Differential impacts of Funneliformis mosseae and Rhizophagus intraradice on soil quality and rice yield in paddy fields: mediated by AMF-rice-rhizosphere microbe interactions

Differential impacts of Funneliformis mosseae and Rhizophagus intraradice on soil quality and rice yield in paddy fields: mediated by AMF-rice-rhizosphere microbe interactions | 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 Differential impacts of Funneliformis mosseae and Rhizophagus intraradice on soil quality and rice yield in paddy fields: mediated by AMF-rice-rhizosphere microbe interactions Minyong Shi, Yanling Wu, Ruotong Wu, Junjie Liu, Feng Shi, Xiaoxu Fan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6936303/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Oct, 2025 Read the published version in Mycorrhiza → Version 1 posted 8 You are reading this latest preprint version Abstract While the positive impact of arbuscular mycorrhizal fungi (AMF) on rice growth has been well established, the specific mechanisms through which different species of AMF regulate rice growth and the rhizosphere microecosystem are still not fully understood. This research investigated two AMF species, Funneliformis mosseae (Fm) and Rhizophagus intraradices (Ri), to uncover their distinct effects on rice rhizosphere soil characteristics, microbial community structure, and rice yield. Field experiments showed that the Fm treatment resulted in a significantly higher yield increase (26.96%) compared to Ri (21.19%). Although both AMF species significantly increased mycorrhizal colonization rates (Fm: 78.23%, Ri: 70.13% at maturity), they induced distinct improvements in soil properties. Specifically, Fm significantly boosted soil enzyme activity, with urease and cellulase activities 47.29% and 24.62%, respectively, higher than Ri Conversely, Ri promoted the accumulation of soil available phosphorus (69.81% higher than Fm). Additionally, the two AMF strains influenced the rhizosphere microbial community through different regulatory mechanisms. Fm significantly enriched carbon cycle-related bacterial groups such as Chloroflexota and Actinomycetota. Ri, however, not only significantly increased microbial α-diversity but also specifically enriched sulfur cycle functional bacterial groups. Crucially, the two AMF species optimized the "AMF-rice-rhizosphere microorganisms" interaction network through differential structural modifications. In the Fm treatment, fungal community network modularity was significantly enhanced, while the bacterial network under Ri treatment exhibited stronger connectivity. This study elucidates the distinct mechanism by which AMF species synergistically enhance rhizosphere soil microenvironment quality and increase rice yield. These findings provide a theoretical basis for the sustainable management of rice fields and suggest new directions for developing environmentally friendly agricultural technologies. Arbuscular mycorrhizal fungi (AMF) Rice rhizosphere soil Soil nutrients Rhizosphere microorganisms Sustainable agriculture Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Global climate change and the increasing population pose a dual threat to the global food security and agricultural sustainability (Gao et al. 2025 ). Projections by the Food and Agriculture Organization of the United Nations (FAO) estimates the global population will surpass 9.7 billion by 2050, requiring a 60% increase in food production compared to current levels (Fu et al. 2023 ; Zhao et al. 2017 ; Saud et al. 2022 ). Concurrently, climate change impacts, including extreme weather events, water resources imbalances, and arable land deterioration, severely impact on global food systems (Zhao et al. 2022 ; Che et al. 2023 ). As a crucial staple crop worldwide, rice production directly affects human welfare and health (Zhang et al. 2018b ). However, the heavy reliance on chemical fertilizers and pesticides in traditional rice farming is triggering diverse environmental problems, while also posing potential risks to human health (Zhang et al. 2018a ). Therefore, given the pressures of climate change and resource scarcity, it has become imperative to explore strategies for reducing fertilizer dependency, enhancing rice resilience, and attaining sustainable rice production in current agricultural research. Sustainable agriculture increasingly relies on microbial inoculants as eco-friendly fertilizers. These living microorganism formulations enhance crop performance, improve nutrient use efficiency, and bolster plant stress resistance, thereby reducing dependence on chemical fertilizers and/or the pesticides (Chen et al. 2023 ). Among these, arbuscular mycorrhizal fungi (AMF) are essential components that form symbiotic relationships with over 80% of plant species. Through their extensive hyphal networks, AMF significantly boost water and nutrient absorption, stimulating plant growth and biomass production (Dong et al. 2021 ). Concurrently, AMF enhance soil enzymatic activities (such as urease, phosphatase) to accelerate organic matter decomposition and nutrient cycling, thereby improving soil health (Jiang et al. 2021 ). However, species-specific mechanisms by which AMF regulate rhizosphere nutrient dynamics and enzyme functions remain poorly understood, particularly in paddy ecosystems where flooded conditions alter microbial functionality Rhizosphere microbial communities plays a critical role in the transformation of soil nutrients and the health of crops through organic matter decomposition, nitrogen fixation, and phosphorus solubilization (Zhang et al. 2022 , Zhalnina et al. 2018 ). AMF impact the structure and function of microbial communities by releasing organic acids, glomalin, and signaling molecules, leading to improved soil nutrient cycling and plant health (Deveau et al. 2018 ). For instance, Funneliformis mosseae (Fm) and Rhizophagus intraradices (Ri) species establish hyphal networks that create unique microenvironments, selectively enriching beneficial bacterial taxa (such as Sphingomonas , Pseudarthrobacter ) involved in nitrogen cycling while suppressing pathogens through resource competition (Zhou et al. 2020 ). This process enhances both rhizosphere soil fertility and plant nutrient uptake efficiency, significantly enhancing plant growth (Zhou et al. 2022 ). However, species-specific regulatory mechanisms of AMF on functional microbial groups remain inadequately characterized, particularly in paddy ecosystems where flooded conditions alter redox potential and microbial functionality. However, the following research questions have been posited: firstly, how do AMF species (Fm and Ri) differentially recruit microbial consortia for carbon, nitrogen and sulfur cycling, and secondly, whether AMF-induced shifts in enzyme activities (for example, urease, phosphatase) correlate with functional gene abundance under anaerobic stress. The scope of this research was restricted to those of two AMF species of Fm and Ri on the impact on soil quality and rice yield. The oblective of current study were (1) to reveal the distinct role of Fm and Ri regulating nutrient dynamics and key enzyme activities in the rice rhizosphere, thereby enhancing rice yield by improving nutrient activation; (2) to investigate how Fm and Ri selectively rhizospheric microbiota diversity, community assembly, and ecological networks, driving functional specialization via formation of an interactive "AMF-rice-microbe" tripartite system. This study systematically elucidates the mechanisms of action of different AMF inoculants within the rice paddy ecosystem, offering a theoretical foundation and technical backing for sustainable management of rice fields and the judicious utilization of AMF. 2. Materials and methods 2.1 Experimental materials The Rice ( Oryza sativa L.) seeds, "Daohuaxiang 2", were provided by the Key Laboratory of Cold Region Ecological Restoration and Resource Utilization of Heilongjiang University. Two arbuscular mycorrhizal fungi (AMF) species were selected for investigation: Funneliformis mosseae (Fm) and Rhizophagus intraradice s (Ri). Both AMF inocula were propagated in pot cultures using sorghum ( Sorghum bicolor L.) as the host plants. The inocula contained spores, hyphae and root fragments, with spore densities of approximately 65 ± 5 spores per gram of inoculum for both species. All fungal inocula were maintained and propagated by the Key Laboratory of Cold Region Ecological Restoration and Resource Utilization in Heilongjiang Province. 2.2 Experimental design The field experiment was carried out at Heilongjiang University's Hulan campus (126°38′35″ E, 45°59′45″ N), covering 81 hectares of land, including 52 hectares of arable soil. The site is characterized by a mid-temperate continental monsoon climate, with an average annual temperature of 3.3°C, 2,661.4 hours of sunshine, and 505.4 mm of precipitation annually. The predominant soil types—black soil and black calcium soil—provide favorable conditions for rice cultivation. Rice seeds were surface-sterilized in 5% NaCl for 30 minutes, rinsed thoroughly with distilled water, and then soaked in distilled water at 25°C for 24 hours. Germination was induced at 28°C for 48 hours. For AMF inoculation, Fm and Ri were blended with seedling substrate at a 5% (w/w) ratio. Seedlings were grown in either AMF-inoculated (Fm or Ri) or non-inoculated (CK) substrates under controlled conditions (light, temperature, and moisture). Each treatment included three replicates (nine trays total), spaced 20 cm apart to prevent cross-contamination. After 36 days, seedlings were manually transplanted to field plots, with 3–5 seedlings per hole. The study employed a randomized complete block design with three treatments (CK, Fm, Ri), each replicated three times (totaling nine experimental plots of 1.5 m × 2 m dimensions). Protective soil ridges were constructed between plots to minimize interference. Standard agronomic practices for rice cultivation were maintained throughout the growing season (Li et al. 2024 ; Wen et al. 2024 ; Shi et al. 2025 ). 2.3 Experimental methods 2.3.1 Mycorrhizal colonization rate in rice roots The arbuscular mycorrhizal (AM) colonization of rice roots was systematically assessed at different growth stages: seedling stage (36 days), tillering stage (56 days), grain-filling stage (109 days), and harvest stage (167 days). For each treatment, five randomly selected plant samples were collected. Approximately 60–80 fresh root segments (1 cm in length) per sample were stained with 0.05% trypan blue. The roots were subsequently cleared, stained, destained, and mounted on slides for microscopic observation. AM fungal colonization rates were calculated using the following formula(Sun et al. 2022 ): $$\:\text{r}\text{o}\text{o}\text{t}\:\text{c}\text{o}\text{l}\text{o}\text{n}\text{i}\text{z}\text{a}\text{t}\text{i}\text{o}\text{n}\:\left(\text{%}\right)=\frac{\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{a}\text{r}\text{b}\text{u}\text{s}\text{c}\text{u}\text{l}\text{a}\text{r}\:\text{m}\text{y}\text{c}\text{o}\text{r}\text{r}\text{h}\text{i}\text{z}\text{a}\:-\:\text{p}\text{o}\text{s}\text{i}\text{t}\text{i}\text{v}\text{e}\:\text{s}\text{e}\text{g}\text{m}\text{e}\text{n}\text{t}\text{s}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{s}\text{e}\text{g}\text{m}\text{e}\text{n}\text{t}\text{s}\:\text{s}\text{t}\text{u}\text{d}\text{i}\text{e}\text{d}}\times\:100\text{\%}$$ 1 2.3.2 Rhizosphere soil nutrients and enzyme activities in paddy fields Soil pH was determined using a calibrated pH meter (FE20, Mettler-Toledo) in a 1:2.5 (wv) soil-water mixture. Organic matter (SOM) was measured by potassium dichromate oxidation with external heating, and total organic carbon (TOC) was analyzed with a TOC analyzer (Multi NC 3100, Analytik Jena). Nutrient contents, including total nitrogen (TN), total phosphorus (TP), available phosphorus (AP), available potassium (AK), nitrate nitrogen (NO₃⁻-N), and ammonium nitrogen (NH₄⁺-N), were assessed using a SmartChem200 discrete autoanalyzer. Soil enzyme activities, including urease (S-UE), cellulase (S-CL), α-glucosidase (S-AGL), β-glucosidase (S-BG), N-acetyl–D-glucosaminidase (S-NAG), and alkaline phosphatase (S-ALP), were evaluated with commercial assay kits (Solarbio, Beijing, China) according to the manufacturer’s instructions. 2.3.3 Soil quality index (SQI) in paddy rhizosphere The rhizosphere soil quality index (SQI) was calculated based on indicators such as nutrient content and enzyme activity (Yuan et al. 2020 ). Initially, all indicator data were standardized to a 0–1 scale. Subsequently, principal component analysis (PCA) was performed using SPSS software. The Kaiser-Meyer-Olkin (KMO) value was 0.690 ( p < 0.001), indicating the suitability of the data for factor analysis. Key parameters relevant to soil health were carefully chosen according to the minimum data set principle to guarantee the effectiveness and precision of the assessment. Principal components with eigenvalues > 1 were extracted, resulting in two principal components, PC1 and PC2, with a cumulative contribution rate of 91.426%, which effectively explained the selected indicators. The weights of each indicator were calculated using factor loading values (λ) (Table S1 ), and the rhizosphere soil quality index was calculated using formula (2). Here, PCi stands for the score of the ith principal component, wi represents the weight of the ith principal component (usually corresponding to its contribution rate), and k indicates the number of principal components selected. 2.3.4 Rice yield and its components Crops from each individual plot were harvested separately to prevent cross-contamination. Panicle density, grain counts, fertility rate, and thousand-grain weight were assessed to gauge growth performance. These measurements, along with plot dimensions, were utilized to determine the theoretical yield and estimate the optimal production potential given the current conditions. Seed-setting rate (%) = (Number of filled grains / Total grains) × 100 (3) Rice yield (kg/ha) = [Panicles/m² × Grains/panicle × Seed-setting rate (%) × 1000-grain weight (g) × 10⁻⁵] × 10⁴ (4) 2.3.5 DNA extraction, sequencing process, and sequence analysis The genomic DNA was isolated from soil samples with the FastPure Soil DNA Isolation Kit. DNA quality was evaluated by 1% agarose gel electrophoresis, while concentration, purity, and integrity were measured using the NanoDrop 2000 (Thermo Scientific, USA). Following the manufacturer’s protocol (Liu et al. 2016 ), the extracted DNA served as the template. The bacterial 16S rRNA V3-V4 region was amplified with primers 338F (5'-ACTCCTACGGGGAGGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'), whereas the fungal ITS V4-V5 region was amplified using ITS1F (5'-CTTGGTCATTTAGAGAGGAAGTAA-3') and ITS2R (5'-GCTGGTCTTCATCGATGC-3'). Fresh soil samples were subsequently submitted to Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai) for 16S rRNA and ITS rRNA sequencing on the Illumina NextSeq 2000 platform. 2.3.6 Statistical analysis Data were statistically analyzed using SPSS 25.0 and visualized through Origin 2022. One-way analysis of variance (ANOVA) was utilized to examine the significance of variances among treatments, and the least significant difference (LSD) test was utilized to evaluate significant variations between groups. The alpha-diversity indices of microbial communities, including the Chao1, Shannon, and Simpson indices, were analyzed using the Wilcoxon rank-sum test in Mothur software (v1.30.2), as well as the R language stats package (v3.3.1) for both the Wilcoxon rank-sum test and Kruskal-Wallis test. Microbial beta-diversity was assessed via PCoA and PERMANOVA (Bray-Curtis distance) using R's vegan package (v3.3.1). Stacked bar plots at the phylum and genus levels were generated in R. Differential taxa were identified using the LEfSe tool (LDA > 3, p < 0.05) ( http:huttenhower.sph.harvard.eduLEfSe ). FAPROTAX and FUNGuild tools ( http:www.funguild.org ) were applied for the classification and annotation of microbial functional groups. The Mantel test was utilized with the vegan package (v3.3.1) in R to examine the relationships between environmental factors, microbial community structure, and rice yield. Species were filtered based on Spearman correlation (r > 0.6, p < 0.05), and the correlation network was visualized using Gephi. Finally, structural equation modeling (SEM) was established using SPSS AMOS 26 to elucidate the multi-level regulatory pathways of "rhizosphere microbiota-paddy soil-rice yield". 