Ecology

Plant biomarker distributionsPlant lipid biomarkers are widely used to reconstruct vegetation and (hydro)climate in ancient environments, and previous studies at Olduvai15,17 have used biomarkers in spatiotemporal landscape reconstructions, although not at the resolution of this study (i.e.,  More

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This study reveals that various measures of bee diversity-including abundance, richness, and assemblage patterns are influenced by vane color and light reflectance patterns when passively sampling bees with vane traps. In particular, brightly colored vanes with higher light reflectance within 400–600 nm range attracted a greater diversity of bees in traps placed in a livestock pasture ecosystem. Effectiveness of blue and yellow vane traps had been compared previously in different ecosystems, for instance in apple orchards17, both woodland and open agriculture farmland13, and adjacent to Helianthus spp. (Asteraceae) field27. In all these studies, blue vane trap captured more bee species and 5–6 times more individuals compared to yellow vane trap.In the current study, we assessed a different design and size of vanes and a wider array of vane colors and reflectance patterns attached to sample collection jars. In particular, we used bright blue and yellow vanes that were made of plastic sheets covered with a micro-prismatic retro-reflective sheeting that provides better daytime and nighttime brightness as well as high visibility and durability. These vanes showed higher light reflectance and captured the most bees and bee species in this study (Table 2). Similar material was used on red vanes as well, but the light reflectance from those vanes was relatively lower, and as a result captured fewer bees. Traps with bright blue vanes performed especially well in terms of rates of bee capture (Fig. 2; 11.1 bees per trap per sampling date) and rates of species accumulation (Fig. 3). Bright yellow traps exhibited the second highest values for capture rates (Fig. 2; 6.6 bees per trap per sampling date) and species accumulation (Fig. 3), but these rates were not deemed significantly different from some other colors in which the reflective sheeting was not used, such as dark yellow, dark blue and purple.Bees use visual clues for detection, recognition, and memorization of floral resources in the foraging landscape7,28. The intensity of light reflected from different colors of vanes in traps affect number of bees attracted toward the trap10. Most bees can recognize colors that fall between 300 to 600 nm visual spectrums29. While the information related to the vision of many solitary and wild bees is not available, in the case of honey bees (Apis mellifera), color vision is trichromatic with highly sensitive photoreceptors at 344 nm (ultraviolet), 436 nm (blue) and 544 nm (green)30.In this study, colored vanes at a higher light reflectance between 400 to 600 nm attracted the highest number bee species in these passive traps. Capture rate differed among traps with different colored vanes in the current study, which can be explained by sensitivity of visual spectrum of bees and variation in the light reflectance of vanes of these traps. For example, bright blue vanes had two peaks of higher light reflectance, initially in 450–455 nm range and second peak with  > 800 nm. Such higher reflectance peak within the optimal range of bee vision may have played an important role in attracting abundant and diverse bee species to these passive traps. Similarly, bright yellow captured second largest number of bees, also had higher light reflectance peak within 600 nm but gradually decreased with increasing wavelength. Though bees have color spectrum from UV to orange31, they are sensitive to color spectrum between blue, green and ultraviolet32, which is a type of trichromatic vision system28. In one study33, red color vanes showed relatively lower light reflectance within 600 nm range, but had higher reflectance later in the spectrum, and this could be a reason why a low number of bees were collected in the traps. Past research showed contradictory views regarding the ability of bees to perceive red color. For instance, an early researcher in this field33, reported that bees