Ecology

The net uptake of CO2 by the biosphere offsets roughly a quarter of current fossil fuel emissions. However, climate change is expected to impact photosynthesis and ecosystem respiration differently. Quantification of these individual processes is required to better understand and predict the consequences for carbon cycling. Variations in oxygen isotope signatures (δ18O and Δ17O) in atmospheric CO2 can be used as tracers for photosynthesis. Δ17O is much less dependent on variations in the hydrological cycle, which often obscure photosynthesis signals in the more widely measured δ18O. Although, measurement techniques for Δ17O in tropospheric CO2 only became sufficiently accurate to interpret variations since the ~2010s, providing new insights into the carbon cycle. More

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It was turtle-nesting season when this photograph was taken one night in June. I am on Needham’s Point beach measuring a critically endangered female hawksbill turtle (Eretmochelys imbricata). As field director of the Barbados sea turtle project, I run the day-to-day conservation activities and train and manage volunteers.We also run research projects that inform our conservation activities. We collect data such as shell length, which can tell us the age at which females become sexually mature and can indicate growth rates. These data help us to keep track of turtle health and survival. For example, if we start seeing smaller turtles, this could indicate that they are maturing faster, or that food is scarce and the turtles are growing more slowly.In August, the baby turtles hatch. I was on call 7 days a week for around 8 hours a day, responding to emergencies. These included hatchlings wandering off in the wrong direction, putting them at risk of being hit by a car or eaten by predators such as rats and cats. We took the hatchlings to a safe spot on the beach and released them. I also had to prepare for the expected swells as Hurricane Ida passed us by: when beaches flood, nests can wash away. We took rescued eggs and premature hatchlings to a makeshift intensive-care unit until they were ready for release. We aim to leave no turtle behind.I have worked at the project for 15 years. I recently finished a master’s degree on the coloration of the Barbados bullfinch (Loxigilla barbadensis) at the University of the West Indies, which hosts the turtle project. Next year I hope to start a PhD, part of which will look at the conflict between tourism and sea-turtle survival in Barbados. Here, interactions between sea turtles and humans occur at every stage of the turtles’ lives and can affect their survival. After my doctorate, I will continue to focus on helping sea turtles in the Caribbean. There is something addictive about making a real-time, tangible difference to their lives.

Nature 598, 532 (2021)
doi: https://doi.org/10.1038/d41586-021-02851-6

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All fungal specimens were collected on Crown Lands for which no permission is required. Specimens of fresh, field-collected mushroom fruiting bodies were either air-dried at a temperature of 30–35 °C or frozen at − 80 °C until processing, and air-dried voucher specimens have been deposited at the Dr. Laurie L. Consaul Herbarium, London, Canada (UWO) and the National Mycological Herbarium of Canada, Ottawa (DAOM) (Table 1).DNA extraction, PCR amplification and sequencingGenomic DNA was extracted from air-dried specimens following Thorn et al.9. Primers ITS1 and ITS6R were used to amplify the ITS region, LS1 and LR3 to amplify ~ 650 bases of the 5’-LSU region and Canth-ef1a983-F and Canth-ef1a-1567-R to amplify Tef-19,41,42,43,44. The PCR products were checked using gel electrophoresis and successful products were cleaned using Bio Basic EZ-10 Spin Column PCR Products Purification Kit. Cleaned PCR products were submitted to the sequencing facility of Robarts Institute (University of Western Ontario) to obtain sequences through Sanger sequencing with amplification primers, and internal sequencing primers CanthITS1_Internal-R, 5.8S-R-Canth, and ITS86R-Canth for the ITS region9. New sequences produced for this study were deposited in GenBank as accessions MN181445–MN181461 and MN206911–MN206945.Phylogenetic analysesSequences of the ITS, LSU and Tef-1 regions were cleaned and assembled with SeqEd v.1.03, then, together with sequences of related species downloaded from GenBank, each region was aligned separately with MAFFT v.745 under the G-INS-i strategy, with “leave gappy regions” selected. The draft Cantharellus cibarius genome (QOWL00000000.1)18 was searched by BLASTn for ribosomal and Tef-1 sequences. The full-length match to our Tef-1 sequences was found in a single scaffold (QOWL01010594_RC), but no ITS sequences and only partial LSU sequences were found (in QOWL01010930_RC, QOWL01012415, QOWL01007530_RC, and QOWL01005980_RC). Alignments were imported into MEGA X46,47, trimmed and concatenated into a single ITS-LSU-Tef-1 dataset, then optimized manually. Phylogenetic trees were constructed with maximum likelihood (ML), with 1000 