3. Results 3.1 Impact of Fm and Ri on arbuscular mycorrhizal colonization efficiency in rice Electron microscopy confirmed the establishment of mutualistic symbiosis between rice roots and both AMF species, showed AMF vesicles and internal hyphal structures within the rice roots (Fig. 1 A, B). Mycorrhizal colonization rates increased progressively across growth stages, with Fm consistently exhibiting the highest colonization rate (78.23% at harvest vs. 70.13% for Ri; p < 0.05). Notably, native AMF colonized control (CK) roots during grain-filling and harvest stages (6.00% and 18.33%, respectively), though at significantly lower rates than inoculated treatments ( p < 0.05; Fig. 1 C). These findings validate the successful formation of enduring mycorrhizal partnerships with rice roots by both AMF inoculants in the natural environment. Importantly, the mycorrhizal colonization rate in the Fm treatment was significantly higher than that in the Ri treatment ( p < 0.05), indicating a greater colonization capacity of Fm in rice roots. 3.2 Influence of Fm and Ri on paddy rhizosphere soil quality and rice yield Inoculation with AMF significantly boosted the rhizosphere nutrient availability. Specifically, Fm and Ri increased SOM by 147.37% and 135.00%, respectively, compared to CK. AK content was also notably higher in Fm and Ri than in CK ( p < 0.05). While, the TN, TP, NO 3 − -N, and NH 4 + -N content were highest in Fm, surpassing CK and Ri significantly ( p < 0.05). Conversely, AP reached its peak in Ri, increasing by 185.81% and 69.81% compared to CK and Fm, respectively. Despite the significant impact of Fm and Ri on rhizosphere soil nutrients, the pH remained consistent across treatments. The enzymatic function also demonstrated a positive response to the inoculation of AMF. Compared to the CK, the activities of S-AGL and S-BG increased by 52.56% and 27.51% in Fm, and and 112.28% and 94.59% in Ri. Moreover, S-UE, S-CL, S-NAG, and S-ALP activities were significantly higher in Fm than in Ri ( p < 0.05), increasing by 47.29%, 24.62%, 23.07%, and 58.80%, respectively (Table S2). A notable rise in the SQI values within the Fm and Ri treatment cohorts as compared to the CK ( p < 0.05). Specifically, SQI values increased by 77.35% and 57.07% in the Fm and Ri relative to the CK (Fig. 2 A). Additionally, the rice yield increased by 26.96% and 21.19% ( p < 0.05) (Fig. 2 B). Linear regression confirmed a strong positive correlation between SQI and yield (R = 0.82, p < 0.001; Fig. 2 C), underscoring that AMF-enhanced soil functionality and rice yield. 3.3 Role of Fm and Ri in shaping rhizosphere microbial diversity Bacterial bacterial α-diversity differed significantly among treatments (Fig. 3 A). Specifically, the Ri treatment showed the highest Chao1 and Shannon indices, indicating greater species richness and even distribution within the bacterial community. In contrast, the Simpson index in the Ri treatment was significantly lower than that in the CK and Fm treatments ( p < 0.05), suggesting the prevalence of specific bacterial species in the Ri treatment. Similar trends were also observed in fungal diversity (Fig. 3 B). Bray-Curtis distance-based PCoA revealed compositional divergence in rhizosphere microbiota (Fig. 3 C, D). Bacterial communities exhibited significant spatial separation among treatments ( p < 0.05), with PCoA1 and PCoA2 collectively explaining 62.44% of variance. Fungal communities showed no significant structural shifts across treatments (PCoA1 and PCoA2 = 61.77% variance). This contrast highlights that Fm and Ri inoculation selectively reshaped bacterial conmmunities, while maintaining fungal community in rice soils. 3.4 Modulation of rhizosphere microbiota composition and function by Fm and Ri Fm and Ri treatments fundamentally reshaped rhizosphere microbial composition, with 43 bacterial phyla, 490 bacterial genera, 14 fungal phyla, and 255 fungal genera identified. Analysis of the top 10 most abundant taxa (Fig. 4 A) revealed that at the bacterial phylum level, Chloroflexota (22.9%), Pseudomonadot a (21.66%), and Acidobacteriota (10.54%) were the dominant phyla across all three treatments, followed by Thermodesulfobacteriota (10.04%), Bacteroidota (7.72%), and Actinomycetota (5.29%), accounting for over 75% of the relative sequence abundance. In particular, Fm and Ri significantly increased Chloroflexota and Acidobacteriota , while decreased Pseudomonadota and Bacteroidota .At the genus level of bacteria, Anaerolinea (3.11%) and Pseudarthrobacter (1.51%) were the prevailing genera in all treatments, with a noticeable increase of both Anaerolinea and Pseudarthrobacter in the rhizosphere soil of Fm and Ri treatments. As for the fungal phylum, Ascomycota (47.41%) and Basidiomycota (18.10%) were the prevailing phyla, followed by Mortierellomycota (4.83%) and Chytridiomycota (3.88%), accounting for over 70% of the relative sequence abundance. Notably, Fm primarily enriched Basidiomycota , Mortierellomycota , and Chytridiomycota , whereas Ri primarily enriched Ascomycota . At the fungal genus level, the Fm enriched Tausonia , Mortierella , and Talaromyces , while the Ri treatment enriched Cladosporium , Psilocybe , Pseudeurotium , and Trichocladium . The LEFSe analysis revealed 58 bacterial biomarkers and nine fungal biomarkers (LDA > 3.0, p < 0.05) in the rice rhizosphere communities (Fig. 4 B). In the rhizosphere soil bacterial community, the top three enriched biomarkers in the Fm treatment were g__norank_f__Aggregatilineaceae , f__norank_o__Chthoniobacterales , and g__norank_o__Chthoniobacterales , while the Ri treatment biomarkers included p__Verrucomicrobiota , o__Pedosphaerales , and c__Verrucomicrobia . At the genus level in the fungal community, g__Pyrenochaetopsis was significantly enriched in the Fm treatment, and g__Ceratobasidiaceae_gen_Incertae_sedis was significantly enriched in the Ri treatment. FAPROTAX and FUNGuild demonstrated AMF-induced metabolic specialization (Fig. 4 C). Compared to the CK treatment, the Fm significantly enhanced functions related to carbon metabolism (such as anaerobic and aerobic heterotrophy). In contrast, the Ri treatment showed significant enhancement in sulfur cycling (such as respiration of sulfur compounds) and energy metabolism. This indicates that Fm and Ri can promote soil carbon and sulfur cycling, thereby driving the mineralization and supply of nutrients in the rhizosphere soil. While, Fm and Ri may decrease the relative abundance of pathogenic bacteria (such as Mycobacterium tuberculosis , parasites, etc.), thereby suppressing the propagation of harmful microorganisms in the rhizosphere soil. The ecological functions revealed that the nutritional types of rhizosphere soil fungi are diverse, including pathogenic, symbiotic, and saprophytic types. The relative abundance of symbiotic fungi (for example, Glomeromycota ) and saprophytic fungi (such as Basidiomycota and Ascomycota ) significantly increased, indicating that the Fm and Ri not only promoted AMF colonization but also significantly enhanced the activity of saprophytic fungi, consistent with the FUNGuild functional prediction results. 3.5 Fm and Ri-induced restructuring of rhizosphere microbial interaction networks Network analysis of rhizosphere microbiota at genus level demonstrated that both Fm and Ri significantly resstructured microbial interaction patterns (Fig. 5 ). It was observed that the network properties of nodes and edges in fungal communities were higher than those of CK, indicating AMF fungi may cause antagonistic fungi to play a central role in the network. This can be confirmed by evidence of negatively correlated connections in Fm. Significantly, the co-occurrence network constructed with Fm exhibited a higher proportion of positive correlations (1,515 edges) in fungal communities, along with a greater average weighted degree, clustering coefficient, and superior modularity compared to the network treated with Ri. These results indicate that the Fm treatment promotes a more stable and functionally organized fungal community structure. In contast, the bacterial co-occurrence network under Ri treatment had a larger proportion of positive edges (3267), with a relatively higher average weighted degree and average clustering coefficient compared to Fm, suggesting that Ri treatment may promote more bacterial mutualistic relationships. 3.6 Correlation analysis of rhizosphere-dominant microbial communities and soil nutrient characteristics This research conducted an analysis on the correlation between the top ten most common bacterial and fungal phyla and the indicators of rhizosphere soil nutrients. The Mantel test results revealed notable disparities in the reaction of fungal and bacterial communities to rhizosphere soil nutrients (Fig. 6 ). Among the bacterial community, a number of prevalent phyla displayed significant positive associations with soil nutrients and enzyme activities. Specifically, Chloroflexota showed highly significant positive correlations with soil SOM and SOC, AK, S-BG, rhizosphere SQI, and rice yield (r > 0.5, p < 0.01). Notably, Acidobacteriota displayed a highly significant positive correlation with soil SOC (r = 0.79, p < 0.001). Furthermore, Pseudomonadota positively correlated significantly with soil SOC (r = 0.53, p < 0.01). Actinomycetota displayed a strong correlation with TP, NO 3 − -N, NH 4 + -N, S-NAG, S-ALP, and rhizosphere SQI, while Verrucomicrobiota exhibited a strong association with soil SOM, AK, S-BG, rhizosphere SQI, and rice yield (r > 0.5, p < 0.01). Furthermore, Verrucomicrobiota demonstrated a highly significant positive correlation with TN (r = 0.68, p < 0.001). In contrast, the fungal community showed weaker correlations, with only Ascomycota correlating very significantly with AK (r = 0.58, p < 0.01) and Monoblepharomycota correlating significantly with TP (r = 0.52, p < 0.01). 4. Discussion 4.1 Differential response of Fm and Ri in regulating the "root symbiosis-rhizosphere soil quality-rice yield" nexus This study confirmed that inoculation with Fm and Ri significantly enhanced root colonization in rice, demonstrating the successful establishment of AM symbiosis under paddy field conditions. Notably, Fm displayed superior colonization efficiency (78.23%) compared to Ri (70.13%) and native fungal strains (18.33%). This competitive advantage likely stems from Fm’s enhanced ability to metabolize rice-derived fatty acids (such as palmitic and oleic acid), facilitated by upregulated lipid transporters such as FatM and RAM2 (Bernaola et al. 2018 ). Additionally, Fm aggressively colonizes root infection sites, forming extensive hyphal networks while prioritizing extraradical hyphae development to optimize phosphorus and nitrogen acquisition (Liu et al. 2024 ; Martin et al. 2024 ). These adaptive traits underscore Fm’s dominance in ecological niche competition. The Fm and Ri treatments significantly enhanced SOM and SOC contents in the rhizosphere soils. This increase aligns with the role of AMF in facilitating the accumulation of organic matter by regulating the enzyme systems in rhizosphere soil (Zhang et al. 2019 ; Zhang et al. 2024a ). Specifically, AMF hyphae accelerate the decomposition of complex carbon compounds, such as cellulose, by secreting S-NAG and S-BG, while the enhanced activity of S-AGL promotes the conversion of simple sugars (Qin et al. 2020 ). Furthermore, the polysaccharides secreted by the hyphae act as "microbial glue", significantly facilitating the formation of soil aggregates (Zhou et al. 2025 ). However, distinct functional specialization was observed between the two AMF species. Fm significantly increased the activities of S-UE and S-CL by 47.29% and 24.62%, respectively, compared to Ri. This promotes mineralization and transformation of organic carbonl; S-UE accelerates urea hydrolysis to provide nitrogen sources, while S-CL enhances cellulose degradation, collectively driving soil carbon cycling (Wang et al. 2023 ). In contrast, Ri notably increased soil AP content by 69.81% compared to Fm, primarily by regulating of S-ALP activity, which effectively hydrolyzes organic phosphorus compounds to release available phosphorus (Li et al. 2025b ). This functional differentiation indicates that Fm tends to optimize energy acquisition by enhancing carbon and nitrogen cycling enzyme systems, whereas Ri improves phosphorus utilization efficiency through the activation of phosphorus mobilization systems. These results reveal the critical role of Fm and Ri in establishing an interactive network of "root symbiosis - rhizosphere soil quality - rice yield", thereby contributing to nutrient cycling in the rhizosphere. This specialized functionality directly improved rhizosphere soil health (Lu et al. 2018 ). Fm treatment resulted in a 9.43% higher rhizosphere SQI compared to Ri, consistent with previous findings on AMF-induced soil enhancement (Chang et al. 2021 ). These improvements were also reflected in crop productivity, as Fm-inoculated plants showed significantly higher yield increases compared to Ri-treated plants ( p < 0.05). Importantly, linear regression analysis revealed that rice yield correlated positively with rhizosphere SQI (R = 0.82, p < 0.001), indicating that Fm increases nutrient availability by stimulating carbon-nitrogen-cycling enzymes (Campo et al. 2020 ) and optimizing soil structure (Madhushan et al. 2023 ; Parvin et al. 2021 ). However, considering the potential influence of abiotic factors (such as climate, water availability), future studies should utilize multifactorial models to better predict the yield-enhancing potential of AMF under field conditions and to clarify the precise role of rhizosphere nutrients and enzymatic activities in maintaining soil health. 4.2 Differential dynamic regulation in rhizosphere microbial communities by Fm and Ri The AMF fungi reduced the number of nodes and links in the bacterial and fungal networks. This suggests that functional microbio, such as those involved in nutrient cycling and antagonistic interactions, play a central role in the network. Evidence from FAPROTAX and FUNGuild revealed that AMF significantly enhanced functions related to carbon metabolism and sulphur cycling while decreasing the relative abundance of pathogenic bacteria. Specially, both AMF treatments reduced pathogen-related risks (Duan et al. 2023 ; Li et al. 2025a ), they exhibited distinct functional specialization: Fm primarily boosted carbon metabolism (including both aerobic and anaerobic heterotrophic processes (Bradley et al. 2016 ; Wang et al. 2021 ), whereas Ri mainly impacted sulfur cycling and energy metabolism (Du et al. 2023 ). These discoveries clarify the specific mechanisms of AMF-mediated regulation of the rhizosphere microbiome and endorse targeted AMF inoculation strategies. Functionally, Fm treatment significantly increased the abundance of organic-degrading Chloroflexota (22.9%) and Actinomycetota (5.29%) through the secretion of organic acids and glomalin (Lahrach et al. 2024 ). The Mantel test results supported these observations, indicating a strong positive relationship between Chloroflexota and SOM (r > 0.5, p < 0.001), suggesting Fm's role in influencing carbon storage by restructuring the microbial community (Dong et al. 2023 ) (Fig. 6 ). Furthermore, Fm notably enhanced S-UE (47.29%) and S-ALP (58.80%) enzyme activities. The significant correlation between Actinomycetota and S-ALP (r > 0.5, p < 0.01) highlights the important role of this phylum in organic phosphorus mineralization (Pii et al. 2016 ). These changes facilitated the development of a highly interconnected bacterial network (4,189 edges), ultimately enhancing rhizosphere microbial stability and the efficiency of soil nutrient cycling (Bao et al. 2022 ; He et al. 2025 ). Alterations in the fungal community reflected distinct ecological functions (Sui et al. 2023 ). AMF strains exhibit distinct regulation of the relative abundance of Ascomycota (Bai et al. 2022 ). Among these, the Fm strain specifically enriches Mortierella , Talaromyces , and Tausonia , which have carbon metabolism functions. These functional groups form a synergistic metabolic network through a "cross-feeding" mechanism (Ujvári et al. 2021 ). Mortierella produces propionate through lipid metabolism, providing a carbon source for Talaromyces (Hnini et al. 2024 ); cellulase secreted by Talaromyces degrades complex polysaccharides, and the resulting glucose further supports the growth of Tausonia (Wen et al. 2023 ; Jin et al. 2024 ). This metabolic interaction significantly enhances the efficiency of rhizosphere carbon cycling (Kakouridis et al. 2024 ; Zhao et al. 2023 ). Ri specifically enriches Monoblepharomycota , whose abundance is significantly positively correlated with the soil TP (r = 0.58, p < 0.01), indicating its critical role in organic phosphorus mineralization within the "Ri-rice" symbiotic system (Yang et al. 2024 ; Ndabankulu et al. 2022 ). Simultaneously, Ri favors the enrichment of Cladosporium and Pseudeurotium , which participate in the sulfur cycle, and these strains may promote the transformation of thiosulfate through the redox enzyme system (Liu et al. 2020 ). 