Impacts of 10 years of elevated CO2 and warming on soil fungal diversity and network complexity in a Chinese paddy field

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Although studies have focused on soil bacterial community responses to climate change in agricultural ecosystems, only few have investigated the dynamic changes in the diversity and complexity of fungal communities in paddy fields. Herein, using internal transcribed spacer (ITS) gene amplicon sequencing and co-occurrence network methods, the responses of soil fungal community to factorial combinations of elevated CO 2 (550 ppm) and canopy warming (+2°C) were explored in an open-air field experiment in Changshu, China, for 10 years. Elevated CO 2 significantly increased the operational taxonomic unit (OTU) richness and Shannon diversity of fungal communities in both rice rhizosphere and bulk soils, whereas the relative abundances of Ascomycota and Basidiomycota were significantly decreased and increased, respectively, by elevated CO 2 . Co-occurrence network analysis showed that elevated CO 2 , warming, and their combination increased the network complexity and negative correlation of the fungal community in rhizosphere and bulk soils, suggesting that these factors enhanced the competition of microbial species. Warming resulted in a more complex network structure by altering topological roles and increasing the numbers of key fungal nodes. Principal coordinate analysis indicated that rice growth stages rather than elevated CO 2 and warming altered soil fungal communities. Specifically, the changes in diversity and network complexity were greater at the heading and ripening stages than at the tillering stage. Furthermore, elevated CO 2 and warming significantly increased the relative abundances of pathotrophic fungi and reduced those of symbiotrophic fungi in both rhizosphere and bulk soils. Overall, the results indicate that long-term CO 2 exposure and warming enhance the complexity and stability of soil fungal community, potentially threatening crop health and soil functions through adverse effects on fungal community functions. Climate change Paddy field Rhizosphere Network complexity Fungal diversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The global atmospheric carbon dioxide (CO 2 ) concentration has increased from 280 ppm in 1850 to 400 ppm today and is predicted to reach 550 ppm by the middle of this century, accompanied by an increase in global mean temperature by 2℃ (Stocker et al., 2014). Increase in atmospheric CO 2 concentration can improve the photosynthetic efficiency of rice, increase CO 2 fixation, and enhance rice yield (Kim et al., 2011 ; Wang et al., 2016 ). Temperature is the key factor determining the length of the rice growth season. Increase in temperature will accelerate the growth and development of rice, resulting in a shortened preheading phase but did not affect the postheading phase (Cai et al., 2016 ). Soil fungi play an important role in regulating soil ecosystem functions such as soil nutrient cycling, organic matter decomposition, and environmental pollutant purification (Austin et al., 2009 ; Yu et al., 2016). Fungal community diversity contributes to ecosystem stability and maintains crop diversity (Antoninka et al., 2011 ; Van Diepen et al., 2011 ). Elevated atmospheric CO 2 concentrations and warming affect the physiological growth of soil crops and the structure and function of terrestrial ecosystems via feedback to the terrestrial ecosystem, along with changes in soil fungal community and function (Graaff et al., 2006 ; Hatfield et al., 2011 ; Zheng et al., 2016 ). It is generally believed that elevated CO 2 levels indirectly affect soil microorganisms by impacting plant photosynthesis (Bruce et al., 2010 ). Higher CO 2 levels promote photosynthesis and increase the amount of root exudates, thereby stimulating the growth and activity of microorganisms and changing the structure and function of microbial communities (Drigo et al., 2010 ; Vestergard et al., 2016). Temperature is the main factor affecting the abundance of fungi (Tedersoo, 2017 ). However, there are still some contradictions concerning the dynamic changes in soil fungal communities with elevated atmospheric CO 2 levels or warming, based on literature. For example, Hayden et al. ( 2012 ) conducted climate change simulation studies on soil microorganisms in Australian grasslands and found that elevated atmospheric CO 2 significantly increased gene abundance and changed the composition of the fungal communities. Tu et al. ( 2015 ) reported that long-term elevated CO 2 did not significantly alter the overall structure and species richness of the fungal community but significantly increased community evenness and diversity. In another study, CO 2 enrichment was found to increase the gene abundance and diversity of soil fungi in an agricultural ecosystem (Liu et al., 2014 ; Liu et al., 2017 ). However, studies have also shown that elevated atmospheric CO 2 levels do not significantly affect soil microbial activity (Austin et al., 2009 ). Temperature increase accelerates the decomposition of soil organic carbon and the uptake of soil nitrogen by plants, resulting in a decrease in organic matter content (Melillo et al., 2017 ). Bacteria have a stronger ability to adapt to environmental changes than fungi which are therefore more susceptible to increase in temperatures (Ali et al., 2018 ). Liu et al. ( 2017 ) reported that warming significantly decreased the abundance of soil fungi in wheat rhizosphere. Recently, it was shown that the diversity of the soil fungal community was lower under warming conditions (Anthony et al., 2021 ). Deslippe et al. ( 2012 ) showed that long-term warming resulted in a significant increase in the relative abundance of fungi in Arctic tundra soil. However, studies have also shown that elevated atmospheric CO 2 levels and warming do not considerably affect the abundance, community composition, or activity of soil fungi (Bergner et al., 2004 ; Austin et al., 2009 ). Thus, the response of soil fungal communities to climate change is still largely unclear. Microbial interactions can form complex networks that collectively act on ecosystem functions (Banerjee et al., 2016 ). Microbial co-occurrence networks can reflect the interaction characteristics among microorganisms, which have been widely used to explore the relationship between microbial communities (Friedman and Alm, 2012 ; Ma et al., 2020 ). However, microbial linkages in co-occurrence networks should be considered as statistically hypothetical interactions (Carr et al., 2019 ), and the term "biological interaction" is therefore used for simplicity. According to previous studies, the soil microbial correlation network model can vary with environmental factors (e.g., drought, warming, and elevated CO 2 concentration). For example, Vries et al. ( 2018 ) demonstrated that drought promotes the destabilization of soil bacterial networks rather than fungal symbiotic networks in grassland ecosystems. Tu et al. ( 2015 ) analyzed the responses of soil fungal communities to long-term elevated CO 2 in an experimental field in Minnesota and indicated that elevated CO 2 increased the complexity of the fungal community network. Warming was found to significantly alter the diversity and structure of soil fungal communities and, additionally, the complexity of fungal networks (Zhou et al., 2021 ). In another study, according to the values of Zi (within-module connectivity) and Pi (among-module connectivity), the roles of species (nodes) were divided into four categories: peripherals, connectors, module hubs, and network hubs (Deng et al., 2012 ). Typically, the nodes that function as hubs or connectors in a network are defined as keystone species (Banerjee et al., 2018 ). Recently, it was shown that long-term warming increased the complexity and abundances of keystone species of a microbial network (Yuan et al., 2021 ). Another study reported that elevated CO 2 simplified the soybean rhizosphere soil fungi network structure by changing the keystone species members. However, to the best of our knowledge, the symbiotic pattern of fungal networks and their responses to climate change in agroecosystems are still not fully understood (Tu et al., 2015 ). In this context, the molecular ecological network (MEN) method was used herein to explore the symbiotic relationship in fungal communities under elevated CO 2 and warming conditions, which can provide new insights into the responses of fungal communities to climate change. The aim of this study is to determine the influence of elevated CO 2 , warming, and their combination on the diversity, community composition, and network complexity of soil fungal communities in paddy soil. It was hypothesized that the diversity and network complexity would increase when exposed to elevated CO 2 , but the positive impacts could be offset by warming. To test this hypothesis, a 10-year open free-air experiment with factorial elevated CO 2 (550 ppm) and warming (by 2°C) was conducted in an agricultural ecosystem. Materials And Methods Site description and experimental setup The field experiment with free-air CO 2 enrichment and warming facilities was established in 2010 in Guli Township (31°30′N, 120°33′E), Changshu, Jiangsu Province, China. In this region, the traditional cropping system is the rotation of summer rice and winter wheat. The area has a typical subtropical monsoon climate, with average annual temperature of 16°C and average annual precipitation of 1,100–1,200 mm. The soil was derived from clay lacustrine deposit and classified as Gleyic Stagnic Anthrosol, with pH (H 2 O) of 7.0, soil organic carbon (SOC) content of 16.2 g∙kg − 1 , and total nitrogen (TN) content of 1.9 g∙kg − 1 . The climate change treatments included ambient environmental conditions (CK), CO 2 increased to 550 ppm (CE), temperature increased by 2°C (WA), and combined elevated CO 2 and temperature (CW). Each treatment consisted of three replicates, and each replicate was conducted in an octagonal ring of 8 m diameter (an area of approximately 50 m 2 ). In total, 12 octagonal rings were set up in this experimental field, and each ring was buffered by 28 m of open field to minimize any treatment cross-over effects (Fig. S1 ). For the CO 2 enrichment treatment groups (CE and CW), pure CO 2 gas was pumped from a storage tank and injected through perforated tubes surrounding the rings. For the warming treatment groups (WA and CW), 12 infrared heating lamps were mounted 1.2 m above the rice canopy in each ring. The treatment groups with elevated CO 2 and warming were maintained consistently over the entire rice growing period. More details of the experimental facility layout, performance, and operation are described by Liu et al. ( 2022 ). Rhizobox Application And Sample Collection Rice ( Oryza sativa L. cv. Changyou 5) was transplanted with a density of 26 hills per m 2 on 10th June, 2020, and harvested on 27th October, 2020. Rhizosphere soil was collected using a rhizobox inserted in each ring plot 2 days before rice transplantation. The dimensions of the rhizobox were 8 × 8 × 15 cm (length × width × height) (Fig. S2). The rhizobox was divided into three sections by a nylon net with a pore diameter of 30 µm to keep in the roots, allowing water and nutrient passage (Xiong et al., 2021 ). To ensure full contact between soil and roots, two rice plants were planted in each rhizobox. The soil in the middle compartment of the rhizobox was treated as rhizosphere soil, and the left and right sides were bulk soil. Soil samples were collected at the tillering, heading, and ripening stages during the rice growth season. A total of 72 samples (four treatment groups × three replicates × three stages × two kinds) were collected from the experimental facilities. All samples were passed through a 2-mm sieve and immediately shipped to the laboratory in an ice box. Soil Dna Extraction And Bioinformatic Analysis Total soil DNA was extracted from 0.5 g soil using a SPINeasy DNA Kit for Soil (MP Biomedicals, LLC), according to the manufacturer’s instructions. The concentration and quality of soil DNA were assessed by 1% agarose gel electrophoresis and a NanoDrop spectrophotometer. The fungal ITS genes were amplified with the primers ITS1F/ITS2R. The PCR amplification product was purified with the SanPrep Column PCR Product Purification Kit (Sangon Biotech Co., China) and quantified using QuantiFluor™-ST (Promega, USA). The purified amplicon libraries were sequenced on the Illumina MiSeq platform. The raw sequences were quality-filtered using the Quantitative Insight into Microbial Ecology (QIIME) software (Caporaso et al., 2010 ) to remove barcodes, primers, fuzzy bases, and low-quality sequences (less than 200 bp). Subsequently, the remaining sequences were subjected to chimerism detection using the Mothur software to remove all chimeric sequences (Schloss et al., 2009 ). The non-chimeric sequences were clustered into operational taxonomic units (OTUs) with 97% similarity using the QIIME software. According to the NCBI GenBank database, the representative sequences in OTU were classified and identified using the BLAST algorithm. Alpha diversity, including OTU richness and Shannon index, was calculated using the Mothur software. The functional prediction of the fungal community was conducted using the FUNGuild database (Louca et al., 2016 ; Nguyen et al., 2016 ). Network Construction And Analysis Molecular ecological networks were used to analyze the intradomain correlation of microbial species at multiple taxon levels. To assess the impact of climate change on the complexity and specificity of the soil fungal