Ecology

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Vörösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).Article 
CAS 

Google Scholar 
Ruhi, A., Messager, M. L. & Olden, J. D. Tracking the pulse of the Earth’s fresh waters. Nat. Sustain. 1, 198–203 (2018).Article 

Google Scholar 
Pearson, C. Short- and medium-term climate information for water management. World Meteorol. Organ. Bull. 57, 173–177 (2008).
Google Scholar 
Tetzlaff, D., Carey, S. K., McNamara, J. P., Laudon, H. & Soulsby, C. The essential value of long-term experimental data for hydrology and water management. Water Resour. Res. 53, 2598–2604 (2017).Article 

Google Scholar 
Carlisle, D. M., Wolock, D. M. & Meador, M. R. Alteration of streamflow magnitudes and potential ecological consequences: a multiregional assessment. Front. Ecol. Environ. 9, 264–270 (2011).Article 

Google Scholar 
Shrestha, S., Kazama, F. & Newham, L. T. H. A framework for estimating pollutant export coefficients from long-term in-stream water quality monitoring data. Environ. Model. Softw. 23, 182–194 (2008).Article 

Google Scholar 
Lepistö, A., Futter, M. N. & Kortelainen, P. Almost 50 years of monitoring shows that climate, not forestry, controls long-term organic carbon fluxes in a large boreal watershed. Glob. Change Biol. 20, 1225–1237 (2014).Article 

Google Scholar 
Hester, G., Ford, D., Carsell, K., Vertucci, C. & Stallings, E. A. Flood Management Benefits of USGS Streamgaging Program (National Hydrologic Warning Council, 2006).Xu, H., Xu, C.-Y., Chen, H., Zhang, Z. & Li, L. Assessing the influence of rain gauge density and distribution on hydrological model performance in a humid region of China. J. Hydrol. 505, 1–12 (2013).Article 

Google Scholar 
Kiang, J. E., Stewart, D. W., Archfield, S. A., Osborne, E. B. & Eng, K. A National Streamflow Network Gap Analysis (USGS, 2013).Deweber, J. T. et al. Importance of understanding landscape biases in USGS gage locations: implications and solutions for managers. Fisheries 39, 155–163 (2014).Article 

Google Scholar 
Tickner, D. et al. Bending the curve of global freshwater biodiversity loss: an emergency recovery plan. BioScience 70, 330–342 (2020).Article 

Google Scholar 
Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).CAS 
Article 

Google Scholar 
Olden, J. D. et al. Hydrologic classification of Tanzanian rivers to support national water resource policy. Ecohydrology. https://doi.org/10.1002/eco.2282 (2021).Lin, P. et al. Global reconstruction of naturalized river flows at 2.94 million reaches. Water Resour. Res. 55, 6499–6516 (2019).Article 

Google Scholar 
Yamazaki, D. et al. MERIT Hydro: a high-resolution global hydrography map based on latest topography dataset. Water Resour. Res. 55, 5053–5073 (2019).Article 

Google Scholar 
Beck, H. E. et al. Bias correction of global high-resolution precipitation climatologies using streamflow observations from 9,372 catchments. J. Clim. 33, 1299–1315 (2020).Article 

Google Scholar 
Do, H. X., Gudmundsson, L., Leonard, M. & Westra, S. The Global Streamflow Indices and Metadata Archive (GSIM)—part 1: the production of a daily streamflow archive and metadata. Earth Syst. Sci. Data 10, 765–785 (2018).Article 

Google Scholar 
Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci. Data 6, 283 (2019).Article 

Google Scholar 
Dobrushin, R. L. Prescribing a system of random variables by conditional distributions. Theory Probab. Appl. 15, 458–486 (1970).Article 

Google Scholar 
Schefzik, R., Flesch, J. & Goncalves, A. Fast identification of differential distributions in single-cell RNA-sequencing data with waddR. Bioinformatics 37, 3204–3211 (2021).CAS 
Article 

Google Scholar 
Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).Article 

Google Scholar 
Wittemyer, G., Elsen, P., Bean, W. T., Burton, A. C. O. & Brashares, J. S. Accelerated human population growth at protected area edges. Science 321, 123–126 (2008).CAS 
Article 

Google Scholar 
Colvin, S. A. R. et al. Headwater streams and wetlands are critical for sustaining fish, fisheries, and ecosystem Services. Fisheries 44, 73–91 (2019).Article 

Google Scholar 
Chen, K. & Olden, J. D. Threshold responses of riverine fish communities to land use conversion across regions of the world. Glob. Change Biol. 26, 4952–4965 (2020).Article 

Google Scholar 
Pardo, I. et al. The European reference condition concept: a scientific and technical approach to identify minimally-impacted river ecosystems. Sci. Total Environ. 420, 33–42 (2012).CAS 
Article 

Google Scholar 
Sauquet, E. et al. Classification and trends in intermittent river flow regimes in Australia, northwestern Europe and USA: a global perspective. J. Hydrol. 597, 126170 (2021).Article 

Google Scholar 
Creed, I. F. et al. Enhancing protection for vulnerable waters. Nat. Geosci. 10, 809–815 (2017).CAS 
Article 

Google Scholar 
Abell, R. et al. Freshwater ecoregions of the world: a new map of biogeographic units for freshwater biodiversity conservation. BioScience 58, 403–414 (2008).Article 

Google Scholar 
Wilhite, D. A. in Coping with Drought Risk in Agriculture and Water Supply Systems: Drought Management and Policy Development in the Mediterranean, Vol. 26 (eds. Iglesias, A. et al.) 3–19 (Springer Science and Business Media, 2009).Winemiller, K. O. et al. Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Science 351, 128–129 (2016).CAS 
Article 

Google Scholar 
Seyfried, M. S. & Wilcox, B. P. Scale and the nature of spatial variability: field examples having implications for hydrologic modeling. Water Resour. Res. 31, 173–184 (1995).Article 

Google Scholar 
Hammond, J. C. et al. Spatial patterns and drivers of nonperennial flow regimes in the contiguous United States. Geophys. Res. Lett. 48, e2020GL090794 (2021).Article 

Google Scholar 
Messager, M. L. et al. Global prevalence of non-perennial rivers and streams. Nature 594, 391–397 (2021).CAS 
Article 

Google Scholar 
Busch, M. H. et al. What’s in a name? Patterns, trends, and suggestions for defining non-perennial rivers and streams. Water 12, 1980 (2020).Article 

Google Scholar 
Zipper, S. C. et al. Pervasive changes in stream intermittency across the United States. Environ. Res. Lett. 16, 084033 (2021).Article 

Google Scholar 
Jaeger, K. L., Olden, J. D. & Pelland, N. A. Climate change poised to threaten hydrologic connectivity and endemic fishes in dryland streams. Proc. Natl Acad. Sci. USA 111, 13894–13899 (2014).CAS 
Article 

Google Scholar 
Beaufort, A., Lamouroux, N., Pella, H., Datry, T. & Sauquet, E. Extrapolating regional probability of drying of headwater streams using discrete observations and gauging networks. Hydrol. Earth Syst. Sci. 22, 3033–3051 (2018).Article 

Google Scholar 
Argerich, A. et al. Comprehensive multiyear carbon budget of a temperate headwater stream: carbon budget of a headwater stream. J. Geophys. Res. Biogeosci. 121, 1306–1315 (2016).CAS 
Article 

Google Scholar 
Molden, D. J., Shrestha, A. B., Nepal, S. & Immerzeel, W. W. in Water Security, Climate Change and Sustainable Development (eds. Biswas, A. K. & Tortajada, C.) 65–82 (Springer, 2016).Kaletová, T. et al. Relevance of intermittent rivers and streams in agricultural landscape and their impact on provided ecosystem services—a Mediterranean case study. Int. J. Environ. Res. Public Health 16, 2693 (2019).Article 

Google Scholar 
Zimmer, M. A. et al. Zero or not? Causes and consequences of zero-flow stream gage readings. WIREs Water 7, e1436 (2020).Article 

Google Scholar 
Wine, M. L. Toward strong science to support equitable water sharing in securitized transboundary watersheds. Biologia 9, 907–915 (2020).Article 

Google Scholar 
Alsdorf, D. E. GEOPHYSICS: tracking fresh water from space. Science 301, 1491–1494 (2003).CAS 
Article 

Google Scholar 
Benstead, J. P. & Leigh, D. S. An expanded role for river networks. Nat. Geosci. 5, 678–679 (2012).CAS 
Article 

Google Scholar 
Allen, D. C. et al. Citizen scientists document long-term streamflow declines in intermittent rivers of the desert southwest, USA. Freshw. Sci. https://doi.org/10.1086/701483 (2019).Joo, H. et al. Optimal stream gauge network design using entropy theory and importance of stream gauge stations. Entropy 21, 991 (2019).Article 

Google Scholar 
Vörösmarty, C. et al. Global water data: a newly endangered species. Eos 82, 54–58 (2001).Article 

Google Scholar 
Jordahl, K. et al. Geopandas/geopandas. Zenodo https://doi.org/10.5281/zenodo.3946761 (2020).Lin, P., Pan, M., Wood, E. F., Yamazaki, D. & Allen, G. H. A new vector-based global river network dataset accounting for variable drainage density. Sci. Data 8, 28 (2021).Article 

Google Scholar 
Yu, S. et al. Evaluating a landscape-scale daily water balance model to support spatially continuous representation of flow intermittency throughout stream networks. Hydrol. Earth Syst. Sci. 24, 5279–5295 (2020).CAS 
Article 

Google Scholar 
Kennard, M. J. et al. Classification of natural flow regimes in Australia to support environmental flow management. Freshw. Biol. 55, 171–193 (2010).Article 

Google Scholar 
Flow/No Flow Observations with Discharge Data from Probabilistic Stream Surveys (US EPA Office of Research and Development, 2021).Rosenbaum, P. R. & Rubin, D. B. The bias due to incomplete matching. Biometrics 41, 103–116 (1985).CAS 
Article 

