Rhizosphere microbial stability and phosphorus availability drive garlic growth differences

Rhizosphere microbial stability and phosphorus availability drive garlic growth differences | 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 Rhizosphere microbial stability and phosphorus availability drive garlic growth differences Rongxin Wang, Shidong He, Linguang Lv, Lingli Li, Dongliang Fang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7336697/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Annals of Microbiology → Version 1 posted 4 You are reading this latest preprint version Abstract Background The rhizosphere microbiome and soil nutrients are critical for crop growth, but their roles in regulating garlic productivity remain unclear. This study aimed to identify key factors driving growth differences in adjacent garlic fields with uniform management. Methods Rhizosphere soils from two adjacent plots (H: vigorous growth; L: stunted growth) were analyzed for physicochemical properties and microbial communities via 16S rRNA and ITS sequencing, combined with network analysis and redundancy analysis (RDA). Results Results showed significantly higher available phosphorus (AP) in H than L. Bacterial communities in H exhibited greater stability and core diversity, with distinct compositional clustering between sites (PERMANOVA, P < 0.001). RDA indicated AP strongly correlated with bacterial community structure (R²=0.7638, P=0.009), and H was enriched with phosphorus-transforming taxa (e.g., Arthrobacter, Thauera). Hierarchical partitioning highlighted bacterial communities as the primary driver of growth differences, followed by AP. Conclusions These findings reveal that AP availability and rhizosphere bacterial stability, mediated by phosphorus-transforming microbes, collectively shape garlic growth, providing insights for optimizing garlic cultivation through microbial management. Rhizosphere microbial community Garlic Available phosphorus Diversity Phosphorus transformation Growth differences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background The rhizosphere, the interface between plant roots and soil, hosts a complex ecosystem involving plants, microorganisms, soil, and other biotic and abiotic components. Within this zone, microbial populations exhibit high activity and participate in intricate biological and ecological processes. These interactions profoundly influence soil health, plant performance, and productivity (Kou et al. 2024 ). The rhizosphere microbiota regulates soil organic matter stability (Nelson et al. 2022 ), nutrient dynamics (van der Heijden et al. 2007 ), and rhizosphere functionality (Mendes et al. 2011 ). The stability of the rhizosphere microecosystem is critical for maintaining soil health and crop productivity. Rhizosphere microbial community composition is shaped by environmental factors such as soil moisture, pH, organic matter content, and nutrient availability (e.g., nitrogen and phosphorus (P)) (Zheng et al. 2019 ; Sun et al. 2022 ). As natural media for plant growth, soil conditions directly impact plant development (Dai et al. 2020 ). Soil microorganisms improve soil structure, increase fertility, and modulate pH (Nannipieri et al. 2017 ). For example, Kallenbach et al . reported that microbial residues contribute to the chemical diversity of soil organic matter (SOM) (Kallenbach et al. 2016 ). He et al . demonstrated that Bacillus species and Pseudomonas putida promote tomato growth through P solubilization, nitrogen fixation, and indole-3-acetic acid (IAA) production (He et al. 2019b ). Microorganisms also increase nutrient bioavailability via metabolic activities that increase the SOM content (Coonan et al. 2020 ) while simultaneously inhibiting plant pathogens (García-Bayona and Comstock 2018 ; Anum et al. 2024 ; Xia et al. 2024a ). Xia et al . identified Bacillus, which antagonizes Fusarium graminearum to mitigate stalk rot, as a core taxon in disease-resistant maize varieties (Xia et al. 2024b ). Collectively, these findings highlight the pivotal role of soil microbes in plant growth and health. Garlic ( Allium sativum L.) is a major economic crop in China, with Jinxiang County in Shandong Province renowned the “Garlic Capital” and “World Garlic Center” owing to its long-standing cultivation practices. However, garlic yields are vulnerable to management practices, particularly when unscientific cultivation reduces soil microbial diversity (Maron et al. 2018 ; Singh and Gupta 2018 ). This decline may result in pathogen accumulation and inefficient nutrient conversion, thereby impairing garlic growth. To address this issue, we collected rhizospheric soil from two adjacent garlic fields exhibiting significant growth disparities and compared the growth performance of garlic. Investigations revealed that both fields had been cultivated with garlic for more than a decade under uniform fertilization and irrigation management, yet growth differences persisted. By analyzing the soil nutrient content and microbial community structure, we aimed to clarify the roles of physicochemical properties and the microbiota in garlic growth. This study focused on identifying the critical roles of soil nutrients and microbial communities in garlic growth, providing theoretical support and technological innovations for precision fertilization and soil management. Our findings aim to mitigate production risks caused by soil degradation and promote sustainable soil health. Materials and methods Research location and sample collection This study was conducted in farmlands of Jinxian County, Jining city, Shandong Province, China (33°02′N, 116°23′E). The region has a warm temperate monsoon climate, with an annual average temperature of 15.5°C and annual precipitation of 668.6 mm in 2023. The main soil type is natural brown soil (C. Li et al. 2021 ). On November 24, 2023, the research team conducted an onsite field investigation of two adjacent garlic fields. These fields shared identical cultivars (Taikong 1) and sowing dates and were free from external nutritional factor disturbances; however, they presented marked differences in growth performance. To facilitate clear distinction, plants with plant heights ranging from 30–50 cm (representing the better-developed plot) were defined as Group H, with the corresponding plot labeled as Plot H. Conversely, plants with heights below 30 cm (from the less vigorous plot) were designated as Group L, and the plot was labeled as Plot L. To ensure the representativeness and reliability of the data, 8 replicate samples were uniformly collected from each plot via the random sampling method. During sampling, intact garlic root systems were carefully excavated, and rhizosphere soil was collected by gently brushing the root surface, yielding 16 rhizosphere soil samples in total. Each sample was divided into two portions: one for physicochemical analysis and the other stored at -80°C for subsequent DNA extraction. Determination of Soil Physical and Chemical Properties The total nitrogen (TN) and total phosphorus (TP) contents were determined via an AutoAnalyser3 soil element flow analyzer (Bran + Luebbe, Hamburg, Germany). The soil pH and electrical conductivity (EC) were measured with a Leici pH meter (Shanghai, China) at a water-to-soil ratio of 5:1 (v/w). Nitrate nitrogen (NO₃⁻-N) and ammonium nitrogen (NH₄⁺-N) were analyzed via hydrazine sulfate reduction and indophenol blue colorimetry, respectively. The soil organic carbon (SOC) content was determined via the potassium dichromate volumetric method (Wang et al. 2023 ). Available phosphorus (AP) was quantified via the molybdate‒ascorbic acid method with a UV‒Vis spectrophotometer (Eppendorf, Germany). The carbon-to-nitrogen (C/N) ratio was calculated as the ratio of SOC to TN (He et al. 2019a ). DNA extraction and sequence analysis The soil DNA was extracted via the FastDNA Spin Kit for Soil (Omega, USA), and the DNA quality was evaluated via 1.2% agarose gel electrophoresis. PCR amplification of the bacterial V5–V7 hypervariable region of the 16S rRNA gene was performed via primers 799F (5′-AACMGGATTAGATACCCKG-3′) and 1193R (5′-ACGTCATCCCCACCTTCC-3′) (Klindworth et al. 2013 ), whereas the fungal ITS1–ITS2 region was amplified via primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′) (Schoch et al. 2012 ). Paired-end sequencing was conducted on an Illumina NovaSeq 6000 platform at Guangdong Magigene Biotechnology Co., Ltd. (Guangzhou, China). Raw FASTQ files were processed according to the QIIME2 standard protocols ( https://docs.qiime2.org/2019.4/tutorials/ ) (Straub et al. 2020 ). After quality filtering, denoising, merging, chimera removal, and alignment via the DADA2 plugin, nonsingleton amplicon sequence variants (ASVs) were retained for downstream analysis. Taxonomic annotation of ASVs was performed via the SILVA v138.1 and UNITE v8.2 databases (Quast et al. 2013 ; Nilsson et al. 2019 ), followed by removal of contaminant sequences (e.g., mitochondria and chloroplasts). To account for differences in sequencing depth, rarefaction was performed to the minimum depth across all samples (47,456 reads for bacteria and 84,758 reads for fungi). These ASV tables were used for