Philippa Kaur

Pot experiment of AG planting
As shown in Fig. 1, compared to CS, the survival rate of 10-year rotation AG decreased, indicating that 2-year-old AG survival rate in RS was lower than that of AG in CS. This confirmed the continued existence of AG continuous cropping obstacles in RS.
The decrease of physicochemical properties and enzyme activity
Plant growth requires water and nutrients. Because soil physicochemical properties influence water and nutrient availability, changes in soil physicochemical properties directly affect AG growth. In the present study, the water content of RS was significantly higher than that of CS under the same management conditions (Table 1). Shu et al.29 found that high soil water content induced root rot disease in AG when sandy loam water content exceeded 30% or that of clay exceeded 50%. Similarly, according to Wang et al.30, the incidence of rust rot positively correlated with soil moisture and rainfall. Therefore, high soil water content, caused by changes in soil physicochemical properties, may negatively affect AG replanting. Furthermore, the pH of RS was significantly lower than that of CS (Table 1). According to Rahman and Punja24, root rot severity at soil pH 5.05 was greater than that at pH 7.0, indicating that acidic conditions can negatively affect AG health. In addition, the available K content in RS was lower than that in CS (Table 1). Sun31 found that AG should be fertilized (N, P, K fertilizer) from emergence to early flowering, when its demand for potassium fertilizer is the highest, suggesting that AG has a high potassium requirement. The levels of ammonium N, nitrate N, available P, and available K, but not of total N and total C, were generally lower in RS than in CS (Table 1), indicating that the cultivation of AG may have long-term negative effects on these soil nutrients. The same trend was observed for soil enzyme activity. Urease, a nickel-containing enzyme, catalyzes the hydrolysis of urea into carbonate and ammonia. Here, urease activity was significantly higher in CS than in RS. Average phosphatase and sucrase activities were also higher in CS than those in RS, although these differences were not significant (Table 2). Yang32 also found that the activities of sucrase, urease, and phosphatase decreased during AG cultivation. In summary, compared to that of CS, RS had lower fertility, but higher soil water content and lower pH, two conditions which are conducive to AG disease, and that may, therefore, present obstacles to AG replanting.
The dual effects of phenolic acids
The results showed that the content of salicylic acid in RS was significantly higher than that in CS. Yang16 found that among the various phenolic acids tested, salicylic acid had the strongest inhibitory effect on AG radicle growth. In our study, higher salicylic acid content in RS may have posed direct autotoxicity to AG. As a major defense hormone, salicylic acid has the function of enhancing immune signals and reprogramming defense transcriptomes33. After planting AG, the soil salicylic acid content increased, which indicated that AG might release more salicylic acid in the growth process to improve immune response to the surrounding environment. Therefore, the role of salicylic acid in the continuous cropping obstacles to AG cultivation deserves further study.
In addition, we found that the content of most phenolic acids, such as p-coumaric, p-hydroxybenzoic, vanillic, caffeic, and cinnamic acid, decreased after AG cultivation, and had not returned to the levels in CS even after 10 years of subsequent crop rotation. AG requires a suitable environment for growth. Before germination in spring, the ginseng farmers’ association uses wheat straw to cover the soil, which not only maintains soil temperature and retains soil moisture, but also improves soil quality and promotes the growth of AG seedlings. Jia et al.34 detected the increase in ferulic, vanillic, cinnamic, and p-hydroxybenzoic acid in a wheat-corn rotation area. In addition, Zheng et al.35 found that straw return, a common method for soil improvement, also increased the concentration of phenolic acids in soil. In our study, the increased phenolic acid content in CS relative to RS may have been beneficial to the growth of AG. Similar to our research results, Jiao et al.36 also found that the content of phenolic acid substances such as syringic, vanillic, p-coumaric, and ferulic acid decreased by 49.1–81% after adding AG root residues (simulating the seasonal AG leaf and fibrous root senescence). Therefore, decreases in the soil contents of some phenolic acids after planting AG may underlie the decline of other soil properties, which is not conducive to the subsequent growth of AG.
As described above, some phenolic acids may be beneficial to the growth of AG; if so, by what mechanism do these beneficial phenolic acids exert their role? Phenolic acids are produced by plants under external stress37,38,39,40. They do have many beneficial functions, such as antibacterial, antioxidant and so on, which can alleviate the stress of plants41. However, with the increase of phenolic acid secretion, some phenolic acids will penetrate into the soil and affect the soil microorganisms. Li et al.42 found that cinnamic acid inhibits Cylindrocarpon destructans (a pathogen of ginseng) growth at high concentrations, while promoting it at low concentrations. Yang et al.43 found that vanillic acid promoted the growth of the pathogens Rhizoctonia solani and Fusarium solani at low concentrations, but inhibited it at high concentrations; many phenolic acid compounds can inhibit the proliferation of Phytophthora cactorum (a pathogenic bacterium that causes AG phytophthora disease) at high concentrations. In addition, Yuan et al.44 found that p-coumaric acid strongly suppressed the in vitro growth of fungi, significantly reducing the decay caused by Alternaria alternata. Therefore, it can be seen that phenolic acids have inhibitory effects on pathogens at higher concentrations. With a decrease in soil phenolic acid content, this inhibitory effect on pathogenic bacteria will be weakened, resulting in an imbalance in the soil microbial composition that affects AG growth performance. Overall, soil phenolic acid content may indirectly affect AG growth performance by affecting soil microorganisms.
