Philippa Kaur

I was filled with hope when I read the first draft of the Global Biodiversity Framework (GBF) in mid-2021. It seemed that the parties to the United Nations Convention on Biodiversity had learnt from bitter experience — the failure of the Aichi Biodiversity Targets, set for the previous decade. Instead of vague aims, the draft framework incorporated most of the advice that the scientific community, myself included, had marshalled. It contained ambitious quantitative thresholds, such as those for the area of ecosystem to be protected, the percentage of genetic diversity to be maintained, and percentage reductions for overall extinction rates, pesticide use and subsidies harmful to biodiversity.Then came the square brackets. In the world of policy, these mark proposed amendments that the parties do not yet agree on. The square brackets proliferated at an alarming rate throughout the GBF text, enclosing, neutralizing and paralysing goals and targets. By July 2021, in a version about 10,200 words long, there were more than 900 pairs of square brackets.Brackets germinated with particular vigour in sections that could make the greatest difference for a better future because of their precision, ambition or conceptual novelty. Almost all quantitative thresholds had been bracketed or had disappeared.
The United Nations must get its new biodiversity targets right
I applaud the new prominence given to gender justice (with a new dedicated Target 22) and to financial resources and capacity building (Target 19). I wonder why other key aspects have not received the same treatment, and have instead been compressed almost beyond recognition. For example, the first draft highlighted that species, ecosystems, genetic diversity and nature’s contribution to people each needed their own specific, verifiable outcomes. Now they have coagulated into one vague yet verbose paragraph.This thicket of square brackets smothers the GBF and the hopes of those of us who see transformative change as the only way forward for life on Earth as we know it.In a titanic effort, a streamlined proposal from the Informal Group on the GBF has halved the brackets to be considered by the parties when they meet in Montreal, Canada, for the 15th Conference of the Parties (COP15) on 7–19 December.We need a text with teeth — and far fewer brackets. This much we have learnt in the 30 years since the foundational 1992 Rio Summit drew attention to the impact of human activities on the environment: a strong, precise, ambitious text does not in itself ensure successful implementation, but a weak, vague, toothless text almost guarantees failure.It was no surprise when the Convention on Biological Diversity officially declared the failure of its ten-year Aichi Targets. People involved at the international interface of biodiversity science and policy were already discussing how to do better in the next decade with the GBF.
Crucial biodiversity summit will go ahead in Canada, not China: what scientists think
The scientific community rose to the occasion. In just three years, we produced the first-ever intergovernmental appraisal of life on Earth and what it means to people: The Global Assessment Report on Biodiversity and Ecosystem Services from IPBES (the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services), which I co-chaired. It was ready in time for the original 2020 date for COP15, before the global disruption caused by COVID-19. It was the most comprehensive ever synthesis of published information on the topic, an inclusive conceptual framework involving various disciplines and knowledge systems, and unprecedented participation of Indigenous peoples.Then, in 2020, we assembled an interdisciplinary team of more than 60 biodiversity scientists across the world, and within a few months produced detailed suggestions for the goals of the GBF. Since then, we have made the best of the many pandemic postponements by issuing a stream of specific, evidence-based recommendations on targets, scenarios and implementation.The scientific advice is convergent. First, the GBF needs to explicitly address each facet of biodiversity; none is a good substitute or umbrella for the others. Second, the biodiversity goals must be more ambitious than ever, accompanied by equally ambitious targets for concrete action and sufficient resources to make them happen. Third, the targets need to be precise, traceable and coordinated.Fourth, formally protecting a proportion of the planet’s most pristine ecosystems will by itself fall far short. Nature must be mainstreamed, incorporated in decisions made for the landscapes in which we live and work every day, well beyond protected areas. Finally, and most crucially, targets must focus on the root causes of biodiversity loss: the ways in which we consume, trade and allocate subsidies, incentives and safeguards.From previous experience, I expected objections to certain sections— pesticides and subsidies, say — but they are everywhere. Only 2 of the 22 targets have no brackets. Ironing out objections takes precious time. Because the framework can be enshrined only by consensus, too many objections can lead to too much compromise.Now, to avert failure, we exhort the governments gathering in Montreal to be brave, long-sighted and open-hearted, and to produce a visionary, ambitious biodiversity framework, grounded in knowledge. The awareness and mobilization of their constituencies has never been greater, the evidence in their hands never clearer. If not now, when?

Competing Interests
The author declares no competing interests. More

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Dataset descriptionSample collectionOur research complies with all relevant ethical regulations following policies at the University of California, San Diego (UCSD). Animal samples that were sequenced were not collected at UCSD and are not for vertebrate animals research at UCSD following the UCSD Institutional Animal Care and Use Committee (IACUC). Samples were contributed by 34 principal investigators of the Earth Microbiome Project 500 (EMP500) Consortium and are samples from studies at their respective institutions (Supplementary Table 1). Relevant permits and ethics information for each parent study are described in the ‘Permits for sample collection’ section below. Samples were contributed as distinct sets referred to here as studies, where each study represented a single environment (for example, terrestrial plant detritus). To achieve more even coverage across microbial environments, we devised an ontology of sample types (microbial environments), the EMP Ontology (EMPO) (http://earthmicrobiome.org/protocols-and-standards/empo/)1, and selected samples to fill out EMPO categories as broadly as possible. EMPO recognizes strong gradients structuring microbial communities globally, and thus classifies microbial environments (level 4) on the basis of host association (level 1), salinity (level 2), host kingdom (if host-associated) or phase (if free-living) (level 3) (Fig. 1a). As we anticipated previously1, we have updated the number of levels as well as states therein for EMPO (Fig. 1b) on the basis of an important additional salinity gradient observed among host-associated samples when considering the previously unreported shotgun metagenomic and metabolomic data generated here (Fig. 3c,d). We note that although we were able to acquire samples for all EMPO categories, some categories are represented by a single study.Samples were collected following the Earth Microbiome Project sample submission guide50. Briefly, samples were collected fresh, split into 10 aliquots and then frozen, or alternatively collected and frozen, and subsequently split into 10 aliquots with minimal perturbation. Aliquot size was sufficient to yield 10–100 ng genomic DNA (approximately 107–108 cells). To leave samples amenable to chemical characterization (metabolomics), buffers or solutions for sample preservation (for example, RNAlater) were avoided. Ethanol (50–95%) was allowed as it is compatible with LC–MS/MS although it should also be avoided if possible.Sampling guidance was tailored for four general sample types: bulk unaltered (for example, soil, sediment, faeces), bulk fractionated (for example, sponges, corals, turbid water), swabs (for example, biofilms) and filters. Bulk unaltered samples were split fresh (or frozen), sampled into 10 pre-labelled 2 ml screw-cap bead beater tubes (Sarstedt, 72.694.005 or similar), ideally with at least 200 mg biomass, and flash frozen in liquid nitrogen (if possible). Bulk fractionated samples were fractionated as appropriate for the sample type, split into 10 pre-labelled 2 ml screw-cap bead beater tubes, ideally with at least 200 mg biomass, and flash frozen in liquid nitrogen (if possible). Swabs were collected as 10 replicate swabs using 5 BD SWUBE dual cotton swabs with wooden stick and screw cap (281130). Filters were collected as 10 replicate filters (47 mm diameter, 0.2 um pore size, polyethersulfone (preferred) or hydrophilic PTFE filters), placed in pre-labelled 2 ml screw-cap bead beater tubes, and flash frozen in liquid nitrogen (if possible). All sample types were stored at –80 °C if possible, otherwise –20 °C.To track the provenance of sample aliquots, we employed a QR coding scheme. Labels were affixed to aliquot tubes before shipping when possible. QR codes had the format ‘name.99.s003.a05’, where ‘name’ is the PI name, ‘99’ is the study ID, ‘s003’ is the sample number and ‘a05’ is the aliquot number. QR codes (version 2, 25 pixels × 25 pixels) were printed on 1.125’ × 0.75’ rectangular and 0.437’ circular cap Cryogenic Direct Thermal labels (GA International, DFP-70) using a Zebra model GK420d printer and ZebraDesigner Pro 3 software for Windows. After receipt but before aliquots were stored in freezers, QR codes were scanned into a sample inventory spreadsheet using a QR scanner.Sample metadataEnvironmental metadata were collected for all samples on the basis of the EMP Metadata Guide, which combines guidance from the Genomics Standards Consortium MIxS (Minimum Information about any Sequence) standard74 and the Qiita Database (https://qiita.ucsd.edu)51. The metadata guide provides templates and instructions for each MIxS environmental package (that is, sample type). Relevant information describing each PI submission, or study, was organized into a separate study metadata file (Supplementary Table 1).MetabolomicsLC–MS/MS sample extraction and preparationTo profile metabolites among all samples, we used LC–MS/MS, a versatile method that detects tens of thousands of metabolites in biological samples. All solvents and reactants used were LC–MS grade. To maximize the biomass extracted from each sample, the