4.3 Deciphering the yield-enhancing mechanisms of AMF via structural equation modeling (SEM) modeling: from rhizosphere microecology to rice productivity Employing the SEM approach, this study revealed that Fm significantly outperformed Ri in promoting rice yield ( p < 0.05). SEM analysis indicated that Fm inoculation primarily influences rice yield through two pathways. Firstly, Fm directly increases rice yield by modifying the composition and structure of the rhizosphere microbial community. This finding supports the "AMF-microbiome" synergistic theory proposed by Zhang et al. ( 2024b ). These microbes secrete growth hormones such as IAA, fix nitrogen, or solubilize phosphorus, thereby directly promoting rice growth (Moran and Durham 2019 ). Secondly, Fm indirectly affects rice yield by modulating rhizosphere the SQI through alterations in the rhizosphere soil microbial community network. In contrast, the regulatory effect of Ri treatment on rice yield was relatively weaker. More importantly, Ri treatment failed to establish a significant association between SQI and rice yield (r = 0.11, p > 0.05), indicating its limited pathway of indirectly affecting yield through soil quality improvement. This study elucidates the key mechanisms by which Fm treatment synergistically promotes rice yield through a dual pathway of "microbial community reconstruction-soil quality improvement", whereas Ri treatment exhibited a weaker yield-promoting effect due to its failure to effectively establish this synergy. This suggests that Fm enhances the rhizosphere soil quality by enriching key functional microbial groups and strengthening the microbial community network, thereby improving rhizosphere soil properties, ultimately achieving a cascade mechanism for stable Fm-rice yield increase (Hao et al. 2024 ). This mechanism elucidates the systemic relationship among "rhizosphere microbes-paddy soil-rice yield", providing a novel perspective for achieving sustainable agricultural development through rhizosphere microecological regulation. These findings not only provide a foundation for optimizing AMF application in agroecosystems but also inspire developing microbiome-mediated technologies for improving soil health and crop yield. 5. Conclusions This study systematically revealed the regulatory mechanisms of Fm and Ri on the AMF, soil microbe and plants interaction network within the paddy ecosystem. Fm demonstrated a superior root colonization capacity and significantly enriching key functional microbial groups involved in carbon cycling ((such as Chloroflexota and Actinomycetota ). This enrichment significantly enhanced soil urease and cellulase activity, thereby promoting soil organic matter accumulation. Conversely, Ri enhanced microbial α-diversity and selectively activated functional microbial groups involved in sulfur or phosphorus transformation ((such as Cladosporium , Pseudeurotium ), effectively promoting soil organic phosphorus mineralization. Fm established a synergistic fungal "cross-feeding" loop among those functional microbiom (such as Mortierella, Talaromyces, and Tausonia ), optimizing carbon-use efficiency. While Ri fostered microbial cooperation via quorum sensing, facilitating sulfur redox cycling. Notably, the Fm significantly improved the SQI, resulting in superior rice yield compared to the Ri, thus confirming the synergistic "soil health-crop yield" enhancement mechanism. Declarations Authors contribution The authors confirm their contribution to the paper as follows: writing - original draft: MS; visualization: YW, RW; Software:JL; investigation: FS; draft manuscript: MS, YW, JL, XF, FS. All authors reviewed the results and approved the final version of the manuscript. Conflict of interest The authors declare that they have no conflict of interest. Funding This paper was supported by the Key R&D Program of Heilongjiang Province (GA23B006),Heilongjiang Province“Double First-Class ” Discipline Collaborative Innovation Achievement Project (LJGXCG2023-088), References Bai B, Liu WD, Qiu XY, Zhang J, Zhang JY, Bai Y (2022) The root microbiome: Community assembly and its contributions to plant fitness. J Integr Plant Biol 64 (2):230-243. Bao XZ, Zou JX, Zhang B, Wu LM, Yang TT, Huang Q (2022) Arbuscular Mycorrhizal Fungi and Microbes Interaction in Rice Mycorrhizosphere. Agronomy-Basel 12 (6):1277. Bernaola L, Cange G, Way MO, Gore J, Hardke J, Stout M (2018) Natural Colonization of Rice by Arbuscular Mycorrhizal Fungi in Different Production Areas. Rice Sci 25 (3):169-174. Bradley AS, Leavitt WD, Schmidt M, Knoll AH, Girguis PR, Johnston DT (2016) Patterns of sulfur isotope fractionation during microbial sulfate reduction. Geobiology 14 (1):91-101. Campo S, Martín-Cardoso H, Olivé M, Pla E, Catala-Forner M, Martínez-Eixarch M, San Segundo B (2020) Effect of Root Colonization by Arbuscular Mycorrhizal Fungi on Growth, Productivity and Blast Resistance in Rice. Rice 13 (1):42. Chang JJ, Sun Y, Tian L, Ji L, Luo SS, Nasir F, Kuramae EE, Tian CJ (2021) The Structure of Rhizosphere Fungal Communities of Wild and Domesticated Rice: Changes in Diversity and Co-occurrence Patterns. Front Microbiol 12:610823. Che WK, Piao JL, Gao Q, Li XB, Li X, Jin F (2023) Response of soil physicochemical properties, soil nutrients, enzyme activity and rice yield to rice straw returning in highly saline-alkali paddy soils. J Soil Sci Plant Nut 23 (3):4396-4411. Chen DL, Saeed M, Ali MNHA, Raheel M, Ashraf W, Hassan Z, Hassan MZ, Farooq U, Hakim MF, Rao MJ, Naqvi SAH, Moustafa M, Al-Shehri M, Negm S (2023) Plant Growth Promoting Rhizobacteria (PGPR) and Arbuscular Mycorrhizal Fungi Combined Application Reveals Enhanced Soil Fertility and Rice Production. Agronomy-Basel 13 (2):550. Deveau A, Bonito G, Uehling J, Paoletti M, Becker M, Bindschedler S, Hacquard S, Hervé V, Labbé J, Lastovetsky OA, Mieszkin S, Millet LJ, Vajna B, Junier P, Bonfante P, Krom BP, Olsson S, van Elsas JD, Wick LY (2018) Bacterial-fungal interactions: ecology, mechanisms and challenges. Fems Microbiol Rev 42 (3):335-352. Dong HY, Sun HY, Chen CL, Zhang MY, Ma DR (2023) Compositional Shifts and Assembly in Rhizosphere-Associated Fungal Microbiota Throughout the Life Cycle of Rice Under Increased Nitrogen Fertilization. Rice 16 (1):34. Dong LJ, Ma LN, He WM (2021) Arbuscular mycorrhizal fungi help explain invasion success of. Appl Soil Ecol 157:103763. Du TY, Hu QF, He HY, Mao WJ, Yang Z, Chen H, Sun LN, Zhai MZ (2023) Long-term organic fertilizer and biofertilizer application strengthens the associations between soil quality index, network complexity, and walnut yield. Eur J Soil Biol 116:103492. Duan SL, Declerck S, Feng G, Zhang L (2023) Hyphosphere interactions between and promote carbon-phosphorus exchange at the peri-arbuscular space in. Environ Microbiol 25 (4):867-879. Fu J, Jian YW, Wang XH, Li L, Ciais P, Zscheischler J, Wang Y, Tang YH, Müller C, Webber H, Yang B, Wu YL, Wang QH, Cui XQ, Huang WC, Liu YQ, Zhao PJ, Piao SL, Zhou F (2023) Extreme rainfall reduces one-twelfth of China's rice yield over the last two decades. Nat Food 4 (5):416-426. Gao YJ, Cui JL, Zhang XM, Hoogenboom G, Wallach D, Huang YQ, Reis S, Lin T, Gu BJ (2025) Cost-effective adaptations increase rice production while reducing pollution under climate change. Nat Food. 6 (3):260–272. Hao HB, Yue YH, Wang Q, Xiao TT, Zhao ZL, Zhang JJ, Chen H (2024) Effects of the rice-mushroom rotation pattern on soil properties and microbial community succession in paddy fields. Front Microbiol 15:1449922. He MY, Li DX, Peng S, Wang YD, Ding QD, Wang YM, Zhang JH (2025) Reduced soil ecosystem multifunctionality is associated with altered complexity of the bacterial-fungal interkingdom network under salinization pressure. Environ Res 269:120863. Hnini M, Rabeh K, Oubohssaine M (2024) Interactions between beneficial soil microorganisms (PGPR and AMF) and host plants for environmental restoration: A systematic review. Plant Stress 11:100391. Jiang FY, Zhang L, Zhou JC, George TS, Feng G (2021) Arbuscular mycorrhizal fungi enhance mineralisation of organic phosphorus by carrying bacteria along their extraradical hyphae. New Phytol 230 (1):304-315. Jin ZX, Jiang FY, Wang LT, Declerck S, Feng G, Zhang L (2024) Arbuscular mycorrhizal fungi and : brothers in arms to shape the structure and function of the hyphosphere microbiome in the early stage of interaction. Microbiome 12 (1):83. Kakouridis A, Yuan MT, Nuccio EE, Hagen JA, Fossum CA, Moore ML, Estera-Molina KY, Nico PS, Weber PK, Pett-Ridge J, Firestone MK (2024) Arbuscular mycorrhiza convey significant plant carbon to a diverse hyphosphere microbial food web and mineral-associated organic matter. New Phytol 242 (4):1661-1675. Lahrach Z, Legeay J, Ahmed B, Hijri M (2024) The composition of the arbuscular mycorrhizal fungal bacteriome is species dependent. Environ Microbiome 19 (1):77. Li HH, Shi F, Zhu SY, He X, Ding YW, Xu TL, Qi DD, Chang W, Dai CL, Song FQ (2024) improves rhizosphere soil pH and nutrient status in sodic soil. Rhizosphere-Neth 32:100973. Li J, Zhou LY, Chen G, Yao MY, Liu ZG, Li XR, Yang XP, Yang Y, Cai DR, Tuerxun Z, Li B, Nie TK, Chen XJ (2025a) Arbuscular mycorrhizal fungi enhance drought resistance and alter microbial communities in maize rhizosphere soil. Environ Technol Inno 37:103947. Li MY, Wang W, Yin HH, Chen YL, Ashraf M, Tao HY, Li SS, Wang WY, Yang CL, Xiao YL, Zhu L, Xiong YC (2025b) The functional role of arbuscular mycorrhizal fungi in enhancing soil organic carbon stocks and stability in dryland. Soil Till Res 248:106443. Liu CS, Zhao DF, Ma WJ, Guo YD, Wang AJ, Wang QL, Lee DJ (2016) Denitrifying sulfide removal process on high-salinity wastewaters in the presence of sp. Appl Microbiol Biot 100 (3):1421-1426. Liu JR, Pellerin A, Izon G, Wang JS, Antler G, Liang JQ, Su PB, Jorgensen BB, Ono S (2020) The multiple sulphur isotope fingerprint of a sub-seafloor oxidative sulphur cycle driven by iron. Earth Planet Sc Lett 536:116165. Liu ZX, Gu HD, Yao Q, Jiao F, Hu XJ, Liu JJ, Jin J, Liu XB, Wang GH (2024) Soil pH and carbon quality index regulate the biogeochemical cycle couplings of carbon, nitrogen and phosphorus in the profiles of Isohumosols. Sci Total Environ 922:171269. Lu S, Lepo JE, Song HX, Guan CY, Zhang ZH (2018) Increased rice yield in long-term crop rotation regimes through improved soil structure, rhizosphere microbial communities, and nutrient bioavailability in paddy soil. Biol Fert Soils 54 (8):909-923. Madhushan KWA, Karunarathna SC, Dissanayake DMD, Priyadarshani TDC, Stephenson SL, Elgorban AM, Dawoud TM, Priyashantha AKH, Dai DQ, Yapa PN, Wang XY (2023) Effect of Arbuscular Mycorrhizal Fungi on Lowland Rice Growth and Yield ( L.) under Different Farming Practices. Agronomy-Basel 13 (11): 2803. Martin FM, Oepik M, Dickie IA (2024) Mycorrhizal research now: from the micro- to the macro-scale. New Phytol 242 (4):1399-1403. Moran MA, Durham BP (2019) Sulfur metabolites in the pelagic ocean. Nat Rev Microbiol 17 (11):665-678. Ndabankulu K, Egbewale SO, Tsvuura Z, Magadlela A (2022) Soil microbes and associated extracellular enzymes largely impact nutrient bioavailability in acidic and nutrient poor grassland ecosystem soils. Sci Rep-Uk 12 (1):12601. Parvin S, Van Geel M, Ali MM, Yeasmin T, Lievens B, Honnay O (2021) A comparison of the arbuscular mycorrhizal fungal communities among Bangladeshi modern high yielding and traditional rice varieties. Plant Soil 462 (1-2):109-124. Pii Y, Borruso L, Brusetti L, Crecchio C, Cesco S, Mimmo T (2016) The interaction between iron nutrition, plant species and soil type shapes the rhizosphere microbiome. Plant Physiol Bioch 99:39-48. Qin MS, Zhang Q, Pan JB, Jiang SJ, Liu YJ, Bahadur A, Peng ZL, Yang Y, Feng HY (2020) Effect of arbuscular mycorrhizal fungi on soil enzyme activity is coupled with increased plant biomass. Eur J Soil Sci 71 (1):84-92. Saud S, Wang DP, Fahad S, Alharby HF, Bamagoos AA, Mjrashi A, Alabdallah NM, AlZahrani SS, AbdElgawad H, Adnan M, Sayyed RZ, Ali S, Hassan S (2022) Comprehensive Impacts of Climate Change on Rice Production and Adaptive Strategies in China. Front Microbiol 13:926059. Shi F, He X, Cao M, Wu RT, Zhang B, Xu TL, Jiang MG, Song FQ (2025) Strategies for plant-microbe symbiosis: Mycorrhizal fungi and helper bacteria to improve cold tolerance in rice. Plant Physiol Bioch 222:109741. Sui JL, He XW, Yi GH, Zhou LM, Liu SQ, Chen QQ, Xiao XH, Wu JY (2023) Diversity and structure of the root-associated bacterial microbiomes of four mangrove tree species, revealed by high-throughput sequencing. Peerj 11:16156. Sun SM, Feng YH, Huang GD, Zhao X, Song FQ (2022) enhances tolerance to cadmium stress by altering host plant hemp ( L.) photosynthetic properties. Environ Pollut 314:120309. Ujvári G, Turrini A, Avio L, Agnolucci M (2021) Possible role of arbuscular mycorrhizal fungi and associated bacteria in the recruitment of endophytic bacterial communities by plant roots. Mycorrhiza 31 (5):527-544. Wang J, Xu CY, Sun QM, Xu JR, Chai YR, Berg G, Cernava T, Ma ZH, Chen Y (2021) Post-translational regulation of autophagy is involved in intra-microbiome suppression of fungal pathogens. Microbiome 9 (1):131. Wang LT, Zhang L, George TS, Feng G (2023) A core microbiome in the hyphosphere of arbuscular mycorrhizal fungi has functional significance in organic phosphorus mineralization. New Phytol 238 (2):859-873. Wen YQ, Xu TL, Qi DD, Chang W, Li K, Fan XX, Zhang MM, Ping Y, Song FQ (2024) combined with biochar can improve the saline-alkali tolerance and energy quality of switchgrass through osmoregulation and gene expression. Sci Hortic-Amsterdam 338:113793. Wen ZL, Yang MK, Han HW, Fazal A, Liao YH, Ren R, Yin TM, Qi JL, Sun SC, Lu GH, Hu SJ, Yang YH (2023) Mycorrhizae Enhance Soybean Plant Growth and Aluminum Stress Tolerance by Shaping the Microbiome Assembly in an Acidic Soil. Microbiol Spectr 11 (2):03310-22 Yang MK, Song YH, Ma HK, Li ZH, Ding JW, Yin TM, Niu KC, Sun SC, Qi JL, Lu GH, Fazal A, Yang YH, Wen ZL (2024) Unveiling the hidden world: How arbuscular mycorrhizal fungi and its regulated core fungi modify the composition and metabolism of soybean rhizosphere microbiome. Environ Microbiome 19 (1):78. Yuan PL, Wang JP, Li CF, Xiao QQ, Liu QJ, Sun ZC, Wang JH, Cao CG (2020) Soil quality indicators of integrated rice-crayfish farming in the Jianghan Plain, China using a minimum data set. Soil Till Res 204:104732. Zhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN, Shi SJ, Cho HJ, Karaoz U, Loqué D, Bowen BP, Firestone MK, Northen TR, Brodie EL (2018) Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol 3 (4):470-480. Zhang L, Shi N, Fan JQ, Wang F, George TS, Feng G (2018a) Arbuscular mycorrhizal fungi stimulate organic phosphate mobilization associated with changing bacterial community structure under field conditions. Environ Microbiol 20 (7):2639-2651. Zhang L, Yan CX, Guo Q, Zhang JB, Ruiz-Menjivar J (2018b) The impact of agricultural chemical inputs on environment: global evidence from informetrics analysis and visualization. Int J Low-Carbon Tec 13 (4):338-352. Zhang L, Zhou JC, George TS, Limpens E, Feng G (2022) Arbuscular mycorrhizal fungi conducting the hyphosphere bacterial orchestra. Trends Plant Sci 27 (4):402-411. Zhang ZC, Ding SL, Diao FW, Jia BB, Shi ZQ, Guo W (2024a) AMF symbiosis drives the rhizosphere microbiome to synergistically improve herbage growth in saline-alkaline soils. Land Degrad Dev 35 (11):3663-3674. Zhang ZC, Zhou ZH, Feng