The change in the relative abundance of key bacteria
Our results showed that there was no significant difference in bacterial α-diversity between 10-year post-ginseng RS and CS, but there were differences in β-diversity, which reflects community composition and structure, between CS and RS. In other words, there were significant differences in the relative abundance of key bacteria in the bacterial community, such as Chlamydiae (phylum level, RS: 0.28%, CS: 0.10%, P = 0.035), within this phylum, the c_Chlamydiae, o_Chlamydiales, f_Simkaniaceae, and g_uncultured; Acidothermus (genus level, RS: 2.40%, CS: 5.40%, P = 0.030); Sphingomonadales (order level, CS: 2.98%, RS: 1.68%, P = 0.002), Sphingomonadaceae (family level, CS: 2.88%, RS: 1.48%, P = 0.004), genera Novosphingobium (CS: 0.03%, RS: 0.20%, P = 0.035) and Sphingomonas (CS: 2.83%, RS: 1.10%, P = 0.000); Rhodanobacter (CS: 0.38%, RS: 3.45%, P = 0.050); Arthrobacter (CS: 0.03%, RS: 0.43%, P = 0.001); Mizugakiibacter (CS: 0.63%, RS: 2.28%, P = 0.048); Jatrophihabitans (CS: 1.15%, RS: 0.75%, P = 0.048); Pseudomonas (RS: 0.15%, CS: 0.03%, P = 0.029) among others (Fig. 4, see Supplementary Table S2).
There was no difference in soil bacterial α-diversity between RS and CS, which may be due to the recovery of soil bacterial diversity after 10 years of rotation. However, the results of the pot experiment showed that RS still presented continuous cropping obstacles, which indicated that restoring soil microbial α-diversity does not alleviate continuous cropping obstacles for AG. Instead, differences in microbial community composition (i.e., β-diversity), particularly the abundances of bacterial taxa that play key roles, may explain the persistence of AG continuous cropping obstacles in RS after 10 years.
Among the differences in microbial community composition, CS had higher relative abundances of some bacterial genera that may be beneficial bacteria. The genus Acidothermus had the highest abundance, and it contained a single species, A. cellulolyticus, which is thermophilic, acidophilic, and has the ability to produce many thermostable cellulose-degrading enzymes45. Therefore, higher cellulose-degrading capacity might exist in CS than that in RS. Sphingomonas, a bacterium with the ability to decompose mono- and polycyclic aromatic compounds, as well as heterocyclic compounds, was more abundant in CS than RS, suggesting that bacterial decay of recalcitrant plant compounds was also higher in CS than RS. In addition, Sphingomonas not only decomposes monoaromatic phenolic acids but also improves plant stress resistance, and it is considered a plant probiotic46. Similar to our results, Li and Jiang23 found that Jatrophihabitans relative abundance in soil used for AG for 4 years was significantly (P  root rot group  > control group; in addition, compared with CS, there was a higher abundance of Rhodanobacter in the soil in which Korean ginseng (Panax ginseng) was grown49. We also found that this genus might be increased by the influence of Panax plants, which warrants further study. Our results showed that Arthrobacter was higher in the RS group, and Jiang et al.48 also found that the relative abundance of Arthrobacter in the root rot group was higher than that in the healthy root group; therefore, we speculate that Arthrobacter might be a factor causing root rot of P. quinquefolius, leading to a continuous cropping obstacle to AG growth. Our results showed that the abundance of Pseudomonas sp. in RS was higher than that in CS (RS: 0.15%, CS: 0.03%, P = 0.029, see Supplementary Table S2). Tan et al.50 showed that Pseudomonas sp. was the main pathogen causing root rot disease in P. notoginseng. In addition, Jiang et al.48 also found that Pseudomonas is abundant in the rhizosphere soils of diseased ginseng roots. Therefore, it is necessary to further study the effects of Pseudomonas species on AG growth. To sum up, the relative abundances of a large number of bacteria that are either confirmed or potentially harmful to other plants increased in RS, which may be an important factor leading to the occurrence of continuous cropping obstacles in the 10-year post-ginseng rotation soil.
As shown in Fig. 6, there are many correlations among the three factors. The abundances of Acidothermus, Sphingomonas, Jatrophihabitans, and Actinospica were each positively correlated with that of available K, caffeic acid, and cinnamic acid, but negatively correlated with that of salicylic acid. Therefore, the interactions among phenolic acids, microorganisms, and soil nutrients evidenced possible “synergistic” or “antagonistic” effects within the microecosystem. Overall, these complex relationships are the main reason for AG continuous cropping obstacles, but it is still unknown which of these factors plays the primary role. Finally, Nitrobacter, Actinospica, Clostridium sensu stricto 1, Thermosporothrix, Holophaga, and Peptoclostridium, also showed significant differences in abundance between RS and CS (Fig. 4), which also should receive more attention. More