recognize red color objects; however, other researchers had reported inability of bees to perceive34 or discriminate red from other colors35,36. It was argued that the bees see up to 650 nm in the visual spectrum and may not miss red colored flowers while foraging. However, other factors such as background (vegetation) color could also be contributing to bees’ ability to navigate different vane or flower colors in a livestock pasture landscape. Generally bees use color contrast to locate flower source, and hence neutral colors such as white are usually ignored29. Ultraviolet signal can make flowers more or less attractive to bees depending on whether it increases or decreases color contrast37. For example, UV color component in yellow38 and red39 flower increases chromatic contrast of these colored flowers with their background contributing attractiveness to the flowers. However, UV-reflecting white flowers decreases attractiveness for bees40.Different species of bees responded to different colors of vane traps. Out of the 49 bee species collected in this study, only nine bee species were found in all vane color types, whereas 14 species were found in only one trap color. For instance, out of five bumble bee species, two were found in all six vane colors, one was found in five colors, and two species (Bombus bimaculatus and B. fervidus) were only found in the traps with bright blue vanes. Many of the species that were only found in one trap color- Calliopsis andreniformis (1, bright yellow), Ceratina dupla (1, bright yellow), Diadasia afflicta (1, bright blue), Diadasia enavata (1, dark blue), Halictus rubicundus (1, dark yellow), Hylaeus mesillae (1, red), Lasioglossum tegulare (1, bright blue), Lasioglossum trigeminum (1, purple), Megachile montivaga (1, dark yellow), Melitoma taurea (1, bright blue), Svastra atripes (1, bright blue), and Triepeolus lunatus (1, dark yellow) were singletons and it was impossible to know if this represented a true preference or pattern. Our analysis of assemblage patterns after aggregating bees at the genus level, did show a gradient-like response in bee-color associations (Fig. 4), ranging from dark blue to yellows (with no strong associations found with red vanes). These patterns may be used to guide future (passive trap-based) sampling efforts to monitor bee diversity or to target specific bee species in livestock pastures or other ecosystems. While the bright blue and yellow traps with reflective sheeting were particularly attractive to bees, dark blue and purple traps also had relatively high levels of abundance and richness and collected higher number of Melissodes. Purple, as a color, is less commonly used than blue and yellow traps in bee monitoring. While this study shows that purple may be a viable option for bee collection, it’s similar assemblage pattern (Fig. 4) and low level of complementarity with dark blue traps (Table 2) suggests that it may be redundant with blue traps that are already commonly used. Differences in species- and sex-specific associations of bees with different colors of sampling traps had also been reported in previous studies41.Most of the bees collected in the current study were from Halictidae family (77.6%) followed by Apidae. However, few bee species in the families Andrenidae, Colletidae, and Megachilidae were collected. Consistent with our findings, others42 reported that bees of the Halictidae family were the most abundant bees in rangeland of Texas. The most common species found in this study were Au. aurata, L. disparile, L. imitatum, and Ag. texanus). In our previous studies we have found similar bee diversity in this study region18. Pollinator species richness and diversity as well as population distribution in livestock pasture vary during the season43. Mid-July to mid-August is the latter half of the summer season in the Southeastern USA, and the sampling period may have missed bee species that emerge earlier in the season and are reported in other studies42,43.Overall, the findings of this study showed that the wild bees responded differently to passive traps with colored vanes of different light wavelength and reflectivity when deployed in a livestock pasture ecosystem. Among six different colors of vanes (dark blue, bright blue, dark yellow, bright yellow, purple and red), the bright blue traps captured the highest number of individuals and species of bees. This could be due to an appropriate match between the visual spectrum of bees and the light reflectance spectrum of vanes, which were made of a micro-prismatic retro-reflective material. 