downstream analyses. A total of 1,062,692 high-quality bacterial sequences were generated and assigned to 6,039 ASVs, whereas fungal samples yielded 2,267,159 sequences assigned to 1,546 ASVs. The sequence data were deposited in the NCBI Sequence Read Archive (SRA) under accession number PRJNA1174389. Data analysis All the statistical analyses and data visualizations were performed in R (v4.3.2, https://www.r-project.org/ ). The core microbial taxa were defined as ASVs with relative abundances > 0.1%. Alpha diversity indices were calculated via the picante package. Principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarities was conducted via the vegan package to evaluate community structure differences between sites, with significance determined via permutation tests (P < 0.05). Redundancy analysis (RDA) and hierarchical partitioning were performed with the rdacca.hp package to identify the contributions of soil physicochemical properties to microbial diversity and community composition, with significance determined at Padj ≤ 0.05. Microbial co-occurrence networks were constructed using ASVs with average relative abundances > 0.1%. Pairwise Spearman rank correlations were calculated via the ggClusterNet package, and significant interactions were filtered using a uniform threshold (|R| >0.7, P < 0.05). Network edges were weighted by correlation strength, and layouts were generated via the Fruchterman–Reingold algorithm in Gephi v.0.9.2 (Li et al. 2023 ). Community stability was evaluated via the average variability degree (AVD) index (Xun et al. 2021 ). Graphical representations (e.g., boxplots, chord diagrams) were generated with the ggplot2 package. Statistical comparisons between groups were performed via the Wilcoxon rank-sum test in IBM SPSS Statistics (IBM Corp., Armonk, NY, USA) (Xia et al. 2024c ). Results Significant differences in garlic growth performance between adjacent fields Analysis of garlic plant height revealed that the mean plant height at site H was 1.44-fold greater than that at site L (Wilcoxon rank-sum test, P < 0.001) (Fig. 1 B–C). To investigate the underlying factors contributing to this discrepancy, we characterized the physicochemical properties and microbial community compositions of rhizosphere soils from both sites. The AP content was significantly greater at site H than at site L To determine whether rhizosphere soil physicochemical properties differed between sites H and L, we measured key parameters in garlic rhizosphere soil samples. Among the analyzed properties, the available phosphorus (AP) content at site H was significantly greater than that at site L (Wilcoxon rank-sum test, P 0.05) (Fig. 2 ). These results highlight AP content as a key factor contributing to the observed differences in garlic growth between the two sites. Significant differences in rhizosphere microbial community diversity To evaluate bacterial and fungal diversity between sites H and L, α diversity indices were calculated for bacteria, core bacterial taxa (defined as ASVs with a mean relative abundance > 0.1% across samples), fungi, and core fungi. The Shannon index of the core bacteria at site H was significantly greater than that at site L (Wilcoxon rank-sum test, P 0.05) (Fig. 3 A, B, D). PCoA based on Bray–Curtis dissimilarities revealed distinct clustering patterns for bacterial and core bacterial communities between sites (PERMANOVA, P 0.05) (Fig. 3 F). Additionally, the exogenous nutrient levels were consistent between the two fields. These results suggest that bacterial community diversity and composition are critical factors contributing to the observed garlic growth disparities between sites H and L. Higher stability of bacterial communities at location H To evaluate rhizosphere microbial network complexity and stability, ecological networks were constructed for bacteria and fungi at sites H and L. Network analysis revealed that bacterial and fungal networks at site H presented greater complexity and connectivity than did those at site L, characterized by more nodes, a higher average degree, a larger network diameter, greater centralization, and greater closeness centralization (Fig. 4 A–B; Table 1 ). Specifically, the bacterial network at site L presented an increased average path length, indicating decreased responsiveness to external perturbations (Table 1 ), whereas the fungal network at site L presented a decreased average path length, reflecting faster adaptive capacity (Table 1 ). Site L also harbored more keystone nodes, suggesting that garlic actively recruits critical microbes under stress. Community stability, measured by the AVD index, was significantly greater at site H for both bacteria and fungi (Fig. 4 D). These results underscore bacterial community stability as a key determinant of garlic growth disparities between sites. Table 1 Network properties of bacteria and fungi Bacteria_H Bacteria_L Fungi_H Fungi_L Nodes 218 219 88 88 Edges 2224 2048 370 367 Connectance 0.094026128 0.085794479 0.096656217 0.095872518 Average Degree 20.40366972 18.70319635 8.409090909 8.340909091 Average Path Length 1.879037574 1.985647338 2.298343637 2.251869175 Diameter 4.023809524 3.85206652 6.111667761 5.307278838 Centralization Degree 0.104130554 0.083930292 0.13322884 0.122518286 Centralization Closeness 0.141528417 0.113956041 0.195748564 0.163661951 The Number of Keystone Nodes 36 41 7 11 The AP content is significantly correlated with rhizosphere bacterial community composition RDA was used to explore the influence of abiotic factors, specifically soil physicochemical properties, on bacterial and fungal communities. The first two axes of the RDA explained 30.63% of the total variation in the bacterial communities. Among these factors, AP had the most significant impact on the bacterial community structure ( R 2 = 0.7638, P = 0.009) (Fig. 5 A, Table 2 ). The RDA results revealed that differences in AP content affected the distribution of bacterial communities, whereas the distribution of fungal communities was not influenced by these abiotic factors (Fig. 5 A, B, Table 2 ). These findings suggest that AP and bacterial communities are crucial factors contributing to the significant differences in garlic growth between sites H and L. Table 2 Results of RDA permutation test for bacteria, fungi communities and soil physical and chemical properties Name Bacteria Fungi R 2 Padj Individual (%) R 2 Padj Individual (%) PH 0.179336 0.5076 1.04 0.010451 0.901 0 EC 0.002077 0.992 0 0.04049 0.858857 0 NH 4 + -N 0.057199 0.892286 0 0.343983 0.711 3.36 NO 3 − -N 0.321534 0.306 1.95 0.106433 0.858857 1.09 AP 0.763771 0.009 3.8 0.069747 0.858857 0 TN 0.139292 0.582 0 0.100856 0.858857 0 TP 0.180848 0.5076 0 0.101308 0.858857 0 C/N 0.20139 0.5076 0.15 0.140689 0.858857 1.08 SOC 0.007786 0.992 0 0.012459 0.901 0.16 Location H is enriched with numerous microorganisms associated with P transformation To quantify the contributions of soil physicochemical properties and microbial communities to garlic plant height at sites H and L, we performed hierarchical partitioning analysis. The results revealed that the bacterial community had the greatest contribution to garlic plant height, followed by AP (Fig. 6 E). To further clarify the role of bacterial communities in determining garlic plant height, we analyzed the species composition of rhizosphere microbial communities at both sites. On the basis of the ASV classification results, Sphingomonas was the most abundant genus among the rhizosphere bacteria at both sites. However, the second most abundant genera differed: Flavobacterium at site H and Bacillus at site L. The most abundant fungus at site H was Mortierella , whereas at site L, it was Fusarium (Fig. 4 A, B), a common soil-borne plant pathogen. To identify the bacterial and fungal genera that differed between the microbial communities at sites H and L, we conducted linear discriminant analysis effect size (LEfSe) analysis. Significant differences were found in the enriched fungi and bacteria between the two sites (Fig. 6 A, B). At site H, 36 bacterial taxa met the score criteria, whereas 22 bacterial taxa met the score criteria at site L (Fig. 6 C). We also found that the main enriched genera at site H, Arthrobacter and Thauera , are typically involved in the transformation of minerals such as P and sulfur, which promotes plant nutrient absorption and inhibits plant pathogens (Fig. 6 C). Among the fungal taxa, 5 groups at site L and only 1 group at site H met the linear discriminant analysis (LDA) score criteria (Fig. 6 D). Additionally, the enriched Penicillium at site L can cause stem-base rot and fruit rot in vegetables and fruits (Fig. 6 D). Overall, these results suggest that the differences in garlic growth at sites H and L are influenced mainly by differences in the composition of rhizosphere microorganisms. Discussion In the two fields where we investigated garlic growth performance, the fertilization rates were standardized. Thus, after excluding interference from exogenous nutrient intrusion, significant differences in available phosphorus (AP) remained between the two fields. The role of P in plants is extremely extensive, as it is involved in critical physiological processes such as