Background: Exploiting soil microorganisms in the rhizosphere of plants can significantly improve agricultural productivity; however, the mechanism by which microorganisms specifically affect agricultural productivity is poorly understood. To clarify this uncertainly, the rhizospheric microbial communities of super rice plants at various growth stages were analysed using 16S rRNA high-throughput gene sequencing; microbial communities were then related to soil properties and rice productivity. Results: The rhizospheric bacterial communities were characterized by the phyla Proteobacteria, Acidobacteria, Chloroflexi, and Verrucomicrobia during all stages of rice growth. Rice production differed by approximately 30% between high- and low-yield sites that had uniform fertilization regimes and climatic conditions, suggesting the key role of microbial communities. Mantel tests showed a strong correlation between soil conditions and rhizospheric bacterial communities, and microorganisms had different effects on crop yield. Among the four growing periods, the rhizospheric bacterial communities present during the heading stage showed a more significant correlation (p < 0.05) with crop yield, suggesting their potential in regulating crop production. The biological properties (i.e., microbes) reflected the situation of agricultural land better than the physicochemical characterics (i.e.,nutrientelements),which provides theoretical support for agronomic production. Molecular ecological network (MEN) analysis suggested that differences in productivity were caused by the interaction between the soil characteristics and the bacterial communities. Conclusions: During theheading stage ofricecropping, therhizospheric microbial community is vital for the resulting rice yield. According to network analysis, the cooperative relationship (i.e., positive interaction) between between microbes may contribute significantly to yield, and the biological properties (i.e., microbes) better reflected the real conditions of agricultural land than did the physicochemical characteristics (i.e., nutrient elements). Keywords: Bacterial diversity, Bacterial community structure, Super hybrid rice, 16S rRNA pyrosequencing technology, Crop yield, Soil physicochemical properties Background e.g. the improper use of chemical fertilizers and pesticides Recent studies have suggested that modern agriculture , have triggered a series of environmental problems [4, 5]. will face substantial challenges over the coming decades Numerous studies have addressed sustainability issues, and , and the market demand for agricultural products one recommended approach is exploiting the soil microor- will increase by at least 70% to 2050 . Over the last ganisms  to sustainably meet agricultural demands . few decades, improper agricultural production methods, Soil microorganisms play important roles in agriculture, par- ticularly in the nutrient supply and in the biocontrol of plant disease [7, 8]. Rhizospheric microorganisms exist within a * Correspondence: firstname.lastname@example.org; email@example.com Zhaohui Wu and Qingshu Liu contributed equally to this work. narrow zone around the root of a plant and are found at Hunan Institute of Microbiology, Changsha 410009, China 11 densities of approximately 10 cells per gram ; School of Minerals Processing and Bioengineering, Central South University, additionally, these microorganisms, are considered as the Changsha 410083, China Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Wu et al. BMC Microbiology (2018) 18:51 Page 2 of 11 plant’s second genome . The rhizospheric microbial is poorly understood, and our study is devoted to statis- community structure is mainly mediated by root exudate tically explaining the links between them. (e.g., sugars, amino acids, siderophores, and enzymes) In the process of production, we found a gap (more . Therefore, the interaction between plant roots and than 30%) in crop production between different super rhizospheric microorganisms potentially influences rice cultivation types with similar, fertilisation regimes ecosystem functioning by promoting the circulation of and latitudinal and longitudinal positions. High- materials . throughput sequencing methods served to collect rhi- Complex biochemical processes occur between rhizo- zospheric microbial community information in soils. spheric microorganisms and plants, and microorganisms The relationship between soil physicochemical attri- enhance soil fertility [13–15], maintain below-ground butes and rhizospheric microorganisms at different ecological