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Site descriptionThe six long-term fertilisation field experiments were located across a latitudinal gradient in China from north to south, spanning three climate zones from the middle temperate zone to the subtropical zone. These sites were chosen to represent the three main agricultural production areas in China. The soils at the sites were black soils (Udolls or Typic Hapludoll according to USDA Soil Taxonomy, BSA) in Hailun (Heilongjiang Province) and Changchun (Jilin Province); fluvo-aquic soils (Aquic Inceptisol according to USDA Soil Taxonomy, FSA) in Yucheng (Shandong Province) and Yuanyang (Henan Province); and red soils (Ultisols according to USDA Soil Taxonomy, RSA) in Jinxian (Jiangxi Province) and Qiyang (Hunan Province). The two most distant sites (from Hailun to Qiyang) were more than 2,500 km apart, and the two closest test sites (from Yucheng to Yuanyang) were at least 300 km apart. Three treatments, i.e., no fertiliser (Control), chemical fertiliser N, P, and K (NPK), and organic manure plus chemical fertiliser (NPKM), have been applied in triplicate plots in each field for 29 years or more. Climate data corresponding to the sampling site coordinates were obtained from the China Meteorological Data Network (http://data.cma.cn/). Further details of experimental sites are provided in Supplementary Data 1.Sample collectionSampling was performed during the silking-maturity period of maize in 2019 and 2020, with the exact date of sampling depending on the developmental stage of plants at each location (Supplementary Data 2). In the FSA and RSA soils, three individual maize plants were selected from each subplot (a total of 27 maize plants per site). From each maize plant, we collected the following compartments in the field: mixed leaves, xylem sap, stem, roots, bulk soil. The mixed leaves sample consisted of the 2nd, 4th, and 6th leaves, which were removed from the plant stem using ethanol-sterilised scissors. To collect xylem sap, a proxy for xylem, we cut off the stem mid-way between the 2nd and 3rd node from the base of the plant, and sterilised absorbent cotton in sterilised bags was placed on the cut end of the shoot (see Supplementary Movie 1 for details of this procedure). Meanwhile, we inserted a steel stick (sterilised, 2 cm diameter, 30 cm length) into the soil at each subplot to simulate the collection of xylem sap and check for contaminants during the field operations (Supplementary Fig. 11). The stem sample consisted of the upper region between the 2nd and 3rd nodes, collected into sterilised bags. To collect root samples, we shook whole roots vigorously to remove all loose soil. The roots and root-adhered soil particles were collected for further separation of the roots and rhizosphere soil in the laboratory. The bulk soil sample was collected from between the rows of maize plants. At the sites with BSA soils, we only collected one individual maize plant from each subplot (a total of nine maize plants per site), because these two long-term experiments have strict requirements for sampling to avoid large-scale damage to the entire test field. Thus, for each plant, the 1st and 2nd, the 3rd and 4th, and the 5th and 6th leaves were collected as three replicates. Similarly, fine roots (< 2 mm) and thick roots ( > 2 mm) were collected separately. Except for this difference, the other operations were the same as those at the other four experimental sites. All samples were placed on ice for transport and further processing within 48 h. The soil parameters of pH, total C (TC) and N (TN), ammonium (NH4+) and nitrate (NO3−), and soil available P (AP) and K (AK) are listed in Supplementary Table 1.Sample processingTo recover the xylem sap absorbed in the cotton, each cotton ball was placed into a 50-mL sterile centrifuge tube with a filter and centrifuged at 6000 × g for 5 min. The collected sap was divided into