samples were prepared depending on their sampling method (for example, bulk, swabs, filter and controls). The bulk samples were transferred into a microcentrifuge tube (polypropylene, PP) and dissolved in 7:3 MeOH:H2O using a volume varying from 600 µl to 1.5 ml, depending on the amounts of sample available, and homogenized in a tissue lyser (QIAGEN) at 25 Hz for 5 min. Then, the tubes were centrifuged at 2,000 × g for 15 min, and the supernatant was collected in a 96-well plate (PP). For swabs, the swabs were transferred into a 96-well plate (PP) and dissolved in 1.0 ml of 9:1 ethanol:H2O. The prepared plates were sonicated for 30 min, and after 12 h at 4 °C, the swabs were removed from the wells. The filter samples were dissolved in 1.5 ml of 7:3 MeOH:H2O in microcentrifuge tubes (PP) and sonicated for 30 min. After 12 h at 4 °C, the filters were removed from the tubes. The tubes were centrifuged at 2,000 × g for 15 min, and the supernatants were transferred to 96-well plates (PP). The process control samples (bags, filters and tubes) were prepared by adding 3.0 ml of 2:8 MeOH:H2O and recovering 1.5 ml after 2 min. After the extraction process, all sample plates were dried with a vacuum concentrator and subjected to solid phase extraction (SPE). SPE was used to remove salts that could reduce ionization efficiency during mass spectrometry analysis, as well as the most polar and non-polar compounds (for example, waxes) that cannot be analysed efficiently by reversed-phase chromatography. The protocol was as follows: the samples (in plates) were dissolved in 300 µl of 7:3 MeOH:H2O and put in an ultrasound bath for 20 min. SPE was performed with SPE plates (Oasis HLB, hydrophilic-lipophilic-balance, 30 mg with particle sizes of 30 µm). The SPE beds were activated by priming them with 100% MeOH, and equilibrated with 100% H2O. The samples were loaded on the SPE beds, and 100% H2O was used as wash solvent (600 µl). The eluted washing solution was discarded, as it contains salts and very polar metabolites that subsequent metabolomics analysis is not designed for. The sample elution was carried out sequentially with 7:3 MeOH:H2O (600 µl) and 100% MeOH (600 µl). The obtained plates were dried with a vacuum concentrator. For mass spectrometry analysis, the samples were resuspended in 130 µl of 7:3 MeOH:H2O containing 0.2 µM of amitriptyline as an internal standard. The plates were centrifuged at 30 × g for 15 min at 4 °C. Samples (100 µl) were transferred into new 96-well plates (PP) for mass spectrometry analysis.LC–MS/MS sample analysisThe extracted samples were analysed by ultra-high performance liquid chromatography (UHPLC, Vanquish, Thermo Fisher) coupled to a quadrupole-Orbitrap mass spectrometer (Q Exactive, Thermo Fisher) operated in data-dependent acquisition mode (LC–MS/MS in DDA mode). Chromatographic separation was performed using a Kinetex C18 1.7 µm (Phenomenex), 100 Å pore size, 2.1 mm (internal diameter) × 50 mm (length) column with a C18 guard cartridge (Phenomenex). The column was maintained at 40 °C. The mobile phase was composed of a mixture of (A) water with 0.1% formic acid (v/v) and (B) acetonitrile with 0.1% formic acid. Chromatographic elution method was set as follows: 0.00–1.00 min, isocratic 5% B; 1.00–9.00 min, gradient from 5% to 100% B; 9.00–11.00 min, isocratic 100% B; followed by equilibration 11.00–11.50 min, gradient from 100% to 5% B; 11.50–12.50 min, isocratic 5% B. The flow rate was set to 0.5 ml min−1.The UHPLC was interfaced to the orbitrap using a heated electrospray ionization source with the following parameters: ionization mode, positive; spray voltage, +3,496.2 V; heater temperature, 363.90 °C; capillary temperature, 377.50 °C; S-lens RF, 60 arbitrary units (a.u.); sheath gas flow rate, 60.19 a.u.; and auxiliary gas flow rate, 20.00 a.u. The MS1 scans were acquired at a resolution (at m/z 200) of 35,000 in the m/z 100–1500 range, and the fragmentation spectra (MS2) scans at a resolution of 17,500 from 0 to 12.5 min. The automatic gain control target and maximum injection time were set at 1.0 × 106 and 160 ms for MS1 scans, and set at 5.0 × 105 and 220 ms for MS2 scans, respectively. Up to three MS2 scans in data-dependent mode (Top 3) were acquired for the most abundant ions per MS1 scans using the apex trigger mode (4–15 s), dynamic exclusion (11 s) and automatic isotope exclusion. The starting value for MS2 was m/z 50. Higher-energy collision induced dissociation (HCD) was performed with a normalized collision energy of 20, 30 and 40 eV in stepped mode. The major background ions originating from the SPE were excluded manually from the MS2 acquisition. Analyses were randomized within plate and blank samples analysed every 20 injections. A quality control mix sample assembled from 20 random samples across the sample types was injected at the beginning, the middle and the end of each plate sequence. The chromatographic shift observed throughout the batch was estimated as less than 2 s, and the relative standard deviation of ion intensity was 15% per replicate.LC–MS/MS data processingThe mass spectrometry data were centroided and converted from the proprietary format (.raw) to the m/z extensible markup language format (.mzML) using ProteoWizard (ver. 3.0.19, MSConvert tool)75. The mzML files were then processed with MZmine 2 toolbox76 using the ion-identity networking modules77 that allow advanced detection for adduct/isotopologue annotations. The MZmine processing was performed on Ubuntu 18.04 LTS 64-bits workstation (Intel Xeon E5-2637, 3.5 GHz, 8 cores, 64 Gb of RAM) and took ~3 d. The MZmine project, the MZmine batch file (.XML format) and results files (.MGF and .CSV) are available in the MassIVE dataset MSV000083475. The MZmine batch file contains all the parameters used during the processing. In brief, feature detection and deconvolution was performed with the ADAP chromatogram builder78 and local minimum search algorithm. The isotopologues were regrouped and the features (peaks) were aligned across samples. The aligned peak list was gap filled and only peaks with an associated fragmentation spectrum and occurring in a minimum of three files were conserved. Peak shape correlation analysis grouped peaks originating from the same molecule and annotated adduct/isotopologue with ion-identity networking77. Finally, the feature quantification table results (.CSV) and spectral information (.MGF) were exported with the GNPS module for feature-based molecular networking analysis on GNPS79 and with SIRIUS export modules.LC–MS/MS data annotationThe results files of MZmine (.MGF and .CSV files) were uploaded to GNPS (http://gnps.ucsd.edu)52 and analysed with the feature-based molecular networking workflow79. Spectral library matching was performed against public fragmentation spectra (MS2) spectral libraries on GNPS and the NIST17 library.For the additional annotation of small peptides, we used the DEREPLICATOR tools available on GNPS80,81. We then used SIRIUS82 (v. 4.4.25, headless, Linux) to systematically annotate the MS2 spectra. Molecular formulae were computed with the SIRIUS module by matching the experimental and predicted isotopic patterns83, and from fragmentation trees analysis84 of MS2. Molecular formula prediction was refined with the ZODIAC module using Gibbs sampling85 on the fragmentation spectra (chimeric spectra or those with poor fragmentation were excluded). In silico structure annotation using structures from biodatabase was done with CSI:FingerID86. Systematic class annotations were obtained with CANOPUS41 and used the NPClassifier ontology87.The parameters for SIRIUS tools were set as follows, for SIRIUS: molecular formula candidates retained, 80; molecular formula database, ALL; maximum precursor ion m/z computed, 750; profile, orbitrap; m/z maximum deviation, 10 ppm; ions annotated with MZmine were prioritized and other ions were considered (that is, [M+H3N+H]+, [M+H]+, [M+K]+, [M+Na]+, [M+H-H2O]+, [M+H-H4O2]+, [M+NH4]+); for ZODIAC: the features were split into 10 random subsets for lower computational burden and computed separately with the following parameters: threshold filter, 0.9; minimum local connections, 0; for CSI:FingerID: m/z maximum deviation, 10 ppm; and biological database, BIO.To establish putative microbially related secondary metabolites, we collected annotations from spectral library matching and the DEREPLICATOR+ tools and queried them against the largest microbial metabolite reference databases (Natural Products Atlas88 and MIBiG89). Molecular networking79 was then used to propagate the annotation of microbially related secondary metabolites throughout all molecular families (that is, the network component).LC–MS/MS data analysisWe combined the annotation results from the different tools described above to create a comprehensive metadata file describing each metabolite feature observed. Using that information, we generated a feature-table including only secondary metabolite features determined to be microbially related. We then excluded very low-intensity features introduced to certain samples during the gap-filling step described above. These features were identified on the basis of presence in negative controls that were universal to all sample types (that is, bulk, filter and swab) and by their relatively low per-sample intensity values. Finally, we excluded features present in positive controls for sampling devices specific to each sample type (that is, bulk, filter or swab). The final feature-table included 618 samples and 6,588 putative microbially related secondary metabolite features that were used for subsequent analysis.We used QIIME 2’s90 (v2020.6) ‘diversity’ plugin to quantify alpha-diversity (that is, feature richness) for each sample and ‘deicode’91 to quantify beta-diversity (that is, robust Aitchison distances, which are robust to both sparsity and compositionality in the data) between each pair of samples. We parameterized our robust Aitchison principal components analysis (RPCA)91 to exclude samples with fewer than 500 features and features present in fewer than 10% of samples. We used the ‘taxa’ plugin to quantify the relative abundance of microbially related secondary metabolite pathways and superclasses (that is, on the basis of NPClassifier) within each environment (that is, for each level of EMPO 4), and ‘songbird’ v1.0.492 to identify sets of microbially related secondary metabolites whose abundances were associated with certain environments. We parameterized our ‘songbird’ model as follows: epochs, 1,000,000; differential prior, 0.5; learning rate, 1.0 × 10−5; summary interval, 2; batch size, 400; minimum sample count, 0; and training on 80% of samples at each level of EMPO 4 using ‘Animal distal gut (non-saline)’ as the reference environment. Environments with fewer than 10 samples were excluded to optimize model training (that is, ‘Animal corpus (non-saline)’, ‘Animal proximal gut (non-saline)’, ‘Surface (saline)’). The output from ‘songbird’ includes a rank value for each metabolite in every environment, which represents the log fold change for a given metabolite in a given environment92. We compared log fold changes for each metabolite from this run to those from (1) a replicate run using the same reference environment and (2) a run using a distinct reference environment: ‘Water (saline)’. We found strong Spearman correlations in both cases (Supplementary Table 8), and therefore focused on results from the original run using ‘Animal distal gut (non-saline)’ as the reference environment, as it has previously been shown to be relatively unique among other habitats. In addition to summarizing the top 10 metabolites for each environment (Supplementary Table 3), we used the log fold change values in our multi-omics analyses described below.We used the RPCA biplot and QIIME 2’s90 EMPeror93 to visualize differences in composition among samples, as well as the association with samples of the 25 most influential microbially related secondary metabolite features (that is, those with the largest magnitude across the first three principal component loadings). We tested for significant differences in metabolite composition across all levels of EMPO using PERMANOVA implemented with QIIME 2’s ‘diversity’ plugin90 and using our robust Aitchison distance matrix as input. In parallel, we used the differential abundance results from ‘songbird’ described above to identify specific microbially related secondary metabolite pathways and superclasses that varied strongly across environments. We then went back to our metabolite feature-table to visualize differences in the relative abundances of those pathways and superclasses within each environment by first selecting features and calculating log-ratios using ‘qurro’94, and then plotting using the ‘ggplot2’ package95 in R96 v4.0.0. We tested for significant differences in relative abundances across environments using Kruskal–Wallis tests implemented with the base ‘stats’ package in R96.GC–MS sample extraction and preparationTo profile volatile small molecules among all samples in addition to what was captured with LC–MS/MS, we used gas chromatography coupled with mass spectrometry (GC–MS). All solvents and reactants were GC–MS grade. Two protocols were used for sample extraction, one for the 105 soil samples and a second for the 356 faecal and sediment samples that were treated as biosafety level 2. The 105 soil samples were received at the Pacific Northwest National Laboratory and processed as follows. Each soil sample (1 g) was weighed into microcentrifuge tubes (Biopur Safe-Lock, 2.0 ml, Eppendorf). H2O (1 ml) and one scoop (~0.5 g) of a 1:1 (v/v) mixture of garnet (0.15 mm, Omni International) and stainless steel (0.9–2.0 mm blend, Next Advance) beads and one 3 mm stainless steel bead (Qiagen) were added to each tube. Samples were homogenized in a tissue lyser (Qiagen) for 3 min at 30 Hz and transferred into 15 ml polypropylene tubes (Olympus, Genesee Scientific). Ice-cold water (1 ml) was used to rinse the smaller tube and combined into the 15 ml tube. Chloroform:methanol (10 ml, 2:1 v/v) was added and samples were rotated at 4 °C for 10 min, followed by cooling at −70 °C for 10 min and centrifuging at 150 × g for 10 min to separate phases. The top and bottom layers were combined into 40 ml glass vials and dried using a vacuum concentrator. Chloroform:methanol (1 ml, 2:1) was added to each large glass vial and the sample was transferred into 1.5 ml tubes and centrifuged at 1,300 × g. The supernatant was transferred into glass vials and dried for derivatization.The remaining 356 samples received from UCSD that included faecal and sediment samples were processed as follows: 100 µl of each sample was transferred to a 2 ml microcentrifuge tube using a scoop (MSP01, Next Advance). The final volume of the sample was brought to 1.5 ml, ensuring that the solvent ratio is 3:8:4 H2O:CHCl3:MeOH by adding the appropriate volumes of H2O, MeOH and CHCl3. After transfer, one 3 mm stainless steel bead (QIAGEN), 400 µl methanol and 300 µl H2O were added to each tube and the samples were vortexed for 30 s. Then, 800 µl chloroform was added and samples were vortexed for 30 s. After centrifuging at 150 × g for 10 min to separate phases, the top and bottom layers were combined in a vial and dried for derivatization.The samples were derivatized for GC–MS analysis as follows: 20 µl of a methoxyamine solution in pyridine (30 mg ml−1) was added to the sample vial and vortexed for 30 s. A bath sonicator was used to ensure that the sample was completely dissolved. Samples were incubated at 37 °C for 1.5 h while shaking at 1,000 r.p.m. N-methyl-N-trimethylsilyltrifluoroacetamide (80 µl) and 1% trimethylchlorosilane solution was added and samples were vortexed for 10 s, followed by incubation at 37 °C for 30 min, with 1,000 r.p.m. shaking. The samples were then transferred into a vial with an insert.An Agilent 7890A gas chromatograph coupled with a single quadrupole 5975C mass spectrometer (Agilent) and an HP-5MS column (30 m × 0.25 mm × 0.25 μm; Agilent) was used for untargeted analysis. Samples (1 μl) were injected in splitless mode, and the helium gas flow rate was determined by the Agilent Retention Time Locking function on the basis of analysis of deuterated myristic acid (Agilent). The injection port temperature was held at 250 °C throughout the analysis. The GC oven was held at 60 °C for 1 min after injection, and the temperature was then increased to 325 °C at a rate of 10 °C min−1, followed by a 10 min hold at 325 °C. Data were collected over the mass range of m/z 50–600. A mixture of FAMEs (C8–C28) was analysed each day with the samples for retention index alignment purposes during subsequent data analysis.GC–MS data processing and annotationThe data were converted from vendor’s format to the .mzML format and processed using GNPS GC–MS data analysis workflow (https://gnps.ucsd.edu)97. The compounds were identified by matching experimental spectra to the public libraries available at GNPS, as well as NIST 17 and Wiley libraries. The data are publicly available at the MassIVE depository (https://massive.ucsd.edu); dataset ID: MSV000083743. The GNPS deconvolution is available in GNPS (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=d5c5135a59eb48779216615e8d5cb3ac), as is the library search (https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=59b20fc8381f4ee6b79d35034de81d86).GC–MS data analysisFor multi-omics analyses including GC–MS data, we first removed noisy (that is, suspected background contaminants and artifacts) features by excluding those with balance scores 1.5–2 kb DNA fragments’ (Oxford Nanopore Technologies). The resulting product consists of uniquely tagged rRNA operon amplicons. The uniquely tagged rRNA operons were amplified in a second PCR, where the reaction (100 µl) contained 2 U Platinum SuperFi DNA Polymerase High Fidelity (Thermo Fisher) and a final concentration of 1X SuperFi buffer, 0.2 mM of each dNTP, and 500 nM of each forward and reverse synthetic primer targeting the tailed primers from above. The PCR cycling parameters consisted of an initial denaturation (3 min at 95 °C) and then 25–35 cycles of denaturation (15 s at 95 °C), annealing (30 s at 60 °C) and extension (6 min at 72 °C), followed by final extension (5 min at 72 °C). The PCR product was purified using the custom bead purification protocol above. Batches of 25 amplicon libraries were barcoded and sent for PacBio Sequel II library preparation and sequencing (Sequel II SMRT Cell 8M and 30 h collection time) at the DNA Sequencing Center at Brigham Young University. Circular consensus sequencing (CCS) reads were generated using CCS v.3.4.1 (https://github.com/PacificBiosciences/ccs) using default settings. UMI consensus sequences were generated using the longread_umi pipeline (https://github.com/SorenKarst/longread_umi) with the following command: longread_umi pacbio_pipeline -d ccs_reads.fq -o out_dir -m 3500 -M 6000 -s 60 -e 60 -f CAAGCAGAAGACGGCATACGAGAT -F AGRGTTYGATYMTGGCTCAG -r AATGATACGGCGACCACCGAGATC -R CGACATCGAGGTGCCAAAC -U ‘0.75;1.5;2;0’ -c 2.Amplicon data analysisFor multi-omics analyses including amplicon sequence data, we processed each dataset for comparison of beta-diversity. For all amplicon data except that for bacterial full-length rRNA amplicons, raw sequence data were converted from bcl to fastq, and then multiplexed files for each sequencing run uploaded as separate preparations to Qiita (study: 13114).For each 16S sequencing run, in Qiita, data were demultiplexed, trimmed to 150 bp and denoised using Deblur122 to generate a feature-table of sub-operational taxonomic units (sOTUs) per sample, using default parameters. We then exported feature-tables and denoised sequences from each sequencing run, used QIIME 2’s ‘feature-table’ plugin to merge feature-tables and denoised reads across sequencing runs, and placed all denoised reads into the GreenGenes 13_8 phylogeny123 via fragment insertion using QIIME 2’s90 SATé-Enabled Phylogenetic Placement (SEPP)124 plugin to produce a phylogeny for diversity analyses. To allow for phylogenetically informed diversity analyses, reads not placed during SEPP (that is, 513 sOTUs, 0.1% of all sOTUs) were removed from the merged feature-table. We then used QIIME 2’s ‘feature-table’ plugin to exclude singleton sOTUs and rarefy the data to 5,000 reads per sample. Rarefaction depths for all amplicon analyses were chosen to best normalize sampling effort per sample while maintaining ≥75% of samples representative of Earth’s environments, and also to maintain consistency with the analyses from EMP release 1. We then used QIIME 2’s90 ‘diversity’ plugin to estimate alpha-diversity (that is, sOTU richness) and beta-diversity (that is, unweighted UniFrac distances). The final feature-table for 16S beta-diversity analysis included 681 samples and 93,260 features. We performed a comparative analysis of the data including and excluding the reads not placed during SEPP, and note that both alpha-diversity (that is, sOTU richness) and beta-diversity (that is, sample–sample RPCA distances) were highly correlated between datasets (Spearman r = 1.0) (Supplementary Fig. 5). We thus proceeded with the SEPP-filtered dataset and used phylogenetically informed diversity metrics where applicable.For 18S data, we used QIIME 2’s90 ‘demux’ plugin’s ‘emp-paired’ method125,126 to first demultiplex each sequencing run, and then the ‘cutadapt’ plugin’s127 ‘trim-paired’ method to trim sequencing primers from reads. We then exported trimmed reads, concatenated R1 and R2 read files per sample, and denoised reads using Deblur’s122,128 ‘workflow’ with default settings, trimming reads to 90 bp, and taking the ‘all.biom’ and ‘all.seqs’ output, for each sequencing run. We then used QIIME 2’s ‘feature-table’ plugin to merge feature-tables and denoised sequences across sequencing runs, and then the ‘feature-classifier’ plugin’s ‘classify-sklearn’ method to classify taxonomy for each sOTU via pre-fitted machine-learning classifiers129 and the SILVA 138 reference database130. We then used QIIME 2’s90 ‘feature-table’ plugin to exclude reads assigned to bacteria and archaea, singleton sOTUs and samples with a total frequency of More