energy transfer, cell division, and nucleic acid synthesis (Wang et al. 2020 ). P is also a structural component of plant cell membranes and plays a vital role in photosynthesis. P deficiency can significantly impair plant growth and development, ultimately reducing crop yield. In our study, the AP content at location H was significantly greater than that at location L (Fig. 2 ), corroborating previous findings that P deficiency disrupts plant growth and development, thereby impacting crop productivity. Plant rhizosphere microorganisms play a pivotal role in plant stress resistance, serving as major drivers of plant defense responses (Zhalnina et al. 2018 ). Stress tolerance is mediated by the collective activities of microbial communities, and more diverse communities are particularly effective at increasing plant stress resistance (Sun et al. 2021 ). This finding is consistent with our findings that the core bacterial community diversity was greater at site H than at site L (Fig. 3 C). Additionally, the bacterial and fungal networks at location H presented greater complexity and connectivity (Fig. 4 A, B, Table 1 ), which is consistent with previous reports that high-diversity microbial communities provide more ecological niches and functional redundancy, thereby strengthening plant stress tolerance. The terrestrial microbiome is recognized as a ubiquitous and essential component of ecosystems, playing critical roles in maintaining organic carbon cycling, enhancing nutrient use efficiency, and supporting productivity (Xun et al. 2021 ). Fundamentally, the sustainability of terrestrial ecosystem functions and services depends on microbiome stability (Griffiths and Philippot 2012 ), which is typically measured by the degree of variation or turnover in microbial communities. In this study, the bacterial community stability at location H was significantly greater than that at location L (Fig. 4 D). The enhanced stability at location H likely indicates greater durability and reliability of ecosystem services, contributing to overall terrestrial ecosystem health. The results of hierarchical partitioning further highlighted the critical role of bacterial communities in garlic growth (Fig. 6 E). Bacterial communities are highly sensitive to soil environmental changes, possibly because of their reliance on small-molecule organic matter and short-distance nutrient exchange in the soil (Yalong Xu et al. 2024 ). In contrast, fungi acquire nutrients through robust mycelial growth, enabling them to form extensive network structures and maintain relatively stable community compositions (He et al. 2024 ). For plants, soil P is often unavailable for direct uptake and requires conversion to AP before absorption. Soil microorganisms play a critical role in this "transformation" process (Shidong He et al. 2024 ). Phosphate-solubilizing bacteria (PSB), which solubilize inorganic P or mineralize organic P to facilitate plant growth (Bargaz et al. 2021 ; Timofeeva et al. 2022 ), were significantly correlated with the AP content at both locations (Fig. 5 A, Table 2 ). Notably, Arthrobacter and Thauera were significantly enriched at location H. Previous studies have demonstrated that these genera possess phosphate-solubilizing capabilities and facilitate P transformation, effectively converting soil P into AP (Vanissa et al. 2020 ; Jiang et al. 2022 ; Ren et al. 2023 ). This likely explains the significant AP content difference between locations and subsequent variations in garlic growth. In many agricultural ecosystems, soil-borne diseases pose severe threats to crop health (Raaijmakers et al. 2008 ). Analysis of the top ten bacterial and fungal genera revealed greater Fusarium abundance at site L (Fig. 6 B). Fusarium species are common pathogens that cause garlic root rot (Garbeva et al. 2004 ), infect various plants and induce diseases such as root rot, stem rot, basal stem rot, blossom blight, and ear blight. These diseases are notoriously difficult to control in agricultural production (Li et al. 2020 ). LEfSe analysis further revealed significant enrichment of Scedosporium at site L (Fig. 6 D), a genus associated with important crop diseases that can cause substantial yield losses if unmanaged (Brauer et al. 2019 ). Taken together, these results underscore the close association between soil microbial communities and plant growth. Further research and targeted management strategies could leverage microbial potential to advance sustainable agriculture. Conclusions This study aimed to investigate the role of garlic rhizosphere microorganisms in plant growth by comparing two adjacent plots with contrasting growth performance. Taken together, these results underscore the close association between soil microbial communities and plant growth in bacterial communities and the AP content across locations. These findings highlight the critical roles of bacterial communities and AP content as key determinants of garlic growth disparities. RDA further revealed strong correlations between bacterial community compositions and soil AP levels, suggesting that bacterial communities indirectly influence garlic growth through regulating P bioavailability. Differential abundance analysis revealed substantial enrichment of Arthrobacter and Thauera at Location H, which are known to directly mediate P transformation processes, providing mechanistic insights into soil nutrient cycling. Additionally, the presence of fungal pathogens in the rhizosphere microbiome was identified as a potential factor contributing to the observed growth disparities. Collectively, these results advance our understanding of the rhizosphere microbial mechanisms underlying garlic growth and provide a theoretical basis for developing microbe-driven management strategies for garlic cultivation. Abbreviations AP Available phosphorus TN Total nitrogen TP Total phosphorus EC Electrical conductivity NO₃⁻-N Nitrate nitrogen NH₄⁺-N Ammonium nitrogen SOC Soil organic carbon C/N Carbon-to-nitrogen ratio IAA Indole-3-acetic acid SOM Soil organic matter ASVs Amplicon sequence variants SRA Sequence Read Archive PCoA Principal coordinate analysis RDA Redundancy analysis AVD Average variability degree LEfSe Linear discriminant analysis effect size LDA Linear discriminant analysis PSB Phosphate-solubilizing bacteria Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing Interests The authors declare that they have no conflicts of interest. Funding This work was supported by the Key Research and Development Project in Shandong Province of China (2024TZXD062, 2023CXPT045, 2023TZXD004), the National Natural Science Foundation of China (42407427, 42377309, 42077027), the ‘First Class Discipline’ Construction Project of Shandong Agricultural University (SKL81103, SKL81110). Author Contributions RX W and SD H planned and designed the study and completed the major data analysis. TT W and LG L assisted with sample collection. DL F, LL L, WC S and Z G were involved in writing the article. X L guided SD H and RX W in drafting the manuscript. Acknowledgments This work was supported by the Key Research and Development Project in Shandong Province of China (2024TZXD062, 2023CXPT045, 2023TZXD004), the National Natural Science Foundation of China (42407427, 42377309, 42077027), and the ‘First Class Discipline’ Construction Project of Shandong Agricultural University (SKL81103, SKL81110). Availability of data and materials The sequence data have been deposited in the NCBI SRA database under accession number PRJNA1174389. References Anum H, Tong Y, Cheng R (2024) Different Preharvest Diseases in Garlic and Their Eco-Friendly Management Strategies. https://doi.org/10.3390/plants13020267 . Plants Bargaz A, Elhaissoufi W, Khourchi S et al (2021) Benefits of phosphate solubilizing bacteria on belowground crop performance for improved crop acquisition of phosphorus. 