two parts; one part was used for bacterial isolation, and the other part (stored at −80 °C) was used for culture-independent bacterial 16 S rRNA gene profiling.Processing of root-associated samplesWe used a modified protocol15 to separate the microbiome living on the plant surface (epiphytes) from the microbiome living within the plant (endophytes). Briefly, 5 g root tissue was weighed into a 100 mL conical flask containing 80 mL sterile PBS and 5 μL Tween 80. The mixture was vortexed, and the liquid was collected as the rhizosphere (root epiphyte) sample. To extract rhizosphere DNA, the sample was centrifuged at 10,000 × g for 5 min, and then 500 mg of the resulting tight pellet containing fine sediment and microorganisms was placed in a Lysing Matrix E tube (supplied in the FastDNA™ Spin Kit for Soil). To obtain endophytes from the root samples, the roots were washed with fresh PBS until the buffer was clear after vortexing. The roots were then sonicated using an ultrasonic cell disruptor (Scientz JY 88- IIN, Ningbo Scientz Biotechnology Co., Ltd., Zhejiang, China) at a low frequency for 10 min (30-s bursts followed by 30-s rests).Processing of leaf-associated samplesThe method for washing leaf samples was similar to that used to wash the roots, except for an additional step before collecting the phyllosphere. Each leaf sample (5 g) was added to a conical flask containing sterile PBS, which was subjected to two 5-min treatments in an ultrasound bath (25 °C, 40 KHZ), with vortexing for 30 s between the two ultrasonication treatments. This procedure released most of the phyllosphere microbes from the leaves. Then, the filtrate was collected and the leaves were washed again using the above procedure. Phyllosphere samples were collected by centrifugation (at 10,000 × g for 20 min) of the accumulated filtrate and were resuspended in 1 mL sodium phosphate buffer (FastDNA™ Spin Kit for Soil) before being transferred to Lysing Matrix E tubes (FastDNA™ Spin Kit for Soil). Finally, the leaf and stem samples were washed and sonicated in the same way as the roots. Sonicated root, leaf, and stem samples were snap-frozen in liquid N2 and stored at −80 °C until analysis.DNA extraction, PCR amplification and sequencingTotal DNAs were extracted from the aforementioned samples with a FastDNA™ Spin Kit for Soil (MP Biomedicals, Solon, OH, USA) following the manufacturer’s instructions. The DNA concentration and purity were measured using a NanoDrop2000 spectrophotometer (NanoDrop2000, Thermo Fisher Scientific, Waltham, MA, USA). The DNAs extracted from soils (bulk and rhizosphere soil) were diluted 10-fold. For 16 S rRNA gene libraries, the V5–V7 region was amplified using the primers 799 F and 1193 R (Supplementary Table 5). Each DNA template was amplified in triplicate (together with a water control) in a 25-μL reaction volume. The PCR conditions were as follows: 12.5 µL 2× EasyTaq PCR SuperMix (TransGen Biotech, Beijing, China), 1.25 µL forward primers (10 µM), 1.25 µL barcoded reverse primers (10 µM), 1.25 µL template DNA, and 8.75 µL ddH2O. The PCR amplification program was as follows: 94 °C for 3 min; 28 cycles of 94 °C for 30 s, 55 °C for 30 s, 72 °C for 90 s; and 72 °C for 90 s. The products were stored at 4 °C until use. After mixing the triplicate PCR products of each sample, the bacterial 16 S rRNA gene amplicons were extracted from a 1% agarose gel using a Gel Extraction Kit (Omega Bio-tek Inc., Norcross, GA, USA). The DNAs were measured using a Quant-iT™ PicoGreen™ dsDNA Assay kit (Thermo Fisher Scientific) and pooled in equimolar concentrations. Sequencing libraries were generated using an Illumina TruSeq DNA PCR-Free Library Preparation Kit (Illumina, San Diego, CA, USA) and were sequenced on the NovaSeq-PE 250 platform (Illumina).16 S rRNA gene amplicon sequence processingPaired-end reads were checked by FastQC v.0.10.137 and merged using the USEARCH 11.0.66738 fastq_mergepairs script. Reads were assigned and demultiplexed to each sample according to the unique barcodes by QIIME 1.9.139. After removing barcodes and primers, low-quality reads were filtered and non-redundant reads were identified using VSEARCH 2.12.040. Unique reads with ≥ 97% similarity were assigned to the same OTU. Representative