Month: April 2025

Patients with inflammatory bowel disease have a significantly increased risk of developing CRC, while long-term aspirin treatment is associated with a significantly decreased risk of CRC. The mechanisms by which chronic inflammation promotes tumor development often involve the immune system. For example, the IL6/STAT pathway discussed earlier is also implicated in cancer formation. Overexpression of IL6 leads to excess STAT3 transcription, causing unwanted cell proliferation not only in T cells but also in the intestinal epithelium. Another inflammatory cytokine of note is TNF α. While the intestinal bacteria can promote inflammation, they may also affect the likelihood of CRC more directly. Once the intestinal mucus layer is thinned, and direct bacterial-epithelial cell interactions occur, certain bacterial strains promote tumor development. E. coli strains bearing the pks island are of particular interest. This genetic locus codes for the secondary metabolite colibactin, along with the enzymes necessary for its production. Colibactin has been shown to crosslink with DNA, producing double-stranded breaks. Furthermore, pks+ E. coli strains have been shown to be prevalent in CRC patients. In one study, big round plant pot nearly two-thirds of CRC patients had pks+ E. coli strains in their intestinal bacteria. In the same study, pks+ E. coli also existed in about 20 percent of healthy individuals.

Colibactin, however, is a reactive and short-lived protein, requiringclose contact with epithelial cells to cause DNA damage. A healthy mucosal barrier keeps colibactin at a distance and reduces the chance of affecting the intestinal epithelium. Evidence for the pathogenic relationship between diets, Fusobacterium nucleatum, and CRC has been emerging. The F. nucleatum levels have been shown to be higher in CRC than in adjacent normal mucosa. Utilizing the molecular pathological epidemiology paradigm and methods, a recent study has shown the association of fiber-rich diets with decreased risk of F. nucleatum-detectable CRC, but not that of F. nucleatum-undetectable CRC. Experimental evidence supports a carcinogenic role of F. nucleatum, as well as its role in modifying therapeutic outcomes. The amount of F. nucleatum in CRC tissue has been associated with proximal tumor location, CpG island methylator phenotype , microsatellite instability , low-level CD3+ T cell infiltrate, high-level macrophage infiltration, and unfavorable patient survival. The amount of F. nucleatum in average increased in CRC from rectum to cecum, supporting the colorectal continuum model. Future studies should examine the role of diets, microbiota, and CRC in detailed tumor locations. Dietary prevention of CRC, then, has two intertwined aims: to reduce inflammation and to promote a healthy intestinal microbiota. As already discussed, preclinical evidence implies that dietary bio-active compounds, particularly anthocyanins, can reduce symptoms of lowgrade chronic inflammation as well as oxidative stress. It can also aid in balancing the intestinal microbiota by promoting the growth of beneficial bacteria and by reducing the populations of pro-inflammatory bacteria.

Clinical trials have had mixed results, but anthocyanins and some polyphenols have shown to counteract against CRC actively. More research, however, is necessary for conclusive results. How, then, are individuals to consume enough bio-active compounds to have an effect on health? Some answers may be found in the food consumption practices of cultures with historically low CRC incidence. Parts of India, for example, have had some of the lowest CRC incidence rates in the world; however, this status has been changing. In recent decades, increasing urbanization and similar factors have led to progressively Westernized diet patterns and lifestyle. CRC incidence rates are similarly rising, lending weight to the hypothesis that the traditional Indian diet may help prevent CRC. Furthermore, Indian immigrants to Western countries have a much higher incidence of CRC compared to Indians in India. Typical components of traditional Indian meals include a broad variety of flavors, as promoted in Ayurvedic medicine, and a variety of other foods. Both are facilitated by using a thali platter to serve the meal. The traditional American main meal includes an entree , one or more carbohydrates , and one or more vegetables. This basic structure can potentially be adapted with inspiration from thali meals by reducing the size of the main dish and serving more vegetables, legumes, pulses, herbs, and spices to accompany it. The inclusion of multiple colors in a meal is desirable, because certain bio-active compounds, particularly anthocyanins are also pigments. Blue, purple, and red-purple colors in plant foods indicate high anthocyanin content. Purple-pigmented potatoes can be prepared in the same way as traditional white potatoes, but the anthocyanin content is significantly higher in the pigmented varieties.

Purple sweet potatoes also contain more anthocyanins than the more common orange varieties and can be easily substituted for them. Other vegetables with red or purple cultivars include carrots, cauliflower, and cabbage. Different colors can indicate the presence of other bio-active compounds, such as orange , yellow , and red/pink . Thus, healthy bio-active compound consumption may be increased by selecting colorful vegetables. Another way to increase consumption of bio-active compounds is to increase their presence in available foods. The content of bio-active compounds in plant foods is highly influenced by genetics. The agricultural industry could greatly impact health by adopting food plant cultivars that produce bio-active compounds in larger amounts than is currently common. New cultivars may need to be developed that retain desirable characteristics such as large size, pest resistance, reduced spoilage, etc., but also have high bio-active content at the time of consumption. bio-active compounds, with some exceptions, tend to deteriorate during storage. Even when compounds have not deteriorated, storage may reduce the anti-inflammatory/antioxidant activity of bio-active compounds to affect health. A second systemic change that would promote increased bio-active compound consumption involves reworking how fruits and vegetables are currently stored and processed, as well as reducing the average storage time and adapting processing to optimize the amount of bio-active compounds. Presently, “nutritional adequacy” does not consider many of the bio-active compounds discussed in this paper. Further clinical studies are needed to support and elucidate the role of bio-active compounds in the prevention and treatment of disease.Recent research provides preclinical evidence that phytochemicals, especially anthocyanins, promote gut microbial health, reduce inflammation, and lower the risk of colorectal cancer. Clinical evidence is sparse but indicates that anthocyanins and other bio-active compounds do have an effect on colon cancer. Both are consistent with low cancer rates in India, where both traditional diet and Ayurvedic medicine promote consumption of many classes of phytochemicals. Long-term, diet-based randomized clinical trials are both difficult to conduct and prohibitively expensive. However, given the strong evidence from basic studies, observational data, and randomized clinical studies with short-term surrogate outcomes, steps should still be taken to improve the consumption of bio-active compounds, particularly in countries which contain a large proportion of CRC patients. Eating a wide variety of plant foods has no ill effects, and is indeed a commonly recommended part of a healthy lifestyle. Increasing bio-active intake among Westerners will require modifications in both individual eating habits and food system practices.Survival of humans will depend on increased agricultural productivity. Agricultural productivity is not only more yield per area, but also higher nutritional content, round plant pot less dependence on fertilizers, and more resilience against environmental hazards. All of these traits impinge upon plant metabolism. Plants carry out a myriad of metabolic reactions that are intricately connected into complex networks. To understand and engineer plant metabolism, it is important that metabolic complements of plant genomes are accurately and consistently annotated across species. To provide the research community with comprehensive information about plant small-molecule metabolism, we previously introduced the Plant Metabolic Network , a plant-specific online resource of metabolic databases .

PMN consists of PlantCyc, a database of all experimentally-supported information found in the literature from any plant species, as well as 22 single-species databases with a mix of experimentally-supported and computationally-predicted information, which allow researchers to explore each species’ unique metabolism. Here we describe the substantial expansion of PMN in both quantity and quality, which includes 126 single-species databases. We demonstrate the utility of the PMN resource by applying recently published omics data to gain insights into plant physiology and cellular level metabolism. Additionally, we systematically compare 126 species in the context of metabolism to identify metabolic domains and pathways that distinguish plant families. Finally, we present new website tools for viewing and analyzing metabolic data including a CoExpression Viewer and subcellular boundaries for metabolic pathways.PMN is a compendium of databases for plant metabolism with a substantial amount of experimentally supported information. The latest release contains 126 databases of organism-specific genome-scale information of small-molecule metabolism alongside the pan-plant reference database PlantCyc . Together, these databases include 1,280 pathways, of which 1,163 have direct experimental evidence of presence in at least one plant species. In addition, PMN 15 includes 1,167,691 proteins encoding metabolic enzymes and transporters where 3,436 have direct experimental evidence for at least one assigned enzymatic function. There are 9,129 reactions , and 7,316 compounds. Compared to the PMN 10 release described in Schläpfer et al. , PMN 15 increases the number of species 4.7-fold and proteins 8-fold, and adds 2,929 more reactions, 2,178 more compounds, 66 more pathways, and 3,229 more references . Data in the PMN databases are represented using structured ontologies consisting of hierarchical classes to which pathways and compounds are assigned by PMN curators, which makes statistical enrichment analyses possible. The pathway and compound ontology classes, alongside the phylogeny of the included species, illustrate the breadth of metabolic information and species included in the database . Prominent specialized metabolism classes such as terpenoids and phenylpropanoids are highly represented in the databases. This large volume of metabolic information makes PMN a unique resource for plant metabolism. The reference database, PlantCyc, is a comprehensive plant metabolic pathway database. PlantCyc 15.0.1 contains experimentally supported metabolic information from 515 species. Most of the data come from a few model and crop species . For example, Arabidopsis thaliana contributes to 43.4% of experimentally supported enzyme information in PlantCyc, followed by 7.46% from Chlamydomonas reinhardtii and 3.37% from Zea mays. Compared to other metabolic pathway databases such as KEGG and Plant Reactome , PlantCychas substantially higher numbers of experimentally supported reaction and pathway data . PlantCyc 15 includes 3,077 experimentally validated reactions with at least one curated enzyme and 1,163 curated pathways . Plant Reactome includes 1,887 and 320 curated reactions and pathways, with 677 reactions and 266 pathways predicted to occur in A. thaliana , while KEGG includes 543 experimentallysupported pathways as of February, 2021, with 136 occurring in A. thaliana. Data on the number of reactions in KEGG that were experimentally validated were not available at the time of publication. The reference information in PlantCyc is incorporated into MetaCyc, which also includes experimentally supported metabolic information from non-plant organisms and is used to predict species-specific pathway databases . In addition to the reference database PlantCyc, PMN 15 contains 126 organism-specific metabolism databases . These databases range widely in the plant lineage including several green algae and nonvascular plants. The majority of the plants are angiosperms with the Poaceae family most highly represented with 25 organisms. There are also 8 pairs of wild and domesticated plants, including rice, wheat, tomato, switchgrass, millet, rose, cabbage, and banana, alongside their wild relatives . Finally, PMN 15 includes 6 medicinal plants : Camptotheca acuminata, Cannabis sativa, Catharanthus roseus, Ginkgo biloba, Salvia miltiorrhiza, and Senna tora. The newest addition to the list of the medicinal plants is Senna tora, which is a rich source for anthraquinones and whose recent genome sequencing and metabolic complement annotation helped discover the first plant gene encoding a type III polyketide synthase catalyzing the first committed step in anthraquinone biosynthesis . This rich collection of species-specific metabolic pathway databases enables a wide range of analyses and comparisons. To promote interoperability and cross-referencing with other databases, PMN databases contain links to several compound databases such as ChEBI , PubChem , and KNApSAcK . PubChem containins over 270 millionchemical entries as of March 2021, and 95% of PMN compounds link to it. ChEBI release 197 has 58,829 entries and serves as a primary source of compound structural information during curation into PMN databases. Within PMN, 65% of compounds link to ChEBI. Examining 50 randomly chosen compounds that are not mapped to ChEBI suggest that the majority of the remaining 35% compounds do not yet exist in ChEBI . KNApSAcK links are comparatively rare, as only 1.7% of compounds have had a KNApSAcK link added by curators.

This modeling tool may improve current management practices by predicting pest pressure independent of trap catches or samples of infested fruit. We also see potential applications of this model for research in other fields of study and for broadening the understanding of how pests interact with the environment.The population projection model was written in the open source statistical environment R version 3.0.2. The model calculated the matrix based on mean temperature input. Briefly, the matrix calculations were based on age-specific regressions of temperature-dependent population parameters as highlighted by Tochen et al.. Whereas immature life stages of D. suzukii may experience different environmental conditions than adults because these life stages are completed within the fruit, in this study, ambient air temperatures were used to predict population dynamics for all life stages. To return age-specific population vectors for 50 age-classes of D. suzukii for each test case, a vector of mean daily temperatures for each site was input into the R statistical interface. The biofix, square pot plastic or the point where the model began in the spring, was determined using methods described in Tochen et al.. Biofix essentially described the earliest point in the season when the temperature allows the population to increase. Calculations for population estimates were initiated on the biofix date of 2 February in Parlier and 1 April in Wilmington and Salem . In Pergine and Sant’Orsola, estimates were initiated on 6 April .

