In ecosystems impacted by anthropogenic disturbance, honey bees may help fill the pollination void left by declines in non-honey bee pollinators . Lastly, where honey bees reach high densities, as reported in some areas of their introduced range , they may exploit enough food resources to compete with other pollinators . These phenomena are of broad ecological, evolutionary, and conservation interest, but to our knowledge, there currently exists no quantitative synthesis on the numerical importance of honey bees as floral visitors in natural ecosystems worldwide, either in their native or introduced range.Here, we use a meta-analysis to address the question of honey bee importance by taking advantage of a recent trend in pollination research—the documentation of community-level plant-pollinator interaction networks . Pollination network studies match the identity and frequency of each type of pollinator visiting each plant species within a locality . While these studies are performed to investigate a variety of questions , data from pollination networks provide an excellent opportunity to investigate the importance of honey bees in natural habitats, not the least because the role of honey bees has rarely been their focus . Here, we compile a database of 80 pollination networks from natural and semi-natural habitats from all continents except Antarctica, as well as several oceanic islands, including regions where honey bees are native and places where they have been introduced. These networks allow us to address four questions regarding the ecological importance of honey bees in natural habitats. What proportion of floral visits in natural habitats are due to honey bees? Do honey bees reach higher numerical dominance in their non-native range? How are honey bee visits distributed among plant species?

For instance, plant plastic pot what proportion of plant species is not visited by honey bees, and for what proportion do honey bees contribute the majority of visits? Finally, network studies often use visitation frequency as a proxy for pollinator importance . To further assess the value of honey bees as pollinators, we compile data on per-visit pollination efficiency of honey bees relative to other floral visitors from studies on 35 plant species. Using these data, we address a fifth research question: How does the per-visit pollination efficiency of honey bees compare to the average non-honey bee pollinator?We used two approaches to compile our data set of pollination networks. First, we performed a literature search using the ISI Web of Science database with the search terms [pollinat* network], [pollinat* web], and [pollinat* visit* community] from October 2014 to August 2016. Second, we downloaded pollination network data from the Interaction Web DataBase of the National Center for Ecological Analysis and Synthesis website and the Web of Life Ecological Networks Database . From the latter two databases, we downloaded all plant-pollinator interaction network datasets available as of December 2014 that reported visitation frequency in addition to the presence / absence of interaction between plant and pollinator taxa. Each data point in our study consists of a weighted pollination network in which the set of interactions between each plant and pollinator pair is weighted by a measure of visitation frequency . We defined a network as the sum of recorded plant-pollinator interactions in all study sites from a single study that fell within a 50-km diameter circle, regardless of the number of study plots that constitute the network. Sites within the same study that are separated by more than 50 km were treated as separate networks. When we encountered networks from different studies that were less than 50km apart, we excluded those studies that sampled a smaller number of plants or pollinators, or documented fewer interactions.

All networks retained for analyses met the following criteria. The data were collected in natural or semi-natural habitats; agricultural, urban, experimental, or otherwise managed habitats were excluded. Each included network consisted of observations on five or more plant species when pooled across study sites; networks that focused only on select plant taxa with specialist pollination syndromes were excluded from analyses. Included networks documented a broad range of pollinators; studies that had a narrow taxonomic scope or that explicitly excluded honey bees from data recording were excluded. Because we are primarily interested in quantifying the importance of honey bees in natural areas free of human interference, we excluded data from study sites that are known to be heavily influenced by honey bee colonies stocked for adjacent agricultural pollination. Thus, our estimates of honey bee numerical importance may be conservative with respect to mosaic landscapes where natural habitats are intermixed with agriculture, but achieve a closer representation of the role of honey bees in natural areas worldwide, overall. We also did not exclude networks from localities outside of the honey bee’s climatic niche, or where honey bees have never been introduced. In all, we obtained 80 networks from 60 peer-reviewed studies, two graduate theses , and our own study of plant-pollinator interactions in San Diego’s scrub habitats .For each network, we obtained the following data from their associated publications or from study authors when data were not available from publications: latitude, longitude, and final year of data collection. When these data were not available and authors could not be reached, we used the approximate geographical center of the study locality listed in the publication, and the year of publication as the last year of data collection.