Google Scholar  More

General experimental proceduresAll chemicals were purchased from Sigma-Aldrich, Acros Organics or Iris BIOTECH. Isotope-labelled chemicals were purchased from Cambridge Isotope Laboratories. Genomic DNA of selected Xenorhabdus and Photorhabdus strains was isolated using the Qiagen Gentra Puregene Yeast/Bact Kit. DNA polymerases (Taq, Phusion and Q5) and restriction enzymes were purchased from New England Biolabs or Thermo Fisher Scientific. DNA primers were purchased from Eurofins MWG Operon. PCR amplifications were carried out on thermocyclers (SensoQuest). Polymerases were used according to the manufacturers’ instructions. DNA purification was performed from 1% Tris-acetate-EDTA (TAE) agarose gel using an Invisorb Spin DNA Extraction Kit (STRATEC Biomedical AG). Plasmids in E. coli were isolated by alkaline lysis. HPLC-UV-MS analysis was conducted on an UltiMate 3000 system (Thermo Fisher) coupled to an AmaZonX mass spectrometer (Bruker) with an ACQUITY UPLC BEH C18 column (130 Å, 2.1 mm × 100 mm, 1.7-μm particle size, Waters) at a flow rate of 0.6 ml min−1 (5–95% acetonitrile/water with 0.1% formic acid, vol/vol, 16 min, UV detection wavelength 190–800 nm). HPLC-UV-HRMS analysis was conducted on an UltiMate 3000 system (Thermo Fisher) coupled to an Impact II qTof mass spectrometer (Bruker) with an ACQUITY UPLC BEH C18 column (130 Å, 2.1 mm × 100 mm, 1.7-μm particle size, Waters) at a flow rate of 0.4 ml min−1 (5–95% acetonitrile/water with 0.1% formic acid, vol/vol, 16 min, UV detection wavelength 190–800 nm). Flash purification was performed on a Biotage SP1 flash purification system (Biotage) by a C18 main column (Interchim, PF50C18HP-F0080, 120 g) with a self-packed pre-column (Interchim, PF-DLE-F0012, Puriflash dry-load empty F0012 Flash column) coupled with a UV detector. HPLC purification was performed on preparative and semipreparative Agilent 1260 systems coupled to a diode array detector (DAD) and a single quadrupole detector with a C18 ZORBAX Eclipse XDB column (9.4 mm × 250 mm, 5 μm, 3 ml min−1; 21.2 mm × 250 mm, 5 μm, 20 ml min−1; 50 mm × 250 mm, 10 μm, 40 ml min−1). Freeze drying was performed using a BUCHI Lyovapor L-300 Continuous system. NMR experiments were carried out on a Bruker AVANCE 500-, 600- or 700-MHz spectrometer equipped with a 5-mm cryoprobe. 2R,3S-IOC (1) and GameXPeptide A (16) were synthesized by WuXi App Tec following the literature (ref. 73 for 2R,3S-1 and ref. 74 for 16).Genome sequencing, assembly and annotationIsolated DNA was sequenced on the Illumina NextSeq 500 platform. DNA libraries were constructed using the Nextera XT DNA preparation kit (Illumina) and whole-genome sequencing was performed using 2× 150-bp paired-end chemistry. A sequencing depth of >50× was targeted for each sample. Adapters and low-quality ends were trimmed with Trimmomatic 0.39 (ref. 75) and the parameters [2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:10 MINLEN:12] using a database of adapter sequences as provided by Illumina. All genomes were assembled using SPAdes v. 3.10.1 (ref. 76) executed with the following parameters: –cov-cutoff auto –careful in paired-end mode plus mate pairs (in cases where accompanying mate-pair libraries were available). Genome annotation was performed using Prokka v. 1.12 (ref. 77) with the following parameters: –usegenus–genus GENUS–addgenes–evalue 0.0001–rfam–kingdom Bacteria–gcode 11–gram –mincontiglen 200. Geneious Prime 2021 was used in genome visualization and analysis.antiSMASH annotations and BiG-FAM preliminary classificationThe antiSMASH 5.0 (ref. 23) web server was employed to mine all the genome sequences for the presence of putative natural-product BGCs. The annotations were conducted using default settings with the extended parameters of ClusterBlast, Cluster Pfam analysis and Pfam-based GO term annotation. The annotated BGCs were summarized for each strain (Supplementary Fig. 1) and visualized in the anvi’o 6.1 (refs. 28,78) layers (Fig. 1 and Supplementary Figs. 3 and 5). We then submitted the antiSMASH job IDs to the biosynthetic gene cluster families database (BiG-FAM 1.0.0)25 for preliminary GCF explorations and classifications of annotated BGCs (Supplementary Table 3), followed by BiG-SCAPE 1.0.0 (ref. 42) refinement with a cutoff of 0.65 (Source Data Fig. 3). The GCFs were double-checked manually via the interactive network (Fig. 3), and corrections were made if necessary. A putative thiopeptide BGC (Xszus_1.region006, Xsze_2.region003, Xsto_4.region001, Xpb_30.3_21.region001, Xmir_10.region001, Xmau_6.region001, Xkoz_3.region001, Xjap_NZ_FOVO01000011.region001, Xish_1.region003, Xhom_ANU1.region005, Xhom_2.region003, Xets_11.region001, XenKK7.region002, XenDL20_c00108_NODE_12.region001, Xekj_19.region001, Xehl_28.region001, Xe30TX1_c0031_NODE_38.region001, Xdo_HBLC131_1.region001, Xdo_FRM16.1.region005, Xbov_NC_013892.1.region004, Ptem_HBLC135_17.region001, Ppb6_4.region001, Plum_TT01_1.region008, Pthr_PT1.1_23.region001, Plau_IT4.1_12.region001, Plum_IL9_35_scf0001.region001, Pbod_HU2.3_20.region001, Plau_HB1.3_105.region001, Plum_EN01_24_scf0009.region001, Pbod_DE6.1_24.region001, Plau_DE2.2_108.region001, Phpb_1.region001, Pbod_LJ_007.region001, Pbod_CN4_25_scf0020.region001, Paeg_BKT4.5_19.region001, P_tem_1.region017 and so on) that exists throughout 45 XP genomes was excluded in the analysis, because it turned out that its annotation by antiSMASH 5.0 is a false positive and early reports suggest that this cluster is responsible for ribosomal methylthiolation79,80. Two BGCs, Xdo_HBLC131_4.region001 encoding the biosynthesis of glidobactins in X. doucetiae HBLC131 and Ptem_HBLC135_2.region002 encoding the biosynthesis of ririwpeptides in P. temperata HBLC135, were artificially integrated into their respective genome by CRAGE39 previously, and thus the two BGCs were also excluded in our analysis.Pangenome analysisBiosynthetic gene cluster boundary definitionThe cluster boundary was defined by antiSMASH with the start nucleotide of the first biosynthetic gene (5′ end) and the stop nucleotide of the last biosynthetic gene (3′ end), and was manually corrected if necessary. Non-structural genes (such as transporters, regulators, transposases and so on) on the outer periphery of an operon were excluded. We compiled a table with contigs of all BGCs encoded by a given genome, BGC start and stop nucleotide positions, BGC classifications by antiSMASH and BiG-SCAPE (see the BiG-SCAPE analysis section), and possible biosynthetic pathways that the BGCs encode (Source Data Fig. 1). These tables would be integrated into the contigs databases of the pangenome for filtering the biosynthetic genes and monitoring distributions of biosynthetic gene homology groups.Interface generationAll genomes were obtained from the National Center for Biotechnology Information (NCBI). Supplementary Table 1 reports their accession numbers. The pangenome analysis herein mainly followed the anvi’o 6.1 pangenomic workflow28,78. After simplifying the header lines of 45 FASTA files for genomes using ‘anvi-script-reformat-fasta’, we converted FASTA files into anvi’o contigs databases by the ‘anvi-gen-contigs-database’ and then decorated the contigs database with hits from HMM models by ‘anvi-run-hmms’. The program ‘anvi-run-ncbi-cogs’ was run to annotate genes in the contigs databases with functions from the NCBI’s Clusters of Orthologous Groups (COGs). Tables of gene caller IDs with start and stop nucleotide positions were exported by ‘anvi-export-table’. By linking the gene caller IDs with BGCs via the start and stop nucleotide positions, genes that fell within a given BGC boundary were considered to be natural product biosynthetic genes (Source Data Fig. 1). Thereafter, the biosynthetic genes were furnished with a classification and a possible compound name, both of which were derived from the BGC that the biosynthetic genes made up. The obtained tables were imported back to contigs databases by ‘anvi-import-functions’. External genome storage was created by ‘anvi-gen-genomes-storage’ to store DNA and amino-acid sequences, as well as functional annotations of each gene. With the genome storage in hand, we used the program ‘anvi-pan-genome’ with the genomes storage database, the flag ‘–use-ncbi-blast’ and the parameter ‘–mcl-inflation 8’. The results were displayed in an interface by ‘anvi-display-pan’. The organization of the pangenome interface as shown in the dendrogram in the centre was represented by ‘presence/absence’ patterns. The core gene bin was characterized by searching the gene homology group (gene homology group represents amino-acid sequences from one or more genomes aligned by muscle81) using filters with ‘Min number of genomes gene homology group occurs, value = 45’. The singleton bin was identified by ‘Max number of genomes gene homology group occurs, value = 1’. The rest of the gene clusters that were neither sorted into the core gene bin nor the singleton bin were appended to the accessory bin. The single-copy-core-gene (scg) bin was found by ‘Min number of genomes gene homology group occurs, value = 45’ and ‘Max number of genes from each genome, value = 1’. The scg bin was refined by ‘Max functional homogeneity index 0.9’ and ‘Min geometric homogeneity index 1’. The resulting protein sequences were exported by ‘anvi-get-sequences-for-gene-clusters’ and aligned using ClustalW 1.2.2, which is incorporated in Geneious Prime 2021. Phylogenetic trees were generated