation of the classification system. One possible reason for the better accuracy performance in the case of XGBoost compared to CNN was that the classification model for XGBoost was created using hand-crafted features which enabled learning differentiating patterns for the four target classes better than the CNN model that automatically extracted the differentiating features. These results, however, were data-dependent, so they should be validated on different datasets to verify the generality of the model. We found that overall, XGBoost was better suited for deployment on RPi because of its faster inference time and better performance than CNN. By using two different predictive models for classification, we demonstrated the flexibility of the system to deploy improved classification models in the future. In this system, we used a DAC to generate EEG signal waveform form European data format files. This provided a reliable way to generate an EEG signal waveform without requiring an actual subject to capture the EEG signal from. We verified that the EEG waveform generated using the DAC on RPi was consistent with the EEG data stored in the EDF file. The verification was done by calculating MSE across the stored and generated signal, which was found to be 0.26, a small value indicating that the generated signal represented the stored signal accurately. Synthesizing EEG signals to replicate the complex and typically nonlinear signal patterns is challenging and the ability to generate EEG signals from an actual recording data file using a DAC simplifies the setup that is required to test an EEG classification deployment system hardware and software chain. It enables the use of several available open-access EEG data files to train classification models and test the deployment system. For future use, the signal generation capability of this system can be simplified for ease of use and expanded to work with a variety of EEG data file types. This can help accelerate mTBI related future research pertaining to portable classification systems that are often constrained by the lack of readily available live EEG signals to test a hardware classification system. In addition to early mTBI detection, blueberry pot the capability of the system to perform live classification on input EEG signals can be extended to cover mTBI related health and sleep monitoring applications in the future. Typically, after the initial diagnosis, TBI patients undergo EEG sleep monitoring in a hospital setup. A portable EEG sleep monitoring system, such as the one described in this work, can enable a subject to self-monitor in home settings and greatly enhances the accuracy, efficiency, and efficacy. The classification system developed in the current work can also provide a replacement of the labor-intensive manual sleep-stage scoring of EEG signals by human experts with an online and automated system with the capability to perform fast sleep staging. Further, our technical approaches can be extended to several other EEG applications, including detection of the onset of epileptic seizures, strokes, and other neurological conditions.

In this work, we used a relatively simple hardware system to capture and digitize EEG signals, which could be improved. Because we generated EEG signals from a datafile containing clean EEG data, this hardware did not include amplification and filtering stages. A practical system designed for field use would require additional hardware and software capabilities to capture and process EEG signals in real-time. We also used a relatively simple metric for comparison of generated and stored EEG signals. While we only used MSE as a metric for this system, for cases where components in the signal path could potentially cause phase changes in the signal, MSE should be coupled another metric such as cross-correlation to verify signal integrity. In terms of hardware, such a system would require amplification, preprocessing, and filtering stages. In software, decimation, normalization, Independent Components Analysis , physiological artifact removal , and filtering stages can be implemented. Further, we used an 8-bit ADC for this proof-of-concept system, but for devices designed for practical use, ADCs typically vary from 16-bit to 24-bit resolution. For example, the OpenBCI Cyton Biosensing system for sampling EEG and other physiological signals uses a 24-bit ADC. We will note that higher resolution ADCs also involve a relatively higher cost and have lower sampling rates as the number of resolution bits increases. In addition, the system in this work was designed for single-channel EEG generation and capture, which limits its use for multichannel EEG applications. It does not directly provide connectivity to wireless EEG headsets. However, several “hardware attached on top” devices are available for RPi, for example, the brain HAT, that makes it possible to connect wireless headsets seamlessly and we anticipate the system in this work to function as intended with the actual streaming EEG data outside the particulars of EEG headset interfacing.Horticultural crops have high economic, and enrich our lives through their aesthetic and nutritional value. Many horticultural species originate from tropical regions and are sensitive to cold at every stage of their lifecycle. Cold stress leads to lower productivity and post-harvest losses in these species, with poor economic and environmental outcomes. Better understanding of the protective mechanisms mediated by hormonal and other signaling pathways may offer solutions to reduce cold-stress induced losses. The papers included in this collection illustrate this concept, examining natural cold-tolerance mechanisms and practical ways for growers to alleviate chilling stress and to reduce crop losses. The studies were remarkably diverse in terms of the species studied , plant organs examined , and approaches used . The papers encompassed the use of basic science, aimed at identifying key genes and their roles in cold signal transduction and protective pathways in fruit and photosynthetic tissues; reverse genetics for proof-of-concept on the hypothesized role of a cold-tolerance transcription factor cloned from an understudied species; and emerging technologies, by using exogenous hormones and signaling compounds to mitigate the harmful effects of chilling. These studies are described below.C-repeat binding factor proteins constitute a transcription factor subfamily known to play a key role in plants against different types of abiotic stress including cold, heat, salinity or dehydration, and thus have been extensively studied. Overexpression of CBFs has been used for the development of genetically modified plants with enhanced stress tolerance and for the investigation of the molecular mechanisms underlying plant stress responses. Using this approach, Yang et al. found that overexpression of three newly identified longan CBF genes enhanced cold tolerance in Arabidopsis by increasing the content of the osmoprotectant proline, reducing the accumulation of reactive oxygen species , and stimulating the expression of cold-responsive genes.