We defined the native status of honey bees based on ; in Great Britain , where the native status of honey bees is uncertain, we treated honey bees as native rather than introduced, but classifying honey bees there as introduced in that location did not substantially alter our results. We also extracted the following information from each study, when available: the proportion of total floral visits contributed by honey bees, the proportion of plant species receiving at least one visit by honey bees, and the rank of honey bees with respect to both the total number of interactions and the proportion of plant species visited. Additionally, we used geographic information system analysis to obtain elevation data and bioclimatic variables for each network based on its GPS coordinates. We also assigned each network as being on an island or a mainland; the latter category includes all continents as well as large islands > 200,000 km2 , namely Great Britain , Honshu , and Greenland. For relevant studies for which raw data were not available, we contacted the corresponding authors to request data, or, in cases where data could not be shared, requested summary statistics on plant-pollinator interactions. When raw numeric data were unavailable from the publication or from authors, we used ImageJ to extract data from figures, where possible . Due to the different methodologies and data-reporting requirements of each study, not all of the above mentioned variables were extracted from all networks. Comparison of honey bees and bumble bees in pollination networks: Because studies vary in the level of detail with which individual species of floral visitors other than Apis mellifera are reported, we cannot reasonably compare frequencies of honey bee visitation with those of other single species across all of our networks. However, data are sufficiently detailed in 66 of our 80 networks to enable comparison of honey bees and bumble bees ; the latter are the only other pollinator group with a similar pattern of local numerical abundance and widespread introduction compared to honey bees . We compared the network-level relative visitation frequency of honey bees with that of all bumble bee species combined using a paired t-test. Since our goal was to compare global patterns of numerical importance, this analysis did not exclude networks in which honey bees, bumble bees, or both taxa were absent. It is worth noting that the leaf cutter bee Megachile rotundata , another widely introduced pollinator , was not reported in any of our 80 networks. Drivers of honey bee visitation frequency among pollination networks worldwide: We used multiple linear regression models to examine environmental factors that may contribute to variation in the network-level frequency of floral visits by honey bees. Networks where honey bees were not recorded were excluded from this analysis because of the variety of reasons that could explain their absence, black plastic plant pots ranging from studies that were outside the geographical or altitudinal range of the honey bee , to studies where honey bees were undetected despite being present in the ecosystem .

Inclusion of networks that documented no honey bee visits using a zero-inflated multiple beta regression model in Program R v.3.3.1 did not qualitatively alter our results . The response variable in these regression models was the proportion of all floral visits in each network contributed by honey bees, logit-transformed to improve normality . To identify the environmental model that best explains network-level honey bee visitation frequency, we generated models containing all possible combinations of the following explanatory variables : latitude, longitude, altitude, land category , and bioclimatic variables relating to temperature and precipitation . To incorporate bioclimatic variables, we first performed Principal Components Analysis to avoid constructing models with highly collinear terms. We performed one PCA for the 11 variables measuring temperature , and a separate PCA for the eight bioclimatic variables measuring precipitation ; these analyses enabled us to reduce bioclimatic variables to the first two principal components of the temperature variables and the first two principal components of the precipitation variables . We used R package glmulti to generate the candidate models and to select the best model using corrected Akaike’s Information Criterion scores. We also used the resulting “best” environmental model to address the questions of whether or not the network-level frequency of honey bee visits depends on their native status and the year of data collection, by adding these two variables to the “best” environmental model, both individually and together. Distribution of honey bee visitation frequency across plant species: We examined the distribution of honey bee relative visitation frequency across plant species as measured by the proportion of visits to each plant species contributed by honey bees. In this analysis, we included 46 networks in which at least one visit by a honey bee was recorded, and data on the proportion of total visits contributed by honey bees were available for each studied plant species. We pooled all plant species from all networks, and did not correct for cases in which the same plant species occurs in more than one network. Given the breadth of geographical areas and ecological contexts represented by networks in our study, the same plant species is expected to be served by different pollinator assemblages in distinct networks. Because plant species receiving few visits overall may tend to have extreme values of proportion of visits by honey bees, we also repeated this analysis after restricting the dataset to plant species with 10 visits recorded. Pollination efficiency of honey bees: We used two approaches to compile our data set. Second, we examined the literature cited sections of each of the studies found through the first approach for additional studies that were not captured in the literature search. Data points in this analysis consist of studies of focal plant species that compared honey bees and at least one other pollinator taxon with respect to pollen deposition, seed set, or fruit set resulting from a single visit by an individual floral visitor . In a small number of cases, we used ImageJ to extract data from figures when raw data were not available. In all, we obtained 33 studies reporting single-visit pollination efficiency data for 35 plant species, spanning 23 plant families . Of these, 19 plant species in 16 families were undomesticated, and 16 plant species in 7 families were grown in agricultural settings. Multiple metrics of per-visit efficiency were available from some studies. We used or calculated seed set data whenever available since it is the most closely related to plant reproductive fitness , fruit set when no seed counts were available, and pollen deposition when measures of seed and fruit set were unavailable. For each plant species in each study, we calculated the average single-visit pollination efficiency of non-honey bee pollinators as the numerical mean efficiency metric of all non-honey bee visitors studied. Then, we calculated the relative single-visit pollination efficiency of honey bees by dividing honey bee pollination efficiency by the average efficiency of non-honey bee floral visitors studied.

Lastly, temporal beta diversity measures the degree to which individual temporal samples at a study site differ from one another with respect to the composition of taxa present, providing insight into the temporal turnover of the taxa that make up an assemblage . While some popular indices of beta diversity are mathematically derived from measures of alpha and gamma diversity , recent advancements in the field of statistics have enabled additional measures of beta diversity, such as multivariate dispersion , that are mathematically independent of measures of alpha and gamma diversity. Impacts of anthropogenic disturbance on temporal gamma diversity always result from changes in temporal alpha diversity, beta diversity, or both . Decreases in temporal alpha and beta diversity may be driven by different aspects of disturbance , and may have different implications for biological interactions and ecosystem function even if different patterns of temporal alpha and beta diversity loss lead to the same net change in temporal gamma diversity . Trends in temporal alpha and beta diversity may also act in opposition such that temporal gamma diversity remains unchanged in spite of the profound alteration to temporal assemblage structure . Thus, isolating the mechanisms through which disturbance impacts an assemblage requires an examination of all three components of temporal diversity . Such approaches may also serve to identify the ecological effects that result from disturbance .In this study, square pots plastic we investigated the impacts of urbanization-induced habitat fragmentation on the seasonal dynamics of a diverse native bee assemblage over a two-year period.