bootstrapping replicates in MEGA X. Analyses were repeated with Bayesian inference using MrBayes 3.2.6 with 4 chains and 5 million generations, discarding the first 25% of trees, when the average standard deviation of split frequencies had stabilized below 0.0148. Tree topologies were compared, and Bayesian prior probabilities transferred to the ML bootstrap tree in Adobe Acrobat.Genetic analysis of carotenoid synthesis genesIn order to design primers to amplify portions of the Al-1 and Al-2 genes in white and golden chanterelles, these genes were first located in three published Cantharellus genome sequences18 using tBLASTn49 to query the genomes with protein sequences from N. crassa (PRJNA132; Al-1 XM_959620.2 and Al-2 XM_960632.3)19. Candidate gene sequences from Cantharellus appalachiensis (QLPK00000000.1; Al-1 Scaffold 4647: QLPK01003932.1 and Al-2 Scaffold 1419: QLPK01001208.1), C. cibarius (QOWL00000000.1; Al-1 Scaffold 15338: QOWL01009792.1 and Al-2 Scaffold 1560: QOWL01001169.1), and C. cinnabarinus (QLPJ00000000.1; Al-1 Scaffold 1057: QLPJ01000880.1 and Al-2 Scaffold 2474: QLPJ01001998.1) were annotated in Geneious using a discontiguous megablast against GenBank to search for homologous motifs50. Based on these alignments, putative ORFs were annotated and aligned, and an overlapping set of PCR primers for AL-1 and AL-2 were designed in Geneious (Table 5). Designed primers were tested for specificity using the BLAST algorithm against the three Cantharellus genomes18. PCR amplified products were assessed for quality, cleaned, sent for sequencing, and assembled as above. Assembled sequences of white and gold samples were compared, along with their putative amino acid products determined using ExPASy51, guided by the translations of the Neurospora crassa Al-1 (XM_959620.2) and Al-2 (XM_960632.3) genes. Partial sequences of the Al-1 and Al-2 genes of gold and white variants have been deposited in GenBank as MW442833–MW442836.Table 5 PCR primers designed to amplify portions of the phytoene desaturase gene (Al-1) and phytoene synthase gene (Al-2) from Cantharellus species, based on genomic sequences from Cantharellus appalachiensis, C. cibarius, and C. cinnabarinus18, listed in “Materials and methods” section.Full size tablePigment analysisField-collected mushroom fruiting bodies were weighed while fresh, wrapped in aluminum foil, and frozen at − 80 °C for pigment analyses, and other samples were weighed fresh and then dried to obtain a conversion for fresh to dry weight. Pigments were extracted with ice-cold 100% acetone at 4 °C and dim light. The supernatant was filtered through a 0.22 µm syringe filter and samples were stored at − 80 °C until analysed. Pigments were separated and quantified by high-performance liquid chromatography (HPLC) as described previously20, with some modifications. The system consisted of a Beckman System Gold programmable solvent module 126, diode array detector module 168 (Beckman Instruments, San Ramon, California, USA), CSC-Spherisorb ODS-1 reverse-phase column (5 mm particle size, 25 × 0.46 cm I.D.) with an Upchurch Perisorb A guard column (both columns from Chromatographic Specialties Inc., Concord, Ontario, Canada). Samples were injected using a Beckman 210A sample injection valve with a 20 μL sample loop. Pigments were eluted isocratically for 6 min with a solvent system of acetonitrile:methanol:0.1 M Tris–HCl (pH 8.0), (72:8:3.5, v/v/v), followed by a 2 min linear gradient to 100% methanol:hexane (75:25, v/v) which continued isocratically for 4 min. Total run time was 12 min. Flow rate was 2 mL min−1. Absorbance was detected at 440 nm and peak areas were integrated by Beckman System Gold software. Retention times and response factors of Chl a, Chl b, lutein and ß-carotene were determined by injection of known amounts of pure standards purchased from Sigma (St. Louis, MO, USA). The retention times of zeaxanthin, antheraxanthin, violaxanthin and neoxanthin were determined by using pigments purified by thin-layer chromatography as described by Diaz et al.52.Extraction and analysis of chanterelle lipidsSamples of each chanterelle species were homogenized to fine powder in a cryomill (Reitch, Germany) and 100 mg of the homogenized powder mixed with 1 mL methanol (MeOH), 1 mL chloroform (CHCl3) and 0.8 mL water following Pham et al.53. The sample mixture was thoroughly vortexed, then centrifuged (Sorvall Legend XT/XF centrifuge; ThermoFisher Scientific, Mississauga, Ontario) at 2500 rpm for 15 min. The organic layer was transferred to new vials, dried under nitrogen and then reconstituted in 1 mL chloroform:methanol (1:1 v/v). Aliquots were then used for either gas chromatography with mass spectrometric and flame ionization detection (GC–MS/FID) or ultra-high-performance liquid chromatography with heated electrospray ionization high resolution accurate mass tandem mass spectrometric analysis (UHPLC-HESI-HRAM/MS–MS) for fatty acids and intact lipids analysis, respectively.For GC–MS/FID analysis, chanterelle fatty acids were converted to fatty acid methyl esters (FAMEs) as follows: To 300 µL aliquot of the lipid extract, 50 μL