Functional re-tooling of rhizosphere guilds is driven by agricultural management and scion genotype in apple

Functional re-tooling of rhizosphere guilds is driven by agricultural management and scion genotype in apple | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Functional re-tooling of rhizosphere guilds is driven by agricultural management and scion genotype in apple Mohammad Zarrabian, Sherif. M Sherif This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8562047/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Microbial Ecology → Version 1 posted 12 You are reading this latest preprint version Abstract Rhizosphere microbiomes are critical for agricultural health, but how they are interactively shaped by management and host genetics in perennial systems remains largely unknown. Using an apple orchard system, we show that long-term agricultural management does not just alter soil biodiversity, but also selects for fundamentally opposing microbial life strategies. Our findings showed that organic management selects resource-decomposition specialists, while conventional management selects abiotic stress-tolerance and xenobiotic remediators. We found that this is achieved via functional retooling, where essential ecosystem services are maintained in both systems, but are performed by different adapted specialists. This was most evident in fungi, where management-driven shifts in taxonomy were tightly coupled to functional capacity. Moreover, challenging the prevailing ecological theory that stress simplifies networks, we found that conventional fungal communities were paradoxically more complex, forming a rigid Stress-Clique of co-dependent survivors, while organic bacterial networks were more modular. This structural divergence provides a new mechanistic framework for rhizosphere assembly. We also showed that the host scion's recruitment of fungi is entirely dependent on the management backdrop, while bacterial recruitment is not. These findings reveal that microbiome-optimized breeding should be conducted within the specific management context of the intended production system. Ecological Filtering Co-occurrence Specialization Functional Redundancy Malus domestica Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Modern agriculture, while striving to meet increasing global demands, has exerted profound ecological stress that threatens the very foundation of terrestrial ecosystems and long-term food security (Delgado-Baquerizo et al. 2023). At the center of this threatened foundation is the soil, a dynamic ecosystem whose microbial diversity sustains terrestrial life (Banerjee et al. 2022). The soil microbiome, encompassing bacteria, fungi, and other microorganisms, performs indispensable services such as nutrient cycling, soil aggregation, organic matter decomposition, and plant health promotion (Singh et al. 2024). Despite the essential role of these intricate microbial networks in agroecosystem functioning, they are highly sensitive to agricultural management, which can profoundly reshape these vital communities. This sensitivity to agricultural management is particularly evident in the contrast between conventional and organic farming, as their fundamentally divergent philosophies create distinct microbial environments (Reganold and Wachter, 2016). For example, organic farming provides slow-release nutrients and diverse carbon substrates that fuel microbial activity and support greater taxonomic diversity (Lupatini et al. 2017). In contrast, synthetic inputs in conventional farming systems can exert toxic effects or select for less diverse, opportunistic communities (Hartmann et al. 2015). Consequently, analyses consistently show that organic farming fosters higher microbial biomass, enhanced enzymatic activity, and altered community composition compared to conventional systems, with differences becoming more pronounced under long-term management (Postma-Blaauw et al. 2010; Mäder et al. 2011). These cumulative, long-term impacts are most evident and best-studied in perennial agroecosystems like apple orchards, where the soil remains relatively undisturbed for decades (Manici et al. 2013). Within these systems, the rhizosphere is a critical hotspot of biological activity fueled by carbon-rich root exudates (Haichar et al. 2014), the composition of which is fundamentally shaped by the plant genotype (both rootstock and scion) to influence microbial assembly (Berendsen et al. 2012; Doornbos et al. 2012; Liu et al. 2018; Van Horn et al. 2021; Chai et al., 2022). Thus, the rhizosphere microbiome in a perennial orchard is co-shaped by two powerful forces: the top-down soil conditions set by long-term management and the bottom-up selective pressures from plant genotype. While recent studies have shown that apple scion genotypes can influence rhizosphere bacteria (Bay et al. 2021; Chai et al. 2022), and that management alters fruit-associated microbes (Lian et al. 2024), the interactive effects of these factors on rhizosphere community assembly remain critically underexplored. Previous investigations have focused on endophytic microbiota or rootstock variation (Araujo, 2022), while management comparisons have typically assessed bulk soil or phyllosphere communities rather than the rhizosphere interface where active selection occurs (Ling et al. 2022; Suman et al. 2022). Although studies in annual crops reveal that genotype effects can be contingent on management (Ling et al. 2022; Pandey and Saharan, 2025), whether similar Management × Scion interactions structure rhizosphere microbiomes in established perennial apple orchards—where decades-long management legacies and grafted architecture create fundamentally different ecological contexts—has not been systematically evaluated. This knowledge gap is significant in apple production, as the grafted nature of apple trees presents a unique opportunity: by holding the rootstock constant while varying the scion, we can isolate above-ground genetic influences on below-ground assembly. Understanding this specific interaction has direct implications for combating critical disorders such as the apple replant disease (ARD) (Yurgel et al. 2025) and for developing microbiome-assisted breeding strategies that perform consistently across farming systems. Therefore, using high-throughput amplicon sequencing of the 16S rRNA gene and ITS region, we sought to: (1) determine which factor—management or scion—serves as the primary driver of microbial diversity; (2) identify significant Management × Scion interactions that indicate context-dependent microbiome assembly; (3) characterize functional guilds and biocontrol taxa whose abundances shift between systems; and (4) elucidate network architectural differences that reveal contrasting strategies of microbial community organization under chemical versus organic management regimes. By integrating taxonomic, functional, and network-level analyses across both kingdoms, this study provides mechanistic insight into the hierarchical filtering processes governing rhizosphere assembly in perennial agroecosystems. 2. Methods and Materials 2.1 Study Site and Experimental Design The study was conducted at paired orchard sites in Winchester, Virginia, USA: a certified organic site (39.115780, -78.284624) and a conventional site (39.146230, -78.274400). The sites were selected for their close proximity, shared soil type, and long-term, distinct management histories. The experiment was established as a 2x2 factorial design comparing two primary factors: (1) Management System: Organic vs. Conventional, (2) Scion Genotype: Liberty vs. Enterprise apple cultivars. This primary design was augmented with a third scion genotype, X6398 (an accession sourced from Adams County Nursery Inc., PA, U.S.A), which was present only within the conventional management system. The Liberty and Enterprise scions were grafted onto a standardized MM.111 rootstock. However, the X6398 scion was grafted onto a different rootstock (G210), representing a critical confounding variable in our design. All trees were six years old and were selected for uniform vigor and health status. For each of the five treatment groups, three individual trees were selected as biological replicates. 2.2. Rhizosphere sample collection Rhizosphere samples were collected during the 2025 growing season at the fruit set stage (fruit size is around 20-25mm). For each replicate tree, sampling was conducted at three equidistant positions around the drip line (approx. 1.5 m from the trunk). At each position, soil was excavated to a depth of ~ 10 cm to access the fine feeder root zone. To isolate the rhizosphere, feeder roots were excised and shaken vigorously by hand to dislodge all loosely adhering bulk soil. The adhered soil was collected by scraping with a sterile scalpel. The three rhizosphere subsamples from each tree were pooled and homogenized in a sterile collection bag, creating a single composite sample for each biological replicate. All samples were immediately placed on dry ice in the field, transported to the laboratory, and stored at -80°C pending DNA extraction. To prevent cross-contamination between biological replicates, all sampling tools (shovels, scalpels, shears) were rigorously sterilized between each tree. The protocol involved washing with deionized water, sterilizing with 5% sodium hypochlorite (1 min), rinsing three times with sterile deionized water, submerging in 75% ethanol (30 sec), and finally rinsing three more times with sterile deionized water. 2.3. Soil DNA extraction, library preparation, and sequencing Total genomic DNA was extracted from 0.25 g of each homogenized rhizosphere sample using the Quick-DNA Soil Microbe Kit (Zymo Research, CA, USA) according to the manufacturer's instructions. The concentration and purity of the extracted DNA were assessed using a Synergy H1 microplate reader (BioTek, Oakville, ON, Canada). Following quantification, library preparation was initiated. For bacterial community analysis, the V3-V4 hypervariable region of the 16S rRNA gene was amplified using the 341F (5´-CCTAYGGGRBGCASCAG-3´) and 806R (5´-GGACTACNNGGGTATCTAAT-3´) primer pair. For fungal community analysis, the full Internal Transcribed Spacer (ITS) region was amplified using the ITS2 (F-5´-GCATCGATGAAGAACGCAGC-3´ and R-5´-TCCTCCGCTTATTGATATGC-3´) primer pair. The resulting amplicons subsequently underwent end-repair, A-tailing, and ligation of Illumina sequencing adapters as part of the library preparation protocol. All library preparation and sequencing were performed by the Novogene company (Sacramento, CA, USA). The final, pooled libraries were sequenced on an Illumina NovaSeq 6000 platform using standard company procedures. 2.4. Data processing and statistical analysis All statistical analyses and data visualizations were performed using the R programming language (v4.3.0). To ensure transparency and reproducibility, the specific R packages and computational workflows utilized for each analysis are detailed in the corresponding subsections below 2.4.1. Sequence processing and taxonomy assignment Raw bacterial (16S rRNA) and fungal (ITS2) amplicon reads were processed separately using the DADA2 package (v1.28.0) (Callahan et al. 2016). Prior to DADA2, primer sequences were removed from all raw reads using Cutadapt (Martin, 2011). Within DADA2, reads were filtered and trimmed based on their quality profiles. Amplicon Sequence Variants (ASVs) were inferred using the DADA2 core algorithm, followed by the merging of paired-end reads and the removal of chimeras. Taxonomy was assigned to bacterial 16S ASVs using the DADA2 assign taxonomy function against the SILVA database (v138.1). Taxonomy for fungal ITS ASVs was assigned against the UNITE database (v9.0) (Abarenkov et al. 2024). The resulting ASV and taxonomy tables were combined with sample metadata to create phyloseq objects (McMurdie & Holmes, 2013). Prior to diversity and compositional analyses, the ASV tables were filtered to remove rare taxa. Rare ASVs were defined as those with a relative abundance below 0.01% and those present in fewer than three samples. Moreover, to confirm sequencing depth, rarefaction curves were generated using the rarecurve function in the vegan package (Oksanen et al. 2022). 2.4.2. Alpha and Beta diversity analyses Alpha diversity was assessed on the filtered ASV data, and Observed Richness (total ASV counts) and the Shannon index were calculated using the estimate richness function in the phyloseq package (McMurdie and Holmes, 2013). Statistical differences in alpha diversity across all five treatment groups were assessed using a one-way ANOVA with the aov function, followed by Fisher's LSD test for post-hoc comparisons. For all primary hypothesis testing of the Management effect (Organic vs. Conventional), a clean, balanced 12-sample subset was used. This subset excluded the confounded X6398 group and contained only the Liberty and Enterprise scions. For beta diversity, community ordination was visualized using PCoA on all 15 samples (Fig. 2 ) to show the outlier X6398 cluster. However, the statistical test (PERMANOVA) was performed only on the clean 12-sample subset. This clean 12-sample subset was subsequently used for all main management comparisons, including LEfSe (Section 2.4.3 ), functional guild statistics (Section 2.4.5 ), and network construction (Section 2.4.6 ). 2.4.3. Community composition visualization The distribution of shared and unique genera (core microbiome) was visualized using an UpSet plot generated with the UpSetR package (Conway et al. 2017). The relative abundances of the top 20 most dominant genera were visualized using stacked bar charts created with the ggplot2 package. 2.4.4. Biomarker and differential abundance analyses To identify statistically characteristic biomarkers for the two management systems, a Linear Discriminant Analysis (LDA) Effect Size (LEfSe) was performed (Segata et al. 2011). To conduct the detailed differential abundance analysis of scion and management effects, we used DESeq2 (Love et al. 2014). Given the unbalanced experimental design, the analysis was split. First, a 2x2 factorial analysis was performed on the balanced data subset using the design formula ~ Farming System + Scion Type + Farming System: Scion Type. Second, a separate analysis was conducted within the conventional system to compare X6398 against Enterprise and Liberty. For all tests, taxa with an adjusted p-value (padj) < 0.05 were considered significant. 2.4.5. Functional profile and guild analyses We performed two distinct types of functional analysis: a broad, community-wide prediction and a narrow, targeted guild analysis. First, for the broad prediction, the functional potential of bacterial communities was predicted from the 16S ASV table using FAPROTAX (Louca et al. 2016), and fungal ecological guilds were assigned using FUNGuild (Nguyen et al. 2016). The resulting functional profiles were visualized using heatmaps and PCoA on a Bray-Curtis dissimilarity matrix. Second, separately from the broad predictions, a custom targeted functional guild analysis was performed to analyze the composition of specific, literature-defined guilds. We performed a literature review to manually curate lists of bacterial genera known for (i) Biocontrol and Plant Growth Promotion (PGP), (ii) Nitrogen Fixation, and (iii) Phosphate Solubilization, as well as fungal genera known for (iv) Biocontrol & PGP, (v) Mycorrhizal associations, and (vi) Nutrient Cycling (Supplementary reference). The relative abundance of all genera from our dataset belonging to these manually curated lists was extracted. The proportional composition of each genus within its respective guild was then calculated for comparison. For the species-level bioremediation taxa analysis, we also used a literature review to identify specific species known for these functions (Dar et al. 2019; Behera et al. 2020; Guerrero Ramírez et al. 2023; Mahalle et al. 2025). To focus on beneficial or saprophytic members, all known plant pathogenic species within these groups were computationally identified and removed. 2.4.6. Microbial co-occurrence network analysis To explore microbial interrelationships, networks were constructed at the genus-level, separately for each management system. The analysis was filtered to include only taxa belonging to phyla identified as statistically significant. The genus-level abundance table was transformed using the Centered Log-Ratio (CLR) method. A Spearman correlation matrix was calculated from the CLR-transformed data, and the correlation matrix was filtered to retain strong, significant correlations (p 0.7). The resulting edge and node lists were used to construct network graphs using the igraph and ggraph packages. 3. Results 3.1. High-throughput sequencing yields robust bacterial and fungal datasets To characterize the apple rhizosphere microbiome, we performed high-throughput sequencing of the 16S rRNA gene and the ITS region. Initial processing yielded 1,511,712 raw bacterial and 1,545,796 raw fungal reads across all samples. Following a quality control pipeline that included filtering, denoising, and chimera removal, a total of 1,368,352 high-quality bacterial 16S and 1,420,918 high-quality fungal ITS sequences were retained for analysis. These high-quality sequences were resolved into 14,337 unique bacterial and 9,782 unique fungal ASVs. A preliminary assessment revealed that a greater number of these total ASVs were associated with samples from the conventional system (10,919 bacterial and 5,937 fungal ASVs) compared to the organic system (3,418 bacterial and 3,845 fungal ASVs). To confirm that our sequencing effort adequately captured microbial diversity, we generated rarefaction curves for all samples. The curves for both bacterial (Fig. S1 A) and fungal (Fig. S1 B) communities approached a clear asymptote, indicating that the sequencing depth was sufficient to sample the richness within the communities comprehensively. This confirmed the dataset's robustness for subsequent ecological analyses. 3.2. Organic management is associated with higher overall microbial richness To focus our analysis on the most relevant and consistently detected taxa, the ASV datasets were filtered once more to remove rare (< 0.01%) variants, resulting in a final core dataset of 3,014 bacterial and 2,700 fungal ASVs for all diversity assessments. We first evaluated gamma diversity (pooled richness) by comparing the main management systems (Fig. S2 ). To avoid the confounding effect of the X6398 scion's G210 rootstock, we performed a clean, 6-vs-6 comparison (pooling Org-Liberty/Org-Enterprise vs. Con-Liberty/Con-Enterprise). This valid comparison revealed that the organic system harbored a greater overall richness for both bacteria (1,392 ASVs) and fungi (1,182 ASVs) to the conventional system (1,052 bacterial ASVs and 990 fungal ASVs). When richness was examined at the individual treatment level, a notable exception to this trend emerged. The organic Liberty scion treatment harbored the highest observed ASV richness for both bacteria (841 ASVs) and fungi (615 ASVs) among all groups (Fig. S2 ). In contrast, the richness within the remaining treatment groups was comparable, ranging from 517–570 ASVs for bacteria and 479–567 ASVs for fungi (Fig. S2 ). 