using the Geneious tree builder utilizing the Jukes–Cantor distance model and the unweighted pair group method with arithmetic mean (UPGMA), and subsequently imported back to anvi’o by ‘anvi-import-misc-data’ and visualized by the interface. The statistical data of BGCs obtained from antiSMASH 5.0 (ref. 23) and BiG-SCAPE42 were imported to the layers of the interface by ‘anvi-import-misc-data’ for visualization.Biosynthetic gene and biosynthetic gene cluster filteringThe bin summary (scg, core, accessory and singleton) with BGC classifications was exported by ‘anvi-summarize’ to monitor the distributions of the biosynthetic gene homology group in the pangenomes (Source Data Fig. 1 and Supplementary Data). In the Excel sheets, ‘core’ and ‘scg’ filters were selected from the ‘bin_name’ column, and the ‘(Blank)’ filter from the ‘BGC_classification’ column was unselected. The table was then sorted by ‘genome_name’ and ‘gene_callers_id’ columns in ascending order. This then displayed consecutive core biosynthetic genes that could possibly make up a BGC. The same procedure was used to filter BGCs in the accessory or singleton region.BiG-SCAPE analysisBGCs in all genome sequences obtained from antiSMASH 5.0 (ref. 23) analyses were compared to reference BGCs from MIBiG repository 2.0 (refs. 41,82) using BiG-SCAPE 1.0.0 (ref. 42) with the PFAM database 32.0 (ref. 83). The analysis was conducted using default settings with the mode ‘auto’, mixing all classes and retaining singletons. Networks were computed for raw distance cutoffs of 0.30–0.95 in increments of 0.05. Results were visualized as a network using Cytoscape 3.7.2 (ref. 84) for a cutoff of 0.65 (Fig. 3 and Source Data Fig. 3). Statistical data for the BGCs were analysed and evaluated using Origin 2020b and Excel from Microsoft Office 365.Strain and culture conditionsWild-type strains and the mutants thereof and E. coli (Supplementary Table 14) were cultivated on lysogeny broth (LB) agar plates at 30 °C overnight, and subsequently inoculated into liquid LB culture at 30 °C with shaking at 200 r.p.m. For compound production, the overnight LB culture was transferred into 5 ml of LB, XPP19 or Sf-900 II SFM medium (1:100, vol/vol) with 2% (vol/vol) Amberlite XAD-16 resins, 0.1% l-arabinose as the inducer for mutants with a PBAD promoter, and selective antibiotics such as ampicillin (Am, 100 µg ml−1), kanamycin (Km, 50 µg ml−1) or chloramphenicol (Cm, 34 µg ml−1) at 30 °C, with shaking at 200 r.p.m.Culture extraction and HPLC-UV-MS analysisThe XAD-16 resins were collected after 72 h and extracted with 5 ml of methanol or ethyl acetate. The solvent was dried under rotary evaporators, and the dried extract was resuspended in 500 μl of methanol or acetonitrile/water (1:1 vol/vol for photoxenobactins), of which 5 μl was injected and analysed by HPLC-UV-MS or HPLC-UV-HRMS. Unless otherwise specified, HPLC-UV-MS and HPLC-UV-HRMS chromatograms in the figures are shown on the same scale. Bruker Compass DataAnalysis 4.3 was used for data collection and analysis of chromatography and MS. MetabolicDetec 2.1 was utilized to differentiate MS profiles between induced and non-induced promoter insertion mutants for identifying possible metabolites produced by targeted BGCs.Construction of PBAD promoter insertion mutantsA 500–800-bp section upstream of the target gene (lpcS, pxbF, rdb1A and xvbA) was amplified with a corresponding primer pair as listed in Supplementary Table 15. The resulting fragments were cloned using Hot Fusion85 into a pCEP_kan or pCEP_cm backbone that was amplified by pCEP_Fw and pCEP_Rv. After transformation of a constructed plasmid into E. coli S17-1 λ pir, clones were verified by PCR with primers pCEP-Ve-Fw and pDS132-Ve-Rv. A wild-type strain (X. bovienii SS-2004, X. szentirmaii DSM 16338, X. budapestensis DSM 16342 or X. vietnamensis DSM 22392) or a deletion mutant (X. szentirmaii ∆hfq, X. budapestensis ∆rdb1P or X. budapestensis ∆rdb1P ∆hfq) was used as a recipient strain. The recipient strain was mated with E. coli S17-1 λ pir (donor) carrying a constructed plasmid (Supplementary Table 16). Both strains were grown in the LB medium to an optical density at 600 nm (OD600) of 0.6 to 0.7, and the cells were washed once with fresh LB medium. Subsequently, the donor and recipient strains were mixed on an LB agar plate in ratios of 1:3 and 3:1, and incubated at 37 °C for 3 h followed by incubation at 30 °C for 21 h. After that, the bacterial cell layer was collected with an inoculating loop and resuspended in 2 ml of fresh LB medium. A 200-μl sample of the resuspended culture was spread out on an LB agar plate with Am/Km or Am/Cm and incubated at 30 °C for two days. Individual insertion clones were cultivated and analysed by HPLC-UV-HRMS, and the genotype of all mutants was verified by plasmid- and genome-specific primers.Construction of deletion mutantsA ~1,000-bp upstream and a ~1,000-bp downstream fragment of hfq in X. budapestensis DSM 16342 were amplified using the primer pairs listed in Supplementary Table 15. The amplified fragments were fused using the complementary overhangs introduced by primers and cloned into the pEB17 vector that was linearized with PstI and BglII by Hot Fusion85. Transformation of E. coli S17-1 λ pir with the resulting plasmid (Supplementary Table 16) and conjugation with X. budapestensis DSM 16342, as well as the generation of double crossover mutants via counterselection on LB plates containing 6% sucrose, were carried out as previously described86. The deletion mutant was verified via PCR using the primer pairs listed in Supplementary Table 15, which yielded a ~2,000-bp fragment for mutants genetically equal to the WT strain and a ~1,000-bp fragment for the desired deletion mutant. The same procedure was used to generate Δrdb1P mutants, during which E. coli S17-1 λ pir carrying pEB17 rdb1P was mated with the X. budapestensis DSM 16342 wild-type and X. budapestensis ∆hfq mutant.Labelling experiments for structural elucidation of photoxenobactins C and D by MSThe cultivation of strains for labelling experiments was carried out as described above. For photoxenobactin C (6) labelling experiments, the overnight culture was transferred into LB medium additionally fed with 4-fluorosalicylate-SNAC, l-methionine-(methyl-d3), l-[U-13C,15N]cysteine and l-[U-34S]cysteine at a final concentration of 1 mM. In terms of inverse feeding experiments, cell pellets of the 100-μl overnight culture were washed once with ISOGRO 13C or 13C,15N medium (100 μl) and resuspended in the corresponding isotope labelling medium (100 μl). The feeding culture in the isotope labelling medium (5 ml) was inoculated with a washed overnight culture (50 μl) and additional l-cysteine was added at a final concentration of 1 mM.For photoxenobactin D (7) labelling experiments, the cell pellets of the 100-μl overnight culture were washed once with ISOGRO 13C or 15N medium (100 μl) and then resuspended in the corresponding isotope labelling medium (100 μl). A 5-ml isotope labelling medium was inoculated with a washed overnight culture (50 μl).Isolation and purificationFor photoxenobactin isolation, 10 ml of LB medium was inoculated with a colony of the X. szentirmaii PBAD pxbF ∆hfq mutant from an LB agar plate and cultivated overnight. A 10-ml culture was taken to inoculate 2 × 100 ml of LB medium (OD600 ≈ 0.1). The 2 × 100-ml cultures were incubated overnight and the whole culture volume (200 ml) was used to inoculate a 20-l LB fermenter (Braun) supplemented with 2% XAD-16 and 0.2% arabinose (antifoam was added when required). Fermenter settings were as follows: 30 °C without pH control, three six-blade impellers 150 r.p.m. After 24 h, 10 l of the culture was collected from the fermenter, and the XAD resins were separated from the cells by filtration. (1) The XAD resins were extracted with 2 × 1 l of ethyl acetate with 1% formic acid, and the combined organic phase was dried under reduced pressure. (2) The culture without XAD was centrifuged and the supernatant was extracted with 3 × 5 l of ethyl acetate with 1% formic acid, and the combined organic layers were dried under reduced pressure. (3) The cell pellet was extracted with 2 × 1 l of ethyl acetate with 1% formic acid, and the organic supernatant was dried under reduced pressure. After 48 h, the remaining 10 l of bacterial culture were extracted as described in steps (1) to (3). The combined extracts from 20 l of culture were fractionated by a flash purification system with a C18 column with a gradient elution of acetonitrile/water 20–100% at 20 ml min−1 (every 10% gradient step was performed with five column volumes, except the 60–70% step, which was performed with ten column volumes). Fractions containing photoxenobactins were combined and dried under reduced pressure. Final purification was achieved via preparative and semipreparative HPLCs with a gradient of 30% acetonitrile/water (0–30 min) and 30–100% acetonitrile/water (30–40 min). The fractions were combined in brown flasks and were immediately freeze-dried to afford photoxenobactin A (4, 0.8 mg), photoxenobactin B (5, 0.6 mg), photoxenobactin C (6, 1.2 mg) and photoxenobactin E (8, 2.2 mg).For the isolation and purification of lipocitides A and B, 2% of XAD-16 resins from a 6-l LB culture of the X. bovienii PBAD lpcS mutant induced by l-arabinose were collected after 72 h of incubation at 30 °C with shaking at 120 r.p.m., and were washed with water and extracted with methanol (3 × 1 