community, network analysis was conducted for each treatment. All analyses were conducted in a pipeline http://mem.rcees.ac.cn:8081 available online (Feng et al., 2022 ). The OTUs which occurred in more than half of the samples were retained without log-transformation prior to obtaining the Spearman correlation coefficient matrix. Based on the random matrix theory (RMT), a uniform threshold was determined for each microbial network. After network construction, the topology characteristics and network randomization were implemented in MENAP. Among-module and inter-module connectivity was computed based on the detected modules and nodes were assigned to network hubs, module hubs, connectors, or peripherals. Finally, networks were visualized in Gephi (version 0.9.2; https://gephi.org/ ) with a Fruchterman-Reingold layout algorithm. To determine the role of nodes in the network, the topological role of each node of the microbial network was defined according to the within-module connectivity (Zi) and among-module connectivity (Pi) of the nodes of the molecular ecological network of the soil microbial community. Network nodes were divided into four categories: (1) peripherals (Zi ≤ 2.5, Pi ≤ 0.62), with few connections, which are basically connected to the internal nodes of the module; (2) module hub (Zi > 2.5, Pi ≤ 0.62), which is highly connected with nodes inside the module; (3) connector (Zi ≤ 2.5, Pi > 0.62), which is highly connected with nodes of other modules; and (4) network hub (Zi > 2.5, Pi > 0.62), which is highly connected to the nodes of other modules as well as those inside the module. It is generally believed that nodes with Zi > 2.5 or Pi > 0.62 are key nodes and play an important role in the connection with nodes within or between modules (Olesen et al., 2007 ; Tian et al., 2022 ). Statistical analysis All statistical analyses were performed using SPSS 20.0 (SPSS Inc., USA), and data visualization was carried out in R (version 4.1.2). The significant difference between climate change treatments ( P < 0.05) was tested by one-way ANOVA, followed by Duncan's test. Principal co-ordinate analysis (PCoA) was conducted to determine the fungal community distribution using the R package “vegan” with the ‘Adonis’ function. Repeated measures ANOVA was employed to determine the primary effects of elevated CO 2 , warming, and growth stage. Results Diversity and richness of the soil fungal community After quality filtering, a total of 4,757,273 qualified reads were obtained from 72 samples, with 52,697–70,616 sequences per sample (Table S1 ). The coverage of each sample was 99.96–100.00%, indicating that the sequencing intensity was sufficient to detect the fungal diversity in all samples (Table S3). As shown in Table 1 , the OTU richness and the Shannon index were significantly increased under elevated CO 2 but only slightly reduced under warming treatment, and these effects were influenced by growth stages (Table 1 ). In rice rhizosphere and bulk soils, elevated CO 2 increased the OTU richness and Shannon index by 9.0–9.7% and 5.3–10.4% ( P 0.05). Warming had no effect on OTU richness or Shannon index in bulk soil but it decreased OTU richness in the rhizosphere ( P = 0.023). At the ripening stage, warming had no effect on OTU richness, whereas at the tillering and heading stages, OTU richness was reduced by 15.68% and 14.59%, respectively. There was no interactive effect of elevated CO 2 and warming on OTU richness and Shannon index for both rhizosphere and bulk soils: the positive effects of elevated CO 2 on OTU richness and Shannon index were moderated by warming. Additionally, there was a significant effect of interaction between CO 2 and rice growth stage on Shannon index in bulk soil ( P < 0.001). Table 1 The OTU richness and Shannon diversity of the soil fungal community in rice rhizosphere and bulk soils under elevated CO 2 and warming. Rhizosphere Bulk Soil OTU richness Shannon OTU richness Shannon Tillering CK 467.67 ± 51.87a 3.49 ± 0.90a 542.33 ± 13.80b 4.56 ± 0.39a CE 464.33 ± 18.61a 3.96 ± 0.09a 548.67 ± 25.11b 4.49 ± 0.12a WA 394.33 ± 20.50b 3.51 ± 0.28a 547.00 ± 19.97b 4.74 ± 0.14a CW 481.67 ± 15.82a 3.86 ± 0.19a 586.00 ± 18.08a 4.46 ± 0.16a Heading CK 656.00 ± 43.21a 4.51 ± 0.14b 663.00 ± 41.58ab 4.76 ± 0.08b CE 676.33 ± 46.92a 5.06 ± 0.14a 700.33 ± 41.24a 5.11 ± 0.22a WA 577.67 ± 21.46b 4.18 ± 0.17c 601.33 ± 53.20b 4.56 ± 0.06b CW 632.00 ± 32.14ab 4.67 ± 0.07b 702.67 ± 35.91a 5.08 ± 0.03a Ripening CK 561.33 ± 57.73b 4.28 ± 0.34b 603.33 ± 60.38b 4.64 ± 0.36b CE 663.00 ± 20.52a 4.91 ± 0.05a 700.67 ± 34.59a 5.24 ± 0.10a WA 591.33 ± 21.78ab 4.57 ± 0.01ab 608.00 ± 26.06b 4.56 ± 0.10b CW 622.33 ± 40.41ab 4.63 ± 0.12ab 673.33 ± 41.40ab 4.89 ± 0.15ab CO 2 effect (%) A 9.0 10.4 9.7 5.3 Warming effect (%) B -5.4 -3.1 -1.1 -1.7 CO 2 < 0.001 0.002 < 0.001 < 0.001 Warming 0.023 0.366 0.64 0.276 Stage < 0.001 < 0.001 < 0.001 < 0.001 CO 2 × Warming 0.271 0.395 0.354 0.445 CO 2 × Stage 0.376 0.625 0.164 < 0.001 Warming × Stage 0.158 0.474 0.218 0.191 CO 2 × Warming × Stage 0.021 0.657 0.272 0.325 The various lowercase letters indicate significance differences at the P < 0.05 level. A: Main effects of elevated CO 2 calculated as ((CE + CW)/(CK + WA)-1) × 100 averaged across the three stages. B: Main effects of warming calculated as ((WA + CW)/(CK + CE)-1) × 100 averaged across the three stages. Structure Composition Of The Soil Fungal Community Ascomycota (29.5–68.8%), Rozellomycota (2.5–21.8%), Mortierellomycota (1.4–15.9%), and Basidiomycota (1.5–8.3%) were the dominant fungal phyla in rice rhizosphere and bulk soils across all treatment groups (Fig. 1 A). Elevated CO 2 significantly decreased the relative abundance of Ascomycota by 11.6–16.7% and increased that of Basidiomycota by 39.6–57.4% in rhizosphere and bulk soil (Table S3). Warming resulted in a significant increase in the relative abundances of Mortierellomycota and Basidiomycota, and the interaction between CO 2 and warming had a significant effect only on the relative abundance of Basidiomycota ( P < 0.001). Additionally, a significant effect of interaction between CO 2 and growth stage on the relative abundance of Basidiomycota was observed: elevated CO 2 increased the relative abundance of Basidiomycota at the heading and ripening stages but had no impact at the tillering stage. PERMANOVA and PCoA results revealed that the soil fungal communities were significantly affected by elevated CO 2 , warming, and growth stage (Table 2 and Fig. 1 B). The fungal communities in the tillering stage were clearly separated from those in the heading and ripening stages along the PCoA1 axis (Fig. 1 B). The PCoA showed a clear separation between CK and the other treatment groups (CE, WA, and CW) in both rhizosphere and bulk soils (Fig. S3). Table 2 The statistical significance of PERMANOVA for dissimilarity analysis of the fungal composition in rice rhizosphere and bulk soils with elevated CO 2 (CO 2 ), warming and growth stage (Stage). Rhizosphere Bulk Soil R 2 P R 2 P Fungal composition CO 2 0.105 0.001 0.096 0.001 Warming 0.096 0.001 0.102 0.001 Stage 0.305 0.001 0.355 0.001 CO 2 ×Warming 0.061 0.001 0.063 0.001 CO 2 ×Stage 0.089 0.001 0.072 0.001 Warming×Stage 0.104 0.001 0.081 0.001 CO 2 ×Warming×Stage 0.095 0.001 0.065 0.001 Residual 0.145 0.166 Total 1 1 As shown in Fig. S4, the relative abundances of the top 10 fungal genera (> 1%) were determined under elevated CO 2 and warming. The fungal genera were significantly affected by climate change only in the rhizosphere. Elevated CO 2 significantly increased the relative abundances of Pyrenochaetopsis , Cosmospora , and Metarhizium but reduced those of Pseudeurotium , Cladorrhinum , and Podospora . Warming significantly increased the relative abundances of Cladorrhinum and Podospora but decreased those of Fusicolla and Sutellinia in the rhizosphere. Functional Composition Of The Soil Fungal Community Seven trophic modes were detected in this study. Among them, saprotrophs (61.7–82.9%), saprotroph-symbiotrophs (5.1–33.3%), and pathogens (1.5–15.1%) were the most abundant functional guilds across all treatment groups (Fig. S5). Individual functional guilds in rhizosphere and bulk soils showed diverse responses to elevated CO 2 and warming (Table 3 ). The relative abundances of saprotrophs and symbiotrophs were significantly decreased, whereas those of pathotrophs, pathotroph-saprotrophs, and saprotroph-symbiotrophs in both rhizosphere and bulk soils were increased. Warming significantly increased the relative abundances of pathotrophs and saprotroph-symbiotrophs and decreased those of symbiotrophs and pathotroph-saprotroph-symbiotrophs. Notably, significant effects of interactions between growth stage and CO 2 /warming were observed on the relative abundances of most fungal functional guilds. Table 3 Effects of elevated CO 2 , warming and growth stage on the relative abundance of fungal functional guilds in rhizosphere and bulk soils. Saprotroph Pathotroph Symbiotroph Pathotroph-Saprotroph Saprotroph- Symbiotroph Pathotroph- Symbiotroph Pathotroph-Saprotroph-Symbiotroph Rhizosphere CO 2 effect (%) A -9.5 42.9 -14.6 71.9 41.6 54.7 34.9 Warming effect (%) B 3.6 49.7 -46.2 -66. 8 14.7 -94.4 -8.2 CO 2 * *** * ** ** ns * Warming ns *** *** *** ns ns ** Stage *** * * * ** ns ** CO 2 ×Warming * *** ns ** * ns ** CO 2 ×Stage *** *** * ns * ns * Warming×Stage ns ** *** * *** ns * CO 2 ×Warming×Stage ** * ns *** * ns ** Bulk Soil CO 2 effect (%) A -14.9 95.9 -27.3 36.9 23.6 99.9 20.1 Warming effect (%) B -7.0 46.9 -28.4 -39.3 32.5 -80.6 -39.9 CO 2 ** *** ** ** ** ns * Warming ns ** ** * ns ns * Stage *** *** *** * *** ns ** CO 2 ×Warming ns ** * ** ns ns ** CO 2 ×Stage *** *** *** ** ** ns ns Warming×Stage ns *** ** * * ns * CO 2 ×Warming×Stage * *** ** * * ns *** The “***”, “**”, “*” and “ns” indicate significant levels with P < 0.001, P < 0.01, P 0.05), respectively. A: Main effects of elevated CO 2 calculated as ((CE + CW)/(CK + WA)-1) × 100 averaged across the three stages. B: Main effects of warming calculated as ((WA + CW)/(CK + CE)-1) × 100 averaged across the three stages. Characteristics Of The Fungal Co-occurrence Network Based on the random matrix theory (RMT) method, co-occurrence networks of the soil fungal community were constructed for climate change treatment groups and growth stages, respectively (Fig. 2 and Fig. S6). Compared to CK, elevated CO 2 and warming greatly increased the complexity of the fungal network in both rhizosphere and bulk soils. Elevated CO 2 , warming, and their interaction strongly increased the edges, linkage density, average degree, especially the negative correlation, and decreased the average path distance and modularity (Table 4 ). Further analysis showed that the nodes, edges, linkage density, average degree, average clustering coefficient, as well as negative correlation of fungal networks were higher in the heading and ripening stages than in the tillering stage (Table S4). The network topologies of both rhizosphere and bulk soils differed significantly from those of randomly generated networks across all treatment groups (t test: P < 0.01), suggesting that the fungal co-occurrence network constructed in this study has features of small-world and modularity (Table 4 and Table S4). Table 4 Topological properties of co-occurrence network in rice rhizosphere and bulk soils as influenced by elevated CO 2 and warming. Rhizosphere Bulk Soil CK CE WA CW CK CE WA CW Empirical Network Positive correlation 62.1% 53.8% 43.8% 48.4% 56.1% 45.52% 46.50% 41.85% Negative correlation 37.9% 46.2% 56.2% 51.6% 43.9% 54.48% 53.50% 58.15% Nodes 113 122 116 116 127 119 121 134 Edges 372 428 425 469 328 435 357 454 Linkage density 3.292 3.508 3.664 4.043 2.583 3.655 2.950 3.388 Average degree(avgK) 6.584 7.016 7.328 8.086 5.165 7.311 5.901 6.776 Average clustering coefficient (avgCC) 0.335 0.347 0.364 0.345 0.298 0.376 0.342 0.337 Average path distance (GD) 4.439 3.709 3.697 3.496 3.774 4.131 3.726 3.296 Modularity 0.518 0.458 0.400 0.460 0.586 0.538 0.624 0.522 Random Networks Average clustering coefficient (avgCC) 0.035 ± 0.013* 0.048 ± 0.013* 0.058 ± 0.013* 0.054 ± 0.015* 0.024 ± 0.010* 0.051 ± 0.013* 0.028 ± 0.012* 0.031 ± 0.011* Average path distance (GD) 2.775 ± 0.036* 2.764 ± 0.048* 2.696 ± 0.046* 2.630 ± 0.038* 3.099 ± 0.048* 2.724 ± 0.041* 2.923 ± 0.040* 2.818 ± 0.034* Modularity 0.309 ± 0.009* 0.292 ± 0.008* 0.264 ± 0.008* 0.264 ± 0.007* 0.381 ± 0.010* 0.282 ± 0.008* 0.346 ± 0.011* 0.318 ± 0.009* Parameters of random networks were generated from randomly rewired (100 times) empirical networks. The presented parameters are mean values and standard derivations of random networks. Significant differences between empirical networks and random networks were determined by t-test. * P < 0.01. Topological Role Of The Fungal Network Nodes To identify the role of the nodes in each network, Zi and Pi were calculated for each node. In the rhizosphere, one module hub and connector were observed in CK and CE treatment groups, two module hubs and three connectors in WA, and one module hub and two connectors in CW (Fig. 3 ). The module hubs and connectors in CK, CE, and CW treatment groups were Ascomycota and Basidiomycota, whereas Ascomycota and Mortierellomycota were the module hubs and connectors in the WA treatment group (Table S5). In bulk soil, there was one connector, namely Ascomycota, in CK, and there were five connectors, including Ascomycota and Basidiomycota, in WA and CW treatment groups. The module hub in CW belonged to Chytridiomycota (Table S5). Further analysis showed that there was one connector at the tillering stage, two module hubs and two connectors at the heading stage, and three module hubs at the ripening