All Seurat objects were then integrated together using the approach from , applying the Select Integration Features, PrepSCT Integration, Find Integration Anchors, and Integrate Data functions from the Seurat R package,using 5000 variable features, 20 principal components, and otherwise default parameters. Cell clusters were computed using the Seurat functions, Find Neighbors and Find Clusters, 20 principal components and a resolution parameter of 0.8. Index of Cell Identity scores were computed using a combination of existing ATH1 microarray and RNA-seq single cell datasets . Briefly, arrays were normalized using the gcrma R package, and RNA-seq data were trimmed using the bbduk tool, and mapped using bbmap . Transcript counts were quantified using the feature Counts tool . Raw RNA-seq counts were then normalized using the edgeR package , with the “upperquartile” method. Normalized reads were then further normalized with the gcrma-normalized microarray data using the Feature-Specific Quantile Normalizations method to obtain a dataset consisting of both RNA-seq and microarray-based cell-type specific transcriptome measurements. This dataset was then used to build an ICI specification matrix using the methods described by . This specification table was then used to compute ICI scores for each cell in the integrated single-cell dataset, along with p-values derived from random permutation. To map the single-cell data to metabolic domains, pathways, and enzymes, we used AraCyc v.17.0, which includes 8556 metabolic genes and 650 pathways.

We used the pathway-metabolic domain mapping file version 2.0 to map the pathways to 13 metabolic domains. To avoid biases introduced by small sample size to the cell type specificity analysis,drainage pot we only included pathways containing at least 10 genes whose transcripts were detected in the single cell data described above. Based on these criteria, 198 out of 650 pathways were included in this analysis. To compute cell type specificity at the transcript level, we first calculated the expression level for a pathway or domain per cell type by taking the average of expression values for all the genes annotated to this pathway or domain within this cell type. The cell type specificity was defined as the cell type for which the expression level of a pathway or domain was at least 1.5-fold higher than their background expression, which was calculated by taking the average of expression values for all the genes annotated to this pathway or domain in all cells. Since the expression levels of a pathway or domain per cell type could be influenced by gene expression outliers, we only included the cell types in which more than 50% of genes associated with the pathway or domain showed higher expression than their background expression based on a Wilcoxon test followed by a multiple hypothesis test adjustment using FDR with a threshold of 0.01. The background expression level of a gene was calculated by taking the average of its expression values in all the cells included in this study. Heatmaps were generated using the R package ggplot2 v.3.3.4. To compute cell type specificity at the pathway level, we first selected the set of pathways containing at least 10 genes whose transcripts were captured by the single cell transcriptomic data to avoid biases that could be introduced by small sample size.

Based on these criteria, 30% Arabidopsis pathways were included in this analysis. In a metabolic network, isozymes are defined as enzymes encoded by different genes catalyzing the same reaction, which are usually the result of gene duplication events. To investigate whether isozymes tend to be expressed in different cells compared to enzymes catalyzing different reactions within the same pathway, we analyzed gene expression pattern similarity between a pair of enzymes across Arabidopsis root cells by computing Spearman’s correlation. To prevent having correlations between self, we removed enzymes that are mapped to more than one reaction in a pathway as well as pathways that contain only one reaction. Spearman’s correlation coefficients were computed using the function cor in R. Significant correlation coefficients were determined using an R package scran v.1.18.5 . Distribution of Spearman’s rho was compared using a one-way ANOVA followed by post-hoc adjustment with Tukey’s test in R. The box plot was generated using the R package ggplot2 v.3.3.4. Microbial communities affect plant health and productivity. For agricultural crops, microbes can affect nutrient mobilization and transport, often promoting plant growth and disease resistance . In turn, understanding and managing microbiome assembly could enhance agricultural sustainability by reducing reliance on external inputs, enhancing yields, and potentially contributing to the maintenance of both biodiversity and the functioning of agricultural landscapes . Yet, despite the growing recognition of the importance of the microbiome to crop productivity, processes governing the assembly of microbiomes for many crop species are still largely unclear . Agricultural landscapes are often spatially heterogeneous. Accruing through shifts in land tenure over time, this heterogeneity reflects a landscape’s composition and configuration .