The population matrices were initiated with 100 flies in the population vector for 41–50 day-old females based on the assumption that females of this age group represent flies that would be emerging from diapause in spring. The logtransformed sum of D. suzukii from all life stages for each day represented the total population estimate except where age distributions are considered. For daily age distribution of D. suzukii from Parlier, Salem and Wilmington during 2013, 1–3 day-old D. suzukii were classified as eggs, 4–7 day-olds were larvae, 8–9 day-olds were pupae, and 10–50 day-olds were classified as adults. Among the most important assumptions of the model are that populations of D. suzukii would not be limited by host availability, are not density dependent, do not exhibit Allee effects, and that response to current temperature is not dependent on previous temperature exposure.Seasonal weekly trap catches of D. suzukii were recorded in all study sites, except Riva del Garda, but model estimations for this location was included because the climate here is much different from the other locations studied in Italy. Trap counts were pooled data from commercial blueberry fields in Wilmington ; unsprayed apricot, blackberry, blueberry, cherry, peach, and citrus orchards in Parlier ; commercial blueberry fields and surrounding blackberry vegetation in Salem ; strawberry, blackberry, cherry and blueberry fields in Pergine ; and unsprayed strawberry and raspberry fields in Sant’Orsola . In Wilmington and Salem, traps were made of clear plastic cups, ca. 1 liter in volume each. Each trap had 6–15 entrance holes 4.5– 9 mm in diameter.

Trap baits in Wilmington consisted of a yeast and sugar water mixture containing 6 g yeast and 40 g sugar dissolved in 710 ml water. In Salem, traps were baited with 100– 200 ml natural apple cider vinegar and 1–3 ml unscented liquid soap to break water surface tension. In Parlier the traps were made to the specifications of the ‘‘Haviland Trap’’ design for D. suzukii monitoring. A 750-ml plastic container served as the basin for each trap. A 7.5-cm diameter hole was cut in the lid, over which a piece of 0.6-cm wire mesh was attached. Each trap was covered with a Pherocon trap cover , which had a built-in wire hanger. Each trap was filled with 250– 300 ml of apple cider vinegar with 15 ml of unscented soap added as a surfactant to each container of vinegar. In Trentino the containers were 1000-ml graduated white polyethylene bottles filled with 200 ml apple cider vinegar . All traps were placed near the fruiting level of host plants or on stable surfaces in shaded areas and were checked weekly. The contents of each trap were collected into a separate container that was taken to the laboratory for processing, and at the same time, the traps were refilled with fresh apple cider vinegar and unscented soap, as described above, in the field. The liquid and contents from each trap sample were strained in the laboratory and the numbers of adult SWD collected were recorded by gender. All data from traps were analyzed to display mean weekly D. suzukii per trap for each of the regions. In Wilmington, winter and late dormant mean temperatures were never below 0uC and reached 20uC multiple times prior to 31 April .

At this site, mean high summer temperatures exceeded 25uC multiple times during July, and mean low temperatures were below 10uC during November. In Parlier, winter and late dormant temperatures were never below 0uC and were higher than observed in both Salem and Wilmington during this period . Here temperatures were above 25uC multiple times prior to 31 April. Mean daily temperatures were consistently above 25uC during June–September, and daily means in Parlier sometimes exceeded 33uC. In Parlier, temperatures dropped to below 10uC during November. In Salem, early-season temperatures were warmer during January and February of 2012, compared to 2013 . Temperatures observed from March through May were slightly higher during 2013 compared to 2012. In Salem, winter and late dormant temperatures were never below 0uC or above 15.3uC until 31 April 2013 . Daily mean temperatures gradually increased to 25uC during July, after which daily mean temperatures dropped to below 10uC during November. For the Italian sites Riva del Garda had higher late dormant and fall temperatures than Pergine and then by Sant’Orsola . Summer temperatures were similarly ranked higher in Riva del Garda, followed by Pergine and then Sant’Orsola during 2013. In Pergine during winter 2012 , late dormant temperatures were frequently below 0uC and were as high as 20uC before 31 April. Fluctuations of temperature were more pronounced in 2012 in Pergine, compared to 2013 . Very low temperatures were recorded in Pergine from 3–12 February 2012, followed by relatively warm temperatures from 24 February to 13 March. Temperatures were also comparatively low from 9–11 April 2012. In Pergine, daily mean temperatures increased to 25uC during July, after which they dropped to below 10uC during November. Mean temperatures were well below 0uC during December.Population estimates using temperature data indicate that D. suzukii populations are able to increase to high levels in all of the studied locations . The population estimates in all regions broadly tracked demographic trends of D. suzukii caught in traps . When comparing early-season population estimates between Wilmington, Parlier, and Salem , the population estimates were highest in 2013 in Wilmington followed by Parlier and then Salem. However, the population estimate for Salem surpassed Wilmington by 15 June and surpassed Parlier on 16 July, as Salem population estimates continued to climb while the latter sites experienced declining populations after reaching the first peak of their bimodal distributions. In Parlier, the early-season population peaked on 16 June, subsequently decreasing to a low on 10 September before increasing to a second population peak on 9 November, large plastic planting pots then decreasing again as winter progressed. In Wilmington, the population curve peaked on 21 June, then the population curve declined slightly for an extended period, followed by a second period of population increase beginning on 19 September to a population peak in November. In Salem, populations consistently increased from 25 April to a peak on 22 October, followed by a steep decrease. When comparing population estimates between seasons for the initial harvest period of early- to mid-season blueberries in Salem , the majority of model outputs for this period estimated greater populations for 2013.

When comparing populations along the elevation gradient of the three Italian sites, higher early-season populations were predicted at the lowest elevation Riva del Garda, followed by Pergine and then Sant’Orsola . In Pergine, greater population numbers were estimated for the majority of the growing season during 2013 compared to 2012 . In all model predictions, immature life stages comprised by far the majority of the population, except at the beginning or end of the season when adults tended to dominate . One exception was Wilmington, where temperatures remained favorable for reproduction into the late fall so that immature stages remained a majority of the population . In Salem, fall temperatures initially caused cessation of reproduction, leaving a majority of adults, but December temperatures allowed for some reproductive activity to occur . In the early spring, a higher relative percentage of adults occurred due to the overwintering adults that were initiating their first reproduction. In part, this was an artifact of initiating the model with only older adult females. In the fall, environmental conditions became unfavorable for reproduction but may not have had strong effects on adult survival. Overall, no populations reached a completely stable age structure, but the highest relative stability for each site occurred in the middle of the season. Stability of age structure was the highest in Wilmington, followed by Parlier and finally Salem, which had a high degree of instability . Demarcation of distinct generations was very clear for the first part of the season in Parlier and Salem , but during the mid season at these sites and in Wilmington , it was very difficult to distinguish individual generations to distinguish complete generations from partial generations.In Wilmington, D. suzukii counts were first recorded on 5 May 2013 at one fly per trap with an erratic increase to a peak in numbers at 26 flies per trap on 26 July . After this period, the trap numbers gradually decreased to six flies per trap until 4 December, at which point D. suzukii trapping was discontinued. In Parlier, two population peaks were found during the crop season, one during the early part of the season, followed by a long mid-summer period without fly captures, and a second peak during the latter portion of the season . Adult D. suzukii were first caught on 19 March 2013 at one fly per trap and increased to a high of six flies per trap on 16 May, after which they decreased to zero on 27 July. The trap numbers remained at this level until 19 September, after which numbers continued to increase into December. In Salem and Wilmington only one population peak was observed during the summer period . During 2012 in Salem , D. suzukii trap counts consistently increased starting on 5 July from one fly per trap per week to a maximum average of 17 flies per trap on 6September. During 2013 in Salem , the first D. suzukii trap counts were observed on 30 May at an average of three flies per trap per week and gradually increased until 10 September, when a maximum of 27 flies per trap was observed. The first trap counts during 2013 were therefore consistently recorded four weeks before those found in 2012 and higher levels of flies were found in traps during 2013 in Salem. In Pergine and Sant’Orsola, fly counts were first observed 23 June 2013 , and on 7 July 2013 in respectively. In Italy, one population peak was visible each year for Pergine and Sant’Orsola. In 2012 in Pergine, the first flies were trapped on 25 July, approximately four weeks before those caught during 2013. The mean number of flies per trap per week increased to a peak of 4.6 on 10 September 2012 to a peak of 11.3 on 29 August 2013. Infested fruit was first found in 2013 on 24 June in Pergine , and on 19 July in Sant’Orsola . First fruit infestation in Pergine in 2012 was determined on 24 June , compared to 28 July 2013 .In order to compare environmental differences between all of the regions in this study, we illustrate degree-day accumulation for 2013 . When comparing differences between seasons, we found basic differences in accumulation for Salem and Pergine during 2012 and 2013. Of the three US locations, accumulation was initially the greatest in 2013 in Wilmington, followed by Parlier and then Salem . However, accumulation of degree-days increased at a higher rate in Parlier and exceeded the accumulation in Wilmington by 3 March.

The fragment sites exhibited marked reductions in bee species richness and assemblage evenness relative to reserve sites , making this an excellent system in which to examine the extent to which ecological filtering causes the restructuring of assemblages. Our dataset also possesses a number of other desirable properties. First, since the original intent of data collection was to examine the effect of habitat fragmentation in isolation from other effects of urbanization-induced landscape change , we selected study plots representative of intact CSS flora to the extent feasible. Accordingly, reserves and fragments did not differ with respect to the plot-level species richness or composition of native insect-pollinated plants . Second, study plots exhibited no spatial autocorrelation with respect to the species composition of native bee assemblages, minimizing the potential for patterns in the spatial arrangement of study plots to drive patterns of bee taxonomic or functional distribution. Lastly, reserves and fragments did not differ with respect to overall bee abundance ; minimizing the potential for sampling effects to contribute to any differences that we detect between reserve and fragment plots with respect to bee taxonomic or functional composition. Functional trait assignment and analyses of functional diversity: Every identified bee species was assigned a category with respect to each of the following natural history traits: lecty , large plastic pots for plants nest location, nest building behavior, sociality, body size, and flight season.

Table 2-1 lists each trait, how it is analyzed in functional diversity models , and its method of assignment; Table 2-S1 lists the bee species and their associated traits. With the exception of body size and flight season, all traits were assigned to individual species using literature syntheses for the species or species group and subgenus , as well as revisionary publications on lower taxa and our own field observations. Due to the lack of data for many species in our region, we also relied on phylogenetic inference when such data are available and appropriate: e.g., all Lasioglossum species were scored as polylectic, ground-nesting, actively constructing nests, and eusocial . Cleptoparasitic bees are included in all analyses, although excluding them does not qualitatively alter our results. Cleptoparasites are classified to a unique lecty category , the same nest location as their presumed hosts, and always as nest renters. Although some species of Sphecodes in our system may be social parasites of eusocialHalictini species , we classified all cleptoparasites as solitary because they do not exhibit reproductive division of labor . Two traits, body size and flight season, were not assigned based on published data. To estimate body size, we measured the intertegular lengths of three haphazardly selected females of each solitary species or four haphazardly selected females of each eusocial species, when possible. For species for which no females were collected, we measured the intertegular lengths of males. To assign flight season, we assembled an individual-level database of collection dates from our own field data and the database of the University of California, Riverside Entomology Museum.

Since climatic conditions may drive intraspecific variation in the timing and duration of bee flight seasons, we including only specimen records from south of 36.00° latitude, within 80 km of the Pacific coast, and below an altitude of 1000 m. From this database, we performed 1,000 random subsamples of 30 specimens per species and calculated the tenth and ninetieth percentile collection dates . We then scored each bee species with respect to whether or not they are active in the early, middle, and late part of our study period using these percentiles. Rather than assigning quantitative measures of flight season duration and median flight date , our approach of assigning flight season as presence-absence in coarser-grained season categories minimizes biases inherently present in most databases, such as non-uniform distribution of sampling dates and non-random captures of different taxa by different collectors. This approach also minimizes the impact of the sampling effect, in which rarer taxa in the database are likely to be recorded as having shorter flight seasons . For our metric of functional diversity, we chose functional dispersion , a widely used metric that can provide insight into how native bee functional diversity responds to anthropogenic alterations of natural habitat . FDis is calculated as the mean distance of each species from its site level, multivariate centroid of functional traits . Thus, FDis is mathematically independent of species richness and can take into account differences in the relative abundances of functional trait combinations . These attributes make FDis relatively insensitive to rare species and functionally equivalent species; thus FDis particularly well-suited for our dataset, in which we have detected strong differences between reserves and fragments in both species richness and assemblage evenness Statistical analyses: Except where noted otherwise, we analyzed data from 2011 and 2012 separately because of differences in sampling location and frequency.