The mixture was centrifuged at 20,000 g for 15 minutes, after which 0.4 ml of supernatant was extracted, mixed with 0.4 ml of 2:1 chloroform:methanol mixture, and centrifuged at 12,000 g for 5 minutes. 50ul of supernatant was combined with 50ul of Amplex®Red working solution, which was prepared according to manufacturer instructions, in a black 96-well plate with a transparent flat bottom. Absorbance was read using an Epoch-2 microplate reader at 560nm. For H2O2 quantification, a standard curve was generated following the manufacturer’s instructions. Frozen grape powder was homogenized for 10min in 0.1% trichloroacetic acid and then centrifuged at 10,000 g for 5 minutes. 1mL of the supernatant was combined with 4mL 20% trichloroacetic acid containing 0.5% thiobarbituric acid, then the mixture was heated for 15 minutes at 95°C then immediately cooled in an ice bath for 5 minutes. After centrifugation at 10,000g for 10 minutes, 100 of supernatant was plated into a Corning® black-walled, clear, flat-bottomed 96-well plate and absorbance were read at 532 and 600 nm. Concentration was calculated using the MDA molar extinction coefficient 155,000 M-1 cm-1 with the following equation: MDA concentration = •1010, accounting for aliquot volumes and dilution factors.The effect of the irrigation treatments on indicators of vine water stress, hydrogen peroxide/lipid oxidation products, and osmotic potential in the berry was examined through one-way analysis of variance for each sample date. We determined the onset and rate of berry cell death, shrivel, and ROS accumulation by fitting piece wise linear regression models between time and percent cell vitality, the berry shrivel index, and H2O2 concentrations. The onset is the date these variables transitioned from relatively constant to rapidly changing, defined as the fitted breakpoints of the piece wise regression models, plastic plants pots and the rate is the slope of these variables over time after onset. We tested for significant irrigation treatment effects on onset and rate by examining their 95% confidence intervals for overlap.

Furthermore, Correlation between shrivel and % living tissue and peroxide and % living tissue was determined through the Standardized Major Axis Tests and Routines package in R using the sma function. We also tested for significant treatment differences between treatments by one-way analysis of variance for TSS, TA, and pH at harvest, when they are most relevant to growers. Because 7/25 was not used for perimeter analysis, nor was 8/1 for cell vitality, both sets of data were omitted in order to achieve a date-by-date correlation. Out of 540 berries sampled, 248 cross sections were viable for image analysis due to damage incurred in the mesocarp from the razor blade hitting and dislodging seeds. Measurements of percent living tissue and perimeter/total area were taken on different yet mostly overlapping subsets of images due to a fraction of images lacking a smooth perimeter or consistent dye stain. For cell vitality analysis, n = 68, 65, and 72 for treatments 1, 2, and 3 respectively; and for the shrivel index analysis, n = 74, 65, and 78 for treatments 1, 2, and 3 respectively. In tracing representative areas of the berry for cell vitality analysis, the selected area routinely excluded the pedicel residue, which did not typically take up the stain. Due to the violations of equal variance caused by these asymmetries, the non-parametric One-Way test in R was used for both percent living tissue and shrivel index on the last sampling date, and, likewise, the Games-Howell test was used as a posthoc test to compare treatment groups.Our augmented irrigation regimen commencing on the estimated date of onset, i.e. the late treatment, was effective in significantly reducing the rate but not the onset of cell death. Onset was defined as the fitted break point of the piece wise linear regression models, when the rate of change in cell vitality significantly increased. The onset of cell death occurred 92 ± 1 DAA for the control, 89 ± 8 DAA for the early treatment, and 83 ± 4 DAA for the late treatment . The 95% confidence intervals for these estimates overlapped, indicating that onset was not significantly different between treatments. The fitted slope after the break point indicates the rate of cell death after onset. Post-onset slopes were -2.06 ± 0.22 for the control, -1.21 ± 0.53 for the early treatment, and -0.90 ± 0.11 for the late treatment .