3.3. Organic Liberty scion fosters higher bacterial alpha-diversity Beyond assessing simple ASV counts, we evaluated local-scale alpha-diversity using the Shannon Diversity Index. Figure 1 displays all five treatment groups for exploratory comparison, including the distinct X6398 group (G210 rootstock), alongside results from a 5-group one-way ANOVA. However, to rigorously test our main hypotheses, the primary statistical analysis (Two-way ANOVA) was performed on the balanced 12-sample subset (2x2 design), and the reported p -values for main effects are derived from this model. To compare the X6398 group against the other treatments, Fisher's Least Significant Difference (LSD) test was incorporated based on the one-way ANOVA. This analysis reinforced our previous findings for the bacterial communities, revealing a significant difference among the treatment groups (ANOVA, p = 0.049). Specifically, the organic Liberty treatment—which we had already identified as having the highest ASV richness (Fig S2 )—also exhibited a significantly higher bacterial alpha-diversity than the conventional Enterprise treatment (Fig. 1 ). No other significant pairwise differences were detected for bacterial diversity. Interestingly, the fungal communities displayed a distinct pattern (ANOVA, p = 0.01, Fig. 1 ). The significant effect was primarily driven by the Conventional Liberty treatment, which was found to harbor a significantly lower fungal alpha-diversity compared to all other treatment groups (Fig. 1 ). 3.4. Farming system drives a major shift in microbial community composition Having established that management and scion can influence the level of microbial diversity, we next investigated whether they drive shifts in the overall community composition (beta-diversity). To visualize compositional differences, we performed Principal Coordinates Analysis (PCoA) on Bray-Curtis dissimilarity matrices and tested for statistical significance using a Permutational Multivariate Analysis of Variance (PERMANOVA) (Fig. 2 ). However, to generate a statistically valid test of the management effect, a PERMANOVA was performed only on the clean 12-sample subset (excluding the confounded X6398 group). For the bacterial community, the combined model (management and scion) was statistically significant and explained around 40% of the total variation (R² = 0.43, p = 0.03, Fig. 2 A). In spite of insignificant model, the PCoA plot revealed almost a separation of samples based on management system, along the PCoA1 axis (9.8% of variance, Fig. 2 A). Subsequently, to investigate the influence of scion and rootstock combinations, we plotted the PCoA associated with the conventional management system separately (Figure S3A). This exploratory analysis revealed a surprising finding: the Enterprise scion on rootstock MM111 separated distinctly from the other two combinations along the PCoA2 axis (10.5% of the variance). The effect of the management system was even more pronounced for the fungal community. The overall model was significant and explained 43% of the total variation (R² = 0.39, p = 0.04, Fig. 2 B). The strong separation was clear in the PCoA plot, where fungal communities from the organic and conventional systems formed two highly distinct and non-overlapping clusters along the PCoA1 axis (12.6% variance, Fig. 2 B). The PCoA analysis for the conventional farming subset (Fig. S3B) demonstrated clear separation among the different scions sharing the MM.111 rootstock. Furthermore, the unique X6398 combination (on G.20 rootstock) also clustered distinctly from the MM.111-based samples. 3.5. Core microbiome analysis reveals taxa unique to management systems The significant compositional shifts observed in our beta-diversity analysis (Section 3.4 ) prompted an investigation into their taxonomic drivers. We sought to determine whether these community-level differences were due to the presence of unique genera or to random shifts within a common pool of taxa. To differentiate these shared and exclusive components, we visualized the distribution of all observed bacterial and fungal genera using an UpSet plot (Fig. 2 C). Across all five treatment combinations, we identified a stable core microbiome consisting of 41 bacterial and 61 fungal genera, representing taxa present in all groups (Fig. 2 C). The primary source of variation, however, was the distribution of treatment-specific genera. This was particularly evident in the conventional X6398 group, which harbored the highest number of unique taxa (30 bacterial and 25 fungal genera not found in any other group, Fig. 2 C). In sharp contrast, the organic Enterprise treatment possessed the fewest unique genera (14 bacterial, 15 fungal, Fig. 2 C). Moreover, we identified a distinct set of 9 bacterial and 19 fungal genera shared exclusively by the two organic treatments (Fig. 2 C). Conversely, 4 bacterial and 5 fungal genera were shared by the corresponding two conventional treatments (Liberty and Enterprise, Fig. 2 C). 3.6. Dominant genera reveal management-specific patterns and high intra-group variability While the UpSet plot (Fig. 2 C) identified genera based on their presence or absence across treatments, we next quantified the relative contribution of the most dominant taxa to the community structure. To achieve this, we visualized the relative abundances of the top 20 most abundant bacterial (Fig. 3 A) and fungal (Fig. 3 B) genera. The bacterial communities featured several highly abundant genera, such as Bradyrhizobium , Methylothermalis , and Mycolicibacterium , that were dominant across all conventional and organic treatments. However, a key observation was the significant taxonomic heterogeneity within treatment replicates. For example, within the conventional Enterprise group, Flavobacterium was highly abundant in replicate 3 but was not among the top 20 genera in replicates 1 and 2 (Fig. 3 A). Similarly, Streptomyces was absent from the top 20 in conventional Enterprise replicate 2 while being present in the other two replicates (Fig. 3 A). Stark, management-driven patterns were evident in the fungal communities (Fig. 3 B). Xenodidymella , for instance, was highly abundant across all organic samples but present at only very low levels in conventional samples (Fig. 3 B). Similarly, Aspergillus was found to be more abundant in the organic samples while remaining a minor component in the conventional ones (Fig. 3 B). Despite these clear management-level trends, the fungal communities also mirrored the bacteria in exhibiting high intra-group variability. Within the conventional Liberty treatment, for instance, replicate 2 was characterized by a high abundance of Neocosmospora while replicate 3 was dominated by Talaromyces (Fig. 3 B). Notably, neither of these genera was ranked among the top 20 most abundant in the other conventional Liberty replicates (Fig. 3 B). 3.7. LEfSe analysis identifies statistically distinct biomarker taxa for each management system After visually identifying dominant genera across all 15 samples (Section 3.6 ), we next statistically pinpointed the biomarkers characteristic of each management system. To do this, we performed a Linear Discriminant Analysis (LDA) Effect Size (LEfSe) analysis (Fig. S4) using the clean, 12-sample subset (excluding the confounded X6398 group). This provided valid statistical validation for management-driven patterns. For the bacterial communities, the analysis revealed a strong asymmetric distribution of biomarkers (Fig. S4A). The organic system was significantly enriched by a vast array of taxa, including numerous genera from the phylum Actinobacteria (e.g., Nakamurella , Cellulomonas , Rhodococcus , Rhizocola ) and Mycolicibacterium , which we had also identified as a dominant genus in Section 3.6 . In stark contrast, only three taxa— Streptosporangium , Phyllobacterium , and Phyllobacteriaceae —were identified as significant biomarkers for the conventional system (Fig. S4A). Notably, other dominant genera like Bradyrhizobium and Streptomyces were not identified as biomarkers, suggesting they represent a stable core community rather than a differentially abundant one. For the fungal communities, LEfSe confirmed the management-driven patterns observed in the Top 20 genera plot (Fig. 4SB). The organic system was enriched with a wide array of fungal taxa, including Xenodidymella , Aspergillus , Penicillium , Cladosporium , and Beauveria (Fig. 4SB). Conversely, the conventional system was characterized by a large, yet entirely different, group of fungal biomarkers, including Fusarium , Metarhizium , Aureobasidium , Nigrospora , and Cosmospora (Fig. 4SB). These results show that the visual differences in dominant genera (Fig. S4) are characteristic of each management system, consistent with the significant community-level shifts established by our PERMANOVA (Fig. 2 ) 3.8. Predicted functional profiles differ by management system but show high scion-specific variation Building on the identification of taxonomic biomarkers, our analysis then focused on the functional implications of these community shifts. We first predicted the broad functional potential of the bacterial communities using FAPROTAX and visualized the overall landscape with a clustered heatmap based on z-scores (Fig. S5). The hierarchical clustering of samples (Top dendrogram, Fig. S5) immediately revealed that, unlike the distinct taxonomic separation (Fig. 2 A and B), the predicted functional profiles did not form clear, top-level clusters based on management system alone. Instead, the clustering highlighted strong scion- and replicate-specific patterns, with samples from different management systems interspersing (Fig. S5). This lack of strong management-level separation was confirmed by a Principal Coordinates Analysis (PCoA) of the functional profiles (Fig. 6SA). While PCoA1 and PCoA2 explained a combined 90% of the variance, the organic and conventional groups were not clearly delineated, and some scions (notably organic Liberty) mixed and clustered with the other samples (Fig. 6SA). Despite this high visual variability in the overall profile, we performed statistical tests on the clean 12-sample (6-vs-6) subset to determine if specific key functions, within FAPROTAX results, were consistently different between the two management systems (Fig. S7). This analysis revealed that functions related to organic matter decomposition, specifically aromatic compound degradation (p = 0.018) and aromatic hydrocarbon degradation (p = 0.01), were significantly higher in the organic system. Fermentation was also predicted to be significantly more abundant in the organic system (p = 0.008). In contrast, no significant differences were observed for other important functions, including plant pathogens ( p = 0.917), Ureolysis ( p = 0.414), or nitrogen fixation ( p = 0.145). Given that these three decomposition-related functions were significantly elevated in the organic system, we performed a follow-up analysis to test for scion-level differences within each management system (Fig. S8). Within the conventional system, no significant differences were observed among the scions for any of the three functions ( p > 0.05). However, within the organic system, a significant effect of scion type was detected for aromatic hydrocarbon degradation ( p = 0.034), which was significantly higher in Liberty than in Enterprise (Fig. S7). While FAPROTAX provided broad functional predictions, we also performed a separate, literature taxonomic-based guild analysis, calculated from the clean 12-sample subset, to examine the composition of three key beneficial groups: Biocontrol & Plant Growth-Promoting Rhizobacteria (PGPR), Nitrogen Fixers, and Phosphate Solubilizers. This allowed us to compare the relative abundance of known genera within these specific functional groups between the two management systems (Fig. 4 ). Within the Biocontrol & PGPR guild, Streptomyces was the dominant genus in both systems, accounting for 90.6% of the guild's composition in conventional and 99.6% in organic samples (Fig. 4 ). However, the conventional system's guild also retained a notable proportion of Paenibacillus (4.9%) and Bacillus (4.4%), which were functionally replaced in the organic system (Fig. 4 ). The composition of the Nitrogen Fixers guild was highly similar between systems (Fig. 4 ). Both were dominated by Bradyrhizobium (60.9% conventional, 60.1% organic), Mesorhizobium (25.3% and 19.3%), and Rhizobium (11.0% and 12.5%, Fig. 4 ). A stark contrast was observed in the Phosphate Solubilizer guild (Fig. 4 ). While the conventional system's guild was dominated by Flavobacterium (70.6%), it also included a substantial proportion of Bacillus (26.6%). In the organic system, this guild was composed entirely (100%) of Flavobacterium (Fig. 4 ). 3.9. Fungal ecological guilds, unlike bacterial functions, show a strong separation by management system In parallel with the bacterial functional analysis, we next characterized the ecological functions of the fungal communities. We first assigned fungal taxa to ecological guilds using the FUNGuild database and visualized the overall functional landscape with a clustered heatmap based on z-scores (Fig. S9). Similar to the bacterial functional heatmap, the hierarchical clustering of samples did not reveal a perfect top-level separation by management system, instead highlighting strong sample-specific patterns. However, when this functional profile was visualized using PCoA, a clear and significant separation driven by management did emerge (Fig. S6B). Samples from the conventional system clustered tightly along the primary axis (PCoA1, 23.0%), while the organic samples formed a separate group (Fig. S5B). Similar to the fungal taxonomic profiles (Fig. 2 B), scion type appeared to have no clear clustering effect, with scions being intermixed within their respective management group (Fig. S5B). Given this strong management separation in the PCoA, we statistically compared the relative abundances of four major ecological guilds using the valid 12-sample subset (Fig. S10). This analysis identified the specific drivers for the separation: a statistically significant difference was observed for endophytic fungi, which were significantly more abundant in the organic system ( p = 0.012, Fig. S10). A similar, though non-significant, trend was observed for plant pathogenic fungi, which tended to be more abundant in the organic system ( p = 0.066, Fig. S10). No significant differences were detected for fungal parasites ( p = 0.94, Fig. S10) or decomposer saprotrophs ( p = 0.17, Fig. S10). A follow-up analysis tested for scion-level effects on these guilds within each management system (Fig. S11), revealing no statistically significant differences in the abundances of endophytes, plant pathogens, fungal parasites, or decomposer saprotrophs among scions in either the conventional or the organic system (all p > 0.05, Fig. S11). This suggests the management system, rather than scion, is the primary factor influencing these broad fungal ecological roles. Beyond the broad ecological assignments from FUNGuild, we performed a more targeted compositional analysis on the 12-sample subset for three key beneficial fungal guilds: Biocontrol & PGPF, Mycorrhizal, and Nutrient Cycling (Fig. 5 )-which were selected a priori based on their essential roles in apple tree health and orchard productivity. This analysis revealed distinct compositional profiles driven by management (Fig. 5 ). Within the Biocontrol & PGPF guild, a clear shift was observed. The conventional system was dominated by Metarhizium (53.6%), followed by Trichoderma (26.2%). In contrast, the organic system was dominated by Trichoderma (57.1%), while Metarhizium was reduced to 16.7% (Fig. 5 ). The Mycorrhizal guild also showed a management-driven substitution. While Glomus was the most abundant genus in both systems (52.3% conventional, 51.7% organic), the second-most abundant genus in the conventional system was Rhizophagus (33.4%). In the organic system, Rhizophagus was minimal (8.1%), and Acaulospora was highly abundant (35.9%) (Fig. 5 ). Finally, the Nutrient Cycling guild in the conventional system was dominated by Talaromyces (51.4%) and Aspergillus (32.6%). The organic system displayed a different profile, with a sharp reduction in Talaromyces (15.9%) and a co-dominance of Penicillium (41.3%) and Aspergillus (40.0%) (Fig. 5 ). 3.10. Management systems select for unique bioremediation taxa Having examined the compositional shifts within shared beneficial guilds, our focus shifted to the bioremediation potential held by specific taxa. Our earlier microbial functional analysis revealed that functions related to organic matter decomposition were significantly elevated in the organic system (Figures S7 and S10). To further investigate this, we performed a deeper analysis correlating these predicted functions with the exclusive taxa (genera and species found in only one management system). To test the main and interactive effects of management and scion, we performed a two-way ANOVA (2x2 factorial) on the clean 12-sample subset (excluding the confounded X6398 group). The figures in this section (Figs. 6 and 7 ) visualize all five treatment groups for exploratory comparison, but the reported p -values (Farming, Scion, Interaction) and any significance letters are derived only from this valid 2x2 ANOVA. The X6398 group is shown for visual context but was excluded from this statistical test due to its confounding G210 rootstock, and therefore does not receive significance letters. For the bacterial communities, we focused on FAPROTAX functions related to complex organic compound degradation. We then examined the abundances of specific, non-pathogenic taxa known for these capabilities (Fig. 6 ). This detailed analysis revealed significant, system-specific enrichments. For example, Sphingosinicella cucumeris was significantly more abundant in the organic system, while Phyllobacterium zundukense was a significant bioremediation-marker for the conventional system (Fig. 6 ). Other known degrader genera, such as Rhodococcus spp. and Sphingomonas spp., were present across treatments, though Rhodococcus spp. showed a non-significant trend of higher abundance in the organic system (Fig. 6 ). We then compared specific, non-pathogenic saprophytic fungal taxa at the species level (Fig. 7 ). This revealed strong, differential responses to management. Metarhizium robertsii and Absidia aquabaelensis were both significantly higher in the conventional system (Fig. 7 ). Metarhizium was most abundant in the Liberty scion (Interaction, p = 0.005, Fig. 7 ), while Absidia was highest in the Enterprise scion (Interaction, p = 0.01, Fig. 7 ). Conversely, several taxa were enriched in the organic system. For instance, Mucor hiemalis (Farming, p = 0.001, Fig. 7 ) and Aspergillus aureolus (Farming, p = 0.051, Fig. 7 ) were significantly (or tended to be) more abundant in organic samples. Lecanicillium primulinum also showed higher abundance in the organic system, particularly in the Enterprise scion (Interaction, p = 0.03, Fig. 7 ). 