In order to avoid biases associated with variation in aerial netting efficacy in different terrains, bees collected by aerial netting were excluded from our main analyses; however, inclusion of these netted specimens in our analyses yielded qualitatively similar results . We also repeated all analyses at the genus level using genus-level mean or modal averages for functional traits to ensure that particularly species-rich genera did not disproportionately influence our findings. All analyses were conducted in R version 3.3.1 . Taxonomic and functional alpha diversity: To assess plot-level alpha diversity, we calculated Shannon-Weiner diversity H and FDis, where each diversity metric was weighted by relative abundances of each species. To account for variation in the number of bees sampled per plot, we calculated both diversity metrics after rarefying our data to the lowest plot-level bee abundance recorded each year . Diversity metrics in reserves and fragments were compared with two-sample t-tests. Additionally, we constructed a linear mixed-effects model , lmerTest , and MuMIn to examine the relationship between taxonomic and functional diversity. In this linear mixed-effects model, data from the two years were combined; functional diversity was the dependent variable, Shannon-Weiner diversity was the independent variable, and study year, study plot identity, and habitat category were included as random effects. While a direct comparison of diversity metrics provides information on how habitat categories differ from one another, assessing whether observed differences are driven by stochastic or deterministic processes requires testing null models . Here, we generated random bee communities for each study plot that have species richness and Shannon-Weiner diversity equivalent to their respective observed communities in order to test whether observed differences in functional diversity are simply due to underlying differences in species richness . In this analysis, we generated random communities for each study plot by first permuting observed species-level individual abundances across all species within the species pool for the study year in question , and then rarefying each randomly permuted community to the lowest plot level bee abundance recorded each year . This permutation procedure resulted in 10,000 random communities for each study plot in each year. We then assembled 100,000 datasets by randomly selecting one permuted community from each study plot, compared the FDis scores of reserve and fragment plots via two-sample t-tests, and extracted the test statistic of each comparison. Finally, we compared the test statistic of our empirical dataset against the null distribution of test statistics to assess the frequency with which null datasets yielded FDis differences between reserves and fragments that equal or exceed those observed in our empirical dataset. Taxonomic and functional beta diversity and assemblage composition: To assess spatial beta diversity among plots, we used analyses of multivariate dispersion , plastic pot plant containers which compare habitat categories with respect to the degree of compositional similarity among their constituent study plots. Multivariate dispersion is calculated by first performing non-metric multidimensional scaling ordinations based on all combinations of pairwise among-plot dissimilarity in bee taxonomic or functional composition , and then comparing the non-metric distances of plots from the centroids of their respective habitat categories via a permutation test .

In our analysis of taxonomic beta diversity, pairwise among-plot dissimilarity was calculated as the abundance-weighted Bray-Curtis distance between each pair of plots. In our analysis of functional beta diversity, we first calculated the coordinates of each plot’s abundance-weighted functional centroid in multivariate trait space , and then calculated pairwise among-plot dissimilarity as the non-metric distances between these functional centroids. We performed 10,000 permutations for calculations of both functional and taxonomic beta diversity. In addition to examining beta diversity among plots within each habitat category, we also assessed whether reserves and fragments differed from each other with respect to the taxonomic and functional assemblage composition of their bee faunas. To accomplish this comparison, we performed permutational multivariate analyses of variance on the same pairwise among-plot dissimilarity scores used to calculate beta diversity, described above, with 10,000 permutations . Lastly, we also performed a Mantel test to examine the relationship between taxonomic and functional composition, based on the same pairwise distance matrices discussed above. Unbalanced designs such as that used in year 2012 of our study are known to introduce bias into PERMANOVA tests when within-group heterogeneity is unequal among habitat categories . Thus, to aid in the interpretation of our results, we also performed tests of beta diversity and assemblage composition for the 2012 dataset on random subsamples of 6 fragment plots and examined the proportion of results in which the findings of the subsamples agreed with those of the full dataset with all 11 fragment plots included.To assess the drivers underlying differences between reserves and fragments with respect to taxonomic and functional diversity and composition, we performed two additional analyses. First, we used two-sample t-tests to compare bee assemblages in reserve and fragment plots with respect to the relative representation of each functional trait. In this analysis, we used the plot-level mean average for intertegular length , and the plot-level proportional representation by each categorical or binary state for all other traits. Proportion data were logit-transformed prior to analysis as recommended by Warton and Hui , and all calculations were weighted by the relative abundance of each species. Second, we performed an indicator species analysis to identify bee species or functional groups associated with each habitat category. We used the Indval.g association index in the indicator analysis to account for the unbalanced sampling design in 2012. To assign bee species to functional groups, we constructed a dendrogram of all bee species collected in the study using hierarchical clustering based on functional trait data . We used Ward’s algorithm to perform hierarchical clustering , and assigned bees into 25 functional groups based on their positions in the dendrogram. Functional group membership of each species is given in Table 2-S1. Geographical range sizes: For bee taxa identified to described species, we calculated their geographical range size based on our field data and the database of the Bee Research Laboratory of the United States Department of Agriculture. This database includes specimens collected from throughout the United States, Canada, and Mexico; and represents one of the most comprehensive and unbiased databases of bees in our study region. These data enabled us to calculate range size for 171 bee species ; range size calculations were not possible for species with too few specimen records or taxa not identified to described species . Using geographical information systems analyses available via ArcGIS and QGIS , we mapped individual records of each bee species and constructed concave polygons bounding the set of location data points for each species. Range size for each species was calculated as the internal area of each species’ concave polygon, which yields the smallest area that encompasses the set of location data points for each species and allows for more accurate determination of species range size compared to convex polygons. We then calculated the average range size of all bee individuals at each study plot , and compared average range sizes between reserve and fragment plots using two-sample t-tests.Functional trait distribution among species is often influenced by phylogeny , including in bees . Thus, to aid in the interpretation of our results in view of phylogenetic signals present in our data, we quantified the variation in each trait attributed to each major taxonomic rank by constructing nested generalized linear mixed-effects models . In these models, the value of each trait is the dependent variable, and taxonomic ranks were included as random effects .

To our knowledge, all other herbivore species included in this analysis were native to the study region. We modelled the presence of each herbivore species using generalized linear models fit using the best predictors of herbivore damage, as estimated in the models described above: mean temperature of the coldest quarter,extracted from WorldClim 2.0 data at 30‐s resolution , and human population density . Twenty‐five percent of the occurrence records were assigned for testing, and the remaining 75% were used to train the models. Pseudoabsences were generated by randomly extracting 1.5 × the number of observations from the background data , delimited as New England. We used ANOVAs to compare the regression coefficients from the GLMs for insect herbivores grouped by host plant. Regression coefficients were weighted by the model AUC score , a measure of reliability of the estimates from the climate occupancy models. Thus, more reliable models contributed more to parameter estimation. Post hoc comparisons among pairs of host plant species were made using Tukey’s Honest Significant Difference.Using herbarium specimens spanning 112 years of rapid global change in the northeastern USA, we found a significant increase in herbivory over the past century. This trend is consistent across four ecologically and phylogenetically distinct host plant species with very different herbivore communities.

We suggest our observation of increasing herbivory through time is most likely driven by insect herbivore responses to warmer winter and early spring temperatures. Two environmental drivers—temperature and urbanization— explain much of the trend in herbivory through time, square pot but they have opposing effects. Over space and time, higher temperatures were associated with greater herbivory and higher probabilities of occupancy for 66 of 69 known insect herbivore associates. In contrast, high human population density was associated with lower herbivory and reduced occupancy for most insect herbivores that showed significant responses. In this region of the US, warming is projected to surpass 2°C by 2040 and is expected to be greater in winter than in summer . Our results indicate that damage to plants by insect herbivores may continue to increase with climate change but that, locally, urbanization may counteract this more general trend.Our analyses indicate that warming winter temperatures may drive increasing herbivory over time. Herbivory preserved in herbarium specimens was positively associated with mean winter to early spring temperature and decreasing latitude. Almost half of the variation in herbivory through time and across latitudes could be explained by temperature. These findings support theory as well as some empirical evidence suggesting that herbivory is largely driven by winter temperature at mid latitudes . As wintersin the northeastern US are projected to warm more than other seasons , we suggest herbivory may continue to increase in the future. We found compelling circumstantial evidence that insect herbivores known to feed on our focal plants also prefer warmer winters.

The vast majority of herbivore species examined had higher occupancy probabilities where winter temperatures were warmer. Occupancies of the other three species were not significantly related to winter temperatures, but the trend was also positive. While the association between winter temperatures and herbivore occurrence was estimated using contemporary data across space, we suggest this relationship is likely to be reflected in patterns through time. First, herbivore ranges may have extended northward in response to milder winters . Second, resident herbivore species have become more abundant due to greater survival in milder winters , and are therefore more frequently observed. Our data are also consistent with the possibility that shifting phenology might contribute to growing herbivory pressure. There is strong evidence indicating that many butterfly species are flying earlier in the UK, US, Canada, and Spain . Butterfly species that emerge earlier may have more generations within a year now than they did several decades ago , allowing more rapid population growth. Warming early in the growing season could also restructure phenological interactions so that they are more synchronous than they were historically , which may increase herbivory if host plants avoid herbivore damage by timing leaf‐out to be asynchronous with herbivore emergence. Warmer early springs might also alter phenological matching between herbivores and their natural enemies, reducing natural biological control of herbivores, as lower trophic levels may be more sensitive to climatic warming than higher trophic levels .

Some evidence suggests that this is the case for Lycaenid butterflies in our study region, which have advanced their flight more than has been shown for birds, which are their potential predators . Independent from its effect on phenology, warming may affect herbivores indirectly through altering host plant nutritional quality. In some cases, warming can induce water stress that alters plant nutritional quality, which can increase herbivore egg production on water stressed relative to unstressed plants . However, if this were the case here, we would expect that higher summer temperatures would be most closely associated with greater herbivore damage, contrary to results from our model comparisons. While warming may have driven increasing herbivory over time, urbanization was associated with reduced herbivory. This negative relationship was consistent with our herbivore occupancy models. Herbivore species that responded significantly were more likely to show significant negative than positive associations with human population density. It is possible that herbivores that showed positive responses favour urban areas because of factors such as the urban heat island effect ,natural enemy release , or higher host plant quality on urban compared to rural plants . However, overall patterns in our data add to mounting evidence that urban development locally reduces diversity in Lepidoptera, which are major herbivores. We focused on Lepidoptera in this study because they are well‐represented in observations. Future studies could extend efforts to collect long‐term data for other herbivorous taxa, such as beetles and grasshoppers, to determine if there is a more general reduction in herbivore diversity and damage with urbanization. Trends in herbivory through time across the four focal plant species were remarkably consistent. The relative importance of the different abiotic variables, however, varied among plant species. Different plant–herbivore relationships are likely sensitive to different drivers. For example, Q. bicolor did not show significant trends towards increasing herbivory with increasing temperature or decreasing latitude, even though the vast majority of herbivore species associated with Q. bicolor are more likely to occur where temperatures are warmer. Factors other than temperature and herbivore occurrence might thus be stronger drivers of herbivory in this host species. Geographical and temporal variation in driver intensity across the host range might also contribute to explaining different responses among species. For example, the negative association between herbivore damage and human population density was strongest for V. angustifolium. This species has a larger range than Q. bicolor and C. ovata that captures a larger urbanization gradient , providing a greater opportunity to detect the effects of human population density.Herbarium specimens collected by botanists provide long‐term estimates of herbivory that span the time frame of anthropogenic environmental change, filling a major data gap. Observational studies tend to span much shorter time frames, with herbivory studies rarely spanning more than 1–2 years . Field warming experiments are also typically short‐term and often address effects on herbivores rather than herbivory . We suggest that data from herbarium specimens may provide opportunities to assess herbivory across unprecedented temporal, spatial, and phylogenetic scales .