of C18:0 alkane (1 mg mL−1 in chloroform: methanol 1:1 v/v) was added as internal standards and the samples dried under nitrogen and the fatty acids esterified by adding 400 µL methanolic HCl (1.5 N). The samples were then incubated in a pre-heated oven at 60 °C for 30 min. After incubation, 0.8 mL of distilled water was added to the cooled samples and the FAMEs extracted with 3 aliquots each of 500 μL of hexane. The fractions were combined, dried under N2, re-suspended in 50 μL hexane, and the FAMEs analyzed using a Trace 1300 gas chromatograph coupled to a Flame Ionization Detector and TSQ 8000 mass spectrometer (Thermo Fisher Scientific). The FAMEs were separated on a BPX70 high-resolution column (10 m × 0.1 mm ID × 0.2 μm, Canadian Life Science, Peterborough, Ontario) using helium as the carrier gas at a flow rate of 1 mL min−1. One μL of each sample was injected in split mode (1:15) using a Tri-plus auto-sampler (Thermo Fisher Scientific). The operation conditions were as follows: initial oven temperature set at 50 °C for 0.75 min, increased to 155 °C at 4 °C min−1, ramped to 210 °C at 6 °C min−1, then 240 °C at 15 °C min−1 and final temperature held for 2 min. Methylated fatty acids were determined by comparison with retention times and mass spectra obtained from commercial standards (Supelco 37 component mix, Supelco PUFA No. 3, Sigma Aldrich, Oakville, Ontario) and the NIST database (Thermo Fisher Scientific). Standard curves were employed to determine the amount of individual fatty acids, and values are presented as nmole%.For the UHPLC-HESI-HRAM/MS–MS analysis, a Q-Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific) coupled to an automated Dionex UltiMate 3000 UHPLC system was used to analyze the intact chanterelle lipids according to our previously published method53. Briefly, the intact lipids were resolved using an Accucore C30 column (150 mm × 2 mm I.D., particle size: 2.6 µm, pore diameter: 150 Å) and the following solvent systems: (i) Solvent A consisted of acetonitrile:water (60:40 v/v) containing 10 mM ammonium formate and 0.1% formic acid and (ii) Solvent B consisted of isopropanol:acetonitrile:water (90:10:1 v/v/v) with 10 mM ammonium formate and 0.1% formic acid. The conditions used for separation were 30 °C (column oven temperature), flow rate of 0.2 mL min−1, and 10 µL of sample injected. The gradient system used was as follow: solvent B increased to 30% in 3 min; 43% in 5 min, 50% in 1 min, 90% in 9 min, 99% in 8 min, and finally maintained at 99% for 4 min. The column was re-equilibrated for 5 min before each new injection. Full scans and tandem MS acquisitions were performed in both negative and positive modes using the following parameters: sheath gas: 40, auxiliary gas: 2, ion spray voltage: 3.2 kV, capillary temperature: 300 °C; S-lens RF: 30 V; mass range: 200–2000 m/z; full scan at 70,000 m/z resolution; top-20 data-dependent MS/MS resolution at 35,000 m/z, collision energy of 35 (arbitrary unit); injection time of 35 min for C30RP chromatography; isolation window: 1 m/z; automatic gain control target: 1e5 with dynamic exclusion setting of 5.0 s. The instrument was externally calibrated to 1 ppm using electrospray ionization (ESI); negative and positive calibration solutions (Thermo Fisher Scientific) were used to calibrate the instrument at 1 ppm. Tune parameters were optimized using PC 18:1(9Z)/18:1(9Z), Cer d18:1/18:1(9Z), PG 18:1(9Z)/18:1(9Z), sulfoquinovosyl diacylglycerols [SQDG] 18:3(9Z,12Z,15Z)/16:0, monogalactosyl diglyceride [MGDG] 18:3(9Z,12Z,15Z)/16:3(7Z,10Z,13Z), and digalactosyldiacylglycerol [DGDG] 18:3(9Z,12Z,15Z)/18:3(9Z,12Z,15Z) lipid standards (Avanti Polar Lipids, Alabaster, AL, USA) in both negative and positive ion modes. The data were processed using either X-Calibur 4.0 (Thermo Fisher Scientific) or LipidSearch version 4.1 (Mitsui Knowledge Industry, Tokyo, Japan) software packages.Phenolics analysis by GC–MSReagent grade phenolic acid standards including benzoic acids, p-hydroxybenzoic acid, vanillic acid, gallic acid, 3,4-dihydroxybenzoic acid, syringic acid, gentisic acid, veratric acid, salicylic acid, cinnamic acid, o-coumaric acid, m-coumaric acid, p-coumaric acid, ferulic acid, sinapic acid, caffeic acid, sodium hydroxide, N,O-Bis(trimethylsilyl)trifluoroacetamide (BSTFA-TCMS) were purchased from Sigma Aldrich. Methanol, ethyl acetate, and hydrochloric acid (36% w/v) were purchased from VWR (Mississauga, Ontario, Canada). For alkaline hydrolysis of powdered chanterelles, 100 µL of aqueous 3,4-dihydroxybenzoic acid solution (0.2 mg mL−1) was added to a mixture containing 4 g of sample in 8 mL 1 M sodium hydroxide. The resultant mixture was incubated in the dark for 24 h at 25 °C on an orbital shaker (50 rpm). The pH of the reaction mixture was adjusted to 2.0–2.5 using concentrated HCl then vortexed. The organic components were extracted four times with 4 mL methanol: ethyl acetate (1:3 ratio) into pre-weighed vials. The solvent was evaporated under nitrogen at 35 °C to determine the crude extraction yield. The extracts were resuspended in 1 mL ethyl acetate, vortexed, then 300 µL of