SC, Guo PR, Wang YC, Hao BH, Guo W, Li FY (2024b) Synergistic effects of AMF and PGPR on improving saline-alkaline tolerance of Leymus chinensis by strengthening the link between rhizosphere metabolites and microbiomes. Environ Technol Inno 36:103900. Zhang ZF, Zhang JC, Xu GP, Zhou LW, Li YQ (2019) Arbuscular mycorrhizal fungi improve the growth and drought tolerance of Zenia insignis seedlings under drought stress. New Forest 50 (4):593-604. Zhao C, Piao SL, Wang XH, Huang Y, Ciais P, Elliott J, Huang MT, Janssens IA, Li T, Lian X, Liu YW, Müller C, Peng SS, Wang T, Zeng ZZ, Peñuelas J (2017) Plausible rice yield losses under future climate warming. Nat Plants 3 (1):16202. Zhao W, Chen ZB, Yang XQ, Sheng LY, Mao H, Zhu SX (2023) Metagenomics reveal arbuscular mycorrhizal fungi altering functional gene expression of rhizosphere microbial community to enhance resistance to Cr stress. Sci Total Environ 895:164970. Zhao WX, Chou JM, Li JN, Xu Y, Li YM, Hao YD (2022) Impacts of Extreme Climate Events on Future Rice Yields in Global Major Rice-Producing Regions. Int J Env Res Pub He 19 (8):4437. Zhou J, Guo PX, Huang SP, Liu CY, Wang YK, Li FM, Chen WP, Zhang Q, Shi LL, Yang HS (2025) Long-term diverse straw management influences arbuscular mycorrhizal fungal community structure and plant growth in a rice-rotated wheat cropping system. J Environ Manage 374:124227. Zhou JC, Chai XF, Zhang L, George TS, Wang F, Feng G (2020) Different Arbuscular Mycorrhizal Fungi Cocolonizing on a Single Plant Root System Recruit Distinct Microbiomes. Msystems 5 (6):e00929-20. Zhou JC, Zhang L, Feng G, George TS (2022) Arbuscular mycorrhizal fungi have a greater role than root hairs of maize for priming the rhizosphere microbial community and enhancing rhizosphere organic P mineralization. Soil Biol Biochem 17:108713. Table 1 Table 1. Structural characteristics of microbial interaction networks in rice rhizosphere soils. Indicator Bacteria Fungi Treatment CK Fm Ri CK Fm Ri Nodes 437±16.00a 411±19.00a 436±35.00a 187±33.00a 213±13.00a 191±17.00a Edges 7636±215.00c 4189±188.00b 6487±236.00a 2447±169.00b 3046±49.00a 2360±235.00b Positive edges 3827±175.00a 2026±616.00b 3267±243.00a 1268±189.00a 1515±202.00a 1282±184.00a Negative edges 3809±247.00a 2163±162.00c 3220±342.00b 1179±173.00a 1531±341.00a 1078±91.00a Average degree 34.947±225.00a 20.384±395.00c 29.757±811.00b 26.171±1608.00a 28.601±1659.00a 24.712±343.00b Average weighted degree 33.998±456.00a 19.824±900.00c 28.987±62.00b 25.755±2096.00a 28.138±679.00a 24.237±548.00b Network diameter 6±1.00a 7±2.00a 6±1.00a 5±1.00a 3±1.00a 4±1.00a Graph density 0.08±0.02a 0.05±0.01a 0.068±0.03a 0.141±0.04a 0.135±0.04a 0.13±0.04a Modularity 0.55±0.03b 0.558±0.03a 0.496±0.03a 0.535±0.04a 0.518±0.03a 0.508±0.03a Average clustering coefficient 0.556±0.07a 0.457±0.04a 0.514±0.09a 0.667±0.07a 0.714±0.11a 0.629±0.08a Average path length 2.664±0.14a 2.988±0.49a 2.596±0.30a 2.217±0.22a 1.933±0.07a 2.185±0.29a All values are means ± standard errors, n = 3. Different lowercase letters within the same row indicate significant differences among treatments ( p < 0.05). Treatments: CK (control), Fm ( Funneliformis mosseae ), Ri ( Rhizophagus intraradices ). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 27 Oct, 2025 Read the published version in Mycorrhiza → Version 1 posted Editorial decision: Revision requested 03 Sep, 2025 Reviews received at journal 04 Aug, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor assigned by journal 23 Jun, 2025 Submission checks completed at journal 23 Jun, 2025 First submitted to journal 20 Jun, 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-6936303","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475907115,"identity":"2fbaa15c-ae89-4e7a-bfcf-c1ea0f936e74","order_by":0,"name":"Minyong Shi","email":"","orcid":"","institution":"Ministry of Education \u0026, Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Minyong","middleName":"","lastName":"Shi","suffix":""},{"id":475907116,"identity":"81dbab7a-6a9a-4dae-a739-84bc6f9cc33f","order_by":1,"name":"Yanling Wu","email":"","orcid":"","institution":"Ministry of Education \u0026, Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Yanling","middleName":"","lastName":"Wu","suffix":""},{"id":475907117,"identity":"c9b18f9b-3d79-4bc5-a02d-4482ce1ee917","order_by":2,"name":"Ruotong Wu","email":"","orcid":"","institution":"Ministry of Education \u0026, Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Ruotong","middleName":"","lastName":"Wu","suffix":""},{"id":475907118,"identity":"113e8b7f-f4bb-4bb4-9de9-a7f5b42d1574","order_by":3,"name":"Junjie Liu","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Liu","suffix":""},{"id":475907119,"identity":"7f3f73ba-f2aa-4b1e-ba72-36bcae1af402","order_by":4,"name":"Feng Shi","email":"","orcid":"","institution":"Ministry of Education \u0026, Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Shi","suffix":""},{"id":475907120,"identity":"414d418d-11dd-4ac3-9b02-6ac2a79b9186","order_by":5,"name":"Xiaoxu Fan","email":"","orcid":"","institution":"Ministry of Education \u0026, Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxu","middleName":"","lastName":"Fan","suffix":""},{"id":475907121,"identity":"eb2c837d-a1ba-42a7-a4e1-4ff27e51903a","order_by":6,"name":"Fuqiang Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIie3RoQ7CMBCA4WuWFLMwuwXCXqFkEgSPcjNMzU9CSDrDM/AY6GuagOkDkCDYK+Aq6QhBIU4i+ouqfknvChCL/WESrkSwhoWc9MQjmXA1wRaqaeqQR4oDVSOpT/lG8Yi6EBqPq0bngOC7M4M4QzbFptWzPYmjuzPIjdAKb1s9J0yE5pDHoMLDbCNzVDxS7EhRihb5JAOH4yxLHZZsWLOEr7TPsLGy7Hsz+I5BPiXvk9j3vyQWi8ViP3sB18hEAN9La68AAAAASUVORK5CYII=","orcid":"","institution":"Ministry of Education \u0026, Heilongjiang University","correspondingAuthor":true,"prefix":"","firstName":"Fuqiang","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2025-06-20 07:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6936303/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6936303/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00572-025-01238-z","type":"published","date":"2025-10-27T15:57:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85492791,"identity":"83f879c2-7007-4d01-8c66-5e39028546e0","added_by":"auto","created_at":"2025-06-26 13:16:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":662701,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMycorrhizal colonization status and colonization rates in rice roots under different treatments. (A) Fm-induced AMF colonization (400×); (B) Ri-mediated AMF structures (400×) (both seedling stage); (C) Colonization rates across growth stages. Data: mean ± SD (n=3). Different letters denote significant differences (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026lt;0.05). Treatments: CK (control), Fm (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFunneliformis mosseae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e), Ri (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eRhizophagus intraradices\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6936303/v1/302a208aedad63722fa15d70.png"},{"id":85492792,"identity":"6a81d101-cfda-49a9-bd12-96bee17143aa","added_by":"auto","created_at":"2025-06-26 13:16:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":260930,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTreatment effects on rice yield and rhizosphere quality. (A) Grain yield; (B) Rhizosphere SQI; (C) Yield-Rhizosphere SQI correlation. Values = mean ± SD (n=3). Different letters denote significant differences (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026lt;0.05). Treatments: CK (control), Fm (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFunneliformis mosseae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e), Ri (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eRhizophagus intraradices\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6936303/v1/a969a81facca893d1204ba57.png"},{"id":85493774,"identity":"e318aa3f-9b31-4710-8279-8be45d79834c","added_by":"auto","created_at":"2025-06-26 13:24:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":323646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial diversity patterns across treatments. (A) Bacterial α-diversity (Chao1/Shannon/Simpson); (B) Corresponding fungal indices; (C-D) Community β-diversity (PCoA) for bacteria and fungi, respectively. Values = mean ± SD (n=3). Treatments: CK (control), Fm (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFunneliformis mosseae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e), Ri (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eRhizophagus intraradices\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6936303/v1/60149f847ba43697a38515e5.png"},{"id":85492794,"identity":"d03dacab-7941-4c41-8abc-6015d33db47b","added_by":"auto","created_at":"2025-06-26 13:16:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":733786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRhizosphere microbial composition and function across treatments. (A) Dominant bacterial/fungal taxa (top 10 at phylum/genus levels); (B) LDA cladogram showing differentially abundant taxa; (C) Functional predictions (FAPROTAX for bacteria, FUNGuild for fungi). Data: mean ± SD (n=3). Treatments: CK (control), Fm (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFunneliformis mosseae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e), Ri (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eRhizophagus intraradices\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6936303/v1/35f723e7e16481ff0ff100ec.png"},{"id":85492797,"identity":"cb1b92b6-d2d0-425e-b2e3-0b62d4124be0","added_by":"auto","created_at":"2025-06-26 13:16:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1080940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRhizosphere microbial co-occurrence networks across treatments. (A) Bacterial networks; (B) Fungal networks. Data represent mean ± SD (n=3). Treatments: CK (control), Fm (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFunneliformis mosseae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e), Ri (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eRhizophagus intraradices\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6936303/v1/61c6ac20269e7defbb1424ef.png"},{"id":85492806,"identity":"2c589d0d-1183-496a-bd65-660cb679b8d0","added_by":"auto","created_at":"2025-06-26 13:16:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":815342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis between rhizosphere microbial community structure and environmental factors. (A) Mantel tests examining relationships between predominant bacterial phyla (top 10) and soil nutrients; (B) Corresponding analysis for dominant fungal phyla. Treatments: CK (control), Fm (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFunneliformis mosseae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e), Ri (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eRhizophagus intraradices\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6936303/v1/ae9b057989b472473562d98f.png"},{"id":85492808,"identity":"9eb532ba-0f0e-4eb7-8735-6635cd1bd27d","added_by":"auto","created_at":"2025-06-26 13:16:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":194651,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural equation modeling of rhizosphere microbiome-soil-rice yield interactions. (A) Fm treatment pathways: microbial diversity/structure-soil properties-yield relationships; (B) Ri treatment pathways. Significant paths (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u0026lt; 0.05) shown as solid arrows.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6936303/v1/bd2aa60643798a5a3db8f94e.png"},{"id":95040533,"identity":"4595dc77-bd82-49ca-ab52-04853fbc4b85","added_by":"auto","created_at":"2025-11-03 16:09:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5931422,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6936303/v1/47e2e33e-6964-4bed-afd4-d46ffbd06525.pdf"},{"id":85492795,"identity":"4c3623af-771d-40dd-9709-62c3537040f6","added_by":"auto","created_at":"2025-06-26 13:16:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18146,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6936303/v1/a706e4202040a94c15f8609b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differential impacts of Funneliformis mosseae and Rhizophagus intraradice on soil quality and rice yield in paddy fields: mediated by AMF-rice-rhizosphere microbe interactions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal climate change and the increasing population pose a dual threat to the global food security and agricultural sustainability (Gao et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Projections by the Food and Agriculture Organization of the United Nations (FAO) estimates the global population will surpass 9.7\u0026nbsp;billion by 2050, requiring a 60% increase in food production compared to current levels (Fu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saud et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Concurrently, climate change impacts, including extreme weather events, water resources imbalances, and arable land deterioration, severely impact on global food systems (Zhao et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Che et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a crucial staple crop worldwide, rice production directly affects human welfare and health (Zhang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). However, the heavy reliance on chemical fertilizers and pesticides in traditional rice farming is triggering diverse environmental problems, while also posing potential risks to human health (Zhang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). Therefore, given the pressures of climate change and resource scarcity, it has become imperative to explore strategies for reducing fertilizer dependency, enhancing rice resilience, and attaining sustainable rice production in current agricultural research.\u003c/p\u003e \u003cp\u003eSustainable agriculture increasingly relies on microbial inoculants as eco-friendly fertilizers. These living microorganism formulations enhance crop performance, improve nutrient use efficiency, and bolster plant stress resistance, thereby reducing dependence on chemical fertilizers and/or the pesticides (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Among these, arbuscular mycorrhizal fungi (AMF) are essential components that form symbiotic relationships with over 80% of plant species. Through their extensive hyphal networks, AMF significantly boost water and nutrient absorption, stimulating plant growth and biomass production (Dong et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Concurrently, AMF enhance soil enzymatic activities (such as urease, phosphatase) to accelerate organic matter decomposition and nutrient cycling, thereby improving soil health (Jiang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, species-specific mechanisms by which AMF regulate rhizosphere nutrient dynamics and enzyme functions remain poorly understood, particularly in paddy ecosystems where flooded conditions alter microbial functionality\u003c/p\u003e\u003cp\u003eRhizosphere microbial communities plays a critical role in the transformation of soil nutrients and the health of crops through organic matter decomposition, nitrogen fixation, and phosphorus solubilization (Zhang et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Zhalnina et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). AMF impact the structure and function of microbial communities by releasing organic acids, glomalin, and signaling molecules, leading to improved soil nutrient cycling and plant health (Deveau et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, \u003cem\u003eFunneliformis mosseae\u003c/em\u003e (Fm) and \u003cem\u003eRhizophagus intraradices\u003c/em\u003e (Ri) species establish hyphal networks that create unique microenvironments, selectively enriching beneficial bacterial taxa (such as \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003ePseudarthrobacter\u003c/em\u003e) involved in nitrogen cycling while suppressing pathogens through resource competition (Zhou et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This process enhances both rhizosphere soil fertility and plant nutrient uptake efficiency, significantly enhancing plant growth (Zhou et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, species-specific regulatory mechanisms of AMF on functional microbial groups remain inadequately characterized, particularly in paddy ecosystems where flooded conditions alter redox potential and microbial functionality. However, the following research questions have been posited: firstly, how do AMF species (Fm and Ri) differentially recruit microbial consortia for carbon, nitrogen and sulfur cycling, and secondly, whether AMF-induced shifts in enzyme activities (for example, urease, phosphatase) correlate with functional gene abundance under anaerobic stress.