Extent and location of critical natural assetsCritical natural assets providing the 12 local NCP (Fig. 1a) occupy only 30% (41 million km2) of total land area (excluding Antarctica) and 24% (34 million km2) of marine Exclusive Economic Zones (EEZs), reflecting the steep slope of the aggregate NCP accumulation curve (Fig. 1b). Despite this modest proportion of global land area, the shares of countries’ land areas that are designated as critical can vary substantially. The 20 largest countries require only 24% of their land area, on average, to maintain 90% of current levels of NCP, while smaller countries (10,000 to 1.5 million km2) require on average 40% of their land area (Supplementary Data 1). This high variability in the NCP–area relationship is primarily driven by the proportion of countries’ land areas made up by natural assets (that is, excluding barren, ice and snow, and developed lands), but even when this is accounted for, there are outliers (Extended Data Fig. 2). Outliers may be due to spatial patterns in human population density (for example, countries with dense population centres and vast expanses with few people, such as Canada and Russia, require far less area to achieve NCP targets) or large ecosystem heterogeneity (if greater ecosystem diversity yields higher levels of diverse NCP in a smaller proportion of area, which may explain patterns in Chile and Australia).The highest-value critical natural assets (the locations delivering the highest magnitudes of NCP in the smallest area, denoted by the darkest blue or green shades in Fig. 1c) often coincide with diverse, relatively intact natural areas near or upstream from large numbers of people. Many of these high-value areas coincide with areas of greatest spatial congruence among multiple NCP (Extended Data Fig. 3). Spatially correlated pairs of local NCP (Supplementary Table 4) include those related to water (flood risk reduction with nitrogen retention and nitrogen with sediment retention); forest products (timber and fuelwood); and those occurring closer to human-modified habitats (pollination with nature access and with nitrogen retention). Coastal risk reduction, forage production for grazing, and riverine fish harvest are the most spatially distinct from other local NCP. In the marine realm, there is substantial overlap of fisheries with coastal risk reduction and reef tourism (though not between the latter two, which each have much smaller critical areas than exist for fisheries).Number of people benefitting from critical natural assetsWe estimate that ~87% of the world’s current population, 6.4 billion people, benefit directly from at least one of the 12 local NCP provided by critical natural assets, while only 16% live on the lands providing these benefits (and they may also benefit; Fig. 2a). To quantify the number of beneficiaries of critical natural assets, we spatially delineate their benefitting areas (which varies on the basis of NCP: for example, areas downstream, within the floodplain, in low-lying areas near the coast, or accessible by a short travel). While our optimization selects for the provision of 90% of the current value of each NCP, it is not guaranteed that 90% of the world’s population would benefit (since it does not include considerations for redundancy in adjacent pixels and therefore many of the areas selected benefit the same populations), so it is notable that an estimated 87% do. This estimate of ‘local’ beneficiaries probably underestimates the total number of people benefitting because it includes only NCP for which beneficiaries can be spatially delineated to avoid double-counting, yet it is striking that the vast majority, 6.1 billion people, live within 1 h travel (by road, rail, boat or foot, taking the fastest path17) of critical natural assets, and more than half of the world’s population lives downstream of these areas (Fig. 2b). Material NCP are often delivered locally, but many also enter global supply chains, making it difficult to delineate beneficiaries spatially for these NCP. However, past studies have calculated that globally more than 54 million people benefit directly from the timber industry18, 157 million from riverine fisheries19, 565 million from marine fisheries20 and 1.3 billion from livestock grazing21, and across the tropics alone 2.7 billion are estimated to be dependent on nature for one or more basic needs22.Fig. 2: People benefitting from and living on critical natural assets (CNA).a,b, ‘Local’ beneficiaries were calculated through the intersection of areas benefitting from different NCP, to avoid double-counting people in areas of overlap; only those NCP for which beneficiaries could be spatially delineated were included (that is, not material NCP that enter global supply chains: fisheries, timber, livestock or crop pollination). Bars show percentages of total population globally and for large and small countries (a) or the percentage of relevant population globally (b). Numbers inset in bars show millions of people making up that percentage. Numbers to the right of bars in b show total relevant population (in millions of people, equivalent to total global population from Landscan 2017 for population within 1 h travel or downstream, but limited to the total population living within 10 km of floodplains or along coastlines 80%) of their populations benefitting from critical natural assets, but small countries have much larger proportions of their populations living within the footprint of critical natural assets than do large countries (Fig. 2a and Supplementary Data 2). When people live in these areas, and especially when current levels of use of natural assets are not sustainable, regulations or incentives may be needed to maintain the benefits these assets provide. While protected areas are an important conservation strategy, they represent only 15% of the critical natural assets for local NCP (Supplementary Table 5); additional areas should not necessarily be protected using designations that restrict human access and use, or they could cease to provide some of the diverse values that make them so critical23. Other area-based conservation measures, such as those based on Indigenous and local communities’ governance systems, Payments for Ecosystem Services programmes, and sustainable use of land- and seascapes, can all contribute to maintaining critical flows of NCP in natural and semi-natural ecosystems24.Overlaps between local and global prioritiesUnlike the 12 local NCP prioritized here at the national scale, certain benefits of natural assets accrue continentally or even globally. We therefore optimize two additional NCP at a global scale: vulnerable terrestrial ecosystem carbon storage (that is, the amount of total ecosystem carbon lost in a typical disturbance event25, hereafter ‘ecosystem carbon’) and vegetation-regulated atmospheric moisture recycling (the supply of atmospheric moisture and precipitation sustained by plant life26, hereafter ‘moisture recycling’). Over 80% of the natural asset locations identified as critical for the 12 local NCP are also critical for the two global NCP (Fig. 3). The spatial overlap between critical natural assets for local and global NCP accounts for 24% of land area, with an additional 14% of land area critical for global NCP that is not considered critical for local NCP (Extended Data Fig. 4). Together, critical natural assets for securing both local and global NCP require 44% of total global land area. When each NCP is optimized individually (carbon and moisture NCP at the global scale; the other 12 at the country scale), the overlap between carbon or moisture NCP and the other NCP exceeds 50% for all terrestrial (and freshwater) NCP except coastal risk reduction (which overlaps only 36% with ecosystem carbon, 5% with moisture recycling; Supplementary Table 4).Fig. 3: Spatial overlaps between critical natural assets for local and global NCP.Red and teal denote where critical natural assets for global NCP (providing 90% of ecosystem carbon and moisture recycling globally) or for local NCP (providing 90% of the 12 NCP listed in Fig. 1), respectively, but not both, occur; gold shows areas where the two overlap (24% of the total area). Together, local and global critical natural assets account for 44% of total global land area (excluding Antarctica). Grey areas show natural assets not defined as ‘critical’ by this analysis, though still providing some values to certain populations. White areas were excluded from the optimization.Full size imageSynergies can also be found between NCP and biodiversity and cultural diversity. Critical natural assets for local NCP at national levels overlap with part or all of the area of habitat (AOH, mapped on the basis of species range maps, habitat preferences and elevation27) for 60% of 28,177 terrestrial vertebrates (Supplementary Data 3). Birds (73%) and mammals (66%) are better represented than reptiles and amphibians (44%). However, these critical natural assets represent only 34% of the area for endemic vertebrate species (with 100% of their AOH located within a given country; Supplementary Data 3) and 16% of the area for all vertebrates if using a more conservative representation target framework based on the IUCN Red List criteria (though, notably, achieving Red List representation targets is impossible for 24% of species without restoration or other expansion of existing AOH; Supplementary Data 4). Cultural diversity (proxied by linguistic diversity) has far higher overlaps with critical natural assets than does biodiversity; these areas intersect 96% of global Indigenous and non-migrant languages28 (Supplementary Data 5). The degree to which languages are represented in association with critical natural assets is consistent across most countries, even at the high end of language diversity (countries containing >100 Indigenous and non-migrant languages, such as Indonesia, Nigeria and India). This high correspondence provides further support