extract transferred into a pre-weighed vial, dried under nitrogen, and 50 µL of BSTFA-TCMS and 50 µL of pyridine added. The resultant mixture was incubated at 70 °C in darkness for 30 min then transferred to GC vials for GC–MS analysis. Standard solutions were derivatized in a similar manner.A Thermo Scientific Trace 1300 gas chromatograph coupled to a Triple Quad mass spectrometer (Thermo Fisher Scientific) was used for the analysis and the compounds resolved on a ZB-5MS non-polar stationary phase column (30 m × 0.25 mm I.D., 0.25 μm film thickness, Phenomenex, Torrance, CA, USA) with helium as the carrier gas (flow rate of 0.6 mL min−1). One microliter of the standard or sample was injected in basic mode (15:0) using a Tri-plus auto-sampler. The oven temperature program was as follows: the initial oven temperature was 70 °C (held for 1 min), was increased at 12 °C min−1 to 220 °C (held for 3 min), 15 °C min−1 to reach 250 °C and held for 1 min. Identification of the phenolic acids (as trimethylsilyl ether, TMS) was based on the comparison of their retention times and mass spectra with that of the NIST library and commercial standards, with quantities calculated and expressed as nmole%.
Analysis of the volatile profile of chanterelles by SPME-GC/MSVolatile metabolites were extracted and analysed by Solid-Phase Microextraction and Gas Chromatography/Mass Spectrometry (SPME-GC/MS) following Vidal et al.54. Briefly, 100 mg of sample powder obtained after cryo homogenization was placed in 10 mL headspace glass vials and kept at 50 °C for 5 min (sample equilibration) before volatile metabolites extraction and analysis began. A divinylbenzene/carboxen/polydimethylsyloxane (DVB/CAR/ PDMS) coated fibre (1 cm long, 50/30 μm film thickness; Supelco, Sigma-Aldrich), was inserted into the headspace of the sample vial and held there for 60 min55,56. Chanterelle volatile composition was analyzed using a Trace 1300 gas chromatograph coupled to a TSQ 8000 Triple Quadrupole mass spectrometer (Thermo Fisher Scientific). The extracted volatile compounds were separated using a ZB-5MS non-polar stationary phase column (30 m × 0.25 mm I.D., 0.25 μm film thickness; Phenomenex) with He used as the carrier at a flow rate of 1 mL min−1. After extraction the fibre was desorbed for 10 min in the injection port and the instrument operated as follows: splitless mode with a purge time of 5 min, initial oven temperature set at 50 °C (5 min hold) and increased to 290 °C at 4 °C min−1 (2 min hold). Ion source and quadrupole mass analyzer temperatures were set at 230 and 150 °C respectively, injector and detector temperatures held at 250 and 290 °C respectively, mass spectra ionization energy set at 70 eV, and data acquisition done in scan mode. After each sample desorption, the fiber was cleaned for 10 min at 250 °C in the conditioning station. Volatile compounds were identified by matching the obtained mass spectra with those of available standards, and mass spectra from commercial libraries NIST/EPA/NIH (version 2.2, Thermo Fisher Scientific) or the scientific literature55,56. Volatile compounds in the chanterelle samples were semi-quantified based on the area counts × 10−6 of the base peak. Compounds with lower abundances than 10−6 area counts were considered as traces. Although the chromatographic response factor of each compound is different, the area counts determined are useful for comparison of the relative abundance of each compound in the different samples analysed55,56.Statistics and reproducibilityResults of analyses of lipids and phenolics are presented as means and standard errors of 4 replicates and those of head-space analyses of volatiles are based on 2 replicates (Tables 2, 3, 4). The values of all dependent variables were tested for normal distribution and homoscedasticity by Shapiro–Wilk and Levene tests, respectively. For variables with homogeneous variance, parametric one-way analysis of variance (ANOVA) was used to determine if there were significant differences between chanterelle samples. Where significance was detected, the means were compared with Fisher’s Least Significant Difference (LSD), α = 0.05. Where the assumption of normality was not met (pentanal, 3-octen-2-one, 2-nonanone, 2-undecanone, (E)-α-Ionone, 3-hydroxy-α-ionene, all phenolic acids, and some minor fatty acids 10:0, 15:0, 14:1, 16:1n5, 20:1n9 and 24:1n9), no significance was detected after the data were treated with a non-parametric Kruskal–Wallis test and significance (p  More