The significance of the late treatment’s effect on the rate of cell death is demonstrated by the non-overlapping 95% confidence intervals between the control and late treatment. There were no final treatment differences found on cell vitality for the last sampling date. Figure 6 displays the cell vitality means while Figure 7 shows the segmented fit over the means.Our late irrigation treatment, a 40% augmented pulse starting at the beginning of cell death, generated two significant effects in our experiment: it reduced the rate of cell death in the berry mesocarp tissue and produced less shriveled, more turgid berries at harvest. We also hypothesized that the early treatment would preemptively reduce water stress and postpone cell death. We correctly anticipated the date of cell death onset , yet despite the optimal timing of our treatments the early irrigation regime was not successful in reducing water stress or cell death. While the late pulse reduced the rate of cell death, its effect on shrivel at harvest was not present for the rest of the experiment. This asymmetry is highlighted by the lack of overall correlation between shrivel and cell death for the late pulse, and the lack of a steady decline in turgidity . It was only for the control that significant correlation was observed, which may be explained by the lower error observed in the piece wise linear regression model for the control than the late or early treatments . Unexpectedly, the greater turgidity in the late treatment berries was not reflected in the osmotic potential, °Brix, or TA measurements at harvest, when we would have expected solutes to be more dilute than treatments with greater shrivel . While significant, difference in shrivel were not great enough to affect solute concentrations. The cell death onset and rates we found were largely within the range observed in other studies, blueberry pot but it can be difficult to draw definitive conclusions from comparing the onset and rate of cell death among experiments with many changing variables . Cabernet Sauvignon has only been measured in one other study with cell death beginning at 100 DAA and 40 days after veraison for field-grown vines in Napa Valley , compared to 92 DAA and 34 DAV for our control vines in Davis .

Given Davis is a warmer climate than Oakville, this may explain why our vines exhibited cell death onset earlier. These differences could also reflect a lack of precision in estimating onset dates, since fluctuations in %living tissue measurements can weaken trends with time, as in this study . This could also reflect differences in growing conditions or typical variation within cultivars. The reported ranges of cell death onset dates for any given cultivar vary by as much as 12 days. Syrah onset tends to fall between 87-96 DAA after which tissue vitality sharply declines while Chardonnay’s cell death much slower at an approximately constant rate. . Our rates of cell death fell within the reported range of living tissue analysis performed thus far on grape berries. The Cabernet in Krasnow et al. , between 100-150DAA, lost cellvitality at a rate of approximately -0.83 %LT/DAA while our control and early treatments for Cabernet had a rate of -1.21 and -2.06 respectively. Metabolic processes sensitive to temperature have been connected to cell death, however Xiao et al. and Bonada et al. have both observed no impact of elevated temperature on cell death rate. For Bonada et al. , within the range of 80-120DAA, Syrah lost living tissue at a rate of -0.62 %LT/DAA and Chardonnay at -0.53. The greatest rate of cell death was reported by Xiao et al with living tissue in Syrah diminishing at rates as high as -5.17 %LT/DAA. Interestingly, Xiao et al. found that irrigation postponed cell death onset much later than the non-irrigated vines, yet the irrigated vines crashed twice as fast as the non-irrigated vines , which may have developed more extensive root systems with greater access to water. Conversely, our late treatment vines exhibited the slowest cell death and the earliest onset, highlighting the efficacy in slowing cell death. Our results for cell vitality and shrivel analysis accord with the reported role that xylem backflow plays in the relationship between cell death and LSD. Cell death occurs prior to LSD by reducing turgor pressure and accelerating water loss through xylem backflow, thus a more negative ψx would enhance the pulling force for water out of the berry . Since ψmd and berry osmotic potentials were nearly the same across treatments, the observed differences in cell death rate and shrivel index cannot be attributed to differences in xylem water potential or the berry solute concentrations. In the late treatment berries cell death was slower and cell vitality remained greater from 92DAA onwards , which indicates more water retention and less backflow. Less water loss in berries from cell death supports our finding that the late treatment berries maintained the greatest turgidity for almost all dates during that same period, from 92DAA to harvest. Another factor to consider affecting xylem backflow is pedicel hydraulic conductivity.