stage in the rhizosphere (Fig. 4 ). The connectors at the tillering and heading stages belonged to Ascomycota, whilst the module hubs at the tillering, heading, and ripening stages belonged to Ascomycota, Basidiomycota, and Rozellomycota, respectively (Table S6). However, there was no module hub or connector at the tillering and heading stages, whereas one module and two connectors were observed at the ripening stage in the bulk soil. Discussion Diversity and functional composition of the soil fungal community Soil fungal communities are formed by aboveground vegetation through root exudates during plant growth and development (Broeckling et al., 2008 ; Bever et al., 2010 ). This study found significant differences in the α-diversity indices of the soil fungal community across different rice growth stages. The OTU richness and Shannon diversity were higher at the heading and ripening stages than at the tillering stage (Table 1 ), indicating greater physiological metabolic activity of rice at the latter stages of rice. The PCoA also showed that the soil fungal community of samples from the heading and ripening stages grouped together and were clearly separated from those at the tillering stage (Fig. 1 B and Table 2 ), suggesting that the fungal community is significantly influenced by rice growth stages. This is in agreement with Hannula et al. ( 2012 ), who reported that different plant growth stages showed different soil nutrient contents and soil temperatures. Thus, there is a greater influence on soil fungal composition and diversity at the rapid vegetation growing stages. Breidenbach et al. ( 2016 ) and Edwards et al. ( 2018 ) found that rice growth stage can influence microbial community structure in the rhizosphere. In this study, elevated CO 2 significantly increased fungal OTU richness and Shannon diversity in both rhizosphere and bulk soils (Table 1 ), consistent with the findings of previous studies (Liu et al., 2014 ; Tu et al., 2015 ). Elevated CO 2 stimulates C3 plant photosynthesis and root exudate production, leading to a greater soil organic C and C:N ratio, which facilitates fungal growth (Blagodatskaya et al., 2010 ; Bhattacharyya et al., 2013 ). In the same experiment, it was found that fungal α-diversity in rice and wheat soils increased by increasing the input of organic C and reducing the soil pH (Gao et al., 2022 ). In this study, warming had no effect on OTU richness and Shannon index, except for a significant decrease in OTU richness of the rhizosphere soil ( P = 0.023). This is in agreement with the finding of Lorberau et al. ( 2017 ) that warming does not alter fungal richness and diversity. However, a significant increase in fungal diversity under warming was found in an alpine meadow (Wang et al., 2017 ). The distinct responses may be due to the differences in plant host and warming conditions (air canopy warming vs soil warming). In this experiment, the + 2℃ warming of the air canopy resulted in a small increase in soil temperature (< 1℃, Liu et al., 2014 ), which could be in the range of fungal growth fluctuations and indirectly affected fungal diversity by influencing plant growth. This study also found that elevated CO 2 and warming significantly changed the composition of the soil fungal communities in both rhizosphere and bulk soils (Table 2 and Fig. S3). Elevated CO 2 and warming may indirectly shift the soil microbial diversity and composition by affecting plant and root growth and altering soil environmental factors (e.g., temperature, moisture, pH, available C, among others) (Weltzin et al., 2003 ; Ebersberger et al., 2004 ; Fernandez et al., 2017 ). In this study, Ascomycota, Rozellomycota, Mortierellomycota, and Basidiomycota were the dominant phyla across the treatment groups and growth stages. Elevated CO 2 significantly decreased the relative abundance of Ascomycota but increased that of Basidiomycota, which is consistent with the study by Tu et al. ( 2015 ) in a grassland region. Lauber et al. ( 2008 ) found that the abundance of Ascomycota was greater in soils with a higher soil pH. The formation of weak acids when CO 2 is dissolved in water leads to soil acidification (Gao et al., 2022 ), which may be responsible for the decrease in abundance of Ascomycota. In addition, elevated CO 2 significantly increased the relative abundance of Basidiomycota, indicating that higher CO 2 levels can promote the growth of above- and below-ground plant parts, thus improving soil aeration; good aeration conditions favor Basidiomycete growth. In this study, the relative abundances of Ascomycota and Basidiomycota under warming were increased in paddy soil, especially that of Basidiomycota, whose content was significantly increased in both rhizosphere and bulk soils. Under warming conditions, the quantity of the plant litter can be increased, and a large part of the litter may contain substances that are difficult to decompose, which provides favorable nutrient conditions for Ascomycota and Basidiomycota (Cornelissen et al., 2007 ; Wu et al., 2011 ). Therefore, in rice paddies, warming may indirectly affect the soil fungal community by increasing the amount of rice litter. In addition to being influenced by climate change, rhizosphere soil microbes are also largely impacted by plants (Hannula et al., 2012 ). At different plant growth stages, the composition and quantity of root exudates largely differ, and the microbial community structure fluctuates accordingly. Shi et al. ( 2016 ) showed that the composition of enriched and excluded microbes in the rhizosphere differed during different growth stages of soybean, further illustrating the regulation of eukaryotic microbes in the rhizosphere by plant root exudates. Compared with the tillering stage, the relative abundance of Basidiomycetes increased and that of Rozellomycota decreased at the heading and ripening stages (Fig. 1 ), and the growth stage significantly changed the community composition of the soil fungi in rhizosphere and bulk soils (Table S3). Further analysis indicated that the dominant fungal genera in different growth stages responded differently to elevated CO 2 and warming. This is consistent with the report that climate change affects soil microbial communities that perform different functions at different growth stages (Horz et al., 2004 ). The observed OTUs were categorized into fungal functional guilds by the FUNGuild annotation tool (Nguyen et al., 2016 ). The distribution patterns of the fungal functional guilds were clearly influenced by elevated CO 2 , warming, and growth stage (Table 3 ). Based on the above results, both elevated CO 2 and warming significantly increased the relative abundances of pathotrophic fungi. Although there is no study about plant pathogens under elevated CO 2 in agricultural ecosystems, a recent study using a global meta-analysis and a 9-year field experiment found that warming increased the abundances of fungal plant pathogens (Delgado-Baquerizo et al., 2020 ). Soil pathogenic fungi might proliferate under warming, affecting the functions and structure of the forest (Looby and Treseder, 2018 ). In this study, the relative abundances of symbiotrophic fungi were significantly decreased under elevated CO 2 and warming (Table 3 ). Symbiotrophic fungi provide nutrients and water for plant host under environmental stress, which plays an important role in soil health and crop production (Schmidt et al., 2019 ). In paddy soil, reduction in symbiotrophic fungi under elevated O 3 was ascribed to the decrease in plant photosynthesis and nutrient availability (Wang et al., 2022 ). Therefore, the increase and decrease in pathotrophics and symbiotrophics under elevated CO 2 and warming conditions could affect fungal functions and threaten crop production. Although FUNGuild is highly accurate, the ecological functions of many fungi remain unknown. In particular, the ecological functions of soil fungi under global climate change conditions need to be further studied and verified. Network Complexity Of The Soil Fungal Community The microbial co-occurrence network constructed in this study is characterized by scale-free, small world, and modularity. The topological properties are used to define the complexity of the network, which is closely related to the ecosystem functions (Yuan et al., 2021 ). In the present study, the co-occurrence networks of the soil fungal community in the heading and ripening stages were more complex than those in the tillering stage, based on the higher numbers of nodes and edges as well as the higher linkage density, average degree, and clustering coefficient in both rhizosphere and bulk soils (Table S4 and Fig. S6). The more complex network indicates that in the later rice stages, soil nutrient availability for fungal communities is increased due to the high quality and quantity of root exudates and plant residues (Liu et al., 2021 ). The present results indicate that elevated CO 2 and warming altered the topological parameters of the fungal ecological network (Table 4 ). In previous studies, average degree and linkage density have been commonly used to assess the complexity of microbial networks (Montoya et al., 2006 ; Deng et al., 2012 ; Wagg et al., 2019 ). Compared with CK, elevated CO 2 and warming treatments increased the linkage density, average degree, and edge number and decreased the modularity and average path distance of co-occurrence networks (Fig. 2 and Table 4 ), indicating that the complexity of soil fungal networks was improved by elevated CO 2 and warming. These findings are consistent with previous studies reporting that elevated CO 2 and warming increase the complexity of fungal networks (Tu et al., 2015 ; Yuan et al., 2021 ). The positively correlated connections in the co-occurrence network represent the existence of mutual synergistic relationships among microorganisms, whereas the negatively correlated connections represent potential antagonistic effects (Blanchet et al., 2020 ; Chen et al., 2022 ). In this study, elevated CO 2 and warming increased the negative correlation in both rice rhizosphere and bulk soils (Table 4 ), indicating that climate change conditions stimulated competitive relationships among fungal compositions. Ma et al. ( 2020 ) found that microbial network complexity facilitated the growth of microbial flora, leading to more efficient use of soil nutrients. Previous studies found that elevated CO 2 and warming increased the contents of soil organic carbon, total nitrogen, and root exudates in rice paddy soil (Liu et al., 2014 ; Xiong et al., 2019 ; Gao et al., 2022 ), which may increase the competition for soil nutrients among microbes. Additionally, previous studies found that the complexity of microbial networks is positively correlated with α-diversity (Fan et al., 2018 ; Chen et al., 2022 ), indicating that the increase in fungal OTU richness and Shannon index value under elevated CO 2 in this study may have led to the enhanced network complexity. Higher complexity of the microbial network means stronger stability of the whole microbial community, and the competitive relationships will also further enhance the stability (Ochoa-Hueso et al., 2018 ; Wagg et al., 2019 ; Yuan et al., 2021 ). A recent study reported that long-term warming increased the complexity and stability of a microbial network in grassland soil, which are important for maintaining ecosystem functions (Yuan et al., 2021 ). This study also screened keystone species of the fungal community by analyzing the topology of the co-occurrence network. A total of six module hubs and 18 connectors were detected in all molecular ecological networks (Fig. 3 and Table S5), which can be regarded as key nodes that play essential roles in forming the network structure (Banerjee et al., 2018 ). The numbers of module hubs and connectors were higher in the warming treatment groups than in the control, indicating that the fungal network is more complex under warming. This was further supported by the higher node and edge numbers as well as the increased linkage density, average degree, and clustering coefficient under warming conditions compared to the control (Table 1 ). These findings are in agreement with Zhou et al. ( 2021 ), who reported that long-term warming can increase the abundance of keystone species and the complexity of the microbial network in the grassland ecosystem, which may be closely related to ecosystem functions. In the present study, there were five nodes (OTU 2, 473, 626, 301, and 436) and 10 nodes (OTU 758, 156, 641, 345, 481, 323, 197, 134, 369, and 4) in the warming treatment groups in rhizosphere and bulk soils, respectively, whereas only two nodes (OTU 751 and 626) were observed in the control (Fig. 3 ). The higher number of module hubs and connectors under warming condition suggests that the interactions, as well as energy and nutrient flows among the soil fungal community, were more efficient compared to the control (Yu et al., 2018 ). In particular, most of the key nodes were affiliated to the phyla Ascomycetes and Basidiomycetes (Table S5 and Table S6). Previous studies found that Ascomycota can decompose mainly degradable organic matter in soil, whilst Basidiomycota can decompose substances such as lignin and cellulose (Beimforde et al., 2014 ). However, the network was connected by Ascomycota and Basidiomycota, which are both parasitic and saprophytic organisms, facilitating nutrient and energy flow in this network. Moreover, the nodes categorized as module hubs and connectors in the warming network were different from those in the control (Fig. 3 and Table S5), indicating that each OTU played different roles in warming and ambient networks. Overall, the warming-induced change