\u003c/p\u003e \u003cp\u003eThe scope of this research was restricted to those of two AMF species of Fm and Ri on the impact on soil quality and rice yield. The oblective of current study were (1) to reveal the distinct role of Fm and Ri regulating nutrient dynamics and key enzyme activities in the rice rhizosphere, thereby enhancing rice yield by improving nutrient activation; (2) to investigate how Fm and Ri selectively rhizospheric microbiota diversity, community assembly, and ecological networks, driving functional specialization via formation of an interactive \"AMF-rice-microbe\" tripartite system. This study systematically elucidates the mechanisms of action of different AMF inoculants within the rice paddy ecosystem, offering a theoretical foundation and technical backing for sustainable management of rice fields and the judicious utilization of AMF.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental materials\u003c/h2\u003e \u003cp\u003eThe Rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.) seeds, \"Daohuaxiang 2\", were provided by the Key Laboratory of Cold Region Ecological Restoration and Resource Utilization of Heilongjiang University.\u003c/p\u003e \u003cp\u003eTwo arbuscular mycorrhizal fungi (AMF) species were selected for investigation: \u003cem\u003eFunneliformis mosseae\u003c/em\u003e (Fm) and \u003cem\u003eRhizophagus intraradice\u003c/em\u003es (Ri). Both AMF inocula were propagated in pot cultures using sorghum (\u003cem\u003eSorghum bicolor\u003c/em\u003e L.) as the host plants. The inocula contained spores, hyphae and root fragments, with spore densities of approximately 65\u0026thinsp;\u0026plusmn;\u0026thinsp;5 spores per gram of inoculum for both species. All fungal inocula were maintained and propagated by the Key Laboratory of Cold Region Ecological Restoration and Resource Utilization in Heilongjiang Province.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experimental design\u003c/h2\u003e \u003cp\u003eThe field experiment was carried out at Heilongjiang University's Hulan campus (126\u0026deg;38\u0026prime;35\u0026Prime; E, 45\u0026deg;59\u0026prime;45\u0026Prime; N), covering 81 hectares of land, including 52 hectares of arable soil. The site is characterized by a mid-temperate continental monsoon climate, with an average annual temperature of 3.3\u0026deg;C, 2,661.4 hours of sunshine, and 505.4 mm of precipitation annually. The predominant soil types\u0026mdash;black soil and black calcium soil\u0026mdash;provide favorable conditions for rice cultivation.\u003c/p\u003e \u003cp\u003eRice seeds were surface-sterilized in 5% NaCl for 30 minutes, rinsed thoroughly with distilled water, and then soaked in distilled water at 25\u0026deg;C for 24 hours. Germination was induced at 28\u0026deg;C for 48 hours. For AMF inoculation, Fm and Ri were blended with seedling substrate at a 5% (w/w) ratio. Seedlings were grown in either AMF-inoculated (Fm or Ri) or non-inoculated (CK) substrates under controlled conditions (light, temperature, and moisture). Each treatment included three replicates (nine trays total), spaced 20 cm apart to prevent cross-contamination.\u003c/p\u003e \u003cp\u003eAfter 36 days, seedlings were manually transplanted to field plots, with 3\u0026ndash;5 seedlings per hole. The study employed a randomized complete block design with three treatments (CK, Fm, Ri), each replicated three times (totaling nine experimental plots of 1.5 m \u0026times; 2 m dimensions). Protective soil ridges were constructed between plots to minimize interference. Standard agronomic practices for rice cultivation were maintained throughout the growing season (Li et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wen et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Experimental methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Mycorrhizal colonization rate in rice roots\u003c/h2\u003e \u003cp\u003eThe arbuscular mycorrhizal (AM) colonization of rice roots was systematically assessed at different growth stages: seedling stage (36 days), tillering stage (56 days), grain-filling stage (109 days), and harvest stage (167 days). For each treatment, five randomly selected plant samples were collected. Approximately 60\u0026ndash;80 fresh root segments (1 cm in length) per sample were stained with 0.05% trypan blue. The roots were subsequently cleared, stained, destained, and mounted on slides for microscopic observation. AM fungal colonization rates were calculated using the following formula(Sun et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{r}\\text{o}\\text{o}\\text{t}\\:\\text{c}\\text{o}\\text{l}\\text{o}\\text{n}\\text{i}\\text{z}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\left(\\text{%}\\right)=\\frac{\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{a}\\text{r}\\text{b}\\text{u}\\text{s}\\text{c}\\text{u}\\text{l}\\text{a}\\text{r}\\:\\text{m}\\text{y}\\text{c}\\text{o}\\text{r}\\text{r}\\text{h}\\text{i}\\text{z}\\text{a}\\:-\\:\\text{p}\\text{o}\\text{s}\\text{i}\\text{t}\\text{i}\\text{v}\\text{e}\\:\\text{s}\\text{e}\\text{g}\\text{m}\\text{e}\\text{n}\\text{t}\\text{s}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{s}\\text{e}\\text{g}\\text{m}\\text{e}\\text{n}\\text{t}\\text{s}\\:\\text{s}\\text{t}\\text{u}\\text{d}\\text{i}\\text{e}\\text{d}}\\times\\:100\\text{\\%}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Rhizosphere soil nutrients and enzyme activities in paddy fields\u003c/h2\u003e \u003cp\u003eSoil pH was determined using a calibrated pH meter (FE20, Mettler-Toledo) in a 1:2.5 (wv) soil-water mixture. Organic matter (SOM) was measured by potassium dichromate oxidation with external heating, and total organic carbon (TOC) was analyzed with a TOC analyzer (Multi NC 3100, Analytik Jena). Nutrient contents, including total nitrogen (TN), total phosphorus (TP), available phosphorus (AP), available potassium (AK), nitrate nitrogen (NO₃⁻-N), and ammonium nitrogen (NH₄⁺-N), were assessed using a SmartChem200 discrete autoanalyzer. Soil enzyme activities, including urease (S-UE), cellulase (S-CL), α-glucosidase (S-AGL), β-glucosidase (S-BG), N-acetyl\u0026ndash;D-glucosaminidase (S-NAG), and alkaline phosphatase (S-ALP), were evaluated with commercial assay kits (Solarbio, Beijing, China) according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Soil quality index (SQI) in paddy rhizosphere\u003c/h2\u003e \u003cp\u003eThe rhizosphere soil quality index (SQI) was calculated based on indicators such as nutrient content and enzyme activity (Yuan et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Initially, all indicator data were standardized to a 0\u0026ndash;1 scale. Subsequently, principal component analysis (PCA) was performed using SPSS software. The Kaiser-Meyer-Olkin (KMO) value was 0.690 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating the suitability of the data for factor analysis. Key parameters relevant to soil health were carefully chosen according to the minimum data set principle to guarantee the effectiveness and precision of the assessment. Principal components with eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1 were extracted, resulting in two principal components, PC1 and PC2, with a cumulative contribution rate of 91.426%, which effectively explained the selected indicators. The weights of each indicator were calculated using factor loading values (λ) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and the rhizosphere soil quality index was calculated using formula (2).\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"535\" height=\"40\"\u003e\u003c/p\u003e\u003cp\u003eHere, PCi stands for the score of the ith principal component, wi represents the weight of the ith principal component (usually corresponding to its contribution rate), and k indicates the number of principal components selected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Rice yield and its components\u003c/h2\u003e \u003cp\u003eCrops from each individual plot were harvested separately to prevent cross-contamination. Panicle density, grain counts, fertility rate, and thousand-grain weight were assessed to gauge growth performance. These measurements, along with plot dimensions, were utilized to determine the theoretical yield and estimate the optimal production potential given the current conditions.\u003c/p\u003e \u003cp\u003eSeed-setting rate (%) = (Number of filled grains / Total grains) \u0026times; 100 (3)\u003c/p\u003e \u003cp\u003eRice yield (kg/ha) = [Panicles/m\u0026sup2; \u0026times; Grains/panicle \u0026times; Seed-setting rate (%) \u0026times; 1000-grain weight (g) \u0026times; 10⁻⁵] \u0026times; 10⁴ (4)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 DNA extraction, sequencing process, and sequence analysis\u003c/h2\u003e \u003cp\u003eThe genomic DNA was isolated from soil samples with the FastPure Soil DNA Isolation Kit. DNA quality was evaluated by 1% agarose gel electrophoresis, while concentration, purity, and integrity were measured using the NanoDrop 2000 (Thermo Scientific, USA). Following the manufacturer\u0026rsquo;s protocol (Liu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the extracted DNA served as the template. The bacterial 16S rRNA V3-V4 region was amplified with primers 338F (5'-ACTCCTACGGGGAGGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'), whereas the fungal ITS V4-V5 region was amplified using ITS1F (5'-CTTGGTCATTTAGAGAGGAAGTAA-3') and ITS2R (5'-GCTGGTCTTCATCGATGC-3'). Fresh soil samples were subsequently submitted to Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai) for 16S rRNA and ITS rRNA sequencing on the Illumina NextSeq 2000 platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eData were statistically analyzed using SPSS 25.0 and visualized through Origin 2022. One-way analysis of variance (ANOVA) was utilized to examine the significance of variances among treatments, and the least significant difference (LSD) test was utilized to evaluate significant variations between groups.\u003c/p\u003e \u003cp\u003eThe alpha-diversity indices of microbial communities, including the Chao1, Shannon, and Simpson indices, were analyzed using the Wilcoxon rank-sum test in Mothur software (v1.30.2), as well as the R language stats package (v3.3.1) for both the Wilcoxon rank-sum test and Kruskal-Wallis test. Microbial beta-diversity was assessed via PCoA and PERMANOVA (Bray-Curtis distance) using R's vegan package (v3.3.1). Stacked bar plots at the phylum and genus levels were generated in R. Differential taxa were identified using the LEfSe tool (LDA\u0026thinsp;\u0026gt;\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp:huttenhower.sph.harvard.eduLEfSe\u003c/span\u003e\u003cspan address=\"http:huttenhower.sph.harvard.eduLEfSe\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). FAPROTAX and FUNGuild tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp:www.funguild.org\u003c/span\u003e\u003cspan address=\"http:www.funguild.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were applied for the classification and annotation of microbial functional groups. The Mantel test was utilized with the vegan package (v3.3.1) in R to examine the relationships between environmental factors, microbial community structure, and rice yield. Species were filtered based on Spearman correlation (r\u0026thinsp;\u0026gt;\u0026thinsp;0.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the correlation network was visualized using Gephi. Finally, structural equation modeling (SEM) was established using SPSS AMOS 26 to elucidate the multi-level regulatory pathways of \"rhizosphere microbiota-paddy soil-rice yield\".\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Impact of Fm and Ri on arbuscular mycorrhizal colonization efficiency in rice\u003c/h2\u003e \u003cp\u003eElectron microscopy confirmed the establishment of mutualistic symbiosis between rice roots and both AMF species, showed AMF vesicles and internal hyphal structures within the rice roots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). Mycorrhizal colonization rates increased progressively across growth stages, with Fm consistently exhibiting the highest colonization rate (78.23% at harvest vs. 70.13% for Ri; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, native AMF colonized control (CK) roots during grain-filling and harvest stages (6.00% and 18.33%, respectively), though at significantly lower rates than inoculated treatments (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These findings validate the successful formation of enduring mycorrhizal partnerships with rice roots by both AMF inoculants in the natural environment. Importantly, the mycorrhizal colonization rate in the Fm treatment was significantly higher than that in the Ri treatment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a greater colonization capacity of Fm in rice roots.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Influence of Fm and Ri on paddy rhizosphere soil quality and rice yield\u003c/h2\u003e \u003cp\u003eInoculation with AMF significantly boosted the rhizosphere nutrient availability. Specifically, Fm and Ri increased SOM by 147.37% and 135.00%, respectively, compared to CK. AK content was also notably higher in Fm and Ri than in CK (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). While, the TN, TP, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N content were highest in Fm, surpassing CK and Ri significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, AP reached its peak in Ri, increasing by 185.81% and 69.81% compared to CK and Fm, respectively. Despite the significant impact of Fm and Ri on rhizosphere soil nutrients, the pH remained consistent across treatments.\u003c/p\u003e \u003cp\u003eThe enzymatic function also demonstrated a positive response to the inoculation of AMF. Compared to the CK, the activities of S-AGL and S-BG increased by 52.56% and 27.51% in Fm, and and 112.28% and 94.59% in Ri. Moreover, S-UE, S-CL, S-NAG, and S-ALP activities were significantly higher in Fm than in Ri (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), increasing by 47.29%, 24.62%, 23.07%, and 58.80%, respectively (Table S2).\u003c/p\u003e \u003cp\u003eA notable rise in the SQI values within the Fm and Ri treatment cohorts as compared to the CK (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, SQI values increased by 77.35% and 57.07% in the Fm and Ri relative to the CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Additionally, the rice yield increased by 26.96% and 21.19% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Linear regression confirmed a strong positive correlation between SQI and yield (R\u0026thinsp;=\u0026thinsp;0.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), underscoring that AMF-enhanced soil functionality and rice yield.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Role of Fm and Ri in shaping rhizosphere microbial diversity\u003c/h2\u003e \u003cp\u003eBacterial bacterial α-diversity differed significantly among treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Specifically, the Ri treatment showed the highest Chao1 and Shannon indices, indicating greater species richness and even distribution within the bacterial community. In contrast, the Simpson index in the Ri treatment was significantly lower than that in the CK and Fm treatments (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting the prevalence of specific bacterial species in the Ri treatment. Similar trends were also observed in fungal diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBray-Curtis distance-based PCoA revealed compositional divergence in rhizosphere microbiota (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D). Bacterial communities exhibited significant spatial separation among treatments (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with PCoA1 and PCoA2 collectively explaining 62.44% of variance. Fungal communities showed no significant structural shifts across treatments (PCoA1 and PCoA2\u0026thinsp;=\u0026thinsp;61.77% variance). This contrast highlights that Fm and Ri inoculation selectively reshaped bacterial conmmunities, while maintaining fungal community in rice soils.