Xylem blockages that are known to develop post-veraison in grapevines could hinder pedicel hydraulic conductivity and slow down backflow out of the berry , though there is no basis for such a physiological response to our late irrigation pulse. There are established differences in pedicel hydraulic conductivity between grape cultivars post-veraison and it has been reported that grapevines produce tylose blockages in response to pruning wounds and bacterial infection . Yet, there is no reported evidence of grapevines producing xylem gelblockages in relation to water stress during our period of ripening. For future studies, it may be of interest to track phloem inflow to the berries in response to extra water availability during cell death. Post-veraison the phloem remains the main source of water influx into the berry, remaining functional until 25-26 Brix, a range our berries did not exceed during the late treatment pulse, which suggests the phloem likely remained functional . Our results have shown vines receiving additional irrigation during the onset of cell death, despite having an earlier onset of cell death, retained water better than vines with later onset. Since phloem functionality declines steadily during ripening , an earlier onset of cell death comes at a time of potentially greater phloem hydraulic conductivity, rendering an irrigation pulse more effective at providing more phloem inflow to compensate for xylem backflow. Greater sap influx into the berry would introduce more dissolved oxygen and alleviate hypoxic stress due to respiration and ethanolic fermentation, though this has not been experimentally determined.We expected that H2O2 levels to begin to rise at cell death and continue throughout the experiment. Our H2O2 concentrations saw an uptick during the onset of cell death and, with exception of one outlying date, steadily increased throughout cell death proliferation at similar levels for all treatments . Across the three treatments cell death onset coincided with the onset of H2O2 accumulation at 88 DAA . This is evidenced by the significant correlation found between the two variables for the late treatment and the control. It should be noted that it is only coincidental that those same two treatment groups were significantly different in our cell death rate analysis—matching significant H2O2 correlations and cell death rates would not be causally related findings. Whether the concentrations found in our study, between 1-3 nmol/g FW, are great enough to cause the degree of cell death we observed is not clear. Gowder measured H2O2 in combined skin and pulp samples finding concentrations in the range of 4-10 nmol/gFW in Chardonnay, 12-9 in Grenache, and 15-40 nmol/gFW in Syrah between 90-120 DAA with similar percentages of living mesocarp tissue to this experiment. We would expect to find lower values in the mesocarp only given that the skin is known to contain higher levels of H2O2, yet Pilati et al. found values in range of 18-27 nmol/gFW for the mesocarp only of Pinot Noir berries between 70-84 DAA. Interestingly, also in Gowder the three cultivars expressed no clear relationships between H2O2 accumulation and cell death, indicating there is much greater complexity to be addressed in the sources of H2O2, its scavenging, and alternative causes of cell death such as lipoxygenases.

To determine if the effects of PhylloStart bacteria on plant reproductive success would be seen in an environment with more potential sources of phyllosphere bacteria and/or whether early inoculation of plants changed subsequent microbiome assembly in the field, we included a field component in the third trial experiment. After inoculation, we transferred both PhylloStart-inoculated and control plants into the field. These plants were sampled concurrently with the plants from the same cohort that remained in the greenhouse , and their phyllosphere communities were sequenced. We analyzed greenhouse and field locations separately and found a significant effect of PhylloStart inoculation density on bacterial abundances in the greenhouse =10.17, p=0.006, but no effect in the field =4.07, p=0.13. A Dunn Post-Hoc test showed that PhylloStart High treated plants had significantly higher bacterial abundance than the control plants in the greenhouse . Further, while we see that community composition is influenced by PhylloStart treatment in the greenhouse, we see no such effect in the field grown plants. When looking at a PCoA of bacterial community similarity using Bray-Curtis distance we see that the PhylloStart-treated greenhouse plants clearly separate out from the control plants, with the plants treated with high concentrations of PhylloStart distinct from the controls, and the plants treated with low concentrations of PhylloStart falling between the controls and the high inoculation. Meanwhile, the communities associated with the field grown plants differ from those grown in the greenhouse, plant pot with drainage but do not otherwise separate by PhylloStart treatment. When analyzing dissimilarity using an Adonis PERMANOVA, we see a significant effect of PhylloStart treatment , location; ie. field vs. greenhouse , as well as a significant PhylloStart by location interaction .