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Bacterial strains isolation and identificationFifty six bacterial cultures were isolated from both management systems (G1 and G2) of mango orchards (rhizosphere) at CISH, Lucknow, India. Isolation of microorganisms using spread plate methods revealed that the Nutrient agar medium had the highest number of colony appearances compared to the Rose Bengal Agar medium. Microbial enumeration showed organic system enriched with higher bacterial and fungal population than conventional system (Fig. 1). From organic system, thirty seven bacteria were isolated out of which, twenty-three isolates were (G+), and fourteen were (G−). While, in the conventional system, nineteen bacteria were isolated, out of which fifteen were (G+) and four were (G−) isolates.Figure 1Comparative microbial enumeration of organic and conventional treated mango rhizosphere soil the CFU mL−1 of selected samples showing growth of fungus and bacterial populations under two different treatments i.e. organic and conventional. The results are the average of five replicates (n = 5), with bars representing standard error. Significant differences based on the analysis variance (ANOVA) are shown by different letters above the error bars, followed by the post hoc DMRT test (p ≤ 0.05) using the software SPSS.Full size imagePlant growth promotion propertiesFor plant growth promotory properties out of fifty-six bacterial isolates total, ten bacterial cultures (2, 3, 4, 8, 15, 23 and 31) from the organic system showed positive results for phosphate solubilization. In contrast, three bacterial cultures (I1, I8 and I9) from the inorganic system (conventional system) showed positive phosphate solubilization in Pikovaskya’s agar medium. For siderophore production, bacterial cultures (2, 3, 4, 8, 12 and 26) from the organic system showed positive results, while four bacterial cultures (I1, I6, I8 and I9) inorganic system showed positive results. Bacterial cultures (2, 3, 4 and 8) from the organic system showed positive results for K-solubilization, while five bacterial cultures (I1, I2, I7, I8 and I9) from the inorganic system showed positive K-solubilization. A total of ten isolates (7 from organic and 3 from the inorganic system) possessed Zn-solubilizing activity. The test isolated from the organic system showed better Zn (ZnO), Zn3 (PO4)2, and (ZnCO3) solubilization as compared to test culture isolated from the inorganic system (Supplementary S1.8).Acetylene reduction assay (ARA)Results from acetylene reduction assay showed in aerophilic condition, bacterial isolates 1, 3, 4 (from organic treated soil) and I1, I8 and I9 (conventional system) showed 134.8, 37.70, 36.73, 13.15, 16.70 and 12.87 ppm of ethylene tube−1 h−1, respectively. In case of microaerophilic condition, bacterial isolates 4, 9, I9 showed 24.17, 19.14, and 12.71 ppm ethylene, respectively. Results indicate possible use of these bacterial isolates as a bioinoculant agent for horticultural crops, especially mango and other subtropical climate fruit crops.Soil enzymatic studyThe soil enzymatic activity in the organic system (G1) showed better dehydrogenase activity than the conventional system (G2). For both methods, alkaline phosphatase almost showed similar activity (at pH 11), while in the case of acid phosphatase showed better activity in the inorganic system (G2) as compared to the organic system (G1) at pH level 6.5 (Fig. 2). The dehydrogenase enzyme oxidizes the organic matter, and it belongs to the oxidoreductase type of enzyme. In the process of respiration of soil microorganisms, the dehydrogenase enzyme facilitates the transfer of protons and electrons from the substrate to the acceptor. It was significant to observe that the dehydrogenase activity was higher in organic treated soils (0.784 µg TPF g−1 h−1) than in conventional system (0.053 µg TPF g−1 h−1).Figure 2Comparative soil enzymes activities of conventional and organic treated mango rhizosphere soil the dehydrogenase, acid phosphatase and alkaline phosphatase activities were showing in µg TPF formed g−1 of soil h−1 and µg PNP g−1 soil h−1 respectively. The results are the average of five replicates (n = 5), with bars representing standard error. Significant differences based on the analysis variance (ANOVA) are shown by different letters above the error bars, followed by the post hoc DMRT test (p ≤ 0.05) using the software SPSS.Full size imageAlpha biodiversity with samples and rarefaction curvesIn this segment, by measuring Shannon, Chao1, and observed species metrics, we analyze the microbial diversity within the samples. The chao1 metric measures the richness of the ecosystem, while the Shannon metric is the formula for calculating reported OTU abundances and accounts for both prosperity and equality. The rarefaction curve is provided in Fig. 3 for each metric. Using QIIME software, the metric measurement was done. The impact of both treatments on the microbial complexity and abundance in the sample was also revealed using the Shannon diversity Index (depicting richness and evenness) and Chao 1 representing only richness. Shannon’s diversity index of the bacterial community in the treatment (G1 and G2) was 8.06 and 8.12. The Simpson index in ecology is used to quantify biological diversity in a region, which was also nearly similar in both the treatments. Chao 1 richness estimator showed an increase in species richness. Rarefaction analysis conducted to confirm species richness revealed a difference in the number of reads and OTUs between the samples. The Rare fraction curve had a similar pattern for both samples and showed an impact on the bacterial population in the experiment (Fig. 3a–c).Figure 3Shanon (a), Chao1 (b) curves and