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Modulation of rhizosphere microbiota composition and function by Fm and Ri\u003c/h2\u003e \u003cp\u003eFm and Ri treatments fundamentally reshaped rhizosphere microbial composition, with 43 bacterial phyla, 490 bacterial genera, 14 fungal phyla, and 255 fungal genera identified. Analysis of the top 10 most abundant taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) revealed that at the bacterial phylum level, \u003cem\u003eChloroflexota\u003c/em\u003e (22.9%), \u003cem\u003ePseudomonadot\u003c/em\u003ea (21.66%), and \u003cem\u003eAcidobacteriota\u003c/em\u003e (10.54%) were the dominant phyla across all three treatments, followed by \u003cem\u003eThermodesulfobacteriota\u003c/em\u003e (10.04%), \u003cem\u003eBacteroidota\u003c/em\u003e (7.72%), and \u003cem\u003eActinomycetota\u003c/em\u003e (5.29%), accounting for over 75% of the relative sequence abundance. In particular, Fm and Ri significantly increased \u003cem\u003eChloroflexota\u003c/em\u003e and \u003cem\u003eAcidobacteriota\u003c/em\u003e, while decreased \u003cem\u003ePseudomonadota\u003c/em\u003e and \u003cem\u003eBacteroidota\u003c/em\u003e .At the genus level of bacteria, \u003cem\u003eAnaerolinea\u003c/em\u003e (3.11%) and \u003cem\u003ePseudarthrobacter\u003c/em\u003e (1.51%) were the prevailing genera in all treatments, with a noticeable increase of both \u003cem\u003eAnaerolinea\u003c/em\u003e and \u003cem\u003ePseudarthrobacter\u003c/em\u003e in the rhizosphere soil of Fm and Ri treatments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs for the fungal phylum, \u003cem\u003eAscomycota\u003c/em\u003e (47.41%) and \u003cem\u003eBasidiomycota\u003c/em\u003e (18.10%) were the prevailing phyla, followed by \u003cem\u003eMortierellomycota\u003c/em\u003e (4.83%) and \u003cem\u003eChytridiomycota\u003c/em\u003e (3.88%), accounting for over 70% of the relative sequence abundance. Notably, Fm primarily enriched \u003cem\u003eBasidiomycota\u003c/em\u003e, \u003cem\u003eMortierellomycota\u003c/em\u003e, and \u003cem\u003eChytridiomycota\u003c/em\u003e, whereas Ri primarily enriched \u003cem\u003eAscomycota\u003c/em\u003e. At the fungal genus level, the Fm enriched \u003cem\u003eTausonia\u003c/em\u003e, \u003cem\u003eMortierella\u003c/em\u003e, and \u003cem\u003eTalaromyces\u003c/em\u003e, while the Ri treatment enriched \u003cem\u003eCladosporium\u003c/em\u003e, \u003cem\u003ePsilocybe\u003c/em\u003e, \u003cem\u003ePseudeurotium\u003c/em\u003e, and \u003cem\u003eTrichocladium\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe LEFSe analysis revealed 58 bacterial biomarkers and nine fungal biomarkers (LDA\u0026thinsp;\u0026gt;\u0026thinsp;3.0, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the rice rhizosphere communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In the rhizosphere soil bacterial community, the top three enriched biomarkers in the Fm treatment were \u003cem\u003eg__norank_f__Aggregatilineaceae\u003c/em\u003e, \u003cem\u003ef__norank_o__Chthoniobacterales\u003c/em\u003e, and \u003cem\u003eg__norank_o__Chthoniobacterales\u003c/em\u003e, while the Ri treatment biomarkers included \u003cem\u003ep__Verrucomicrobiota\u003c/em\u003e, \u003cem\u003eo__Pedosphaerales\u003c/em\u003e, and \u003cem\u003ec__Verrucomicrobia\u003c/em\u003e. At the genus level in the fungal community, \u003cem\u003eg__Pyrenochaetopsis\u003c/em\u003e was significantly enriched in the Fm treatment, and \u003cem\u003eg__Ceratobasidiaceae_gen_Incertae_sedis\u003c/em\u003e was significantly enriched in the Ri treatment.\u003c/p\u003e \u003cp\u003eFAPROTAX and FUNGuild demonstrated AMF-induced metabolic specialization (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Compared to the CK treatment, the Fm significantly enhanced functions related to carbon metabolism (such as anaerobic and aerobic heterotrophy). In contrast, the Ri treatment showed significant enhancement in sulfur cycling (such as respiration of sulfur compounds) and energy metabolism. This indicates that Fm and Ri can promote soil carbon and sulfur cycling, thereby driving the mineralization and supply of nutrients in the rhizosphere soil. While, Fm and Ri may decrease the relative abundance of pathogenic bacteria (such as \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, parasites, etc.), thereby suppressing the propagation of harmful microorganisms in the rhizosphere soil.\u003c/p\u003e \u003cp\u003eThe ecological functions revealed that the nutritional types of rhizosphere soil fungi are diverse, including pathogenic, symbiotic, and saprophytic types. The relative abundance of symbiotic fungi (for example, \u003cem\u003eGlomeromycota\u003c/em\u003e) and saprophytic fungi (such as \u003cem\u003eBasidiomycota\u003c/em\u003e and \u003cem\u003eAscomycota\u003c/em\u003e) significantly increased, indicating that the Fm and Ri not only promoted AMF colonization but also significantly enhanced the activity of saprophytic fungi, consistent with the FUNGuild functional prediction results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Fm and Ri-induced restructuring of rhizosphere microbial interaction networks\u003c/h2\u003e \u003cp\u003eNetwork analysis of rhizosphere microbiota at genus level demonstrated that both Fm and Ri significantly resstructured microbial interaction patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It was observed that the network properties of nodes and edges in fungal communities were higher than those of CK, indicating AMF fungi may cause antagonistic fungi to play a central role in the network. This can be confirmed by evidence of negatively correlated connections in Fm. Significantly, the co-occurrence network constructed with Fm exhibited a higher proportion of positive correlations (1,515 edges) in fungal communities, along with a greater average weighted degree, clustering coefficient, and superior modularity compared to the network treated with Ri. These results indicate that the Fm treatment promotes a more stable and functionally organized fungal community structure. In contast, the bacterial co-occurrence network under Ri treatment had a larger proportion of positive edges (3267), with a relatively higher average weighted degree and average clustering coefficient compared to Fm, suggesting that Ri treatment may promote more bacterial mutualistic relationships.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlation analysis of rhizosphere-dominant microbial communities and soil nutrient characteristics\u003c/h2\u003e \u003cp\u003eThis research conducted an analysis on the correlation between the top ten most common bacterial and fungal phyla and the indicators of rhizosphere soil nutrients. The Mantel test results revealed notable disparities in the reaction of fungal and bacterial communities to rhizosphere soil nutrients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among the bacterial community, a number of prevalent phyla displayed significant positive associations with soil nutrients and enzyme activities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecifically, \u003cem\u003eChloroflexota\u003c/em\u003e showed highly significant positive correlations with soil SOM and SOC, AK, S-BG, rhizosphere SQI, and rice yield (r\u0026thinsp;\u0026gt;\u0026thinsp;0.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Notably, \u003cem\u003eAcidobacteriota\u003c/em\u003e displayed a highly significant positive correlation with soil SOC (r\u0026thinsp;=\u0026thinsp;0.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, \u003cem\u003ePseudomonadota\u003c/em\u003e positively correlated significantly with soil SOC (r\u0026thinsp;=\u0026thinsp;0.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). \u003cem\u003eActinomycetota\u003c/em\u003e displayed a strong correlation with TP, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, S-NAG, S-ALP, and rhizosphere SQI, while \u003cem\u003eVerrucomicrobiota\u003c/em\u003e exhibited a strong association with soil SOM, AK, S-BG, rhizosphere SQI, and rice yield (r\u0026thinsp;\u0026gt;\u0026thinsp;0.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Furthermore, \u003cem\u003eVerrucomicrobiota\u003c/em\u003e demonstrated a highly significant positive correlation with TN (r\u0026thinsp;=\u0026thinsp;0.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, the fungal community showed weaker correlations, with only \u003cem\u003eAscomycota\u003c/em\u003e correlating very significantly with AK (r\u0026thinsp;=\u0026thinsp;0.58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u003cem\u003eMonoblepharomycota\u003c/em\u003e correlating significantly with TP (r\u0026thinsp;=\u0026thinsp;0.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Differential response of Fm and Ri in regulating the \"root symbiosis-rhizosphere soil quality-rice yield\" nexus\u003c/h2\u003e \u003cp\u003eThis study confirmed that inoculation with Fm and Ri significantly enhanced root colonization in rice, demonstrating the successful establishment of AM symbiosis under paddy field conditions. Notably, Fm displayed superior colonization efficiency (78.23%) compared to Ri (70.13%) and native fungal strains (18.33%). This competitive advantage likely stems from Fm\u0026rsquo;s enhanced ability to metabolize rice-derived fatty acids (such as palmitic and oleic acid), facilitated by upregulated lipid transporters such as FatM and RAM2 (Bernaola et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, Fm aggressively colonizes root infection sites, forming extensive hyphal networks while prioritizing extraradical hyphae development to optimize phosphorus and nitrogen acquisition (Liu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Martin et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These adaptive traits underscore Fm\u0026rsquo;s dominance in ecological niche competition.\u003c/p\u003e \u003cp\u003eThe Fm and Ri treatments significantly enhanced SOM and SOC contents in the rhizosphere soils. This increase aligns with the role of AMF in facilitating the accumulation of organic matter by regulating the enzyme systems in rhizosphere soil (Zhang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Specifically, AMF hyphae accelerate the decomposition of complex carbon compounds, such as cellulose, by secreting S-NAG and S-BG, while the enhanced activity of S-AGL promotes the conversion of simple sugars (Qin et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, the polysaccharides secreted by the hyphae act as \"microbial glue\", significantly facilitating the formation of soil aggregates (Zhou et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, distinct functional specialization was observed between the two AMF species.\u003c/p\u003e \u003cp\u003eFm significantly increased the activities of S-UE and S-CL by 47.29% and 24.62%, respectively, compared to Ri. This promotes mineralization and transformation of organic carbonl; S-UE accelerates urea hydrolysis to provide nitrogen sources, while S-CL enhances cellulose degradation, collectively driving soil carbon cycling (Wang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, Ri notably increased soil AP content by 69.81% compared to Fm, primarily by regulating of S-ALP activity, which effectively hydrolyzes organic phosphorus compounds to release available phosphorus (Li et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). This functional differentiation indicates that Fm tends to optimize energy acquisition by enhancing carbon and nitrogen cycling enzyme systems, whereas Ri improves phosphorus utilization efficiency through the activation of phosphorus mobilization systems. These results reveal the critical role of Fm and Ri in establishing an interactive network of \"root symbiosis - rhizosphere soil quality - rice yield\", thereby contributing to nutrient cycling in the rhizosphere. This specialized functionality directly improved rhizosphere soil health (Lu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Fm treatment resulted in a 9.43% higher rhizosphere SQI compared to Ri, consistent with previous findings on AMF-induced soil enhancement (Chang et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These improvements were also reflected in crop productivity, as Fm-inoculated plants showed significantly higher yield increases compared to Ri-treated plants (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Importantly, linear regression analysis revealed that rice yield correlated positively with rhizosphere SQI (R\u0026thinsp;=\u0026thinsp;0.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that Fm increases nutrient availability by stimulating carbon-nitrogen-cycling enzymes (Campo et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and optimizing soil structure (Madhushan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Parvin et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, considering the potential influence of abiotic factors (such as climate, water availability), future studies should utilize multifactorial models to better predict the yield-enhancing potential of AMF under field conditions and to clarify the precise role of rhizosphere nutrients and enzymatic activities in maintaining soil health.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Differential dynamic regulation in rhizosphere microbial communities by Fm and Ri\u003c/h2\u003e \u003cp\u003eThe AMF fungi reduced the number of nodes and links in the bacterial and fungal networks. This suggests that functional microbio, such as those involved in nutrient cycling and antagonistic interactions, play a central role in the network. Evidence from FAPROTAX and FUNGuild revealed that AMF significantly enhanced functions related to carbon metabolism and sulphur cycling while decreasing the relative abundance of pathogenic bacteria. Specially, both AMF treatments reduced pathogen-related risks (Duan et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e), they exhibited distinct functional specialization: Fm primarily boosted carbon metabolism (including both aerobic and anaerobic heterotrophic processes (Bradley et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), whereas Ri mainly impacted sulfur cycling and energy metabolism (Du et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These discoveries clarify the specific mechanisms of AMF-mediated regulation of the rhizosphere microbiome and endorse targeted AMF inoculation strategies.\u003c/p\u003e \u003cp\u003eFunctionally, Fm treatment significantly increased the abundance of organic-degrading \u003cem\u003eChloroflexota\u003c/em\u003e (22.9%) and \u003cem\u003eActinomycetota\u003c/em\u003e (5.29%) through the secretion of organic acids and glomalin (Lahrach et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Mantel test results supported these observations, indicating a strong positive relationship between \u003cem\u003eChloroflexota\u003c/em\u003e and SOM (r\u0026thinsp;\u0026gt;\u0026thinsp;0.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting Fm's role in influencing carbon storage by restructuring the microbial community (Dong et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Furthermore, Fm notably enhanced S-UE (47.29%) and S-ALP (58.80%) enzyme activities. The significant correlation between \u003cem\u003eActinomycetota\u003c/em\u003e and S-ALP (r\u0026thinsp;\u0026gt;\u0026thinsp;0.