for the importance of safeguarding rights to access critical natural assets, especially for Indigenous cultures that benefit from and help maintain them. Despite the larger land area required for maintaining the global NCP compared with local NCP, global NCP priority areas overlap with slightly fewer languages (92%) and with only 2% more species (60% of species AOH), although a substantially greater overlap is seen with global NCP if Red List criteria are considered (36% compared with 16% for local NCP; Supplementary Data 4). These results provide different insights than previous efforts at smaller scales, particularly a similar exercise in Europe that found less overlap with priority areas for biodiversity and NCP29. However, the 40% of all vertebrate species whose habitats did not overlap with critical natural assets could drive very different patterns if biodiversity were included in the optimization.Although these 14 NCP are not comprehensive of the myriad ways that nature benefits and is valued by people23, they capture, spatially and thematically, many elements explicitly mentioned in the First Draft of the CBD’s post-2020 Global Biodiversity Framework13: food security, water security, protection from hazards and extreme events, livelihoods and access to green and blue spaces. Our emphasis here is to highlight the contributions of natural and semi-natural ecosystems to human wellbeing, specifically contributions that are often overlooked in mainstream conservation and development policies around the world. For example, considerations for global food security often include only crop production rather than nature’s contributions to it via pollination or vegetation-mediated precipitation, or livestock production without partitioning out the contribution of grasslands from more intensified feed production.Gaps and next stepsOur synthesis of these 14 NCP represents a substantial advance beyond other global prioritizations that include NCP limited to ecosystem carbon stocks, fresh water and marine fisheries30,31,32, though still falls short of including all important contributions of nature such as its relational values33. Despite the omission of many NCP that were not able to be mapped, further analyses indicate that results are fairly robust to inclusion of additional NCP. Dropping one of the 12 local NCP at a time results in More

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Simulation experimental set upLaboratory monitoring of leakage migration process can provide an important basis for field tests. The designed and improved ERT device can better describe the migration range of leakage in soil41. In this experiment, a parallel potential monitoring device was used to improve the monitoring of leakage fluid migration. The simulation experiment in the laboratory is carried out in a (100 cm*100 cm*50 cm) plexiglass tank. Sand and clay shall be screened with a 2.36 mm square sieve, watered and compacted with a board to ensure that the soil layer is in close contact with the measuring electrode.Electrode arrangementThe ground wire of high-density electrical method instrument is connected to the electrodes arranged around the bottom of the tank as the power electrode C2, as shown in Fig. 2a. The host is connected to the electrode system. The electrode system consists of 47 electrode grids with a spacing of 0.08 m. The measuring electrode P1 is connected to the mainframe through a wire 0.05 m below the grid center. The geomembrane is located 0.03 m above the measuring electrode P1. The collection device is used as a monitoring system for various leachate. The arrangement of electrodes is shown in Fig. 2b. The power supply electrode C1 is placed at a certain depth in the middle of the saturated sand to provide a constant current. The location of electrode C1 and leakage point is shown in Fig. 2c. The layers from the bottom of the tank are silty clay, geomembrane, silty clay and saturated sand, as shown in Fig. 2d.Figure 2Set-up of leachate migration simulation experiment: (a) Schematic diagram of electrode C2 layout; (b) Schematic diagram of electrical system laying; (c) Position of electrode C1 and leakage point; (d) Schematic diagram of simulated experimental soil layer.Full size imageComposition of monitoring systemThe electrode system is used to monitor the background electric field and artificial electric field of the landfill site. In the experiment, the electrode system is laid in the clay layer under the geomembrane. It is composed of detection electrodes distributed in a grid at a certain distance.The electrical signal conversion system adjusts the measurement mode, sampling accuracy, acquisition frequency and other parameters of the electrode in the field according to the instructions of the mainframe, and transmits the collected electrical signal to the mainframe.The mainframe can control the operation of the monitoring system. The possible leachate points and their pollution range are determined by collecting data. The system mainly includes mainframe and its software system, power supply, etc., as shown in Fig. 3.Figure 3Se2432 parallel electric method instrument.Full size imageLeachate devicePlace 4 leakage bottles above the tank. No.1 and No.4 bottled water are used to simulate the leakage liquid formed by the direct infiltration of rainwater in slag through geomembrane and as a reference. Because Cl-1 is a typical pollutant in the landfill. No. 2 bottle containing 20 g/L NaCl solution is used to simulate inorganic salt leakage in urban life. No. 3 bottle containing 20 ml/L ethanol solution is used to simulate the leakage liquid containing a large amount of organic matter in municipal solid waste. The characteristics of leachate have been summarized in Table1.Table 1 The characteristics of leachate.Full size tableBefore the experiment, configure four solutions, close the injection, use an electric meter to check the conductivity of each measuring point. After each measuring point has no open circuit, supply power to the soil layer through the mainframe to measure the background electric field of the soil. Then open the injection, adjust the flow rate, release the solution at a fixed flow rate, record the soil electric field in the process of leakage every half an hour, collect the potential values of each measuring point, process the data through the potentiometry and potential difference method, and form the relevant potential horizontal profile and longitudinal section of the soil.Principle of potentiometric detection technologyWhen there are leakage points in the landfill, power is supplied to the landfill, and the current forms a current loop through the geomembrane. If there are n (n = 1,2,3…) leakage points in the geomembrane, the power supply current is I, and the artificial electric field will form a leakage electric field at the leakage point, which can be used as a point power supply.$$I = int dI = int j cdot dS$$
(1)
where I is the current intensity, j is the current density vector, and S is the area passing through the current.When there are n leakage points, I will be shunted. If a leakage point is regarded as a finite surface, the current intensity I as:$$I = {I_1} + {I_2} + cdot cdot cdot + {I_{text{n}}} = sumlimits_{i = 1}^n {int_{S_i} {jdS} }$$
(2)
Generally, the power supply current field of landfill site will be affected by the formation medium structure. It is assumed that the formation medium structure is composed of three layers, each layer has uniform properties and stable conductivity, and the layers from top to bottom are: landfill layer, with resistivity of ρ1. The saturated leakage liquid layer above the geomembrane has a resistivity of ρ2. The clay layer under the geomembrane has a resistivity of ρ3. The electrode C1 is arranged in the garbage layer for power supply, and the electrode C2 is arranged at the lower part of the geomembrane away from the electrode system area. The electrode C2 can be regarded as a far pole.Because of the ρ1  > ρ2, the conductivity of the saturated leakage liquid layer at the upper part of the geomembrane is better than that of the landfill layer, so that there is almost no reflected current between the ρ1 layer and the ρ2 layer, that is, the current generated by the power supply electrode C1 is all transmitted to the ρ2 layer. Because of the ρ3  > ρ2, it can be considered that the interface between ρ2 layer and ρ3 layer has both a reflection current, and a transmission current through the leakage point. The potential generated at the detection electrode P1 under the geomembrane is formed by the action of transmission current. The total potential of point P1 is obtained by the superposition of the potential of point power supply passing through n leakage points at P1.$${U_{P1}} = sumlimits_{i = 1}^n {frac{{{I_i}{rho_3}}}{{2pi {{text{r}}_{iP1}}}}}$$
(3)
Parallel potential difference methodThe test adopts pole–pole arrangement, and the calculation formula of apparent resistivity is as follows:$$rho = 2pi {text{aR}}$$
(4)
where ρ is apparent resistivity; a is the distance between electrodes C1 and P1; R is measuring resistivity.When there are loopholes in the geomembrane of the landfill, the leakage liquid will gradually penetrate into the soil layer under the geomembrane through the loopholes, resulting in the change of the conductivity of the soil layer under the geomembrane. The pole-pole acquisition mode of Se2432 parallel electrical instrument is used to obtain the original data (potential difference) of each measuring point on the grid. After current normalization, the apparent resistivity of the soil layer is obtained. The electrical properties of different depths of the soil layer can be obtained by inversion of the apparent resistivity data of the soil layer, so as to determine the occurrence point and distribution range of leakage.The monitoring grid is 5 × 5. The spacing between measuring points is 0.08 m. The measurement method adopted by Se2432 parallel electric method instrument is cross diagonal measurement method. Figure 4 shows that it only needs to measure the potential values on the measuring points on the horizontal, vertical and 45° diagonal lines.Figure 4Schematic diagram of cross-diagonal measurement method.Full size imageTheoretical calculation of test modelTheoretical results of 10 × 10 grid monitoringAccording to the experimental model and statistical data, the resistivity of the clay layer under the geomembrane is assumed ρ = 10 Ω· m, the resistivity ratio of tap water, NaCl solution and ethanol solution after penetrating into the soil layer ρNo.1:ρNo.2:ρNo.3 = 5:3:10. If the four leakage points set by the model are regarded as four conductive resistors, the ratio of the current passing through the four leakage