Bees represent an appropriate taxonomic group for studying how habitat fragmentation affects temporal dynamics because, like many other organisms that occupy seasonal environments, bees exhibit distinct periods of activity that differ among species with respect to both duration and timing of onset . Previous research has demonstrated that anthropogenic disturbance may differentially impact bee species active in different seasons , and that temporal turnover in bee assemblages can contribute to among-habitat differences in site-level bee species richness . Additionally, the key ecosystem function that bees perform is influenced by the season specific pollination effectiveness and temporal complementarity of individual bee species. An explicit consideration of temporal diversity patterns is thus necessary to assess how anthropogenic disturbance affects bee assemblage structure and to identify potential consequences for ecosystem function. Here, we explicitly examined the seasonal dynamics of our focal bee assemblages by simultaneously evaluating their temporal gamma, alpha, and beta diversity. Our use of linear mixed-effects models and analyses of multivariate dispersion distinguishes our study from previous work on temporal patterns in pollinator diversity, the majority of which has focused on quantifying the relative contributions of spatial versus temporal variation in structuring pollinator assemblages . Our approach enabled us to address the following research questions: does habitat fragmentation affect all three components of bee temporal diversity similarly? And how do the effects of habitat fragmentation vary with time? Addressing these research questions allowed us to scrutinize the impacts of habitat fragmentation with a temporal resolution that would be unachievable by pooling temporal samples within study sites.Study System: Between April and August of 2011 and 2012, we documented bee assemblages in the coastal sage scrub ecosystems of San Diego County, California, USA, a global hotspot of bee biodiversity with over 500 bee species documented in the surrounding areas .

We established 1-ha study plots in CSS habitat situated in large natural reserves , and well-preserved habitat fragments embedded within the residential, urban matrix. In 2011, we surveyed four study plots in reserves and four study plots in fragments. In 2012, we surveyed seven study plots in reserves and 11 study plots in fragments. Details regarding the location and treatment classification of each plot are provided in the Table 1-S1. Many of our study plots are located in the same system of reserves and fragments included in earlier studies on the ecological effects of urbanization-induced habitat fragmentation , including bees sampled incidentally in pitfall traps . Permission to conduct field research was obtained from the University of California, San Diego; the Otay-Sweetwater Unit and Tijuana River National Estuarine Research Reserve Unit of the US National Wildlife Refuge; the City of San Diego Open Space Parks Division and Real Estate Division; the City of La Mesa Open Space Division; and the City of Chula Vista Open Space Division. Data collection: We employed bowl trapping and aerial netting to sample bees at all study plots, on sunny days with light wind. Bowl traps consisted of plastic bowls 7 cm in diameter that were white or painted fluorescent blue or fluorescent yellow and filled with ca. 60 ml of unscented detergent solution. During each survey, 30 bowl traps were placed at a study plot before 0900 h and collected after 1500 h. Traps were placed on level ground in an alternating sequence of colors, deployed in two roughly linear transects originating from the corners of each plot and forming an “X” formation near the plot’s center. Traps were placed 5-10 m apart from one another and at least 1 m from the canopy of large shrubs to avoid being shaded. During aerial netting, one researcher walked throughout the study plot and examined blooming plants as well as presumed nesting substrates for bees. Non-Apis bee species were collected regardless of whether they were on flowers, in flight, or in the vicinity of presumed nesting substrates.

In 2011, surveys were performed ca. every 2-3 weeks at each study plot , during which time, 60-min bouts of netting were performed once between 0900 h and 1200 h and once between 1200 h and 1500 h . In 2012, in order to accommodate a larger number of study plots, surveys were performed ca. every 3-5 weeks and included only a single 60-min bout of netting at each plot during each survey. Although seven sites were sampled in both years , the level of sampling employed here seems unlikely to have altered bee assemblages during our study .All collected bees were individually mounted and identified to species or morphospecies within genus using taxonomic keys and the reference collections of the American Museum of Natural History, UC Riverside Entomology Research Museum, California Academy of Sciences, UC Berkeley Essig Museum of Entomology, and UC Davis Bohart Museum of Entomology. Additionally, we also categorized each bee species as a pollen generalist or a pollen specialist based on whether it is documented to exclusively collect pollen from a single plant family. Data used to classify bees as generalists or specialists come from literature accounts for the species  and its subgenus , as well as our own field observations. Bee assemblages often reflect the richness, abundance, and temporal dynamics of their host plant assemblages . Thus, concurrently with the bee sampling, we documented the identities of insect-pollinated native plant species present in each plot in each year; in 2012 we also counted the number of blooming individuals of each plant species in each plot during each survey. We documented blooming plants by walking through pre-planned paths that allowed the observer’s field of view to cover the entirety of the study plot, as in , because many key plant species in our system are patchily distributed and because the thick growth of large, woody shrubs prohibited the use of random linear transects at many of our plots. Statistical analyses: We compared native bee assemblages in reserve versus fragment plots with respect to their temporal gamma, alpha, and beta diversity. In order to avoid human biases associated with aerial netting , our analyses include only bee specimens collected by bowl traps; however, plastic grow pots inclusion of netted specimens in our analyses yielded qualitatively similar results. For analyses requiring species-level identification, we excluded 78 bee individuals not identifiable beyond genus. We also repeated all analyses at the genus level to ensure that particularly species-rich genera did not disproportionately influence our findings; the results of these additional analyses did not alter our main conclusions. Lastly, we verified that reserve and fragment plots did not differ with respect to the composition and temporal dynamics of insect-pollinated native plant assemblages, and that the plot-level compositions of bee assemblages were not spatially autocorrelated . All analyses were conducted in R version 3.3.1 ; packages vegan , MASS , car , and nlme were used in visualizing and analyzing data. Temporal gamma diversity: We define temporal gamma diversity as the diversity of bees at a single study plot, pooled across all temporal samples , with each sample representing the bee specimens collected at one study plot during a single day of data collection. We considered both species richness and assemblage evenness .