structure and are associated with plant health stage (from pre-transplanting to the ripening stage) of (e.g., diseases, pathogens and weed suppression) . the super hybrid rice “Y Liang You 900” was investi- Microbial diversity is also an indicator of soil microor- gated to reveal the inborn mechanism of how micro- ganisms in a region , which provide a vast amount of organisms specifically affect crop productivity. We ecological information in terms of the soil. Although the mainly explored the interaction of microbial commu- relationships between the soil microbial diversity and nities. By comparing the differences between soil mi- the functioning and sustainability of agricultural ecosys- croorganisms in high- and low-yield areas during tems have not been fully elucidated [18, 19], it is ac- discrete periods, we explored the mechanism of how cepted that microbial diversity plays an important role microorganisms specifically affect agricultural prod- in agricultural production [20, 21]. Microbial diversity uctivity based on soil characteristics. Our study con- includes the range of microorganisms and their relative cluded that (1) it is possible improve the average crop abundance in natural habitats . In addition to micro- yield by controlling rational agricultural management bial diversity, microbial community structure also re- in the heading stage, (2) the positive species interac- sponds to the basis of agroecosystem services in tions within communities may contribute to crop agricultural production . Soil microbial communities yield, and(3) the biological properties (i.e., microbes) drive globally important processes , including elem- were better than the physicochemical characteristics ental cycles and energy flows. These microbial commu- (i.e., nutrient elements) in terms of reflecting the ac- nities are involved in various processes that serve tual situation of agricultural land. essential functions in agricultural production by promoting crop absorption of nutrients and inhibiting harmful pathogens [7, 8, 26–28]. For example, soil mi- Methods crobes can promote plant growth through the degrad- Study sites and sampling ation of manure fertilizers and form humus nutrients, Soil samples were collected from Xupu (110°31′E, 27°23′N, which are then easily absorbed by plants. Other microor- Ha), Ningxiang (112°16′E, 28°08′N, Ly), Longhui- ganisms may regulate soil pH, generating favourable Zhaojiachong (110°56′E, 27°27′N, Hb) and Longhui- conditions that permit functional microorganisms to Niuxingzui (110°56′E, 27°29′N, Hc). The sites are paddy work at full capacity and promote production . This fields planted with “Y Liang You 900” super hybrid rice. kind of microorganism often plays a crucial role in the Sampling was conducted on private land, and the land agricultural ecosystem. Explaining the direct effects of owner gave permission for our sampling activity. For all soil microbial communities on crop-growth and yield is sites, the fertilization regime was the same and rice was challenging because the ecosystem functions provides by harvested in October, 2014. The rice yield was calculated most soil microorganisms are not well clear [30, 31]. according to Chen et al. . Rhizospheric soil samples Numerous studies have shown dramatically related re- were collected at four rice development stages: pre- sults that indicate soil community structure is character- transplanting stage (0 weeks), tillering stage (6 weeks), ized by Proteobacteria, Acidobacteria, Actinobacteria, heading stage (14 weeks), and ripening stage (20 weeks). Bacteroidetes, Firmicutes, etc. Nevertheless, further con- When sampling, three biological replicates were sampled sideration of the interactions between the microbial from each site, resulting in a total of 48 samples (4 stages × communities and the external environment has identi- 4 sites × 3 replicates = 48 samples, also see in fied biological and abiotic factors related to crop yield. Additional file 1: Table S2). Rhizosphere soils were sampled Some studies have associated the soil microbial proper- according to Smalla et al. . After sampling, soil samples ties with the transformation of edaphic nutrients, which were separated into two parts, one part was air dried and have an essential role in plant growth that helps obtain stored at 4 °C until physiochemical analysis and the other high yields [10, 32]. However, the innate mechanism of part wasfrozeninliquidnitrogenand stored at − 80 °C how microorganisms specifically affect crop productivity until molecular analysis. Wu et al. BMC Microbiology (2018) 18:51 Page 3 of 11 The yields of super hybrid rice “Y Liang You 900” was rarefied to16, 000 for all samples and the rarefied from four sampling sites are presented in Additional file OTU table was used for all downstream analyses. All se- 1: Table S1. Based on the rice yield, the sites were re- quences were submitted to the NCBI database under the ferred to as ‘low-yield’ sites (Ly, 1.074 kg/m )and ‘high- accession number SRP083104. 