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J Integr Plant Biol. https://doi.org/10.1111/jipb.13053 Xia X, Wei Q, Wu H et al (2024a) Bacillus species are core microbiota of resistant maize cultivars that induce host metabolic defense against corn stalk rot. https://doi.org/10.1186/s40168-024-01887-w . Microbiome Xia X, Wei Q, Wu H et al (2024b) Bacillus species are core microbiota of resistant maize cultivars that induce host metabolic defense against corn stalk rot. Microbiome 12:156. https://doi.org/10.1186/s40168-024-01887-w Xia X, Wei Q, Wu H et al (2024c) Bacillus species are core microbiota of resistant maize cultivars that induce host metabolic defense against corn stalk rot. https://doi.org/10.1186/s40168-024-01887-w . Microbiome Xun W, Liu Y, Li W et al (2021) Specialized metabolic functions of keystone taxa sustain soil microbiome stability. https://doi.org/10.1186/s40168-020-00985-9 . Microbiome Xu Y, Li J, Qiao C et al (2024) Rhizosphere bacterial community is mainly determined by soil environmental factors, but the active bacterial diversity is mainly shaped by plant selection. BMC Microbiol. https://doi.org/10.1186/s12866-024-03611-y Zhalnina K, Louie KB, Hao Z et al (2018) Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. https://doi.org/10.1038/s41564-018-0129-3 Zheng Q, Hu Y, Zhang S et al (2019) Soil multifunctionality is affected by the soil environment and by microbial community composition and diversity. Soil Biol Biochem. https://doi.org/10.1016/j.soilbio.2019.107521 Cite Share Download PDF Status: Published Journal Publication published 05 Jan, 2026 Read the published version in Annals of Microbiology → Version 1 posted Reviewers agreed at journal 25 Aug, 2025 Reviewers invited by journal 19 Aug, 2025 Editor assigned by journal 12 Aug, 2025 First submitted to journal 09 Aug, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7336697","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502411391,"identity":"64fd2c82-b670-45ac-94b8-a73542afa69d","order_by":0,"name":"Rongxin Wang","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Rongxin","middleName":"","lastName":"Wang","suffix":""},{"id":502411392,"identity":"aa957e7b-f697-4639-ae09-139dc493a9c0","order_by":1,"name":"Shidong He","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shidong","middleName":"","lastName":"He","suffix":""},{"id":502411393,"identity":"3fd17a65-1a30-4be9-ba07-cd052bc4ce91","order_by":2,"name":"Linguang Lv","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Linguang","middleName":"","lastName":"Lv","suffix":""},{"id":502411394,"identity":"9431d1e1-7d01-45dc-802a-70cf400a5dad","order_by":3,"name":"Lingli Li","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Lingli","middleName":"","lastName":"Li","suffix":""},{"id":502411395,"identity":"6140526a-c3a7-41b7-a4db-a2597af085b8","order_by":4,"name":"Dongliang Fang","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dongliang","middleName":"","lastName":"Fang","suffix":""},{"id":502411396,"identity":"f21fd000-858a-461a-8e6b-9d34a35c6028","order_by":5,"name":"Taotao Wang","email":"","orcid":"","institution":"Shandong Eengineering and Technology Research Center for Garlic","correspondingAuthor":false,"prefix":"","firstName":"Taotao","middleName":"","lastName":"Wang","suffix":""},{"id":502411397,"identity":"e62fcbe4-d744-4a9f-9c5b-d34c386b4273","order_by":6,"name":"Wenchong Shi","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wenchong","middleName":"","lastName":"Shi","suffix":""},{"id":502411398,"identity":"17c730c6-ebdf-4ba0-87e1-89e48fe8a07e","order_by":7,"name":"Zheng Gao","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Gao","suffix":""},{"id":502411399,"identity":"fadf4458-e38c-4b87-8a77-2b6714f0e6dc","order_by":8,"name":"Xiang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACPmYGhgNAWo6BIQHEZyashQ2qxZgELVA6sYF4Lew8hgd+7qhNn9+e/EyCocI6sYH97AECDmNLONh75njuhjPPzCQYzqQnNvDkJRDQwnzgAG/bsdwNEglmEoxthxMbJHgMCGhhbDj4t+1YuvyM9G8SjP+I0sJ84DBvW00Cw40coC0NRGlhSzgs23bAcMOZN8UWCcfSjdt4cvBr4ec/Y/zxbVudvHx7+sYbH2qsZfvZz+DXAgWHIVQCAyKmCIE6ItWNglEwCkbBiAQAO/tCi9i4S3kAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-0517-3894","institution":"Shandong Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-08-10 04:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7336697/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7336697/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13213-025-01837-3","type":"published","date":"2026-01-05T15:57:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90036494,"identity":"e63c22a2-6aca-4e48-a4af-b911b3e486d5","added_by":"auto","created_at":"2025-08-27 15:54:51","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1970085,"visible":true,"origin":"","legend":"\u003cp\u003eSampling location and garlic plant height. (A) Schematic map of sampling sites in Jinxiang County, Shandong Province. (B) Phenotypic comparison of garlic plant heights at sites H and L. (C) Boxplot showing significant differences in garlic plant heights between sites H and L. Asterisks denote significance levels determined by the Wilcoxon rank-sum test ((*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001); ns = not significant.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7336697/v1/7cb739240830b21ea32f1346.jpeg"},{"id":90035431,"identity":"ae35b52e-4368-4629-ad70-a7777a6e0a2a","added_by":"auto","created_at":"2025-08-27 15:46:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":766754,"visible":true,"origin":"","legend":"\u003cp\u003ePhysical and chemical properties of garlic rhizosphere soil. Asterisks indicate significant differences as represented by the Wilcoxon rank-sum test (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and “ns” means not significant difference.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7336697/v1/c5ab68b6393e4d8829d91cf6.jpeg"},{"id":90035435,"identity":"27bbaa87-fb1f-4ad5-92c4-df8d23b10fa3","added_by":"auto","created_at":"2025-08-27 15:46:51","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1055328,"visible":true,"origin":"","legend":"\u003cp\u003eDiversity and species composition of bacterial and fungal communities. (A) Shannon and Chao1 indices of bacterial communities. (B) Shannon and Chao1 indices of core bacterial communities. (C) Shannon and Chao1 indices of fungal communities. (D) Shannon and Chao1 indices of core fungal communities. (E) PCoA analysis of bacterial and core bacterial communities based on Bray-Curtis distance. (F) PCoA analysis of fungal and core fungal communities based on Bray-Curtis distance. Asterisks indicate significant differences as represented by the Wilcoxon rank-sum test (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and “ns” means not significant difference.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7336697/v1/647c04068d61b94599ef9aa3.jpeg"},{"id":90035445,"identity":"5fd20e3d-8bd1-409c-ac1c-8ffcff460ad5","added_by":"auto","created_at":"2025-08-27 15:46:51","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":16188999,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork analysis and stability evaluation of fungi and bacteria.\u003c/p\u003e\n\u003cp\u003e(A) Bacterial network, nodes are colored according to the phylum level of the species, the size of the nodes represents the degree of connectivity, and the color of the lines represents the correlation, with red indicating positive correlation and green indicating negative correlation. (B) Fungal network, nodes are colored according to the phylum level of the species, the size of the nodes represents the degree of connectivity, and the color of the lines represents the correlation, with red indicating positive correlation and green indicating negative correlation. (C) Degree of freedom of nodes in bacterial and fungal networks. (D) Stability of bacterial and fungal communities. Asterisks indicate significant differences as represented by the Wilcoxon rank-sum test (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and “ns” means not significant difference.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7336697/v1/7f741b31b79ebec8633f6880.jpeg"},{"id":90036497,"identity":"32d868c5-ac69-4d41-83ec-938ecc70f8c7","added_by":"auto","created_at":"2025-08-27 15:54:51","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":590922,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial and fungal RDA analysis. (A) RDA of all ASVs in the bacterial community versus soil physical and chemical factors. (B) RDA of all ASVs in the fungal community versus soil physical and chemical factors.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7336697/v1/b7c53c7216fdd450739537d6.jpeg"},{"id":90036498,"identity":"37f16183-ad09-4162-9346-b58fe85bbd97","added_by":"auto","created_at":"2025-08-27 15:54:51","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6243442,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial and fungal differences and hierarchical partitioning analysis. (A) Proportional Chord Diagram of the Top Ten Bacterial Genera. (B) Proportional Chord Diagram of the Top Ten Bacterial Genera. (C) LEfSe Analysis of Bacterial Genera. (D) LEfSe Analysis of Fungal Genera. Only taxa with absolute LDA scores \u0026gt; 2 are shown. (E)Contribution of Bacterial Communities, Fungal Communities, and Physical-Chemical Factors to Garlic Plant Height.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7336697/v1/d4ea3d4631dc22695d7ad59f.jpeg"},{"id":100069231,"identity":"453a472a-42c6-4783-92ba-bccc0fae1384","added_by":"auto","created_at":"2026-01-12 16:11:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27711369,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7336697/v1/9bc076a6-6638-4699-b470-8745195ab0c0.pdf"}],"financialInterests":"","formattedTitle":"Rhizosphere microbial stability and phosphorus availability drive garlic growth differences","fulltext":[{"header":"Background","content":"\u003cp\u003eThe rhizosphere, the interface between plant roots and soil, hosts a complex ecosystem involving plants, microorganisms, soil, and other biotic and abiotic components. Within this zone, microbial populations exhibit high activity and participate in intricate biological and ecological processes. These interactions profoundly influence soil health, plant performance, and productivity (Kou et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The rhizosphere microbiota regulates soil organic matter stability (Nelson et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), nutrient dynamics (van der Heijden et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and rhizosphere functionality (Mendes et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The stability of the rhizosphere microecosystem is critical for maintaining soil health and crop productivity.