In addition, we examined the proportion of bee individuals represented by generalist species , as generalist bees can exhibit higher tolerance to anthropogenic disturbance compared to their specialist counterparts . Lastly, we also examined the temporal gamma component of bee abundance. We used rarefaction in our analyses of species richness and assemblage evenness to account for among-plot variation in the number of bees sampled. We used the lowest plot-level bee abundance recorded each year as the number of individuals to subsample in our rarefactions. Bee abundance was calculated as the total number of bee individuals collected at each plot averaged across the number of temporal samples. Assemblage evenness and generalist proportion were logit-transformed prior to analysis as recommended by , and bee abundance was cube root-transformed to improve normality. We used Welch’s two sample t-tests to compare fragment and reserve plots for all dependent variables listed above. Given the dependence of bee diversity on the diversity and assemblage composition of their host plant assemblages , we also repeated each analysis with the temporal gamma richness of native plants as an added independent variable . We then compared the corrected Akaike Information Criterion scores of each pair of models with or without plant richness added. Compared to original models that did not include plant richness, models that included plant richness yielded qualitatively similar results in all cases but had poorer or equivalent AIC scores; thus, we did not include plant richness in our final models. Temporal alpha diversity: We define temporal alpha diversity as the diversity of bees collected in a single temporal sample . As in our analyses of temporal gamma diversity, we examined species richness, logit-transformed assemblage evenness, logit-transformed generalist proportion, and cube root-transformed bee abundance. In our analyses of species richness and assemblage evenness, we rarefied each temporal sample to 20 bee individuals to allow for unbiased comparisons between treatments and across temporal samples. In analyses requiring rarefaction, we excluded one sample from the 2011 dataset and nine samples from the 2012 dataset . We chose to rarefy to 20 individuals in order to minimize the number of data points to exclude while retaining sufficient resolution in our data. To examine how bee assemblages in reserves and fragments differ over the course of the study period, we constructed linear mixed-effects models. This approach allowed us to quantify the direction of seasonal trends and to detect treatment-by-sample interactions, neither of which is possible for the additive diversity partitioning approach used by most published studies that examined bee temporal alpha diversity . In each model, treatment , temporal sample , and their interaction were included as fixed effects, and study plot identity was included as a random effect to control for repeated sampling as in . To account for possible non-linear relationships between dependent variables and Julian dates of temporal samples, we constructed second- and third-degree orthogonal polynomial models in addition to first-degree linear models for each dependent variable, and selected the model with the lowest corrected AIC score. When alternative models yielded equivalent AICc scores , the model with the lowest degree was chosen. Lastly, as with our analyses of temporalgamma diversity, we repeated all analyses with the temporal alpha richness of native plants as an added independent variable . Models that included plant richness yielded poorer AIC scores in all cases; thus, we did not include plant richness in our final models. Temporal beta diversity: We define temporal beta diversity as the multivariate dispersion of bee assemblages in distinct temporal samples from the same study plot. We chose this index because of its relative mathematical independence from measures of alpha and gamma diversity , as well as its capability to detect differences among assemblages in both species identity and relative abundance .

The Dothiorella clade and Neos. dimidiatum show no specific pattern. The expanded secondary metabolite proteins were specifically abundant in L. missouriana, L. exigua, B. dothidea, Do. sarmentorum, and Neos. dimidiatum. The P450 family was expanded mostly in Lasiodiplodia species and B. dothidea but contracted in Diplodia species and Neos. dimidiatum. Neofusicoccum species and B. dothidea have an important representation of expanded secreted CAZymes, Diplodia and Dothiorella represent several expanded proteins, however, the numbers in Lasiodiplodia are extremely low. Transporter related genes in the Major Facilitator Superfamily were the most enriched in all the clades analyzed . The secondary metabolite related proteins type 1 Polyketide Synthases were expanded in Neos. dimidiatum, L. missouriana, L. exigua, and Do. sarmentorum, whereas Non-Ribosomal Peptide Synthetases were expanded in B. dothidea and Neos. dimidiatum. For the secreted CAZymes, Neof. nonquaesitum, Neofusicoccum hellenicum, and B. dothidea show an enrichment of the Auxiliary Activity family 3. Also, Do. iberica and Diplodia mutila show an enrichment of the Glycoside Hydrolase Family 3.To identify similarities between species in the Botryosphaeriaceae family, a phylogenetically informed-principal component analysis was applied to the significantly expanded families of virulence functions. These gene families were grouped into the functional categories based on the specialized databases, square pots plastic and the PCA was carried out using the Phyl.PCA . Phyl.PCA considers correlations among species due to phylogenetic relatedness, while correcting the matrices for nonindependence among observations .