No statistical methods were used to predetermine sample size. The experiments were not randomized, and investigators were not blinded to allocation during experiments and outcome assessment.
Etymology
The designation ‘Azoamicus’ combines the prefix azo- (New Latin, pertaining to nitrogen) with amicus (Latin, masculine noun, friend); thus giving azoamicus (‘friend that pertains to nitrogen’), alluding to its role as denitrifying endosymbiont. ‘Ciliaticola’ combines ciliate (referring to a group of ciliated protozoa) with the suffix -cola (derived from the Latin masculine noun incola, dweller, inhabitant), thus meaning ‘dwelling within a ciliate’.
Geochemical profiling
We carried out sampling for geochemical profiling in September 2016 and October 2018 at a single station located in the deep, southern lake basin of Lake Zug (about 197-m water depth) (47° 06′ 00.8′′ N, 8° 29′ 35.0′′ E). In September 2016, we used a multi-parameter probe to measure conductivity, turbidity, depth (pressure), temperature and pH (XRX 620, RBR). Dissolved oxygen was monitored online with normal and trace micro-optodes (types PSt1 and TOS7, Presens) with detection limits of 125 and 20 nM, respectively, and a response time of 7 s. In October 2018, we used a CTD (CTD60, Sea&Sun Technology) equipped with a Clark-type oxygen sensor (accuracy ± 3%, resolution 0.1%) to record oxygen profiles.
Sample collection
Water for bulk DNA and RNA analyses was collected in September 2016 and October 2018. Sample collection for DNA and RNA extraction in September 2016 has previously been described46. In October 2018, water was sampled with a Niskin bottle (Hydro-Bios) from 160 m, 170 m and 180 m. For each depth, 2 l of lake water was directly filtered on board our boat onto 0.22-μm Sterivex filter cartridges (Merck Millipore) using a peristaltic pump, subsequently purged with RNAlater preservation solution (Life Technologies) and stored at −20 °C until further processing. For fluorescence in situ hybridization (FISH) analyses, water from the same depths was fixed on board the boat with formaldehyde (1.5% final concentration; Electron Microscopy Sciences) and incubated in a chilled cool box for about 6 h before filtration onto 3-μm polycarbonate filters (Merck Millipore). Additional FISH samples using the same approach were collected in May 2019 from 189 m water depth.
Water for incubation experiments and single-ciliate PCR was sampled in May 2019 from 189-m water depth using a 10 l Go-Flo bottle (General Oceanics), filled into 2.5-l glass bottles without headspace, closed with butyl rubber stoppers and kept cold (at about 4 °C) and dark until further handling. During sampling, oxygen contamination was minimized by overflowing the bottle with anoxic lake water.
For combined FISH and differential interference contrast microscopy analyses, individual live ciliates were picked from lake water (from 186-m depth, collected February 2020) and directly fixed on microscope slides. In brief, microscope slides were treated with 0.1 mg ml−1 poly-l-lysine for 10 min at room temperature, washed with MilliQ water and dried. Ciliates were pre-enriched by gravity flow of bulk lake water through a 5-μm membrane filter and picked using a glass capillary under a binocular microscope. Picked ciliates were transferred into a droplet of formaldehyde (2% in 0.1× PBS, pH 7.6) on poly-l-lysine-coated microscope slides, incubated (for 1 h at room temperature) and washed with MilliQ water. FISH was performed as described in ‘Double-labelled oligonucleotide probe fluorescence in situ hybridization and microscopy’.
Nutrient measurements
Water samples for measurements of nutrients (ammonium, NOx and nitrite) were retrieved with a syringe sampler from 15 discrete depths at and below the base of the oxycline. Forty ml of water was directly injected into a 50-ml Falcon tube containing 10 ml of OPA reagent for fluorometric ammonium quantification47. In 2018, ammonium concentration was determined using the same method, except that the lake water was immediately sterile-filtered after sampling and frozen at −20 °C until further processing. For NOx quantification, 10 ml of water was sterile-filtered into a 15-ml Falcon tube and combined nitrate and nitrite concentration was determined by a commercial QuAAtro Segmented Flow Analyzer (SEAL Analytical).
Clone library construction and Sanger sequencing
‘Candidatus A. ciliaticola’-specific 16S rRNA gene primers were designed on the basis of the ‘Ca. A. ciliaticola’ circular metagenome-assembled genome sequence. Primers targeted the intergenic spacer regions about 50 bp up- and downstream of the 16S rRNA gene, resulting in a 1,568-bp-long PCR product. For clone library construction, the ‘Ca. A. ciliaticola’ 16S rRNA gene was amplified by a nested PCR approach from the same DNA extract used for metagenome sequencing obtained in September 2016 from 160-m water depth using the newly designed ‘Ca. A. ciliaticola’-specific primers (eub62A3_29F and eub62A3_1547R) followed by PCR amplification with general bacterial 16S rRNA gene primers (8F and 1492R) (Supplementary Table 2). Cloning and construction of the clone library is described in more detail in Supplementary Methods. Inserts of purified plasmids from five clones were sequenced by Sanger sequencing using the BigDye Terminator v.3.1 sequencing kit (Thermo Fisher Scientific) and primers M13f or M13r. The sequencing PCR contained 3 μl purified plasmid, 0.5 μl 10× sequencing buffer, 0.5 μl primer (10 μM) and 1 μl BigDye reagent. The PCR reactions were performed as follows: 99 cycles (1 °C s−1 ramp) of denaturation (10 s at 96 °C), annealing (5 s at 60 °C) and elongation (4 min at 60 °C). The PCR products were purified using gel filtration (Sephadex G-50 Superfine, Amersham Bioscience) followed by Sanger sequencing (3130xl genetic analyser, Applied Biosystems). The Sanger sequences were quality-trimmed and assembled using Sequencher v.5.4.6 and standard settings before trimming vector and primer sequences.
Probe design for fluorescence in situ hybridization