sequences were selected using UPARSE41 and the classify.seqs command in mothur42 was used to taxonomically classify each OTU with reference to the SILVA 138 database43. The OTUs classified as host plastids, cyanobacteria, and others not present in our samples were removed from the dataset.Statistical analysisAlpha and beta-diversity analyses were conducted with R script as described in previous studies44 and on the QIIME239 platform. PerMANOVA analyses were performed using the ‘Adonis’ function implemented in the vegan package45 of R. We used the microbial source-tracking method FEAST46 to determine the potential origin of the microbiota inhabiting the various compartments of maize plants. Bacterial OTUs were assigned into multiple functional groups using FAPROTAX v.1.2.147. All analyses were conducted in the R Environment48 except for beta-diversity analyses. All plots were generated with ggplot249 and GraphPad Prism 8.0.0 (GraphPad Software, San Diego, CA, USA, www.graphpad.com).Distance-decay relationship analysesWe conducted distance-decay relationship analyses to assess the relationship between the similarity of communities in individual plant compartments and spatial distance, edaphic distance, and climatic distance. We used the Geosphere package to calculate the geographic distances in km from the latitude and longitude coordinates, and calculated edaphic and climatic distances separately as the Euclidian distance. We used the ‘vegdist’ function in the vegan package to calculate the Bray–Curtis similarity of microbial communities. Here, the variation in the slope of distance decay reflects the degree to which the similarity of microbial communities in plant compartments varies with environmental distances. The relationships between the Bray–Curtis similarity of each compartment and specific soil or climatic factors were determined by calculating Pearson’s correlation (r) values. The significance of r values was assessed with the Mantel test implemented in the vegan package.Differential abundance testingA negative binomial generalised linear model was implemented with the edgeR package50 to detect differences in OTU abundance among samples. We compared individual plant compartments (RS, RE, VE, SE, LE, and P) against bulk soil (BS) and conducted pairwise comparisons among fertilisation treatments. For each comparison, after constructing the DGEList object and filtering out low counts, the calcNormFactors function was used to obtain normalisation factors and the estimateDisp function was used to estimate tagwise, common, and trended dispersions. We then used the glmFit function to test the differential OTU abundance. The corresponding P values were corrected for multiple tests using FDR with α = 0.05.Core taxa selectionWe used the UpSetR package51 to visualise the OTUs that overlapped among all plant compartments and soils. The overlapping OTUs were defined as those detected in at least one sample from each compartment. We further identified the union of overlapped OTUs and enriched OTUs in the xylem using the EVenn online tool52. Importantly, abundance–occupancy analyses were conducted to identify the core OTUs across environmental gradients. We calculated occupancy with the most conservative approach, which restricted the core to only those OTUs that were detected in all xylem sap samples (i.e., occupancy=1).Triple-qPCR to verify FAPROTAX resultsTo detect contamination with plant organelles, sample DNAs were amplified using the universal primer pair 799 F/1193R53, which amplifies both bacterial 16 S and mitochondrial 18 S rRNA but not chloroplast sequences; the mitochondrial-specific primer pair mito1345F/mito4130R54,55, which only amplifies mitochondrial 18 S rRNA, and the nifH gene primers PolF/PolR56 (Supplementary Table 5). To reflect the potential N-fixation of bacterial communities in each plant compartment, the following ratio was calculated:$${{{{{rm{Relative}}}}}},{{{{{rm{nitrogen}}}}}},{{{{{rm{fixation}}}}}},{{{{{rm{potential}}}}}}=frac{{nifH},{{{{{rm{gene}}}}}}}{{{{{{rm{bacterial}}}}}},{{{{{rm{16S}}}}}},{{{{{rm{and}}}}}},{{{{{rm{mitochondrial}}}}}},{{{{{rm{18S}}}}}},{{{{{rm{rRNA}}}}}}-{{{{{rm{mitochondrial}}}}}},{{{{{rm{18S}}}}}},{{{{{rm{rRNA}}}}}}}$$
(1)