3.11. Management system inversely reshapes bacterial and fungal network complexity The previous analyses identified key shifts in community composition, function, and the enrichment of unique taxa. To understand how these community-wide changes affect the interrelationships among microbes, we next constructed co-occurrence networks on the 12-sample subset, excluding X6398 (Fig. 8 ). The analysis was specifically filtered to visualize interactions only among taxa belonging to phyla that were identified as statistically significant within the organic and conventional systems. The bacterial phyla included in this analysis were Actinobacteriota , Bacillota , Bacteroidota , Chloroflexi , Nitrospirota , and Pseudomonadota , while the fungal phyla included Ascomycota , Basidiomycota , and Mucoromycota . For the bacterial communities, the network of significant phyla in the organic system fostered a larger and more complex set of interactions (Nodes: 68, Edges: 374, Fig. 8 ) compared to the network of significant phyla in the conventional system (Nodes: 57, Edges: 288, Fig. 8 ). This suggests a higher degree of interaction among the key bacterial players in the organic soil. A striking, contrasting pattern was observed for the fungal communities. The network of significant fungal taxa in the conventional system was substantially larger and more densely connected (Nodes: 131, Edges: 1248, Fig. 8 ) than the network in the organic system (Nodes: 96, Edges: 594, Fig. 8 ). The conventional network featured more than double the number of interactions, indicating a far more complex web of co-occurrence within this key fungal group. Overall, these filtered network topologies reveal that the management system fundamentally reshapes interaction patterns among the most responsive microbial phyla. While the organic system was associated with a more complex network of its key bacterial taxa, the conventional system was characterized by a dramatically more complex and interconnected network of its key fungal taxa. 3.12. Differential abundance analysis pinpoints key taxa driven by management and scion The network analysis highlighted broad shifts in interaction complexity among key phyla. However, to identify the specific taxa (the nodes) driving these changes at a finer resolution, and to parse the complex, interacting effects of management and scion, we performed a differential abundance analysis (Figs. 9 and S11 ). Due to the unbalanced experimental design (with Liberty and Enterprise present in both systems, but X6398 only in the conventional system), the analysis was strategically split into two parts: (1) a 2x2 factorial analysis on the balanced portion of the design, and (2) a separate analysis within the conventional system to compare X6398 against Enterprise and Liberty. For the 2x2 factorial analysis of bacterial communities (Fig. 9 A-C), the main effect of Farming System was the strongest driver. Aldersonia and Methylocystis were significantly enriched in the organic system, while Phyllobacterium and Paractino planes were enriched in the conventional system (Fig. 9 A). The main effect of scion (Fig. 9 B) showed Actinacidiphila and Rhabdothermincola enriched in Enterprise, whereas Phyllobacterium and Paractinoplanes were enriched in Liberty. A significant interaction effect was observed for Paractinoplanes , Actinacidiphila , and Methylocystis (Fig. 9 C), indicating their response to scion depended on the farming system. For the fungal communities in the factorial analysis (Fig. 9 D-F), the farming system effect was also pronounced. A large group of fungi, including Monilinia , Cucitella , and Tortiopsora , were significantly enriched in the organic system. Conversely, Zygoratorulaspora , Ceratobasidium , Colarella , Nectria , and Flammocladiella were characteristic of the conventional system (Fig. 9 D). The main effect of scion revealed Phaeopopca , Sistotrema , Neptunomyces , and Tiankongomelaia were enriched in Enterprise, while Flammocladiella was enriched in Liberty (Fig. 9 E). The interaction effect was strong, with a large number of fungi, including Flammocladiella , Phragmographa , and Wickerhamomyces , showing a significant interaction, highlighting that the scion's influence on fungi is highly dependent on management (Fig. 9 E). The second part of the analysis compared the X6398 scion against Enterprise and Liberty within the conventional system. When comparing X6398 vs. Enterprise bacteria, X6398 was significantly enriched in Bacillus , Solirubrobacter , Paenibacillus , Streptibioticus , Peribacilus , Povalibacter , and Niallia (Fig. S12). For fungi, X6398 was enriched in Cladobotryum, Codinaea, Gongronella, Dictyosporium, Ascospirella , and Phaeosaria , while Enterprise was enriched in a large group including Wickerhamomyces , Polyschema , Emericellopsis , Dendryphion , Gibellulopsis , Enterocarpus , Neroroussoella , and Vishniacozyma (Fig S12). Finally, when comparing X6398 vs. Liberty bacteria, no taxa were found to be differentially abundant. For fungi (Fig. S12), Liberty was enriched in Solicoccozyma and Linnemannia , while X6398 was enriched in Arachnomyces and Plectosphaerella (Fig S12). 4. Discussion Our study provides critical evidence that long-term agricultural management may not act as a disruptive force, but act as an ecological filter that fundamentally restructures the rules of rhizosphere assembly. While previous studies have established that management shifts community composition (Schmidt et al. 2019), our data advance this field by demonstrating that these shifts represent a coherent, system-level adaptation rather than simple biotic loss. We posit that organic and conventional systems select for fundamentally opposing life strategies: resource-acquisition specialists in organic versus stress-tolerance specialists in conventional. This finding challenges the simplistic narrative of conventional microbiomes as merely degraded (Ray et al. 2020), pointing instead to highly adapted, functionally distinct, alternative stable states. 4.1. Deterministic filtering drives microbial specialization and functional retooling The stark, non-overlapping separation of fungal communities (Fig. 2 B) stands in contrast to the more variable bacterial response (Fig. 2 A). This indicates that fungi are the primary responders to the dominating abiotic pressures of conventional management, aligning with theory suggesting fungi lack the rapid adaptive plasticity of bacteria (Schmidt et al. 2019). This key difference in filtering dictates how each kingdom responds to host genetics. The bacterial community, being less constrained by the soft filter of management, appears to respond to host (scion) cues more independently (Fig. 9 B and C). Conversely, the fungal community is so deterministically locked by the hard filter of management that the scion's influence becomes entirely context-dependent, resulting in the massive web of Scion × Management interactions observed in Fig. 9 F. This double-filter effect, where management dictates the available pool from which the scion recruits, is a core finding of our study. This difference between kingdoms extended beyond just taxonomic composition to their functional architecture. For bacteria, both their taxonomic composition and their predicted functional profiles were highly variable, with neither showing a clear separation by management (Fig. S5A). This suggests a high degree of functional redundancy—the principle where different bacterial species can perform the same ecosystem function. This redundancy allows the community to maintain a stable functional output, even as the specific species (the taxonomy) changes. For fungi, we observed the exact opposite; the taxonomic composition and their predicted functional profiles both showed a strong, clear separation by management (Fig. S5B). This demonstrates a tight coupling between fungal identity and function. The implication is that management-driven shifts in fungal community composition directly translate into changes in fungal functional capacity, positioning fungi as the primary drivers of ecosystem-level functional change in this system. This tale of two kingdoms strongly supports a hypothesis of microbial specialization, where each system selects for a community adapted to its specific challenges. The organic system appears to select specialization in resource decomposition, evidenced by the enrichment of bacterial and fungal functions related to degrading complex compounds in each system. For example, organic decomposer specialists like Sphingosinicella cucumeris , and Mucor hiemalis (Figs. 6 and 7 ). Conversely, the conventional system appears to select for specialization in abiotic stress tolerance and xenobiotic remediation. The enrichment of Metarhizium robertsii and Phyllobacterium zundukense (Fig. 6 , 7 )—taxa with known capabilities to degrade pollutants, including organotins, nonylphenols, and other xenobiotics, while exhibiting remarkable stress tolerance through enhanced oxidative stress management systems (Różalska et al. 2014; Siewiera et al. 2015; Hermans et al. 2023) —suggests a community of remediators and survivors adapted to the farm's chemical inputs. This pattern of specialization was most evident in the functional retooling of key beneficial guilds, rather than a simple loss of function. In the fungal biocontrol guild (Fig. 5 ), the conventional system was dominated by Metarhizium (53.6%), a robust, stress-tolerant entomopathogen with documented capabilities for surviving extreme environmental conditions, including heat, UV radiation, and oxidative stress (Wang et al. 2017; Wang et al. 2019; Paixão et al. 2021). In contrast, the organic system was dominated by Trichoderma (57.1%), a classic biocontrol agent that thrives via mycoparasitism, directly parasitizing other fungal species through the production of cell wall-degrading enzymes and secondary metabolites (Sood et al. 2020; Guzmán-Guzmán et al. 2023; Poveda, 2021) —a strategy likely more successful in the biotically complex organic environment. This retooling extended to nutrient cycling and even the phosphate-solubilizing guild (Fig. 5 ). This confirms that distinct chemical and biological environments select for entirely different specialists to fulfill the same crucial ecosystem functions. Understanding the specific compatibility of these key biocontrol agents, such as Trichoderma and Bacillus , with the unique inputs of each system, from synthetic fungicides to novel biopesticides, is therefore a critical next step. This work is essential not only for predicting their real-world ecological success (Zarrabian et al. 2025), but also for establishing the refined ecotoxicological frameworks required for these next-generation agricultural technologies (Zarrabian and Sherif, 2024). 4.2 Stochastic variability within deterministic frameworks: reconciling replicate heterogeneity While deterministic processes (niche-based selection) and stochastic processes (ecological drift, dispersal limitation) simultaneously influence community assembly along a continuum (Schmidt et al . 2019; Araujo et al . 2022), our results reveal a complex interplay between these mechanisms. Despite strong deterministic management effects on overall community composition (Section 3.4 ), we observed substantial replicate-to-replicate variation within treatment groups (Fig. 3 ). This pattern aligns with theoretical frameworks suggesting that strong deterministic factors can paradoxically intensify stochastic assembly at local scales (Bay et al. 2021; Lian et al. 2024). We propose that while management acts as a primary deterministic filter setting the regional species pool (as evidenced by Fig. 2 C), stochastic processes—including ecological drift and dispersal limitation—play greater roles in structuring rare taxa and determining local-scale abundance patterns within this filtered pool (Pantigoso et al. 2022). The influence of deterministic environmental filtering relative to stochastic factors may be maximized at extreme ends of environmental gradients, while stochastic processes become more important at intermediate conditions or shorter temporal scales (Bay et al. 2021; Navarro-Noya et al. 2022). This framework helps explain the divergence in diversification patterns between our systems. As highlighted by the Shannon diversity index (Fig. 1 ), the organic system represents a state of significantly higher diversification, where the soft management filter allows a vast array of taxa to coexist and interact stochastically. In contrast, the conventional system acts as a "hard filter where harsh deterministic selection eliminates sensitive taxa. This is most clearly observed in the fungal community, where the Conventional Liberty treatment exhibits a significant diversity crash (Fig. 1 ). While the surviving stress-tolerant taxa in the conventional system may still vary stochastically in local abundance based on microsite heterogeneity or priority effects, their overall diversification is strictly limited by the high-pressure environment. Thus, while both systems exhibit replicate-level variation, it is the organic system that facilitates the highest degree of microbial diversification. 4.3 The stress-clique vs. functional modules: divergent network architectures Our network analysis reveals that management does not just change the level of complexity, it fundamentally reshapes the architecture of microbial interactions. Contrary to prevailing theory that stress simplifies ecological networks and reduces compositional complexity (Landi et al. 2018), we observed a dramatically more complex fungal network in the high-stress conventional system (Fig. 8 ). The filtered network analysis of key responding phyla showed that conventional fungi formed a network with 131 nodes and 1,248 edges—more than double the 594 edges in the organic fungal network (96 nodes) (Fig. 8 ). We propose the stress-clique hypothesis to explain this. Extreme abiotic filtering likely eliminates casual species, forcing the few surviving, highly-adapted taxa (such as the Aureobasidium and Metarhizium identified in our LEfSe analysis, Fig. S4B) into dense, obligate co-dependencies to survive. This represents a rigid complexity—a tightly interconnected community adapted to extreme chemical stress, where surviving taxa display increased interdependence for mutual support. In contrast, the complex organic bacterial network reflects a healthy, modular food web. The organic bacterial network (68 nodes, 374 edges) was larger and more complex than the conventional (57 nodes, 288 edges, Fig. 8 ). This is best illustrated by the distinct sub-modules visible in the network structure (Fig. 8 ). For example, a separated cluster containing Rhodococcus , Methylocystis , and Nocardia likely represents a specialized functional module dedicated to degrading specific complex hydrocarbons—a role these genera are well-known for (Ivshina et al. 2023; Płaza et al. 2018; Semrau et al. 2011; Im and Semrau, 2011; Azadi and Shojaei, 2020; Hesham et al. 2006; Płaza et al. 2018). This functional compartmentalization allows the organic microbiome to process diverse inputs simultaneously without cross-interference, a hallmark of a stable, mature ecosystem that is absent in the monolithic stress-clique of the conventional farming system. 4.4. The organic Liberty anomaly: When scion and management synergize Among all treatment combinations, organic Liberty emerged as a striking outlier, harboring the highest microbial richness for both bacteria (841 ASVs) and fungi (615 ASVs)—substantially exceeding all other groups (Fig. S2 ). This exceptional diversity was not merely a richness artifact; organic Liberty also exhibited higher bacterial alpha-diversity (Shannon Index) than the other (Fig. 1 ) and demonstrated significantly elevated aromatic hydrocarbon degradation capacity compared to Enterprise in the organic system (Fig. S7). Therefore, we propose two non-mutually exclusive hypotheses to explain this synergistic diversity effect: (1) The niche amplification hypothesis: This hypothesis posits that Liberty’s unique traits interact with the organic environment to create a hyper-heterogeneous landscape. The organic system inherently provides high resource heterogeneity (Schmidt et al. 2019) and, on the other hand, the scion, which is known to influence exudates (Lu et al. 2020; Chai et al. 2022), may produce a more diverse exudate cocktail that further amplifies this background heterogeneity. Thereby creating a multitude of unique microniches that can support a broader array of specialized microbial consortia. (2) The reduced host selectivity hypothesis: This hypothesis proposes a host-mediated mechanism. Plants actively shape their microbiome through selective exudate secretion and immune modulation (Marasco et al. 2022). If the Liberty genotype possesses a less stringent microbial filter, it may permit colonization by a wider range of microbes. Critically, this Liberty-specific diversity enrichment was absent in the conventional system, where conventional Liberty actually harbored the lowest fungal alpha-diversity among all groups (Fig. 1 ). This stark system-dependence underscores that scion effects are not intrinsic properties but are contingent on the management backdrop—a theme we explore further in Section 4.6 . 4.5. X6398: A unique genetic background reveals host-specific microbial recruitment The unique experimental setup of the X6398 scion, which was grafted onto a different rootstock (G210) than the MM.111 used for all other treatments, allows for a compelling discussion of the rootstock's confounding influence. The X6398 combination was a profound outlier, harboring the highest number of unique taxa among all treatment groups (Fig. 2 C). This exceptional uniqueness and the specific enrichments (Fig. S12) cannot be conclusively attributed to the X6398 scion's genetic background. These differences are likely driven by a combination of the scion and the G210 rootstock's distinct root exudation profile and below-ground architecture. Despite this confoundment, the data offer valuable insight into recruitment capacity: The X6398 environment successfully recruits stress-tolerant PGP taxa, showing significant enrichment of Bacillus (Hashem et al. 2019) and Gongronella (Wang et al. 2024) compared to the other conventional scions. This suggests that certain rootstock-scion combinations may possess an enhanced ability to recruit protective microbial guilds under agrochemical stress. Future studies should evaluate X6398 in organic systems or on MM.111 rootstock to disentangle whether its unique microbiome reflects intrinsic genotype effects or management × scion interactions. 