\u003c/p\u003e\u003cp\u003eRhizosphere microbial community composition is shaped by environmental factors such as soil moisture, pH, organic matter content, and nutrient availability (e.g., nitrogen and phosphorus (P)) (Zheng et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As natural media for plant growth, soil conditions directly impact plant development (Dai et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Soil microorganisms improve soil structure, increase fertility, and modulate pH (Nannipieri et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, Kallenbach \u003cem\u003eet al\u003c/em\u003e. reported that microbial residues contribute to the chemical diversity of soil organic matter (SOM) (Kallenbach et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). He \u003cem\u003eet al\u003c/em\u003e. demonstrated that \u003cem\u003eBacillus\u003c/em\u003e species and \u003cem\u003ePseudomonas putida\u003c/em\u003e promote tomato growth through P solubilization, nitrogen fixation, and indole-3-acetic acid (IAA) production (He et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). Microorganisms also increase nutrient bioavailability via metabolic activities that increase the SOM content (Coonan et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) while simultaneously inhibiting plant pathogens (Garc\u0026iacute;a-Bayona and Comstock \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Anum et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xia et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Xia \u003cem\u003eet al\u003c/em\u003e. identified Bacillus, which antagonizes Fusarium graminearum to mitigate stalk rot, as a core taxon in disease-resistant maize varieties (Xia et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Collectively, these findings highlight the pivotal role of soil microbes in plant growth and health.\u003c/p\u003e\u003cp\u003eGarlic (\u003cem\u003eAllium sativum\u003c/em\u003e L.) is a major economic crop in China, with Jinxiang County in Shandong Province renowned the \u0026ldquo;Garlic Capital\u0026rdquo; and \u0026ldquo;World Garlic Center\u0026rdquo; owing to its long-standing cultivation practices. However, garlic yields are vulnerable to management practices, particularly when unscientific cultivation reduces soil microbial diversity (Maron et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Singh and Gupta \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This decline may result in pathogen accumulation and inefficient nutrient conversion, thereby impairing garlic growth. To address this issue, we collected rhizospheric soil from two adjacent garlic fields exhibiting significant growth disparities and compared the growth performance of garlic. Investigations revealed that both fields had been cultivated with garlic for more than a decade under uniform fertilization and irrigation management, yet growth differences persisted. By analyzing the soil nutrient content and microbial community structure, we aimed to clarify the roles of physicochemical properties and the microbiota in garlic growth. This study focused on identifying the critical roles of soil nutrients and microbial communities in garlic growth, providing theoretical support and technological innovations for precision fertilization and soil management. Our findings aim to mitigate production risks caused by soil degradation and promote sustainable soil health.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch location and sample collection\u003c/h2\u003e\u003cp\u003eThis study was conducted in farmlands of Jinxian County, Jining city, Shandong Province, China (33\u0026deg;02\u0026prime;N, 116\u0026deg;23\u0026prime;E). The region has a warm temperate monsoon climate, with an annual average temperature of 15.5\u0026deg;C and annual precipitation of 668.6 mm in 2023. The main soil type is natural brown soil (C. Li et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On November 24, 2023, the research team conducted an onsite field investigation of two adjacent garlic fields. These fields shared identical cultivars (Taikong 1) and sowing dates and were free from external nutritional factor disturbances; however, they presented marked differences in growth performance. To facilitate clear distinction, plants with plant heights ranging from 30\u0026ndash;50 cm (representing the better-developed plot) were defined as Group H, with the corresponding plot labeled as Plot H. Conversely, plants with heights below 30 cm (from the less vigorous plot) were designated as Group L, and the plot was labeled as Plot L. To ensure the representativeness and reliability of the data, 8 replicate samples were uniformly collected from each plot via the random sampling method. During sampling, intact garlic root systems were carefully excavated, and rhizosphere soil was collected by gently brushing the root surface, yielding 16 rhizosphere soil samples in total. Each sample was divided into two portions: one for physicochemical analysis and the other stored at -80\u0026deg;C for subsequent DNA extraction.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDetermination of Soil Physical and Chemical Properties\u003c/h3\u003e\n\u003cp\u003eThe total nitrogen (TN) and total phosphorus (TP) contents were determined via an AutoAnalyser3 soil element flow analyzer (Bran\u0026thinsp;+\u0026thinsp;Luebbe, Hamburg, Germany). The soil pH and electrical conductivity (EC) were measured with a Leici pH meter (Shanghai, China) at a water-to-soil ratio of 5:1 (v/w). Nitrate nitrogen (NO₃⁻-N) and ammonium nitrogen (NH₄⁺-N) were analyzed via hydrazine sulfate reduction and indophenol blue colorimetry, respectively. The soil organic carbon (SOC) content was determined via the potassium dichromate volumetric method (Wang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Available phosphorus (AP) was quantified via the molybdate‒ascorbic acid method with a UV‒Vis spectrophotometer (Eppendorf, Germany). The carbon-to-nitrogen (C/N) ratio was calculated as the ratio of SOC to TN (He et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eDNA extraction and sequence analysis\u003c/h3\u003e\n\u003cp\u003eThe soil DNA was extracted via the FastDNA Spin Kit for Soil (Omega, USA), and the DNA quality was evaluated via 1.2% agarose gel electrophoresis. PCR amplification of the bacterial V5\u0026ndash;V7 hypervariable region of the 16S rRNA gene was performed via primers 799F (5\u0026prime;-AACMGGATTAGATACCCKG-3\u0026prime;) and 1193R (5\u0026prime;-ACGTCATCCCCACCTTCC-3\u0026prime;) (Klindworth et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), whereas the fungal ITS1\u0026ndash;ITS2 region was amplified via primers ITS1F (5\u0026prime;-CTTGGTCATTTAGAGGAAGTAA-3\u0026prime;) and ITS2R (5\u0026prime;-GCTGCGTTCTTCATCGATGC-3\u0026prime;) (Schoch et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Paired-end sequencing was conducted on an Illumina NovaSeq 6000 platform at Guangdong Magigene Biotechnology Co., Ltd. (Guangzhou, China). Raw FASTQ files were processed according to the QIIME2 standard protocols (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.qiime2.org/2019.4/tutorials/\u003c/span\u003e\u003cspan address=\"https://docs.qiime2.org/2019.4/tutorials/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Straub et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). After quality filtering, denoising, merging, chimera removal, and alignment via the DADA2 plugin, nonsingleton amplicon sequence variants (ASVs) were retained for downstream analysis. Taxonomic annotation of ASVs was performed via the SILVA v138.1 and UNITE v8.2 databases (Quast et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nilsson et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), followed by removal of contaminant sequences (e.g., mitochondria and chloroplasts). To account for differences in sequencing depth, rarefaction was performed to the minimum depth across all samples (47,456 reads for bacteria and 84,758 reads for fungi). These ASV tables were used for downstream analyses. A total of 1,062,692 high-quality bacterial sequences were generated and assigned to 6,039 ASVs, whereas fungal samples yielded 2,267,159 sequences assigned to 1,546 ASVs. The sequence data were deposited in the NCBI Sequence Read Archive (SRA) under accession number PRJNA1174389.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eAll the statistical analyses and data visualizations were performed in R (v4.3.