Two separate analyses were conducted using the clock-calibrated tree presented previously and the tables of the number of genes classified as secreted CAZymes and Secondary Metabolism . Due to the close phylogenetic relationship of the Botryosphaeriaceae family, the set of secreted CAZymes is remarkably similar. However, there is a clear separation of the species that are considered to be more virulent , those belonging to the genera Neofusicoccum, Lasiodiplodia, and Botryosphaeria . At the same time, we observe a close cluster of Neofusicoccum species which are separated from the other groups mostly by the abundance of AA1, AA3, and GH5. In addition, the genus Lasiodiplodia is tightly clustered together with B. dothidea. This is driven by the abundance of AA9, GH28, and GH3, with the last family being more abundant in Lasiodiplodia species. The close clustering of Neofusicoccum, Botryosphaeria, and Lasiodiplodia is driven mostly by their similar profile of GH16 and AA3. Neoscytalidium dimidiatum is well separated from the rest of the species by the higher presence of GH76 and PL3 proteins. The PCA on secondary metabolite genes shows a similar separation of the most virulent genera from the others . Lasiodiplodia species are grouped together by similarly high profiles of T1PKS, Beta-lactone and T1PKS/NRPS clusters. Neofusicoccum species are grouped due to high numbers of terpene synthases and NRPS-like clusters. Botryosphaeria dothidea is separated because of its high abundance of NRPS, T1PKS, Terpenes, Beta-lactone, and NRPS-like clusters.In this study, we describe the genome sequences of seventeen well-known canker-causing fungal species in the Botryosphaeriaceae. The genomes assembled coupled with in-planta experiments allowed us to start analyzing the pathogenicity levels and the virulence factor profiles within this important fungal family. The level of completeness of the assembled genomes is consistent across all the drafts based on the expected and assembled genome sizes. This behavior is also confirmed by the high representation of conserved genes .

The completeness of the genomes, as well as the protein-coding genes and the repetitive DNA content, are similar to those of other woodcolonizing fungi of grape, such as Diaporthe ampelina DA912 , Di. seriata DS831 , and L. theobromae LA-SOL3 . Apart from the estimated completeness of the genomes, it is necessary to understand some of the limitations of the short reads technology, like copy number errors, chimeric contigs, and under-representation of repetitive regions . The functional annotation of the seventeen Botryosphaeriaceae species presents a broad and variableprofile of virulence factors that are used in different ways by fungi to colonize and survive in their hosts . The results show a great variation in the number of genes identified with a functional category, and these differences were usually associated with the genus of each species like those observed by Baroncelli et al. in Colletotrichum and Morales-Cruz et al. in other grapevine trunk pathogens. Researchers are inclined to think that the gene content is associated with the lifestyle and the variety of hosts . The expansion or contraction of a gene family usually occurs on functions that are under positive or negative selection. For instance, the genes related to host colonization and defense are under high pressure, therefore, it is common to encounter duplications or even losses. On the other hand, genes related to growth are more conserved and usually selected against these changes . Gene duplication events are crucial as they are considered to be one of the main processes that generate functional innovation . This process plays one of the most important roles in fungal adaptation and divergence . Host colonization during infection is mostly driven by gene expression of some groups of well-known proteins, namely, the secreted CAZymes, cytochrome P450 monooxygenases, peroxidases, and secondary metabolite-producing proteins .

The Botryosphaeriaceae family has a variable profile of these sets of genes, with the most virulent and aggressive species having, on average, greater numbers of annotated genes in these categories . In grape and pistachio, species in the genera Neofusicoccum and Lasiodiplodia, are typically more virulent than species in the genera Diplodia and Dothiorella . GH functions of β-glucosidases, β-xylosidases, glucanases, L-arabinofuranosidase, and galactanase were present in all the pathogens in this study and significantly more in Neofusicoccum and Lasiodiplodia. In the same way as the GH, AA functions like cellobiose dehydrogenases, alcohol oxidases, pyranoseoxidase were more abundant among Neofusicoccum species and B. dothidea. GH and AA play a critical role in the degradation of the host cell wall compounds , which is involved with the degree of pathogenicity within these genera, albeit on grape, the host we examined. Marsberg et al. ; Massonnet et al. , and Félix et al. , found similar numbers of CAZymes in Neof. parvum, L. theobromae, and B. dothidea, respectively. P450s are instrumental to the development of all organisms. These enzymes are involved in many aspects of primary and secondary metabolisms and are responsible for xenobiotic detoxification and degradation . Virulence may in part reflect the ability of some species to better tolerate and, further, to metabolize phenolic compounds produced by the host. Both Neof. parvum and Di. seriata can eliminate the stilbene piceid and its derivative resveratrol in vitro , but the former is better able to tolerate resveratrol derivatives ampelopsin A, hopeaphenol, isohopeaphenol, miyabenol C, and ε-viniferin, which are produced at higher levels in planta in response to Neof. parvum versus Di. seriata infection . Therefore, it is not unexpected to see a variable profile amongst genera in the Botryosphaeriaceae family and even within a single genus. As presented in Figure 2, some superfamilies are abundant in Neofusicoccum, Lasiodiplodia and Botryosphaeria genera, but other superfamilies are especially more numerous in the Basidiomycetes species included in this study. On the other hand, for most of the superfamilies presented, Sa. cerevisiae shows a considerable lack of such annotated genes, but CYP53 and CYP578 the counts are comparable with the rest of the species. This variation is sourced by the constant evolution and adaptation of the microorganism and hosts to their specific environment . As plants evolve new defense mechanisms and compounds against pathogens, plastic grow pots the fungi diversify their methods to degrade these compounds or generate new metabolites to attack their hosts . The Botryosphaeriaceae species in this study and the two Basidiomycetes present a set of fungal peroxidases that range from 41 to 62. As for the previous putative virulence factors, Neofusicoccum, Lasiodiplodia, and Botryosphaeria genera have the most annotated peroxidases, however, in this case, Diplodia also showed a comparable amount. Manganese peroxidase was only found in the two basidiomycetes. This enzyme has a critical role in the degradation of lignocellulose compounds by basidiomycetes , therefore it is very common in white-rot fungi such as F. mediterranea and St. hirsutum . Ascomycetes that rot wood are characterized as soft-rot fungi, which do not degrade lignin by producing manganese peroxidase, but instead “alter” lignin by producing lignin peroxidases, peroxidases, polyphenol oxidases, and laccases .