To visualize ‘Ca. A. ciliaticola’ cells in the environment, we designed a specific FISH probe on the basis of the ‘Ca. A. ciliaticola’ circular metagenome-assembled genome 16S rRNA gene sequence and closely related sequences within the clade eub62A3. The ‘Ca. A. ciliaticola’ 16S rRNA gene sequence was imported into Arb48 v.6.1 and aligned to the SILVA SSU Ref NR 99 132 database using the SINA-Aligner49. A FISH probe specific for ‘Ca. A. ciliaticola’ and most members of clade eub62A3 was designed (probe eub62A3_813 5′ CTAACAGCAAGTTTTCATCGTTTA 3′) (Supplementary Table 3) using the probe design tool implemented in Arb, and further manually refined and evaluated in silico using MathFISH50. The newly designed probe eub62A3_813 targets ‘Ca. A. ciliaticola’ and 78% of the ‘Ca. Azoamicus’ subgroup A and B sequences included in SILVA SSU Ref NR 99 138 (7 out of 9; the 2 sequences that are not targeted belong to ‘Ca. Azoamicus’ subgroup B), and shows no nontarget hits. Some sequences in the database had only 1 or 2 weak mismatches (98% identity with the 16S rRNA gene sequence of ‘Ca. A. ciliaticola’. The remaining reads shared >94% identity with ‘Ca. A. ciliaticola’ and all shared as top hits sequences belonging to ‘Ca. Azoamicus’ subgroup A when blasted against the NCBI nr database (accessed June 2020).
Clone-FISH
Clone-FISH was performed as previously described51. In brief, a purified plasmid containing the ‘Ca. A. ciliaticola’ 16S rRNA gene sequence (as described in ‘Clone library construction and Sanger sequencing’) in the correct orientation (confirmed by PCR using M13F and 1492R primers) was transformed into electrocompetent Escherichia coli JM109(DE3) cells (Promega) by electroporation using the Cell Porator and Voltage Booster System (Gibco) with settings 350 V, 330 μF capacitance, low ohm impedance, fast charge rate and 4 kΩ resistance (Voltage Booster). After electroporation, cells were transferred into SOC medium (Sigma Aldrich), incubated for 1 h at 37 °C and plated onto LB plates containing 100 mg l−1 kanamycin. After incubation overnight at 37 °C, 4 clones were picked and the presence of the insert was checked with PCR (primers M13F and 1492R) as described in ‘Clone library construction and Sanger sequencing’, followed by gel electrophoresis. An insert-positive clone was selected at random and grown in LB medium containing 100 mg l−1 kanamycin at 37 °C until optical density at 600 nm reached 0.37. Transcription of the plasmid insert was induced using isopropyl β-d-1-thiogalactopyranoside (IPTG) (1 mM final concentration). After addition of IPTG, cells were incubated for 1 h at 37 °C followed by addition of 170 mg l−1 chloramphenicol and subsequent incubation for 4 h. Cells were collected by centrifugation, fixed in 2% formaldehyde solution for 1 h at room temperature, washed and stored at 4 °C in phosphate-buffered saline (pH 7.4) containing 50% ethanol until further processing. Formamide melting curves52 were carried out using a ‘Ca. Azoamicus’-specific, HRP-labelled probe (eub62A3_813) (Extended Data Fig. 5). In brief, cells were applied to glass slides. Permeabilization with lysozyme, peroxidase inactivation, hybridization (10%, 30%, 35%, 40%, 45% and 50% formamide) and tyramide signal amplification (Oregon Green 488) was performed as previously described53. For each formamide concentration, images of three fields of view were recorded using the same exposure time for all formamide concentrations, which was optimized at 10% formamide all same settings using an Axio Imager 2 microscope (Zeiss) and analysed using Daime 2.154.
Double-labelled oligonucleotide probe fluorescence in situ hybridization and microscopy
Hybridization with double-labelled oligonucleotide probes (terminally double-labelled with Atto488 dye; details of the probe are in Supplementary Table 3) (Biomers) and counterstaining with DAPI was performed as previously described55. In brief, samples (either cut filter sections or ciliates picked and fixed on a glass slide, as described in ‘Sample collection’) were incubated in hybridization buffer containing 25% formamide and 5 ng DNA μl−1 probe (the same concentration was used for eub62A3_813 competitor 1 and 2) for 3 h at 46 °C, and subsequently washed in prewarmed washing buffer (5 mM EDTA, 159 mM NaCl) for 30 min at 48 °C. After a brief MilliQ water wash, samples were incubated for 5 min at room temperature in DAPI solution (1 μg ml−1), briefly washed in MilliQ water and air-dried. Filter sections were mounted onto glass slides. Samples were embedded in Prolong Gold Antifade Mountant (Thermo Fisher Scientific), and left to cure for 24 h. Confocal laser scanning and differential interference contrast microscopy were performed on a Zeiss LSM 780 (Zeiss, 63× oil objective, 1.4 numerical aperture, with differential interference contrast prism). Z-stack images were obtained to capture entire ciliate cells and fluorescence images of FISH probe and DAPI signals were projected into 2D for visualization. Cell counting was performed using a Axio Imager 2 microscope (Zeiss) in randomly selected fields of view (40× objective, grid length = 312.5 μm) on polycarbonate filters (3-μm pore size, 32-mm effective filter diameter; Merck Millipore) onto which 0.5 l PFA-fixed lake water was filtered.
For light microscopy, live ciliates were picked from Lake Zug water (May 2019, 189 m) and prepared for live ciliate imaging using light microscopy as previously described56 on an Axio Imager 2 microscope (Zeiss). For image acquisition and processing, Zeiss ZEN (blue edition) 2.3 was used.
Scanning electron microscopy
Ciliates sampled in February 2020 were individually picked under a binocular microscope, and washed in droplets of sterile-filtered lake water. Washed ciliates were then transferred into approximately 200 μl fixative on top of a polylysine-coated silicon wafer (0.1 mg ml−1 poly-l-lysine for 10 min at room temperature) and fixed for 1 h at room temperature. The fixative contained 2.5% glutaraldehyde (v/v, electron microscopy grade) in PHEM buffer57 (pH 7.4). Fixed ciliates attached to the silicon wafer were dehydrated in an ethanol series (30%, 50%, 70%, 80%, 96% and 100%) before automated critical-point drying (EM CPD300, Leica). Scanning electron microscopy was performed on a Quanta FEG 250 (FEI). Images were obtained using FEI xTM v.6.3.6.3123 at an acceleration voltage of 2 kV under high vacuum conditions and were captured using an Everhart–Thornley secondary electron detector. The image represents an integrated and drift corrected array of 128 images captured with a dwell time of 50 ns.
Single-ciliate PCR