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The core microbial taxa were defined as ASVs with relative abundances\u0026thinsp;\u0026gt;\u0026thinsp;0.1%. Alpha diversity indices were calculated via the picante package. Principal coordinate analysis (PCoA) based on Bray\u0026ndash;Curtis dissimilarities was conducted via the vegan package to evaluate community structure differences between sites, with significance determined via permutation tests (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Redundancy analysis (RDA) and hierarchical partitioning were performed with the rdacca.hp package to identify the contributions of soil physicochemical properties to microbial diversity and community composition, with significance determined at \u003cem\u003ePadj\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05. Microbial co-occurrence networks were constructed using ASVs with average relative abundances\u0026thinsp;\u0026gt;\u0026thinsp;0.1%. Pairwise Spearman rank correlations were calculated via the ggClusterNet package, and significant interactions were filtered using a uniform threshold (|R| \u0026gt;0.7, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Network edges were weighted by correlation strength, and layouts were generated via the Fruchterman\u0026ndash;Reingold algorithm in Gephi v.0.9.2 (Li et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Community stability was evaluated via the average variability degree (AVD) index (Xun et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Graphical representations (e.g., boxplots, chord diagrams) were generated with the ggplot2 package. Statistical comparisons between groups were performed via the Wilcoxon rank-sum test in IBM SPSS Statistics (IBM Corp., Armonk, NY, USA) (Xia et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024c\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSignificant differences in garlic growth performance between adjacent fields\u003c/h2\u003e\n \u003cp\u003eAnalysis of garlic plant height revealed that the mean plant height at site H was 1.44-fold greater than that at site L (Wilcoxon rank-sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB\u0026ndash;C). To investigate the underlying factors contributing to this discrepancy, we characterized the physicochemical properties and microbial community compositions of rhizosphere soils from both sites.\u003c/p\u003e\n \u003cp\u003eThe \u003cstrong\u003eAP content was significantly greater at site H than at site L\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo determine whether rhizosphere soil physicochemical properties differed between sites H and L, we measured key parameters in garlic rhizosphere soil samples. Among the analyzed properties, the available phosphorus (AP) content at site H was significantly greater than that at site L (Wilcoxon rank-sum test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas no significant differences were detected in pH, EC, TN, TP, NH₄⁺-N, NO₃⁻-N, SOC, or the C/N ratio (all *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These results highlight AP content as a key factor contributing to the observed differences in garlic growth between the two sites.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSignificant differences in rhizosphere microbial community diversity\u003c/h3\u003e\n\u003cp\u003eTo evaluate bacterial and fungal diversity between sites H and L, \u0026alpha; diversity indices were calculated for bacteria, core bacterial taxa (defined as ASVs with a mean relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;0.1% across samples), fungi, and core fungi. The Shannon index of the core bacteria at site H was significantly greater than that at site L (Wilcoxon rank-sum test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). No significant differences were detected in bacterial, fungal, or core fungal \u0026alpha; diversity (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, B, D).\u003c/p\u003e\n\u003cp\u003ePCoA based on Bray\u0026ndash;Curtis dissimilarities revealed distinct clustering patterns for bacterial and core bacterial communities between sites (PERMANOVA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE). In contrast, the fungal and core fungal communities presented no significant structural differences (PERMANOVA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF). Additionally, the exogenous nutrient levels were consistent between the two fields. These results suggest that bacterial community diversity and composition are critical factors contributing to the observed garlic growth disparities between sites H and L.\u003c/p\u003e\n\u003ch3\u003eHigher stability of bacterial communities at location H\u003c/h3\u003e\n\u003cp\u003eTo evaluate rhizosphere microbial network complexity and stability, ecological networks were constructed for bacteria and fungi at sites H and L. Network analysis revealed that bacterial and fungal networks at site H presented greater complexity and connectivity than did those at site L, characterized by more nodes, a higher average degree, a larger network diameter, greater centralization, and greater closeness centralization (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;B; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, the bacterial network at site L presented an increased average path length, indicating decreased responsiveness to external perturbations (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), whereas the fungal network at site L presented a decreased average path length, reflecting faster adaptive capacity (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Site L also harbored more keystone nodes, suggesting that garlic actively recruits critical microbes under stress. Community stability, measured by the AVD index, was significantly greater at site H for both bacteria and fungi (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). These results underscore bacterial community stability as a key determinant of garlic growth disparities between sites.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNetwork properties of bacteria and fungi\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBacteria_H\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBacteria_L\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFungi_H\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFungi_L\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEdges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConnectance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094026128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085794479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096656217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095872518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.40366972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.70319635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.409090909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.340909091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage Path Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.879037574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.985647338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.298343637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.251869175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.023809524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.85206652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.111667761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.307278838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentralization Degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104130554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083930292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13322884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122518286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentralization Closeness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.141528417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.113956041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195748564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163661951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe Number of Keystone Nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe \u003cstrong\u003eAP content is significantly correlated with rhizosphere bacterial community composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRDA was used to explore the influence of abiotic factors, specifically soil physicochemical properties, on bacterial and fungal communities. The first two axes of the RDA explained 30.63% of the total variation in the bacterial communities. Among these factors, AP had the most significant impact on the bacterial community structure (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.7638, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The RDA results revealed that differences in AP content affected the distribution of bacterial communities, whereas the distribution of fungal communities was not influenced by these abiotic factors (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings suggest that AP and bacterial communities are crucial factors contributing to the significant differences in garlic growth between sites H and L.