2 2 yield’ sites (Ha, 1.543 kg/m ; Hb, 1.517 kg/m ; and Hc, 1.589 kg/m ). The yield of ‘high-yield’ sites was Statistical analysis approximately 30% higher than the yield of the ‘low- Soil microbial diversity indices, including the Shannon yield’ site. Wiener, Inverse Simpson, and Pielou evenness indices, were calculated using the R version 3.3.2 (https://www.r- Soil properties project.org/) platform with the package vegan 2.4.2. . Soil pH was measured using a pH metre and by dissolv- Detrended correspondence analysis (DCA) for detecting ing 5 g of each soil sample in 25 mL of distilled water. the variation in microbial community composition The nitrogen contents, including total nitrogen (TN) among sites/stages was also performed using the vegan and available nitrogen (AN), were determined by the package. Permutational multivariate analysis of variance Kjeldahl procedure . Phosphorus (P) was determined (Adonis) was used to test the effects of soil variables on photo metrically as orthophosphate with using a crop yield. Standard and Mantel tests were carried out vanado-molybdate method , and potassium (K) was to identify the correlations between environmental fac- determined using inductively coupled plasma-atomic tors and soil bacterial communities (based on Euclidean emission spectroscopy (ICP-AES) . Additionally, soil distance). The relationship between soil properties and organic matter (SOM) was analysed using the potassium microbial microorganisms in the heading stage was fur- dichromate method by titration with ammonium ferrous ther analysed by the PLSPM model (partial least-squares sulphate (i.e., Mohr’s salt solution) . path) using the plspm package . Additional statistical analysis including one-way ANOVA and Pearson correl- DNA extraction, PCR amplification and MiSeq sequencing ation analysis was also completed using R version 3.3.2, The soil samples were homogenized in liquid nitrogen and a p value less than 0.05 was considered significant. and mixed completely, and 1 g of each soil sample was A molecular ecological network (MEN) using an RMT- used for DNA extraction. Soil microbial genomic DNA based network approach was built on the IEG website was extracted using a soil microbial DNA extraction kit (http://ieg4.rccc.ou.edu/) to investigate the interaction (MOBIO, San Diego, USA). The hyper-variable region between microbes [49–51] and the networks; finally, the (V4) of prokaryotic 16S rRNA  was amplified using result were visualized using Cytoscape 3.4.0. the primer pair 515F (5′-GTGCCAGCMGCCGCGG- TAA-3′) and 806R (5′-GGACTACHVGGTWTCTAAT- 3′). PCR products were recovered and the quality Results and quantity of recovered PCR products was determined Soil properties using a Nano-drop spectrophotometer (Thermo Fisher The pH varied from 4 to 7 (Additional file 1: Table S3), Scientific, Waltham, MA, USA). Purified PCR products and the lowest pH was at the Ly site. The Ly site had a were subjected to the MiSeq platform (Illumina, San lower concentration of soil nutrients (N, P, and K) than Diego, CA) with a 500-cycle kit (2 × 250 bp paired-ends) the other sites; particularly, the SOM (soil organic mat- for sequencing. Data processing was conducted accord- ter) showed the largest difference between Ly and the ing to Tao et al.  and Yin et al. . Specifically, high-yield sites. Permutational multivariate analysis of reads were assigned to different samples according to variance (Adonis) was conducted using three methods the barcode sequence and primers were removed. The (Bray, Euclid and Horn), and analyses indicated that the left and right reads were then merged with a minimum rice yield was closely associated with the soil physio- of 10 bp overlap and less than 5% mismatches using chemical properties (Additional file 1: Table S4). The Flash . Ambiguous bases (N) were removed from the DCA based on soil environmental factors (Fig. 1) merged sequences and chimers were removed by Uchim showed the low-yield site had very different soil proper- . Finally, high-quality sequences were clustered using ties compared to the high-yield sites. Pearson correlation UCLUST  at 97% similarity level , and the OTU