l) to yield a crude extract (5.3 g after evaporation). The extract was dissolved in methanol and was subjected to preparative HPLC with a C18 column using an acetonitrile/water gradient (0.1% formic acid) for 0–32 min, 55–80%, 40 ml min−1 to afford lipocitides A (17, 4.8 mg) and B (18, 9.0 mg).Two percent of XAD-16 resins from a 12-l LB culture of the X. budapestensis PBAD rdb1A ∆rdb1P ∆hfq mutant induced by l-arabinose were collected after 72 h of incubation at 30 °C with shaking at 120 r.p.m. and washed with water and extracted with methanol (3 × 2 l) to yield a crude extract (15.3 g after evaporation). The extract was subject to a Sephadex LH-20 column eluted with methanol. The fraction (2.8 g) containing pre-rhabdobranins was subjected to preparative HPLC with a C18 column using an acetonitrile/water gradient (0.1% formic acid) for 0–20 min, 15–35%, 40 ml min−1 to afford a fraction (206 mg) mainly containing pre-rhabdobranin D, which was further purified by semipreparative HPLC with a C18 column using an acetonitrile/water gradient (0.1% formic acid) for 0–24 min, 5–53%, 3 ml min−1 to afford pre-rhabdobranin D (27, 59.1 mg).Benzobactin A (28) and its methyl ester (29), which were detected in X. vietnamensis PBAD xvbA, were also produced by Pseudomonas chlororaphis subsp. piscium DSM 21509 (unpublished). Owing to the high production level in Pseudomonas chlororaphis subsp. piscium DSM 21509, 28 and 29 were isolated from the Pseudomonas strain. Four percent of XAD-16 resins from a 12-l XPP culture of Pseudomonas chlororaphis subsp. piscium DSM 21509 PBAD pbzA mutant induced by l-arabinose were collected after 72 h of incubation at 30 °C with shaking at 120 r.p.m., and washed with water and extracted with methanol (3 × 2 l) to yield a crude extract (95.4 g after evaporation). The extract was dissolved in methanol and subjected to preparative HPLC with a C18 column using an acetonitrile/water gradient (0.1% formic acid) for 0–18 min, 5–59%, 20 ml min−1 to afford ten fractions. Fractions 2 (95.6 mg) and 3 (50.7 mg) were further purified by semipreparative HPLC with a C18 column using an acetonitrile/water gradient (0.1% formic acid) for 0–35 min, 5–95%, 3 ml min−1 to afford benzobactin A (28, 3.2 mg) and its methyl ester (29, 0.9 mg), respectively.NMR spectroscopyMeasurements were carried out using 1H and 13C NMR, 1H-13C heteronuclear single quantum coherence (HSQC), 1H-13C heteronuclear multiple bond correlation (HMBC), 1H-1H correlation spectroscopy (COSY), 1H-13C heteronuclear multiple quantum correlation/1H-1H correlation spectroscopy (HMQC-COSY) and 1H-13C heteronuclear single quantum coherence/1H-1H total correlation spectroscopy (HSQC-TOCSY). Chemical shifts (δ) were reported in parts per million (ppm) and referenced to the solvent signals. Data are reported as follows: chemical shift, multiplicity (br = broad, s = singlet, d = doublet, t = triplet, dd = doublet of doublet, m = multiplet and ov = overlapped) and coupling constants (in hertz). Bruker TopSpin 4.0 was used for NMR data collection and spectral interpretation.General synthetic proceduresThe Fmoc protecting group was removed with 2 ml of 40% piperidine/dimethylformamide (DMF; 5 min) followed by 2 ml of 20% piperidine/DMF (10 min). Washings between coupling and deprotection steps were performed with DMF (five syringe volumes) and dichloromethane (DCM) (five syringe volumes). Resin loadings were determined by Fmoc cleavage from a weighted resin sample87. The combined filtrates containing Fmoc cleavage products were quantified spectrophotometrically at 301 nm using a UV–vis spectrophotometer with Hellma absorption cuvettes with a path length of 1 cm. Loadings were calculated (in mmol resin) using Lambert–Beer’s law with ɛ = 7,800 M−1 cm−1: loading (mmol) = ({frac{{{rm{Abs}},{({rm{sample}})}}}{{varepsilon l}}} times V), where ɛ is the molar extinction coefficient, V is the sample volume in liter and l is the optical path length in cm. Final cleavage was achieved by shaking the resin in 2 ml of a mixture of TFA/TIPS/H2O (95:2.5:2.5) for 1 h. The filtrate was then collected and the resin washed three times (2 ml each) with DCM, and the combined filtrates were dried under reduced pressure.Syntheses of lipocitide AFmoc-protected Rink Amide resin (192 mg, 0.52 mmol g−1, 0.1 mmol) was placed in a polypropylene 6-ml syringe vessel fitted with polyethylene porous filter disks and swollen in 3 ml of DMF for 10 min. Subsequently, the Fmoc-protected resin was deprotected and then washed as described in the general synthetic procedures. Fmoc-d-Cit-OH (198.0 mg, 0.5 mmol, 5 equiv.), 1-hydroxy-7-azabenzotriazole (HOAT, 0.83 ml, 0.5 mmol, 5 equiv.), hexafluorophosphate azabenzotriazole tetramethyl uronium (HATU, 190.5 mg, 0.5 mmol, 5 equiv.) and N,N-diisopropylethylamine (DIPEA, 170 μl, 1.0 mmol, 10 equiv.) were dissolved in 1.5 ml of dry DMF. After 5 min, the clear solution was added to the resin and shaken at room temperature overnight. The resin was washed and loading was calculated (79.2%) as described in the general synthetic procedures. Acylation of Fmoc-l-Ala-OH (74.1 mg, 0.24 mmol, 3 equiv.), Fmoc-d-Leu-OH (84.8 mg, 0.24 mmol, 3 equiv.) and myristic acid (54.8 mg, 0.24 mmol, 3 equiv.) were carried out using the abovementioned procedure. Final cleavage was performed as described in the general synthetic procedures, and the crude product (70.8 mg) was purified by HPLC to obtain lipocitide A (17, Supplementary Fig. 100; 24.3 mg, 54.0%) as a white solid.Syntheses of lipocitide B2-CTC resin (63 mg, 1.6 mmol g−1, 0.1 mmol) was placed in a polypropylene 6-ml syringe vessel fitted with polyethylene porous filter disks. The resin was incubated with Fmoc-d-Cit-OH (119.0 mg, 0.3 mmol, 3 equiv.) and DIPEA (153 μl, 0.9 mmol, 9 equiv.) in 1.5 ml of dry DCM at room temperature overnight. The resin was washed and loading was calculated (56.7%) as described in the general synthetic procedures. Acylations of Fmoc-l-Ala-OH (52.9 mg, 0.17 mmol, 3 equiv.), Fmoc-d-Leu-OH (60.1 mg, 0.17 mmol, 3 equiv.) and myristic acid (38.9 mg, 0.24 mmol, 3 equiv.) were performed with additional HOAT (0.47 ml, 0.28 mmol, 5 equiv.), HATU (108 mg, 0.28 mmol, 5 equiv.) and DIPEA (96 μl, 0.56 mmol, 10 equiv.). Final cleavage was carried out as described in the general synthetic procedures, and the crude (54.2 mg) was purified by HPLC to obtain lipocitide B (18, Supplementary Fig. 101; 18.6 mg, 57.6%) as a white solid.Synthesis of S-(2-acetamidoethyl)4-fluoro-2-hydroxybenzothioate (4-fluorosalicylate SNAC)To a solution of 4-fluorosalicylic acid (156 mg, 1.0 mmol, 1.0 equiv.) and hydroxybenzotriazole (HOBt, 162 mg, 1.2 mmol, 1.2 equiv.) in 45 ml of THF, N,N′-dicyclohexylcarbodiimide (DCC, 248 mg, 1.2 mmol, 1.2 equiv.) was added, followed by N-acetylcysteamine (112 µl, 1.0 mmol, 1.0 equiv.). After 1 h at room temperature, K2CO3 (138 mg, 1.0 mmol, 1.2 equiv.) was added and the reaction was stirred for an additional 2 h. The reaction mixture was then filtered and concentrated by rotary evaporation. The solid residue was dissolved in ethyl acetate and washed with sat. NaHCO3 (50 ml) and water (50 ml). The organic layer was dried over MgSO4, concentrated, and purified by flash chromatography (1–10% MeOH in CHCl3) to give 26 mg (10%) S-(2-acetamidoethyl)4-fluoro-2-hydroxybenzothioate (Supplementary Fig. 102).IC50 value determination with the purified yeast 20S proteasome core particleYeast 20S proteasome core particle (yCP) from Saccharomyces cerevisiae was purified according to previously described methods88,89. The concentration of purified yCP was determined spectrophotometrically at 280 nm. yCP (final concentration: 0.05 mg ml−1 in 100 mM Tris-HCl, pH 7.5) was mixed with dimethyl sulfoxide (DMSO) as a control or serial dilutions of IOC (1) in DMSO, thereby not surpassing a final concentration of 10% (vol/vol) DMSO. After an incubation time of 45 min at room temperature, fluorogenic substrates Boc-Leu-Arg-Arg-AMC (AMC, 7-amino-4-methylcoumarin), Z-Leu-Leu-Glu-AMC and Suc-Leu-Leu-Val-Tyr-AMC (final concentration of 200 µM) were added to measure the residual activity of caspase-like (C-L, β1 subunit), trypsin-like (T-L, β2 subunit) and chymotrypsin-like (ChT-L, β5 subunit), respectively. The assay mixture was incubated for another 60 min at room temperature, then diluted 1:10 in 20 mM Tris-HCl, pH 7.5. The AMC molecules released by hydrolysis were measured in triplicate with a Varian Cary Eclipse fluorescence spectrophotometer (Agilent Technologies) at λexc = 360 nm and λem = 460 nm. Relative fluorescence units were normalized to the DMSO-treated control. The calculated residual activities were plotted against the logarithm of the applied inhibitor concentration and fitted with GraphPad Prism 9.0.2. IC50 values were deduced from the fitted data. These depend on enzyme concentration and are comparable within the same experimental settings.Crystallization and structure determination of the yCP in complex with IOC (1)Crystals of the yCP were grown in hanging drops at 20 °C, as previously described88,89. The protein concentration used for crystallization was 40 mg ml−1 in Tris/HCl (20 mM, pH 7.5) and EDTA (1 mM). The drops contained 1 μl of protein and 1 μl of the reservoir solution (30 mM magnesium acetate, 100 mM 2-(N-morpholino)ethanesulfonic acid (pH 6.7) and 10% (wt/vol) 2-methyl-2,4-pentanediol). Crystals appeared after two days and were incubated with 1 at a final concentration of 10 mM for at least 24 h. Droplets were then