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of RDA permutation test for bacteria, fungi communities and soil physical and chemical properties\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBacteria\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFungi\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePadj\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndividual (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePadj\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndividual (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.179336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.858857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.892286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.343983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.321534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.106433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.858857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.763771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.069747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.858857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.139292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.858857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.180848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.101308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.858857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.140689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.858857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eLocation H is enriched with numerous microorganisms associated with P transformation\u003c/h2\u003e\n \u003cp\u003eTo quantify the contributions of soil physicochemical properties and microbial communities to garlic plant height at sites H and L, we performed hierarchical partitioning analysis. The results revealed that the bacterial community had the greatest contribution to garlic plant height, followed by AP (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE). To further clarify the role of bacterial communities in determining garlic plant height, we analyzed the species composition of rhizosphere microbial communities at both sites. On the basis of the ASV classification results, \u003cem\u003eSphingomonas\u003c/em\u003e was the most abundant genus among the rhizosphere bacteria at both sites. However, the second most abundant genera differed: \u003cem\u003eFlavobacterium\u003c/em\u003e at site H and \u003cem\u003eBacillus\u003c/em\u003e at site L. The most abundant fungus at site H was \u003cem\u003eMortierella\u003c/em\u003e, whereas at site L, it was \u003cem\u003eFusarium\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA, B), a common soil-borne plant pathogen. To identify the bacterial and fungal genera that differed between the microbial communities at sites H and L, we conducted linear discriminant analysis effect size (LEfSe) analysis. Significant differences were found in the enriched fungi and bacteria between the two sites (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). At site H, 36 bacterial taxa met the score criteria, whereas 22 bacterial taxa met the score criteria at site L (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). We also found that the main enriched genera at site H, \u003cem\u003eArthrobacter\u003c/em\u003e and \u003cem\u003eThauera\u003c/em\u003e, are typically involved in the transformation of minerals such as P and sulfur, which promotes plant nutrient absorption and inhibits plant pathogens (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). Among the fungal taxa, 5 groups at site L and only 1 group at site H met the linear discriminant analysis (LDA) score criteria (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD). Additionally, the enriched \u003cem\u003ePenicillium\u003c/em\u003e at site L can cause stem-base rot and fruit rot in vegetables and fruits (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD). Overall, these results suggest that the differences in garlic growth at sites H and L are influenced mainly by differences in the composition of rhizosphere microorganisms.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the two fields where we investigated garlic growth performance, the fertilization rates were standardized. Thus, after excluding interference from exogenous nutrient intrusion, significant differences in available phosphorus (AP) remained between the two fields. The role of P in plants is extremely extensive, as it is involved in critical physiological processes such as energy transfer, cell division, and nucleic acid synthesis (Wang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). P is also a structural component of plant cell membranes and plays a vital role in photosynthesis. P deficiency can significantly impair plant growth and development, ultimately reducing crop yield. In our study, the AP content at location H was significantly greater than that at location L (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e), corroborating previous findings that P deficiency disrupts plant growth and development, thereby impacting crop productivity. Plant rhizosphere microorganisms play a pivotal role in plant stress resistance, serving as major drivers of plant defense responses (Zhalnina et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Stress tolerance is mediated by the collective activities of microbial communities, and more diverse communities are particularly effective at increasing plant stress resistance (Sun et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This finding is consistent with our findings that the core bacterial community diversity was greater at site H than at site L (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Additionally, the bacterial and fungal networks at location H presented greater complexity and connectivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which is consistent with previous reports that high-diversity microbial communities provide more ecological niches and functional redundancy, thereby strengthening plant stress tolerance.\u003c/p\u003e\u003cp\u003eThe terrestrial microbiome is recognized as a ubiquitous and essential component of ecosystems, playing critical roles in maintaining organic carbon cycling, enhancing nutrient use efficiency, and supporting productivity (Xun et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fundamentally, the sustainability of terrestrial ecosystem functions and services depends on microbiome stability (Griffiths and Philippot \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which is typically measured by the degree of variation or turnover in microbial communities. In this study, the bacterial community stability at location H was significantly greater than that at location L (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The enhanced stability at location H likely indicates greater durability and reliability of ecosystem services, contributing to overall terrestrial ecosystem health. The results of hierarchical partitioning further highlighted the critical role of bacterial communities in garlic growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Bacterial communities are highly sensitive to soil environmental changes, possibly because of their reliance on small-molecule organic matter and short-distance nutrient exchange in the soil (Yalong Xu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, fungi acquire nutrients through robust mycelial growth, enabling them to form extensive network structures and maintain relatively stable community compositions (He et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor plants, soil P is often unavailable for direct uptake and requires conversion to AP before absorption. Soil microorganisms play a critical role in this \"transformation\" process (Shidong He et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Phosphate-solubilizing bacteria (PSB), which solubilize inorganic P or mineralize organic P to facilitate plant growth (Bargaz et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Timofeeva et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), were significantly correlated with the AP content at both locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, \u003cem\u003eArthrobacter\u003c/em\u003e and \u003cem\u003eThauera\u003c/em\u003e were significantly enriched at location H. Previous studies have demonstrated that these genera possess phosphate-solubilizing capabilities and facilitate P transformation, effectively converting soil P into AP (Vanissa et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ren et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This likely explains the significant AP content difference between locations and subsequent variations in garlic growth.\u003c/p\u003e\u003cp\u003eIn many agricultural ecosystems, soil-borne diseases pose severe threats to crop health (Raaijmakers et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Analysis of the top ten bacterial and fungal genera revealed greater \u003cem\u003eFusarium\u003c/em\u003e abundance at site L (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). \u003cem\u003eFusarium\u003c/em\u003e species are common pathogens that cause garlic root rot (Garbeva et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), infect various plants and induce diseases such as root rot, stem rot, basal stem rot, blossom blight, and ear blight. These diseases are notoriously difficult to control in agricultural production (Li et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). LEfSe analysis further revealed significant enrichment of \u003cem\u003eScedosporium\u003c/em\u003e at site L (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), a genus associated with important crop diseases that can cause substantial yield losses if unmanaged (Brauer et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Taken together, these results underscore the close association between soil microbial communities and plant growth. Further research and targeted management strategies could leverage microbial potential to advance sustainable agriculture.