When analyzing each location separately with a pairwise Adonis, we see that there are significant differences between PhylloStart high and control treated plants in the greenhouse , but no difference between PhylloStart high and low , or PhylloStart low and control in the greenhouse. The plant microbiome is increasingly recognized for its role in shaping plant health, but most work to date has focused on below ground associations between plant-microbe interactions. Thus far,most evidence for an impact of the above-ground, phyllosphere microbiome on their hosts has focused on disease or herbivore resistance . Our initial experiment established that greenhouse-grown plants develop a significantly more abundant microbial community when inoculated with a synthetic microbial community . When inoculated onto plants early in development, we found that our taxa represent the dominant members of the phyllosphere and that overall bacterial densities were far higher in amended plants relative to controls . With two additional greenhouse studies, we determined that these microbial associations lead to a significant increase in the total number of fruit produced by greenhouse-grown tomato plants . We also found that, in a growth chamber trial, the bacterial community provided nutrient status dependent protection from P. syringae establishment. In contrast, plants that were transplanted into a field environment did not appear to benefit from the initial inoculation of PhylloStart bacteria . Given the minimal development of the phyllosphere community in non-treated greenhouse control plants , our findings suggest that greenhouses present an ideal location to study the effect of microbial amendments on agriculturally relevant plant traits, and support previous work finding that greenhouse-grown plants develop bacterial communities distinct from outdoor environments .

The extraordinarily low background levels of bacterial colonization allowed us to examine the importance of phyllosphere bacteria to plant fitness, where we found that the application of PhylloStart bacteria was associated with increased total fruit production . Further, that we do not see a fruit number/size tradeoff in this study suggests that the microbial amendment is increasing investment in above-ground biomass, rather than simply redirecting resources from fruit size to number, as is commonly observed in seeds for example . Our results add to a body of work describing how fruit yield can be affected by both local nutrient conditions and microbial associations but extend the latter to the above ground tissues. In contrast to the greenhouse, we did not see evidence for either establishment or impact of PhylloStart amendment in the field. In this case, PhylloStart bacteria were not found at significant abundances on these plants after a month in the field , and their initial community structure did not seem to shape the future composition of the phyllosphere communities . While this would seem to contradict results finding initial colonizers dominate plant microbiome assembly , priority effects often depend on the identities of the earlycolonizing species and their environments. For example, when wood disks were pre-colonized with fungi and placed in a field for six months, retention of initial colonizers in the eventual community varied between ascomycetes and basidiomycetes, and from season to season . In the tomato plant phyllosphere, PhylloStart bacteria may have been overwhelmed by dispersal from neighboring plants , or from other sources. Further, the field conditions during which we ran our trials may have differed from those under which we initially quantified the phyllosphere composition to design PhylloStart. Previous work has shown that community composition will depend on both plant host genotype as well as local environmental conditions .

It remains possible that ‘local’ or otherwise well-adapted isolates may have yielded better performance. Further field trials under a broad spectrum of conditions and locations, as well as with larger sample sizes, are needed to determine whether bacterial amendments to the phyllosphere can potentially confer benefits to commercial field tomato production. There are various mechanisms by which the phyllosphere community might provide the observed benefits to its host. These include: 1) altering plant hormone signaling, either directly by producing phytohormones or indirectly through the elicitation of a plant response; 2) by increasing the nutrients available to the plant either through enhanced nutrient fixation or availability; and 3) through reducing stress, either environmental or due to pathogen pressure . While our study does not seek to explain the mechanism underlying observed biostimulant effects, it likely relies on a combination of these factors. However, that the effects of Azomite fertilization and PhylloStart inoculation acted primarily in an additive fashion suggests that altered nutrient acquisition is not a particularly dominant force. Like many phyllosphere microbiome studies, our experimental design did not specifically exclude the possible movement of bacteria to the soil , and it thus remains possible that some fraction of the inoculated bacteria colonized the below-ground compartment. However, recent studies have found that phyllosphereand rhizosphere-associated bacteria are predominately adapted for survival in their respective niches , and, when paired with our 16S rRNA sequencing results showing robust and long-term establishment of the community on the leaf surfaces, we think it is more likely that the effect is mediated through phyllosphere interactions directly. With this in mind, future work exploring the mechanisms of phyllosphere associated growth promotion should specifically differentiate any effects impacting above versus below ground responses to the PhylloStart bacteria, either through reciprocal translocation of the species or by physically preventing inoculation of or migration to the soil. One specific potential mechanism for the increased reproductive success is linked to the phytohormone auxin , which is a major regulator of plant growth, is commonly produced by bacteria inhabiting the phyllosphere , and has been linked to increased biomass accumulation in rice and corn . In this context, increased fruit yield could be mediated by the action of auxin in decreasing flower abscission , potentially leaving more flowers available to set. Of note, we did not observe a significant change in flower number across the first trial. Using BLAST to search the genomes of the PhylloStart bacteria, we found that several members have matches for idpC , a key protein in auxin production . Future research is needed to confirm that these bacteria can produce auxin in planta, growing blueberries in pots and if this may explain some of their plant-beneficial effects. It is also possible that the PhylloStart bacteria alter the plant’s response to environmental cues, allowing the plant to better optimize its growth strategy and invest more resources in reproduction.