complemented with a cryoprotecting buffer (30% (wt/vol) 2-methyl-2,4-pentanediol, 15 mM magnesium acetate, 100 mM 2-(N-morpholino)ethanesulfonic acid, pH 6.9) and vitrified in liquid nitrogen. The dataset from the yCP:IOC complex was collected using synchrotron radiation (λ = 1.0 Å) at the X06SA-beamline (Swiss Light Source). X-ray intensities and data reduction were evaluated using the XDS program package version 5 February 2021 (Supplementary Table 17)90. Conventional crystallographic rigid body, positional and temperature factor refinements were carried out with REFMAC5 5.0.32 (ref. 91) and the CCP4 Program Suite 7.1.016 (ref. 92) using coordinates of the yCP structure as the starting model (PDB 5CZ4)50. Model building was performed by the programs SYBYL-X and COOT 0.8.7 (ref. 93). The final coordinates yielded excellent residual factors, as well as geometric bond and angle values. Coordinates were confirmed to fulfil the Ramachandran plot and have been deposited in the RCSB (PDB 7O2L).Haemocyte-spreading assaysSpodoptera exigua larvae were collected from Welsh onion (Allium fistulsum L.) fields in Andong, Korea. Insects were reared in the laboratory under the following conditions: 25 ± 2 °C constant temperature, 16:8 h (light/dark) photoperiod and 60 ± 5% relative humidity. Larvae were reared on an artificial diet94 and 10% sucrose solutions were fed to adult insects. Fifth instar larvae were used in all experiments. For analysing haemocyte behaviours in vivo, fifth instar larvae of S. exigua were co-injected with 1 µl of heat-killed (95 °C for 10 min) E. coli TOP10 (2.4 × 104 cells per larva) with the test compound (0–1,000 ng per larva) by using a Hamilton microsyringe (Reno). At 1 h post-injection, 10 µl of haemolymph from each larva was collected on the glass slide and incubated for 5 min inside a dark wet chamber at room temperature. The medium was replaced with 3.7% of formaldehyde dissolved in phosphate buffered saline (PBS) and incubated for 10 min. After washing three times with PBS, cells were permeabilized with 0.2% Triton X-100 in PBS for 2 min at room temperature. After incubation, the slides were washed with PBS three times. Blocking was performed using 5% skimmed milk (Invitrogen) dissolved in PBS, followed by incubation for 10 min. After washing once with PBS, the cells were incubated with fluorescein isothiocyanate (FITC)-tagged phalloidin in PBS for 1 h at room temperature. After washing three times, the cells were incubated with 4′,6-diamidino-2-phenylindole (DAPI, 1 mg ml−1, Thermo Scientific) in PBS for nucleus staining. Finally, after washing twice in PBS, cells were observed under a fluorescence microscope (DM2500, Leica) at ×400 magnification. Haemocyte spreading was determined by the extension of F-actin out of the original cell boundary. For the in vitro assay, ~100 μl of haemolymph was collected into 400 μl of anticoagulation buffer (ACB; 186 mM NaCl, 17 mM Na2EDTA, 41 mM citric acid, pH 4.5). After adding ACB, the medium was incubated for 30 min on ice. After centrifugation at 300g for 5 min, 400 μl of supernatant was discarded. The rest of the suspension was gently mixed with 200 μl of TC100 insect tissue culture medium (Welgene). From this suspension, 10 µl of haemolymph was collected on the glass slide. The slides were co-injected with 1 µl of E. coli TOP10 (2.4 × 104 cells per larva) with the test compound (0–1,000 ng per larva), followed by the procedure described above. Means were compared by a least squared difference (LSD) test of one-way analysis of variance (ANOVA) using POC GLM of the SAS program (SAS Institute, 1989) and discriminated at type I error = 0.05.Nodulation assaysE. coli TOP10 was heat-killed by incubating at 95 °C for 10 min. Fifth-instar larvae of S. exigua were injected with 1 µl of bacteria (2.4 × 104 cells per larva) using a Hamilton microsyringe along with 1 µl of different concentrations (10, 50, 100, 500 and 1,000 ppm) of inhibitors. Control larvae were injected with bacteria and DMSO. At 8 h after bacterial injection, nodules were counted by dissecting larvae under a stereomicroscope (Stemi SV 11, Zeiss) at ×50 magnification.Phenoloxidase activity assaysThe PO activity from plasma was estimated as previously described95. Briefly, DOPA (l-3,4-dihydroxyphenylalanine) was used as a substrate for determining PO activity from treated larvae plasma. For PO activation, each fifth-instar larva of S. exigua was challenged with 2.4 × 104 cells of heat-killed E. coli TOP10. Different inhibitors were co-injected (1 µg per larva) along with E. coli TOP10. After 8 h of bacterial challenge, haemolymph was collected from treated larvae in a 1.5-ml tube containing a few granules of phenylthiocarbamide (Sigma-Aldrich) to prevent melanization. Haemocytes were separated from plasma by centrifuging at 4 °C for 5 min at 300g. A reaction volume of 200 µl consisted of 180 µl of 10 mM DOPA in PBS (pH 7.4) and 20 µl of plasma. Absorbance was measured using a VICTOR multi-label plate reader (PerkinElmer) at 490 nm. PO activity was expressed as ΔABS per min per µl of plasma. Each treatment was replicated three times with independent samples.Measurement of nitric oxideThe NO was indirectly quantified by measuring its oxidized form, nitrate (NO3−), using the Griess reagent of a Nitrate/Nitrite Colorimetric Assay Kit (Cayman Chemical). Fifth-instar larvae were injected with 1 µl of heat-killed E. coli TOP10 (2.4 × 104 cells per larva) using a Hamilton microsyringe along with 1 µl of the test compound. Haemolymph was collected from each sample 1 h post infection. A 150-μl volume of haemolymph from three L5 larvae was collected and homogenized in 350 μl of 100 mM PBS pH 7.4 with a homogenizer (Ultra-Turrax T8, Ika Laboratory). After centrifugation at 14,000g for 20 min at 4 °C, the supernatant was used to measure the nitrate amounts, and the total protein was measured in each sample by a Bradford assay. The samples were analysed in a 200-μl final reaction volume. Briefly, 80 μl of samples were added to the wells, then 10 μl of enzyme cofactor mixture and 10 μl of nitrate reductase mixture were added. After incubation at room temperature for 1 h, 50 μl of Griess reagent R1 and immediately 50 μl of Griess reagent R2 were added to each well. The plate was left at room temperature for 10 min for colour development. For a standard curve to quantify the nitrate concentrations of the samples, nitrates with final concentrations of 0, 5, 10, 15, 20, 25, 30 and 35 μM in a 200-μl reaction volume were used. The absorbance was recorded at 540 nm on a VICTOR multi-label plate reader. Our measurements used three larvae per sample, and we repeated the treatment with three biological samples.Galleria injection assaysPrecultures of X. szentirmaii DSM wild-type strain and the mutants thereof were grown in LB medium and inoculated into fresh cultures at an OD600 of 0.1. Cells were grown to exponential phase (OD600 ≈ 1) and then diluted to an OD600 of 0.00025. A 5-µl volume of the diluted bacterial culture was injected into the last left pro-leg of the larvae (LB medium as a negative control). G. mellonella larvae were kept at 4 °C for 10 min before injection. After infection, the larvae were incubated at 25 °C. Dead Galleria larvae were frozen at −20 °C, then at −80 °C, and freeze-dried for one day. Freeze-dried larvae were ground. Every injection experiment was aliquoted into two portions, one of which was extracted with 25 ml of acetone/ethyl acetate (vol/vol, 1:1) while the other one was extracted with acetone/methanol. Extracts were dried and resuspended in 3 ml of acetonitrile/water (1:1, vol/vol) with a tenfold dilution for HPLC-MS-UV analysis. To compare the survival percentage of G. mellonella larvae infected with the WT strain and mutants and to determine median lethal time (LT50) values, Kaplan–Meier curves were generated by GraphPad PRISM 8.4.3.Cytotoxicity assaysHepG2 cells (hepatoblastoma cell line; ACC 180, DSMZ) were cultured under conditions recommended by the depositor, and cells were propagated in Dulbecco’s modified Eagle medium supplemented with 10% fetal bovine serum. To determine the cytotoxicity of test compounds, cells were seeded at 6 × 103 cells per well of 96-well plates in 120 μl of complete medium. After 2 h of equilibration, compounds were added in serial dilution in 60 µl of complete medium. Compounds as well as the solvent control and doxorubicin as an in-assay positive control (IC50 of 0.06 ± 0.01 µg ml−1) were tested as duplicates in two independent experiments. After 5 days of incubation, 20 μl of 5 mg ml−1 MTT (thiazolyl blue tetrazolium bromide) in PBS was added per well, and the cells were further incubated for 2 h at 37 °C. The medium was then discarded and cells were washed with 100 μl of PBS before adding 100 μl of 2-propanol/10 N HCl (250:1) to dissolve the formazan granules. The absorbance at 570 nm was measured using a microplate reader (Tecan Infinite M200Pro with Tecan iControl 2.0), and cell viability was expressed as a percentage relative to the respective solvent control. IC50 values were determined by sigmoidal curve fitting using GraphPad PRISM 8.4.3.Statistical analysisIn Fig. 5d,e,g,j,k, means were compared using an LSD test of one-way ANOVA using POC GLM of the SAS program (SAS Institute, 1989) for continuous variables and discriminated at type I error = 0.05. The results were plotted using Sigma Plot 12.0.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this Article. More