In addition, square plastic plant pots equivalent data from herbarium specimens on plant–pollinator interactions and plant–pathogen interactions may be used to further tailor land management strategies to changing environmental conditions. However, data from herbaria present challenges that require careful consideration . The spatial resolution of older specimens is coarse and, in our data, limited to the county level within the US. In addition, plant collectors tend to avoid damaged specimens, and thus, absolute values of herbivore damage are likely underestimates. Nonetheless, we have shown that it is possible to detect meaningful variation in herbivory that can be contrasted between species and time periods. While collecting biases could in theory confound interpretations—for example, if more recent collectors are more likely to collect specimens with herbivore damage, leading to an apparent increase in herbivore damage through time—we find no evidence to support any such bias. And, importantly, we can see no reason why collection of damaged specimens should be correlated with temperature or urbanization. In addition, we note that we observe increasing herbivory with day of year . As herbivory is cumulative through the growing season, these data indicate that, even if collectors show bias towards selecting more intact specimens, we are still able to detect expected temporal trends in herbivory preserved within herbarium collections. One exciting prospect is that long‐term herbivory data from herbarium specimens may provide the opportunity to compare effects of contemporary temperature change to predictions from fossils. Fossils are one source of long‐term data that may help in generating predictions of how plant–herbivore interactions will respond to projected anthropogenic change. It is currently unclear how reliably responses to temperature across epochs should predict effects of the rapid, anthropogenic change we are experiencing today. Because herbarium specimens, like fossils, can be scored for the presence and absence of herbivory, it should be possible to answer this question and assess whether patterns across millennia can predict effects of recent global change. Comparing herbivory on herbarium specimens and fossils would require adjusting the methods we have developed here to derive comparable fossil and herbarium data on herbivory that could be placed on a common axis. Our results hint that herbivory responses to contemporary and paleontological climate change might be congruent.Domesticated livestock are an integral part of agriculture as many high-quality foods come from their production such as eggs, milk, and meat. Ruminants, such as cattle, goats, and sheep have a specialized stomach, breaking down into four different chambers with the largest being the rumen. The rumen is an anaerobic environment that can hold anywhere between 113 to 226 liters of feed materials and fluids dependent on the age and size of the animal and hosts a variety of microbes such as bacteria, fungi, archaea, and protozoa. Anaerobic fungi possess carbohydrate-active enzymes such as cellulase that can break down structural carbohydrates . Bacteria are the most abundant microbes in the rumen and can contain billions within 1 milliliter of rumen fluid . Each having their own niches, they also contain enzymes that can break down carbohydrates ranging from plant cell walls to simpler carbohydrates like starch. Through their metabolism, they produce byproducts that can be utilized by the host in the form of volatile fatty acids . Acetate, propionate, and butyrate are the three major VFA that are metabolized and used by the host as their primary energy source . The proportion of VFA is highly dependent on the feed composition with acetate associated with higher fiber degradation and propionate with starch degradation . Microbes themselves also become a nutrient for the host as they are the source of microbial crude protein when they are digested and absorbed in the lower digestive tract . Through fermentation and digestion, host animals can utilize the VFA and MCP to promote growth, reproduction, and create products such as milk and meat . This symbiotic relationship between ruminants and microbes allows them to access a variety of plant-based feed sources without competing with humans. Aside from VFA and MCP, fermentation also produces other byproducts such as CO2 and hydrogen . It is crucial that the H2 formed from fermentation does not go unregulated as too much will cause the rumen environment pH to decrease . Many microbes in the rumen cannot function and may even die off if the rumen becomes too acidic . This can lead to a decrease in fermentation and depression in growth and production, and in some cases detrimental to the health of the animal . However, there are microbes in the rumen that can utilize hydrogen as substrates for their own metabolism. The main H2 utilizers are hydrogenotrophic methanogens by reducing CO2 with H2 to form methane gas. Other microbes found in the rumen can also utilize hydrogen that can act as competitors to methanogens such as homoacetogens, nitrate-, sulfate-, and fumarate-reducing bacteria. Homoacetogens produce acetate using both H2 and CO2, however, methanogens are more efficient at utilizing H2 when resources are limited . Even though sulfate and nitrate are thermodynamically more efficient, substrates for bacteria to use are limited as the concentrations are low in a ruminant’s diet .

Plots of UC 92 were vacuumed in 2019 and monitored with water traps in 2021 to check the difference in L. hesperus densities. In 2019, plots in the sprayed block had on average 0.5 L. hesperus while plots in the unsprayed block had on average 1.5 L. hesperus. In 2021, water traps collected every 3 days for 2 weeks after the insecticide treatment indicated that in UC 92 plots there were an average of 0.125 L. hesperus in the sprayed section and an average of 0.375 L. hesperus in the unsprayed section. These data indicate that there was a difference in L. hesperus densities between the two treatment blocks. High tolerance of damage by L. hesperus was defined by dividing the yield of untreated plots by the yield of insecticide treated plots . If the quotient was high, the variety was highly defended against L. hesperus. The most desirable varieties for breeding are those with high tolerance to L. hesperus and high yield. It should be noted that the term “tolerance” is used here as a place holder for the true mechanism of resilience. It is possible that some varieties may be resistant rather than tolerant. A resistant variety can avoid damage while a tolerant variety can recover from damage. Sixteen commercial cultivars from the United States were included in this study . On average, square pot these elite lines yielded 65% as much in conditions unprotected by insecticide as compared to conditions with protection of insecticide. Pat and Kingstone had the highest L. hesperus tolerance while UC Lee , UC 92 , and Fordhook had the lowest.

UC Haskell performed the best when both yield and L. hesperus tolerance are accounted for.Among the 56 heirloom and international cultivars, the average yield in unprotected conditions compared to protected conditions was 73%. Noir De Kisenyi – G26196 , a baby, black-and-white-seeded, bush type from Rwanda, and Hopi 50 , a baby, white-seeded, vine type from the United States, were the least susceptible to L. hesperus but did not compete well on overall yield. The American heirloom variety, Jackson Wonder, had the best combination of high L. hesperus tolerance and an average yield of 2338.4 g/plot when treated with insecticide and 1945.4 g/plot when unprotected by insecticide. Many of the international and heirloom varieties included in this study have already been incorporated into the UC Davis Lima Bean Breeding Program. These results will offer additional guidance in parent selection and the choice of varieties used in future research on insect defense mechanisms.When comparing total number of L. hesperus per plot and yield per plot, yield did not appear to be affected by the density of L. hesperus, as shown by the nearly horizontal trend line . In this group of varieties, UC 92 appeared to be an outlier with low yields but also a low number of L. hesperus. UC 92 is the only variety in this study from the Andean gene pool of Lima bean. While the yields of Andean and Mesoamerican varieties are comparable, Andean lines tend to have much larger seeds. As a result, the loss of a single seed due to L. hesperus herbivory would constitute a loss of a higher percentage of yield than when a single small seed is lost .It should also be noted that data from this experimental design may over represent resistance or tolerance traits. On a typical California farm, a single variety will be planted across several if not tens of acres. While L. hesperus adults are highly mobile, they have less choice in an actual farm setting than in this experimental field.

Nymphs were not considered in this study because they were crushed by the strong pressure of the vacuumand difficult to identify with the available expertise. Nymphs are much less mobile than adults and therefore would have less ability to choose between varieties. It is interesting to note the strong difference in the average number of L. hesperus per plot between the two highest yielding varieties, UC Haskell and UC Beija Flor. The large difference in average L. hesperus might be attributable to sampling bias caused by the denser canopy of the indeterminate UC Haskell compared to the determinate UC Beija Flor. It could also be that these two varieties have different mechanisms of resistance or tolerance to L. hesperus. UC Haskell and UC Beija Flor share as a parent UC Cariblanco N, but their resistance probably comes from their other respective parents. UC Beija Flor is an F10 progeny from a cross of UC Cariblanco N and CIAT accession G25165 . This accession was included in the diversity panel yield comparison under insecticide treated and untreated conditions described above. It has an average sprayed yield of 1650.8 g per plot and an average unsprayed yield of 1106.4 g per plot. This puts its average yield with insecticide protection above average but its estimated tolerance of L. hesperus below average. UC Haskell is an F10 progeny from a cross of UC Cariblanco N and an accession introduced from CIAT accession “P&T 4255” . This accession has been less well studied at it is unknown what degree or mechanism of resilience it may offer to insect herbivory. These results open several avenues of research to pursue in the future. From further analysis of these and other data it may be possible to distinguish resistance and tolerance traits.

Another area of future exploration should be the differences between the defense phenotypes of Lima beans in the Andean and Mesoamerican gene pools. Unlike the Mesoamerican gene pool, the range of Andean Lima beans falls completely outside the native range of the genus L. hesperus and therefore may have fewer defense adaptations to it and closely related species . In figure 12, the only Andean variety, UC 92, supports very low L. hesperus densities. However, as shown in Figure 11, it is very sensitive to L. hesperus with nearly a 50% drop in yield in the unsprayed plots compared to the sprayed plots. This combination of data may indicate that the variety has some resistance traits but very little tolerance to L. hesperus herbivory. Future studies should include additional Andean lines for a more robust comparison of defense phenotypes between the two gene pools.There was a very significant effect of variety on levels of cyanide in flowers, but the effect was not significant in pods . Large-seeded Lima beans, UC 92 and UC Lee had significantly less cyanide in their flowers than UC Haskell. Henderson Bush and UC Beija Flor had intermediate levels of cyanide in flowers that were not significantly different from UC Haskell, UC 92, or UC Lee . Cyanide was not detectable in the mature seeds. The presence of L. hesperus had no effect on the level of cyanide in flowers but it did affect the level of cyanide in pods . Pods collected from plants with L. hesperus had higher levels of cyanide . This may be due to increased enzymatic activity increasing the cyanogenic capacity rather than potential as this was found in Lima bean leaves . There was no significant difference in cyanide levels when considering the interactions between treatment and variety. The duration of the L. hesperus presence, as measured by “Time” in the study, did not affect the level of cyanide inflowers or pods. Nor was there a significant difference in cyanide levels when considering the interaction of treatment and time, square plastic plant pots or time and variety.From the greenhouse experiment of cyanogenic capacity with and without the presence of L. hesperus, insects from each of the cages were counted four weeks after flowering and three weeks after the L. hesperus had been introduced. For thorough counts, all plants were completely deconstructed with each leaf carefully checked for nymphs and the surface of the potting soil carefully searched. It is interesting to note that the two varieties known to be resistant or tolerant of L. hesperus, UC Haskell and UC Beija Flor, have very different numbers of surviving adults and new nymphs. It is possible that cyanogenesis may be responsible for this difference. In quantified studies of greenhouse grown samples without L. hesperus, UC Beija Flor was estimated to have a cyanogenic capacity of 153.4 nM/30 minutes of volatile HCN released from floral bud samples and 224.1 nM/30 minutes of volatile HCN released from immature pod samples .

In the same study, UC Haskell only had a cyanogenic capacity of 126.86 nM/30 minutes of volatile HCN released from floral bud samples and 63.4 nM nM/30 minutes of volatile HCN released from immature pod samples. Given that survival of adult and nymph L. hesperus is more strongly correlated with cyanide in immature pods than in flowers this difference in cyanogenic capacity could be contributing to the higher number of insects surviving on UC Haskell as compared to UC Beija Flor. It should be noted however that this was a no-choice experiment and in the field collected vacuum samples, in which there were many varieties to choose from, more L. hesperus were captured on UC Beija Flor than UC Haskell. This could be due to sampling bias due to the denser canopy of the indeterminate UC Haskell or it could indicate that a mechanism other than cyanogenesis is contributing to L. hesperus preference and survival.This study has several limitations. One is that no wild or weedy types were included for comparison with the domesticated forms. While growing these accessions can be challenging in confined greenhouse space, including them in a similar analysis could shed light on how the trait of cyanogenesis has been affected by domestication. Another limitation of the study was the use of discrete semiquantitative measurements using Fiegl Anger paper. While this method of measuring cyanogenic capacity is well established, safe, and high throughput, it does not provide a true quantitative measurement, or the continuous sampling provided by the enclosed detection systems used for studies of volatilized cyanide .This study found that the survival and reproduction of L. hesperus was negatively correlated with the cyanogenic capacity of their host plant. This indicates that selecting plants with higher cyanogenic capacity in their flowers and young pods may be an effective way to control L. hesperus. The evidence does not support the hypothesis that cyanogenic capacity was induced by the presence of L. hesperus. For consumer safety, future research should determine if there is a relationship between the cyanogenic glucoside content of flowers, immature pods, and the mature seeds they grow into as a correlation has been found between the cyanogenic content of mature seeds and the cotyledons from the seedlings those seeds grow into .Farmland covers more than 35% of Earth’s ice-free terrestrial area, and agriculture is expanding and intensifying in many regions to meet the growing demands of human populations . This trend threatens biodiversity and the ecosystem services on which agriculture depends, including crop pollination . Indeed, recent reviews have highlighted how multiple anthropogenic pressures lead to a decline in wild pollinators such as bees, flies, beetles, and butterflies . However, practices to enhance wild pollinators in agroecosystems are still in development , and considerable uncertainty remains regarding their effects on crop yield and farmers’ profits. Here we review recent research on the topic, including the impacts of certain practices on wild pollinators, crop pollination, yield, and profits . We focus on practices that enhance the carrying capacity of habitats for wild insect assemblages that may then provide crop pollination services; practices to conserve or manage a particular pollinator species are outside our scope although they have received attention elsewhere . We offer general science-based advice to land managers and policy makers and highlight knowledge gaps. Throughout, we emphasize the need to consider population-level processes, rather than just short-term behavioral responses of pollinators to floral resources.Plant–pollinator interactions are typically very general, with many pollinators being rewarded with pollen, nectar, or other resources from several plant species , and with most angiosperms being pollinated by multiple insect species . Humans benefit from this generalized nature of pollination systems, as exotic crops brought far from their ancestral ranges can find effective pollinators within native insect assemblages .