in the network structure and the topological roles of the keystone OTUs may be related to soil nutrient availability. However, further studies are needed to determine the ecological functions of the keystone species. Conclusions This study shows that elevated CO 2 , warming, and growth stages clearly altered the diversity, composition, and network structure of fungal communities in both rhizosphere and bulk soils. The responses of soil fungal communities were greater at the later growth stages than at the tillering stage, whereas α-diversity and network complexity (e.g., nodes, edges, linkage density, average degree, and negative correlation) were greatly increased at the heading and ripening stages. Elevated CO 2 significantly increased OTU richness and Shannon diversity, whereas the relative abundance of Ascomycota significantly decreased under elevated CO 2 and that of Basidiomycota increased compared with the control. Elevated CO 2 and warming increased the network complexity and negative correlation of the fungal community in rhizosphere and bulk soils, suggesting that both elevated CO 2 and warming enhanced the stability of the soil fungal community. Furthermore, the individual functional composition of the soil fungal community showed diverse responses to elevated CO 2 and warming. The relative abundances of pathotrophic fungi were significantly increased and those of symbiotrophs were decreased under elevated CO 2 and warming conditions. Overall, soil fungal communities were considerably altered by elevated CO 2 , warming, and growth stages, potentially influencing ecosystem function and threatening food production under future climate change conditions. Abbreviations PCoA: principal coordinates analysis ; OTU: operational taxonomic units ; ITS: internal transcribed spacer ; MENs: molecular ecological networks Declarations Declaration of competing interests All authors declare that they have no competing financial interests. Ethics Approval This article does not contain any study with humans or other animals. Acknowledgments This work was financially supported by the University Natural Science Research Project of Anhui Province (KJ2021A0531), the University Excellent Young Talent Project of Anhui Province (gxgwfx2019066), and the Natural Science Foundation of Anhui Province, China (2008085QC102). References Ali, R.S., Poll, C., Kandeler, E., 2018. Dynamics of soil respiration and microbial communities: Interactive controls of temperature and substrate quality. Soil Biology and Biochemistry 127, 60–70. Anthony, M.A., Knorr, M., Moore, J.A.M., Simpson, M., Frey, S.D., 2021. 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Zheng, Y., Chen, L., Luo, C.Y., Zhang, Z.H., Wang, S.P., Guo, L.D., 2016. Plant Identity Exerts Stronger Effect than Fertilization on Soil Arbuscular Mycorrhizal Fungi in a Sown Pasture. Microbial Ecology 72, 647–658. Zhou, Y., Sun, B., Xie, B., Feng, K., Zhang, Zhaojing, Zhang, Zheng, Li, S., Du, X., Zhang, Q., Gu, S., Song, W., Wang, L., Xia, J., Han, G., Deng, Y., 2021. Warming reshaped the microbial hierarchical interactions. Global Change Biology 27, 6331–6347. Additional Declarations No competing interests reported. Supplementary Files SurportingInformation.docx Cite Share Download PDF Status: Published Journal Publication published 29 May, 2023 Read the published version in Microbial Ecology → Version 1 posted Editorial decision: Major revision 25 Apr, 2023 Reviews received at journal 29 Mar, 2023 Reviewers agreed at journal 23 Mar, 2023 Reviewers invited by journal 21 Mar, 2023 Editor assigned by journal 21 Mar, 2023 Submission checks completed at journal 21 Mar, 2023 First submitted to journal 21 Mar, 2023 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. 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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-2718550","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":185311559,"identity":"3a72d49a-7e6d-4dfe-afb2-f6c976b9aa27","order_by":0,"name":"Ke Gao","email":"","orcid":"","institution":"Huaibei Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Gao","suffix":""},{"id":185311560,"identity":"7d07514a-d942-4e7f-8b20-9aef2ca63eeb","order_by":1,"name":"Weijie Li","email":"","orcid":"","institution":"Huaibei Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weijie","middleName":"","lastName":"Li","suffix":""},{"id":185311561,"identity":"a99e66fa-86bd-47c3-8db8-8047089c9020","order_by":2,"name":"Zhihui Zhang","email":"","orcid":"","institution":"Huaibei Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhihui","middleName":"","lastName":"Zhang","suffix":""},{"id":185311562,"identity":"8a54b97c-8810-474c-8751-b0d125472795","order_by":3,"name":"Li Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACxmYQWSDBwyZ/+MCBDxVEazGQkOOTYEs8OOMM0XYZMBjLSfAYH+ZtIUIxczvzs4dfDCwS26R7PhzgbWCQ5xc7QMhhbObGMgYSiW0yZzcckNzBYDhzdgIhLQxm0hIgLQy5Gw4YnmFIMLhNUAv7N6iWnAcHgCQxWnjMJD8YSBizSeQwHDhIpJYyaVAgs/EcMzjYcEaCsF8M+49vk/xRUccj3978+POfCht5fmlCWhqAAc2D4EvgVw4C8iDH/SCsbhSMglEwCkYyAAAw5UF3YbSoYwAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese Academy of Science","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Jiang","suffix":""},{"id":185311563,"identity":"342c2d86-3423-4162-9030-5b29a40a4c4d","order_by":4,"name":"Yuan Liu","email":"","orcid":"","institution":"Huaibei Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2023-03-21 12:44:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2718550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2718550/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00248-023-02248-0","type":"published","date":"2023-05-29T21:03:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":34734841,"identity":"3c50077f-0bc7-45dd-8af7-b689364751c8","added_by":"auto","created_at":"2023-03-23 19:36:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":237095,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance (A) and principal coordinate analysis (PCoA) (B) of the soil fungal community in rice rhizosphere and bulk soils under elevated CO\u003csub\u003e2\u003c/sub\u003e and warming.\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-2718550/v1/5904d19d66611b95b9e334e2.png"},{"id":34734843,"identity":"86f9912e-5924-4f9f-94df-8fc3de963509","added_by":"auto","created_at":"2023-03-23 19:36:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1544717,"visible":true,"origin":"","legend":"\u003cp\u003eThe complexity and interactions of the soil fungal community in rice rhizosphere (A) and bulk soils (B) under elevated CO\u003csub\u003e2\u003c/sub\u003e and warming. The size of a node is proportional to the relative abundance of OTUs, and the colors of nodes indicate different fungal phyla of network topology. Red and blue edges represent positive and negative relationships, respectively.\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-2718550/v1/ded2f2312bf9e92d02482182.png"},{"id":34734840,"identity":"92dd2bb5-5fab-4ebf-a717-13cb3150d883","added_by":"auto","created_at":"2023-03-23 19:36:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":391926,"visible":true,"origin":"","legend":"\u003cp\u003eRole distribution of the fungal network nodes in rice rhizosphere (A) and bulk soils (B) under elevated CO\u003csub\u003e2\u003c/sub\u003e and warming.\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-2718550/v1/3bbcc02923cfc602f38219d8.png"},{"id":34734839,"identity":"3b25abcf-6b66-45a6-a0a7-cff2c62cbba5","added_by":"auto","created_at":"2023-03-23 19:36:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":283884,"visible":true,"origin":"","legend":"\u003cp\u003eRole distribution of the fungal network nodes in rice rhizosphere (A) and bulk soils (B) in the three growth stages.\u003c/p\u003e","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-2718550/v1/1ff4db2c7c8bae4f7ac2a75b.png"},{"id":44730271,"identity":"07018e24-f3b2-4da8-b83f-388db53227b6","added_by":"auto","created_at":"2023-10-16 21:29:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1605537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2718550/v1/fbf85407-5198-40c8-bfbe-19c2c8e81616.pdf"},{"id":34734842,"identity":"c1cdb795-8a49-4843-bc7c-64ef7323e6a7","added_by":"auto","created_at":"2023-03-23 19:36:15","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1678769,"visible":true,"origin":"","legend":"","description":"","filename":"SurportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2718550/v1/ccf4e5a35c9dc8dfb7ed3ebb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impacts of 10 years of elevated CO2 and warming on soil fungal diversity and network complexity in a Chinese paddy field","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global atmospheric carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) concentration has increased from 280 ppm in 1850 to 400 ppm today and is predicted to reach 550 ppm by the middle of this century, accompanied by an increase in global mean temperature by 2℃ (Stocker et al., 2014). Increase in atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration can improve the photosynthetic efficiency of rice, increase CO\u003csub\u003e2\u003c/sub\u003e fixation, and enhance rice yield (Kim et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Temperature is the key factor determining the length of the rice growth season. Increase in temperature will accelerate the growth and development of rice, resulting in a shortened preheading phase but did not affect the postheading phase (Cai et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Soil fungi play an important role in regulating soil ecosystem functions such as soil nutrient cycling, organic matter decomposition, and environmental pollutant purification (Austin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yu et al., 2016). Fungal community diversity contributes to ecosystem stability and maintains crop diversity (Antoninka et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Van Diepen et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Elevated atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentrations and warming affect the physiological growth of soil crops and the structure and function of terrestrial ecosystems via feedback to the terrestrial ecosystem, along with changes in soil fungal community and function (Graaff et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Hatfield et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is generally believed that elevated CO\u003csub\u003e2\u003c/sub\u003e levels indirectly affect soil microorganisms by impacting plant photosynthesis (Bruce et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Higher CO\u003csub\u003e2\u003c/sub\u003e levels promote photosynthesis and increase the amount of root exudates, thereby stimulating the growth and activity of microorganisms and changing the structure and function of microbial communities (Drigo et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Vestergard et al., 2016). Temperature is the main factor affecting the abundance of fungi (Tedersoo, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, there are still some contradictions concerning the dynamic changes in soil fungal communities with elevated atmospheric CO\u003csub\u003e2\u003c/sub\u003e levels or warming, based on literature. For example, Hayden et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) conducted climate change simulation studies on soil microorganisms in Australian grasslands and found that elevated atmospheric CO\u003csub\u003e2\u003c/sub\u003e significantly increased gene abundance and changed the composition of the fungal communities. Tu et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported that long-term elevated CO\u003csub\u003e2\u003c/sub\u003e did not significantly alter the overall structure and species richness of the fungal community but significantly increased community evenness and diversity. In another study, CO\u003csub\u003e2\u003c/sub\u003e enrichment was found to increase the gene abundance and diversity of soil fungi in an agricultural ecosystem (Liu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, studies have also shown that elevated atmospheric CO\u003csub\u003e2\u003c/sub\u003e levels do not significantly affect soil microbial activity (Austin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Temperature increase accelerates the decomposition of soil organic carbon and the uptake of soil nitrogen by plants, resulting in a decrease in organic matter content (Melillo et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Bacteria have a stronger ability to adapt to environmental changes than fungi which are therefore more susceptible to increase in temperatures (Ali et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Liu et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that warming significantly decreased the abundance of soil fungi in wheat rhizosphere. Recently, it was shown that the diversity of the soil fungal community was lower under warming conditions (Anthony et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Deslippe et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) showed that long-term warming resulted in a significant increase in the relative abundance of fungi in Arctic tundra soil. However, studies have also shown that elevated atmospheric CO\u003csub\u003e2\u003c/sub\u003e levels and warming do not considerably affect the abundance, community composition, or activity of soil fungi (Bergner et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Austin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Thus, the response of soil fungal communities to climate change is still largely unclear.