points is INo. 1:INo. 2:INo. 3:INo. 4 = 6:10:3:6.The calculation model is 10 × 10 grid, and the spacing of measuring points is 0.05 m. The potential value on each measuring point is calculated according to Eq. 3, and the obtained data is processed with surfer software to obtain the potential contour map, as shown in Fig. 5. Among them, points 1, 2, 3 and 4 are the leakage positions of water, NaCl solution, ethanol solution and water respectively, and the spacing between leakage points is 0.15 m.Figure 510 × 10 Grid theory detection potential contour map.Full size imageFigure 5 shows that the leakage fields formed by the four kinds of leaking liquids interfere with each other from the theoretical calculation results. The leachate current at point 2 is larger, the high potential closed loop is obvious, and its center corresponds to the leakage center. The reason for this is that the NaCl solution contains conductive particles that increase the conductivity of the leak point. Point 1 and 4 are the same as water, and the leakage electric field is almost the same. Its closed loop is obvious, and the high potential center also corresponds to their leakage position. There is almost no closed loop effect at point 3 under the influence of 1, 2 and 4. The results show that the leakage field formed by high resistance leakage liquid is not easy to be detected by potentiometric detection, and low resistance leakage is suitable to be detected by potentiometric detection.Theoretical results of 12 × 12 grid monitoringThe resistivity of the clay layer under the geomembrane is assumed ρ = 10Ω·m. In consideration of the mutual influence between the leachate and appropriately reduce its influence effect, the resistivity ratio of water, NaCl solution, and ethanol solution after penetrating into the soil layer is set as ρNo.1:ρNo.2:ρNo.3 = 20:15:24, the ratio of the current passing through the four leakage points is INo.1:INo.2:INo.3:INo.4 = 6:8:5:6. And adjust the distance between the two points to 0.28 m. 12 × 12 grid was used for detection, and the spacing of detection points is 0.04 m. Calculate the potential value of each detection point according to Eq. 3, and use Surfer to obtain the detection contour map of four kinds of leakage, as shown in Fig. 6.Figure 612 × 12 Grid theory detection potential contour map.Full size imageTheoretical calculation results show that when the distance between the leakage points is large and the distance between the detection points is small, the potentiometric method can detect the leakage position of various leachates well. At the same time, the diffusion range of different leachates in the same plane is roughly the same, and they all gradually diffuse outward from the center of the leakage point, and the potential value gradually decrease. Point 2 has the largest potential closed loop range, which also has a certain impact on the leakage points of adjacent points 1 and 3. Point 1 and point 4 are water leakage. Affected by different leakage liquids, the leakage electric field of the two same leakage liquids is obviously different. The potential closed loop range of point 1 is larger than that of point 4. Point 3 is the leakage of ethanol solution. Because its resistance is the largest, the range of potential closed loop is the smallest.Figure 7 shows that the leakage fields around the leachates are funnel-shaped, and its size is related to the type of leachate. Therefore, different network density should be designed for different types of leakage liquid, so as to use the most economical scheme to detect the leakage point.Figure 712 × 12 Grid theory detects potential 3d view.Full size imageInterpretation and discussion of resultsLaboratory simulation experiment researchFigure 8a shows the background electric field potential of soil layer. The four injection pipes are opened at the same time and adjusted to the same flow rate. Under the condition of continuous leakage, monitor the leakage field potential at an interval of 1 h. Figure 8b shows the leakage electric field potential value for 1 h. Reduce the injection pipes flow rate to 1/2 of the initial value. Figure 8c shows the monitoring results of 2 h soil layer leakage field potential. Figure 8d shows the soil leakage field potential monitored after 30 min of sealing the injection pipes.Figure 8Leakage field potential diagram of soil layer: (a) Background electric field of soil layer; (b) Potential distribution of soil layer after 1 h of leakage; (c) Potential distribution of soil layer after 2 h of leakage; (d) Potential distribution of soil layer after closing the injection tube for 30 min.Full size imageFigure 8a shows that the background potential contour of the experimental soil layer is at a lower value. Few current lines pass through the monitoring area. A dense closed potential circle of high potential value is formed at point 2. The current flow at point 2 is greater than the other points 1, 3 and 4. The analysis result may be that in the process of watering and compaction, the clay layer under the geomembrane is not uniform, and the compaction degree of the soil layer is different, resulting in different potential values ​​obtained by monitoring. The permeability at point 2 is better than other points, so when the flow rate of the leakage liquid is large, the leakage liquid under the geomembrane gathers near point 2 and spreads out around. After the clay is watered and compacted, the soil compaction is small and the pore water content is large, resulting in a high potential abnormal area in the lower left corner of point 3.Point 2 forms a closed loop of anomaly potential contour much higher than the background electric field, while the value of potential contour coil at leakage point 3 is lower than the surrounding value. It can be analyzed that positions 2 and 3 are leakage points. The leachate at point 2 is a high concentration NaCl solution containing more conductive particles, which will reduce the resistivity of the soil layer under the geomembrane at point 2. Thus, the passing current is increased to form a high potential closed loop. The leachate at point 3 is ethanol solution, which will increase the resistivity of the soil layer under the geomembrane at point 3. So as to reduce the passing current and form a low potential closed loop. Figure 8b shows that the potential contour is consistent with the influence of NaCl solution and ethanol solution on the soil layer under the geomembrane. It can be concluded that point 2 and point 3 are leakage points. The electric field formed after water leakage at point 1 and point 4 cannot clearly distinguish the leakage points.During the monitoring process, the leachate was continuously released from the injection pipe, and the results reflected the dynamic characteristics. Figure 8b shows the phenomenon that the leachate from point 1 and point 4 aggregates around point 2, which is consistent with the inference of better permeability at point 2. Figure 8b,c show that when the flow rate of the leachate is changed and the flow rate of the injection pipe is reduced, the high-potential region of the entire electric field is reduced. Under the influence of gravity, the leachate will migrate longitudinally, and the closed-loop abnormally high-potential regions and abnormally low-potential regions at points 2 and 3 also decrease.Compared with the surrounding potential contours, the difference is more obvious. Figure 8d shows that when the injection pipe stops leaking for a period of time, the leachate migrates longitudinally along the leakage point. At this time, the electric field of the soil layer is similar to the original background electric field, but the potential value is higher than the background electric field, indicating that the leachate is stagnant in the pores of the soil layer, it is the result of changing the electrical properties of the soil layer. The parallel potential method can collect the potential value of each point in the field at one time, which provides a basis for real-time monitoring of landfill leachate.Figure 9 shows the inversion results of the horizontal section of the experimental model. The blue area corresponds to the distribution range of the low resistance anomaly. There are no jump or distortion points in the profile. The resistivity in the longitudinal direction basically shows a change from low to high. The upper layer seepage liquid migrates, and the bottom soil layer is characterized by low humidity and high resistivity. The low-resistance areas formed by the leakage of NaCl solution are widely distributed in the horizontal section. The distribution range is 0–0.28 m, and the migration scale of the leakage liquid can be clearly seen. The morphological characteristics of water leakage in different parts are basically the same. The distribution range is 0–0.18 m. The leakage of ethanol solution is only reflected at 0–0.06 m, and the distribution range is the smallest at the same depth. The ethanol solution also had the slowest migration rate.Figure 9Inversion map of plane section at different depths.Full size imageFigure 10 shows the inversion results of the X–Z longitudinal section of the test model. The two apparent resistivity profiles at Y = 0.24 m and Y = 0.32 m show that there is no low-resistance area in the shallow layer on the soil layer, indicating that the geomembrane in this area is not damaged. The low resistance zone in the middle is caused by the lateral migration of leakage fluid. The low-resistance anomaly area at the top of the profile can be judged as a leak point or formed by the migration of nearby leachate. Combined with the horizontal section, the leakage depth is similar, and the lateral migration speed of leachate is faster than the longitudinal migration speed. Four leak points can be distinguished, delineating the general location of the leak.Figure 10X–Z longitudinal section on different Y axes.Full size imagePhysical model experimentThe potential value of each electrode was monitored after 2 h of leakage, and the resistivity profiles at different positions were obtained by the potential difference method.It can be seen from Fig. 11 that the potential difference method can monitor the leakage of leachate in different directions. The morphological features of the plume formed by the downward migration of the leak point are approximately funnel-shaped in longitudinal section. The affected area of ​​the soil layer can be obtained in time. Figure 11b shows that the potential difference at the monitoring point is very different on both sides. After 2 h of leakage, a large amount of leakage liquid exists in the soil layer. When the water content in the soil layer increases, the diffusion rate of the ethanol solution