Our results were also consistent with those previously reported in goldfish, which suggests the most common mechanism for duplicate gene retention in these allopolyploid cyprinines since their 4R event is due to dosage constraints. However, mechanisms for duplicate gene retention are not strictly inferable because a multilevel set of phenomena that range across WGD.Allopolyploids face the unique challenge of integrating two subgenomes, which evolved independently in the diploid progenitors since their most recent common ancestor, that now reside in a single nucleus. One way to resolve potential genetic or epigenetic conflicts is “subgenome dominance”, which results in one subgenome being dominant over the ‘submissive’ subgenome. The dominant subgenome not only has higher gene expression but also retains a greater number of ohnologs compared to the submissive subgenome. To better understand the dynamics among subgenomes of our three sequenced allotetraploids, we compared their gene loss , gene expression level, the density of TEs near ohnologs, constraint on conserved noncoding sequences , DNA methylation patterns and 3D genome structure. To examine gene fractionation differences among subgenomes, blueberry grow bag size gene retention patterns between the two subgenomes of the three allopolyploid species were examined relative to the diploid references from zebrafish, O. macrolepis and Sc. acanthopterus.

These results revealed that in all cases, the maternal subM showed slightly higher gene retention rates relative to the paternal subP . Compared to the reference zebrafish, subM showed 2.815% higher gene retention in L. capito, 0.427% higher gene retention in P. rabaudi, and 0.819% higher gene retention in S. sinensis relative to subP. However, these patterns are not supportive of strong subgenome dominance pattterns as has been reported in some plant allopolyploids . Ohnolog retention bias of certain sets of genes, including BUSCO genes towards one subgenome has been recently reported for the Prussian carp, goldfish and common carp. Similarly, we found that the number of BUSCO singleton genes in maternal subM was significantly higher than those in subP for all three allotetraploids . For example, subM of S. sinensis has retained 609 complete and single copy BUSCO genes, compared to only 448 in subP . Next, we performed GO analysis of the genes that returned to single copy in subP and subM. Functional enrichment analysis revealed that similar GO term classes were identified for all species, including mitochondrial related processes, nc/rRNA processes and DNA repair . These GO terms were also identified as returning to singleton state post-WGD from a previous analysis of the Prussian carp, goldfish, and common carp genomes.A previous study investigating subgenome dominance in octoploid strawberry revealed that the dominant subgenome retained a significantly greater number of tandem duplicated genes. Here, we uncovered a similar pattern for retained tandem gene duplications being biased towards the maternal subM in all three allotetraploid cyprinids .

First, significantly more tandem duplicates are encoded on the maternal subM compared to the paternal subP . Second, a greater number of tandem gene arrays were observed in the maternal subM compared to paternal subP . Lastly, the maternal subM genomes contained a greater number of larger tandem arrays than the paternal subP . An analysis of protein family domains revealed an enrichment of functions associated with the immune system for retained tandem duplicates in these subgenomes .We also tested the hypothesis that DNA methylation patterns in genes and TEs in the extant relatives of diploid progenitor species,and thus subgenomes within allotetraploids, may explain observed subgenome expression bias patterns. Whole-genome bisulfite sequencing of the muscle tissue from two diploid ancestors and three allotetraploids was performed . Levels of CH methylation were very low in genes of all five species , which was also observed in the common carp genome and is typical of somatic tissues in humans. Therefore, we focused on CG methylation for all subsequent analyses. A similar pattern of CG methylation was observed within the gene body and 2 kb flanking regions in case of the diploid Sc. acanthopterus and the three allotetraploid species . However, for O. macrolepis, there was much lower CG methylation levels ~1 kb upstream up to the transcriptional start site and higher levels throughout the gene body and ~1 kb downstream. No difference in CG methylation was observed among the diploid and subgenomes of tetraploid species . However, this analysis of the entire set of ohnologs may obscure more subtle differences.

To examine this, we next analyzed CG methylation for genes with biased expression in muscle tissue towards either the paternal subP or maternal subM. Interestingly, CG methylation levels of expression biased genes towards the subgenome A were lower from ~1.5 kb upstream to TSS compared with subM levels . Similarly, the upstream region of subM bias genes for all species showed lower CG methylation levels in this same region than those of the corresponding regions of duplicated genes in subP . This suggests that CG methylation levels in upstream regions of genes may have a role in observed expression bias towards a particular subgenome. Further, to determine if there are any significant differences in TE CG methylation between subP and subM of tetraploid species, we investigated CG methylation of TEs that are in 1 kb vicinity of 7040 positionally conserved syntenic ohnologs and at the whole genome level. We found some degree of variation in mCG levels between subgenome TEs that were found in 1 kb vicinity of 7040 duplicate orthologs . However, elevated levels of TE methylation in subP were observed in L. capito which was opposite to what was observed in S. sinensis and P. rabaudi where subM showed higher methylation levels. This phenomenon was also observed for TE methylation at the whole genome level . This opposite trend of TE methylation in L. capito in comparison to S. sinensis and P. rabaudi can be attributed to the difference in the TE density of respective genomes .Cypriniformes represent the largest clade of freshwater fish with ~600 described species in the family Cyprinidae, which has experienced multiple rounds of independent WGD. The phylogenetic relationships, evolutionary history, and the genetic basis of previously reported subgenome dominance of these polyploids has remained poorly understood. In this study, high-quality genomes of twenty-one cyprinid fishes, including subgenome-resolved allotetraploid genomes from three tribes, were de novo assembled and analyzed to investigate subgenome evolution at the genetic and epigenetic levels. Our results are supportive of previous reports for subgenome dominance at both the gene retention and transcriptome level. In addition, we observed that the dominant subgenome retained a greater number of tandem duplicates with a functional bias towards immune related processes. Our phylogenetic analyses revealed that S. sinensis, L. capito, and P. rabaudi are allopolyploids and that observed dominance is consistently towards the subgenome contributed by the maternal parent. Also, the most recent polyploid event in P. rabaudi is likely shared with common carp and goldfish. Functional enrichment analyses revealed similar GO term classes, blueberry box including mitochondrial related processes, for the genes that returned to single copy in all examined allopolyploids. The observed consistent bias towards the maternal subgenome donor, alongside the bias towards mitochondrial functions, suggests that observed subgenome dominance patterns in these allopolyploid fish may be due to maternal dominance. The maternal contributed nuclearencoded genes that interact with mitochondrial encoded genes may be favored to maintain proper cytonuclear interactions. The mitochondrial proteome contains products from over a thousand genes, while the mitochondrial genome encodes approximately only 13 proteins.

The vast majority of genes are now nuclear genome encoded following the horizontal gene transfer from the organellar genome to the nuclear genome over the past hundred million years. However, these nuclear genes might encode dosage-sensitive proteins that function in either organellar signaling networks or macromolecular complexes that must maintain proper stoichiometric balance with interacting partner that are encoded in the organellar genome. Furthermore, the sequence of the proteins encoded by both organellar and nuclear-encoded mitochondrial genes may have diverged among the diploid progenitors. Thus, there’s a possibility for incompatibilities to arise from “mismatches” between the genes contributed by the paternal subgenome and the organellar genomes contributed by the maternal parents in allopolyploids. The model that we are proposing here is that observed dominance patterns in these allopolyploids is to preserve proper cytonuclear interactions, and ultimately, core cellular functions. Nonetheless, we cannot exclude the possibility that some ofthe observed subgenome expression differences, particularly at the individual gene level, is due to differences in DNA methylation and transposable element density differences as hypothesized in previous studies. We observed that methylation levels at CG sites in upstream regions of genes, ~1.5 kb upstream to the transcriptional start site, may have a role in observed expression bias towards the maternal subM genome. Epigenetic factors, including changes in methylation at certain CG sites, have been previously shown to alter gene expression and involved in maternal imprinting including of nuclear encoded mitochondrial and DNA repair genes. We also observed that the dominantly expressed ohnolog, from either subgenome, in some cases, had significantly lower TE densities. This suggests that both maternal dominance and TE differences are likely contributing to observed independently repeated subgenome dominance patterns in allopolyploid cyprinid fishes. To the best of our knowledge, this is the first study to show the potential role of maternal dominance in contributing to subgenome dominance in any allopolyploid animal. Future studies of other allopolyploids are needed to determine if these observed patterns are shared by other polyploid animals or are potentially unique to cyprinids. Furthermore, our multi-species comparisons suggest that genetic divergence of the diploid progenitors, for the allopolyploids and divergence times examined in this study, did not contribute to subgenome expression dominance. However, it is important to note the possibility that the divergence of the diploid species in each allopolypoid wasn’t sufficiently different to observe additive subgenome expression dominance effects. Lastly, we also examined genome organization using Hi-C data and selective constraints on noncoding regulatory sequences, which revealed no significant differences among subgenomes. These new reference genomes and various datasets should serve as a powerful platform for the community to further investigate genome evolution of cyprinids, and as a valuable resource for a wide range of studies including modeling human disease.Genomic DNA degradation and contamination was monitored on agarose gels. DNA purity was checked using the NanoPhotometer spectrophotometer . DNA concentration was measured using Qubit DNA Assay Kit in Qubit 2.0 Flurometer . Microgram genomic DNA spiked with lambda DNA were fragmented by sonication to 200-300 bp with Covaris S220, followed by end repair and adenylation. Cytosine-methylated barcodes were ligated to sonicated DNA as per manufacturer’s instructions. Then these DNA fragments were treated twice with bisulfite using EZ DNA Methylation-GoldTM Kit , before the resulting single-strand DNA fragments were PCR amplificated using KAPA HiFi HotStart Uracil + ReadyMix . Library concentration was quantified by Qubit 2.0 Flurometer and sequenced by Novaseq platform w producing 24.39-55.95 Gb raw bases with a bisulfite conversion rate of 99.57–99.75%. MethylC-seq data for each sample were aligned to their respective genomes and methylation called using the methylpy pipeline v.1.4.6. This pipeline uses Cutadapt v.4.1 for adapter trimming, Bowtie 2 v.2.4.4 for alignment, and Picard v.2.26.10 to mark duplicate reads. Spiked-in unmethylated lambda phage DNA was used as a control to calculate non-conversion rates from bisulfite treatment . Gene and TE metaplots were made as previously done using custom scripts and pybedtools v.0.9.0. Gene/TE bodies were divided into 20 bins, and the weighted methylation level calculated across all genes/TEs. For gene bodies, only exonic cytosines were included. This process was repeated for both 2 kb upstream and downstream regions, and the data plotted in R with ggplot2. To examine the effects of neighboring TEs on genicmethylation, we used bedtools v.2.30.0 to identifying genes with an intersecting TE within 1 kb.Domestication has altered the interactions among crop plants, herbivorous insects, and higher trophic levels of agroecosystems . While selection has resulted in enhanced agronomic traits like yield, defensive traits such as toxic compounds have been reduced or removed . A consistent pattern of reduced defense has not been established across species, but crop plants tend to be more vulnerable to herbivory than their wild relatives . Domesticated beans may be more attractive to insect herbivores than their wild relatives, but they are also more effective at recruiting parasitoids .

Genomic selection , first introduced by Meuwissen et al. , has been adopted by alfalfa breeding programs in recent years for improvement of a variety of complex traits . By enabling breeders to perform marker-only selection, a cycle of selection could be completed in less than a year, addressing one of the biggest impediments to faster genetic gain – the need for multi-year evaluations . The majority ofGS applications in alfalfa to date are based on phenotypic and genotypic data at the individual plant level . However, alfalfa is marketed as synthetic cultivars with breeding based on the evaluation of families, not individual plants. This is particularly relevant when attempting to evaluate yield. An alternative approach to GS is pooling individuals and genotyping family bulks rather than individuals . Phenotypic data can then be collected at the family level from densely planted plots, a better representation of commercial yield, and the relationship matrix is built based on allele frequency marker data, rather than individual genotyping calls. High quality phenotypic data is essential for maximizing the predictive ability of a GS model . Field trials are inherently variable, particularly in the case of perennial crops with multiple harvests where spatial and temporal differences can have a large impact the quality of phenotypic data. Randomization, blocking, pe grow bag and complex trial designs are commonly used to account for field variation; however, they are often inadequate to account for all the spatial trends in large experiments .