Amundson R, Berhe AA, Hopmans JW, Olson C, Sztein AE, Sparks DL. Soil and human security in the 21st century. Science. 2015;348:1261071.PubMed 
Article 
CAS 

Google Scholar 
Borrelli P, Robinson DA, Fleischer LR, Lugato E, Ballabio C, Alewell C, et al. An assessment of the global impact of 21st century land use change on soil erosion. Nat Commun. 2017;8:2013.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Carvalho FP. Pesticides, environment, and food safety. Food Energy Secur. 2017;6:48–60.Article 

Google Scholar 
Santos VB, Araújo ASF, Leite LFC, Nunes LAPL, Melo WJ. Soil microbial biomass and organic matter fractions during transition from conventional to organic farming systems. Geoderma. 2012;170:227–31.CAS 
Article 

Google Scholar 
Tilman D, Fargione J, Wolff B, D’Antonio C, Dobson A, Howarth R, et al. Forecasting agriculturally driven global environmental change. Science. 2001;292:281–4.CAS 
PubMed 
Article 

Google Scholar 
Tu C, Louws FJ, Creamer NG, Paul Mueller J, Brownie C, Fager K, et al. Responses of soil microbial biomass and N availability to transition strategies from conventional to organic farming systems. Agric Ecosyst Environ. 2006;113:206–15.Article 

Google Scholar 
Blundell R, Schmidt JE, Igwe A, Cheung AL, Vannette RL, Gaudin ACM, et al. Organic management promotes natural pest control through altered plant resistance to insects. Nat Plants. 2020;6:483–91.CAS 
PubMed 
Article 

Google Scholar 
Verbruggen E, Röling WFM, Gamper HA, Kowalchuk GA, Verhoef HA, van der Heijden MGA. Positive effects of organic farming on below-ground mutualists: large-scale comparison of mycorrhizal fungal communities in agricultural soils. N. Phytol. 2010;186:968–79.CAS 
Article 

Google Scholar 
Lupatini M, Korthals GW, de Hollander M, Janssens TKS, Kuramae EE. Soil microbiome is more heterogeneous in organic than in conventional farming system. Front Microbiol. 2017;7:2064.PubMed 
PubMed Central 
Article 

Google Scholar 
Cheng H, Zhang D, Ren L, Song Z, Li Q, Wu J, et al. Bio-activation of soil with beneficial microbes after soil fumigation reduces soil-borne pathogens and increases tomato yield. Environ Pollut. 2021;283:117160.CAS 
PubMed 
Article 

Google Scholar 
Shahi DK, Kachhap S, Kumar A, Agarwal BK. Organic agriculture for plant disease management. In: Singh KP, Jahagirdar S, Sarma BK. (eds). Emerging Trends in Plant Pathology. 2021. Springer, Singapore, pp 643–62.Francioli D, Schulz E, Lentendu G, Wubet T, Buscot F, Reitz T. Mineral vs organic amendments: microbial community structure, activity and abundance of agriculturally relevant microbes are driven by long-term fertilization strategies. Front Microbiol. 2016;7:1446.PubMed 
PubMed Central 
Article 

Google Scholar 
Sanchez-Barrios A, Sahib MR, DeBolt S. “I’ve got the magic in me”: the microbiome of conventional vs organic production systems. In: Singh DP, Singh HB, Prabha R. (eds). Plant-Microbe Interactions in Agro-Ecological Perspectives: Volume 1: Fundamental Mechanisms, Methods and Functions. 2017. Springer, Singapore, pp 85–95.Chowdhury SP, Babin D, Sandmann M, Jacquiod S, Sommermann L, Sørensen SJ, et al. Effect of long-term organic and mineral fertilization strategies on rhizosphere microbiota assemblage and performance of lettuce. Environ Microbiol. 2019;21:2426–39.Article 
CAS 

Google Scholar 
Weller DM. Pseudomonas biocontrol agents of soilborne pathogens: Looking back over 30 years. Phytopathology 2007;97:250–6.PubMed 
Article 

Google Scholar 
Tao C, Li R, Xiong W, Shen Z, Liu S, Wang B, et al. Bio-organic fertilizers stimulate indigenous soil Pseudomonas populations to enhance plant disease suppression. Microbiome 2020;8:137.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mazurier S, Corberand T, Lemanceau P, Raaijmakers JM. Phenazine antibiotics produced by fluorescent Pseudomonads contribute to natural soil suppressiveness to Fusarium wilt. ISME J. 2009;3:977–91.CAS 
PubMed 
Article 

Google Scholar 
Yuan J, Zhao M, Li R, Huang Q, Rensing C, Shen Q. Lipopeptides produced by B. amyloliquefaciens NJN-6 altered the soil fungal community and non-ribosomal peptides genes harboring microbial community. Appl Soil Ecol. 2017;117–8:96–105.Article 

Google Scholar 
Kiesewalter HT, Lozano-Andrade CN, Strube ML, Kovács ÁT. Secondary metabolites of Bacillus subtilis impact the assembly of soil-derived semisynthetic bacterial communities. Beilstein J Org Chem. 2020;16:2983–98.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Banerjee S, Schlaeppi K, van der Heijden MGA. Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567–76.CAS 
PubMed 
Article 

Google Scholar 
Zhang Z, Han X, Yan J, Zou W, Wang E, Lu X, et al. Keystone microbiomes revealed by 14 years of field restoration of the degraded agricultural soil under distinct vegetation scenarios. Front Microbiol. 2020;11:1915.PubMed 
PubMed Central 
Article 

Google Scholar 
Shang X, Cai X, Zhou Y, Han X, Zhang C-S, Ilyas N, et al. Pseudomonas inoculation stimulates endophytic Azospira population and induces systemic resistance to bacterial wilt. Front Plant Sci. 2021;12:1964.Article 

Google Scholar 
Tyc O, Song C, Dickschat JS, Vos M, Garbeva P. The ecological role of volatile and soluble secondary metabolites produced by soil bacteria. Trends Microbiol. 2017;25:280–92.CAS 
PubMed 
Article 

Google Scholar 
Cornforth DM, Foster KR. Competition sensing: the social side of bacterial stress responses. Nat Rev Microbiol. 2013;11:285–93.CAS 
PubMed 
Article 

Google Scholar 
Berg G, Mahnert A, Moissl-Eichinger C. Beneficial effects of plant-associated microbes on indoor microbiomes and human health? Front Microbiol. 2014;5:15.PubMed 
PubMed Central 

Google Scholar 
Straight PD, Willey JM, Kolter R. Interactions between Streptomyces coelicolor and Bacillus subtilis: Role of surfactants in raising aerial structures. J Bacteriol. 2006;188:4918–25.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
González O, Ortíz-Castro R, Díaz-Pérez C, Díaz-Pérez AL, Magaña-Dueñas V, López-Bucio J, et al. Non-ribosomal peptide synthases from Pseudomonas aeruginosa play a role in cyclodipeptide biosynthesis, quorum-sensing regulation, and root development in a plant host. Micro Ecol. 2017;73:616–29.Article 
CAS 

Google Scholar 
Zhao M, Yuan J, Zhang R, Dong M, Deng X, Zhu C, et al. Microflora that harbor the NRPS gene are responsible for Fusarium wilt disease-suppressive soil. Appl Soil Ecol. 2018;132:83–90.Article 

Google Scholar 
Caulier S, Nannan C, Gillis A, Licciardi F, Bragard C, Mahillon J. Overview of the antimicrobial compounds produced by members of the Bacillus subtilis group. Front Microbiol. 2019;10:302.PubMed 
PubMed Central 
Article 

Google Scholar 
Tambadou F, Lanneluc I, Sablé S, Klein GL, Doghri I, Sopéna V, et al. Novel nonribosomal peptide synthetase (NRPS) genes sequenced from intertidal mudflat bacteria. FEMS Microbiol Lett. 2014;357:123–30.CAS 
PubMed 

Google Scholar 
Prieto C. Characterization of nonribosomal peptide synthetases with NRPSsp. In: Evans BS. (ed). Nonribosomal Peptide and Polyketide Biosynthesis: Methods and Protocols. 2016. Springer, New York, NY, pp 273–8.Yuan J, Ruan Y, Wang B, Zhang J, Waseem R, Huang Q, et al. Plant growth-promoting rhizobacteria strain Bacillus amyloliquefaciens NJN-6-enriched bio-organic fertilizer suppressed Fusarium wilt and promoted the growth of banana plants. J Agric Food Chem. 2013;61:3774–80.CAS 
PubMed 
Article 

Google Scholar 
Yuan J, Li B, Zhang N, Waseem R, Shen Q, Huang Q. Production of bacillomycin- and macrolactin-type antibiotics by Bacillus amyloliquefaciens NJN-6 for suppressing soilborne plant pathogens. J Agric Food Chem. 2012;60:2976–81.CAS 
PubMed 
Article 

Google Scholar 
Xiong W, Song Y, Yang K, Gu Y, Wei Z, Kowalchuk GA, et al. Rhizosphere protists are key determinants of plant health. Microbiome. 2020;8:27.PubMed 
PubMed Central 
Article 

Google Scholar 
Thakur MP, Geisen S. Trophic regulations of the soil microbiome. Trends Microbiol. 2019;27:771–80.CAS 
PubMed 
Article 