analysis showed various correlations between the rice table was constructed after removing the false positive yield and the soil properties (Additional file 1: Table S5). sequences (singletons). Taxa assignment was carried out Among all soil properties, the available nitrogen (AN) by blasting the sequences to the RDP database  with and available potassium (AK) were significantly (Pearson 50% confidence. The rarefaction curve is shown in correlation > 0.650; p < 0.05) and positively associated Additional file 1: Figure S1. To reduce the variations with rice yield. In addition, TN, TP, and SOM were also caused by different sequencing depth, sequencing depths significantly correlated (p < 0.05) with crop yield in all Wu et al. BMC Microbiology (2018) 18:51 Page 4 of 11 differed slightly between the pre-transplant and heading stages in the Ha and Hc sites; however, the bacterial di- versity at the Ly site was similar during all stages of crop development. In addition, two-way ANOVA indicated that both stage and site had significant effects on micro- bial community diversity indices (Table 1). Pearson cor- relation showed the microbial community diversity during the pre-transplanting and heading stages was positively correlated (p < 0.05) with rice yield (Fig. 2). Adonis (permutational multivariate analysis of variance) also showed the microbial community during the pre- transplanting stage and the heading stage had a signifi- cant effect on crop yield (Additional file 1: Table S6), Fig. 1 Detrended correspondence analysis (DCA) of soil whereas the tillering and ripening stages did not show environmental factors with all samples from pre-transplanting stage significant effects. to ripening stage Network analysis on relationships between key microbial stages of development. Furthermore, pH was substan- communities tially correlated with yield during the tillering stage. To explore the interactions between rhizospheric mi- crobes, RMT-based molecular ecological networks (MEN) Bacterial diversity were constructed and analysed. All sites had similar RMT The microbial community diversity indices, including thresholds (0.88~ 0.90). However, the number of nodes Shannon and Simpson diversity and Pielou evenness and links was lower in the Ly site than in the other sites were significantly higher in the high-yield sites than in (Figs. 3 and 4, Additional file 1: Figures S2 and S3). There the low-yield site, and generally, the diversity indices were more positive interactions between OTUs in the were the highest during the tillering stage. The diversity high-yield site (Ha, Hb and Hc) network than that in the Table 1 Rhizospheric microbial community diversity at different developmental stages at four sites Shannon diversity Simpson Pielou evenness Ha_0p 7.01 ± 0.08abcde 0.9971 ± 0.0005ab 347.61 ± 60.66abcd Ha_2p 7.28 ± 0.11a 0.9981 ± 0.0004a 553.05 ± 116.91a Ha_3p 7.08 ± 0.03abcd 0.9976 ± 0.0005ab 421.17 ± 78.66abcd Ha_4p 6.77 ± 0.05e 0.9948 ± 0.0026b 223.77 ± 95.43d Hb_0p 7.05 ± 0.13abcde 0.9978 ± 0.0006ab 488.8 ± 131.09abc Hb_2p 7.28 ± 0.06a 0.9983 ± 0.0002a 583.58 ± 62.07a Hb_3p 7.19 ± 0.06ab 0.9982 ± 0.0001a 548.53 ± 21.6ab Hb_4p 7.17 ± 0.01abc 0.9982 ± 0.0001a 551.81 ± 16.64ab Hc_0p 7.03 ± 0.1abcde 0.9977 ± 0.0003ab 442.72 ± 61.61abcd Hc_2p 7.07 ± 0.17abcde 0.9975 ± 0.0009ab 427.48 ± 139.1abcd Hc_3p 7.05 ± 0.14abcde 0.9977 ± 0.0005ab 453.29 ± 85.08abcd Hc_4p 6.87 ± 0.13cde 0.9969 ± 0.0005ab 328.54 ± 58.07bcd Ly_0p 6.86 ± 0.12de 0.9962 ± 0.0012ab 277.73 ± 73.87bcd Ly_2p 6.94 ± 0.1bcde 0.9971 ± 0.0007ab 363.47 ± 92.08abcd Ly_3p 6.9 ± 0.11bcde 0.9957 ± 0.0021ab 264.65 ± 101.48bcd Ly_4p 6.84 ± 0.04de 0.996 ± 0.001ab 257.85 ± 56.84 cd Two-way ANOVA Shannon Simpson Pielou evenness Site effect < 0.001 0.001 < 0.001 Stage effect < 0.001 0.034 < 0.001 Cross effect 0.026 0.182 0.082 0p: pre-transplanting stage; 2p: tillering stage; 3p: heading stage; and 4p: ripening stage. The results are shown as the means and S.D. of three biological replicates. Values that do not share letters are different at the p < 0.05 level following Tukey’s t-test. Site, stage and cross effects were accessed by two-way ANOVA Wu et al. BMC Microbiology (2018) 18:51 Page 5 of 11 Fig. 2 Dcomparing the differences in microbial diversity indices was carried out by one-way analysis of variance (ANOVA) before the transplanting and heading stages. Error bars are based on the standard error and different lowercase letters indicate significant differences at the level of 0.05 as indicated by the ANOVA results. (a): Shannon Wiener index; (b): Inverse Simpson index; (c)Linear regression