increases, showing high resistance characteristics. At the same time, due to the action of gravity, there is a lot of vertical migration, and the potential value changes greatly. The profile clearly shows that the distribution area of ​​high potential difference is large, and the distribution of low potential is small. Figure 11c shows that since the migration rate of leachate in the horizontal direction is greater than that in the vertical direction, the potential difference of the monitoring point in the middle region is smaller, and a closed region of a high-potential circle is formed in the middle. The difference between the two results in a smaller potential difference area. Figure 11d shows that almost all the low-potential areas on the monitoring point are on the left side, because the leakage rate of NaCl solution in the horizontal direction is similar to that in the vertical direction under the condition of good soil compaction. At this time, a large number of conductive particles are contained, resulting in a large high-potential region. The difference between the two forms a large area of ​​low potential difference on the left. This is in good agreement with the lower resistance characteristics of the NaCl solution. Figure 11e shows that the two low-resistance regions correspond to the two leakage centers. The low potential difference region is formed by migration around the leak point. The migration speed in the horizontal direction is similar to that in the vertical direction, and the water migration speed on the left is slower than that of the sodium chloride solution on the right. Figure 11e,f show that the monitoring results are the same, but the resulting potential difference is also increased. This is affected by the distance between the monitoring point and the leak point. When the monitored point and the leakage point are located on the same section, the soil layer is the most severely affected area by leakage. Through the change of the potential difference, the leakage range and the location of the leakage point can be better judged.Figure 11Electrical resistivity tomograms of profile: (a) Resistivity of the slitting profile Y = 0; (b) Resistivity of the slitting profile Y = 0.08; (c) Resistivity of the slitting profile Y = 0.16; (d) Resistivity of the slitting profile Y = 0.24; (e) Resistivity of the slitting profile Y = 0.32; (f) Resistivity of the slitting profile Y = 0.4.Full size image More

Variation in environmental parameters across space and time in Bohai BayThe environmental parameters of samples collected near the Tianjin coastal area from different stations and seasons exhibited high temporal and spatial heterogeneity. The seawater temperature was 28.09 ± 0.53 °C in Aug, 17.48 ± 2.36 °C in May, and 19.55 ± 1.26 °C in Oct (Table 1). The seasonal variation in seawater temperature corresponded to the meteorological characteristics in Bohai Bay, with warm seawater in summer and relatively cool seawater in spring. The salinity was 29.69 ± 2.71‰ in Aug, 33.19 ± 0.33‰ in May, and 30.15 ± 1.63‰ in Oct. Seasonal variations in salinity may be mainly related to freshwater loading. According to the precipitation observed data of Bohai Bay in previous years, the rainfall amount and days in summer are the most19, which may lead to the increase in runoff and the relatively low salinity in summer. Chlorophyll a (Chl a) was highest in May, with lower levels in Aug and Oct. The dissolved inorganic nitrogen (DIN) was significantly higher in May and Aug than in Oct. The higher level of DIN in May and Aug may be related to terrestrial input and supply for the demand of phytoplankton growth. In October, the temperature and DIN content were both not suitable for phytoplankton growth, and Chl_a showed the lowest value. Spatially, the DIN distribution across the three seasons was rather similar, with high values observed in nearshore waters and low values in offshore waters (Dataset S1 & Fig. S1), which suggested that terrestrial input was an important source of DIN. The pH, soluble reactive phosphate (SRP) and chemical oxygen demand (COD) showed relatively higher values in October than in August and May, which may be caused by the dead phytoplankton release and terrestrial loadings through coasts and rivers. The dissolved oxygen (DO), conductivity and depth did not show significant variation among sampling times (Table 1), while the conductivity and depth had relatively higher values at offshore stations (Dataset S1) since the more remote the sampling water was, the greater the depth was in Bohai Bay and the closer it was to the open sea with higher salinity and conductivity. The ordination plot showed distinct partitioning of samples from nearshore and offshore sites along principal component axis 1 (PC1) (Fig. 1). The ordination plot could explain 73.49% of the total variation in the geo-physical–chemical parameters and revealed a linear positive correlation between different parameters (Fig. 1). AN, DIN, nitrate and Chl_a were most crucial in the partitioning of samples from May and the other 2 months; salinity, longitude, depth and conductivity were crucial for the partitioning of samples from offshore and nearshore stations; pH, COD, SRP, nitrite and temperature were crucial for the partitioning of samples from nearshore stations in August and October and samples from offshore stations. Overall, the principal component analysis (PCA) plot clearly showed both the temporal and spatial variation of the measured environmental parameters, indicating that complex biogeochemical processes and hydrodynamic conditions lead to the variation among sites and seasons.Table 1 The independent-samples t test of environmental variables and α-diversity among different months.Full size tableFigure 1Biplot of the principal component analysis (PCA) for environmental parameters in the seawater samples of the Bohai Bay coastal area across different seasons and sites. The two principal components (PC1 and PC2) explained 73.49% of the total variation in the environmental data and showed clear partitioning of offshore samples (in blue font) from other nearshore samples along PC1 and partitioning of May samples from August and October along PC2. The variables transparency and latitude were strongly correlated with PC1, and the variables ammonia nitrogen (AN), COD, pH, soluble reactive phosphate (SRP), and nitrite were strongly correlated with PC2. Chlorophyll a (Chl_a), dissolved inorganic nitrogen (DIN), nitrate and DO were mainly positively correlated with samples from May, while salinity, longitude, depth and conductivity were mainly positively correlated with offshore samples. Blue arrows represent environmental parameters, and circles in color represent sampling points.Full size imageProkaryotic α/β-diversity variationMeasures of α-diversity showed significant differences in shannon, evenness, faith_pd and OTU richness between samples from May/Aug and Oct (Fig. 2, Table 1). Principal coordinates analysis (PCoAs) based on weighted UniFrac (WUF) distance and unweighted UniFrac (UUF) distance showed that the PCC from different sampling months separated across the first and second principal coordinates (Fig. 3A-B). Both the analysis of similarity (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA/ADONIS) results indicated that the prokaryotic communities varied significantly across different sampling months when using a WUF distance metric (ANOSIM, r = 0.709, P = 0.001; ADONIS, R2 = 40.0%, P = 0.001) and UUF distance metric (ANOSIM, r = 0.934, P = 0.001; ADONIS, R2 = 38.7%, P = 0.001). At the same time, the prokaryotic α– and β-diversity both showed high within-month variability in Aug (Figs. 2, 3C–D), which indicated that the community varied greatly among different sites in Aug.Figure 2Alpha diversity of shannon, eveness, faith_pd (phylogenetic diversity) and OTU richness value of the prokaryotic community of all the samples from different stations at different sampling times.Full size imageFigure 3Principal coordinate analysis (PCoA) based on unweighted (A) and weighted (B) UniFrac distances for prokaryotic communities in the surface waters; box plots showing the unweighted (C) and weighted (D) UniFrac distances among each station at different sampling times.Full size imageCorrelation between prokaryotic α/β-diversity and physical, chemical and geographic factorsThe α-diversity measurements exhibited significant positive correlations with temperature, pH, SRP, AN and un_ionN (Dataset S2). The correlation between α-diversity indexes and geo factors (longitude and latitude) was not strong or significant both in samples across the three sampling times or from each sampling time (Dataset S2).The environmental variation significantly correlated with β-diversity among the three seasons (r_weighted = 0.4558, r_unweighted = 0.4631, P = 0.001, Table 2), with pH, AN, temperature, un_ionN, COD, nitrite, SRP, salinity, DO and DIN as the main individual determinants. However, it did not show significant correlations with β-diversity at any sampling time except in Oct (Table S1).Table 2 Spearman’s rank correlation between environmental/spatial variability (Euclidean distance) and prokaryotic β-diversity (weighted/unweighted UniFrac distance) among all samples from different season.Full size tableThe geographic distance was not correlated with prokaryotic β-diversity (variation in community composition; r  0.05; Table 2) among the three sampling times. However, samples from Aug and Oct exhibited a significant correlation between β-diversity and geographic distance (Table S1).Factors driving the PCC variationPERMANOVA using the UUF/WUF distance indicated that temperature variation explained the largest part of community variation among the investigated factors (34.90%/19.83%, P = 0.001, Dataset S3), with AN (31.84%/13.56%, P = 0.001) and salinity (12.91%/6.21%, P = 0.001) as the second and third most significant sources of variation.The variance partitioning analysis (VPA) conducted on both UUF/WUF distances showed that almost 100% percent of the variation in PCC among all three sampling times was explained by the detected environmental factors. In May, no environmental or spatial factors could be selected as significantly explain the PCC variation; in Aug, the joint effects of environmental and spatial factors could explain 49.5% of the variation; in Oct, based on WUF distance, the spatial factors could purely explain 10.5%, environmental factors could purely explain 38.8%, their joint effects could explain 28.2%, and based on UUF distance, the joint effects of environmental factors and trend could explain 13.7% of the PCC variation. These results indicated dramatic shifts in the spatial or environmental factor effects on the PCC