Google Scholar 
Müller MS, Scheu S, Jousset A. Protozoa drive the dynamics of culturable biocontrol bacterial communities. PLOS ONE. 2013;8:e66200.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Geisen S, Mitchell EAD, Adl S, Bonkowski M, Dunthorn M, Ekelund F, et al. Soil protists: A fertile frontier in soil biology research. FEMS Microbiol Rev. 2018;42:293–323.CAS 
PubMed 
Article 

Google Scholar 
Gao Z, Karlsson I, Geisen S, Kowalchuk G, Jousset A. Protists: puppet masters of the rhizosphere microbiome. Trends Plant Sci. 2019;24:165–76.CAS 
PubMed 
Article 

Google Scholar 
Jousset A, Lara E, Wall LG, Valverde C. Secondary metabolites help biocontrol strain Pseudomonas fluorescens CHA0 to escape protozoan grazing. Appl Environ Microbiol. 2006;72:7083–90.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liu H, Xiong W, Zhang R, Hang X, Wang D, Li R, et al. Continuous application of different organic additives can suppress tomato disease by inducing the healthy rhizospheric microbiota through alterations to the bulk soil microflora. Plant Soil. 2018;423:229–40.CAS 
Article 

Google Scholar 
Chen D, Wang X, Zhang W, Zhou Z, Ding C, Liao Y, et al. Persistent organic fertilization reinforces soil-borne disease suppressiveness of rhizosphere bacterial community. Plant Soil. 2020;452:313–28.CAS 
Article 

Google Scholar 
Müller JP, Hauzy C, Hulot FD. Ingredients for protist coexistence: Competition, endosymbiosis and a pinch of biochemical interactions. J Anim Ecol. 2012;81:222–32.PubMed 
Article 

Google Scholar 
Guo S, Xiong W, Hang X, Gao Z, Jiao Z, Liu H, et al. Protists as main indicators and determinants of plant performance. Microbiome. 2021;9:64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ren F, Sun N, Xu M, Zhang X, Wu L, Xu M. Changes in soil microbial biomass with manure application in cropping systems: a meta-analysis. Soil Tillage Res. 2019;194:104291.Article 

Google Scholar 
Berendsen RL, Pieterse CMJ, Bakker PAHM. The rhizosphere microbiome and plant health. Trends Plant Sci. 2012;17:478–86.CAS 
PubMed 
Article 

Google Scholar 
Raaijmakers JM, Paulitz TC, Steinberg C, Alabouvette C, Moënne-Loccoz Y. The rhizosphere: A playground and battlefield for soilborne pathogens and beneficial microorganisms. Plant Soil. 2009;321:341–61.CAS 
Article 

Google Scholar 
Compant S, Cambon MC, Vacher C, Mitter B, Samad A, Sessitsch A. The plant endosphere world – bacterial life within plants. Environ Microbiol. 2021;23:1812–29.PubMed 
Article 

Google Scholar 
Oliverio AM, Geisen S, Delgado-Baquerizo M, Maestre FT, Turner BL, Fierer N. The global-scale distributions of soil protists and their contributions to belowground systems. Sci Adv. 2020;6:eaax8787.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dumack K, Fiore-Donno AM, Bass D, Bonkowski M. Making sense of environmental sequencing data: ecologically important functional traits of the protistan groups Cercozoa and Endomyxa (Rhizaria). Mol Ecol Resour. 2020;20:398–403.PubMed 
Article 

Google Scholar 
Romdhane S, Spor A, Banerjee S, Breuil M-C, Bru D, Chabbi A, et al. Land-use intensification differentially affects bacterial, fungal and protist communities and decreases microbiome network complexity. Environ Microbiome. 2022;17:1.PubMed 
PubMed Central 
Article 

Google Scholar 
Jousset A, Rochat L, Péchy-Tarr M, Keel C, Scheu S, Bonkowski M. Predators promote defence of rhizosphere bacterial populations by selective feeding on non-toxic cheaters. ISME J. 2009;3:666–74.CAS 
PubMed 
Article 

Google Scholar 
Yu GY, Sinclair JB, Hartman GL, Bertagnolli BL. Production of iturin A by Bacillus amyloliquefaciens suppressing Rhizoctonia solani. Soil Biol Biochem. 2002;34:955–63.CAS 
Article 

Google Scholar 
Romero D, de Vicente A, Rakotoaly RH, Dufour SE, Veening J-W, Arrebola E, et al. The iturin and fengycin families of lipopeptides are key factors in antagonism of Bacillus subtilis toward Podosphaera fusca. Mol Plant Microbe Interact. 2007;20:430–40.CAS 
PubMed 
Article 

Google Scholar 
Xu Z, Mandic-Mulec I, Zhang H, Liu Y, Sun X, Feng H, et al. Antibiotic bacillomycin D affects iron acquisition and biofilm formation in Bacillus velezensis through a Btr-mediated FeuABC-dependent pathway. Cell Rep. 2019;29:1192–1202.e5.CAS 
PubMed 
Article 

Google Scholar 
Huang J, Wei Z, Tan S, Mei X, Shen Q, Xu Y. Suppression of bacterial wilt of tomato by bioorganic fertilizer made from the antibacterial compound producing strain Bacillus amyloliquefaciens HR62. J Agric Food Chem. 2014;62:10708–16.CAS 
PubMed 
Article 

Google Scholar 
Wang B, Shen Z, Zhang F, Raza W, Yuan J, Huang R, et al. Bacillus amyloliquefaciens strain W19 can promote growth and yield and suppress Fusarium wilt in banana under greenhouse and field conditions. Pedosphere. 2016;26:733–44.CAS 
Article 

Google Scholar 
Shen Z, Ruan Y, Chao X, Zhang J, Li R, Shen Q. Rhizosphere microbial community manipulated by 2 years of consecutive biofertilizer application associated with banana Fusarium wilt disease suppression. Biol Fertil Soils. 2015;51:553–62.CAS 
Article 

Google Scholar 
Jeger MJ, Eden-Green S, Thresh JM, Johanson A, Waller JM, Brown AE. Banana diseases. In: Gowen S. (ed). Bananas and Plantains. 1995. Springer Netherlands, Dordrecht, pp 317–81.Edwards J, Johnson C, Santos-Medellín C, Lurie E, Podishetty NK, Bhatnagar S, et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc Natl Acad Sci. 2015;112:E911–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fierer N, Jackson JA, Vilgalys R, Jackson RB. Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Appl Environ Microbiol. 2005;71:4117–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jiménez-Fernández D, Montes-Borrego M, Navas-Cortés JA, Jiménez-Díaz RM, Landa BB. Identification and quantification of Fusarium oxysporum in planta and soil by means of an improved specific and quantitative PCR assay. Appl Soil Ecol. 2010;46:372–82.Article 

Google Scholar 
Mori K, Iriye R, Hirata M, Takamizawa K. Quantification of Bacillus species in a wastewater treatment system by the molecular analyses. Biotechnol Bioprocess Eng. 2004;9:482–9.CAS 
Article 

Google Scholar 
Ayuso-Sacido A, Genilloud O. New PCR primers for the screening of NRPS and PKS-I systems in actinomycetes: detection and distribution of these biosynthetic gene sequences in major taxonomic groups. Micro Ecol. 2005;49:10–24.CAS 
Article 

Google Scholar 
Fu L, Penton CR, Ruan Y, Shen Z, Xue C, Li R, et al. Inducing the rhizosphere microbiome by biofertilizer application to suppress banana Fusarium wilt disease. Soil Biol Biochem. 2017;104:39–48.CAS 
Article 

Google Scholar 
Claesson MJ, O’Sullivan O, Wang Q, Nikkilä J, Marchesi JR, Smidt H, et al. Comparative analysis of pyrosequencing and a phylogenetic microarray for exploring microbial community structures in the human distal intestine. PLOS ONE. 2009;4:e6669.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
White TJ, Bruns T, Lee S, Taylor J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ. (eds). PCR Protocols. 1990. Academic Press, San Diego, pp 315–22.Gardes M, Bruns TD. ITS primers with enhanced specificity for basidiomycetes – application to the identification of mycorrhizae and rusts. Mol Ecol. 1993;2:113–8.CAS 
PubMed 
Article 

Google Scholar 
Bass D, Silberman JD, Brown MW, Pearce RA, Tice AK, Jousset A, et al. Coprophilic amoebae and flagellates, including Guttulinopsis, Rosculus and Helkesimastix, characterise a divergent and diverse rhizarian radiation and contribute to a large diversity of faecal-associated protists. Environ Microbiol. 2016;18:1604–19.CAS 
PubMed 
Article 

Google Scholar 
Geisen S, Vaulot D, Mahé F, Lara E, Vargas C de, Bass D. A user guide to environmental protistology: primers, metabarcoding, sequencing, and analyses. BioRxiv 2019;850610:1–34.Xiong W, Jousset A, Li R, Delgado-Baquerizo M, Bahram M, Logares R, et al. A global overview of the trophic structure within microbiomes across ecosystems. Environ Int. 2021;151:106438.PubMed 
Article 

Google Scholar 
Xiong W, Li R, Ren Y, Liu C, Zhao Q, Wu H, et al. Distinct roles for soil fungal and bacterial communities associated with the suppression of vanilla Fusarium wilt disease. Soil Biol Biochem. 2017;107:198–207.CAS 
Article 

Google Scholar 
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): A catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013;41:D597–D604.CAS 
PubMed 
Article 

Google Scholar 
Xiong W, Li R, Guo S, Karlsson I, Jiao Z, Xun W, et al. Microbial amendments alter protist communities within the soil microbiome. Soil Biol Biochem. 2019;135:379–82.CAS 
Article 

Google Scholar 
Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–93.CAS 
PubMed 
Article 