analysis of the relationship between crop yield and Shannon Wiener diversity index during pre-transplanting stage; and (d) Linear regression analysis of the relationship between crop yield and Shannon Wiener diversity index at the heading stage low-yield site (Ly) network. Nodes with high degrees in Ly and the other sites increased when the stage changed the Ly networks were classified into Bacteroides (OTU_ from the pre-transplanting stage to the heading stage 1367 and OTU_742) and Proteobacteria (OTU_807 and (Fig. 5d). An increase in unique OTUs in site Ly indi- OTU_275), while in high-yield site networks, nodes with cated that the difference between high- and low-yield high degree were classified into Acidobacteria, Actinobac- sites increased. Detrended correspondence analysis teria and Planctomycete. In addition, site Hb had the most (DCA) showed that the microbial community structure complicated network (Additional file 1: Figure S3) with differed more substantially during three of the crop- the most links and the maximal degree, presenting more growth stages than in the pre-transplanting stage (Fig. intricate topological properties than the other sites. 5a and b; Additional file 1: Figure S4c and d). Further- more, the community structure differences between the Microbial community composition and structure low- and high-yield sites were most obvious during the All sequences were clustered into 14,332 operational heading stage; therefore, we focused on the rhizospheric taxonomic units (OTUs) and were assigned into 469 microbial community during the heading stage genera. The permutational multivariate analysis of vari- (Additional file 1: Table S9). Proteobacteria, Acidobac- ance (Adonis) from three methods (Bray, Euclid and teria, and Chloroflexi were the most abundant phyla Horn) at the OTUs (Additional file 1: Table S7) and the found in the rhizospheric microbial community dur- genus level (Additional file 1: Table S8) showed that the ing the heading stage (Additional file 1: Table S9a), microbial community structure during the heading stage and each accounted for more than 10% of the com- was closely associated with the yield. A venn diagram munity. At the class level, Deltaproteobacteria was (Fig. 5c and Additional file 1: Figure S4) showed there the most abundant class in all samples, and was were 87 OTUs shared by sites Ha, Hb, and Hc during followed by Anaerolineae, Betaproteobacteria, Alpha- the pre-transplanting stage, but this was not found at proteobacteria. (Additional file 1:Table S9b). the Ly site. During the crop-growth period, 184 OTUs were shared by high-yield sites. Whereas the number of Relationship between soil characteristics, microorganisms shared OTU taxa in the four sites reduced from 600 and crop yield during the pre-transplanting stage to 370 during the Mantel tests were carried out to determine the correla- crop-growth period. The number of unique OTUs in site tions between the soil physical and chemical properties Wu et al. BMC Microbiology (2018) 18:51 Page 6 of 11 Fig. 3 Network constructed by thethe highest level (highly degree) of bacterial communities at site Hc. Each node signifies an OTU that could corresponds to a microbial population. Colours of the nodes indicate different major phyla. Blue and red lines represent positive and negative path coefficients, respectively and the soil microorganisms (Table 2). The results in- (Pearson correlation = 0.743–0.752; p < 0.01; Additional file 1: dicated that soil conditions had a significant influence on Table S10) with pH, whereas Chloroflexi exhibited a signifi- the bacterial community structure (p < 0.05) during the cant negative correlation with pH (Pearson correlation = − 0. pre-transplanting and heading stages. During the heading 621; p < 0.05, Additional file 1:Table S10).Overall,pH stage, there was a significantly negative correlation (Pearson showed significant effects on constraining the bacterial correlation = − 0.647; p < 0.05, Additional file 1:Table S10) community. between the yield and Crenarchaeota (Additional file 1: Further analyses showed 17 phyla had significant ef- Table S10). In addition, soil environmental factors, such as fects on soil fertility and crop production. Some genera AK, had a significant negative correlation (Pearson correl- had significant impact on soil fertility as shown in ation = − 0.666, p < 0.05; Additional file 1:Table S10)with Additional file 1: Table S11. The genera Geobacter, Crenarchaeota, but a significant positive correlation with Syntrophorhabdus, Phaselicystis, Thiobacter, and Chon- crop