Specifically, crop production occurs on patches of land that exist within habitat mosaics containing patches of the same crop, alternative commodities, and seminatural vegetation. Such variation in land cover around a crop field may strongly affect local abiotic and biotic conditions. Most studies assessing the effects of spatial context, however, have focused primarily on plants and animals , but effects of landscape-level drivers on plant-associated microbiomes have received less attention. This is a problematic knowledge gap, as microbes often disperse over long distances, and studies show that spillover of microbes from agricultural into natural habitats is affected by landscape context and dispersal ability of individual taxa . Many microbes are often affected strongly by environmental conditions, and abiotic variation across landscapes can sometimes predict outbreaks of pathogenic microbes . At the orchard scale, management practices employed to control pests and disease can also shape microbiome assembly and structure. Agricultural producers often rely on agrochemicals to prevent establishment or directly suppress both pests and pathogens. As part of an integrated pest management program, these practices can vary in intensity across orchards, including the frequency of application, the active ingredients of chemical controls, and how they are coupled with other biological or cultural control strategies . Indeed, the application of antibiotics, fungicides, or microbiological control agents can leave distinct signatures on the microbiome associated with tree fruits . Though their application can often have direct, suppressive effects on the abundance of targeted, pathogenic taxa , non-target effects on associated yeasts and bacteria have also been observed . Here, we assessed how local- and landscape-level processes affected the diversity and structure of microbe communities associated with pear flowers in Washington State. We focused on microbes on flowers, as these ephemeral structures produce the fruit but are also the primary infection site for pathogens such as the bacterium Erwinia amylovora, the causal agent of fire blight . As a consequence, pear orchards are typically heavily managed during bloom to minimize disease risk while promoting pollination . Such management tactics range from the use of managed honeybees to the application of diverse bactericides for control of fire blight. We predicted that floral microbiota would be impacted by orchard management practices and the abiotic and biotic landscape conditions. Such work provides important insights into microbial colonization and community structure pre- and postpollination, important windows for production.Orchard pest management practices were significantly associated with pear flower bacterial and fungal diversity . Considered alone, conventional and biological-based integrated pest management -managed orchards were found to have ;60% higher bacterial diversity than those managed organically ,drainage planter pot while organically managed orchards exhibited the highest fungal diversity . Yet the positive effects of organic management on fungal diversity were not significant in the multiple variate linear model when controlling for land cover and climate . In these linear regression models , both organic and bIPM management styles reduced bacterial and fungal diversity, although the negative influence of organic management on fungal diversity was weak. Land cover was also associated with bacterial and fungal diversity: bacterial diversity declined with increasing proportion of habitat containing forest or pear, while fungal diversity increased with pear crop cover . Microclimatic conditions were also associated with both bacterial and fungal diversity, though minimum temperature was the only variable of significant effect on fungal diversity and minimum vapor pressure deficit for bacterial diversity in the top Akaike information criterion -selected model .Focal bacterial and fungal genera of concern were first investigated to assess the scale of spatial auto-correlation, as well as potential associations with aspects of landscape context. Positive spatial auto-correlation was exhibited for each of the nine taxa examined, but only at the shortest distances of less than 1 km.

Using canonical correlation analysis to assess how landscape and management variables were associated with the microbial species composition, we found significant associations between predictors and bacterial and fungal communities. The three bacterial genera associated with disease suppression were distributed very differently in association with the factors of interest. More specifically, the relative abundance of Bacillus,bacteria commonly applied to suppress disease in pear, was most strongly associated with organic management , followed closely by the amount of surrounding forest and then geographic distance. These top factors, aligned with axis 1, were negatively associated with Pseudomonas, while bIPM was the most important predictor of Pantoea . Similar to Pantoea, bIPM and organic management best predicted the presence of Aureobasidium, a beneficial fungus aligned with axis 1, and Monilinia to a lesser degree. Minimum temperature and minimum VPD best predicted Botrytis, Cladosporium, and Mycosphaerella, as well as Monilinia , all pathogenic fungi of concern for pears. Finally, the proportion of forest in the landscape and geographic distance were associated with the distribution of these fungal genera of interest .Pre- and post harvest diseases that can take hold during bloom threaten production and the quality of yield, however . Here, we investigated how local orchard-level IPM practices interacted with landscape-level growing conditions to influence the structure and diversity of microbiota associated with pear flowers, potential sites for infection. Our analyses revealed that the orchard management scheme can significantly influence the structure and diversity of both bacterial and fungal communities. Beyond local orchard-level management, land cover and climate were also found to be significant predictors of microbe diversity, and bacterial and fungal communities were affected by different habitat types found in landscapes surrounding orchards. Finally, fungal alpha and beta diversity were more greatly affected by microclimatic conditions experienced in orchards than bacteria. In the sections that follow, we discuss these findings in light of understanding the key drivers of floral microbiome structure in this system.Bacterial and fungal alpha diversity responded differently to orchard management schemes. Bacterial diversity was significantly higher in conventional and bIPM orchards than organic orchards; however, the opposite pattern was observed for fungi. Organic orchards had a high relative abundance of Bacillus, likely because of its application as a biological control agent. The strong effect of orchard management on bacterial diversity suggests that application of Bacillus reduced bacterial diversity, which may occur through resource competition, priority effects, or mass effects. Bacillus species have shown promise in limiting the establishment and development of the bacterial pathogen E. amylovora,the causal agent of fire blight , and may also affect other floral microbes. Indeed, increased fungal diversity in organically managed orchards could be a consequence of Bacillus application, although we were unable to directly assess if fungal abundance was affected in our study. In contrast to bacteria applied for biological control, we observed that Aureobasidium had a higher relative abundance in conventional and bIPM orchards than organic ones . Background levels of some microbial taxa may be high and more prevalent in the presence of particular landscape and climate conditions . These patterns may represent preferential use of these biological treatments across orchards in our survey. Though unable to confirm whether ASVs recovered in our data set are these exact commercial strains, biologicals applied to pear flowers often have a high recovery rate in surveys .Our results show that habitat patches with alternate tree fruit crops were negatively associated with both bacterial and fungal diversity on pear flowers and appeared to be primary drivers of microbial community structure . Pear orchards in the Wenatchee River Valley are primarily located in narrow, intermountain areas with highly variable elevation and land cover, including forest, additional pear orchards, and those dedicated to production of other deciduous fruits, namely, apple.