The former enzyme was found in higher numbers in the genus Neofusicoccum compared to other genera within the family. The hybrid ascorbate-cytochrome C peroxidase was over represented in the genera Neofusicoccum, Lasiodiplodia, and Botryosphaeria and is associated directly with the detoxification of ROS . The wide array of transporters annotated in this study suggests a high adaptation to toxic compounds, either produced by other microorganisms, the host, or potentially chemical synthesized fungicides . The number of proteins in the Major Facilitator Superfamily and Superfamily in Neofusicoccum, Lasiodiplodia, and Botryosphaeria were more numerous than the other Botryosphaeriaceae species. Protein members of the MFS family may have different functions in the influx/efflux of molecules between cells and the exterior environment, and several cases of fungicide resistances have been associated with the overexpression of certain MFS channels . The former genera have been reported to have lower sensitivities to almost full resistance to different synthetic fungicides . Similar behavior was observed in Do. sarmentorum, were the ATP-binding Cassette ishighly represented. The ABC superfamily plays different roles in fungicide resistance, mycelial growth, and overall pathogenicity . In addition, the array of secondary metabolite gene clusters is more expanded in the Botryosphaeriaceae family than in the Basidiomycetes except for terpene synthase gene clusters. T1PKS, NRPS, and hybrids of T1PKS-NRPS produce toxic polyketides and toxic polypeptides, which kill host cells and leads to disease development . To evaluate the potential differences in virulence within the Botryosphaeriaceae family in more detail, we executed a Computational Analysis of gene Family Evolution . By identifying species and gene families with higher rates of gain and loss can help us to better understand the differences in pathogenicity as it relates to the numbers of copies of virulence genes . Six hundred and sixty-six gene families of the proteins analyzed in this study have a significantly higher than expected rate of gain/loss. The annotation of putative virulence factors in Neofusicoccum, Lasiodiplodia, and Botryosphaeria shows an average expansion of these gene families, even if some of the species shows a contraction, the overall clade rate is positive. Among those expanded or contracted families there is a set of functions that are over represented. The secreted CAZymes seem to be expanding in Neof. hellenicum, Neof. nonquaesitum, B. dothidea, Di. mutila, Do. iberica, and Do. sarmentorum, whereas the Dothiorella species show contractions in some families. However, almost no significant gain/loss of secreted CAZymes appears to be occurring in the genomes of Lasiodiplodia species. The opposite scenario is observed for the P450s, where Lasiodiplodia appears to be actively evolving, showing major expansions in three of the four species in this study. Also, B. dothidea and three Neofusicoccum species show an expansion of these families. On the other side, Neos. dimidiatum, B. dothidea, Do. sarmentorum, L. exigua, and L. missouriana are actively expanding their secondary metabolite gene clusters. Finally, the wide variety of transporters present in fungi, is the result of the positive selection pressure over them. The need of the fungi to adapt to new environments and hosts had selected for multiple mutations that diversifies the transporters functions . The MFS displays the largest effect of expansion and contraction among all the species. Botryosphaeria dothidea, L. missouriana, L. exigua, and Di. mutila appear to be actively expanding the MFS transporters. However, Neos. dimidiatum, Di. seriata, Neofusicoccum vitifusiforme, Neof. australe, and Neof. mediterraneum are contracting MFS transporters. Phylo PCAs results support the idea that within the Botryosphaeriaceae family, Neofusicoccum, Lasiodiplodia, and Botryosphaeria genera are the most virulent . There was a very clear separation of these species from the Diplodia, Dothiorella, and Neoscytalidium. The secreted CAZymes that cause the clustering of the Neofusicoccum species are usually associated with laccases, cellobiose dehydrogenases, and cellulase activities. These enzymes usually target components of the plant cell wall such as lignin, cellulose, cellobiose . Among the functions driving the clustering of Lasiodiplodia and Botryosphaeria, the lytic polysaccharide monooxygenases are one of the most important. They have a role in the oxidative degradation of various biopolymers such as cellulose and chitin.