Recent work has focused on the phenomenon of microbiome-dependent ontogenic timing , by which the presence of certain bacterial species acts as essential cues in the developmental timing of their host organism . For example, the composition of the Boechera stricta soil-associated bacterial community has been found to significantly alter the timing and duration of flowering . Further research is needed to assess the potential role that host-associated microbes play in developmental timing. Throughout these trials we saw no evidence of an interaction between the nutrient status of the plant and the effect of the PhylloStart bacteria; instead, the bacterial and Azomite treatments additively increased the total yield. Given these observations, we were curious if the PhylloStart community would show the nutrient dependent pathogen protection found in our lab’s previous work . Indeed, we found, in a growth chamber experiment, that the addition of this community limited the growth of the pathogen P. syringae in nutrient-limited plants, but that this effect disappeared when organic phosphorus fertilizer was added . These results are in line with the stress gradient hypothesis, which posits that inter-species interactions should become more facilitative under adverse conditions , and highlight the important role that phyllosphere bacteria play in stress response. Indeed, previous work in this system has shown that the PhylloStart bacteria up-regulate defense responses in Arabidopsis and subsequently reducing pathogen colonization, . In summary, we find that the presence of phyllosphere-associated bacteria benefit their plant host when grown in a microbially depauperate greenhouse environment, through an increase in reproductive success as measured by total fruit production, with further evidence for pathogen resistance. These results are important for understanding the role of microbial communities in host outcomes and are broadly relevant in an agricultural context where, for example, 32% of domestic and 56% of imported tomatoes in the United States are grown in greenhouses that may not provide adequate colonization of phyllosphere bacteria . Further, we show that bacterial inoculation provides an additive increase in fruit production when applied with a common supplement containing micronutrients, opening avenues for further optimization of agricultural production by harnessing the biostimulant properties of phyllosphere microbes.The detachment of a grape berry from its pedicel generally damages the berry because the vascular tissues and associated parenchyma, collectively known as “the brush”, remain attached to the pedicel and are pulled out of the berry on detachment, leaving an open wound sometimes called a “wet” stem scar on the berry’s stem-end. Berry detachment may also remove pieces of skin or cause the whole berry to rupture. Such mechanical damage can reduce the yield and quality of machine-harvested grapes for wine or raisins. Stem-end picking damage also limits the quality and storage life of stemless table grapes. Certain plant growth regulators known as “abscission agents” activate an abscission zone at the pedicel-fruit boundary. The activation of this abscission zone reduces fruit detachment force and promotes the development of dry stem scars . Abscission agents could reduce picking damage and thereby serve as harvest aides if treated grapes can be harvested after the abscission zone is activated, but before the fruit abscises. However, once the abscission zone has been activated, development proceeds quickly and may lead to excessive preharvest fruit drop .The first compound tested as an abscission agent for grape was ethephon, a phosphonic-acid compound that decomposes to release the gaseous plant hormone ethylene. Ethephon can induce the abscission of mature grape berries within 7 to 14 days after treatment, but high dosages are needed. The use of ethephon as an abscission agent for grapes would require an application dosage which is higher, and a preharvest interval that is shorter, than those for the current registered use of ethephon on grapes in order to enhance berry color. Such changes could be expected to increase ethephon residues on treated fruit, and it seems unlikely that regulatory agencies would approve a use that could increase ethephon residues on grapes since existing residues are already a concern. However, it should be noted that Ferrara et al. found that effective dosages of ethephon did not result in excessive residues. Jasmonates, including methyl jasmonate, a natural product, have also been shown to induce the abscission of various fleshy fruits, including blueberry , orange , and tomato.

A direct one-to-one comparison was made between the transcriptomes of the samples S and LS at the same time of cold storage, given the notion that this analysis would outperform the general profile comparison to identify the candidates to be involved in tolerance/susceptibility to cold . Figure 2A shows how the number of differentially expressed genes at each time decreased with storage time , thus confirming PCA results . Functional enrichment analysis showed that by 1 week of cold storage, the transcripts with higher levels of expression in fruits CS1-LS were preferentially related to energy production, RNA translation and protein assembly, the antioxidant system, structure maintenance, and genes with unknown functions . As 1 week cold storage is critical timing i.e. when maximum differences were shown when later transferring fruits to shelf life ripening , these functions may play a prominent role in the tolerant/sensitive character of fruits . By 2 weeks of cold exposure, only the genes with unknown functions were over represented in the tolerant pool , whereas a significant enrichment was noted for the genes linked to amino acid metabolism, pyruvate, signal transduction and transport in the genes at higher levels in CS2S. Interestingly, most of the genes expressed at higher levels in S fruits by 2 weeks had already reached this state by 1-week of cold storage . As two weeks of cold exposure results in mealiness upon shelf life in both S and LS fruits , square plastic pot but with large differences in MI severity, high levels of these genes may correlate negatively with the tolerant character of fruits.