\u003c/p\u003e \u003cp\u003eMicrobial interactions can form complex networks that collectively act on ecosystem functions (Banerjee et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Microbial co-occurrence networks can reflect the interaction characteristics among microorganisms, which have been widely used to explore the relationship between microbial communities (Friedman and Alm, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, microbial linkages in co-occurrence networks should be considered as statistically hypothetical interactions (Carr et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the term \"biological interaction\" is therefore used for simplicity. According to previous studies, the soil microbial correlation network model can vary with environmental factors (e.g., drought, warming, and elevated CO\u003csub\u003e2\u003c/sub\u003e concentration). For example, Vries et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) demonstrated that drought promotes the destabilization of soil bacterial networks rather than fungal symbiotic networks in grassland ecosystems. Tu et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) analyzed the responses of soil fungal communities to long-term elevated CO\u003csub\u003e2\u003c/sub\u003e in an experimental field in Minnesota and indicated that elevated CO\u003csub\u003e2\u003c/sub\u003e increased the complexity of the fungal community network. Warming was found to significantly alter the diversity and structure of soil fungal communities and, additionally, the complexity of fungal networks (Zhou et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In another study, according to the values of Zi (within-module connectivity) and Pi (among-module connectivity), the roles of species (nodes) were divided into four categories: peripherals, connectors, module hubs, and network hubs (Deng et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Typically, the nodes that function as hubs or connectors in a network are defined as keystone species (Banerjee et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recently, it was shown that long-term warming increased the complexity and abundances of keystone species of a microbial network (Yuan et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Another study reported that elevated CO\u003csub\u003e2\u003c/sub\u003e simplified the soybean rhizosphere soil fungi network structure by changing the keystone species members. However, to the best of our knowledge, the symbiotic pattern of fungal networks and their responses to climate change in agroecosystems are still not fully understood (Tu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this context, the molecular ecological network (MEN) method was used herein to explore the symbiotic relationship in fungal communities under elevated CO\u003csub\u003e2\u003c/sub\u003e and warming conditions, which can provide new insights into the responses of fungal communities to climate change.\u003c/p\u003e \u003cp\u003eThe aim of this study is to determine the influence of elevated CO\u003csub\u003e2\u003c/sub\u003e, warming, and their combination on the diversity, community composition, and network complexity of soil fungal communities in paddy soil. It was hypothesized that the diversity and network complexity would increase when exposed to elevated CO\u003csub\u003e2\u003c/sub\u003e, but the positive impacts could be offset by warming. To test this hypothesis, a 10-year open free-air experiment with factorial elevated CO\u003csub\u003e2\u003c/sub\u003e (550 ppm) and warming (by 2\u0026deg;C) was conducted in an agricultural ecosystem.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSite description and experimental setup\u003c/h2\u003e \u003cp\u003eThe field experiment with free-air CO\u003csub\u003e2\u003c/sub\u003e enrichment and warming facilities was established in 2010 in Guli Township (31\u0026deg;30\u0026prime;N, 120\u0026deg;33\u0026prime;E), Changshu, Jiangsu Province, China. In this region, the traditional cropping system is the rotation of summer rice and winter wheat. The area has a typical subtropical monsoon climate, with average annual temperature of 16\u0026deg;C and average annual precipitation of 1,100\u0026ndash;1,200 mm. The soil was derived from clay lacustrine deposit and classified as Gleyic Stagnic Anthrosol, with pH (H\u003csub\u003e2\u003c/sub\u003eO) of 7.0, soil organic carbon (SOC) content of 16.2 g∙kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and total nitrogen (TN) content of 1.9 g∙kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe climate change treatments included ambient environmental conditions (CK), CO\u003csub\u003e2\u003c/sub\u003e increased to 550 ppm (CE), temperature increased by 2\u0026deg;C (WA), and combined elevated CO\u003csub\u003e2\u003c/sub\u003e and temperature (CW). Each treatment consisted of three replicates, and each replicate was conducted in an octagonal ring of 8 m diameter (an area of approximately 50 m\u003csup\u003e2\u003c/sup\u003e). In total, 12 octagonal rings were set up in this experimental field, and each ring was buffered by 28 m of open field to minimize any treatment cross-over effects (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For the CO\u003csub\u003e2\u003c/sub\u003e enrichment treatment groups (CE and CW), pure CO\u003csub\u003e2\u003c/sub\u003e gas was pumped from a storage tank and injected through perforated tubes surrounding the rings. For the warming treatment groups (WA and CW), 12 infrared heating lamps were mounted 1.2 m above the rice canopy in each ring. The treatment groups with elevated CO\u003csub\u003e2\u003c/sub\u003e and warming were maintained consistently over the entire rice growing period. More details of the experimental facility layout, performance, and operation are described by Liu et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRhizobox Application And Sample Collection\u003c/h3\u003e\n\u003cp\u003eRice (\u003cem\u003eOryza sativa\u003c/em\u003e L. cv. Changyou 5) was transplanted with a density of 26 hills per m\u003csup\u003e2\u003c/sup\u003e on 10th June, 2020, and harvested on 27th October, 2020. Rhizosphere soil was collected using a rhizobox inserted in each ring plot 2 days before rice transplantation. The dimensions of the rhizobox were 8 \u0026times; 8 \u0026times; 15 cm (length \u0026times; width \u0026times; height) (Fig. S2). The rhizobox was divided into three sections by a nylon net with a pore diameter of 30 \u0026micro;m to keep in the roots, allowing water and nutrient passage (Xiong et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To ensure full contact between soil and roots, two rice plants were planted in each rhizobox. The soil in the middle compartment of the rhizobox was treated as rhizosphere soil, and the left and right sides were bulk soil. Soil samples were collected at the tillering, heading, and ripening stages during the rice growth season. A total of 72 samples (four treatment groups \u0026times; three replicates \u0026times; three stages \u0026times; two kinds) were collected from the experimental facilities. All samples were passed through a 2-mm sieve and immediately shipped to the laboratory in an ice box.\u003c/p\u003e\n\u003ch3\u003eSoil Dna Extraction And Bioinformatic Analysis\u003c/h3\u003e\n\u003cp\u003eTotal soil DNA was extracted from 0.5 g soil using a SPINeasy DNA Kit for Soil (MP Biomedicals, LLC), according to the manufacturer\u0026rsquo;s instructions. The concentration and quality of soil DNA were assessed by 1% agarose gel electrophoresis and a NanoDrop spectrophotometer. The fungal ITS genes were amplified with the primers ITS1F/ITS2R. The PCR amplification product was purified with the SanPrep Column PCR Product Purification Kit (Sangon Biotech Co., China) and quantified using QuantiFluor\u0026trade;-ST (Promega, USA). The purified amplicon libraries were sequenced on the Illumina MiSeq platform. The raw sequences were quality-filtered using the Quantitative Insight into Microbial Ecology (QIIME) software (Caporaso et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) to remove barcodes, primers, fuzzy bases, and low-quality sequences (less than 200 bp). Subsequently, the remaining sequences were subjected to chimerism detection using the Mothur software to remove all chimeric sequences (Schloss et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The non-chimeric sequences were clustered into operational taxonomic units (OTUs) with 97% similarity using the QIIME software. According to the NCBI GenBank database, the representative sequences in OTU were classified and identified using the BLAST algorithm. Alpha diversity, including OTU richness and Shannon index, was calculated using the Mothur software. The functional prediction of the fungal community was conducted using the FUNGuild database (Louca et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eNetwork Construction And Analysis\u003c/h3\u003e\n\u003cp\u003eMolecular ecological networks were used to analyze the intradomain correlation of microbial species at multiple taxon levels. To assess the impact of climate change on the complexity and specificity of the soil fungal community, network analysis was conducted for each treatment. All analyses were conducted in a pipeline \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mem.rcees.ac.cn:8081\u003c/span\u003e\u003cspan address=\"http://mem.rcees.ac.cn:8081\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e available online (Feng et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The OTUs which occurred in more than half of the samples were retained without log-transformation prior to obtaining the Spearman correlation coefficient matrix. Based on the random matrix theory (RMT), a uniform threshold was determined for each microbial network. After network construction, the topology characteristics and network randomization were implemented in MENAP. Among-module and inter-module connectivity was computed based on the detected modules and nodes were assigned to network hubs, module hubs, connectors, or peripherals. Finally, networks were visualized in Gephi (version 0.9.2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gephi.org/\u003c/span\u003e\u003cspan address=\"https://gephi.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a Fruchterman-Reingold layout algorithm.\u003c/p\u003e \u003cp\u003eTo determine the role of nodes in the network, the topological role of each node of the microbial network was defined according to the within-module connectivity (Zi) and among-module connectivity (Pi) of the nodes of the molecular ecological network of the soil microbial community. Network nodes were divided into four categories: (1) peripherals (Zi\u0026thinsp;\u0026le;\u0026thinsp;2.5, Pi\u0026thinsp;\u0026le;\u0026thinsp;0.62), with few connections, which are basically connected to the internal nodes of the module; (2) module hub (Zi\u0026thinsp;\u0026gt;\u0026thinsp;2.5, Pi\u0026thinsp;\u0026le;\u0026thinsp;0.62), which is highly connected with nodes inside the module; (3) connector (Zi\u0026thinsp;\u0026le;\u0026thinsp;2.5, Pi\u0026thinsp;\u0026gt;\u0026thinsp;0.62), which is highly connected with nodes of other modules; and (4) network hub (Zi\u0026thinsp;\u0026gt;\u0026thinsp;2.5, Pi\u0026thinsp;\u0026gt;\u0026thinsp;0.62), which is highly connected to the nodes of other modules as well as those inside the module. It is generally believed that nodes with Zi\u0026thinsp;\u0026gt;\u0026thinsp;2.5 or Pi\u0026thinsp;\u0026gt;\u0026thinsp;0.62 are key nodes and play an important role in the connection with nodes within or between modules (Olesen et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS 20.0 (SPSS Inc., USA), and data visualization was carried out in R (version 4.1.2). The significant difference between climate change treatments (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was tested by one-way ANOVA, followed by Duncan's test. Principal co-ordinate analysis (PCoA) was conducted to determine the fungal community distribution using the R package \u0026ldquo;vegan\u0026rdquo; with the \u0026lsquo;Adonis\u0026rsquo; function. Repeated measures ANOVA was employed to determine the primary effects of elevated CO\u003csub\u003e2\u003c/sub\u003e, warming, and growth stage.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDiversity and richness of the soil fungal community\u003c/h2\u003e \u003cp\u003eAfter quality filtering, a total of 4,757,273 qualified reads were obtained from 72 samples, with 52,697\u0026ndash;70,616 sequences per sample (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The coverage of each sample was 99.96\u0026ndash;100.00%, indicating that the sequencing intensity was sufficient to detect the fungal diversity in all samples (Table S3).\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the OTU richness and the Shannon index were significantly increased under elevated CO\u003csub\u003e2\u003c/sub\u003e but only slightly reduced under warming treatment, and these effects were influenced by growth stages (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In rice rhizosphere and bulk soils, elevated CO\u003csub\u003e2\u003c/sub\u003e increased the OTU richness and Shannon index by 9.0\u0026ndash;9.7% and 5.3\u0026ndash;10.4% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The increases in OTU richness and Shannon under elevated CO\u003csub\u003e2\u003c/sub\u003e were much larger at the heading and ripening stages than at the tillering stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Warming had no effect on OTU richness or Shannon index in bulk soil but it decreased OTU richness in the rhizosphere (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023). At the ripening stage, warming had no effect on OTU richness, whereas at the tillering and heading stages, OTU richness was reduced by 15.68% and 14.59%, respectively. There was no interactive effect of elevated CO\u003csub\u003e2\u003c/sub\u003e and warming on OTU richness and Shannon index for both rhizosphere and bulk soils: the positive effects of elevated CO\u003csub\u003e2\u003c/sub\u003e on OTU richness and Shannon index were moderated by warming. Additionally, there was a significant effect of interaction between CO\u003csub\u003e2\u003c/sub\u003e and rice growth stage on Shannon index in bulk soil (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe OTU richness and Shannon diversity of the soil fungal community in rice rhizosphere and bulk soils under elevated CO\u003csub\u003e2\u003c/sub\u003e and warming.