4.6. Scion acts as a secondary, permissive filter Finally, our findings refine the understanding of host-microbe selection in agriculture by establishing a double-filter model. While management acts as the primary, deterministic filter that sets the available microbial pool, the apple scion acts as a secondary, permissive filter. The significant host-genotype effects we observed are driven entirely by the scion, confirming that above-ground signals mediated by the scion—including phytohormones, photosynthate allocation, and specific root exudates triggered by scion genotype—can actively shape the rhizosphere microbiome even across a standardized root system (Chai et al. 2022; Marasco et al. 2022). This observation is consistent with the established principle that host genetics define the breadth of potential microbial partnerships, a mechanism that has been clearly demonstrated in other plant-symbiont systems (Zarrabian et al. 2022) Crucially, our interaction analysis reveals that this secondary filter functions very differently for bacteria versus fungi. For bacteria, the interaction landscape is remarkably simple, with very few taxa showing significant management × scion effects (Fig. 9 C). This suggests that when a scion recruits specific bacteria, it tends to do so consistently, regardless of the management backdrop. In sharp contrast, the fungal community displays a highly complex web of management × scion interactions (Fig. 1 6F). The massive cloud of significantly interacting taxa indicates that the scion's recruitment strategy for fungi is entirely dependent on the farming system. Because the management filter radically changes the available fungal pool (as seen in Section 4.1 ), the plant is forced to recruit completely different fungal partners in an organic soil versus a conventional one. This demonstrates that breeding for microbiome-optimized crops cannot be a one-size-fits-all approach. A scion selected for beneficial fungal associations in an organic farm may fail to recruit those same partners in a conventional orchard, because management will have already filtered them out. The exceptional diversity of organic Liberty versus the depleted diversity of conventional Liberty (lowest fungal alpha-diversity, Fig. 1 ) exemplifies this context-dependency. Therefore, microbiome-assisted breeding programs should explicitly account for the destination farming system. 5. Conclusion This study demonstrates that the management system is the primary determinant of the apple rhizosphere microbiome, acting as a fundamental filter that shapes microbial diversity, composition, and interaction networks. Organic management played a decisive role in enhancing both alpha and beta diversification; specifically, the organic system fostered higher overall microbial richness and diversity, with the Organic-Liberty combination representing a peak in bacterial diversity. In contrast, conventional management was associated with a significant diversity crash in fungal communities, particularly in the Liberty scion. The role of the scion genotype was found to be highly context-dependent. While bacterial recruitment showed relative stability across systems, fungal recruitment was governed by strong Management × Scion interactions. This suggests that the scion’s ability to recruit beneficial partners is not an intrinsic trait but is contingent upon the species pool permitted by the management system. Furthermore, the unique microbial signature of the X6398 scion suggests that rootstock genetics (G210) may provide an additional layer of recruitment capacity, though this remains a compelling area for future research to disentangle from scion effects. Based on these observations, we propose the Stress-Clique hypothesis to explain the observed network topologies. We hypothesize that the hyper-dense fungal co-occurrence networks in conventional systems represent a state of obligate co-dependency driven by agrochemical stress. While these communities appear complex, their high interconnectivity may render them vulnerable to cascading failures if key hub taxa are disrupted by new management inputs. Conversely, the more modular networks observed in organic systems may indicate greater ecological resilience. Finally, these ecological insights provide a roadmap for microbiome-assisted breeding. We conclude that: (1) breeding for microbial traits must be system-specific, as a genotype’s performance in an organic orchard may not translate to a conventional orchard; (2) breeders should prioritize fungal partnerships, as they are the most responsive to scion-management interactions; and (3) selection should target functional guilds rather than individual taxa, recognizing that different specialists may fulfill identical ecological roles under different management filters. Declarations Declaration of Interest Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This project was partially funded by the Biotechnology Risk Assessment Research Grants Program (BRAG) (Grant# 33522–38314). Author Contribution **Mohammad Zarrabian** : Writing – original draft, writing – review & editing, methodology, visualization, software, investigation, formal analysis, data curation.**Sherif M. Sherif** : conceptualization, writing – review & editing, supervision, project administration, and funding acquisition. Acknowledgments The authors wish to express their gratitude to Ms. Diane Kearns for providing access to her organic and conventional orchards for this study. Sincere thanks are also extended to Mr. Gerald Michaels III and Ms. Katherine Furcho for their assistance in collecting the rhizosphere soil and extracting microbiome DNA. 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Mol Plant Microbe Interact 35(11):1006–1017. https://doi.org/10.1094/MPMI-04-22-0087-R Additional Declarations No competing interests reported. Supplementary Files SupplementaryReference.docx SupplementaryFigure.docx Cite Share Download PDF Status: Published Journal Publication published 22 Apr, 2026 Read the published version in Microbial Ecology → Version 1 posted Editorial decision: Revision requested 21 Mar, 2026 Reviews received at journal 15 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviews received at journal 23 Feb, 2026 Reviewers agreed at journal 22 Feb, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor assigned by journal 12 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 09 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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08:10:35","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150908,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/bd6f29d048f3ea39c5f464b6.html"},{"id":100229459,"identity":"e014329e-a5b8-4626-9b46-d49de8b9c761","added_by":"auto","created_at":"2026-01-14 11:13:24","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91337,"visible":true,"origin":"","legend":"\u003cp\u003eRhizosphere Microbial Alpha-Diversity in apple orchard. Boxplots illustrating the Shannon diversity index for rhizosphere bacterial (blue) and fungal (red) communities across five distinct apple scion and management system combinations. The treatments are: Conventional-Enterprise (Con-Ent), Conventional-Liberty (Con-Lib), Conventional-X63 (Con-X63), Organic-Enterprise (Org-Ent), and Organic-Liberty (Org-Lib). The Con-X6398 group is confounded by a different (G210) rootstock and is shown for visual comparison. The letters indicate significant differences (p \u0026lt; 0.05) from an exploratory one-way ANOVA across all five groups, followed by a Fisher's LSD test. The primary statistical test for the Management and Scion effects is a two-way ANOVA performed on the clean 12-sample (Org/Con-Liberty and Org/Con-Enterprise), and those p-values are reported in the main text.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/e7f9c658462504359b34cb97.jpeg"},{"id":100229460,"identity":"e1ff313c-6133-43ab-b3c4-dc06c6ada843","added_by":"auto","created_at":"2026-01-14 11:13:24","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":347991,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Coordinates Analysis (PCoA) of rhizosphere microbial Beta-diversity (A and B). PCoA plots illustrating the separation of microbial community structure based on Bray-Curtis dissimilarity for (A) bacterial (16S) and (B) fungal (ITS) communities. Samples are colored by management system (Conventional = red, Organic = black) and shaped by scion cultivar (Enterprise = circle, Liberty = triangle, X6398 = star). The percentage of variation explained by each principal coordinate axis is shown in parentheses. The PERMANOVA statistic for the overall model is displayed on each plot. C: UpSet Plot of shared bacterial and fungal genera. Distribution of shared and unique genera for bacteria (red) and fungi (black) across the five treatment combinations. Vertical Bars: The height of each bar represents the number of genera found in a specific intersection of treatment groups. The numbers above each bar indicate the count for bacteria (red) and fungi (black).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/3b0962166bae3dedfbb52f67.jpeg"},{"id":100369815,"identity":"a96d383f-cc8b-4d94-a997-b02f08317c1e","added_by":"auto","created_at":"2026-01-16 07:59:31","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":357312,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic composition of the top 20 most abundant microbial genera. Stacked bar plots showing the relative abundance (%) of the top 20 most abundant (A) bacterial and (B) fungal genera. Each bar represents an individual biological replicate, grouped by treatment on the x-axis. The y-axis indicates the relative abundance. Genera are identified in the legend, corresponding to the colored sections of the bars.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/c701830ab9385c468e8afcfb.jpeg"},{"id":100371063,"identity":"3ffc01e3-a69f-4c15-bfe8-211bf9e9098b","added_by":"auto","created_at":"2026-01-16 08:09:19","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":276626,"visible":true,"origin":"","legend":"\u003cp\u003eRelative composition of key beneficial bacterial guilds. Pie charts illustrating the relative abundance of bacterial genera within three pre-defined functional guilds: Biocontrol \u0026amp; PGPR (Plant Growth-Promoting Rhizobacteria), Nitrogen Fixers, and Phosphate Solubilizers. Charts are organized by management system, with conventional (top row) and organic (bottom row). Percentages indicate the proportion of each genus relative to the total number of sequences classified within that specific functional guild for each management system.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/d3e374db9fef53775cdd2db1.jpeg"},{"id":100229466,"identity":"b6b1b03f-36e8-4cf1-ada8-2c024296afdf","added_by":"auto","created_at":"2026-01-14 11:13:24","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":305660,"visible":true,"origin":"","legend":"\u003cp\u003eRelative composition of key beneficial fungal guilds. Pie charts illustrate the relative abundance of fungal genera within three pre-defined functional guilds: Biocontrol \u0026amp; PGPF (Plant Growth-Promoting Fungi), Mycorrhizal, and Nutrient Cycling. Charts are organized by management system, with conventional (top row) and organic (bottom row). Percentages indicate the proportion of each genus relative to the total number of sequences classified within that specific functional guild for each management system.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/721224d078d5e9500d712aac.jpeg"},{"id":100371027,"identity":"7f092f71-83a9-48ac-af70-c898f4f2c6d1","added_by":"auto","created_at":"2026-01-16 08:09:13","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":447508,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of key bacterial taxa associated with bioremediation and decomposition. Bar plots show the relative abundance (%) in the rhizosphere of all five scion/management groups. Error bars represent the standard error (n=3). The Conventional_X6398 group, which is on a confounding (G210) rootstock, is included for visual comparison but was excluded from all statistical tests. All p-values (Farming, Scion, Interaction) and significance letters are derived from a two-way ANOVA (2x2 factorial) performed only on the four balanced (MM.111 rootstock) treatment groups. Different letters indicate significant differences (Fisher's LSD, p \u0026lt; 0.05) among these four groups. A missing interaction p-value indicates the test returned NA because the taxon was absent (zero variance) from all samples in one of the main factors.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/3e34592e0d603346c6354b5a.jpeg"},{"id":100229465,"identity":"a1c4aa7b-fb82-460c-a132-6188e6531c78","added_by":"auto","created_at":"2026-01-14 11:13:24","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":287632,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of key fungal taxa associated with bioremediation and decomposition.\u003cstrong\u003e \u003c/strong\u003eBar plots show the mean relative abundance (%) in the rhizosphere of all five scion/management groups. Error bars represent the standard error (n=3). The Conventional_X6398 group, which is on a confounding (G210) rootstock, is included for visual comparison but was excluded from all statistical tests. All p-values (Farming, Scion, Interaction) and significance letters are derived from a two-way ANOVA (2x2 factorial) performed only on the four balanced (MM.111 rootstock) treatment groups. Different letters indicate significant differences (Fisher's LSD, p \u0026lt; 0.05) among these four groups. A missing interaction p-value indicates the test returned NA because the taxon was absent (zero variance) from all samples in one of the main factors.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/107f960fa8ef504a2dbd653d.jpeg"},{"id":100370967,"identity":"4474deaf-d146-4fd0-b917-bfc6e5649cc7","added_by":"auto","created_at":"2026-01-16 08:09:05","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":438687,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial co-occurrence networks for bacterial and fungal communities under organic and conventional management. Networks were constructed at the genus level, separately for each management system and kingdom. The analysis was run on the 12-sample subset, excluding X6398. The analysis was filtered to include only genera belonging to phyla that were identified as statistically significant within each management system. Each node represents a genus, colored by its respective phylum. Edges represent significant Spearman correlations (p \u0026lt; 0.05, r \u0026gt; |0.6|) based on CLR-transformed abundances, with green lines indicating positive correlations and red lines indicating negative correlations. The total number of nodes and edges is displayed for each of the four networks, illustrating the divergent complexity.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/d8cd33cf100abab2f6327593.jpeg"},{"id":100229479,"identity":"0b51e450-8e9d-417c-be8e-b6a47b8715f4","added_by":"auto","created_at":"2026-01-14 11:13:25","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":341248,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential abundance analysis of bacterial and fungal taxa. The left column (A-C) displays bacterial taxa, and the right column (D-F) displays fungal taxa. The rows of volcano plots show statistical comparisons: the main effect of Farming System (A, D), the main effect of Scion (Liberty vs. Enterprise) (B, E), and the Farming × Scion interaction (C, F). The x-axis represents the log2 fold change between groups, and the y-axis represents the -log10 adjusted p-value (padj). Taxa with a padj \u0026lt; 0.05 and a log2 fold change \u0026gt; |1.0| are highlighted in color and labeled; grey points represent non-significant taxa.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/3ef7018218f5429949fc6e44.jpeg"},{"id":107928047,"identity":"deaf439d-67c7-4429-a8e1-75e29579400c","added_by":"auto","created_at":"2026-04-27 16:06:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3367324,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/c844fdb4-a2cc-4b5b-bc82-41ba39ee944b.pdf"},{"id":100371565,"identity":"f071d6ea-f530-4de6-b75e-0d888961abba","added_by":"auto","created_at":"2026-01-16 08:10:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":32679,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryReference.docx","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/7cd6eb34a2f67a7eef824e1f.docx"},{"id":100229486,"identity":"bc2ccd2c-a62a-488c-99e7-fa51062a2a3d","added_by":"auto","created_at":"2026-01-14 11:13:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":58114280,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-8562047/v1/49dc1cc9aee89eb75438c98e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Functional re-tooling of rhizosphere guilds is driven by agricultural management and scion genotype in apple","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eModern agriculture, while striving to meet increasing global demands, has exerted profound ecological stress that threatens the very foundation of terrestrial ecosystems and long-term food security (Delgado-Baquerizo \u003cem\u003eet al.\u003c/em\u003e 2023). At the center of this threatened foundation is the soil, a dynamic ecosystem whose microbial diversity sustains terrestrial life (Banerjee \u003cem\u003eet al.\u003c/em\u003e 2022). The soil microbiome, encompassing bacteria, fungi, and other microorganisms, performs indispensable services such as nutrient cycling, soil aggregation, organic matter decomposition, and plant health promotion (Singh \u003cem\u003eet al.\u003c/em\u003e 2024). Despite the essential role of these intricate microbial networks in agroecosystem functioning, they are highly sensitive to agricultural management, which can profoundly reshape these vital communities.\u003c/p\u003e \u003cp\u003eThis sensitivity to agricultural management is particularly evident in the contrast between conventional and organic farming, as their fundamentally divergent philosophies create distinct microbial environments (Reganold and Wachter, 2016). For example, organic farming provides slow-release nutrients and diverse carbon substrates that fuel microbial activity and support greater taxonomic diversity (Lupatini \u003cem\u003eet al.\u003c/em\u003e 2017). In contrast, synthetic inputs in conventional farming systems can exert toxic effects or select for less diverse, opportunistic communities (Hartmann et al. 2015). Consequently, analyses consistently show that organic farming fosters higher microbial biomass, enhanced enzymatic activity, and altered community composition compared to conventional systems, with differences becoming more pronounced under long-term management (Postma-Blaauw \u003cem\u003eet al.\u003c/em\u003e 2010; M\u0026auml;der \u003cem\u003eet al.\u003c/em\u003e 2011).\u003c/p\u003e \u003cp\u003eThese cumulative, long-term impacts are most evident and best-studied in perennial agroecosystems like apple orchards, where the soil remains relatively undisturbed for decades (Manici \u003cem\u003eet al.\u003c/em\u003e 2013). Within these systems, the rhizosphere is a critical hotspot of biological activity fueled by carbon-rich root exudates (Haichar \u003cem\u003eet al.\u003c/em\u003e 2014), the composition of which is fundamentally shaped by the plant genotype (both rootstock and scion) to influence microbial assembly (Berendsen \u003cem\u003eet al.\u003c/em\u003e 2012; Doornbos \u003cem\u003eet al.\u003c/em\u003e 2012; Liu \u003cem\u003eet al.