After 3 weeks in the cold, only the highly expressed genes in tolerant fruits showed signal transduction as an over represented class . In this case, the genes differed from those identified as being over represented at 1 and 2 weeks . At this time, both S and LS developed mealy fruits with MI 1.0 and MI 0.8, respectively , but S was probably much more severely affected or underwent other downstream processes. In order to analyze if the transcript program in the cold may have a direct effect on eventual mealiness development during shelf life, a Pearson correlation analysis was conducted between the gene expression values and the projected MI will be achieved when subjected to shelf life ripening after cold exposure . This ‘‘projected MI’’ correlation analysis resulted in 113 directly correlated genes and 159 inversely correlated genes according to their pattern of expression in the cold . The functional enrichment analysis indicated that genes directly correlated to projected MI were enriched in RNA transcription and RNA posttranscriptional regulation. A further inspection revealed genes related to RNA bio-genesis and processing, splicing, RNA transcription machinery and the transcription factors . In addition, genes correlated positively with the projected MI were also enriched in transport category , that includes transporters for auxin, anthocyanin, amino acid, peptides, sulfate, carbohydrates and metal-ions . No functional enrichment was observed for those genes which correlated negatively with projected MI . However, a detailed inspection indicated that this set of genes contained calcium-related genes, including a transcription factor of the CAMTA family, and genes related to antioxidant systems which could participate in the regulation of this transient tolerance mechanism.The possibility that, in addition to cold-inducible mechanisms, some sort of tolerance mechanism may already be partly preprogrammed in tolerant fruits was investigated. The direct comparison between S and LS fruits at mature stage resulted in 63 differentially expressed genes .

Out of them, 13 genes we high expressed in fruits T and some have to do with flavonoid metabolism , structure protection and that forms part of a cycle that generates asparagine for more energyeconomical nitrogen remobilization under darkness and stress conditions. Several cell wall modifying activities were also differentially expressed between fruits S and LS . As no differences at the maturity stage were between pools , it is likely that differences in the expression levels of these genes at harvest may protect fruits and/or contribute to develop the tolerance program at least in the early stages of the cold response. HCA of samples M, R and CS showed that genes differentially expressed between fruits S and LS at harvest qualified in fruits LS as ripening genes . Notwithstanding, it is most interesting to note these genes were characterized by continuing the ripening program during cold storage , which did not happen so clearly in fruits S . However and as expected this behavior of the differential M genes is the exception rather than the rule for ripening genes. As seen in Figure 3B, a similar analysis with a set of 862 ripening genes showed that although cold affect the expression many of ripening genes, is quite effective stopping the molecular ripening program in fruits LS. This result is in agreement with the findings from PC2 . The main expression differences between LS and S fruits involved changes occurring in the same direction in R and cold stored fruits. In fruits LS, the expression of several ripening genes during cold storage remained at the same or higher level that they were in the M stage, but achieved similar expression levels to fruits R in the sensitive backgrounds . Apart from the delayed or attenuated ripening program in the fruits LS during cold storage, these fruits also showed specific ripening processes that became activated during cold storage , which is in agreement with the findings for genes differentially expressed at harvest . A more detailed analysis of shelf life ripening conditions and mealiness will be addressed in a future manuscript .In this section we wanted to see if there were similarities between the adaptation mechanisms operating in peach fruits stored in cold and darkness and those well-characterized in the cold acclimation of Arabidopsis plants grown in day/night regimes.