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eRhizosphere\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eBulk Soil\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOTU richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOTU richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTillering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e467.67\u0026thinsp;\u0026plusmn;\u0026thinsp;51.87a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e542.33\u0026thinsp;\u0026plusmn;\u0026thinsp;13.80b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e464.33\u0026thinsp;\u0026plusmn;\u0026thinsp;18.61a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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align=\"left\" colname=\"c3\"\u003e \u003cp\u003e656.00\u0026thinsp;\u0026plusmn;\u0026thinsp;43.21a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e663.00\u0026thinsp;\u0026plusmn;\u0026thinsp;41.58ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e676.33\u0026thinsp;\u0026plusmn;\u0026thinsp;46.92a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e700.33\u0026thinsp;\u0026plusmn;\u0026thinsp;41.24a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e577.67\u0026thinsp;\u0026plusmn;\u0026thinsp;21.46b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e601.33\u0026thinsp;\u0026plusmn;\u0026thinsp;53.20b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e632.00\u0026thinsp;\u0026plusmn;\u0026thinsp;32.14ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e702.67\u0026thinsp;\u0026plusmn;\u0026thinsp;35.91a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eRipening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e561.33\u0026thinsp;\u0026plusmn;\u0026thinsp;57.73b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e603.33\u0026thinsp;\u0026plusmn;\u0026thinsp;60.38b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e663.00\u0026thinsp;\u0026plusmn;\u0026thinsp;20.52a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e700.67\u0026thinsp;\u0026plusmn;\u0026thinsp;34.59a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e591.33\u0026thinsp;\u0026plusmn;\u0026thinsp;21.78ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e608.00\u0026thinsp;\u0026plusmn;\u0026thinsp;26.06b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e622.33\u0026thinsp;\u0026plusmn;\u0026thinsp;40.41ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e673.33\u0026thinsp;\u0026plusmn;\u0026thinsp;41.40ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e effect (%) \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWarming effect (%) \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWarming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e \u0026times; Warming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e \u0026times; Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWarming \u0026times; Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e \u0026times; Warming \u0026times; Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe various lowercase letters indicate significance differences at the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level. A: Main effects of elevated CO\u003csub\u003e2\u003c/sub\u003e calculated as ((CE\u0026thinsp;+\u0026thinsp;CW)/(CK\u0026thinsp;+\u0026thinsp;WA)-1) \u0026times; 100 averaged across the three stages. B: Main effects of warming calculated as ((WA\u0026thinsp;+\u0026thinsp;CW)/(CK\u0026thinsp;+\u0026thinsp;CE)-1) \u0026times; 100 averaged across the three stages.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStructure Composition Of The Soil Fungal Community\u003c/h3\u003e\n\u003cp\u003eAscomycota (29.5\u0026ndash;68.8%), Rozellomycota (2.5\u0026ndash;21.8%), Mortierellomycota (1.4\u0026ndash;15.9%), and Basidiomycota (1.5\u0026ndash;8.3%) were the dominant fungal phyla in rice rhizosphere and bulk soils across all treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Elevated CO\u003csub\u003e2\u003c/sub\u003e significantly decreased the relative abundance of Ascomycota by 11.6\u0026ndash;16.7% and increased that of Basidiomycota by 39.6\u0026ndash;57.4% in rhizosphere and bulk soil (Table S3). Warming resulted in a significant increase in the relative abundances of Mortierellomycota and Basidiomycota, and the interaction between CO\u003csub\u003e2\u003c/sub\u003e and warming had a significant effect only on the relative abundance of Basidiomycota (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, a significant effect of interaction between CO\u003csub\u003e2\u003c/sub\u003e and growth stage on the relative abundance of Basidiomycota was observed: elevated CO\u003csub\u003e2\u003c/sub\u003e increased the relative abundance of Basidiomycota at the heading and ripening stages but had no impact at the tillering stage. PERMANOVA and PCoA results revealed that the soil fungal communities were significantly affected by elevated CO\u003csub\u003e2\u003c/sub\u003e, warming, and growth stage (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The fungal communities in the tillering stage were clearly separated from those in the heading and ripening stages along the PCoA1 axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The PCoA showed a clear separation between CK and the other treatment groups (CE, WA, and CW) in both rhizosphere and bulk soils (Fig. S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe statistical significance of PERMANOVA for dissimilarity analysis of the fungal composition in rice rhizosphere and bulk soils with elevated CO\u003csub\u003e2\u003c/sub\u003e (CO\u003csub\u003e2\u003c/sub\u003e), warming and growth stage (Stage).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRhizosphere\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eBulk Soil\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFungal composition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u0026times;Warming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u0026times;Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarming\u0026times;Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u0026times;Warming\u0026times;Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig. S4, the relative abundances of the top 10 fungal genera (\u0026gt;\u0026thinsp;1%) were determined under elevated CO\u003csub\u003e2\u003c/sub\u003e and warming. The fungal genera were significantly affected by climate change only in the rhizosphere. Elevated CO\u003csub\u003e2\u003c/sub\u003e significantly increased the relative abundances of \u003cem\u003ePyrenochaetopsis\u003c/em\u003e, \u003cem\u003eCosmospora\u003c/em\u003e, and \u003cem\u003eMetarhizium\u003c/em\u003e but reduced those of \u003cem\u003ePseudeurotium\u003c/em\u003e, \u003cem\u003eCladorrhinum\u003c/em\u003e, and \u003cem\u003ePodospora\u003c/em\u003e. Warming significantly increased the relative abundances of \u003cem\u003eCladorrhinum\u003c/em\u003e and \u003cem\u003ePodospora\u003c/em\u003e but decreased those of \u003cem\u003eFusicolla\u003c/em\u003e and \u003cem\u003eSutellinia\u003c/em\u003e in the rhizosphere.\u003c/p\u003e\n\u003ch3\u003eFunctional Composition Of The Soil Fungal Community\u003c/h3\u003e\n\u003cp\u003eSeven trophic modes were detected in this study. Among them, saprotrophs (61.7\u0026ndash;82.9%), saprotroph-symbiotrophs (5.1\u0026ndash;33.3%), and pathogens (1.5\u0026ndash;15.1%) were the most abundant functional guilds across all treatment groups (Fig. S5). Individual functional guilds in rhizosphere and bulk soils showed diverse responses to elevated CO\u003csub\u003e2\u003c/sub\u003e and warming (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The relative abundances of saprotrophs and symbiotrophs were significantly decreased, whereas those of pathotrophs, pathotroph-saprotrophs, and saprotroph-symbiotrophs in both rhizosphere and bulk soils were increased. Warming significantly increased the relative abundances of pathotrophs and saprotroph-symbiotrophs and decreased those of symbiotrophs and pathotroph-saprotroph-symbiotrophs. Notably, significant effects of interactions between growth stage and CO\u003csub\u003e2\u003c/sub\u003e/warming were observed on the relative abundances of most fungal functional guilds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of elevated CO\u003csub\u003e2\u003c/sub\u003e, warming and growth stage on the relative abundance of fungal functional guilds in rhizosphere and bulk soils.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaprotroph\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePathotroph\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSymbiotroph\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePathotroph-Saprotroph\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSaprotroph-\u003c/p\u003e \u003cp\u003eSymbiotroph\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePathotroph-\u003c/p\u003e \u003cp\u003eSymbiotroph\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePathotroph-Saprotroph-Symbiotroph\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eRhizosphere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e effect (%) \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming effect (%) \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-66. 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-94.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u0026times;Warming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u0026times;Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming\u0026times;Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u0026times;Warming\u0026times;Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eBulk Soil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e effect (%) \u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming effect (%) \u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-80.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-39.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u0026times;Warming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u0026times;Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming\u0026times;Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e\u0026times;Warming\u0026times;Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eThe \u0026ldquo;***\u0026rdquo;, \u0026ldquo;**\u0026rdquo;, \u0026ldquo;*\u0026rdquo; and \u0026ldquo;ns\u0026rdquo; indicate significant levels with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and no significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), respectively. A: Main effects of elevated CO\u003csub\u003e2\u003c/sub\u003e calculated as ((CE\u0026thinsp;+\u0026thinsp;CW)/(CK\u0026thinsp;+\u0026thinsp;WA)-1) \u0026times; 100 averaged across the three stages. B: Main effects of warming calculated as ((WA\u0026thinsp;+\u0026thinsp;CW)/(CK\u0026thinsp;+\u0026thinsp;CE)-1) \u0026times; 100 averaged across the three stages.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eCharacteristics Of The Fungal Co-occurrence Network\u003c/h3\u003e\n\u003cp\u003eBased on the random matrix theory (RMT) method, co-occurrence networks of the soil fungal community were constructed for climate change treatment groups and growth stages, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. S6). Compared to CK, elevated CO\u003csub\u003e2\u003c/sub\u003e and warming greatly increased the complexity of the fungal network in both rhizosphere and bulk soils. Elevated CO\u003csub\u003e2\u003c/sub\u003e, warming, and their interaction strongly increased the edges, linkage density, average degree, especially the negative correlation, and decreased the average path distance and modularity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Further analysis showed that the nodes, edges, linkage density, average degree, average clustering coefficient, as well as negative correlation of fungal networks were higher in the heading and ripening stages than in the tillering stage (Table S4). The network topologies of both rhizosphere and bulk soils differed significantly from those of randomly generated networks across all treatment groups (t test: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that the fungal co-occurrence network constructed in this study has features of small-world and modularity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTopological properties of co-occurrence network in rice rhizosphere and bulk soils as influenced by elevated CO\u003csub\u003e2\u003c/sub\u003e and warming.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eRhizosphere\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eBulk Soil\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eEmpirical Network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41.85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e53.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e58.