\u003c/em\u003e 2018; Van Horn \u003cem\u003eet al.\u003c/em\u003e 2021; Chai et al., 2022). Thus, the rhizosphere microbiome in a perennial orchard is co-shaped by two powerful forces: the top-down soil conditions set by long-term management and the bottom-up selective pressures from plant genotype. While recent studies have shown that apple scion genotypes can influence rhizosphere bacteria (Bay \u003cem\u003eet al.\u003c/em\u003e 2021; Chai \u003cem\u003eet al.\u003c/em\u003e 2022), and that management alters fruit-associated microbes (Lian \u003cem\u003eet al.\u003c/em\u003e 2024), the interactive effects of these factors on rhizosphere community assembly remain critically underexplored. Previous investigations have focused on endophytic microbiota or rootstock variation (Araujo, 2022), while management comparisons have typically assessed bulk soil or phyllosphere communities rather than the rhizosphere interface where active selection occurs (Ling \u003cem\u003eet al.\u003c/em\u003e 2022; Suman \u003cem\u003eet al.\u003c/em\u003e 2022). Although studies in annual crops reveal that genotype effects can be contingent on management (Ling \u003cem\u003eet al.\u003c/em\u003e 2022; Pandey and Saharan, 2025), whether similar Management \u0026times; Scion interactions structure rhizosphere microbiomes in established perennial apple orchards\u0026mdash;where decades-long management legacies and grafted architecture create fundamentally different ecological contexts\u0026mdash;has not been systematically evaluated.\u003c/p\u003e \u003cp\u003eThis knowledge gap is significant in apple production, as the grafted nature of apple trees presents a unique opportunity: by holding the rootstock constant while varying the scion, we can isolate above-ground genetic influences on below-ground assembly. Understanding this specific interaction has direct implications for combating critical disorders such as the apple replant disease (ARD) (Yurgel \u003cem\u003eet al.\u003c/em\u003e 2025) and for developing microbiome-assisted breeding strategies that perform consistently across farming systems. Therefore, using high-throughput amplicon sequencing of the 16S rRNA gene and ITS region, we sought to: (1) determine which factor\u0026mdash;management or scion\u0026mdash;serves as the primary driver of microbial diversity; (2) identify significant Management \u0026times; Scion interactions that indicate context-dependent microbiome assembly; (3) characterize functional guilds and biocontrol taxa whose abundances shift between systems; and (4) elucidate network architectural differences that reveal contrasting strategies of microbial community organization under chemical versus organic management regimes. By integrating taxonomic, functional, and network-level analyses across both kingdoms, this study provides mechanistic insight into the hierarchical filtering processes governing rhizosphere assembly in perennial agroecosystems.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Site and Experimental Design\u003c/h2\u003e \u003cp\u003eThe study was conducted at paired orchard sites in Winchester, Virginia, USA: a certified organic site (39.115780, -78.284624) and a conventional site (39.146230, -78.274400). The sites were selected for their close proximity, shared soil type, and long-term, distinct management histories. The experiment was established as a 2x2 factorial design comparing two primary factors: (1) Management System: Organic vs. Conventional, (2) Scion Genotype: Liberty vs. Enterprise apple cultivars. This primary design was augmented with a third scion genotype, X6398 (an accession sourced from Adams County Nursery Inc., PA, U.S.A), which was present only within the conventional management system. The Liberty and Enterprise scions were grafted onto a standardized MM.111 rootstock. However, the X6398 scion was grafted onto a different rootstock (G210), representing a critical confounding variable in our design. All trees were six years old and were selected for uniform vigor and health status. For each of the five treatment groups, three individual trees were selected as biological replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Rhizosphere sample collection\u003c/h2\u003e \u003cp\u003eRhizosphere samples were collected during the 2025 growing season at the fruit set stage (fruit size is around 20-25mm). For each replicate tree, sampling was conducted at three equidistant positions around the drip line (approx. 1.5 m from the trunk). At each position, soil was excavated to a depth of ~\u0026thinsp;10 cm to access the fine feeder root zone.\u003c/p\u003e \u003cp\u003eTo isolate the rhizosphere, feeder roots were excised and shaken vigorously by hand to dislodge all loosely adhering bulk soil. The adhered soil was collected by scraping with a sterile scalpel. The three rhizosphere subsamples from each tree were pooled and homogenized in a sterile collection bag, creating a single composite sample for each biological replicate. All samples were immediately placed on dry ice in the field, transported to the laboratory, and stored at -80\u0026deg;C pending DNA extraction.\u003c/p\u003e \u003cp\u003eTo prevent cross-contamination between biological replicates, all sampling tools (shovels, scalpels, shears) were rigorously sterilized between each tree. The protocol involved washing with deionized water, sterilizing with 5% sodium hypochlorite (1 min), rinsing three times with sterile deionized water, submerging in 75% ethanol (30 sec), and finally rinsing three more times with sterile deionized water.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Soil DNA extraction, library preparation, and sequencing\u003c/h2\u003e \u003cp\u003eTotal genomic DNA was extracted from 0.25 g of each homogenized rhizosphere sample using the Quick-DNA Soil Microbe Kit (Zymo Research, CA, USA) according to the manufacturer's instructions. The concentration and purity of the extracted DNA were assessed using a Synergy H1 microplate reader (BioTek, Oakville, ON, Canada). Following quantification, library preparation was initiated. For bacterial community analysis, the V3-V4 hypervariable region of the 16S rRNA gene was amplified using the 341F (5\u0026acute;-CCTAYGGGRBGCASCAG-3\u0026acute;) and 806R (5\u0026acute;-GGACTACNNGGGTATCTAAT-3\u0026acute;) primer pair. For fungal community analysis, the full Internal Transcribed Spacer (ITS) region was amplified using the ITS2 (F-5\u0026acute;-GCATCGATGAAGAACGCAGC-3\u0026acute; and R-5\u0026acute;-TCCTCCGCTTATTGATATGC-3\u0026acute;) primer pair. The resulting amplicons subsequently underwent end-repair, A-tailing, and ligation of Illumina sequencing adapters as part of the library preparation protocol. All library preparation and sequencing were performed by the Novogene company (Sacramento, CA, USA). The final, pooled libraries were sequenced on an Illumina NovaSeq 6000 platform using standard company procedures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data processing and statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses and data visualizations were performed using the R programming language (v4.3.0). To ensure transparency and reproducibility, the specific R packages and computational workflows utilized for each analysis are detailed in the corresponding subsections below\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Sequence processing and taxonomy assignment\u003c/h2\u003e \u003cp\u003eRaw bacterial (16S rRNA) and fungal (ITS2) amplicon reads were processed separately using the DADA2 package (v1.28.0) (Callahan \u003cem\u003eet al.\u003c/em\u003e 2016). Prior to DADA2, primer sequences were removed from all raw reads using Cutadapt (Martin, 2011). Within DADA2, reads were filtered and trimmed based on their quality profiles. Amplicon Sequence Variants (ASVs) were inferred using the DADA2 core algorithm, followed by the merging of paired-end reads and the removal of chimeras. Taxonomy was assigned to bacterial 16S ASVs using the DADA2 assign taxonomy function against the SILVA database (v138.1). Taxonomy for fungal ITS ASVs was assigned against the UNITE database (v9.0) (Abarenkov \u003cem\u003eet al.\u003c/em\u003e 2024). The resulting ASV and taxonomy tables were combined with sample metadata to create phyloseq objects (McMurdie \u0026amp; Holmes, 2013). Prior to diversity and compositional analyses, the ASV tables were filtered to remove rare taxa. Rare ASVs were defined as those with a relative abundance below 0.01% and those present in fewer than three samples. Moreover, to confirm sequencing depth, rarefaction curves were generated using the rarecurve function in the vegan package (Oksanen \u003cem\u003eet al.\u003c/em\u003e 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Alpha and Beta diversity analyses\u003c/h2\u003e \u003cp\u003eAlpha diversity was assessed on the filtered ASV data, and Observed Richness (total ASV counts) and the Shannon index were calculated using the estimate richness function in the phyloseq package (McMurdie and Holmes, 2013). Statistical differences in alpha diversity across all five treatment groups were assessed using a one-way ANOVA with the aov function, followed by Fisher's LSD test for post-hoc comparisons.\u003c/p\u003e \u003cp\u003eFor all primary hypothesis testing of the Management effect (Organic vs. Conventional), a clean, balanced 12-sample subset was used. This subset excluded the confounded X6398 group and contained only the Liberty and Enterprise scions.\u003c/p\u003e \u003cp\u003eFor beta diversity, community ordination was visualized using PCoA on all 15 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to show the outlier X6398 cluster. However, the statistical test (PERMANOVA) was performed \u003cem\u003eonly\u003c/em\u003e on the clean 12-sample subset. This clean 12-sample subset was subsequently used for all main management comparisons, including LEfSe (Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e2.4.3\u003c/span\u003e), functional guild statistics (Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e2.4.5\u003c/span\u003e), and network construction (Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e2.4.6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Community composition visualization\u003c/h2\u003e \u003cp\u003eThe distribution of shared and unique genera (core microbiome) was visualized using an UpSet plot generated with the UpSetR package (Conway \u003cem\u003eet al.\u003c/em\u003e 2017). The relative abundances of the top 20 most dominant genera were visualized using stacked bar charts created with the ggplot2 package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Biomarker and differential abundance analyses\u003c/h2\u003e \u003cp\u003eTo identify statistically characteristic biomarkers for the two management systems, a Linear Discriminant Analysis (LDA) Effect Size (LEfSe) was performed (Segata \u003cem\u003eet al.\u003c/em\u003e 2011). To conduct the detailed differential abundance analysis of scion and management effects, we used DESeq2 (Love \u003cem\u003eet al.\u003c/em\u003e 2014). Given the unbalanced experimental design, the analysis was split. First, a 2x2 factorial analysis was performed on the balanced data subset using the design formula\u0026thinsp;~\u0026thinsp;Farming System\u0026thinsp;+\u0026thinsp;Scion Type\u0026thinsp;+\u0026thinsp;Farming System: Scion Type. Second, a separate analysis was conducted within the conventional system to compare X6398 against Enterprise and Liberty. For all tests, taxa with an adjusted p-value (padj)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5. Functional profile and guild analyses\u003c/h2\u003e \u003cp\u003eWe performed two distinct types of functional analysis: a broad, community-wide prediction and a narrow, targeted guild analysis. First, for the broad prediction, the functional potential of bacterial communities was predicted from the 16S ASV table using FAPROTAX (Louca \u003cem\u003eet al.\u003c/em\u003e 2016), and fungal ecological guilds were assigned using FUNGuild (Nguyen \u003cem\u003eet al.\u003c/em\u003e 2016). The resulting functional profiles were visualized using heatmaps and PCoA on a Bray-Curtis dissimilarity matrix. Second, separately from the broad predictions, a custom targeted functional guild analysis was performed to analyze the composition of specific, literature-defined guilds. We performed a literature review to manually curate lists of bacterial genera known for (i) Biocontrol and Plant Growth Promotion (PGP), (ii) Nitrogen Fixation, and (iii) Phosphate Solubilization, as well as fungal genera known for (iv) Biocontrol \u0026amp; PGP, (v) Mycorrhizal associations, and (vi) Nutrient Cycling (Supplementary reference). The relative abundance of all genera from our dataset belonging to these manually curated lists was extracted. The proportional composition of each genus within its respective guild was then calculated for comparison. For the species-level bioremediation taxa analysis, we also used a literature review to identify specific species known for these functions (Dar \u003cem\u003eet al.\u003c/em\u003e 2019; Behera \u003cem\u003eet al.\u003c/em\u003e 2020; Guerrero Ram\u0026iacute;rez \u003cem\u003eet al.\u003c/em\u003e 2023; Mahalle \u003cem\u003eet al.\u003c/em\u003e 2025).\u003c/p\u003e \u003cp\u003eTo focus on beneficial or saprophytic members, all known plant pathogenic species within these groups were computationally identified and removed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.6. Microbial co-occurrence network analysis\u003c/h2\u003e \u003cp\u003eTo explore microbial interrelationships, networks were constructed at the genus-level, separately for each management system. The analysis was filtered to include only taxa belonging to phyla identified as statistically significant. The genus-level abundance table was transformed using the Centered Log-Ratio (CLR) method. A Spearman correlation matrix was calculated from the CLR-transformed data, and the correlation matrix was filtered to retain strong, significant correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, r\u0026thinsp;\u0026gt;\u0026thinsp;0.7). The resulting edge and node lists were used to construct network graphs using the igraph and ggraph packages.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1. High-throughput sequencing yields robust bacterial and fungal datasets\u003c/h2\u003e \u003cp\u003eTo characterize the apple rhizosphere microbiome, we performed high-throughput sequencing of the 16S rRNA gene and the ITS region. Initial processing yielded 1,511,712 raw bacterial and 1,545,796 raw fungal reads across all samples. Following a quality control pipeline that included filtering, denoising, and chimera removal, a total of 1,368,352 high-quality bacterial 16S and 1,420,918 high-quality fungal ITS sequences were retained for analysis. These high-quality sequences were resolved into 14,337 unique bacterial and 9,782 unique fungal ASVs. A preliminary assessment revealed that a greater number of these total ASVs were associated with samples from the conventional system (10,919 bacterial and 5,937 fungal ASVs) compared to the organic system (3,418 bacterial and 3,845 fungal ASVs).\u003c/p\u003e \u003cp\u003eTo confirm that our sequencing effort adequately captured microbial diversity, we generated rarefaction curves for all samples. The curves for both bacterial (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA) and fungal (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB) communities approached a clear asymptote, indicating that the sequencing depth was sufficient to sample the richness within the communities comprehensively. This confirmed the dataset's robustness for subsequent ecological analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Organic management is associated with higher overall microbial richness\u003c/h2\u003e \u003cp\u003eTo focus our analysis on the most relevant and consistently detected taxa, the ASV datasets were filtered once more to remove rare (\u0026lt;\u0026thinsp;0.01%) variants, resulting in a final core dataset of 3,014 bacterial and 2,700 fungal ASVs for all diversity assessments. We first evaluated gamma diversity (pooled richness) by comparing the main management systems (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). To avoid the confounding effect of the X6398 scion's G210 rootstock, we performed a clean, 6-vs-6 comparison (pooling Org-Liberty/Org-Enterprise vs. Con-Liberty/Con-Enterprise). This valid comparison revealed that the organic system harbored a greater overall richness for both bacteria (1,392 ASVs) and fungi (1,182 ASVs) to the conventional system (1,052 bacterial ASVs and 990 fungal ASVs).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen richness was examined at the individual treatment level, a notable exception to this trend emerged. The organic Liberty scion treatment harbored the highest observed ASV richness for both bacteria (841 ASVs) and fungi (615 ASVs) among all groups (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). In contrast, the richness within the remaining treatment groups was comparable, ranging from 517\u0026ndash;570 ASVs for bacteria and 479\u0026ndash;567 ASVs for fungi (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Organic Liberty scion fosters higher bacterial alpha-diversity\u003c/h2\u003e \u003cp\u003eBeyond assessing simple ASV counts, we evaluated local-scale alpha-diversity using the Shannon Diversity Index. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays all five treatment groups for exploratory comparison, including the distinct X6398 group (G210 rootstock), alongside results from a 5-group one-way ANOVA. However, to rigorously test our main hypotheses, the primary statistical analysis (Two-way ANOVA) was performed on the balanced 12-sample subset (2x2 design), and the reported \u003cem\u003ep\u003c/em\u003e-values for main effects are derived from this model. To compare the X6398 group against the other treatments, Fisher's Least Significant Difference (LSD) test was incorporated based on the one-way ANOVA.\u003c/p\u003e \u003cp\u003eThis analysis reinforced our previous findings for the bacterial communities, revealing a significant difference among the treatment groups (ANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). Specifically, the organic Liberty treatment\u0026mdash;which we had already identified as having the highest ASV richness (Fig \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u0026mdash;also exhibited a significantly higher bacterial alpha-diversity than the conventional Enterprise treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No other significant pairwise differences were detected for bacterial diversity.