Ciliates were picked from Lake Zug water (189 m, 2019) under the binocular microscope with a glass micropipette, and subsequently washed twice in drops of sterile nuclease-free water (Ambion) before being transferred into lysis buffer. DNA was extracted using MasterPure DNA purification kit (Ambion) following the manufacturer’s instructions with a final elution in 1× TE buffer (25 μl). Overall, DNA was extracted from four individual ciliates (S1–S4), five (C5) and ten pooled ciliates (C10) as well as no ciliate (control). 16S (‘Ca. A. ciliaticola’) and 18S rRNA gene sequences (ciliate host) were then separately amplified by PCR using primer pairs eub62A3_29F/_1547R and cil_384F/_1147R. PCR reactions (20 μl) with ‘Ca. A. ciliaticola’-specific primers (eub62A3_29F/_1547R) were performed as described in ‘Clone library construction and Sanger sequencing’ with following modifications: 5 μl template, 58 °C annealing temperature and 40 amplification cycles. PCR with ciliate-specific primers (cil_384F/_1147R) was performed analogously with following modifications: 55 °C annealing temperature, 50 s elongation and 35 amplification cycles. The PCR reactions with primer pairs eub62A3_29F/_1547R were further amplified in a second round of PCR (under the same conditions) using 2 μl PCR reaction from the first round. For each PCR step, successful amplification of products was checked using gel electrophoresis as described in Supplementary Methods. PCR reactions were subsequently purified using QIAquick PCR purification Kit (Qiagen) according to the manufacturer’s instructions with a final elution in sterile nuclease-free water (25 μl) (Ambion). Purified PCR reactions were subsequently sequenced using Sanger sequencing and processed as described in ‘Clone library construction and Sanger sequencing’ with the following modifications: primers eub62A3_29F, eub62A3_1547R (annealing temperature 58 °C) or cil_384F, cil_1147R (annealing temperature 55 °C). Two of the single-ciliate DNA extracts amplified with the endosymbiont-specific primers either showed a faint (S4) or no (S2) band and were not sequenced.
Nucleic acid extraction from lake water
Bulk DNA and RNA extraction as well as metagenome and metatranscriptome sequencing of lake water samples collected in 2016 have previously been described46. For samples from 2018, filters were purged of RNAlater, briefly rinsed with nuclease-free water and removed from the Sterivex cartridge. RNA and DNA was then extracted from separate filters using PowerWater RNA or DNA isolation kits (MoBio Laboratories) according to the manufacturer’s instructions.
Metagenome and bulk metatranscriptome sequencing
For metagenomic sequencing, DNA libraries were prepared as recommended by the NEBNext Ultra II FS DNA Library Prep Kit for Illumina (New England Biolabs). Sequencing-by-synthesis was performed on the Illumina HiSeq2500 sequencer (Illumina Inc.) with the 2 × 250-bp read mode. For metatranscriptomic sequencing, rRNA was removed (Ribo-Zero rRNA Removal Kit for bacteria (Illumina)) and an RNA-sequencing library was prepared according to the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs). Sequencing-by-synthesis was performed on the Illumina HiSeq3000 sequencer (Illumina) with the 1 × 150-bp read mode. Library preparation and sequencing was performed by the Max Planck Genome Centre Cologne (http://mpgc.mpipz.mpg.de/home/). Detailed information for each metagenomic and metatranscriptomic dataset can be found in Supplementary Table 4.
Genome assembly and finishing
The genome of ‘Ca. A. ciliaticola’ was reconstructed from a metagenomic dataset sampled in 2018, as follows: metagenomic reads (MG_18_C) were trimmed using Trimmomatic58 v.0.32 as previously described46 and assembled using metaSPAdes59 v.3.13.0 and k-mer lengths of 21, 33, 55, 77, 99 and 127. From this assembly, contigs with high similarity to the previously reconstructed ‘Ca. A. ciliaticola’ genome from the metagenome samples from 2016 (further details are provided in Supplementary Methods) were identified by blastn (identity >95%) and metagenomic reads were mapped back to the contigs using BBmap60 v.35.43 (minid = 0.98). Mapped reads were subsequently reassembled using SPAdes v.3.13.0 with mismatch corrector and coverage threshold enabled (–careful –cov-cutoff 60), resulting in the assembly of a single contig (292,647 bp) that was circularized by trimming the identical overlapping ends (127 bp) giving rise to the closed genome (292,520 bp). The start position was set in an intergenic spacer region near the maximum of the GC disparity curve generated with oriFinder61 v.1.0. The two independently assembled circular metagenome-assembled genomes (from 2016 and 2018 metagenomes (Supplementary Methods)) shared 99.99% identity. For all subsequent analyses, the genome reconstructed from the 2018 dataset was used owing to higher coverage.
Genome annotation and comparative analyses
Genome annotation was performed using a modified version of Prokka62 v.1.13.3 to allow annotation of genes that overlap with tRNA genes. The annotation of key metabolic genes was manually inspected and refined using searches against NCBI non-redundant protein or conserved domain database63. Transmembrane transporters were predicted and classified using the Transporter Automatic Annotation Pipeline web service64 and the Transporter Classification Database65. Pseudogenes were predicted using pseudo finder66 v.0.11 and standard settings. Circular genome maps were created using DNAplotter67 v.18.1.0.
For comparative analyses, protein-coding CDS encoded in the genomes of insect endosymbionts (C. ruddii PV, AP009180.1 and B. aphidicola BCc, CP000263.1), mitochondrion of J. libera (NC_021127) and a free-living relative of ‘Ca. A. ciliaticola’ (L. clemsonensis, CP016397.1) were downloaded from NCBI GenBank. Additionally, protein-coding CDS of the ciliate endosymbiont C. taeniospiralis (PGGB00000000.1) were obtained using Prokka annotation. Classification of functional categories was performed using the eggNOG-mapper v.1 web service68 with mapping mode DIAMOND and standard settings. The classification of the functional category C (energy production and conversion) for ‘Ca. A. ciliaticola’ was modified to also include norB and nirK, which were grouped by eggNOG into different categories (Q and P, respectively).
Multiple sequence alignment of ‘Ca. A. ciliaticola’ and other plastidic and bacterial ATP/ADP translocases was generated using MuscleWS69 v.3.8.31 with default settings implemented in Jalview70 v.2.11.1.0.
Metatranscriptomic analyses of bulk water samples
Raw metatranscriptomic Illumina reads trimming and removal of rRNA sequences was performed as previously described46. Non-rRNA reads were then mapped to the genome of ‘Ca. A. ciliaticola’ using Bowtie271 v.2.2.1.0 and standard parameters. Sorted and indexed BAM files were generated using samtools72 v.0.1.19 and transcripts per feature (based on the Prokka annotation) were quantified using EDGE-pro73 v.1.3.1 and standard settings. Gene transcription was subsequently quantified as transcripts per million74 (TPM) using the formula:

$${{rm{T}}{rm{P}}{rm{M}}}_{{rm{i}}}=frac{{c}_{i}}{{l}_{i}}times frac{1}{{sum }_{j}frac{{c}_{i}}{{l}_{i}}}times {10}^{6}$$

to assign each feature (i) a TPM value, in which c = feature count, l = length (in kb) and j = all features.
Read coverage visualization and plotting was performed using pyGenomeTracks75 (average coverage over 100-bp bins) implemented in deepTools276 v.3.2.0.
Phylogenetic analyses
The full-length 16S rRNA gene sequence was retrieved from the circular metagenome-assembled genome of ‘Ca. A. ciliaticola’ using RNAmmer77 v.1.5, aligned using the SILVA incremental aligner49 (SINA 1.2.11) and imported to the SILVA SSU NR99 database45 (release 132) using ARB48 v.6.1. Additional closely related 16S rRNA gene sequences were identified by BLASTN in the NCBI non-redundant nucleotide database and JGI IMG/M 16S rRNA Public Assembled Metagenomes (retrieved July 2018) and also imported into ARB. A maximum-likelihood phylogenetic tree of 16S rRNA gene sequences was calculated using RAxML78 v.8.2.8 integrated in ARB with the GAMMA model of rate heterogeneity and the GTR substitution model with 100 bootstraps. The alignment was not constrained by a weighting mask or filter. For the complete tree shown in Extended Data Fig. 4, additional ‘Ca. A. ciliaticola’ sequences obtained from a clone library and individual single ciliates were added to the tree using the Parsimony ‘Quick add’ algorithm implemented in ARB.
For the ciliate phylogeny, sequences obtained from Sanger sequencing of picked ciliates were added to the EukRef-Ciliphora30 Plagiopylea subgroup alignment using MAFFT79 online service version 7 (argument:–addfragments). An additional metagenome-assembled full-length 18S rRNA gene sequence assigned to Plagiopylea was obtained using phyloFlash80 v.3.0 from one of the Lake Zug metagenomes (MG_18_C) and also added to the alignment (argument:–add). A phylogenetic tree was calculated using IQ-TREE webserver (http://iqtree.cibiv.univie.ac.at) running IQ-TREE81 1.6.11 with default settings and automatic substitution model selection (best-fit model: TIM2+F+I+G4). Phylogenetic trees were visualized using the Interactive Tree of Life v.4 web service82.
For the ATP/ADP translocase phylogenetic tree, amino acid sequences were retrieved from NCBI RefSeq (250 top hits) and NCBI nr (15 top hits) (both accessed June 2019) using NCBI blastp web-service83 with the amino acid sequence of ATP/ADP translocase sequence of ‘Ca. A. ciliaticola’ (ESZ_00147) as query. Additional amino acid sequences of characterized nucleotide transporters listed in Supplementary Table 8 were also added. After removal of duplicates, sequences were clustered at 95% identity using usearch84 v.8.0.1623 and aligned using MUSCLE69 v.3.8.31. Phylogenetic tree construction using IQ-TREE (best-fit model: LG+F+I+G4) and visualization was performed as described for the 18S rRNA gene phylogenetic tree.
Incubation experiments
Incubation experiments were performed to provide experimental evidence for the denitrifying activity linked to the ciliate host. Three incubations were set up that contained (a) no ciliates (filtered fraction), (b) lake water that was enriched in ciliates (enriched fraction) and (c) bulk lake water. For these experiments, lake water was size-fractionated using a 10-μm polycarbonate filter (Merck Millipore) under N2 atmosphere in a glove bag at 12 °C. Enriched and filtered fractions were obtained by gravity filtration of 0.5 l water until 0.25 l supernatant was left. Thus, in the enriched fraction, ciliates from 0.5 l lake water were concentrated in 0.25 l lake water. In the filtered fraction, organisms larger than 10 μm (including ciliates) were filtered out. Both the enriched water (plus filter) and the filtered water were transferred to separate serum bottles (no headspace) and closed with butyl rubber stoppers. For bulk incubations, unfiltered water was directly filled into 250 ml serum bottles under N2 atmosphere.
Denitrification potential was assessed by measuring the production of 30N2 over time in 15N-nitrite and 15N-nitrate amended incubations by isotope ratio mass spectrometry (Isoprime Precision running ionOS v.4.04, Elementar). A 30 ml helium headspace was set for the 250 ml serum bottles and the water was degassed with helium for 10 min to ensure anoxic conditions and reduce N2 background. A 15N-labelled mixture of nitrate and nitrite (20 μM and 5 μM final concentration, respectively; Sigma Aldrich) was supplied at a 99% labelling percentage and the water was incubated for a total of 30 h at 4 °C in the dark. Subsamples of the headspace were taken at regular time intervals during the incubation by withdrawing 3 ml of the gaseous headspace and simultaneously replacing the removed volume by helium. This gas sample was transferred into 12 ml Exetainers (LabCo) that were pre-filled with helium-degassed water and stored until analysis. 30N2 in the gas samples was measured on an isotope ratio mass spectrometer, and denitrification rates were calculated from the slope of the linear increase of 30N2 in the headspace over the time course of the experiments. The rate of 30N2 production was corrected for dilution of the headspace introduced by subsampling and by the measured total 15N labelling percentage. ‘Ca. A. ciliaticola’-containing ciliate abundance in the different incubation bottles was assessed by microscopic counts after cell fixation, FISH hybridization (eub62A3_813) and DAPI staining as described in ‘Double-labelled oligonucleotide probe fluorescence in situ hybridization and microscopy’.
Statistics and reproducibility
No statistical methods were used to predetermine sample size and experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.
In Fig. 1a, the scanning electron microscopy image is a representative of n = 6 recorded images that were obtained from 1 experiment. In Fig 1b, the differential interference contrast image is a representative of n = 6 recorded images that were obtained from 1 experiment.
In Fig. 2c, Extended Data Fig. 2i. FISH fluorescence images (eub62A3_813 probe) are representative of n = 33 recorded images that were obtained from 5 independent experiments of 3 biological replicate samples.
In Fig. 2c, Extended Data Fig. 2f, h, j. DAPI fluorescence images are representative of n = 21 recorded images that were obtained from 5 independent experiments of 3 biological replicate samples.
In Extended Data Fig. 2a, the FISH fluorescence image (Arch915 probe) is representative of n = 6 images that were obtained from 1 experiment. In Extended Data Fig. 2b, d, F420 autofluorescence images are representative of n = 11 recorded images that were obtained from 3 independent experiments of 1 sample. In Extended Data Fig. 2c, g, FISH fluorescence images (EUB-I probe) are representative of n = 15 images obtained from 3 independent experiments of 2 biological replicate samples. In Extended Data Fig. 2e, the FISH fluorescence image (NON338 probe) is representative of n = 15 recorded images that were obtained from 3 independent experiments of 2 biological replicate samples.
In Extended Data Fig. 5a, each of the 6 FISH fluorescence images (eub62A3_813 probe) is representative of n = 3 images from 1 experiment.
For the fluorescence images shown, the number of analysed cells was typically much higher (n  > 100) than the ones that were eventually recorded.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this paper. More