We wanted to see if the patterns of gene expression for the peach homologues of Arabidopsis genes in cold/dehydration regulons were consistent with the differential cold responses in S and LS peaches. First we analyzed the overlap between the response of cold stored peach fruits and those to various stimuli, including abiotic/ biotic stresses and hormones . Gene-bygene comparisons revealed that the vast majority of the cold regulated genes in our peach cold storage experiment have Arabidopsis orthologs, which have been described as being regulated by cold , or by ABA . Similarly to Arabidopsis, approximately 30% of peach cold-regulated genes were found to be associated with drought and/or salinity treatments . More strikingly however, approximately 35% of the cold-responsive genes in peach were known pathogen responsive genes or have been postulated to play a role in pathogen resistance . Furthermore, the genes described as being regulated by darkness in Arabidopsis account for up to 3.7% of peach cold-regulated genes ,square pot indicating that, although its contribution to all cold-regulated genes was less than those also involved in other stresses, dark stress could contribute to the differences observed in the cold response between peach fruits and Arabidopsis plants . Second, a list of Arabidopsis genes reported in cold regulons and dehydration regulons was used to identify homologous peach genes that were present on Chillpeach microarray . In total, 163 Chillpeach unigenes corresponded to the genes found in at least in one of the previously defined cold and/or dehydration Arabidopsis regulons . The expression profiles of these genes in response to cold storage were compared to those described for Arabidopsis and scored as matching when they behave similarly. More than 60% of the genes associated to the regulons CBF, HOS9, ICE and DREB2 correlated well with both the known Arabidopsis WT cold response pattern and the Arabidopsis mutant expression . That is, the ortologs genes to those up-regulated in Arabidopsis in response to cold showed higher expression levels in LS peach fruits than in high sensitive ones, while the genes down-regulated in Arabidopsis had higher levels in high sensitive peach fruits than in low sensitive ones. In contrast, most of the genes in HOS15, ZAT12, ESK, AREB, MYB, ZF/HD-NAC presented low correlation levels . Therefore, these latter are less likely to contribute to the differences in response to cold between the S and LS pools of fruits. The individual participation of each regulon to the differential response to cold between fruits S and LS was assessed by studying their contribution to the traits/trends observed in the global dataset analysis. For this purpose, we performed both PCA and 2D-HCA using the gene expression values for all the genes in each regulon as input datasets and quantitatively evaluate the importance of each regulon to discriminate samples S from LS and to separate the samples that would eventually became mealy, or not, by assessing by the number of genes well correlated with Arabidopsis in the gene expression models . The importance to discriminate samples S from samples LS was calculated by multiplying the number of genes that correlated well by the variance explained by PC2.

The importance of an operon to separate the samples that would eventually become mealy, or not , was quantified by dividing the number of genes in that operon that correlated well by the weight of the nearest node to CS1-LS. Both PCA and 2D-HCA revealed that regulon ICE1 was the one most contributing to discriminate samples LS and S, as to separate samples CS1-LS from the rest of cold-stored fruits that developed mealiness when submitted to shelf life ripening . Furthermore, this analysis also indicated that the regulon CBF1 was the next major regulon in discriminating between samples LS and S , while emphasized the relevance of HOS9 to separate CS1-LS from the remaining samples . The rest of the cold operons produced no such separation between CS1 S and LS, or did so but to a lesser extent . The expression pattern of the subsets the genes appertaining to the regulons ICE1 , CBF and HOS9 across the different samples showed that although extended exposure to cold debilitated the response of ICE-CBF regulated genes, fruits LS were able to maintain a longer and greater response for many of the genes in the regulon in the cold. In the case of HOS9 regulon, many of its members were up-regulated or without change in LS fruits as compared to M fruits .The same bulked samples used in this microarray experiment were used to validate the results by using medium-throughput qRT-PCR over a set of genes selected because they 1) contributed to separate samples S from samples LS at 1 week of cold storage , 2) showed a differential expression in, both, the M stage and 1-week of cold storage , and 3) showed differences at harvest . In order to examine at the single sibling level the reliability of the differential gene expression patterns obtained from the pools, the analysis was performed also on 15 individual genotypes of the pop-DG population . The qRTPCR results obtained from the pools and from the individual lines making up this pools indicate that 72.5% of the genes had the same expression pattern in the microarray experiment as in the qRT-PCR experiment . However, the magnitude of expression varied slightly in many of the genes and samples tested . Furthermore qRT-PCR experiments conducted on individual pop-DG siblings revealed that 42 out of the 50 genes validated in the pools were consistent with the expected patterns for which they were selected . These results support the validity of our approach and indicate that the genes selected from the microarray analysis could be either involved in chilling tolerance and/or be associated with the differential response to chilling response, and for some of them could even prove to general enough to hold true in individual fruits/plants.Since cold induced mealiness is not observed until the cold stored fruit are allowed to ripen, the chilling sensitivity phenotype of each fruit in the cold was estimated from the protracted mealiness incidence observed for equivalent fruit samples after shelf life ripening . Although mealiness, probably, a downstream effect of cold stress in peach fruits , it is the best phenotyping tool to assess the effect of cold on peach fruit, and has be used successfully to identify CI QTLs in peach. For BSGA we use Chillpeach microarray, interrogating part of peach genome. This provides only an incomplete picture of the genes behind the process; that is partially compensated by Chillpeach microarray being enriched in fruit-specific and cold responsive genes.