\u003c/p\u003e \u003cp\u003eInterestingly, the fungal communities displayed a distinct pattern (ANOVA, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The significant effect was primarily driven by the Conventional Liberty treatment, which was found to harbor a significantly lower fungal alpha-diversity compared to all other treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Farming system drives a major shift in microbial community composition\u003c/h2\u003e \u003cp\u003eHaving established that management and scion can influence the level of microbial diversity, we next investigated whether they drive shifts in the overall community composition (beta-diversity). To visualize compositional differences, we performed Principal Coordinates Analysis (PCoA) on Bray-Curtis dissimilarity matrices and tested for statistical significance using a Permutational Multivariate Analysis of Variance (PERMANOVA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, to generate a statistically valid test of the management effect, a PERMANOVA was performed only on the clean 12-sample subset (excluding the confounded X6398 group).\u003c/p\u003e \u003cp\u003eFor the bacterial community, the combined model (management and scion) was statistically significant and explained around 40% of the total variation (R\u0026sup2; = 0.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In spite of insignificant model, the PCoA plot revealed almost a separation of samples based on management system, along the PCoA1 axis (9.8% of variance, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Subsequently, to investigate the influence of scion and rootstock combinations, we plotted the PCoA associated with the conventional management system separately (Figure S3A). This exploratory analysis revealed a surprising finding: the Enterprise scion on rootstock MM111 separated distinctly from the other two combinations along the PCoA2 axis (10.5% of the variance).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe effect of the management system was even more pronounced for the fungal community. The overall model was significant and explained 43% of the total variation (R\u0026sup2; = 0.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The strong separation was clear in the PCoA plot, where fungal communities from the organic and conventional systems formed two highly distinct and non-overlapping clusters along the PCoA1 axis (12.6% variance, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The PCoA analysis for the conventional farming subset (Fig. S3B) demonstrated clear separation among the different scions sharing the MM.111 rootstock. Furthermore, the unique X6398 combination (on G.20 rootstock) also clustered distinctly from the MM.111-based samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Core microbiome analysis reveals taxa unique to management systems\u003c/h2\u003e \u003cp\u003eThe significant compositional shifts observed in our beta-diversity analysis (Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e) prompted an investigation into their taxonomic drivers. We sought to determine whether these community-level differences were due to the presence of unique genera or to random shifts within a common pool of taxa. To differentiate these shared and exclusive components, we visualized the distribution of all observed bacterial and fungal genera using an UpSet plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eAcross all five treatment combinations, we identified a stable core microbiome consisting of 41 bacterial and 61 fungal genera, representing taxa present in all groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The primary source of variation, however, was the distribution of treatment-specific genera. This was particularly evident in the conventional X6398 group, which harbored the highest number of unique taxa (30 bacterial and 25 fungal genera not found in any other group, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In sharp contrast, the organic Enterprise treatment possessed the fewest unique genera (14 bacterial, 15 fungal, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Moreover, we identified a distinct set of 9 bacterial and 19 fungal genera shared exclusively by the two organic treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Conversely, 4 bacterial and 5 fungal genera were shared by the corresponding two conventional treatments (Liberty and Enterprise, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Dominant genera reveal management-specific patterns and high intra-group variability\u003c/h2\u003e \u003cp\u003eWhile the UpSet plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) identified genera based on their presence or absence across treatments, we next quantified the relative contribution of the most dominant taxa to the community structure. To achieve this, we visualized the relative abundances of the top 20 most abundant bacterial (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and fungal (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) genera.\u003c/p\u003e \u003cp\u003eThe bacterial communities featured several highly abundant genera, such as \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eMethylothermalis\u003c/em\u003e, and \u003cem\u003eMycolicibacterium\u003c/em\u003e, that were dominant across all conventional and organic treatments. However, a key observation was the significant taxonomic heterogeneity within treatment replicates. For example, within the conventional Enterprise group, \u003cem\u003eFlavobacterium\u003c/em\u003e was highly abundant in replicate 3 but was not among the top 20 genera in replicates 1 and 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Similarly, \u003cem\u003eStreptomyces\u003c/em\u003e was absent from the top 20 in conventional Enterprise replicate 2 while being present in the other two replicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eStark, management-driven patterns were evident in the fungal communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). \u003cem\u003eXenodidymella\u003c/em\u003e, for instance, was highly abundant across all organic samples but present at only very low levels in conventional samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Similarly, \u003cem\u003eAspergillus\u003c/em\u003e was found to be more abundant in the organic samples while remaining a minor component in the conventional ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Despite these clear management-level trends, the fungal communities also mirrored the bacteria in exhibiting high intra-group variability. Within the conventional Liberty treatment, for instance, replicate 2 was characterized by a high abundance of \u003cem\u003eNeocosmospora\u003c/em\u003e while replicate 3 was dominated by \u003cem\u003eTalaromyces\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Notably, neither of these genera was ranked among the top 20 most abundant in the other conventional Liberty replicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7. LEfSe analysis identifies statistically distinct biomarker taxa for each management system\u003c/h2\u003e \u003cp\u003eAfter visually identifying dominant genera across all 15 samples (Section \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003e3.6\u003c/span\u003e), we next statistically pinpointed the biomarkers characteristic of each management system. To do this, we performed a Linear Discriminant Analysis (LDA) Effect Size (LEfSe) analysis (Fig. S4) using the clean, 12-sample subset (excluding the confounded X6398 group). This provided valid statistical validation for management-driven patterns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the bacterial communities, the analysis revealed a strong asymmetric distribution of biomarkers (Fig. S4A). The organic system was significantly enriched by a vast array of taxa, including numerous genera from the phylum Actinobacteria (e.g., \u003cem\u003eNakamurella\u003c/em\u003e, \u003cem\u003eCellulomonas\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, \u003cem\u003eRhizocola\u003c/em\u003e) and \u003cem\u003eMycolicibacterium\u003c/em\u003e, which we had also identified as a dominant genus in Section \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003e3.6\u003c/span\u003e. In stark contrast, only three taxa\u0026mdash;\u003cem\u003eStreptosporangium\u003c/em\u003e, \u003cem\u003ePhyllobacterium\u003c/em\u003e, and \u003cem\u003ePhyllobacteriaceae\u003c/em\u003e\u0026mdash;were identified as significant biomarkers for the conventional system (Fig. S4A). Notably, other dominant genera like \u003cem\u003eBradyrhizobium\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e were not identified as biomarkers, suggesting they represent a stable core community rather than a differentially abundant one.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the fungal communities, LEfSe confirmed the management-driven patterns observed in the Top 20 genera plot (Fig.\u0026nbsp;4SB). The organic system was enriched with a wide array of fungal taxa, including \u003cem\u003eXenodidymella\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e, \u003cem\u003eCladosporium\u003c/em\u003e, and \u003cem\u003eBeauveria\u003c/em\u003e (Fig.\u0026nbsp;4SB). Conversely, the conventional system was characterized by a large, yet entirely different, group of fungal biomarkers, including \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003eMetarhizium\u003c/em\u003e, \u003cem\u003eAureobasidium\u003c/em\u003e, \u003cem\u003eNigrospora\u003c/em\u003e, and \u003cem\u003eCosmospora\u003c/em\u003e (Fig.\u0026nbsp;4SB). These results show that the visual differences in dominant genera (Fig. S4) are characteristic of each management system, consistent with the significant community-level shifts established by our PERMANOVA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Predicted functional profiles differ by management system but show high scion-specific variation\u003c/h2\u003e \u003cp\u003eBuilding on the identification of taxonomic biomarkers, our analysis then focused on the functional implications of these community shifts. We first predicted the broad functional potential of the bacterial communities using FAPROTAX and visualized the overall landscape with a clustered heatmap based on z-scores (Fig. S5). The hierarchical clustering of samples (Top dendrogram, Fig. S5) immediately revealed that, unlike the distinct taxonomic separation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B), the predicted functional profiles did not form clear, top-level clusters based on management system alone. Instead, the clustering highlighted strong scion- and replicate-specific patterns, with samples from different management systems interspersing (Fig. S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis lack of strong management-level separation was confirmed by a Principal Coordinates Analysis (PCoA) of the functional profiles (Fig.\u0026nbsp;6SA). While PCoA1 and PCoA2 explained a combined 90% of the variance, the organic and conventional groups were not clearly delineated, and some scions (notably organic Liberty) mixed and clustered with the other samples (Fig.\u0026nbsp;6SA).\u003c/p\u003e \u003cp\u003eDespite this high visual variability in the overall profile, we performed statistical tests on the clean 12-sample (6-vs-6) subset to determine if specific key functions, within FAPROTAX results, were consistently different between the two management systems (Fig. S7). This analysis revealed that functions related to organic matter decomposition, specifically aromatic compound degradation (p\u0026thinsp;=\u0026thinsp;0.018) and aromatic hydrocarbon degradation (p\u0026thinsp;=\u0026thinsp;0.01), were significantly higher in the organic system. Fermentation was also predicted to be significantly more abundant in the organic system (p\u0026thinsp;=\u0026thinsp;0.008). In contrast, no significant differences were observed for other important functions, including plant pathogens (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.917), Ureolysis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.414), or nitrogen fixation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.145). Given that these three decomposition-related functions were significantly elevated in the organic system, we performed a follow-up analysis to test for scion-level differences within each management system (Fig. S8). Within the conventional system, no significant differences were observed among the scions for any of the three functions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, within the organic system, a significant effect of scion type was detected for aromatic hydrocarbon degradation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), which was significantly higher in Liberty than in Enterprise (Fig. S7).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile FAPROTAX provided broad functional predictions, we also performed a separate, literature taxonomic-based guild analysis, calculated from the clean 12-sample subset, to examine the composition of three key beneficial groups: Biocontrol \u0026amp; Plant Growth-Promoting Rhizobacteria (PGPR), Nitrogen Fixers, and Phosphate Solubilizers. This allowed us to compare the relative abundance of known genera within these specific functional groups between the two management systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Within the Biocontrol \u0026amp; PGPR guild, \u003cem\u003eStreptomyces\u003c/em\u003e was the dominant genus in both systems, accounting for 90.6% of the guild's composition in conventional and 99.6% in organic samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, the conventional system's guild also retained a notable proportion of \u003cem\u003ePaenibacillus\u003c/em\u003e (4.9%) and \u003cem\u003eBacillus\u003c/em\u003e (4.4%), which were functionally replaced in the organic system (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The composition of the Nitrogen Fixers guild was highly similar between systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Both were dominated by \u003cem\u003eBradyrhizobium\u003c/em\u003e (60.9% conventional, 60.1% organic), \u003cem\u003eMesorhizobium\u003c/em\u003e (25.3% and 19.3%), and \u003cem\u003eRhizobium\u003c/em\u003e (11.0% and 12.5%, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A stark contrast was observed in the Phosphate Solubilizer guild (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003e). While the conventional system's guild was dominated by \u003cem\u003eFlavobacterium\u003c/em\u003e (70.6%), it also included a substantial proportion of \u003cem\u003eBacillus\u003c/em\u003e (26.6%). In the organic system, this guild was composed entirely (100%) of \u003cem\u003eFlavobacterium\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Fungal ecological guilds, unlike bacterial functions, show a strong separation by management system\u003c/h2\u003e \u003cp\u003eIn parallel with the bacterial functional analysis, we next characterized the ecological functions of the fungal communities. We first assigned fungal taxa to ecological guilds using the FUNGuild database and visualized the overall functional landscape with a clustered heatmap based on z-scores (Fig. S9). Similar to the bacterial functional heatmap, the hierarchical clustering of samples did not reveal a perfect top-level separation by management system, instead highlighting strong sample-specific patterns. However, when this functional profile was visualized using PCoA, a clear and significant separation driven by management did emerge (Fig. S6B). Samples from the conventional system clustered tightly along the primary axis (PCoA1, 23.0%), while the organic samples formed a separate group (Fig. S5B). Similar to the fungal taxonomic profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), scion type appeared to have no clear clustering effect, with scions being intermixed within their respective management group (Fig. S5B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven this strong management separation in the PCoA, we statistically compared the relative abundances of four major ecological guilds using the valid 12-sample subset (Fig. S10). This analysis identified the specific drivers for the separation: a statistically significant difference was observed for endophytic fungi, which were significantly more abundant in the organic system (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, Fig. S10). A similar, though non-significant, trend was observed for plant pathogenic fungi, which tended to be more abundant in the organic system (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.066, Fig. S10). No significant differences were detected for fungal parasites (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.94, Fig. S10) or decomposer saprotrophs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17, Fig. S10). A follow-up analysis tested for scion-level effects on these guilds within each management system (Fig. S11), revealing no statistically significant differences in the abundances of endophytes, plant pathogens, fungal parasites, or decomposer saprotrophs among scions in either the conventional or the organic system (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig. S11). This suggests the management system, rather than scion, is the primary factor influencing these broad fungal ecological roles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBeyond the broad ecological assignments from FUNGuild, we performed a more targeted compositional analysis on the 12-sample subset for three key beneficial fungal guilds: Biocontrol \u0026amp; PGPF, Mycorrhizal, and Nutrient Cycling (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e5\u003c/span\u003e)-which were selected a priori based on their essential roles in apple tree health and orchard productivity. This analysis revealed distinct compositional profiles driven by management (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Within the Biocontrol \u0026amp; PGPF guild, a clear shift was observed. The conventional system was dominated by \u003cem\u003eMetarhizium\u003c/em\u003e (53.6%), followed by \u003cem\u003eTrichoderma\u003c/em\u003e (26.2%). In contrast, the organic system was dominated by \u003cem\u003eTrichoderma\u003c/em\u003e (57.1%), while \u003cem\u003eMetarhizium\u003c/em\u003e was reduced to 16.7% (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The Mycorrhizal guild also showed a management-driven substitution. While \u003cem\u003eGlomus\u003c/em\u003e was the most abundant genus in both systems (52.3% conventional, 51.7% organic), the second-most abundant genus in the conventional system was \u003cem\u003eRhizophagus\u003c/em\u003e (33.4%). In the organic system, \u003cem\u003eRhizophagus\u003c/em\u003e was minimal (8.1%), and \u003cem\u003eAcaulospora\u003c/em\u003e was highly abundant (35.9%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Finally, the Nutrient Cycling guild in the conventional system was dominated by \u003cem\u003eTalaromyces\u003c/em\u003e (51.4%) and \u003cem\u003eAspergillus\u003c/em\u003e (32.6%). The organic system displayed a different profile, with a sharp reduction in \u003cem\u003eTalaromyces\u003c/em\u003e (15.9%) and a co-dominance of \u003cem\u003ePenicillium\u003c/em\u003e (41.3%) and \u003cem\u003eAspergillus\u003c/em\u003e (40.0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.10. Management systems select for unique bioremediation taxa\u003c/h2\u003e \u003cp\u003eHaving examined the compositional shifts within shared beneficial guilds, our focus shifted to the bioremediation potential held by specific taxa. Our earlier microbial functional analysis revealed that functions related to organic matter decomposition were significantly elevated in the organic system (Figures S7 and S10). To further investigate this, we performed a deeper analysis correlating these predicted functions with the exclusive taxa (genera and species found in only one management system).