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study aimed to investigate the role of garlic rhizosphere microorganisms in plant growth by comparing two adjacent plots with contrasting growth performance. Taken together, these results underscore the close association between soil microbial communities and plant growth in bacterial communities and the AP content across locations. These findings highlight the critical roles of bacterial communities and AP content as key determinants of garlic growth disparities. RDA further revealed strong correlations between bacterial community compositions and soil AP levels, suggesting that bacterial communities indirectly influence garlic growth through regulating P bioavailability. Differential abundance analysis revealed substantial enrichment of \u003cem\u003eArthrobacter\u003c/em\u003e and \u003cem\u003eThauera\u003c/em\u003e at Location H, which are known to directly mediate P transformation processes, providing mechanistic insights into soil nutrient cycling. Additionally, the presence of fungal pathogens in the rhizosphere microbiome was identified as a potential factor contributing to the observed growth disparities. Collectively, these results advance our understanding of the rhizosphere microbial mechanisms underlying garlic growth and provide a theoretical basis for developing microbe-driven management strategies for garlic cultivation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAvailable phosphorus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal phosphorus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eElectrical conductivity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNO₃⁻-N\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNitrate nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNH₄⁺-N\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmmonium nitrogen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSOC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSoil organic carbon\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eC/N\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCarbon-to-nitrogen ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIAA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIndole-3-acetic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSOM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSoil organic matter\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASVs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmplicon sequence variants\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSRA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSequence Read Archive\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCoA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal coordinate analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRDA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRedundancy analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAVD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAverage variability degree\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLEfSe\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLinear discriminant analysis effect size\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLDA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLinear discriminant analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePSB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphate-solubilizing bacteria\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Key Research and Development Project in Shandong Province of China (2024TZXD062, 2023CXPT045, 2023TZXD004), the National Natural Science Foundation of China (42407427, 42377309, 42077027), the \u0026lsquo;First Class Discipline\u0026rsquo; Construction Project of Shandong Agricultural University (SKL81103, SKL81110).\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eRX W and SD H planned and designed the study and completed the major data analysis. TT W and LG L assisted with sample collection. DL F, LL L, WC S and Z G were involved in writing the article. X L guided SD H and RX W in drafting the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis work was supported by the Key Research and Development Project in Shandong Province of China (2024TZXD062, 2023CXPT045, 2023TZXD004), the National Natural Science Foundation of China (42407427, 42377309, 42077027), and the \u0026lsquo;First Class Discipline\u0026rsquo; Construction Project of Shandong Agricultural University (SKL81103, SKL81110).\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eThe sequence data have been deposited in the NCBI SRA database under accession number PRJNA1174389.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnum H, Tong Y, Cheng R (2024) Different Preharvest Diseases in Garlic and Their Eco-Friendly Management Strategies. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants13020267\u003c/span\u003e\u003cspan address=\"10.3390/plants13020267\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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Soil Biol Biochem. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2019.107521\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2019.107521\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amoa","sideBox":"Learn more about [Annals of Microbiology](https://www.springer.com/journal/13213)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/amoa/default.aspx","title":"Annals of Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Rhizosphere microbial community, Garlic, Available phosphorus, Diversity, Phosphorus transformation, Growth differences","lastPublishedDoi":"10.21203/rs.3.rs-7336697/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7336697/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rhizosphere microbiome and soil nutrients are critical for crop growth, but their roles in regulating garlic productivity remain unclear. This study aimed to identify key factors driving growth differences in adjacent garlic fields with uniform management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRhizosphere soils from two adjacent plots (H: vigorous growth; L: stunted growth) were analyzed for physicochemical properties and microbial communities via 16S rRNA and ITS sequencing, combined with network analysis and redundancy analysis (RDA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults showed significantly higher available phosphorus (AP) in H than L. Bacterial communities in H exhibited greater stability and core diversity, with distinct compositional clustering between sites (PERMANOVA, P \u0026lt; 0.001). RDA indicated AP strongly correlated with bacterial community structure (R²=0.7638, P=0.009), and H was enriched with phosphorus-transforming taxa (e.g., Arthrobacter, Thauera). Hierarchical partitioning highlighted bacterial communities as the primary driver of growth differences, followed by AP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings reveal that AP availability and rhizosphere bacterial stability, mediated by phosphorus-transforming microbes, collectively shape garlic growth, providing insights for optimizing garlic cultivation through microbial management.\u003c/p\u003e","manuscriptTitle":"Rhizosphere microbial stability and phosphorus availability drive garlic growth differences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 15:46:46","doi":"10.21203/rs.3.rs-7336697/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-25T13:05:48+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-19T08:15:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-12T22:28:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Microbiology","date":"2025-08-10T00:07:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amoa","sideBox":"Learn more about [Annals of Microbiology](https://www.springer.com/journal/13213)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/amoa/default.aspx","title":"Annals of Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"31dca549-bdbc-4c64-b598-60ca0e2ff88b","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:03:05+00:00","versionOfRecord":{"articleIdentity":"rs-7336697","link":"https://doi.org/10.1186/s13213-025-01837-3","journal":{"identity":"annals-of-microbiology","isVorOnly":false,"title":"Annals of Microbiology"},"publishedOn":"2026-01-05 15:57:57","publishedOnDateReadable":"January 5th, 2026"},"versionCreatedAt":"2025-08-27 15:46:46","video":"","vorDoi":"10.1186/s13213-025-01837-3","vorDoiUrl":"https://doi.org/10.1186/s13213-025-01837-3","workflowStages":[]},"version":"v1","identity":"rs-7336697","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7336697","identity":"rs-7336697","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}