Author notes
These authors jointly supervised this work: Elizabeth A. Archie and José M. Montoya.

Affiliations

Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
Johannes R. Björk & Elizabeth A. Archie

Theoretical and Experimental Ecology Station, CNRS-University Paul Sabatier, Moulis, France
Johannes R. Björk & José M. Montoya

Natural History Museum, London, UK
Cristina Díez-Vives

School of Biological Sciences, University of Auckland, Auckland, New Zealand
Carmen Astudillo-García

Authors
Johannes R. Björk

Cristina Díez-Vives

Carmen Astudillo-García

Elizabeth A. Archie

José M. Montoya

Corresponding authors
Correspondence to Johannes R. Björk or Elizabeth A. Archie or José M. Montoya. More

Screening of solvent and adjuvant
The results of solvent screening are shown in Table 1. The original pesticide could not be completely dissolved using a single solvent. However, 5% N-methyl-2-pyrrolidone + 10% cyclohexanone could completely dissolve the original pesticide. There was no solid precipitation at room temperature, so the formulation could be used for the subsequent experiment. According to Table 2, a mixture of sulfonate adjuvants (70b) and fatty alcohol polyoxyethylene ether adjuvants (AEO-4, -5, -7, -9, 992) could stabilize the system in a single, transparent, homogeneous phase. Therefore, sulfonate adjuvant (70b) was selected and mixed with five adjuvants of the AEO series to prepare thidiazuron·diuron ultra-low-volume sprays, numbered 1–5 (as shown in Table 3).
Table 1 Selection of solvent type and dosage (%: mass fraction).
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Table 2 Selection of adjuvants type and dosage (%: mass fraction).
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Table 3 Ultra-low-volume formulations used in this study.
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Surface tension measurement
The critical surface tension of cotton leaves is 63.30–71.81 mN/m. Figure 1 shows that the surface tension of each sample was 31.67–33.37 mN/m, which was much lower than the critical surface tension of the leaf, indicating the agent was able to completely wet the leaf and be fully distributed on the leaf surface. The maximum surface tension of the reference product was 38.90 mN/m. Under the same dosage of adjuvant, sample 5 with adjuvant 992 had the smallest surface tension of 31.67 mN/m.
Figure 1

Surface tensions of different samples. Different letters (a–d) indicate significant differences between means. Means followed by the same letter are not significant at the 5% significance level by the LSD test (LSD = 0.05). Vertical bars indicate a standard deviation of the mean. The detailed data of the histogram is shown in Supplementary Table S1.

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Contact angle measurement
According to Young’s equation, the smaller the surface tension, the smaller the contact angle40,41. Figure 2 shows the contact angle of different samples on cotton leaves and the change in contact angle over time. The contact angles of oil agents containing the adjuvant 992, AEO-7 and AEO-9 were smaller than that of the reference product, and the spreading effect was superior to that of the reference product. In the surface tension test, sample 5 had the smallest surface tension of 31.67 mN/m; this sample showed the minimum initial contact angle (39°) and a static contact angle (22°). The surface tension of the reference product was 38.90 mN/m., with the maximum initial contact angle (65.5°). Therefore, the relationship between surface tension and contact angle conformed to Young’s equation.
Figure 2

Contact angles of different samples on cotton leaves in 0–10 s. The detailed data of drawing the contact Angle curve is shown in Supplementary Table S2.

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Volatilization rate measurement
As shown in Fig. 3, the volatilization rate of the oil agent was much lower than that of the reference product. The volatilization rate of the five treatments was 5.80–8.74%, while the volatilization rate of the reference product was 22.97%. The volatilization rate of the oil agent met the quality requirements of an ultra-low-volume spray (≤ 30%). A low volatilization rate helps with spraying defoliants in hot and dry areas such as Xinjiang, effectively preventing evaporation of the droplets and increasing deposition.
Figure 3

Volatilization of different samples on filter paper. Different letters (a–e) indicate significant differences between means. Means followed by the same letter are not significant at the 5% significance level by the LSD test (LSD = 0.05). Vertical bars indicate a standard deviation of the mean. The detailed data of the histogram is shown in Supplementary Table S3.

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Viscosity measurement
Viscosity is an important factor affecting the atomization performance of a formulation42. Figure 4 shows that the viscosity of the five oil agents ranged from 12.9 to 18.3 mPa s, meeting the quality requirements of an ultra-low-volume spray ( 20 V), the droplet size distribution tended to be stable. This coincided with data shown in Fig. 6, where the inflection point appeared when rotation speed was 9600 rpm (voltage = 20 V).
Figure 6

Relationship between the rotation speed of the centrifugal spray atomizer and droplet size. D10: 10% cumulative volume diameter, D50: 50% cumulative volume diameter, D90: 90% cumulative volume diameter. The detailed data of drawing the curve is shown in Supplementary Table S6.

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Figure 7

Relationship between the rotation speed of the centrifugal spray atomizer and the fog droplet spectrum. The detailed data of drawing the curve is shown in Supplementary Table S6.

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Therefore, we determined that the optimal working conditions for the rotary atomizer were achieved by setting the DC voltage stabilized power supply current to 1.00 A and voltage to 20 V, which were used for subsequent experiments.
Atomization performance
The relationship between viscosity and droplet spectrum are shown in Table 4 and Fig. 8. The cumulative volume diameter for the five treatments was less than 150 μm meeting the requirements of the ULV spray32. The cumulative volume diameter for the five treatments was larger than that for the reference product, the width of the droplet spectrum was narrower, and the droplet distribution was more uniform. Droplet size affects the drift of droplets43. The D10 of the reference product was 25.62 μm under these working conditions. This droplet size was highly susceptible to drift and deposition on non-target organisms. Water suspension was not suitable for this application at low dosage.
Table 4 Droplet size and droplet size distribution of different sample sprays.
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Figure 8

Relationship between formulation viscosity and droplet spectrum. The detailed data of drawing the figure is shown in Supplementary Table S7.

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As presented in Table 4, droplet size increased with increasing viscosity, which influenced the droplet spectrum. The results in Fig. 8 show that the span of droplet size decreased with the increase of viscosity, indicating that droplets with more uniform distribution could be obtained by increasing the viscosity of the formulation41.
Droplet deposition effect
We tested the efficacy of the ULV spray formulation by spraying cotton plants using an UAV. The test results in Table 5 indicate that increasing the dosage of application would increase droplet size, coverage, and deposition density. At the same application dosage, the droplet size of the ultra-low-volume spray was slightly larger than that of the reference product, and the coverage and deposition density were greater than those of the reference product. The droplet spectral width (Rs) of the five treatments was less than 1, and the coefficient of variation was less than 7%, indicating that the droplet distribution was relatively uniform. Among treatments, T2 had the narrowest Rs and coefficient of variation (CV), where the droplet size distribution was the most uniform. For the ultra-low-volume spray, at the application dosage of 4.5–9.0 L/ha, the droplet coverage gradually increased from 0.85 to 4.15%; the droplet deposition densities were 15.63, 17.24, 28.45, and 42.57 pcs/cm2, which were larger than requirements suggested in the literature. The droplet coverage of the reference product (T5) was 0.73%, and the deposition density was only 11.32 pcs/cm2.
Table 5 Droplet size, coverage, deposition density, spectral width and variation coefficient for each treatment.
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Efficacy trials
The efficacy of cotton defoliant is reflected in the defoliation rate and boll opening rate of cotton after application. Therefore, we surveyed the defoliation rate and boll opening rate of cotton in the test area 3–15 days after application. The results are shown in Figs. 9 and 10.
Figure 9

Defoliation rate 3–15 days after treatment. The detailed data of drawing the curve is shown in Supplementary Table S8.