Residual maximum likelihood has become commonplace in plant breeding to estimate variance components and calculate genetic parameters using linear mixed models . Best linear unbiased predictions of random effects have proven effective in predicting breeding values to guide breeding decisions . Mixed-model analysis has the advantage of handling unbalanced data and can incorporate additional information using covariance matrices, providing more accurate predictions . In field trials, experimental units in close proximity to one-another are expected to be more highly correlated than those far apart, and successive harvests likely will be more highly correlated than harvests separated by a greater time interval. Therefore, a better way of managing spatial and temporal variation is to include covariance matrices in the analysis to account for spatial and temporal trends in the data .The training populations used in this study were part of the UC Davis elite non-dormant alfalfa breeding program. Plants were selected from an evaluation trial in Davis, CA and crossed in the greenhouse in 2018 resulting in two populations – UCAL1960 and UCAL1970. In 2019, seed from both populations was germinated in the greenhouse and transplanted to the field at the UC Davis Plant Sciences Farm. Approximately 200 plants from each population were grown in adjacent areas that were each placed under mesh cages to enable pollination with leaf cutter bees . The seed from each plant was harvested individually and the highest seed yielding families of each population were included in the trial.

The entries consisted of 105 half-sib families from UCAL1960, 88 half-sib families from UCAL1970, balanced bulks from each population, and three cultivar checks: Highline, UC Impalo and CUF. A field trial was established at two locations, one in Yolo County and the other in Solano County , on the UC Davis Plant Sciences Farm near Davis, CA in May 2020. Although the two sites are close together, the Solano location is in a floodplain of the nearby Putah Creek. The locations are in a Mediterranean environment characterized by hot, dry summers, cool winters, and moderate annual rainfall, which falls predominantly in the cooler months from November to March . The soil type is a Yolo silt clay loam . Soil tests were conducted prior to planting to adjust P, K, and pH according to soil test recommendations. Seeds were sown in trays in the greenhouse in March 2020 and seedlings transplanted in early May. The trial at each location was a randomized complete block design consisting of two replicates, each with 7 rows and 29 ranges. Plots consisted of 24 plants laid out in a regular 4 × 6 grid with 20 cm spaces between plants. There was a 30 cm space between rows and a 110 cm space between ranges to allow room for mechanical harvesting. This trial was managed as a high-yielding alfalfa stand; soil tests were conducted each year, with amendments made accordingly. The trial was initially irrigated using sprinklers, but after plants were well established, flood irrigation was used to match crop evapotranspiration. Weeds were managed by a combination of manual removal and herbicides, and insect pests were monitored and controlled with insecticide as needed.Dry matter yield was measured by mechanical harvest when the plants had reached 10% bloom.

A self-propelled forage plot harvester with a flail chopper and weigh bin was used to cut plots uniformly at 7.5 cm and measure the fresh weight. There was a total of 12 harvests, three in the establishment year, seven in the first full year of production , and two in the second production year . Each harvest, 20 hand grab samples were collected throughout the day of harvest and weighed before being dried at 60°C in a forced-air drying oven for 7 days. Plot yield was recorded on a dry matter basis after adjusting the fresh weights with the average dry matter percentage.The first fully expanded trifoliolate leaf from the growing tip of the plant was collected from every plant within each family from a single replication of the trial and pooled for DNA extraction. Subsequently, tissue samples were lyophilized and ground. DNA was extracted from the ground tissue using DNeasy 96 plant kits quantified using a BioTek Synergy H1 Microplate Reader with Take3 Micro-volume plate. Genotyping-by-sequencing libraries were constructed using the protocol of Elshire et al. modified as described previously in Li et al. . Briefly, 100 ng of DNA from each sample was digested with PstI enzyme. Two different types of adapters, a barcode adapter and common adapter, were ligated with the DNA fragment using T4 DNA ligase. Equal volumes of the ligation product from each sample were pooled together and purified using AMPure XP beads . The ligation product was then amplified with the Phusion High-Fidelity PCR Master mix , using 50 ng of DNA as the template and 25 nmole of each PCR primer in a 50 ul reaction system. PCR was performed under the following thermal cycling conditions: 72°C for 5 min, denaturation at 98°C for 30 sec, followed by 12 cycles with 10 sec of denaturation at 98°C, 30 sec of primer annealing at 65°C and 30 sec extension at 72°C. The resulting library was again purified using the AMPure XP beads . The DNA fragment sizes were checked using a Bioanalyzer then all families were pooled and sequenced with Illumina Nextseq. The raw reads were compressed using clumpify software from the BBTools package , then trimmed using Cutadapt . The Tassel 5.0 GBS v2 pipeline was used for SNP discovery . Default parameters were used for all steps unless otherwise stated. First, tags and taxa are identified from the raw FastQ files and stored in a local database using GBSSeqToTagDBPlugin. Next, distinct tags from the database were retrieved and reformatted to be aligned with the reference genome using the TagExportToFastqPlugin. Bowtie2 v2.5.1was used to align the filtered reads with the Medicago sativa refence genome developed by Shen et al. using the –very-sensitive-local input. The resulting SAM file created from this step was then used to update the local database with the SAMToGBSdbPlugin. DiscoverySNPCallerPluginV2 then identified SNPs from the aligned tags and updated the database. Finally, ProductionSNPCallerPluginV2 was used to generate a VCF formatted genotype file. SNPs were further filtered with VCFTools using the following criteria: biallelic variants only, growing bags minor allele frequency > 0.05, minimum sequencing depth for each data point > 4, minimum mean read depth over all samples > 30, and maximum missing data of 10%. After filtering, 39,229 SNPs remained for downstream analysis and genomic prediction.

Alfalfa has suffered from a lack of yield improvement over the last 30 years of breeding and research. Traditional recurrent phenotypic selection, although useful for the improvement of simply inherited traits, has been unsuccessful in translating yield gains to commercial producers. In this study, genomic selection was incorporated into the UC Davis alfalfa breeding program across two elite non-dormant populations in an effort to select families for improved yield potential. Whereas previous predictive models in alfalfa have been developed based on genotyping single plants and phenotypic information from space plants or short family rows, this study used bulk genotyping of families, as demonstrated by Andrade et al. , and uses phenotypic data on densely transplanted mini-sward plots. The goal was to better capture yield that could be expected in a commercial planting, while reducing genotyping costs, and aligning the genotyping with current breeding methodology in alfalfa, which is also performed on a family level. To further improve the quality of phenotypic data, spatial and temporal variation were modeled using variance/covariance structures. The predictive ability of GS models is affected by a range of factors, incluing the number, density and distribution of genetic markers, population size and structure, the degree of LD, and the quality of phenotypic data. The LD decay in this study was moderate , but our marker density was sufficient to provide coverage of the genome. In a study optimizing whole-genome sequencing in autotetraploid blueberry , the predictive ability of genomic selection increased rapidly with the inclusion of more markers; however, a plateau was reached with 10,000 markers or more. GBS therefore should provide sufficient markers for accurate GS in alfalfa, as we noted previously . Population structure can induce bias in genomic predictions if not incorporated into the prediction model. Organized breeding programs can develop significant population structure, as observed in this study, where the two populations included in the training population were related, yet clearly distinct. Here we accounted for population structure by including the first two principal components from a PCA in the model. Population size is perhaps the most important factor influencing the predictive ability of GS. A significant increase in predictive ability for fruit yield in blueberry occurred by increasing population size from 120 to ~1500, with an ideal size of 1000 or more . We evaluated relatively few families in this study which may have impacted the predictive ability of our model, even though it represented a maximum number of families we could reasonably harvest and manage. High throughput phenotyping could be a solution to greatly increase the number of families evaluated for DMY in alfalfa without a significant increase in cost to the breeder . Remotely assessing biomass from drone-based cameras and harvesting a subset of plots would allow a breeder to evaluate significantly more families with a similar cost to current phenotyping methods. High quality phenotypic data is essential for producing accurate genomic predictions. The correlation between harvests was significant and positive in this study, and spatial variation is inherent in field trials. Accounting for spatial variation and temporal correlation between repeated measures improves the quality of phenotypic data, thus allowing for more accurate predictions which may translate to genetic gain. Moving forward, we intend to generate a range of populations based on both phenotypic and genomic selection from this yield evaluation trial. These will then be evaluated alongside other elite material for DMY to assess if there has been any improvement. Due to the lengthy breeding process of perennial forages, it will take several years to determine whether these methods have been successful. We will continue to develop the predictive model as more material is evaluated and adjust accordingly. To improve the predictive ability of the model moving forward a combination of evaluating a greater number of families and improving the quality of phenotypic data through better modeling will be imposed. Increasing the size of the training population could be facilitated without a significant increase in costs by using modern high-throughput phenotyping tools, such as drone based remote sensing. With decreasing costs of genotyping, improved computational software and the availability of genomic resources , genomic selection is becoming increasingly available to more resource limited breeding programs like alfalfa. There is still much research required to assess whether actual yield gain can be achieved; however, these studies provide a baseline for future studies to investigate potential yield improvement.

The main correction for this type of mapping project would showing suburban and residential areas do have some ecological habitat value for bees. However, showing how much habitat value a residential parcel has is not simple, but subsequent student designers tried with other techniques shown in section 5.9, below.The goal for the studio students was to create an extensive poster emphasizing designing for bees in a city of their choice. The final project had them concentrate at the scale of up to a few kilometers or miles maximum. One of the best projects include Figure 5, which shows a regional bee habitat design for the SLO area. This student was careful to use both GIS land cover data for more precise habitat mapping as well as mapping information . In their map titled, “The Network Map”, they were able to precisely prescribe where to place new habitat for “pollinator usability” which in ecological terms relates to ensuring foraging habitats are close enough together for continuity between pollinator patches. This student also does a wonderful job demonstrating three simple design techniques in their category “Pollinator Pockets” which could be implemented at various landscape types. So, for example, with major streets, slim narrow planters could be installed; with residential areas, seed packet programs could be implemented; and finally with parks, plastic plant pot suitable foraging flowers could be planted in groups. All three of these design techniques add together for the desired result of increased pollinator habitat circuity and connectivity.

This design solution is akin to adding sunflowers throughout a town to bolster bee network connectivity, as seen in The Great Sunflower Project in Sonoma, California, which was very well received. Overall, Roa’s project is an outstanding example of assessing a city’s pollinator habitat and offering design solutions. This is the sort of spatial resolution landscape designers should be striving for to create bee habitat network plans. This project is a shining example of how bee or pollinator habitat analysis and design could be accomplished. Another final project worth discussing is a pollinator habitat project for densely urban, Glendora, California. This student opted to trace every portion of vegetation from an aerial imagery map using Illustrator . This process, at this scale was painstaking and took several days, but the results are fascinating. They were able to capture the distribution of vegetation through this highly urbanized area. We realized that even the proportion of green spaces were visible between neighborhoods at this scale . Moreover, this project helps to show the proportion of vegetated space versus hardscape in very urbanized areas. This project could have been stronger by identifying how far their focal bee could travel and/or possibly occupy habitat patches within this map. Additionally, this project lacks a vision that the previous one, Figure 5, showed for implementation in typical design situations. However, the attention to detail in mapping green space was outstanding and should not be overshadowed by these constructive criticisms.

Finally, this sort of mapping technique, if used in GIS could be very helpful for improving the urban pollinator network and quantifying existing conditions as well as potential habitat modifications. By applying landscape categorizations to real landscapes, it was possible to graphically demonstrate how bees perceive the greater landscape. Only then, once habitat deficiencies are identified can designers go about fixing habitat short-falls. How different bee genera experience the same landscape will also be important to understand and see spatially, as dynamic landscapes create geographic isolation or continuity depending on bee genera. Future studies should work to untangle how urban habitat performs; provides habitat for various focal bees.This research focuses on developing the field of landscape architecture to better aid in bee conservation design at multiple scales. My goal is to build resilient pollination landscapes for the future. As a profession we must invest in helping stabilize landscapes and their functionality or the effects climate change could be much worse. Climate change is causing ecological stress for bees, both in terms of environmental, including phenological, mismatches , but also in terms of increased physiological issues, including bee body temperature. Bees are keystone species and conserving them, saves so much more than just bees. Pollination functionality underlines the very basis for nearly all terrestrial ecosystems on Earth. Every day, landscape architects and designers create plans to change Earth’s lands and with every plant they choose, they either provide foraging habitat for a bee… or not. Planting designs need to serve functional pollination purpose, to feed the local fauna. Landscape designers, should put an emphasis on bee conservation now to hopefully preserve and prioritize pollination ecosystem services.