observed species (c) obtained for the samples (G1 and G2).Full size imageBacterial diversity analysis at phyla levelTaxonomic study of the 16S rRNA gene amplicon reads yielded seven classifiable bacterial phyla. Six phyla, namely Acidobacteria, Actinobacteria, Bacteroides, Proteobacteria, Firmicutes, and Chloroflexi were dominant in both the systems. The Organically treated soil (G1) sample harbored a higher percentage of Bacteroidetes (14.55%), Actinobacteria (7.45%), and Proteobacteria (10.82%) as compared to conventional treatment (G2) 8.98%, 5.71%, and 6.64%, respectively. However, phylum Acidobacteria(13.6%), Firmicutes(4.84%), and Chloroflexi (2.56) were higher abundance in conventional treatment as compared to the organic treatment, which showed the same phyla with lesser quantity, i.e., 5.63%, 0.91%, and 0.79% respectively (Fig. 4a).Figure 4Comparative microbiome (a-phylum and b-order) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 region) approach.Full size imageDistribution of bacterial community at order levelThe bacterial orders in both systems were diversified. The most abundant orders in organic and conventional systems were Chitinophagales (Organic-11.32%, Conventional-43%), Elev-16S-573 (Organic-3.09%, Conventional-8.69%), Pedosphaerales (Organic-1.56%, Conventional-3.55%), Opitutales (Organic-2.46%, Conventional-0.27%), Chthoniobacterales (Organic-1.35%, Conventional-2.84%), Bacillales (Organic-0.91%, Conventional-4.84%) and Solibacterales (Organic-1.39%, Conventional-2.26%) (Fig. 4b).Bacterial community distribution at family levelBacterial family members were identified and enriched including Pedosphaeraceae (O-1.56%, C-3.55%), Opitutaceae (O-2.46%, C-0.27%), Chthoniobacteraceae (O-1.03%, C-2.68%), Steroidobacteraceae (O-2.05%, C-0.73%), Bacillaceae (O-0.77%, C-4.55%), Chitinophagaceae (O-10.99%, C-5.06%), and Xanthomonadaceae (O-1.39%, C-0.06%) and other families (Fig. 5a).Figure 5Comparative microbiome (a-family and b-genus) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 region) approach.Full size imageBacterial community distribution at the genus levelComparative abundance of unidentified genus in organic system were uncultured soil bacterium, Glycomyces, Chitinophaga, Lysobacter, Udaeobacter, Bacillus (not detected, 1.85%, 4.77%, 1.19%,1.03% and 0.75% respectively) whereas same genus-group were observed in conventional system with different percentage i.e., 0.11%, not detected, 0.56%, 0.04%, 2.67%, 4.54% respectively (Fig. 5b).Bacterial communities at species levelBecause most of the species were unidentified and uncultured bacterium based on relative abundance, they could not be assigned a species name in either sample. Few species are identified in both systems, like Sphingomonas sp. (O-1.57%, C-1.05%), Bacillus drentensis (O-0.25%, C-2.65%), and Chitinophaga sp. (O-4.64%, C-0.11%) (Fig. 6).Figure 6Comparative microbiome (Species) analysis of organic (G1) and conventional (G2) treated mango orchards soil by using metagenomic (V3 and V4 regions) approach.Full size imageHeat map and PCA analysisUnder long-term exposure of organic and conventional treatments, a microbial shift was observed in the rhizosphere microbiome of mango orchards. Based on percent abundance, nine different microbial genera Acidobacteria, Actinobacteria, Bacteroidetes and Proteobacteria formed Cluster I. While, Firmicutes, Chloroflexi and Opitutales were abundances in cluster II. Cluster III includes Chitinobacterales, Bacillales, Chitinophagarales and Otherales genera. Whereas cluster IV (Elev7-16S-573, Otherales, Solibacterales and Pedobacteriaceae), cluster V (Opitutaceae, Chitnobacteraceae, Bacillaceae, Chitinophagaceae and Otherales), cluster VI (Xanthomonadaceae, Uncultured soil bacterium, Candidatus-Udaeobacter, Lysobacter and Bacillus), cluster VII (Chitinophaga, Glycomyces and Other), cluster VIII (Uncultured bacterium and Others) and cluster IX (Bacillus drentensis and Others) (Fig. 7). The cluster I observed with the highest abundance was closely related to clusters II and III. Cluster IV to IX created large groups and is distantly related to cluster I to III of the microbial groups in organic and conventional systems (Fig. 7). In the organic system (G1), microbial groups like Proteobacteria, Actinobacteria, Bacteroidetes, and Opitutaceae were largely dominated and provided benefits to the mango rhizosphere in terms of nutrient availability, plant growth promotion, and protection against biotic and abiotic stress. Phylum Proteobacteria and Actinobacteria are closely linked with the rhizosphere and identified as potential PGPR. Acidobacteria and firmicutes, on the other hand, were dominated primarily by conventional systems and serve as a bio-indicator of anthropogenic stress caused by excessive chemical fertilizer application. Undefined Acidobacteria is oligotrophic in nature and considered as an indicator of low organic carbon and acidic environment. To desire higher productivity, the indiscriminate use of chemical fertilizers or pesticides in conventional systems leads to low nutrient availability, microbial shift, less PGPR, and