All qPCR assays were run on a QuantStudio 6 Flex using SYBR Green Pro Taq HS Premix (Accurate Biotechnology, Changsha, China) in a 20 µL volume containing 200 nM of each primer and approximately 50 ng DNA per reaction. All three primer sets were amplified in a three-step qPCR57 run at 95 °C for 15 s, 55 °C for 30 s and 72 °C for 40 s for 40 cycles followed by a melting curve analysis. The amplification efficiency varied from 83 to 117%.Isolation of bacteria from xylem sapTo isolate strains, four gradient dilutions (10−3, 10−4, 10−5, and 10−6) of xylem sap were incubated on TSB, R2A, and Ashby’s Nitrogen-Free Agar media for 5–7 days at 30 °C (Supplementary Data 9). After incubation, colonies were selected based on their character and colony morphology and were purified by triple serial colony isolation. The isolates were subjected to Sanger sequencing and identified on the basis of PCR analyses with 27 F and 1492 R primers, and alignment against reference. 16S rRNA gene sequences using the BLAST algorithm. Isolates belonging to the core taxa were identified by comparing the 16 S rRNA V5–V7 regions against the highly abundant OTUs ( > 0.01%) using UCLUST with 98.65% similarity;58 this threshold has been reported to accurately distinguish two species. Cladograms were visualised by iTOL.v6.459. The isolated cultures were stored in 30% (v/v) glycerol.Nitrogen-fixing capacity of bacterial isolatesWe used three different methods to evaluate the N-fixing capacity of bacterial isolates. First, we observed growth on Ashby’s N-Free medium, and documented which strains grew well after streaking of diluted cultures. Then, these strains were analysed by PCR to detect nifH with the PolF/PolR primer set56. The positive control was the N-fixing strain, Azotobacter chroococcum ACCC10006 (Agricultural Culture Collection of China). Strains that did not yield a PCR product with this primer set were analysed using other nitrogenase gene primers including nifH-F/nifH-R60 primers and the nested PCR primers FGPH19/PolR (outer primers) and PolF/AQER (inner primers)56 (Supplementary Table 5). We also conducted acetylene reduction assays (ARA)61,62,63 to quantify the nitrogenase activity of putative N-fixing strains. Each tested strain was initially incubated overnight in TSB medium and then washed twice with sterile 0.9% NaCl solution. After centrifugation and re-suspension, the bacterial pellet was added to a 20 mL serum vial containing 5 mL Dobereiner’s N-free liquid medium (Supplementary Data 9), reaching a final OD600 of ~0.1. The vials were first flushed with argon to evacuate air, and then 1% and 10% of the headspace was replaced with pure and fresh O2 and C2H2, respectively. After incubation at 30 °C for 12 h, the gas phase was analysed with a gas chromatograph (Agilent Technologies 6890 N). Data are presented as mean values from five replicate cultures. To test the hypothesis that the core non-N-fixers might assist N-fixation by modifying the oxygen concentration, the nitrogenase activity was measured as described above except that the headspace atmosphere in the sealed vial was not adjusted to 1% O2 by flushing with argon gas so that the initial oxygen concentration was that found in ambient air.Draft whole-genome sequencing of cross-referenced core strainsIsolated genomic DNA was extracted with a TIANamp Bacteria DNA Kit (Tiangen Biotech, Beijing, China). The purified genomic DNA was used to construct a sequencing library, which was generated using the NEB Next® Ultra™ DNA Library Prep Kit for Illumina (NEB, Beverly, MA, USA) following the manufacturer’s recommendations. Pooled libraries were sequenced on the NovaSeq-PE 150 platform. After trimming low-quality reads by fastq64, the clean reads were assembled into draft genomes (excluding contigs of < 300 bp) by SPAdes 3.13.165. Gene prediction and annotation were performed by NCBI PGAP66, and the putative genes were further annotated by searching against the eggNOG database67 by emaper. The functional mapping and analysis pipeline (FMAP 0.15)68 was used to align the filtered reads using BLAST against a KEGG Filtered UniProt69 reference cluster (e  70%) and to calculate the number of reads mapping to each KEGG Orthologous group (KO). Other data manipulation was performed using perl scripts developed