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) highlights the important role of this phylum in organic phosphorus mineralization (Pii et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These changes facilitated the development of a highly interconnected bacterial network (4,189 edges), ultimately enhancing rhizosphere microbial stability and the efficiency of soil nutrient cycling (Bao et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; He et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlterations in the fungal community reflected distinct ecological functions (Sui et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). AMF strains exhibit distinct regulation of the relative abundance of \u003cem\u003eAscomycota\u003c/em\u003e (Bai et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Among these, the Fm strain specifically enriches \u003cem\u003eMortierella\u003c/em\u003e, \u003cem\u003eTalaromyces\u003c/em\u003e, and \u003cem\u003eTausonia\u003c/em\u003e, which have carbon metabolism functions. These functional groups form a synergistic metabolic network through a \"cross-feeding\" mechanism (Ujv\u0026aacute;ri et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). \u003cem\u003eMortierella\u003c/em\u003e produces propionate through lipid metabolism, providing a carbon source for \u003cem\u003eTalaromyces\u003c/em\u003e (Hnini et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); cellulase secreted by \u003cem\u003eTalaromyces\u003c/em\u003e degrades complex polysaccharides, and the resulting glucose further supports the growth of \u003cem\u003eTausonia\u003c/em\u003e (Wen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jin et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This metabolic interaction significantly enhances the efficiency of rhizosphere carbon cycling (Kakouridis et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ri specifically enriches \u003cem\u003eMonoblepharomycota\u003c/em\u003e, whose abundance is significantly positively correlated with the soil TP (r\u0026thinsp;=\u0026thinsp;0.58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating its critical role in organic phosphorus mineralization within the \"Ri-rice\" symbiotic system (Yang et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ndabankulu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Simultaneously, Ri favors the enrichment of \u003cem\u003eCladosporium\u003c/em\u003e and \u003cem\u003ePseudeurotium\u003c/em\u003e, which participate in the sulfur cycle, and these strains may promote the transformation of thiosulfate through the redox enzyme system (Liu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e4.3 Deciphering the yield-enhancing mechanisms of AMF via structural equation modeling (SEM) modeling: from rhizosphere microecology to rice productivity\u003c/p\u003e \u003cp\u003eEmploying the SEM approach, this study revealed that Fm significantly outperformed Ri in promoting rice yield (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). SEM analysis indicated that Fm inoculation primarily influences rice yield through two pathways. Firstly, Fm directly increases rice yield by modifying the composition and structure of the rhizosphere microbial community. This finding supports the \"AMF-microbiome\" synergistic theory proposed by Zhang et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). These microbes secrete growth hormones such as IAA, fix nitrogen, or solubilize phosphorus, thereby directly promoting rice growth (Moran and Durham \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Secondly, Fm indirectly affects rice yield by modulating rhizosphere the SQI through alterations in the rhizosphere soil microbial community network. In contrast, the regulatory effect of Ri treatment on rice yield was relatively weaker. More importantly, Ri treatment failed to establish a significant association between SQI and rice yield (r\u0026thinsp;=\u0026thinsp;0.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating its limited pathway of indirectly affecting yield through soil quality improvement.\u003c/p\u003e \u003cp\u003eThis study elucidates the key mechanisms by which Fm treatment synergistically promotes rice yield through a dual pathway of \"microbial community reconstruction-soil quality improvement\", whereas Ri treatment exhibited a weaker yield-promoting effect due to its failure to effectively establish this synergy. This suggests that Fm enhances the rhizosphere soil quality by enriching key functional microbial groups and strengthening the microbial community network, thereby improving rhizosphere soil properties, ultimately achieving a cascade mechanism for stable Fm-rice yield increase (Hao et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This mechanism elucidates the systemic relationship among \"rhizosphere microbes-paddy soil-rice yield\", providing a novel perspective for achieving sustainable agricultural development through rhizosphere microecological regulation. These findings not only provide a foundation for optimizing AMF application in agroecosystems but also inspire developing microbiome-mediated technologies for improving soil health and crop yield.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study systematically revealed the regulatory mechanisms of Fm and Ri on the AMF, soil microbe and plants interaction network within the paddy ecosystem. Fm demonstrated a superior root colonization capacity and significantly enriching key functional microbial groups involved in carbon cycling ((such as \u003cem\u003eChloroflexota\u003c/em\u003e and \u003cem\u003eActinomycetota\u003c/em\u003e). This enrichment significantly enhanced soil urease and cellulase activity, thereby promoting soil organic matter accumulation. Conversely, Ri enhanced microbial α-diversity and selectively activated functional microbial groups involved in sulfur or phosphorus transformation ((such as \u003cem\u003eCladosporium\u003c/em\u003e, \u003cem\u003ePseudeurotium\u003c/em\u003e), effectively promoting soil organic phosphorus mineralization. Fm established a synergistic fungal \"cross-feeding\" loop among those functional microbiom (such as \u003cem\u003eMortierella, Talaromyces, and Tausonia\u003c/em\u003e), optimizing carbon-use efficiency. While Ri fostered microbial cooperation via quorum sensing, facilitating sulfur redox cycling. Notably, the Fm significantly improved the SQI, resulting in superior rice yield compared to the Ri, thus confirming the synergistic \"soil health-crop yield\" enhancement mechanism.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm their contribution to the paper as follows: writing - original draft: MS; visualization: YW, RW; Software:JL; investigation: FS; draft manuscript: MS, YW, JL, XF, FS. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper was supported by the Key R\u0026amp;D Program of Heilongjiang Province (GA23B006),Heilongjiang Province“Double First-Class ” Discipline Collaborative Innovation Achievement Project (LJGXCG2023-088),\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBai B, Liu WD, Qiu XY, Zhang J, Zhang JY, Bai Y (2022) The root microbiome: Community assembly and its contributions to plant fitness. J Integr Plant Biol 64 (2):230-243.\u003c/li\u003e\n\u003cli\u003eBao XZ, Zou JX, Zhang B, Wu LM, Yang TT, Huang Q (2022) Arbuscular Mycorrhizal Fungi and Microbes Interaction in Rice Mycorrhizosphere. Agronomy-Basel 12 (6):1277.\u003c/li\u003e\n\u003cli\u003eBernaola L, Cange G, Way MO, Gore J, Hardke J, Stout M (2018) Natural Colonization of Rice by Arbuscular Mycorrhizal Fungi in Different Production Areas. Rice Sci 25 (3):169-174.\u003c/li\u003e\n\u003cli\u003eBradley AS, Leavitt WD, Schmidt M, Knoll AH, Girguis PR, Johnston DT (2016) Patterns of sulfur isotope fractionation during microbial sulfate reduction. Geobiology 14 (1):91-101.\u003c/li\u003e\n\u003cli\u003eCampo S, Mart\u0026iacute;n-Cardoso H, Oliv\u0026eacute; M, Pla E, Catala-Forner M, Mart\u0026iacute;nez-Eixarch M, San Segundo B (2020) Effect of Root Colonization by Arbuscular Mycorrhizal Fungi on Growth, Productivity and Blast Resistance in Rice. Rice 13 (1):42.\u003c/li\u003e\n\u003cli\u003eChang JJ, Sun Y, Tian L, Ji L, Luo SS, Nasir F, Kuramae EE, Tian CJ (2021) The Structure of Rhizosphere Fungal Communities of Wild and Domesticated Rice: Changes in Diversity and Co-occurrence Patterns. Front Microbiol 12:610823.\u003c/li\u003e\n\u003cli\u003eChe WK, Piao JL, Gao Q, Li XB, Li X, Jin F (2023) Response of soil physicochemical properties, soil nutrients, enzyme activity and rice yield to rice straw returning in highly saline-alkali paddy soils. J Soil Sci Plant Nut 23 (3):4396-4411.\u003c/li\u003e\n\u003cli\u003eChen DL, Saeed M, Ali MNHA, Raheel M, Ashraf W, Hassan Z, Hassan MZ, Farooq U, Hakim MF, Rao MJ, Naqvi SAH, Moustafa M, Al-Shehri M, Negm S (2023) Plant Growth Promoting Rhizobacteria (PGPR) and Arbuscular Mycorrhizal Fungi Combined Application Reveals Enhanced Soil Fertility and Rice Production. Agronomy-Basel 13 (2):550.\u003c/li\u003e\n\u003cli\u003eDeveau A, Bonito G, Uehling J, Paoletti M, Becker M, Bindschedler S, Hacquard S, Herv\u0026eacute; V, Labb\u0026eacute; J, Lastovetsky OA, Mieszkin S, Millet LJ, Vajna B, Junier P, Bonfante P, Krom BP, Olsson S, van Elsas JD, Wick LY (2018) Bacterial-fungal interactions: ecology, mechanisms and challenges. Fems Microbiol Rev 42 (3):335-352.\u003c/li\u003e\n\u003cli\u003eDong HY, Sun HY, Chen CL, Zhang MY, Ma DR (2023) Compositional Shifts and Assembly in Rhizosphere-Associated Fungal Microbiota Throughout the Life Cycle of Rice Under Increased Nitrogen Fertilization. Rice 16 (1):34.\u003c/li\u003e\n\u003cli\u003eDong LJ, Ma LN, He WM (2021) Arbuscular mycorrhizal fungi help explain invasion success of. Appl Soil Ecol 157:103763.\u003c/li\u003e\n\u003cli\u003eDu TY, Hu QF, He HY, Mao WJ, Yang Z, Chen H, Sun LN, Zhai MZ (2023) Long-term organic fertilizer and biofertilizer application strengthens the associations between soil quality index, network complexity, and walnut yield. Eur J Soil Biol 116:103492.\u003c/li\u003e\n\u003cli\u003eDuan SL, Declerck S, Feng G, Zhang L (2023) Hyphosphere interactions between and promote carbon-phosphorus exchange at the peri-arbuscular space in. Environ Microbiol 25 (4):867-879.\u003c/li\u003e\n\u003cli\u003eFu J, Jian YW, Wang XH, Li L, Ciais P, Zscheischler J, Wang Y, Tang YH, M\u0026uuml;ller C, Webber H, Yang B, Wu YL, Wang QH, Cui XQ, Huang WC, Liu YQ, Zhao PJ, Piao SL, Zhou F (2023) Extreme rainfall reduces one-twelfth of China\u0026apos;s rice yield over the last two decades. Nat Food 4 (5):416-426.\u003c/li\u003e\n\u003cli\u003eGao YJ, Cui JL, Zhang XM, Hoogenboom G, Wallach D, Huang YQ, Reis S, Lin T, Gu BJ (2025) Cost-effective adaptations increase rice production while reducing pollution under climate change. Nat Food. 6 (3):260\u0026ndash;272.\u003c/li\u003e\n\u003cli\u003eHao HB, Yue YH, Wang Q, Xiao TT, Zhao ZL, Zhang JJ, Chen H (2024) Effects of the rice-mushroom rotation pattern on soil properties and microbial community succession in paddy fields. Front Microbiol 15:1449922.\u003c/li\u003e\n\u003cli\u003eHe MY, Li DX, Peng S, Wang YD, Ding QD, Wang YM, Zhang JH (2025) Reduced soil ecosystem multifunctionality is associated with altered complexity of the bacterial-fungal interkingdom network under salinization pressure. Environ Res 269:120863.\u003c/li\u003e\n\u003cli\u003eHnini M, Rabeh K, Oubohssaine M (2024) Interactions between beneficial soil microorganisms (PGPR and AMF) and host plants for environmental restoration: A systematic review. Plant Stress 11:100391.\u003c/li\u003e\n\u003cli\u003eJiang FY, Zhang L, Zhou JC, George TS, Feng G (2021) Arbuscular mycorrhizal fungi enhance mineralisation of organic phosphorus by carrying bacteria along their extraradical hyphae. New Phytol 230 (1):304-315.\u003c/li\u003e\n\u003cli\u003eJin ZX, Jiang FY, Wang LT, Declerck S, Feng G, Zhang L (2024) Arbuscular mycorrhizal fungi and : brothers in arms to shape the structure and function of the hyphosphere microbiome in the early stage of interaction. Microbiome 12 (1):83.\u003c/li\u003e\n\u003cli\u003eKakouridis A, Yuan MT, Nuccio EE, Hagen JA, Fossum CA, Moore ML, Estera-Molina KY, Nico PS, Weber PK, Pett-Ridge J, Firestone MK (2024) Arbuscular mycorrhiza convey significant plant carbon to a diverse hyphosphere microbial food web and mineral-associated organic matter. New Phytol 242 (4):1661-1675.\u003c/li\u003e\n\u003cli\u003eLahrach Z, Legeay J, Ahmed B, Hijri M (2024) The composition of the arbuscular mycorrhizal fungal bacteriome is species dependent. Environ Microbiome 19 (1):77.\u003c/li\u003e\n\u003cli\u003eLi HH, Shi F, Zhu SY, He X, Ding YW, Xu TL, Qi DD, Chang W, Dai CL, Song FQ (2024) improves rhizosphere soil pH and nutrient status in sodic soil. Rhizosphere-Neth 32:100973.\u003c/li\u003e\n\u003cli\u003eLi J, Zhou LY, Chen G, Yao MY, Liu ZG, Li XR, Yang XP, Yang Y, Cai DR, Tuerxun Z, Li B, Nie TK, Chen XJ (2025a) Arbuscular mycorrhizal fungi enhance drought resistance and alter microbial communities in maize rhizosphere soil. Environ Technol Inno 37:103947.\u003c/li\u003e\n\u003cli\u003eLi MY, Wang W, Yin HH, Chen YL, Ashraf M, Tao HY, Li SS, Wang WY, Yang CL, Xiao YL, Zhu L, Xiong YC (2025b) The functional role of arbuscular mycorrhizal fungi in enhancing soil organic carbon stocks and stability in dryland. Soil Till Res 248:106443.\u003c/li\u003e\n\u003cli\u003eLiu CS, Zhao DF, Ma WJ, Guo YD, Wang AJ, Wang QL, Lee DJ (2016) Denitrifying sulfide removal process on high-salinity wastewaters in the presence of sp. Appl Microbiol Biot 100 (3):1421-1426.\u003c/li\u003e\n\u003cli\u003eLiu JR, Pellerin A, Izon G, Wang JS, Antler G, Liang JQ, Su PB, Jorgensen BB, Ono S (2020) The multiple sulphur isotope fingerprint of a sub-seafloor oxidative sulphur cycle driven by iron. Earth Planet Sc Lett 536:116165.\u003c/li\u003e\n\u003cli\u003eLiu ZX, Gu HD, Yao Q, Jiao F, Hu XJ, Liu JJ, Jin J, Liu XB, Wang GH (2024) Soil pH and carbon quality index regulate the biogeochemical cycle couplings of carbon, nitrogen and phosphorus in the profiles of Isohumosols. Sci Total Environ 922:171269.\u003c/li\u003e\n\u003cli\u003eLu S, Lepo JE, Song HX, Guan CY, Zhang ZH (2018) Increased rice yield in long-term crop rotation regimes through improved soil structure, rhizosphere microbial communities, and nutrient bioavailability in paddy soil. Biol Fert Soils 54 (8):909-923.\u003c/li\u003e\n\u003cli\u003eMadhushan KWA, Karunarathna SC, Dissanayake DMD, Priyadarshani TDC, Stephenson SL, Elgorban AM, Dawoud TM, Priyashantha AKH, Dai DQ, Yapa PN, Wang XY (2023) Effect of Arbuscular Mycorrhizal Fungi on Lowland Rice Growth and Yield ( L.) under Different Farming Practices. Agronomy-Basel 13 (11): 2803.\u003c/li\u003e\n\u003cli\u003eMartin FM, Oepik M, Dickie IA (2024) Mycorrhizal research now: from the micro- to the macro-scale. New Phytol 242 (4):1399-1403.\u003c/li\u003e\n\u003cli\u003eMoran MA, Durham BP (2019) Sulfur metabolites in the pelagic ocean. Nat Rev Microbiol 17 (11):665-678.\u003c/li\u003e\n\u003cli\u003eNdabankulu K, Egbewale SO, Tsvuura Z, Magadlela A (2022) Soil microbes and associated extracellular enzymes largely impact nutrient bioavailability in acidic and nutrient poor grassland ecosystem soils. Sci Rep-Uk 12 (1):12601.\u003c/li\u003e\n\u003cli\u003eParvin S, Van Geel M, Ali MM, Yeasmin T, Lievens B, Honnay O (2021) A comparison of the arbuscular mycorrhizal fungal communities among Bangladeshi modern high yielding and traditional rice varieties. Plant Soil 462 (1-2):109-124.\u003c/li\u003e\n\u003cli\u003ePii Y, Borruso L, Brusetti L, Crecchio C, Cesco S, Mimmo T (2016) The interaction between iron nutrition, plant species and soil type shapes the rhizosphere microbiome. Plant Physiol Bioch 99:39-48.\u003c/li\u003e\n\u003cli\u003eQin MS, Zhang Q, Pan JB, Jiang SJ, Liu YJ, Bahadur A, Peng ZL, Yang Y, Feng HY (2020) Effect of arbuscular mycorrhizal fungi on soil enzyme activity is coupled with increased plant biomass. Eur J Soil Sci 71 (1):84-92.\u003c/li\u003e\n\u003cli\u003eSaud S, Wang DP, Fahad S, Alharby HF, Bamagoos AA, Mjrashi A, Alabdallah NM, AlZahrani SS, AbdElgawad H, Adnan M, Sayyed RZ, Ali S, Hassan S (2022) Comprehensive Impacts of Climate Change on Rice Production and Adaptive Strategies in China. Front Microbiol 13:926059.\u003c/li\u003e\n\u003cli\u003eShi F, He X, Cao M, Wu RT, Zhang B, Xu TL, Jiang MG, Song FQ (2025) Strategies for plant-microbe symbiosis: Mycorrhizal fungi and helper bacteria to improve cold tolerance in rice. Plant Physiol Bioch 222:109741.\u003c/li\u003e\n\u003cli\u003eSui JL, He XW, Yi GH, Zhou LM, Liu SQ, Chen QQ, Xiao XH, Wu JY (2023) Diversity and structure of the root-associated bacterial microbiomes of four mangrove tree species, revealed by high-throughput sequencing. Peerj 11:16156.\u003c/li\u003e\n\u003cli\u003eSun SM, Feng YH, Huang GD, Zhao X, Song FQ (2022) enhances tolerance to cadmium stress by altering host plant hemp ( L.) photosynthetic properties. Environ Pollut 314:120309.