yield. Crop yield and AN were also significantly dromyces (in addition to Cystobacter) of the phylum Pro- positively correlated (Pearson correlation = 0.668; p < 0.05; teobacteria, had negative impacts on crop yield. Additional file 1: Table S10). In addition, Proteobacteria However, the abundance of the Proteobacteria was posi- and Bacteroidetes showed a significant positive correlation tively correlated with soil fertility. This also stressed that Wu et al. BMC Microbiology (2018) 18:51 Page 7 of 11 Fig. 4 Network constructed by thethe highest level (highly degree) of bacterial communities in site Ly. Each node signifies an OTU that corresponds to a microbial population. Colours of the nodes indicate different major phyla. Blue and red lines represent positive and negative path coefficients, repectively the structure and function of the bacterial community The bacterial diversity and the major bacterial communi- was diverse and complex at the phylum level. Armatimo- ties were substantially related to the yield of super rice, nadetes-gp2 and Armatimonadetes-gp5 in Armatimona- but the structure of the bacterial communities was in- detes showed contradictory correlation with soil fertility significant. The main soil factors affecting the yield and crop production. Gp17, Gp6, Gp24, and Gp25, of super rice were pH and SOM, which were also which belong to Acidobacteria, were significantly core- closely associated with the structure of soil bacterial lated with production. Spartobacteria incertae sedis in communities. The goodness of fit to the model was Verrucomicrobia,and OD1-sedis in OD1 showed diverse 0.702 (> 0.350), which validates the result and pro- effects on crop yield. Therefore, the overall effect could vides a reference value for our study. be directly observed at the phylum level, as well as on a more precise level when considering the individual ef- Discussion fects of each bacterial community at the genus level. Relationship between rhizospheric microbial community The partial least squares path model (PLSPM, Fig. 6) and rice productivity showed an association between the yield and soil bio- Crop productivity was mainly affected by the flow of en- logical and abiotic factors, in general, at the heading stage. ergy and material in the soil ecosystem which is driven by Wu et al. BMC Microbiology (2018) 18:51 Page 8 of 11 Fig. 5 Analsis of compositions and structures of bacterial communities from four groups. (a) Detrended correspondence analysis (DCA) of 16S rRNA gene sequencing data at the genus level during the tillering stage; (b) Detrended correspondence analysis (DCA) of 16S rRNA gene sequencing data at the genus level during the heading stage; (c) Venn diagrams were calculated by R with the package gplots and based on OTU level during the heading stage. Figures in pictures represent the taxa number of OTUs with common ownership at different sites; (d) Variation trends in OTUs under different classifications from pre-transplanting to heading stages. ∩: Intersection of mathematical symbol; ∪:Union mathematical symbol; and S: Ha∩Hb∩Hc∩Ly (intersection of four sites) soil organisms. Microorganisms located in the plant’s nutrients for microbial communities and regulated micro- rhizosphere play pivotal roles in the soil geochemical cycle bial diversity [56, 57]. The ecosystem properties such as . In the present study, the microbial community robustness and trophic interactions were morestable responded instantly to ecological changes during the pre- in response to environmental fluctuations as diversity in- transplanting and heading stages. Generally, the bacterial creased, for the richness of taxonomic diversity leads to diversity was higher in the high-yield sites than in the low- the extension of niches and the utilization of resources. It yield site, which suggested that high bacterial diversity po- is urgent to improve the stability and sustainability of tentially increases the yield of super rice. This is because farmland ecosystems , and meagre microbial diversity high bacterial diversity maintained a relatively stable eco- makes it difficult to resist the interference of detrimental system in the rhizosphere, which allowed effective nutri- factors. Additionally, a previous study by Laurent Philip- ent cycling . The difference in the microbial pot (2013) showed a loss in microbial diversity could affect communities between the high-yield sites and the low- nutrient cycling in soil , and microbial communities yield site may be a result of different nutrient levels in the were closely related to material cycling. soils. This