A STRUCUTRE analysis found that the optimal K was five . This is the same number of populations identified by Chacón-Sánchez and Martinez-Castillo . Rather than fitting neatly into the categories of MI, MII, AI, AII, and admixed, these samples were optimally divided into MII, AI, AII and two MI groups. The larger of the MI groups included a mixture of wild and domesticated lines while the smaller MI group consisted mainly of wild accessions collected in Mexico. Based on this analysis, 36 lines identified as belonging primarily to the two Andean gene pools were removed from the study. For future publication, a higher threshold of admixture may also be considered for removing some additional genotypes. With the Andean lines removed the remaining population showed significantly less population structure .A GWAS of volatile HCN production in the first 15 minutes of tissue rupture caused by thawing, identified several significant SNPs for flower tissue and one highly significant SNP for pod tissue . The most significant SNPs for flower tissue, on Chromosomes 2 and 4 are located near matches for the BLAST search of the white clover Li/li sequence. The SNP identified in pods is not near the significant alignment of the BLAST search against the Lima bean reference genome of the white clover sequence or the QTL identified in the biparental population. When considering cyanogenesis as a defense trait, the immediate release of HCN following tissue disruption deters an insect herbivore and therefore serves as a resistance trait . As such, it will be most successful against opportunistic, square plant pots generalist herbivores rather than specialist herbivores which would have experineced coevolution with the crop and had more opportunity to adapt to its defenses .

Additional study of these findings may yield great contributions to breeding efforst for L. hesperus resistance. Additional significant SNPs were found in the 15-30 minute exposure window . In flower tissue, SNPs on chromosomes 9, 5, and 7 were closely located to significant matches from the BLAST of the white clover Ac/ac gene sequence on the Lima bean reference genome. In pod tissue, a significant SNP on chromosome 6 was also closely located to a match for the Ac/ac sequence.Prior QTL analysis of HCN in floral buds, immature pods, and leaves of a RIL population identified significant loci for volatile HCN on chromosome 5 . This QTL is very close in position to one found by the GWAS analysis of HCN in flowers defrosting for 15-30 minutes, PL05_36471809. There is also a significant alignment with the white clover sequence for the Li/li gene in a nearby region of chromosome 5 . It is interesting to note that there is evidence of β-glucosidase activity being induced by the presence of insect herbivores . The greenhouse from which the samples in this study were collected had a stable infestation of thrips but was free of the larger herbivores typically found in field settings. It is therefore possible that if this study were repeated with field-collected samples, this locus would have a stronger effect.Cyanogenesis is a complex trait in Lima bean with multiple SNPs closely associated with the expression of cyanogenesis. Highly significant SNPs found in flowers during the first 15 minutes after tissue disruption are close matches for the white clover Li/li gene sequence. This could contribute to the effectiveness of cyanogenesis as a resistance trait that deters insect herbivores . Additional SNPS on chromosomes 9, 5, 7, and 6 found in the 15-30 minute exposure window may be associated with the biosynthesis of cyanogenic glucosides as they are close to matches of the white clover Ac/ac gene sequence.

Finally, a QTL on chromosome 5 was in close proximity to previously identified QTL for cyanogeneis in flowers, pods, and leaves as well as the white clover sequence for Li/li. Further analysis and research is needed to clarify the function and expression of genes located near the significant SNPs identified by this study and solidify understanding of the genetic architecture of cyanogenesis in Lima beans. Several additional steps will be take to advance this research prior to publicaiton. First, the STRUCTURE and GWAS analyses will be reexamined to consider higher thresholds of admixture. Next, confidence intervals and markers flanking the significant SNPS will be analyzed to increase certainty about the relationship between these findings and the BLAST search maches as well as previously identified QTL from the RIL population. A study of genome annotations and the expression atlas will also be undertaken to identify clues about the function of genes near these significant SNPs. The results from wild and domesticated accessions will also be compared to determine how the matches for Li/li and Ac/ac genes may have been affected by domestication. Lastly accessions with extreme phenotypes will be identified and their associated genotypes used for breeding, further mapping, and validation studies.Amplifying defense traits that protect plants from insect herbivores through plant breeding has the potential to increase yields while reducing pesticide use and associated concerns for human and environmental health . This is a particularly important strategy for organic systems in which conventional pesticides cannot be used. Lima beans are an important grain legume globally and the most economically important dry bean grown in California where their primary insect pest is the Western Tarnished Plant Bug . Lima beans are a model experimental organism for studying anti-herbivore defense traits . Within this body of literature, many studies have focused on the trait of cyanogenesis . Several experiments have been conducted in recent years to identify specific mechanisms that contribute to the tolerance or resistance traits that protect some Lima bean accessions from damage by L. hesperus .