variation at different sampling times in Bohai Bay (Table 3).Table 3 Variance partitioning analysis of prokaryotic community in Bohai Bay according to seawater environmental factors and geospatial factors. The spatial factors including linear trend and PCNM variables. Forward selection procedures were used to select the best subset of environmental, trend, and PCNM variables explaining community variation, respectively. The community variation was calculated on the weighted and unweighted UniFrac distance matrix, respectively. Monte Carlo permutation test was performed on each set without the effect of the other by permuting samples freely (999 permutations).Full size tableDistinct seasonal features at the phylum and OTU levelsThere were notable differences in the proportions of various phyla among different seasons (sampling month). In May, there was a greater proportion of Alphaproteobacteria (41.41%), Planctomycetes (6.42%), Actinobacteria (3.86%), Firmicutes (1.48%), Acidobacteria (0.45%), TM7 (0.16%), Tenericutes (0.16%), OD1 (0.13%), and WPS-2 (0.09%) than in Aug and Oct, whereas Gammaproteobacteria (44.23%), GN02 (0.08%) and SAR406 (0.04%) were depleted in May and Aug but enriched in Oct. In Aug, Bacteroidetes (13.98%), Deltaproteobacteria (6.93%), Verrucomicrobia (4.5%), Chloroflexi (0.36%), Lentisphaerae (0.97%), TM6 (0.25%), Nitrospirae (0.08%), Chlamydiae (0.07%), Chlorobi (0.07%), Spirochaetes (0.04%) and OP8 (0.03%) were significantly enriched than in the other two sampling times (Duncan test; Table S2).At the OTU level, OTUs with relative abundance  > 0.01% (1040 OTUs) were used to perform the difference analysis, and 175 OTUs in May, 281 OTUs in Aug, and 210 OTUs in Oct were identified as seasonal specific OTUs (ssOTUs). The cooccurrence network showed that the ssOTUs were clustered separately (Fig. 4A). Furthermore, the separation of the three modules contained most of the ssOTUs specific to different seasons (Fig. 4A-B). All the ssOTUs of different seasons comprised a taxonomically broad set of prokaryotes at the phylum (phylum Proteobacteria is grouped at the class level) level (Fig. 4C) belonging to various phyla with different proportions. Betaproteobacteria, Verrucomicrobia, Gemmatimonadetes, Epsilonproteobacteria, PAUC34f., and Euryarchaeota did not show significant differences among the three sampling times at the phylum level, but features belonging to these phyla showed differences at the OTU level (Fig. 4C, Dataset S4). In addition, the phylum ssOTUs belonging to, such as Alphaproteobacteria, Gammaproteobacteria, Bacteroidetes, Actinobacteria, and Deltaproteobacteria, were not only enriched at one sampling time (Dataset S4) but also enriched at the other two sampling times (Fig. 4C, Dataset S4). These results revealed that different seasons do not strictly select specific microbial lineages at the phylum level, but a finer level analysis could more strictly reflect the seasonal variation.Figure 4Co-occurrence patterns of seasonal sensitive OTUs (A). Co-occurrence network visualizing significant correlations (ρ  > 0.7, P  0.01%. Different colors represent ssOTUs in May (green), Aug (red) and Oct (blue). Cumulative relative abundance (as counts per million, CPM; y-axis in × 1000) of all the sensitive modules in the networks (B). The phylum attribution of ssOTUs in each season (C). The y-axis is the percentage of the number of OTUs that belong to a particular phylum that accounts for the total number of all the OTUs.Full size imageRegression analysis between the relative abundance of modules to which the ssOTUs belonged and the environmental factors was also conducted, and module 1 abundance, to which the Aug-ssOTUs belonged, showed a significant positive correlation with temperature (R2 = 0.77, P = 6.609e−62), AN (R2 = 0.43, P = 7.416e−25), and un_ionN (R2 = 0.75, P = 1.366e−58) and a negative correlation with SRP (R2 = 0.81, P = 6.762e-17). This may be caused by the functional role of the microbes in Aug. In the Aug-ssOTUs, Deltaproteobacteria showed a higher ratio than in the other 2 months (Fig. 4c), and in the following functional analysis, Deltaproteobacteria contributed to the genes related to nitrogen fixation, which may help to explain why there was a positive correlation of Aug-ssOTUs to AN and un_ionN. The module 2 abundance to which the May-ssOTUs belonged showed a significant negative correlation with pH (R2 = 0.65, P = 4.026e−44), temperature (R2 = 0.19, P = 2.325e−10), un_ionN (R2 = 0.025, P = 0.01779), and SRP (R2 = 0.12, P = 4.104e−07) and a positive correlation with AN (R2 = 0.26, P = 5.174e−14). In the May-ssOTUs, the ratio of Alphaproteobacteria was the highest, and Alphaproteobacteria were reported to be pH-sensitive groups in marine environments20, which prefer neutral pH environments21. In this study, the pH in May was 8.04 ± 0.07, in Aug was 8.39 ± 0.09, in Oct was 8.38 ± 0.07, and the pH in May was the closest to neutral, and the ratio decreased with increasing pH in Oct and Aug. The abundance of module 3, to which the Oct-ssOTUs belonged, showed a significant positive correlation with SRP (R2 = 0.81, P = 0.16e-10) and pH (R2 = 0.054, P = 0.00075) and a negative correlation with temperature (R2 = 0.44, P = 2.276e−25), AN (R2 = 0.75, P = 4.51e−58), and un_ionN (R2 = 0.6, P = 3.995e-39) (Fig. S2). Phosphate has been identified to limit primary productivity22, which is of great importance in the structure of dominant bacterial taxa in marine environments23. In the Oct-ssOTUs, the ratio of Gammaproteobacteria was the highest, as reported. Gammaproteobacteria was significantly explained by SRP during the seasonal variation in the Western English Channel, with Rho equal to 0.7523, which suggested the sensitivity of it to SRP, and in that study, it also showed a negative correlation between temperature and Gammaproteobacteria and a positive correlation between SRP and Gammaproteobacteria. Although the correlation was not significant, the variation trend was consistent, which indicates that the phenomenon observed in this study was not unexpected. In addition, most ammonia-oxidizing bacteria belong to the Betaproteobacteria and Gammaproteobacteria classes are chemolithoautotrophs that oxidize ammonia to nitrite24. Gammaproteobacteria and Betaproteobacteria both had higher ratios in Oct-ssOTUs, and the functional prediction results also showed that pmoA/amoA and pmoB/amoB, which encode ammonia monooxygenase, were mainly contributed by OTUs from Gammaproteobacteria and Betaproteobacteria (Dataset S10). The utilization of ammonia may explain the negative correlation between the Oct-ssOTUs and AN.Community assembly processes across different sampling months and sitesBased on the analysis of phylogenetic turnover, unweighted βNTI mostly ranged from -2 to 2 across different sites at a single sampling time in May, Aug and Oct, revealing that PCC variations across different sampling sites at a single time were mostly affected by stochastic processes. The unweighted βNTI was greater than 2 across May–Aug, May–Oct and Aug-Oct (Fig. 5A), which revealed that the variations in PCC across different sampling times were mostly affected by deterministic processes. The RCbray values across any two sampling times were equal to 1, and in each sampling time, the RCbray values ranged from − 1 to 1 (Fig. 5B). Combining the βNTI and RCbray values, the community assembly processes were quantified at each sampling time and at any two sampling times. As shown in Fig. 5C, turning over of the community during different sampling times was mainly governed by selection; among the different sites in May and Oct, it was mainly governed by “undominated” processes; community turn over in Aug was mainly governed by the influence of “Dispersal Limitation”. These results indicated that the shifts in the assembly of prokaryotic communities during different sampling times were caused by strong “heterogeneous selection” (βNTI  > 2), and the community variation at each sampling time was mainly caused by stochastic processes.Figure 5Patterns of distribution of unweighted βNTI (A) and RCbray (B) values across different sampling times. Quantification of the features that impose community assembly processes in and among different sampling times. (C) Pie charts give the percent of turnover in community composition governed primarily by Selection acting alone (white fill), Dispersal Limitation (green line fill), Homogenizing Dispersal (blue line fill) and undominated process (cyan fill).Full size imagePrediction of the metabolic potential at different sampling timesThe NSTI scores of each sample ranged from 0.033 to 0.096, with a mean of 0.058 (Dataset S5). Microbial functions were detected in all the samples from the three sampling times, and it was found that the relative abundances of 242 pathways were significantly changed between samples from May and samples from Aug (Dataset S6). The relative abundances of 321 pathways were significantly changed between samples from May and Oct (Dataset S7), and the relative abundances of 370 pathways were significantly changed between samples from Aug and Oct (Dataset S8).Genes related to energy metabolism were given more attention. For nitrogen metabolism genes relevant with nitrogen fixation (nifD, nifK) were detected only enriched in Aug, while genes relevant with nitrate reduction and denitrification (narG, narZ, nxrA, narH, narY, nxrB, narI, narV, nirD, nasA, nasB) were detected enriched in May, genes related with ammonia oxidation were both detected enriched in Oct and Aug. For sulfur metabolism, genes relevant with thiosulfate oxidation (soxA, soxB, soxC, soxX, soxY and soxZ) were only enriched in Aug, while genes relevant with sulfate and sulfite reduction (cysNC, aprA, aprB, cysJ, cysI, cysK, dsrA) were detected enriched in May and Oct (Fig. 6).Figure 6The LEfSe analysis indicated significantly differential abundances of PICRUSt predicted genes relevant to energy metabolism in different months of samples.Full size imageProkaryotic taxa contributed to the significantly varied functional genes related to nitrogen and sulfur metabolism at different sampling times. At the species level, the taxa contributing to nifK and nifD mainly belonged to Deltaproteobacteria and Firmicutes, and the taxa contributing to the sox-series genes mainly belonged to Alphaproteobacteria and Gammaproteobacteria (Fig. S3). The denitrification-related functional genes that were enriched in May were mainly contributed by members from Alphaproteobacteria and Gammaproteobacteria. The taxa contributing to dsrA, aprA and aprB were mainly from Deltaproteobacteria, including members of Desulfarculaceae, Desulfobacteraceae, Desulfobulbaceae, Desulfovibrionaceae and Syntrophobacteraceae (Fig. S4). More