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEdges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLinkage density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage degree(avgK)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage clustering coefficient (avgCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage path distance (GD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRandom Networks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage clustering coefficient (avgCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.058\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.054\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.024\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.051\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.028\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.031\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage path distance (GD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.775\u0026thinsp;\u0026plusmn;\u0026thinsp;0.036*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.764\u0026thinsp;\u0026plusmn;\u0026thinsp;0.048*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.696\u0026thinsp;\u0026plusmn;\u0026thinsp;0.046*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.630\u0026thinsp;\u0026plusmn;\u0026thinsp;0.038*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.099\u0026thinsp;\u0026plusmn;\u0026thinsp;0.048*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.724\u0026thinsp;\u0026plusmn;\u0026thinsp;0.041*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.923\u0026thinsp;\u0026plusmn;\u0026thinsp;0.040*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.818\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.309\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.292\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.264\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.264\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.381\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.282\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.346\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.318\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eParameters of random networks were generated from randomly rewired (100 times) empirical networks. The presented parameters are mean values and standard derivations of random networks. Significant differences between empirical networks and random networks were determined by t-test. *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eTopological Role Of The Fungal Network Nodes\u003c/h3\u003e\n\u003cp\u003eTo identify the role of the nodes in each network, Zi and Pi were calculated for each node. In the rhizosphere, one module hub and connector were observed in CK and CE treatment groups, two module hubs and three connectors in WA, and one module hub and two connectors in CW (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The module hubs and connectors in CK, CE, and CW treatment groups were Ascomycota and Basidiomycota, whereas Ascomycota and Mortierellomycota were the module hubs and connectors in the WA treatment group (Table S5). In bulk soil, there was one connector, namely Ascomycota, in CK, and there were five connectors, including Ascomycota and Basidiomycota, in WA and CW treatment groups. The module hub in CW belonged to Chytridiomycota (Table S5). Further analysis showed that there was one connector at the tillering stage, two module hubs and two connectors at the heading stage, and three module hubs at the ripening stage in the rhizosphere (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The connectors at the tillering and heading stages belonged to Ascomycota, whilst the module hubs at the tillering, heading, and ripening stages belonged to Ascomycota, Basidiomycota, and Rozellomycota, respectively (Table S6). However, there was no module hub or connector at the tillering and heading stages, whereas one module and two connectors were observed at the ripening stage in the bulk soil.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDiversity and functional composition of the soil fungal community\u003c/h2\u003e \u003cp\u003eSoil fungal communities are formed by aboveground vegetation through root exudates during plant growth and development (Broeckling et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bever et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This study found significant differences in the α-diversity indices of the soil fungal community across different rice growth stages. The OTU richness and Shannon diversity were higher at the heading and ripening stages than at the tillering stage (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating greater physiological metabolic activity of rice at the latter stages of rice. The PCoA also showed that the soil fungal community of samples from the heading and ripening stages grouped together and were clearly separated from those at the tillering stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting that the fungal community is significantly influenced by rice growth stages. This is in agreement with Hannula et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who reported that different plant growth stages showed different soil nutrient contents and soil temperatures. Thus, there is a greater influence on soil fungal composition and diversity at the rapid vegetation growing stages. Breidenbach et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Edwards et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that rice growth stage can influence microbial community structure in the rhizosphere. In this study, elevated CO\u003csub\u003e2\u003c/sub\u003e significantly increased fungal OTU richness and Shannon diversity in both rhizosphere and bulk soils (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), consistent with the findings of previous studies (Liu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Elevated CO\u003csub\u003e2\u003c/sub\u003e stimulates C3 plant photosynthesis and root exudate production, leading to a greater soil organic C and C:N ratio, which facilitates fungal growth (Blagodatskaya et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bhattacharyya et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the same experiment, it was found that fungal α-diversity in rice and wheat soils increased by increasing the input of organic C and reducing the soil pH (Gao et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, warming had no effect on OTU richness and Shannon index, except for a significant decrease in OTU richness of the rhizosphere soil (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023). This is in agreement with the finding of Lorberau et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) that warming does not alter fungal richness and diversity. However, a significant increase in fungal diversity under warming was found in an alpine meadow (Wang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The distinct responses may be due to the differences in plant host and warming conditions (air canopy warming vs soil warming). In this experiment, the +\u0026thinsp;2℃ warming of the air canopy resulted in a small increase in soil temperature (\u0026lt;\u0026thinsp;1℃, Liu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which could be in the range of fungal growth fluctuations and indirectly affected fungal diversity by influencing plant growth.\u003c/p\u003e \u003cp\u003eThis study also found that elevated CO\u003csub\u003e2\u003c/sub\u003e and warming significantly changed the composition of the soil fungal communities in both rhizosphere and bulk soils (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. S3). Elevated CO\u003csub\u003e2\u003c/sub\u003e and warming may indirectly shift the soil microbial diversity and composition by affecting plant and root growth and altering soil environmental factors (e.g., temperature, moisture, pH, available C, among others) (Weltzin et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ebersberger et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Fernandez et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, Ascomycota, Rozellomycota, Mortierellomycota, and Basidiomycota were the dominant phyla across the treatment groups and growth stages. Elevated CO\u003csub\u003e2\u003c/sub\u003e significantly decreased the relative abundance of Ascomycota but increased that of Basidiomycota, which is consistent with the study by Tu et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in a grassland region. Lauber et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) found that the abundance of Ascomycota was greater in soils with a higher soil pH. The formation of weak acids when CO\u003csub\u003e2\u003c/sub\u003e is dissolved in water leads to soil acidification (Gao et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which may be responsible for the decrease in abundance of Ascomycota. In addition, elevated CO\u003csub\u003e2\u003c/sub\u003e significantly increased the relative abundance of Basidiomycota, indicating that higher CO\u003csub\u003e2\u003c/sub\u003e levels can promote the growth of above- and below-ground plant parts, thus improving soil aeration; good aeration conditions favor Basidiomycete growth. In this study, the relative abundances of Ascomycota and Basidiomycota under warming were increased in paddy soil, especially that of Basidiomycota, whose content was significantly increased in both rhizosphere and bulk soils. Under warming conditions, the quantity of the plant litter can be increased, and a large part of the litter may contain substances that are difficult to decompose, which provides favorable nutrient conditions for Ascomycota and Basidiomycota (Cornelissen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, in rice paddies, warming may indirectly affect the soil fungal community by increasing the amount of rice litter. In addition to being influenced by climate change, rhizosphere soil microbes are also largely impacted by plants (Hannula et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). At different plant growth stages, the composition and quantity of root exudates largely differ, and the microbial community structure fluctuates accordingly. Shi et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) showed that the composition of enriched and excluded microbes in the rhizosphere differed during different growth stages of soybean, further illustrating the regulation of eukaryotic microbes in the rhizosphere by plant root exudates. Compared with the tillering stage, the relative abundance of Basidiomycetes increased and that of Rozellomycota decreased at the heading and ripening stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and the growth stage significantly changed the community composition of the soil fungi in rhizosphere and bulk soils (Table S3). Further analysis indicated that the dominant fungal genera in different growth stages responded differently to elevated CO\u003csub\u003e2\u003c/sub\u003e and warming. This is consistent with the report that climate change affects soil microbial communities that perform different functions at different growth stages (Horz et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed OTUs were categorized into fungal functional guilds by the FUNGuild annotation tool (Nguyen et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The distribution patterns of the fungal functional guilds were clearly influenced by elevated CO\u003csub\u003e2\u003c/sub\u003e, warming, and growth stage (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on the above results, both elevated CO\u003csub\u003e2\u003c/sub\u003e and warming significantly increased the relative abundances of pathotrophic fungi. Although there is no study about plant pathogens under elevated CO\u003csub\u003e2\u003c/sub\u003e in agricultural ecosystems, a recent study using a global meta-analysis and a 9-year field experiment found that warming increased the abundances of fungal plant pathogens (Delgado-Baquerizo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Soil pathogenic fungi might proliferate under warming, affecting the functions and structure of the forest (Looby and Treseder, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this study, the relative abundances of symbiotrophic fungi were significantly decreased under elevated CO\u003csub\u003e2\u003c/sub\u003e and warming (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Symbiotrophic fungi provide nutrients and water for plant host under environmental stress, which plays an important role in soil health and crop production (Schmidt et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In paddy soil, reduction in symbiotrophic fungi under elevated O\u003csub\u003e3\u003c/sub\u003e was ascribed to the decrease in plant photosynthesis and nutrient availability (Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, the increase and decrease in pathotrophics and symbiotrophics under elevated CO\u003csub\u003e2\u003c/sub\u003e and warming conditions could affect fungal functions and threaten crop production. Although FUNGuild is highly accurate, the ecological functions of many fungi remain unknown. In particular, the ecological functions of soil fungi under global climate change conditions need to be further studied and verified.