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Figure 10

Boll opening rate 3–15 days after treatment. The detailed data of drawing the curve is shown in Supplementary Table S9.

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Figure 9 indicates that the defoliation rates of the five treatments 15 days after the pesticide treatment were 59.82%, 63.96%, 71.40%, 77.84%, and 54.58%, respectively. The defoliation rates of T1, T2, and T5 were less than 70%.
Application of the ultra-low-volume spray at 4.50 L/ha or 6.00 L/ha and the reference product at 6.00 L/ha had a poor defoliation effect. T4 (9.00 L/ha) was superior to the others, and the defoliation rate reached 77.84% 15 days after application. As shown in Fig. 10, the boll opening rates of the five treatments were 58.54%, 67.74%, 95.35%, 100%, and 44.68% 15 days after application. Similarly, the boll opening rates of T1, T2, and T5 were poor, with the boll opening rate of the control T5 only 44.68%. We analyzed significant differences between the defoliation rates and boll opening rates of the five treatments. The results showed that the defoliation rate and boll opening rate associated with the thidiazuron·diuron ultra-low-volume spray on cotton plants were significantly different from those of the reference product.
Overall, the defoliation rate and boll opening rate produced by the ultra-low-volume spray were superior to those produced by the reference product. This result was consistent with data shown in Table 5. The higher the droplet coverage rate, the higher the droplet deposition density and the higher the defoliation rate and boll opening rate. T1, T2 and T5 had poor deposition effect on cotton plants, and the effective pesticide utilization rate was low, resulting in dissatisfactory defoliation rates and boll opening rates. Both the droplet coverage rate and the droplet deposition density of T3 and T4 were large. Therefore, droplets of pesticide solution could deposit more easily and uniformly on cotton leaves, allowing the plants to defoliate and open their bolls easily. More

Indian prime minister Indira Gandhi meets Maurice Strong, who chaired the 1972 Stockholm Conference on the Human Environment. Gandhi saw UNEP’s potential at a time when other countries doubted its value.Credit: Yutaka Nagata/UN Photo

The United Nations Environment Programme (UNEP) will be 50 next year. But the globe’s green watchdog, which helped to create the Intergovernmental Panel on Climate Change (IPCC), very nearly didn’t exist.
During talks hosted by Sweden in 1972, low- and middle-income countries were concerned that such a body would inhibit their industrial development. Some high-income countries also questioned its creation. UK representative Solly Zuckerman, a former chief scientific adviser to prime ministers including Winston Churchill, said the science did not justify warnings that human activities could have irreversible consequences for the planet. The view in London was that, on balance, environmental pollution was for individual nations to solve — not the UN.
But the idea of UNEP had powerful supporters, too. India’s prime minister, Indira Gandhi, foresaw its potential in enabling industry to become cleaner and more humane. And the host nation made a wise choice in picking Canadian industrialist Maurice Strong to steer the often fractious talks to success. He would become UNEP’s first executive director. Two decades later, Strong re-emerged to chair the 1992 Earth Summit in Rio de Janeiro, Brazil, which created three landmark international agreements: to protect biodiversity, safeguard the climate and combat desertification.
UNEP has chalked up some impressive achievements in science and legislation. In 1988, working with the World Meteorological Organization, it co-founded the IPCC, whose scientific assessments have been pivotal to global climate action. It also responded to scientists’ warnings about the hole in the ozone layer, leading to the creation of the 1987 Montreal Protocol, an international law to phase out ozone-depleting chemicals.
Strong’s successors would go on to identify emerging green-policy issues and nudge them into the mainstream. UNEP has pushed the world of finance to think about how to stop funding polluting industries. It has also advocated working with China to green its rapid industrial growth — including the Belt and Road Initiative to develop global infrastructure. It is essential that this work continues.
UNEP also accelerated the creation of environment ministries around the world. Their ministers sit on the programme’s governing council; at their annual meeting last week, they reflected on what UNEP must do to tackle the environmental crisis. Although the environment is a rising priority for governments, businesses and civil society, progress on the UN’s flagship Sustainable Development Goals — in biodiversity, climate, land degradation, pollution, finance and more — is next to non-existent. Moreover, the degradation of nature is putting hard-won gains at risk, argues a report that UNEP commissioned as part of its half-century commemorations.
The report, Making Peace with Nature, assesses much of the same literature as would a climate- or land-degradation assessment, but its key strength is in how it brings together researchers from across environmental science. In doing so, UNEP is helping to accelerate a mode of working that should be standard. If, for example, there is to be an assessment of how climate change affects biodiversity, it makes much more sense for this to be carried out by a joint team from the IPCC and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) than by researchers from just one of these organizations.
The UNEP report’s authors stop short of recommending such changes to the architecture of the UN’s scientific advisory bodies. That is a missed opportunity. Also missing is a discussion and recommendations on how to make countries more accountable for their environmental pledges.
Both these actions are sorely needed if the world is to take more meaningful steps to battle climate change and biodiversity loss. Countries have become expert in capturing data and reporting them to UN organizations. But there is no mechanism that holds nations to account. For example, there is no system to ensure compliance with targets for the Sustainable Development Goals.
Last week, the UN produced a report in which countries published their progress towards commitments under the 2015 Paris climate agreement, known as nationally determined contributions. The agreement includes almost 200 countries, but just 75 reported their data. There are few incentives for success and no penalties for failure. Without such measures, it is hard to see how meaningful change could ever happen.
In the past, researchers have proposed that UNEP’s member states upgrade its powers so it becomes more of a compliance body — a World Environment Organization that, like the World Trade Organization, has the power to censure countries for failing to keep to agreements. But this has been resisted as too radical a step, which would upend the autonomy of the UN biodiversity and climate organizations that UNEP itself helped to bring into being.
Twenty years ago, there might have been some justification for such a view, but now, with the world on a path to extreme climate change, any action will need to be radical, including considering how to give UNEP more teeth.
UNEP helped to lay the foundations for a scientific consensus on environmental decline, and it should be proud of the body of law that has been enacted globally. Alas, such measures risk being too little, too late. As it embarks on a year of reflection ahead of its anniversary, member states must consider what more they need to do to empower UNEP to tackle the planetary emergency. More

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