in-house.Potted plant experimentTwo potted plant experiments were performed to (1) reproduce the endophytic behaviour of isolates from xylem sap; and (2) verify their N-fixation potential in maize. We constructed SynComs consisting of two diazotrophs (K. variicola MNAZ1050 and Citrobacter sp. MNAZ1397) and two non-N-fixers (Acinetobacter sp. ACZLY512 and R. epipactidis YCCK550) based on a N-fixing capacity test. Each individual strain was cultured overnight in TSB medium at 30 °C and 180 rpm, then cells were collected by centrifugation and the pellet was suspended in sterile 0.9% NaCl solution. Four bacterial suspensions were mixed in equal amounts to a final OD600 of ~0.2.The potted plants were grown in plastic pots filled with loose a soilless mixture consisting of perlite and vermiculite (sterilised by autoclaving). The nutrients needed for plant growth were added as base fertilisers (Supplementary Data 10). The seeds of maize “Zhengdan 958” were surface-disinfected for 15 min with sodium hypochlorite (approximately 2% active chlorine, with 200 μL Tween 80) and washed for 5 min, five times, with sterile water. The final rinse water (100 μL) was spread on TSA medium to check for other attached bacteria. Seeds were allowed to germinate, and then germinated seeds with similar primary root lengths were selected for inoculation with SynComs. Maize plants were grown in a greenhouse under a 16-h light/8-h photoperiod at 30 °C/25 °C (day/night).Colonisation of maize tissues by GFP-tagged SynComsTo verify the endophytic behaviour, each of the four members of SynComs was tagged with green fluorescent protein (GFP) (vector pCPP6529-GFPuv). One GFP-tagged and three other wild bacterial suspensions were mixed as described above, giving a total of four different GFP-tagged combinations. The control was SynComs with no GFP tags. For inoculation, maize seedlings with the endosperm removed were soaked in four GFP-tagged combined solutions for 30 min. The same bacterial suspension was applied to the potted plants at days 10 and 30 after transplanting. After 63 days, stem samples from between the 2nd and 3rd nodes were surface-sterilised with 70% ethanol, collected, and then sectioned using a Leica VT 1000 S vibratome (Leica, Nussloch, Germany). Thin sections (60 μm) and xylem sap were observed under a confocal laser scanning microscope (CLSM, Zeiss LSM 880 confocal microscope, Jena, Germany). 15N isotope dilution methodTo verify the N-fixation potential of endophytes in maize, the N fertiliser was replaced with 15N-labelled (NH4)2SO4 (30 % 15N atom, Shanghai Research Institute of Chemical Industry, China). Maize seedlings with endosperm removed were soaked in a bacterial suspension of SynComs (Treatment group) or autoclaved SynComs (Control group) for 30 min. The same SynComs, active or autoclaved, were re-applied to the potted plants at 10 and 30 days after transplanting, as described above. The roots, stems and leaves were harvested separately from each treatment (four replicates) on day 65. The roots were washed with deionised water to remove adhering isotope residues. The N content and 15N enrichment of plant tissue were determined using Elementar vario PYRO cube elemental analyser (Vario PYRO Cube, Elementar, Hanau, Germany) and Isoprime 100 isotope mass spectrometer (Isoprime, Cheadle, United Kingdom). The plants inoculated with autoclaved SynComs were used as the reference to calculate BNF with the following equations34:$$% {{{{{rm{Ndfa}}}}}}=(1{{mbox{-}}}{ % }^{15}{{{{{rm{Na}}}}}}.{{{{{rm{e}}}}}}{.}_{{{{{{rm{I}}}}}}}/{ % }^{15}{{{{{rm{Na}}}}}}.{{{{{rm{e}}}}}}{.}_{{{{{{rm{UI}}}}}}})times 100$$ (2) $${{{{{{rm{N}}}}}}}_{2}{{mbox{-}}}{{{{{rm{fixed}}}}}}=left(1-{ % }^{15}{{{{{rm{Na}}}}}}.{{{{{rm{e}}}}}}{.}_{{{{{{rm{I}}}}}}}/{ % }^{15}{{{{{rm{Na}}}}}}.{{{{{rm{e}}}}}}{.}_{{{{{{rm{UI}}}}}}}right)times {{{{{{rm{N; yield}}}}}}.}_{{{{{{rm{I}}}}}}}$$ (3) where %Ndfa is the percentage of N derived from air, %15Na.e. (%15N atom excess) is the enrichment in plants inoculated with SynComs (I) and autoclaved SynComs (UI), N2-fixed is N derived from air, and N yield.I is the total N content of the whole inoculated plant.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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