developing the environment for Acidobacteria, Firmicutes and Chloroflexia group of microorganisms. Principal component analysis (PCA) was performed for both systems (organic-component 1; conventional-component 2). The total variables of principal component analysis were the percentage of different parameters such as alkaline phosphatase, acid phosphatase, DHA, Acetylene reduction assay (ARA1, ARA2, ARA3), and CFU mL−1 (bacteria and fungi). The results of PCA yielded two components that explained 100% of the total variance in the data and had an Eigen value of 6.1 for component 1. In contrast, 1.8 for component 2 and together they described 100% of the total variance in the data (Fig. 8). In the organic system, the loading factor with score plot indicates that component-1 is positively associated with DHA, ARA1, ARA2, alkaline phosphatase, acid phosphatase while negatively correlated with CFU ARA3 activity. Component-1 explains the 76.42% variance of the experimental data, while component-2 explains 23.58%. The second component (PC2) represents the positive association with DHA, ARA1, ARA2, ARA3 activity, and CFU while negatively correlated with alkaline phosphatase and acid phosphatase. In the conventional system, the loading factor with score plot indicates that component-1 is positively associated with single variable acid phosphatise while negatively correlated with DHA, ARA1, ARA2, ARA3, CFU, and alkaline phosphatase activity. The second component (PC2) of the conventional system showed positive association with DHA, ARA1, ARA2, ARA3 activity, and CFU, while the negative association with alkaline phosphatase and acid phosphatase.Figure 7Comparative (G1 organic and G2 conventional) heat map of dominant microbial diversity and their clusters in terms of T1 (phylum), T2 (order), T3 (family), T4 (Genus) and T5 (Species).Full size imageFigure 8PCA analysis of different parameters for organic and conventional systems.Full size image More

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Influence of soil organic matter on zeta potentialIn this study, the zeta potential of a clayey red soil was compared among 4 types of long-term treatments including manure, NPK + straw, NPK and CK in a subtropical climate. Generally, the manure treatment which also had the greatest concentration of SOC resulted in the highest clay zeta potential (less intense charge imbalance), while NPK + straw did not result in the second highest zeta potential as expected compared to the NPK and CK treatments. Variation in clay zeta potential among types of fertilization might be related with their different SOM content, because SOM had an influence on the zeta potentials via affecting the negative charges of soils19. The zeta potential of manure and NPK + straw treatments having high SOC agreed with earlier studies in Marchuk et al.9 that decreases of SOC via NaOH treatments decreased the negative zeta potential value9, where Claremont soil originally having high SOC (2.2%) displayed a greater degree of decline in negative zeta potential (from − 29 to − 34.9 mV) than Urrbrae having lower SOC (1.4%) (− 66.3 to − 68 mV). However, zeta potential in water dispersible clay responded to SOC contrastly in the study of Melo et al.12 , where Londrina soil with high SOC (5–20 g kg−1) displayed lower negative zeta potential values in water dispersible clay than that in Rondon soil (SOC 5 to 12 g kg−1) in subtropical Brazil.Differences of SOC effect on zeta potential in our study and other studies were probably because ionic strength in bulk solution also affected the intensity of soil charge imbalance. Generally, in tropical and subtropical Ferralsols, high amounts of SOM that was released following the breakdown of macroaggregate provided an excess of negative charges and intensified the imbalance in charge, resulting in more negative in zeta potential of clay12. In contrast to Ferralsols in Brazil, red soil (highly-weathered) in our study showed higher negative zeta potential in manure soils with higher SOM. This was because high ionic strength in bulk solution might counterbalance the negative charges from SOM, and attenuated the imbalance in charges. Hence, manure treatment which provided greater EC and Ca2+, Mg2+ concentration and possibly higher ionic strength was reasonable to allow for more charge balance and greater negative zeta potential values than other treatment.In this study, NPK + straw treatment exhibited similar negative zeta potential values as that in NPK but slightly lower than manure, probably due to the effect of SOM functional group from straw and soil solution concentration. Straw can increase the humin content as reported in the study of Sheng et al.11, and then a decrease of negative zeta potential can be induced as addition of humic acid on a Luvisol20. But the negative humic effect from straw on zeta potential was probably stronger than the positive effect from the increased bulk soil solution concentration in NPK + straw relative to NPK in Fig. 3 where increase of bulk solution concentration was found to increase the negative charge numbers and the negative zeta