\u003c/li\u003e\n\u003cli\u003eUjv\u0026aacute;ri G, Turrini A, Avio L, Agnolucci M (2021) Possible role of arbuscular mycorrhizal fungi and associated bacteria in the recruitment of endophytic bacterial communities by plant roots. Mycorrhiza 31 (5):527-544.\u003c/li\u003e\n\u003cli\u003eWang J, Xu CY, Sun QM, Xu JR, Chai YR, Berg G, Cernava T, Ma ZH, Chen Y (2021) Post-translational regulation of autophagy is involved in intra-microbiome suppression of fungal pathogens. Microbiome 9 (1):131.\u003c/li\u003e\n\u003cli\u003eWang LT, Zhang L, George TS, Feng G (2023) A core microbiome in the hyphosphere of arbuscular mycorrhizal fungi has functional significance in organic phosphorus mineralization. New Phytol 238 (2):859-873.\u003c/li\u003e\n\u003cli\u003eWen YQ, Xu TL, Qi DD, Chang W, Li K, Fan XX, Zhang MM, Ping Y, Song FQ (2024) combined with biochar can improve the saline-alkali tolerance and energy quality of switchgrass through osmoregulation and gene expression. Sci Hortic-Amsterdam 338:113793.\u003c/li\u003e\n\u003cli\u003eWen ZL, Yang MK, Han HW, Fazal A, Liao YH, Ren R, Yin TM, Qi JL, Sun SC, Lu GH, Hu SJ, Yang YH (2023) Mycorrhizae Enhance Soybean Plant Growth and Aluminum Stress Tolerance by Shaping the Microbiome Assembly in an Acidic Soil. Microbiol Spectr 11 (2):03310-22\u003c/li\u003e\n\u003cli\u003eYang MK, Song YH, Ma HK, Li ZH, Ding JW, Yin TM, Niu KC, Sun SC, Qi JL, Lu GH, Fazal A, Yang YH, Wen ZL (2024) Unveiling the hidden world: How arbuscular mycorrhizal fungi and its regulated core fungi modify the composition and metabolism of soybean rhizosphere microbiome. Environ Microbiome 19 (1):78.\u003c/li\u003e\n\u003cli\u003eYuan PL, Wang JP, Li CF, Xiao QQ, Liu QJ, Sun ZC, Wang JH, Cao CG (2020) Soil quality indicators of integrated rice-crayfish farming in the Jianghan Plain, China using a minimum data set. Soil Till Res 204:104732.\u003c/li\u003e\n\u003cli\u003eZhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN, Shi SJ, Cho HJ, Karaoz U, Loqu\u0026eacute; D, Bowen BP, Firestone MK, Northen TR, Brodie EL (2018) Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol 3 (4):470-480.\u003c/li\u003e\n\u003cli\u003eZhang L, Shi N, Fan JQ, Wang F, George TS, Feng G (2018a) Arbuscular mycorrhizal fungi stimulate organic phosphate mobilization associated with changing bacterial community structure under field conditions. Environ Microbiol 20 (7):2639-2651.\u003c/li\u003e\n\u003cli\u003eZhang L, Yan CX, Guo Q, Zhang JB, Ruiz-Menjivar J (2018b) The impact of agricultural chemical inputs on environment: global evidence from informetrics analysis and visualization. Int J Low-Carbon Tec 13 (4):338-352.\u003c/li\u003e\n\u003cli\u003eZhang L, Zhou JC, George TS, Limpens E, Feng G (2022) Arbuscular mycorrhizal fungi conducting the hyphosphere bacterial orchestra. Trends Plant Sci 27 (4):402-411.\u003c/li\u003e\n\u003cli\u003eZhang ZC, Ding SL, Diao FW, Jia BB, Shi ZQ, Guo W (2024a) AMF symbiosis drives the rhizosphere microbiome to synergistically improve herbage growth in saline-alkaline soils. Land Degrad Dev 35 (11):3663-3674.\u003c/li\u003e\n\u003cli\u003eZhang ZC, Zhou ZH, Feng SC, Guo PR, Wang YC, Hao BH, Guo W, Li FY (2024b) Synergistic effects of AMF and PGPR on improving saline-alkaline tolerance of Leymus chinensis by strengthening the link between rhizosphere metabolites and microbiomes. Environ Technol Inno 36:103900.\u003c/li\u003e\n\u003cli\u003eZhang ZF, Zhang JC, Xu GP, Zhou LW, Li YQ (2019) Arbuscular mycorrhizal fungi improve the growth and drought tolerance of Zenia insignis seedlings under drought stress. New Forest 50 (4):593-604.\u003c/li\u003e\n\u003cli\u003eZhao C, Piao SL, Wang XH, Huang Y, Ciais P, Elliott J, Huang MT, Janssens IA, Li T, Lian X, Liu YW, M\u0026uuml;ller C, Peng SS, Wang T, Zeng ZZ, Pe\u0026ntilde;uelas J (2017) Plausible rice yield losses under future climate warming. Nat Plants 3 (1):16202.\u003c/li\u003e\n\u003cli\u003eZhao W, Chen ZB, Yang XQ, Sheng LY, Mao H, Zhu SX (2023) Metagenomics reveal arbuscular mycorrhizal fungi altering functional gene expression of rhizosphere microbial community to enhance resistance to Cr stress. Sci Total Environ 895:164970.\u003c/li\u003e\n\u003cli\u003eZhao WX, Chou JM, Li JN, Xu Y, Li YM, Hao YD (2022) Impacts of Extreme Climate Events on Future Rice Yields in Global Major Rice-Producing Regions. Int J Env Res Pub He 19 (8):4437.\u003c/li\u003e\n\u003cli\u003eZhou J, Guo PX, Huang SP, Liu CY, Wang YK, Li FM, Chen WP, Zhang Q, Shi LL, Yang HS (2025) Long-term diverse straw management influences arbuscular mycorrhizal fungal community structure and plant growth in a rice-rotated wheat cropping system. J Environ Manage 374:124227.\u003c/li\u003e\n\u003cli\u003eZhou JC, Chai XF, Zhang L, George TS, Wang F, Feng G (2020) Different Arbuscular Mycorrhizal Fungi Cocolonizing on a Single Plant Root System Recruit Distinct Microbiomes. Msystems 5 (6):e00929-20.\u003c/li\u003e\n\u003cli\u003eZhou JC, Zhang L, Feng G, George TS (2022) Arbuscular mycorrhizal fungi have a greater role than root hairs of maize for priming the rhizosphere microbial community and enhancing rhizosphere organic P mineralization. Soil Biol Biochem 17:108713.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Structural characteristics of microbial interaction networks in rice rhizosphere soils.\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"691\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBacteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 271px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFungi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eFm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eRi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eFm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003eRi\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e437\u0026plusmn;16.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e411\u0026plusmn;19.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e436\u0026plusmn;35.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e187\u0026plusmn;33.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e213\u0026plusmn;13.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e191\u0026plusmn;17.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEdges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7636\u0026plusmn;215.00c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4189\u0026plusmn;188.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6487\u0026plusmn;236.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2447\u0026plusmn;169.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3046\u0026plusmn;49.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2360\u0026plusmn;235.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive edges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3827\u0026plusmn;175.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2026\u0026plusmn;616.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3267\u0026plusmn;243.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1268\u0026plusmn;189.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1515\u0026plusmn;202.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1282\u0026plusmn;184.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative edges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3809\u0026plusmn;247.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2163\u0026plusmn;162.00c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3220\u0026plusmn;342.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1179\u0026plusmn;173.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1531\u0026plusmn;341.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e1078\u0026plusmn;91.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e34.947\u0026plusmn;225.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e20.384\u0026plusmn;395.00c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e29.757\u0026plusmn;811.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e26.171\u0026plusmn;1608.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e28.601\u0026plusmn;1659.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e24.712\u0026plusmn;343.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage weighted degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e33.998\u0026plusmn;456.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e19.824\u0026plusmn;900.00c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e28.987\u0026plusmn;62.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e25.755\u0026plusmn;2096.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e28.138\u0026plusmn;679.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e24.237\u0026plusmn;548.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNetwork diameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6\u0026plusmn;1.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7\u0026plusmn;2.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6\u0026plusmn;1.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e5\u0026plusmn;1.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3\u0026plusmn;1.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e4\u0026plusmn;1.00a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGraph density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.08\u0026plusmn;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.05\u0026plusmn;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.068\u0026plusmn;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.141\u0026plusmn;0.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.135\u0026plusmn;0.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.13\u0026plusmn;0.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModularity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.55\u0026plusmn;0.03b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.558\u0026plusmn;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.496\u0026plusmn;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.535\u0026plusmn;0.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.518\u0026plusmn;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.508\u0026plusmn;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage clustering coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.556\u0026plusmn;0.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.457\u0026plusmn;0.04a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.514\u0026plusmn;0.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.667\u0026plusmn;0.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.714\u0026plusmn;0.11a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e0.629\u0026plusmn;0.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage path length\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2.664\u0026plusmn;0.14a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.988\u0026plusmn;0.49a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.596\u0026plusmn;0.30a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2.217\u0026plusmn;0.22a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1.933\u0026plusmn;0.07a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e2.185\u0026plusmn;0.29a\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\u003e\u003cstrong\u003eAll values are means \u0026plusmn; standard errors, n = 3. Different lowercase letters within the same row indicate significant differences among treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Treatments: CK (control), Fm (\u003cem\u003eFunneliformis mosseae\u003c/em\u003e), Ri (\u003cem\u003eRhizophagus intraradices\u003c/em\u003e).\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"mycorrhiza","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcor","sideBox":"Learn more about [Mycorrhiza](http://link.springer.com/journal/572)","snPcode":"572","submissionUrl":"https://submission.nature.com/new-submission/572/3","title":"Mycorrhiza","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Arbuscular mycorrhizal fungi (AMF), Rice rhizosphere soil, Soil nutrients, Rhizosphere microorganisms, Sustainable agriculture","lastPublishedDoi":"10.21203/rs.3.rs-6936303/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6936303/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile the positive impact of arbuscular mycorrhizal fungi (AMF) on rice growth has been well established, the specific mechanisms through which different species of AMF regulate rice growth and the rhizosphere microecosystem are still not fully understood. This research investigated two AMF species, \u003cem\u003eFunneliformis mosseae\u003c/em\u003e (Fm) and \u003cem\u003eRhizophagus intraradices\u003c/em\u003e (Ri), to uncover their distinct effects on rice rhizosphere soil characteristics, microbial community structure, and rice yield. Field experiments showed that the Fm treatment resulted in a significantly higher yield increase (26.96%) compared to Ri (21.19%). Although both AMF species significantly increased mycorrhizal colonization rates (Fm: 78.23%, Ri: 70.13% at maturity), they induced distinct improvements in soil properties. Specifically, Fm significantly boosted soil enzyme activity, with urease and cellulase activities 47.29% and 24.62%, respectively, higher than Ri Conversely, Ri promoted the accumulation of soil available phosphorus (69.81% higher than Fm). Additionally, the two AMF strains influenced the rhizosphere microbial community through different regulatory mechanisms. Fm significantly enriched carbon cycle-related bacterial groups such as \u003cem\u003eChloroflexota\u003c/em\u003e and \u003cem\u003eActinomycetota.\u003c/em\u003e Ri, however, not only significantly increased microbial α-diversity but also specifically enriched sulfur cycle functional bacterial groups. Crucially, the two AMF species optimized the \"AMF-rice-rhizosphere microorganisms\" interaction network through differential structural modifications. In the Fm treatment, fungal community network modularity was significantly enhanced, while the bacterial network under Ri treatment exhibited stronger connectivity. This study elucidates the distinct mechanism by which AMF species synergistically enhance rhizosphere soil microenvironment quality and increase rice yield. These findings provide a theoretical basis for the sustainable management of rice fields and suggest new directions for developing environmentally friendly agricultural technologies.\u003c/p\u003e","manuscriptTitle":"Differential impacts of Funneliformis mosseae and Rhizophagus intraradice on soil quality and rice yield in paddy fields: mediated by AMF-rice-rhizosphere microbe interactions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-26 13:16:29","doi":"10.21203/rs.3.rs-6936303/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-03T09:59:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T15:51:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145920280945708371002381950977472888130","date":"2025-07-02T09:06:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186789570247212022213527595942043228763","date":"2025-06-24T17:47:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T09:05:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-23T15:11:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-23T13:20:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Mycorrhiza","date":"2025-06-20T07:16:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"mycorrhiza","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcor","sideBox":"Learn more about [Mycorrhiza](http://link.springer.com/journal/572)","snPcode":"572","submissionUrl":"https://submission.nature.com/new-submission/572/3","title":"Mycorrhiza","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5280ed8c-1500-4e8d-9cf5-4db9ca06b23e","owner":[],"postedDate":"June 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:05:56+00:00","versionOfRecord":{"articleIdentity":"rs-6936303","link":"https://doi.org/10.1007/s00572-025-01238-z","journal":{"identity":"mycorrhiza","isVorOnly":false,"title":"Mycorrhiza"},"publishedOn":"2025-10-27 15:57:22","publishedOnDateReadable":"October 27th, 2025"},"versionCreatedAt":"2025-06-26 13:16:29","video":"","vorDoi":"10.1007/s00572-025-01238-z","vorDoiUrl":"https://doi.org/10.1007/s00572-025-01238-z","workflowStages":[]},"version":"v1","identity":"rs-6936303","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6936303","identity":"rs-6936303","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}