One mechanism that has been considered is the production of various polygalacturonase inhibiting proteins in the cell walls of Lima bean that bind to L. hesperus salivary enzymes and mitigate attempted digestion of the cell wall . This trait was found to be strongly influenced by environmental variables such as pest pressure and insecticide treatments but the study design did not permit differentiation of these results as the primary goal was QTL mapping . Cyanogenesis is a trait of particular interest since it is known to be an effective anti-herbivore defense trait in wild Lima beans that has been selected against during domestication . Several QTL have been identified for cyanogenesis in flowers, immature pods, and leaves . However, these studies have not yet determined if cyanogenesis is an effective trait in the defense of Lima beans against L. hesperus specifically. L. hesperus predominantly feed on the flowers and immature pod tissue of Lima bean and if cyanide is an effective deterrent or toxin for L. hesperus then increased expression of cyanogenesis in these tissue types could be amplified through breeding without risk to the human consumers of mature seeds which are known to have low cyanogenic capacity . The final part of this study aims to determine how cyanogenesis affects L. hesperus survival and development as well as test if cyanogenic capacity can be induced by the presence of L. hesperus.Many economically important crops with high protein content and great importance for indigenous food systems are members of the legume family . Several of these legume crops are cyanogenic . It appears that this trait has evolved independently several times in the legume family. Within the legume genus Phaseolus, there are five domesticated crop species but only one, Lima bean, is cyanogenic . In addition to Lima bean, five other cross-compatible, Phaseolus species within the Polystachios group of section Paniculati are also cyanogenic . This and other evidence indicate that despite being a widespread trait, cyanogenesis evolved independently multiple times through the recruitment of similar genes .In an extensive screening of wild, weedy, garden pots square and cultivated forms of Lima bean, all were found to be cyanogenic. However, there is variability within and between populations . Domesticated forms typically have much less cyanogenic potential , the amount of stored cyanogenic glucosides, and cyanogenic capacity , the amount of cyanide released when damage occurs . Cyanogenic potential is determined by the biosynthesis and accumulation of cyanogenic glucosides . Cyanogenic capacity is primarily determined by genetic factors but there is also a significant influence of plant age and other environmental factors . Cyanogenesis is typically considered a constitutive trait with strong genetic control by two Mendelian genes . However, there is great variation in the trait within populations and even within an individual plant . Previous studies have found cyanogenic potential and capacity to vary based on the age and tissue type being measured . For example, in Lima bean, young leaves have higher cyanogenic potential than mature leaves .

Wild Lima bean seeds by contrast have very high cyanogenic potential but low cyanogenic capacity, likely due to the low moisture content inhibiting β-glucosidase activity . Additionally, there is evidence that cyanogenic capacity may be locally induced by the presence of insect herbivores even if cyanogenic potential is constitutive . Temperature, humidity, seasonal dynamics, water-stress, and nutrient availability may also affect cyanogenesis .Cyanogenesis is very nitrogen intensive with a one-to-one ratio of nitrogen and carbon in each molecule of hydrogen cyanide . The availability of nitrogen can be a limiting factor for plant growth . Therefore, it has been hypothesized that cyanogenic glucosides evolved first as an intermediate nitrogen storage compound and only later evolved into a defense compound . In the case of Lima beans, evidence supports the hypothesis that cyanogenic glucosides primarily serve as an anti-herbivore defense more so than a nitrogen storage mechanism . Lima bean plants with high cyanogenic glucoside content in leaves had lower above ground biomass than low cyanogenic glucoside content plants when no herbivores are present, but this difference was less in the presence of herbivores . This could indicate that there is a high cost to producing cyanogenic glucosides. Alternatively, these plants may be investing in a strong defense of their vegetative tissue so that a smaller above ground biomass can produce higher yield. Additionally, seeds of Lima bean with high cyanogenic glucoside content had lower germination rates but produce seedlings that had high cyanogenic glucoside content and supported lower growth rates of the generalist herbivore Spodoptera littoralis . In addition to having tradeoffs with growth and vigor, plants with high cyanogenic glucosides have lower investment in other defense mechanisms . In Lima bean, a negative correlation was found between cyanogenic glucosides and volatile organic compound emissions . This evidence indicates that in Lima bean, cyanogenicglucosides serve primarily as an antiherbivore defense compound rather than a nitrogen storage mechanism.Cyanogenesis is an anti-herbivore defense trait found in many plant families . It is especially common in crop plants. While an estimated 11% of all plant species are cyanogenic, the trait is present in approximately 21% of the major world food crops . Given that humans have long known of several effective methods of detoxifying cyanogenic foods, including leaching, cooking, and fermenting, it is possible that crops with this trait were specifically selected by early farmers for their superior defense against insect herbivores . Despite its value as an anti-herbivore defense trait, cyanogenesis has been selected against during the process of domestication. With the notable exception of sorghum, most crops have lower levels of pre-cyanogenic compounds than their wild relatives . This may be because, though satisfactory, our methods of detoxifying foods do not fully eliminate cyanide. Specifically, in the example of Lima bean, the enzyme linamarase rapidly hydrolyzes cyanogenic glucosides during cooking but becomes denatured at 141 °C . If cyanogenic glucosides remain unhydrolyzed when that cooking temperature is reached, their cyanide will be released within the consumers digestive track . This can be tolerated at low levels, but chronic cyanide intoxication can cause severe symptoms including degenerative neuropathy, paralysis, blindness, and premature death . Given the severe consequence of chronic or acute cyanide intoxication as well as the bitter taste, it is understandable that cyanogenesis was selected against in crop plants . The toxicity of HCN comes from asphyxiation when it binds to cytochrome oxidase, a key enzyme in the mitochondrial respiratory pathway . This chemistry makes it toxic to both animal and plant cells. It is therefore necessary for plants to store pre-cyanogenic compounds, typically cyanogenic glycosides, separately from enzymes that cleave the compound and form HCN . The result of this arrangement is a possible difference in the HCNp and the HCNc of a plant.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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