Strategic solutions to habitat deficiencies, education and also raising public awareness are all important for resilient landscapes. Now is the time to act. Its time to make great strides in educating not only the new generations of designers, but also, with the greater public. Through implementing designs that celebrate local pollinator diversity, biology, and ecology, we can help to support nature’s and our own, human, future as well.For the last decade or so, horticultural and landscape researchers have been developing sustainable California plant varieties and palettes. Most research on sustainable themes has been completed on low water use landscape plants, but more recently, wildlife friendly too. Field trials for future suitable plants have been completed to help identify, promote and produce climate suitable plantings . For over a decade the UC Davis Arboretum and Public Garden has placed a strong emphasis on using drought tolerant plants, regardless of their geographic origin, the published list being called “The Arboretum All-Stars.” Notably, many of these plants, of non-California origin exhibit traits which this research project has found are also attractive to and functional for foraging native bees. Importantly, this plant palette has been carefully crafted to first prioritize low water use, which is an essential quality facing California’s Central Valley future facing climate change . Consider that in similar urban butterfly studies, it is estimated that around 40% of native host plants which once existed in the California Central Valley habitat areas no longer exist where they once grew and exotic species now provide essential host plant habitat . Thus, it is inappropriate to attempt to eradicate all non-native plants, as many are currently native bee foraging resources. The non-invasive exotic plants that native bees currently use may provide important habitat resources in the future if native plants cannot survive extreme climate change, while the exotic species may be more resilient.Preserving and enhancing pollination ecosystem services are extremely important in landscapes facing great uncertainties with climate change. Ecosystem services are a tool which can be used to help buffer the negative effects of drought, extreme storms, increased or decreased precipitation. Pollination ecosystem services are infrastructure and essential not only for conservation, nature and biodiversity, but also for much of the food humans depend on . Between one-third and two-thirds of the food we eat is the result of insect pollination. Aside from that, the next top priority in California should be water conservation. While pollinator plant lists exist, it is important to explore their success in a real-world setting and examine which ecological aspects could still be improved upon. Ecologically, landscape design can be used to provide and promote bee habitat and connectivity of habitat.Tree death is a natural part of forest dynamics , nursery pots but increasing rates of mortality can result when climatic conditions exceed a species’ physiological threshold . Although directional climate change has historically resulted in shifts in the distributions of species and ecosystems , comparatively rapid shifts in tree distributions attributed to anthropogenic climate change have been documented on all six plant-covered continents . Recent research has focused predominantly on causal mechanisms of tree death, feedbacks to the climate system, and predictive modeling . Ecologists generally agree that trees and forests in temperate regions will shift to higher latitudes and upward in elevation due to warming trends . However, understanding how forests will behave at the ‘‘trailing ends’’ is limited . Stand development patterns following forest mortality events are of considerable interest because they indicate future structure and composition of affected forests, and the ability of these forests to maintain biodiversity and other ecosystem services .

Although widespread mortality events can have negative impacts to ecosystem services , there may be benefits that are also important for adaptation in the human dimension . A global overview of climate induced forest mortality provides a detailed assessment of events driven by climatic water/heat stress since 1970; few of these documented dieback events provide opportunity to examine vegetation changes that occur over a longer time frame. Yellow-cedar , a species distributed from the northern Klamath Mountains of California to Prince William Sound in Alaska, has been dying in southeast Alaska since the late 1800s with intensifying rates observed in the 1970s and 1980s . Recent research reveals a complex ‘‘tree injury pathway’’ where climate change plays a key role in a web of interactions leading to widespread yellow-cedar mortality, referred to as yellow-cedar decline . Prominent factors in this injury pathway include cold tolerance of roots, timing of dehardening, and regional trends of reduced snowpack at low elevations . Early springtime thaws trigger dehardening and reduce snow cover that insulates soil and shallow fine roots from periodic extreme cold events; this can lead to injury of yellow-cedar roots to initiate tree mortality, which is predominantly limited to lower elevations . Despite the extent of research on the mechanisms of decline, overstory and understory dynamics in declining stands are not well understood . The direct loss of yellow-cedar has important ecological, economic, and cultural implications; however, other changes are also relevant in these forests that emerge in response to decline. Researchers are just beginning to understand the influence of dead cedars on watershed nutrient export . Economically and culturally, yellow-cedar trees are important because they provide valuable products for Alaska Native communities and the forest industry . These coastal forests also provide forage for the Sitka black-tailed deer , an important game animal throughout the region. Since the 1980s, much forest-related research in southeast Alaska has addressed the implications of various active forest management regimes on habitat of this commonly hunted species and biodiversity ; aspects of this research centered on old growth habitat and the effects of land use practices, such as clearcutting or partial cutting on forage . To date, researchers have not addressed the effects of yellow-cedar decline on the availability of key forage species. Death of yellow-cedar and the shifts in plant community dynamics in forests affected by decline can have cascading effects on the human-natural system by affecting the ecosystem services these forests provide . We studied the process of forest development using a chronosequence to compare forests unaffected by widespread mortality with those affected at different time points over approximately one century. Considering size classes from seedlings to large trees across the chronosequence, our analysis of the conifer species populations at various life history stages, including death, documented changes occurring in forests affected by decline, and extended a view of forest composition and structure into the future. We hypothesized that: western hemlock and other conifers increase in importance as the contribution of yellow-cedar to the conifer community structure is reduced over time, seedling and sapling regeneration increases as yellow-cedars die and the canopy opens, community composition of understory plants changes over time such that shrubs increase in abundance, and the volume of key forage species for the Sitka black-tailed deer increases in forests affected by decline. Our study illustrates the long-term consequences for many plant species when a single tree species suffers from climate-induced mortality.Modern climate in the southeast region of Alaska is mild and hypermaritime with year round precipitation, absence of prolonged dry periods, and comprised of comparatively mild season conditions than continental climates at similar latitudes . Mean annual rainfall measured in Sitka and Gustavus, the two closest towns to the remote, outer coast study area, measure 2200 and 1700 mm, respectively. The high rainfall that occurs throughout the Alexander Archipelago, combined with its unique island geography, geologic history, and absence of fires maintain some of the most expansive old-growth forests found in North America.

These results suggest that the existing literature identifies few actual foraging associations for the vast majority of bee genera in California, This dearth of information shows the importance of this study and the need for further research in this area. The model independence tests for each respective bee genus observed in the Arboretum reveals that 15 of the 28 models are highly significant at levels below 0.01 with degrees of freedom ranging from 1-237. The remaining models were found to be lacking independence mostly due to a low frequency of use from our observations for those bee genera.According to these results, it is possible to build foraging models for bees based on floral foraging preferences. However, while the aggregate model evaluation scored quite well overall with Table 1 plants being utilized for forage by bees, analysis of the bee-to-plant associations demonstrated that the individual models were not as predictive at the finer scale of bees to specific plant genera, mostly due to the high omission rates. Thus, bees were found to be using many more plants than the predictive plants alone in Table 1. Future research should focus on better understanding bee-to-plant associations. Furthermore, plastic flower buckets wholesale investigating the spatial consequences of bee-to-plant associations is essential in studying habitat connectivity and fragmentation for bee genera.

Studying bees at the genus level was effective for observation of their foraging preferences and trends, which require specific conservation strategies. The variability of results between bee genera emphasizes that, for more effective conservation, beegenera should be studied individually from each other, using an autecological approach, and not aggregated to achieve more effective conservation. Overall, there was much variability in the degree to which bee genera utilized Table 1 plants or not . In contrast to Frankie’s estimate of 17 common bee genera in California , we found 27 bee genera observed to be foraging in the Arboretum, five of which were not predicted to have been there . The 22 predicted bee genera included: Agapostemon, Andrena, Anthidium, Anthophora, Apis, Ashmeadiella, Bombus, Ceratina, Coelioxys, Diadasia, Eucera, Habropoda, Halictus, Hoplitus, Hylaeus, Lasioglossum, Megachile, Melissodes, Osmia, Peponapis, Svastra, and Xylocopa. At 27 observed bee genera, our findings demonstrated only one more bee genus than Robbin Thorp’s confirmed personal collection of 26 bee genera found in Davis, California. It is a good sign that our findings closely confirm the local expert’s specimen collection. Map accuracy was very helpful in determining the presence or absence of bees. Interestingly, as a whole, bees utilized 84 of the 134 plants they were predicted to use in the Arboretum per Table 1. Since bees utilized 297 forage plants, 213 plants were novel plants not included in Table 1.

Error analysis demonstrated variability in predictive success between bee genera. The genera Anthophora, Diadasia, and Habropoda demonstrated the highest true positive fractions; theywere the most predictable in terms of their feeding preferences, ranging from 0.3-0.5. On average, for all bees, the sensitivity score was low, at 0.14, meaning only 14% of the literature’s plants were predicted ‘correctly’ with the corresponding bee association. Conversely, the false negative fraction of 0.86 means that 86% of bee foraging was not predicted via the existing literature models. Therefore, it is valuable to compare separately multiple bee genera and also their foraging preference associations within the same study. Studies which aggregate all bees risk erroneous conclusions regarding the relationships between bee and plant genera. Similarly, studies of only one bee genera are unlikely to be comprehensive enough to encompass conservation of a diversity of bees. We have demonstrated that different angles of model analysis can yield varied ranges of success. Our findings indicate the existing plant lists currently lack effectiveness to comprehensively predict bee genera foraging in California. These results emphasize how more needs to be learned about bee genera foraging patterns. We suggest more research should be done to collect and publish bee association data. Moreover, these findings also show how strongly plant selection can influence garden habitats for bee genera. Table 3 shows how the Arboretum gardens vary greatly in their ability to attract bees and also sustain them over time.

Gardens which facilitate the most foraging over time represent models for how urban planting schemes could attempt to accommodate and conserve California’s bees. The Mary Wattis Brown California native garden and Ruth Storer Arboretum All-Star garden perform the best at providing bee habitat. Therefore, future garden design to support bees should be based off of the planting characteristics of both garden types,maximizing plants best at supporting bee foraging. Furthermore, almost all bees were found to forage on exotic plant species which were not emphasized on some of the recommended suitable plant lists . The observed opportunistic foraging nature of bees shows that reconciliation ecology seems in part suitable for maximizing bee habitat conservation and design. This study presents compelling evidence that that current suggested plant lists have significant inadequacies as habitat for bees. Furthermore, this research has identified many plants within the Arboretum’s novel plant communities that are highly functional as habitat for California’s native bees . To further improve bee habitat relationship model information, habitat elements required to support bee nesting need to be included. In future research, it would be valuable to add further layers of information, such as nesting resources, to increase the helpfulness of the model toward building or predicting comprehensive suitable habitat, not just foraging. For example, the primary means of reproduction by most native bees is usually one of three strategies : ground nesting types, cavity nesting in trees or plant stems, and plant stems, both woody and herbaceous. There may be also be garden physical form factors regarding pollinator attractiveness and this should be addressed in future research. For example, the age of the plants, degree of under or overstory, sun aspect ratio, and more. Detailed bee landscape design is explored in Chapter 2 in depth.Since the existing bee-plant lists seemed to be under performing, we conducted further investigations into these list inadequacies. The existing literature plant list included 61%native California plants and 38% non-native, while our foraging study showed nearly an inverse in the actual feeding trends, with 43% native California plants and 57% non-native. In addition, among plants which were not listed in Table 1, “unexpected novel” foraging plants, only 36% were California natives, while 64% were non-natives. Scientists may have overvalued the forage attractiveness of native plants for bees and undervalued the positive function of nonnative plants. For example, an urban ecology study in the Central Valley of California by Shapiro found 40% of lepidopteran faunal host plants are non-native plants. While California native plants contribute many evolutionary and ecological aspects to a site, black flower buckets our study showed that bees in horticultural environments exhibit trends of feeding opportunistically and without majority preference for the native status of a plant. As climate change increasingly puts stresses on native plants forcing range contractions and expansions, exotic plant species may ultimately need to be utilized to maintain and conserve native bees.