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNetwork Complexity Of The Soil Fungal Community\u003c/h3\u003e\n\u003cp\u003eThe microbial co-occurrence network constructed in this study is characterized by scale-free, small world, and modularity. The topological properties are used to define the complexity of the network, which is closely related to the ecosystem functions (Yuan et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the present study, the co-occurrence networks of the soil fungal community in the heading and ripening stages were more complex than those in the tillering stage, based on the higher numbers of nodes and edges as well as the higher linkage density, average degree, and clustering coefficient in both rhizosphere and bulk soils (Table S4 and Fig. S6). The more complex network indicates that in the later rice stages, soil nutrient availability for fungal communities is increased due to the high quality and quantity of root exudates and plant residues (Liu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The present results indicate that elevated CO\u003csub\u003e2\u003c/sub\u003e and warming altered the topological parameters of the fungal ecological network (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In previous studies, average degree and linkage density have been commonly used to assess the complexity of microbial networks (Montoya et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wagg et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Compared with CK, elevated CO\u003csub\u003e2\u003c/sub\u003e and warming treatments increased the linkage density, average degree, and edge number and decreased the modularity and average path distance of co-occurrence networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), indicating that the complexity of soil fungal networks was improved by elevated CO\u003csub\u003e2\u003c/sub\u003e and warming. These findings are consistent with previous studies reporting that elevated CO\u003csub\u003e2\u003c/sub\u003e and warming increase the complexity of fungal networks (Tu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The positively correlated connections in the co-occurrence network represent the existence of mutual synergistic relationships among microorganisms, whereas the negatively correlated connections represent potential antagonistic effects (Blanchet et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, elevated CO\u003csub\u003e2\u003c/sub\u003e and warming increased the negative correlation in both rice rhizosphere and bulk soils (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), indicating that climate change conditions stimulated competitive relationships among fungal compositions. Ma et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that microbial network complexity facilitated the growth of microbial flora, leading to more efficient use of soil nutrients. Previous studies found that elevated CO\u003csub\u003e2\u003c/sub\u003e and warming increased the contents of soil organic carbon, total nitrogen, and root exudates in rice paddy soil (Liu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Xiong et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which may increase the competition for soil nutrients among microbes. Additionally, previous studies found that the complexity of microbial networks is positively correlated with α-diversity (Fan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), indicating that the increase in fungal OTU richness and Shannon index value under elevated CO\u003csub\u003e2\u003c/sub\u003e in this study may have led to the enhanced network complexity. Higher complexity of the microbial network means stronger stability of the whole microbial community, and the competitive relationships will also further enhance the stability (Ochoa-Hueso et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wagg et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A recent study reported that long-term warming increased the complexity and stability of a microbial network in grassland soil, which are important for maintaining ecosystem functions (Yuan et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study also screened keystone species of the fungal community by analyzing the topology of the co-occurrence network. A total of six module hubs and 18 connectors were detected in all molecular ecological networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table S5), which can be regarded as key nodes that play essential roles in forming the network structure (Banerjee et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The numbers of module hubs and connectors were higher in the warming treatment groups than in the control, indicating that the fungal network is more complex under warming. This was further supported by the higher node and edge numbers as well as the increased linkage density, average degree, and clustering coefficient under warming conditions compared to the control (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings are in agreement with Zhou et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who reported that long-term warming can increase the abundance of keystone species and the complexity of the microbial network in the grassland ecosystem, which may be closely related to ecosystem functions.\u003c/p\u003e \u003cp\u003eIn the present study, there were five nodes (OTU 2, 473, 626, 301, and 436) and 10 nodes (OTU 758, 156, 641, 345, 481, 323, 197, 134, 369, and 4) in the warming treatment groups in rhizosphere and bulk soils, respectively, whereas only two nodes (OTU 751 and 626) were observed in the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The higher number of module hubs and connectors under warming condition suggests that the interactions, as well as energy and nutrient flows among the soil fungal community, were more efficient compared to the control (Yu et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In particular, most of the key nodes were affiliated to the phyla Ascomycetes and Basidiomycetes (Table S5 and Table S6). Previous studies found that Ascomycota can decompose mainly degradable organic matter in soil, whilst Basidiomycota can decompose substances such as lignin and cellulose (Beimforde et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, the network was connected by Ascomycota and Basidiomycota, which are both parasitic and saprophytic organisms, facilitating nutrient and energy flow in this network. Moreover, the nodes categorized as module hubs and connectors in the warming network were different from those in the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table S5), indicating that each OTU played different roles in warming and ambient networks. Overall, the warming-induced change in the network structure and the topological roles of the keystone OTUs may be related to soil nutrient availability. However, further studies are needed to determine the ecological functions of the keystone species.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study shows that elevated CO\u003csub\u003e2\u003c/sub\u003e, warming, and growth stages clearly altered the diversity, composition, and network structure of fungal communities in both rhizosphere and bulk soils. The responses of soil fungal communities were greater at the later growth stages than at the tillering stage, whereas α-diversity and network complexity (e.g., nodes, edges, linkage density, average degree, and negative correlation) were greatly increased at the heading and ripening stages. Elevated CO\u003csub\u003e2\u003c/sub\u003e significantly increased OTU richness and Shannon diversity, whereas the relative abundance of Ascomycota significantly decreased under elevated CO\u003csub\u003e2\u003c/sub\u003e and that of Basidiomycota increased compared with the control. Elevated CO\u003csub\u003e2\u003c/sub\u003e and warming increased the network complexity and negative correlation of the fungal community in rhizosphere and bulk soils, suggesting that both elevated CO\u003csub\u003e2\u003c/sub\u003e and warming enhanced the stability of the soil fungal community. Furthermore, the individual functional composition of the soil fungal community showed diverse responses to elevated CO\u003csub\u003e2\u003c/sub\u003e and warming. The relative abundances of pathotrophic fungi were significantly increased and those of symbiotrophs were decreased under elevated CO\u003csub\u003e2\u003c/sub\u003e and warming conditions. Overall, soil fungal communities were considerably altered by elevated CO\u003csub\u003e2\u003c/sub\u003e, warming, and growth stages, potentially influencing ecosystem function and threatening food production under future climate change conditions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePCoA: principal coordinates analysis\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003eOTU: operational taxonomic units\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003eITS: internal transcribed spacer\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003eMENs: molecular ecological networks\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any study with humans or other animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the University Natural Science Research Project of Anhui Province (KJ2021A0531), the University Excellent Young Talent Project of Anhui Province (gxgwfx2019066), and the Natural Science Foundation of Anhui Province, China (2008085QC102).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAli, R.S., Poll, C., Kandeler, E., 2018. 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Global Change Biology 27, 6331\u0026ndash;6347. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate change, Paddy field, Rhizosphere, Network complexity, Fungal diversity","lastPublishedDoi":"10.21203/rs.3.rs-2718550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2718550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFungal communities play essential roles in ecosystems and are involved in soil formation, waste decomposition, nutrient cycling, and plant nutrient supply. Although studies have focused on soil bacterial community responses to climate change in agricultural ecosystems, only few have investigated the dynamic changes in the diversity and complexity of fungal communities in paddy fields. Herein, using internal transcribed spacer (ITS) gene amplicon sequencing and co-occurrence network methods, the responses of soil fungal community to factorial combinations of elevated CO\u003csub\u003e2\u003c/sub\u003e (550 ppm) and canopy warming (+2°C) were explored in an open-air field experiment in Changshu, China, for 10 years. Elevated CO\u003csub\u003e2\u003c/sub\u003e significantly increased the operational taxonomic unit (OTU) richness and Shannon diversity of fungal communities in both rice rhizosphere and bulk soils, whereas the relative abundances of Ascomycota and Basidiomycota were significantly decreased and increased, respectively, by elevated CO\u003csub\u003e2\u003c/sub\u003e. Co-occurrence network analysis showed that elevated CO\u003csub\u003e2\u003c/sub\u003e, warming, and their combination increased the network complexity and negative correlation of the fungal community in rhizosphere and bulk soils, suggesting that these factors enhanced the competition of microbial species. Warming resulted in a more complex network structure by altering topological roles and increasing the numbers of key fungal nodes. Principal coordinate analysis indicated that rice growth stages rather than elevated CO\u003csub\u003e2\u003c/sub\u003e and warming altered soil fungal communities. Specifically, the changes in diversity and network complexity were greater at the heading and ripening stages than at the tillering stage. Furthermore, elevated CO\u003csub\u003e2\u003c/sub\u003e and warming significantly increased the relative abundances of pathotrophic fungi and reduced those of symbiotrophic fungi in both rhizosphere and bulk soils. Overall, the results indicate that long-term CO\u003csub\u003e2\u003c/sub\u003e exposure and warming enhance the complexity and stability of soil fungal community, potentially threatening crop health and soil functions through adverse effects on fungal community functions.\u0026nbsp; \u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Impacts of 10 years of elevated CO2 and warming on soil fungal diversity and network complexity in a Chinese paddy field","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-23 19:36:10","doi":"10.21203/rs.3.rs-2718550/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-04-25T12:24:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-03-29T04:24:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90bbe58e-523e-4025-804e-abf42dbaef55","date":"2023-03-24T00:47:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-03-22T01:07:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-03-21T13:20:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-03-21T13:20:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbial Ecology","date":"2023-03-21T12:33:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meco","sideBox":"Learn more about [Microbial Ecology](https://www.springer.com/journal/248)","snPcode":"248","submissionUrl":"https://submission.nature.com/new-submission/248/3","title":"Microbial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"dd7e0c5d-30f6-4f8d-90ae-92fddb0905d4","owner":[],"postedDate":"March 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:12:32+00:00","versionOfRecord":{"articleIdentity":"rs-2718550","link":"https://doi.org/10.1007/s00248-023-02248-0","journal":{"identity":"microbial-ecology","isVorOnly":false,"title":"Microbial Ecology"},"publishedOn":"2023-05-29 21:03:16","publishedOnDateReadable":"May 29th, 2023"},"versionCreatedAt":"2023-03-23 19:36:10","video":"","vorDoi":"10.1007/s00248-023-02248-0","vorDoiUrl":"https://doi.org/10.1007/s00248-023-02248-0","workflowStages":[]},"version":"v1","identity":"rs-2718550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2718550","identity":"rs-2718550","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}