was further supported by the Mantel test, which showed that the soil physiochemical properties had signifi- Rhizospheric microbial community was crucial for rice cant effects on the soil microbial communities. Resident production in the heading stage plants shaped and restructured rhizospheric microbial The heading stage is a critical period for crop produc- communities via root exudates , which provides tion; during this time, crops exhibit the most rapid Table 2 Pearson correlation between microbial community diversity and rice yield Pearson Yield correlation Before transplanting Tillering stage Heading stage Ripening stage Shannon.Wiener 0.614* 0.613* 0.650* 0.197 Simpson 0.579* 0.485 0.720** 0.286 Pielou 0.658* 0.477 0.594* 0.182 The stars indicate the significance level: *: P < 0.05, **: P < 0.01 Wu et al. BMC Microbiology (2018) 18:51 Page 9 of 11 Effects of microbial species interactions on rice productivity Distinct sites had uniform fertilization regimes and simi- lar climatic conditions, so the main difference in prod- uctivity was due to the rhizospheric microbial communities. In agricultural production, rhizospheric microorganisms are always associated with a consider- able yield within a stable farmland ecosystem . Ac- cording to the molecular ecology networks, rhizospheric bacterial communities that played pivotal roles in the site Ly (Fig. 4), such as Bacteroidetes and Proteobacteria, are always classified as ‘copiotrophs’ (R-strategists), hav- ing a high growth rate in nutrient-rich conditions . Owing to efficient metabolism in decomposing organic matter, the R-strategists should be chosen as the centre Fig. 6 Partial least squares path modelling (PLSPM) of the association of communication among microbial communities, espe- between the yield of super rice and soil biological and abiotic factors cially in artificially controlled nutrient-rich conditions. A during the heading stage. Goodness-of-fit of the model is 0.702. Blue farmland is a relatively complete ecosystem that keeps a and orange arrows represent positive and negative path coefficients, dynamic balance between the input (contrived fertilizers) respectively. *p < 0.05, **p <0.01, **p <0.01 and output (plant growth) energetic processes; thus, due to rich resources leading to niche diversification and a growth and development of their entire life. It is a crit- reduction in the intensity of competition between co- ical period for determining the amount of grains. Dur- occurring communities , the excellent decomposers ing this stage, crops respond to external conditions and communicators with powerful metabolic rates, i.e., . Abundant nutrients, water, temperature, illumin- R-strategists, should present a positive role in the node ation, and other external conditions are required during of energy flows and material cycling. After all, co- this period, suggesting that this is an excellent time for occurrences may indicate a benign or mutually beneficial farmers to increase production by means of topdress- relationship. Conversely, the results showed that many ing. Soil fertility, a fundamental factor for plant growth, R-strategists were negatively linked with their nearest is mainly dependent on microbial transformations. neighbour in site Ly due to competition for resources, Thus it is not surprising that the microbial community which implies a decline in microenvironment vitality. was most closely related to rice production during the One possible explanation is that mutual exclusivity be- heading stage. The association between the super rice tween bacterial communities caused by competition ex- with soil characteristics and the microbial microorgan- ists widely in the low-yield area, especially within similar isms during the heading stage was further analysed niches. In addition, high-yield sites differed considerably using the PLSPM (partial least-squares path, Fig. 6). from site Ly in terms of most members of Actinobac- Bacterial communities such as Blastopirellula in the teria, Acidobacteria, and Planctomycetes. We infer that Planctomycete, are good indicators of soil fertility be- member of Acidobacteria and Actinobacteria had stron- cause the microorganisms respond to topdressing more ger adaptability and resilience, which enable their sur- rapidly than do the soil physicochemical characteristics vival under stressful conditions , and regulated the , and microorganisms represent the true situation surrounding environmental attributes through feedback of the farmland; this is of great significance when estab- mechanisms. The microbial community