\u003c/p\u003e \u003cp\u003eTo test the main and interactive effects of management and scion, we performed a two-way ANOVA (2x2 factorial) on the clean 12-sample subset (excluding the confounded X6398 group). The figures in this section (Figs.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e) visualize all five treatment groups for exploratory comparison, but the reported \u003cem\u003ep\u003c/em\u003e-values (Farming, Scion, Interaction) and any significance letters are derived only from this valid 2x2 ANOVA. The X6398 group is shown for visual context but was excluded from this statistical test due to its confounding G210 rootstock, and therefore does not receive significance letters.\u003c/p\u003e \u003cp\u003eFor the bacterial communities, we focused on FAPROTAX functions related to complex organic compound degradation. We then examined the abundances of specific, non-pathogenic taxa known for these capabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This detailed analysis revealed significant, system-specific enrichments. For example, \u003cem\u003eSphingosinicella cucumeris\u003c/em\u003e was significantly more abundant in the organic system, while \u003cem\u003ePhyllobacterium zundukense\u003c/em\u003e was a significant bioremediation-marker for the conventional system (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Other known degrader genera, such as \u003cem\u003eRhodococcus\u003c/em\u003e spp. and \u003cem\u003eSphingomonas\u003c/em\u003e spp., were present across treatments, though \u003cem\u003eRhodococcus\u003c/em\u003e spp. showed a non-significant trend of higher abundance in the organic system (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe then compared specific, non-pathogenic saprophytic fungal taxa at the species level (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This revealed strong, differential responses to management. \u003cem\u003eMetarhizium robertsii\u003c/em\u003e and \u003cem\u003eAbsidia aquabaelensis\u003c/em\u003e were both significantly higher in the conventional system (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e). \u003cem\u003eMetarhizium\u003c/em\u003e was most abundant in the Liberty scion (Interaction, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e), while \u003cem\u003eAbsidia\u003c/em\u003e was highest in the Enterprise scion (Interaction, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Conversely, several taxa were enriched in the organic system. For instance, \u003cem\u003eMucor hiemalis\u003c/em\u003e (Farming, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e) and \u003cem\u003eAspergillus aureolus\u003c/em\u003e (Farming, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.051, Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e) were significantly (or tended to be) more abundant in organic samples. \u003cem\u003eLecanicillium primulinum\u003c/em\u003e also showed higher abundance in the organic system, particularly in the Enterprise scion (Interaction, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.11. Management system inversely reshapes bacterial and fungal network complexity\u003c/h2\u003e \u003cp\u003eThe previous analyses identified key shifts in community composition, function, and the enrichment of unique taxa. To understand how these community-wide changes affect the interrelationships among microbes, we next constructed co-occurrence networks on the 12-sample subset, excluding X6398 (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The analysis was specifically filtered to visualize interactions only among taxa belonging to phyla that were identified as statistically significant within the organic and conventional systems. The bacterial phyla included in this analysis were \u003cem\u003eActinobacteriota\u003c/em\u003e, \u003cem\u003eBacillota\u003c/em\u003e, \u003cem\u003eBacteroidota\u003c/em\u003e, \u003cem\u003eChloroflexi\u003c/em\u003e, \u003cem\u003eNitrospirota\u003c/em\u003e, and \u003cem\u003ePseudomonadota\u003c/em\u003e, while the fungal phyla included \u003cem\u003eAscomycota\u003c/em\u003e, \u003cem\u003eBasidiomycota\u003c/em\u003e, and \u003cem\u003eMucoromycota\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFor the bacterial communities, the network of significant phyla in the organic system fostered a larger and more complex set of interactions (Nodes: 68, Edges: 374, Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e8\u003c/span\u003e) compared to the network of significant phyla in the conventional system (Nodes: 57, Edges: 288, Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This suggests a higher degree of interaction among the key bacterial players in the organic soil. A striking, contrasting pattern was observed for the fungal communities. The network of significant fungal taxa in the conventional system was substantially larger and more densely connected (Nodes: 131, Edges: 1248, Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e8\u003c/span\u003e) than the network in the organic system (Nodes: 96, Edges: 594, Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The conventional network featured more than double the number of interactions, indicating a far more complex web of co-occurrence within this key fungal group.\u003c/p\u003e \u003cp\u003eOverall, these filtered network topologies reveal that the management system fundamentally reshapes interaction patterns among the most responsive microbial phyla. While the organic system was associated with a more complex network of its key bacterial taxa, the conventional system was characterized by a dramatically more complex and interconnected network of its key fungal taxa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.12. Differential abundance analysis pinpoints key taxa driven by management and scion\u003c/h2\u003e \u003cp\u003eThe network analysis highlighted broad shifts in interaction complexity among key phyla. However, to identify the specific taxa (the nodes) driving these changes at a finer resolution, and to parse the complex, interacting effects of management and scion, we performed a differential abundance analysis (Figs.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003eS11\u003c/span\u003e). Due to the unbalanced experimental design (with Liberty and Enterprise present in both systems, but X6398 only in the conventional system), the analysis was strategically split into two parts: (1) a 2x2 factorial analysis on the balanced portion of the design, and (2) a separate analysis within the conventional system to compare X6398 against Enterprise and Liberty.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the 2x2 factorial analysis of bacterial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-C), the main effect of Farming System was the strongest driver. \u003cem\u003eAldersonia\u003c/em\u003e and \u003cem\u003eMethylocystis\u003c/em\u003e were significantly enriched in the organic system, while \u003cem\u003ePhyllobacterium\u003c/em\u003e and \u003cem\u003eParactino planes\u003c/em\u003e were enriched in the conventional system (Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). The main effect of scion (Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003eB) showed \u003cem\u003eActinacidiphila\u003c/em\u003e and \u003cem\u003eRhabdothermincola\u003c/em\u003e enriched in Enterprise, whereas \u003cem\u003ePhyllobacterium\u003c/em\u003e and \u003cem\u003eParactinoplanes\u003c/em\u003e were enriched in Liberty. A significant interaction effect was observed for \u003cem\u003eParactinoplanes\u003c/em\u003e, \u003cem\u003eActinacidiphila\u003c/em\u003e, and \u003cem\u003eMethylocystis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003eC), indicating their response to scion depended on the farming system.\u003c/p\u003e \u003cp\u003eFor the fungal communities in the factorial analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003eD-F), the farming system effect was also pronounced. A large group of fungi, including \u003cem\u003eMonilinia\u003c/em\u003e, \u003cem\u003eCucitella\u003c/em\u003e, and \u003cem\u003eTortiopsora\u003c/em\u003e, were significantly enriched in the organic system. Conversely, \u003cem\u003eZygoratorulaspora\u003c/em\u003e, \u003cem\u003eCeratobasidium\u003c/em\u003e, \u003cem\u003eColarella\u003c/em\u003e, \u003cem\u003eNectria\u003c/em\u003e, and \u003cem\u003eFlammocladiella\u003c/em\u003e were characteristic of the conventional system (Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). The main effect of scion revealed \u003cem\u003ePhaeopopca\u003c/em\u003e, \u003cem\u003eSistotrema\u003c/em\u003e, \u003cem\u003eNeptunomyces\u003c/em\u003e, and \u003cem\u003eTiankongomelaia\u003c/em\u003e were enriched in Enterprise, while \u003cem\u003eFlammocladiella\u003c/em\u003e was enriched in Liberty (Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). The interaction effect was strong, with a large number of fungi, including \u003cem\u003eFlammocladiella\u003c/em\u003e, \u003cem\u003ePhragmographa\u003c/em\u003e, and \u003cem\u003eWickerhamomyces\u003c/em\u003e, showing a significant interaction, highlighting that the scion's influence on fungi is highly dependent on management (Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eThe second part of the analysis compared the X6398 scion against Enterprise and Liberty within the conventional system. When comparing X6398 vs. Enterprise bacteria, X6398 was significantly enriched in \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eSolirubrobacter\u003c/em\u003e, \u003cem\u003ePaenibacillus\u003c/em\u003e, \u003cem\u003eStreptibioticus\u003c/em\u003e, \u003cem\u003ePeribacilus\u003c/em\u003e, \u003cem\u003ePovalibacter\u003c/em\u003e, and \u003cem\u003eNiallia\u003c/em\u003e (Fig. S12). For fungi, X6398 was enriched in Cladobotryum, \u003cem\u003eCodinaea, Gongronella, Dictyosporium, Ascospirella\u003c/em\u003e, and \u003cem\u003ePhaeosaria\u003c/em\u003e, while Enterprise was enriched in a large group including \u003cem\u003eWickerhamomyces\u003c/em\u003e, \u003cem\u003ePolyschema\u003c/em\u003e, \u003cem\u003eEmericellopsis\u003c/em\u003e, \u003cem\u003eDendryphion\u003c/em\u003e, \u003cem\u003eGibellulopsis\u003c/em\u003e, \u003cem\u003eEnterocarpus\u003c/em\u003e, \u003cem\u003eNeroroussoella\u003c/em\u003e, and \u003cem\u003eVishniacozyma\u003c/em\u003e (Fig S12). Finally, when comparing X6398 vs. Liberty bacteria, no taxa were found to be differentially abundant. For fungi (Fig. S12), Liberty was enriched in \u003cem\u003eSolicoccozyma\u003c/em\u003e and \u003cem\u003eLinnemannia\u003c/em\u003e, while X6398 was enriched in \u003cem\u003eArachnomyces\u003c/em\u003e and \u003cem\u003ePlectosphaerella\u003c/em\u003e (Fig S12).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study provides critical evidence that long-term agricultural management may not act as a disruptive force, but act as an ecological filter that fundamentally restructures the rules of rhizosphere assembly. While previous studies have established that management shifts community composition (Schmidt \u003cem\u003eet al.\u003c/em\u003e 2019), our data advance this field by demonstrating that these shifts represent a coherent, system-level adaptation rather than simple biotic loss. We posit that organic and conventional systems select for fundamentally opposing life strategies: resource-acquisition specialists in organic versus stress-tolerance specialists in conventional. This finding challenges the simplistic narrative of conventional microbiomes as merely degraded (Ray \u003cem\u003eet al.\u003c/em\u003e 2020), pointing instead to highly adapted, functionally distinct, alternative stable states.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Deterministic filtering drives microbial specialization and functional retooling\u003c/h2\u003e \u003cp\u003eThe stark, non-overlapping separation of fungal communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) stands in contrast to the more variable bacterial response (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). This indicates that fungi are the primary responders to the dominating abiotic pressures of conventional management, aligning with theory suggesting fungi lack the rapid adaptive plasticity of bacteria (Schmidt \u003cem\u003eet al.\u003c/em\u003e 2019). This key difference in filtering dictates how each kingdom responds to host genetics. The bacterial community, being less constrained by the soft filter of management, appears to respond to host (scion) cues more independently (Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003eB and C). Conversely, the fungal community is so deterministically locked by the hard filter of management that the scion's influence becomes entirely context-dependent, resulting in the massive web of Scion \u0026times; Management interactions observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003eF. This double-filter effect, where management dictates the available pool from which the scion recruits, is a core finding of our study.\u003c/p\u003e \u003cp\u003eThis difference between kingdoms extended beyond just taxonomic composition to their functional architecture. For bacteria, both their taxonomic composition and their predicted functional profiles were highly variable, with neither showing a clear separation by management (Fig. S5A). This suggests a high degree of functional redundancy\u0026mdash;the principle where different bacterial species can perform the same ecosystem function. This redundancy allows the community to maintain a stable functional output, even as the specific species (the taxonomy) changes. For fungi, we observed the exact opposite; the taxonomic composition and their predicted functional profiles both showed a strong, clear separation by management (Fig. S5B). This demonstrates a tight coupling between fungal identity and function. The implication is that management-driven shifts in fungal community composition directly translate into changes in fungal functional capacity, positioning fungi as the primary drivers of ecosystem-level functional change in this system.\u003c/p\u003e \u003cp\u003eThis tale of two kingdoms strongly supports a hypothesis of microbial specialization, where each system selects for a community adapted to its specific challenges. The organic system appears to select specialization in resource decomposition, evidenced by the enrichment of bacterial and fungal functions related to degrading complex compounds in each system. For example, organic decomposer specialists like \u003cem\u003eSphingosinicella cucumeris\u003c/em\u003e, and \u003cem\u003eMucor hiemalis\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Conversely, the conventional system appears to select for specialization in abiotic stress tolerance and xenobiotic remediation. The enrichment of \u003cem\u003eMetarhizium robertsii\u003c/em\u003e and \u003cem\u003ePhyllobacterium zundukense\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e7\u003c/span\u003e)\u0026mdash;taxa with known capabilities to degrade pollutants, including organotins, nonylphenols, and other xenobiotics, while exhibiting remarkable stress tolerance through enhanced oxidative stress management systems (R\u0026oacute;żalska \u003cem\u003eet al.\u003c/em\u003e 2014; Siewiera \u003cem\u003eet al.\u003c/em\u003e 2015; Hermans \u003cem\u003eet al.\u003c/em\u003e 2023) \u0026mdash;suggests a community of remediators and survivors adapted to the farm's chemical inputs.\u003c/p\u003e \u003cp\u003eThis pattern of specialization was most evident in the functional retooling of key beneficial guilds, rather than a simple loss of function. In the fungal biocontrol guild (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the conventional system was dominated by \u003cem\u003eMetarhizium\u003c/em\u003e (53.6%), a robust, stress-tolerant entomopathogen with documented capabilities for surviving extreme environmental conditions, including heat, UV radiation, and oxidative stress (Wang \u003cem\u003eet al.\u003c/em\u003e 2017; Wang \u003cem\u003eet al.\u003c/em\u003e 2019; Paix\u0026atilde;o \u003cem\u003eet al.\u003c/em\u003e 2021). In contrast, the organic system was dominated by \u003cem\u003eTrichoderma\u003c/em\u003e (57.1%), a classic biocontrol agent that thrives via mycoparasitism, directly parasitizing other fungal species through the production of cell wall-degrading enzymes and secondary metabolites (Sood \u003cem\u003eet al.\u003c/em\u003e 2020; Guzm\u0026aacute;n-Guzm\u0026aacute;n \u003cem\u003eet al.\u003c/em\u003e 2023; Poveda, 2021) \u0026mdash;a strategy likely more successful in the biotically complex organic environment. This retooling extended to nutrient cycling and even the phosphate-solubilizing guild (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This confirms that distinct chemical and biological environments select for entirely different specialists to fulfill the same crucial ecosystem functions. Understanding the specific compatibility of these key biocontrol agents, such as \u003cem\u003eTrichoderma\u003c/em\u003e and \u003cem\u003eBacillus\u003c/em\u003e, with the unique inputs of each system, from synthetic fungicides to novel biopesticides, is therefore a critical next step. This work is essential not only for predicting their real-world ecological success (Zarrabian \u003cem\u003eet al.\u003c/em\u003e 2025), but also for establishing the refined ecotoxicological frameworks required for these next-generation agricultural technologies (Zarrabian and Sherif, 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Stochastic variability within deterministic frameworks: reconciling replicate heterogeneity\u003c/h2\u003e \u003cp\u003eWhile deterministic processes (niche-based selection) and stochastic processes (ecological drift, dispersal limitation) simultaneously influence community assembly along a continuum (Schmidt \u003cem\u003eet al\u003c/em\u003e. 2019; Araujo \u003cem\u003eet al\u003c/em\u003e. 2022), our results reveal a complex interplay between these mechanisms. Despite strong deterministic management effects on overall community composition (Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e), we observed substantial replicate-to-replicate variation within treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This pattern aligns with theoretical frameworks suggesting that strong deterministic factors can paradoxically intensify stochastic assembly at local scales (Bay \u003cem\u003eet al.\u003c/em\u003e 2021; Lian \u003cem\u003eet al.