potential in Ultisol and Oxisol15. Therefore, our hypothesis that organic treatments decreased negative zeta potential value of soil was not supported for manure treatment, but was for NPK + straw treatment.NPK + straw’s similar effect on negative zeta potential as NPK treatment was probably also related with their similar pH values. The effect of pH on the potential of clay surfaces can be related to the amount of variable charge on the external surface of the clay particles. Negative zeta potential decreased with rising pH of the solution due to deprotonation of the functional groups on the surface of the organic matter and Fe/Aloxides in NPK + straw treated soils. An increase of soil pH (from 3.5 to 7.5) influenced zeta potential through production of more negative net surface charges on soils in subtropical Australia21,22. Therefore, the pH in our study after KCl adjustment that showed a first increase and then decrease pattern with the increase of concentration, can help to explain the bell shape pattern of negative zeta potential (first decrease and then increase). However, in our study, the pH pattern with increment of KCl concentration was different from the results in study of Yu et al.8 where a continuous decline pattern in pH of two soils (Vertisol and Ultisol) was reported when the KCl concentration increased from 10–5 to 10–1 mol L−1. This is probably because the Ultisol possessed high amount of variable charges from Fe or Al oxides, which resulted in the diffusion layer attracted more positive charged cations (i.e. K+) from bulk solution to balance the increased negative charge on the surface of colloidal particles in order to maintain the electrical neutrality of the system15. This indicated that when KCl concentration was low, between 0 and 10–2 mol L−1, part of K+ was attracted to the diffuse double layer and the remaining K+ hydration allowed for raising in soil pH. When KCl concentration was beyond 10–2 mol L−1, many Al3+ions on soil exchange site were released into solution (0.03 to 0.12 mg L−1) through K+ exchange and probably dropped soil pH (data not shown).Studies also found that the effect of SOM on zeta potential of clay also varied for soils in different climate. Yu et al.8 compared rice straw incorporation effect on two soils (Ultisol and Vertisol) and found that similar SOC content resulted in contrasting effects on surface potential of two types of soils, where surface potential of Ultisol continuously increased while firstly increased and became stable for Vertisol with increase of treated solution concentration. Different SOM effect on soil potential properties of two soils were probably associated with presence of soil variable charges in Ultisol23. SOM and Fe/Al (hydro)oxides in Ultisol carried a larger number of variable surface charges, and resulted in a strong overlapping of oppositely charged electric double layers (EDLs) between SOM and Fe/Al (hydro)oxides at low concentration8. The overlapping of oppositely charged EDLs between SOM and Fe/Al probably yielded in an increase in negative surface charge for Ultisols compared to Vertisol.Effect of SOM and zeta potential on soil aggregationIncrement in content of SOM after additions of straw or other organic treatments can improve aggregate stability6,24,25. The hydrophobic organic compounds that coated around soil particle can act as nucleus of aggregate formation and reduce the destruction effect from water infiltration26,27. The hydrophobic-C/hydrophilic-C increased from 1.04 to 1.07, from 1.22 to 1.27 for chicken manure and maize residues treatments, respectively, when soil water conditions changed from water deficiency to natural rainfall treatment28. This indicated that a small change of hydrophobic-C/hydrophilic-C might result in substantial change in soil water, which was a critical factor of aggregate development28. Xue et al.24 also reported that a small difference of aromatic percentage between tillage + straw and no tillage + straw treatments resulted in significant differences for aggregate ( > 0.25 mm). Hence, small variation in soil hydrophobic-C groups can yield in soil aggregate variation. In our study, the manure treatment, which had higher SOM and hydrophobic-C (aromatic C) while lower hydrophilic-C than other treatments, was probably reasonable to yield in its higher stability than others. In these previous studies, the positive effect of SOM on soil aggregate development was attributed to the increment in van der Waals force between soil particles. However, different from our study, Melo et al.12 reported that Londrina soil with high SOC released greater water dispersible clay (60–80%) than that in Rondon with low SOC (50–70%) after mechanical breakdown of macroaggregate. This was probably due to the repulsive force prevailing attractive force between soil particles as affected by more negative zeta potential or surface potential8.Clay zeta potential influenced the powerful electrostatic fields, soil internal forces and aggregate stability9. Decrease in negative clay zeta potential mainly yielded an increase in the soil microaggregate portion ( More