The UC Davis Arboretum and Public Garden hosts a variety of specialty gardens, boasting plants curated from geographically distant locations. However, the Arboretum plantings have been consciously selected to suit the local Mediterranean climate. The Arboretum tends not to grow plants which require intensive care and high water demands, rather they take low maintenance approach, with policies about not fertilizing and/or spraying for pests, for example, and this is beneficial to bees. Furthermore, the Arboretum has a plant promotion program called the “AllStars” and one hundred Arboretum All-Star plants were selected for climate suitability, some native, some not, with an emphasis on low-water use plants and also attractiveness from the human perspective . Many of these plants were also found to be utilized by bees. This study allowed a unique look at how California native bees perceive unique foraging opportunities, some of which were previously unknown, but seemingly beneficial. Among nonnative plants there are consistent trends of plant origin . Based on observed foraging results, from a bee’s perspective, the Arboretum would improve bee foraging by adding more drought tolerant plants from Africa, Australia, Europe, South Africa, South America, the Canary Islands, and New Zealand.It is essential to better understand bee foraging from a bees’ perspective in order to design suitable habitat. This research has demonstrated the limitations of currently understood bee-toplant associations. The basic connection of a bee to its foraging plants is essential to understand which types of habitats are appropriate or not. WHR accuracy can be improved with extensive empirical testing. This study elucidates the need for further quantitative studies on bee WHR models. Through accommodating native bees in novel ecosystems, such as in gardens and in hedgerows near agricultural crops, valuable pollination services can be conserved for the benefit of people, agriculture, and natural communities.Reconciliation ecology provides one of the best frameworks for resilient bee habitat design. The data from this study have shown there are benefits to using both California native plants as well as non-natives to achieve peak foraging habitat for California native bees, as well as naturalized European honey bees. As seen in the Arboretum’s themed gardens, bees are utilizing the novel plantings which also were selected for drought tolerance and aesthetic beauty. By combining the best foraging plants, landscape designers can significantly improve foraging habitats for native bees. Best management practices and design responses for improving bee habitat are likely to be very specialized to increase foraging optimization at designed garden sites. For example, in California’s Central Valley drought tolerance is a very important plant attribute since water will likely become less available in the future due to climactic water deficit projections . Therefore, a design framework should be responsive to a site’s prioritized ecological needs.Although the data collected for this study is extensive in time and space, it represents just one year of bee-plant visitation in the Arboretum. Thus, interannual variability of weather and climate could produce other visitation patterns. However, we feel we captured much intraannual variability with our sampling approach. This study was conducted in the Central Valley ecoregion of California and this could produce biased results towards the bees associated with this ecoregion. As noted in the Introduction WHR models tend to be regional in nature and therefore results may not transfer to all other areas of California. All methods for bee fieldwork have gone under scrutiny in recent years, which are not unique to this study. It also should be pointed out that this study did not explicitly evaluate nesting habitat at the study site.Development of WHR models for native bees is an important step in conservation planning for these species. They can be significantly improved with additional field studies regarding bee foraging plants. In particular, it is important to study bee-to-plant associations, which are currently not well understood or, in the case of novel associations, underrepresented. More quantitative foraging studies should be undertaken to make accurate foraging models. Results from this study show that more optimal pollinator plantings could be designed and constructed in a more strategic and scientific manner. Using data from this study, plant palettes for ideal garden design for native bee conservation could be created from a bee’s point of view. Many bees in this research project were found to be opportunistic foragers—neither exclusively utilizing native or non-native plants. Thus, bees may have more opportunities to thrive if they are given a broader spectrum of plants, which can be done through strategic conservation actions in both space and time. This study elucidates the feeding preferences of native bees, which can be used to better manage and conserve them in the California landscape. How many other foraging plants would native bees use or prefer? This can only be answered with further empirical studies and assessment of possible plant candidates for native bees.Bees are a diverse suite of insects which provide the greatest percentage of plant pollination in the world . The ecosystem services that bees provide are essential to people and the world’s ecosystems. Bees also pollinate the majority of food plants that humans consume . It is estimated that up to two-thirds of food crops for humans require bee pollination of which native bees have been estimated to contribute, around 50 percent of bee foraging activity .

While there are no studies looking at the connection between floral topography and seed microbial transmission, experiments with flowers and leaves have demonstrated that bacterial dispersal is influenced by plant surface topography and surface water distribution. Conducting similar micro-scale inoculation experiments like these in flower to-seed systems will illuminate how microbes actually move. Seeing that microbes can be florally transmitted to seed, we need to consider studies on the dispersal of floral microbes to understand seed microbial communities at the meso-and macroscales. For example, Belisle et al., 2012 found that yeast frequency in nectar communities of Mimulus aurantiacus was correlated with flower proximity, and they inferred that dispersal limitation was controlled by pollinator behavior. In a study on the floral microbiome across wildflower species of California, Vannette et al. observed that fungi were more dispersal limited between individual flowers and plant species than bacteria. Another major finding has been that pollinators can vector microbes between flowers and influence microbial community patterns. For example, Vannette and Fukami explored the variable effects of dispersal limitation on beta diversity in the nectar microbiome. Using a pollinator exclusion experiment, procona valencia buckets they found that increased dispersal by pollinators raised beta diversity and hypothesized that this increase was due to the stochasticity of dispersal timing which strengthens priority effects .

A pollinator exclusion experiment in B. napus demonstrated that pollinators can also vector bacteria to seeds through flowers, impacting the local and regional diversity . These experiments indicate that dispersal may have unique effects on diversity in flower microbiome meta communities via arrival history. However, all of these studies were performed only on the macro-scale, and they did not characterize dispersal traits. Furthermore, the association between dispersal patterns in floral microbial communities and those in seed communities has yet to be studied. Future experiments should explore if dispersal traits and arrival history consistently enhance beta diversity in flower and seed microbial communities among spatial scales .Ecological drift is defined as random fluctuations in species abundances over time, and can be driven by random birth, death, and migration events . Drift is particularly important when local communities are small and filtering is weak . This is key to note for seed microbial communities because they typically have low population sizes and low species richness . Random migration events may be particularly important for seed microbes, such as those vectored by rain or wind . However, while there is a lot of interest in drift and stochasticity in seed microbe research , drift as a process is difficult to study because it is hard to manipulate. Meta community ecologists have also generally found it difficult to get direct evidence of drift, with limited examples from experiments testing the coexistence of ecologically equivalent taxa . One alternative approach to direct observation used in plant microbiota studies is to fit community data to neutral models, where community members are assumed to be ecologically equivalent, and non-significant variation in community composition across samples is explained by neutral processes.

Rezki et al. took this approach when studying the seed microbiota of R. sativus by fitting fungal and bacterial community data to a Sloan neutral model . This model accounts for neutral birth, death, and immigration rates, and estimates immigration rates into communities based on species frequencies across samples . Immigration rates within the confidence interval of the predicted values imply that drift is structuring the community . Based on the model, they found that bacterial community assembly was driven primarily by drift, while fungal communities were driven more by dispersal . This study indicated that drift is important for some seed microbes, and more model-fitting studies or coexistence experiments are needed.At its core, meta community ecology emphasizes not only how the processes described above play out individually, but also how they interact with each other to produce emergent community patterns across scales. In plant microbiome research, the interaction between abiotic and host filters, also known as genotype-by-environment interactions, has been of growing interest because it provides a more holistic explanation for microbiome variation . Such an explanation can be applied to seed microbial communities, which may vary with seed nutrient profiles, osmotic stress, and water availability. However, as previously mentioned, GxE studies on plant microbiota face a scale problem where genotype and environment become synonymous at the micro-scale. Taking a plant trait-based approach to these studies may make the role of these effects more clear, and can connect micro-and macro-scales via host local adaptation. While not emphasized as much as GxE interactions, the interaction between dispersal and filtering is also important during seed microbiome assembly.

At the micro-scale, variation in the plant surface landscape can create differences in dispersal limitation between taxa. Doan et al. demonstrated this interaction on synthetic leaf surfaces, finding that surface water acted as a conduit for bacterial dispersal. This effect may also be present in floral stigmas, which are highly heterogeneous landscapes . Indeed, in their work on transmission of the pathogen A. citrulli from watermelon flowers and fruit to seeds, Dutta et al. found that inoculum from the flower dispersed more frequently and ended up in deeper seed tissues than inoculum from the fruit. While these examples suggest that heterogeneity in the plant landscape impacts dispersal limitation to seeds, more studies are needed. Dispersal also intersects with species interactions, most clearly through historical contingency or priority effects . In this phenomenon, the arrival order of community members dictates assembly outcomes, typically with an advantage to taxa that arrive first . Priority effects can occur either through niche preemption, where the first colonizers fill all available niches, or by niche modification, where the first colonizers alter the environment and its resulting niches . These effects are often cited as important in seed communities because they have few members . However, priority effect experiments in plant microbiota have typically been done in leaf and wood communities . As such, there is a need to understand the role of priority effects in seed communities. An exciting new approach for studying the multiple, interactive processes of dispersal, filtering, drift, and species interaction is with Joint Species Distribution Models , which extend single-species distributions to community-level dynamics . Leibold et al. used these models in tandem with variation partitioning to explain the internal structure of simulated meta communities. They found that this approach was a promising way to connect meta community pattern data to multiple assembly processes . In the seed microbiology literature, Fort et al. used JSDMs to infer how maternal filtering and abiotic filtering contributed to seed mycobiome variation in Q. petraea seeds . They found that fungal guild influenced which taxa varied with abiotic filters, with elevation selecting saprotrophs and seed specialists, and all taxon co-occurrences were positive associations . While JSDMs were not used in a meta community context for this study, and they are limited in their omission of abundance data, these models provide an integrative approach for looking at seed microbiome assembly.Multiple tools exist for exploring and exposing the effects and interactions of filtering, species interactions, dispersal, and drift on microbial community assembly of individual seeds at multiple spatial scales. However, future work can do a better job of integrating and connecting meta community ecology models to traditional seed microbial ecology studies at micro-, procona buckets meso-and macro-scales. One technical challenge of taking this approach pertains to the interrogation of microbial communities in individual seeds. Culture-based studies of individual seeds report low isolation frequencies, with most seeds containing zero or one microbial taxon . Additionally, most sequence-based studies to date pool seeds by fields or other groupings . As exceptions, Bergmann and Busby and Fort et al. sequenced fungi from individual tree seeds and found that sequencing depth was fairly high. However, the tree species in these studies produce large seeds; sequence-based detection of microbiota might be more difficult in small-seeded species .

Additionally, it is often difficult or impossible to treat individual seeds as independent since experimental treatments or predictors are often applied at the fruit or plant level. To resolve these issues, future work could focus on species where seeds are fertilized independently , or one seed per fruit/plant could be sampled for large-seeded species. Alternatively, seeds could be pooled at the fruit or plant levels, since these are the levels where treatments are often applied and they sufficiently capture the variation in seed microbiota while still allowing for a meta community approach at the meso-and macro-scales. The appropriate level of pooling should be selected based on the transmission pathway of interest . Finally, seeds of large-fruited species could be pooled by parts of the fruit/pod for spatially explicit sampling at the meso-and micro-scales. These scale-explicit pooling approaches, along with the use of additional methods at the meso-and micro-scales , will allow for characterization of microbiota at or near the individual seed level while mitigating issues of low DNA amounts and cross-contamination. At the micro-scale, there are many opportunities to take a traits-based approach to host filtering of seed microbiota. Experiments can go beyond studying if plant traits have an effect to testing what these effects are . These experiments could also take a microbial trait-based approach to host filtering and identify the genes required for successful transmission, which are still largely unknown . This could provide valuable insights into the genes required for transmission across the different pathways . Furthermore, meta genomic analyses across plants, populations, species, etc., could determine if these transmission-associated genes are common across meta communities. Such information could show if there is functional conservation across microbial communities, even if they are taxonomically variable. Finally, micro-scale experiments can also test how microbial community assembly is impacted by the interplay between deterministic and stochastic processes. In addition to these tests of microbial and plant trait impacts, experiments testing the role of dispersal in seed microbial community assembly among spatial scales should be conducted. At the micro-scale, experiments using synthetic microbial communities on stigmas with varying chemistry and topography can demonstrate how dispersal and selection occur between flowers and seeds, and what the role is of plant genetics and microbial adaptations. At the macro-scale, pollinator exclusion experiments similar to those in Prado et al. could be conducted across sites in natural landscapes. By using sites at varying distances and connectivity levels from each other, and analyzing both within- and among-site seed microbial community variation, one may obtain new information about how pollinators and patch connectivity impact multi-scale dispersal ability. These proposed studies would elucidate how dispersal contributes to meta community assembly among spatial scales. Along with these single-process studies, we envision studying the interactions between processes through both observational and experimental studies. As JSDMs continue to be refined to model nested and continuous meta communities, they will provide a way to analyze seed microbiome patterns and their associated assembly processes that is more sophisticated than previous modeling approaches. Additionally, priority effect experiments conducted at multiple points in the seed life cycle may reveal how historical contingencies impact seed microbiome assembly throughout the seed life cycle. Such experiments would also test the Primary Symbiont Hypothesis , which argues that seed communities are dominated by a single microbe with significant functional consequences for the plant. Finally, questions will need to be asked about seed microbiome assembly that go beyond just testing for spatial mechanisms. Primary among these questions is: what fitness benefit does transmission into seeds provide to microbes and their host plants? Such a question gets at the eco-evolutionary dynamics in these microbial meta communities, which can have long-term consequences for both microbes and plants. Because microbial communities behave and evolve at shorter time-scales than macro-organisms , it is feasible to design simple experiments testing how microbes evolve in response to plant defenses, nutrient availability, and micromorphology. Such eco-evolutionary studies may have applications in understanding microbial community shifts with crop domestication . Additionally, both microbes and seeds have dormant stages, which can impact meta community dynamics through tradeoffs with dispersal and delayed responses to environmental conditions . The role of dormancy in seed and plant microbial meta community assembly has yet to be explored, so there is much room to study how dormancy impacts these systems over longer temporal scales. Finally, a hot topic in plant microbiome research is how to modify plant microbial communities for climate resilience and other beneficial traits . However, the impacts of climate change-associated disturbances on plant microbiomes have been limited to pattern-based studies in leaves and roots .