\u003c/em\u003e 2024). We propose that while management acts as a primary deterministic filter setting the regional species pool (as evidenced by Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), stochastic processes\u0026mdash;including ecological drift and dispersal limitation\u0026mdash;play greater roles in structuring rare taxa and determining local-scale abundance patterns within this filtered pool (Pantigoso \u003cem\u003eet al.\u003c/em\u003e 2022). The influence of deterministic environmental filtering relative to stochastic factors may be maximized at extreme ends of environmental gradients, while stochastic processes become more important at intermediate conditions or shorter temporal scales (Bay \u003cem\u003eet al.\u003c/em\u003e 2021; Navarro-Noya \u003cem\u003eet al.\u003c/em\u003e 2022).\u003c/p\u003e \u003cp\u003eThis framework helps explain the divergence in diversification patterns between our systems. As highlighted by the Shannon diversity index (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the organic system represents a state of significantly higher diversification, where the soft management filter allows a vast array of taxa to coexist and interact stochastically. In contrast, the conventional system acts as a \"hard filter where harsh deterministic selection eliminates sensitive taxa. This is most clearly observed in the fungal community, where the Conventional Liberty treatment exhibits a significant diversity crash (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). While the surviving stress-tolerant taxa in the conventional system may still vary stochastically in local abundance based on microsite heterogeneity or priority effects, their overall diversification is strictly limited by the high-pressure environment. Thus, while both systems exhibit replicate-level variation, it is the organic system that facilitates the highest degree of microbial diversification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.3 The stress-clique vs. functional modules: divergent network architectures\u003c/h2\u003e \u003cp\u003eOur network analysis reveals that management does not just change the level of complexity, it fundamentally reshapes the architecture of microbial interactions. Contrary to prevailing theory that stress simplifies ecological networks and reduces compositional complexity (Landi \u003cem\u003eet al.\u003c/em\u003e 2018), we observed a dramatically more complex fungal network in the high-stress conventional system (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The filtered network analysis of key responding phyla showed that conventional fungi formed a network with 131 nodes and 1,248 edges\u0026mdash;more than double the 594 edges in the organic fungal network (96 nodes) (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e8\u003c/span\u003e). We propose the stress-clique hypothesis to explain this. Extreme abiotic filtering likely eliminates casual species, forcing the few surviving, highly-adapted taxa (such as the \u003cem\u003eAureobasidium\u003c/em\u003e and \u003cem\u003eMetarhizium\u003c/em\u003e identified in our LEfSe analysis, Fig. S4B) into dense, obligate co-dependencies to survive. This represents a rigid complexity\u0026mdash;a tightly interconnected community adapted to extreme chemical stress, where surviving taxa display increased interdependence for mutual support.\u003c/p\u003e \u003cp\u003eIn contrast, the complex organic bacterial network reflects a healthy, modular food web. The organic bacterial network (68 nodes, 374 edges) was larger and more complex than the conventional (57 nodes, 288 edges, Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This is best illustrated by the distinct sub-modules visible in the network structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e8\u003c/span\u003e). For example, a separated cluster containing \u003cem\u003eRhodococcus\u003c/em\u003e, \u003cem\u003eMethylocystis\u003c/em\u003e, and \u003cem\u003eNocardia\u003c/em\u003e likely represents a specialized functional module dedicated to degrading specific complex hydrocarbons\u0026mdash;a role these genera are well-known for (Ivshina et al. 2023; Płaza \u003cem\u003eet al.\u003c/em\u003e 2018; Semrau \u003cem\u003eet al.\u003c/em\u003e 2011; Im and Semrau, 2011; Azadi and Shojaei, 2020; Hesham \u003cem\u003eet al.\u003c/em\u003e 2006; Płaza \u003cem\u003eet al.\u003c/em\u003e 2018). This functional compartmentalization allows the organic microbiome to process diverse inputs simultaneously without cross-interference, a hallmark of a stable, mature ecosystem that is absent in the monolithic stress-clique of the conventional farming system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.4. The organic Liberty anomaly: When scion and management synergize\u003c/h2\u003e \u003cp\u003eAmong all treatment combinations, organic Liberty emerged as a striking outlier, harboring the highest microbial richness for both bacteria (841 ASVs) and fungi (615 ASVs)\u0026mdash;substantially exceeding all other groups (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This exceptional diversity was not merely a richness artifact; organic Liberty also exhibited higher bacterial alpha-diversity (Shannon Index) than the other (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and demonstrated significantly elevated aromatic hydrocarbon degradation capacity compared to Enterprise in the organic system (Fig. S7). Therefore, we propose two non-mutually exclusive hypotheses to explain this synergistic diversity effect: (1) The niche amplification hypothesis: This hypothesis posits that Liberty\u0026rsquo;s unique traits interact with the organic environment to create a hyper-heterogeneous landscape. The organic system inherently provides high resource heterogeneity (Schmidt \u003cem\u003eet al.\u003c/em\u003e 2019) and, on the other hand, the scion, which is known to influence exudates (Lu \u003cem\u003eet al.\u003c/em\u003e 2020; Chai \u003cem\u003eet al.\u003c/em\u003e 2022), may produce a more diverse exudate cocktail that further amplifies this background heterogeneity. Thereby creating a multitude of unique microniches that can support a broader array of specialized microbial consortia. (2) The reduced host selectivity hypothesis: This hypothesis proposes a host-mediated mechanism. Plants actively shape their microbiome through selective exudate secretion and immune modulation (Marasco \u003cem\u003eet al.\u003c/em\u003e 2022). If the Liberty genotype possesses a less stringent microbial filter, it may permit colonization by a wider range of microbes.\u003c/p\u003e \u003cp\u003eCritically, this Liberty-specific diversity enrichment was absent in the conventional system, where conventional Liberty actually harbored the lowest fungal alpha-diversity among all groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This stark system-dependence underscores that scion effects are not intrinsic properties but are contingent on the management backdrop\u0026mdash;a theme we explore further in Section \u003cspan refid=\"Sec32\" class=\"InternalRef\"\u003e4.6\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e4.5. X6398: A unique genetic background reveals host-specific microbial recruitment\u003c/h2\u003e \u003cp\u003eThe unique experimental setup of the X6398 scion, which was grafted onto a different rootstock (G210) than the MM.111 used for all other treatments, allows for a compelling discussion of the rootstock's confounding influence. The X6398 combination was a profound outlier, harboring the highest number of unique taxa among all treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This exceptional uniqueness and the specific enrichments (Fig. S12) cannot be conclusively attributed to the X6398 scion's genetic background. These differences are likely driven by a combination of the scion and the G210 rootstock's distinct root exudation profile and below-ground architecture. Despite this confoundment, the data offer valuable insight into recruitment capacity: The X6398 environment successfully recruits stress-tolerant PGP taxa, showing significant enrichment of \u003cem\u003eBacillus\u003c/em\u003e (Hashem \u003cem\u003eet al.\u003c/em\u003e 2019) and \u003cem\u003eGongronella\u003c/em\u003e (Wang \u003cem\u003eet al.\u003c/em\u003e 2024) compared to the other conventional scions. This suggests that certain rootstock-scion combinations may possess an enhanced ability to recruit protective microbial guilds under agrochemical stress. Future studies should evaluate X6398 in organic systems or on MM.111 rootstock to disentangle whether its unique microbiome reflects intrinsic genotype effects or management \u0026times; scion interactions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Scion acts as a secondary, permissive filter\u003c/h2\u003e \u003cp\u003eFinally, our findings refine the understanding of host-microbe selection in agriculture by establishing a double-filter model. While management acts as the primary, deterministic filter that sets the available microbial pool, the apple scion acts as a secondary, permissive filter. The significant host-genotype effects we observed are driven entirely by the scion, confirming that above-ground signals mediated by the scion\u0026mdash;including phytohormones, photosynthate allocation, and specific root exudates triggered by scion genotype\u0026mdash;can actively shape the rhizosphere microbiome even across a standardized root system (Chai \u003cem\u003eet al.\u003c/em\u003e 2022; Marasco \u003cem\u003eet al.\u003c/em\u003e 2022). This observation is consistent with the established principle that host genetics define the breadth of potential microbial partnerships, a mechanism that has been clearly demonstrated in other plant-symbiont systems (Zarrabian \u003cem\u003eet al.\u003c/em\u003e 2022)\u003c/p\u003e \u003cp\u003eCrucially, our interaction analysis reveals that this secondary filter functions very differently for bacteria versus fungi. For bacteria, the interaction landscape is remarkably simple, with very few taxa showing significant management \u0026times; scion effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig20\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). This suggests that when a scion recruits specific bacteria, it tends to do so consistently, regardless of the management backdrop. In sharp contrast, the fungal community displays a highly complex web of management \u0026times; scion interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e6F). The massive cloud of significantly interacting taxa indicates that the scion's recruitment strategy for fungi is entirely dependent on the farming system. Because the management filter radically changes the available fungal pool (as seen in Section \u003cspan refid=\"Sec27\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e), the plant is forced to recruit completely different fungal partners in an organic soil versus a conventional one. This demonstrates that breeding for microbiome-optimized crops cannot be a one-size-fits-all approach. A scion selected for beneficial fungal associations in an organic farm may fail to recruit those same partners in a conventional orchard, because management will have already filtered them out. The exceptional diversity of organic Liberty versus the depleted diversity of conventional Liberty (lowest fungal alpha-diversity, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e) exemplifies this context-dependency. Therefore, microbiome-assisted breeding programs should explicitly account for the destination farming system.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that the management system is the primary determinant of the apple rhizosphere microbiome, acting as a fundamental filter that shapes microbial diversity, composition, and interaction networks. Organic management played a decisive role in enhancing both alpha and beta diversification; specifically, the organic system fostered higher overall microbial richness and diversity, with the Organic-Liberty combination representing a peak in bacterial diversity. In contrast, conventional management was associated with a significant diversity crash in fungal communities, particularly in the Liberty scion.\u003c/p\u003e \u003cp\u003eThe role of the scion genotype was found to be highly context-dependent. While bacterial recruitment showed relative stability across systems, fungal recruitment was governed by strong Management \u0026times; Scion interactions. This suggests that the scion\u0026rsquo;s ability to recruit beneficial partners is not an intrinsic trait but is contingent upon the species pool permitted by the management system. Furthermore, the unique microbial signature of the X6398 scion suggests that rootstock genetics (G210) may provide an additional layer of recruitment capacity, though this remains a compelling area for future research to disentangle from scion effects.\u003c/p\u003e \u003cp\u003eBased on these observations, we propose the Stress-Clique hypothesis to explain the observed network topologies. We hypothesize that the hyper-dense fungal co-occurrence networks in conventional systems represent a state of obligate co-dependency driven by agrochemical stress. While these communities appear complex, their high interconnectivity may render them vulnerable to cascading failures if key hub taxa are disrupted by new management inputs. Conversely, the more modular networks observed in organic systems may indicate greater ecological resilience.\u003c/p\u003e \u003cp\u003eFinally, these ecological insights provide a roadmap for microbiome-assisted breeding. We conclude that: (1) breeding for microbial traits must be system-specific, as a genotype\u0026rsquo;s performance in an organic orchard may not translate to a conventional orchard; (2) breeders should prioritize fungal partnerships, as they are the most responsive to scion-management interactions; and (3) selection should target functional guilds rather than individual taxa, recognizing that different specialists may fulfill identical ecological roles under different management filters.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis project was partially funded by the Biotechnology Risk Assessment Research Grants Program (BRAG) (Grant# 33522\u0026ndash;38314).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**Mohammad Zarrabian** : Writing \u0026ndash; original draft, writing \u0026ndash; review \u0026amp;amp; editing, methodology, visualization, software, investigation, formal analysis, data curation.**Sherif M. Sherif** : conceptualization, writing \u0026ndash; review \u0026amp;amp; editing, supervision, project administration, and funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors wish to express their gratitude to \u003cb\u003eMs. Diane Kearns\u003c/b\u003e for providing access to her organic and conventional orchards for this study. Sincere thanks are also extended to \u003cb\u003eMr. Gerald Michaels III\u003c/b\u003e and \u003cb\u003eMs. Katherine Furcho\u003c/b\u003e for their assistance in collecting the rhizosphere soil and extracting microbiome DNA.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. Abarenkov K, Nilsson RH, Larsson KH et al (2024) The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: sequences, taxa and classifications reconsidered. 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Mol Plant Microbe Interact 35(11):1006\u0026ndash;1017. https://doi.org/10.1094/MPMI-04-22-0087-R\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ecological Filtering, Co-occurrence, Specialization, Functional Redundancy, Malus domestica","lastPublishedDoi":"10.21203/rs.3.rs-8562047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8562047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRhizosphere microbiomes are critical for agricultural health, but how they are interactively shaped by management and host genetics in perennial systems remains largely unknown. Using an apple orchard system, we show that long-term agricultural management does not just alter soil biodiversity, but also selects for fundamentally opposing microbial life strategies. Our findings showed that organic management selects resource-decomposition specialists, while conventional management selects abiotic stress-tolerance and xenobiotic remediators. We found that this is achieved via functional retooling, where essential ecosystem services are maintained in both systems, but are performed by different adapted specialists. This was most evident in fungi, where management-driven shifts in taxonomy were tightly coupled to functional capacity. Moreover, challenging the prevailing ecological theory that stress simplifies networks, we found that conventional fungal communities were paradoxically more complex, forming a rigid Stress-Clique of co-dependent survivors, while organic bacterial networks were more modular. This structural divergence provides a new mechanistic framework for rhizosphere assembly. We also showed that the host scion's recruitment of fungi is entirely dependent on the management backdrop, while bacterial recruitment is not. These findings reveal that microbiome-optimized breeding should be conducted within the specific management context of the intended production system.\u003c/p\u003e","manuscriptTitle":"Functional re-tooling of rhizosphere guilds is driven by agricultural management and scion genotype in apple","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 11:13:19","doi":"10.21203/rs.3.rs-8562047/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-21T20:16:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-15T20:21:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194985971211755188667179641258399925172","date":"2026-02-25T11:25:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33120340174022554426845615707861590690","date":"2026-02-23T15:29:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T07:50:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5926657375391969060048014574912970111","date":"2026-02-23T03:46:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223635023589929785765169474367874250277","date":"2026-01-26T11:07:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160517029748418660538864777440740957970","date":"2026-01-15T23:37:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-12T15:57:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-12T14:26:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-12T14:21:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2026-01-09T14:15:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e2675169-78b3-4afe-a772-8312cfa9168f","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:04:42+00:00","versionOfRecord":{"articleIdentity":"rs-8562047","link":"https://doi.org/10.1007/s00248-026-02774-7","journal":{"identity":"microbial-ecology","isVorOnly":false,"title":"Microbial Ecology"},"publishedOn":"2026-04-22 15:57:43","publishedOnDateReadable":"April 22nd, 2026"},"versionCreatedAt":"2026-01-14 11:13:19","video":"","vorDoi":"10.1007/s00248-026-02774-7","vorDoiUrl":"https://doi.org/10.1007/s00248-026-02774-7","workflowStages":[]},"version":"v1","identity":"rs-8562047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8562047","identity":"rs-8562047","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}