links showed lishing a stable and high-yield agricultural system. In more specific ecological significance than the physico- the present study, some bacterial communities such as chemical characteristics of farmland throughout the en- Chloroflexi, which do not produce oxygen during tire period of rice growth. photosynthesis and have decreased nitrogen-fixation abilities, are abundant in soils; therefore, these bacteria Conclusion are negatively correlated with the yield of super rice The results of the present study demonstrated that the . In contrast to detrimental bacteria, some bacterial gaps in crop yield are strongly associated with variations communities, such as members of Acidobacteria, can in the soil microbial communities during the heading degrade cellulose and adjust soil pH effectively [63, 64]; stage, which were due to the effects of soil characteristic therefore, they are closely related to rice yield. How- before transplanting; thus, in addition to adjusting the ever, the specific features of some Acidobacteria, e.g., microhabitat before transplanting, there is the potential GP6 and GP17, remain unclear. to improve the average crop yield by controlling rational Wu et al. BMC Microbiology (2018) 18:51 Page 10 of 11 agricultural management during the heading stage. In Authors’ contributions PL, QL and ZW: Design and conception of the experiment. ZL, DM, and HY: addition, the positive species interactions within com- Data analysis and manuscript preparation. WC, JS and ZG: Execution of the munities may contribute to yield, as seen through net- experiments. YL, JZ and HL: Participated in discussions and supervised overall work analysis, and the biological properties (i.e., experimental and theoretical works. All authors have read and approved the manuscript. microbes) better reflect real-world farmland situations than do physicochemical characteristics (i.e., nutrient el- Ethics approval and consent to participate ements). However, we have essentially focused on the re- Not applicable. lationships among soil characteristics and the variations Competing interests and interactions of microbial communities. Hypotheses The authors declare that they have no competing interests. can be formed based on correlation analyses and need to be further tested to determine whether they are applic- Publisher’sNote able to other land types. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Additional file Author details Hunan Hybrid Rice Research Center/State Key Laboratory of Hybrid Rice, Additional file 1: Figure S1. Rarefaction curve of 16S rRNA gene Changsha 410125, China. Hunan Institute of Microbiology, Changsha sequencing at four sites (sites Ha, Hb, Hc and Ly). Figure S2. Network 410009, China. College of Plant Protection, Hunan Agricultural University, constructed by the highest level (highly degree) of bacterial communities Changsha 410128, China. School of Minerals Processing and Bioengineering, at site Ha. Figure S3. Network constructed by the highest level (highly Central South University, Changsha 410083, China. Hunan Soil and Fertilizer degree) of bacterial communities at site Hb. Figure S4. Analysis of Institute, Changsha 410125, China. Hunan Institute of Agricultural compositions and structures of bacterial communities from four Information and Engineering, Changsha 410125, China. LongPing Graduate groups.Table S1. Average yield of super hybrid rice “Y Liang You 900” at Institute, Hunan University, Changsha 410125, China. four sampling sites. Table S2. Name and batch of detected samples. Table S3. Mean value ± Received: 29 June 2017 Accepted: 3 April 2018 standard deviation (n = 3) for chemical properties of soils during the four periods . Table S4. Permutational multivariate analysis of variance (Adonis) shows the effects of soil properties at the various stages of crop References transplanting on the yield of super rice using three methods. Table S5. 1. Berg G. Plant–microbe interactions promoting plant growth and health: Pearson positive correlations between crop yield and several physical and perspectives for controlled use of microorganisms in agriculture. Appl chemical soil Microbiol Biot. 2009;84(1):11–8. properties during the four stages of development. Table S6. Permutational 2. Barea J. Future challenges and perspectives for applying microbial multivariate analysis of variance (Adonis) shows the effect of bacterial diversity biotechnology in sustainable agriculture based on a better understanding on crop yield at four stages of development. Table S7. 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