Google Scholar 
Revelle W, Revelle MW. Package ‘psych’. Compr R Arch Netw. 2015;337:338.
Google Scholar 
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289–300.
Google Scholar 
Bargabus RL, Zidack NK, Sherwood JE, Jacobsen BJ. Characterisation of systemic resistance in sugar beet elicited by a non-pathogenic, phyllosphere-colonizing Bacillus mycoides, biological control agent. Physiol Mol Plant Pathol. 2002;61:289–98.CAS 
Article 

Google Scholar 
Bais HP, Fall R, Vivanco JM. Biocontrol of Bacillus subtilis against infection of arabidopsis roots by Pseudomonas syringae is facilitated by biofilm formation and surfactin production. Plant Physiol. 2004;134:307–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cazorla FM, Romero D, Pérez-García A, Lugtenberg BJJ, Vicente Ade, Bloemberg G. Isolation and characterization of antagonistic Bacillus subtilis strains from the avocado rhizoplane displaying biocontrol activity. J Appl Microbiol. 2007;103:1950–9.CAS 
PubMed 
Article 

Google Scholar 
Aneja KR. Experiments in microbiology, plant pathology and biotechnology. 2007. New Age International, New Delhi.Mela F, Fritsche K, de Boer W, van Veen JA, de Graaff LH, van den Berg M, et al. Dual transcriptional profiling of a bacterial/fungal confrontation: Collimonas fungivorans versus Aspergillus niger. ISME J. 2011;5:1494–504.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gao Z. Soil protists: From traits to ecological functions. 2020. Utrecht University.Anderson MJ. Permutational multivariate analysis of variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online. 2017. American Cancer Society, pp 1–15.Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’hara RB, et al. Package ‘vegan’. Community Ecol Package Version. 2013;2:1–295.
Google Scholar 
Breiman L. Random forests. Mach Learn. 2001;45:5–32.Article 

Google Scholar 
Liaw A, Wiener M. Classification and regression by randomForest. R N. 2002;23:18–22.
Google Scholar 
Archer E. rfPermute: Estimate permutation p-values for random forest importance metrics. R Package Version 20 2016.Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.PubMed 
PubMed Central 
Article 

Google Scholar 
Oguntunde PG, Fosu M, Ajayi AE, van de Giesen N. Effects of charcoal production on maize yield, chemical properties and texture of soil. Biol Fertil Soils. 2004;39:295–9.CAS 
Article 

Google Scholar 
Mcdonald JH. Handbook of biological statistics. 2009. Baltimore: sparky house publishing, Baltimore. More

Ferreira, G. S. & Langer, M. C. A pelomedusoid (Testudines, Pleurodira) plastron from the Lower Cretaceous of Alagoas, Brazil. Cretaceous Res. 46, 267–271 (2013).Article 

Google Scholar 
Romano, P. S. R., Gallo, V., Ramos, R. R. C. & Antonioli, L. Atolchelys lepida, a new side-necked turtle from the Early Cretaceous of Brazil and the age of crown Pleurodira. Biol. Lett. 10, 1–10 (2014).Article 

Google Scholar 
Ferreira, G. S., Bronzati, M., Langer, M. C. & Sterli, J. Phylogeny, biogeography and diversification patterns of side-necked turtles (Testudines: Pleurodira). R. Soc. Open Sci. 5, 1–17 (2018).Article 

Google Scholar 
de la Fuente, M. S., Umazano, A. M., Sterli, J. & Carballido, J. L. New chelid turtles of the lower section of the Cerro Barcino formation (Aptian-Albian?), Patagonia, Argentina. Cretaceous Res. 32, 527–537 (2011).Article 

Google Scholar 
Joyce, W. G., Parham, J. F., Lyson, T. R., Warnock, R. C. M. & Donoghue, P. C. J. A divergence dating analysis of turtles using fossil calibrations: An example of best practices. J. Paleontol. 87, 612–634 (2013).Article 

Google Scholar 
Pereira, A. G., Sterli, J., Moreira, F. R. R. & Schrago, C. G. Multilocus phylogeny and statistical biogeography clarify the evolutionary history of major lineages of turtles. Mol. Phylogenet. Evol. 113, 59–66 (2017).PubMed 
Article 

Google Scholar 
Shaffer, H. B., McCartney-Melstad, E., Near, T. J., Mount, G. G. & Spinks, P. Q. Phylogenomic analyses of 539 highly informative loci dates a fully resolved time tree for the major clades of living turtles (Testudines). Mol. Phylogenet. Evol. 115, 7–15 (2017).PubMed 
Article 

Google Scholar 
Rueda-Almonacid, J. Vicente. Las tortugas y los cocodrilianos de los países andinos de trópico (Conservación Internacional, 2007).Georges, A. & Thomson, S. Diversity of Australasian freshwater turtles, with an annotated synonymy and keys to species. Zootaxa 2496, 1–37 (2010).Article 

Google Scholar 
TTWG. Turtles of the World: Annotated Checklist and Atlas of Taxonomy, Synonymy, Distribution, and Conservation Status, 9th ed. Vol. 8 (Chelonian Research Foundation and Turtle Conservancy, 2021).Uetz, P., F. P. A. R. & H. J. The Reptile Database. http://www.reptile-database.org/ (2022).Antonelli, A. et al. Amazonia is the primary source of Neotropical biodiversity. Proc. Natl. Acad. Sci. U.S.A. 115, 6034–6039 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mittermeier, R. A., van Dijk, P. P., Rhodin, A. G. J. & Nash, S. D. Turtle hotspots: An analysis of the occurrence of tortoises and freshwater turtles in biodiversity hotspots, high-biodiversity wilderness areas, and turtle priority areas. Chelonian Conserv. Biol. 14, 2–10 (2015).Article 

Google Scholar 
Cunha, F. A. G., Sampaio, I., Carneiro, J. & Vogt, R. C. A New Species of Amazon Freshwater Toad-Headed Turtle in the Genus Mesoclemmys (Testudines: Pleurodira: Chelidae) from Brazil. Chelonian Conserv. Biol. 20, 151–166 (2021).
Google Scholar 
Brito, E. S. et al. New records of mesoclemmys raniceps (Testudines, chelidae) for the states of amazonas, pará and Rondônia, north Brazil, including the Tocantins basin. Herpetol. Notes 12, 283–289 (2019).
Google Scholar 
Cunha, F. A. G. et al. Distribution of Chelus fimbriata and Chelus orinocensis (Testudines: Chelidae). Chelonian Conserv. Biol. 20, 109–115 (2021).
Google Scholar 
Pritchard, P. Chelus fimbriata (Schneider 1783)—Matamata Turtle. In Conservation Biology of Freshwater Turtles and Tortoises 020.1–020.10 (Chelonian Research Foundation, 2008). https://doi.org/10.3854/crm.5.020.fimbriata.v1.2008.Vogt, R. C. Tartarugas da Amazônia (2008).Holmstrom, W. F. Preliminary observations on prey herding in the Matamata turtle, Chelus fimbriatus (Reptilia, Testudines, Chelidae). J. Herpetol. 12, 573 (1978).Article 

Google Scholar 
Teran, A. F., Vogt, R. C. & de Fatima Soares Gomez, M. Food Habits of an assemblage of five species of turtles in the Rio Guapore, Rondonia, Brazil. J. Herpetol. 29, 536 (1995).Article 

Google Scholar 
Vargas-Ramírez, M. et al. Genomic analyses reveal two species of the matamata (Testudines: Chelidae: Chelus spp.) and clarify their phylogeography. Mol. Phylogenet. Evol. 148 (2020).Lasso, C. A. et al. Conservación y tráfico de la tortuga matamata, Chelus fimbriata (Schneider, 1783) en Colombia: un ejemplo del trabajo conjunto entre el Sistema Nacional Ambiental, ONG y academia. Biota Colombiana 19, 147–159 (2018).Article 

Google Scholar 
Barros, R. M., Sampaio, M. M., Assis, M. F., Ayres, M. & Cunha, O. R. General considerations on the karyotypic evolution of chelonia from the Amazon Region of Brazil. Cytologia 41, 559–565 (1976).Article 

Google Scholar 
Bull, J. J. & Legler, J. M. Karyotypes of side-necked turtles (Testudines: Pleurodira). Can. J. Zool. 58, 828–841 (1980).Viana, P. F. et al. An optimized protocol for obtaining mitotic chromosomes from cultured reptilian lymphocytes. Nucleus 59,1–5 (2016).Article 

Google Scholar 
Mcbee, K., Bickham, J. W., Rhodin, A. G. J. & Mittermeier, R. A. Karyotypic Variation in the Genus Platemys (Testudines: Pleurodira). Copeia 2, 445–449 (1987).
Google Scholar 
Mazzoleni, S. et al. Sex is determined by XX/XY sex chromosomes in Australasian side-necked turtles (Testudines: Chelidae). Scientific Reports 10, 1–11 (2020).Viana, P. F. et al. The Amazonian red side-necked turtle Rhinemys rufipes (Spix, 1824) (Testudines, Chelidae) Has a GSD sex-determining mechanism with an ancient XY sex microchromosome system. Cells 9, 1–15 (2020).Ewert, M. A., Etcheberger, C. R. & Nelson, C. E. Turtle Sex-determining modes and TSD Patterns, and Some TSD Pattern Correlates 21–32 (Smithsonian Books, Washington, 2004).
Google Scholar 
Ferreira-Júnior Paulo. Aspectos Ecológicos da Determinação Sexual em Tartarugas. 39, 139–154 (2009).Martinez, P. A., Ezaz, T., Valenzuela, N., Georges, A. & Marshall Graves, J. A. An XX/XY heteromorphic sex chromosome system in the Australian chelid turtle Emydura macquarii: A new piece in the puzzle of sex chromosome evolution in turtles. Chromosom. Res. 16, 815–825 (2008).CAS 
Article 

Google Scholar 
Lee, L. S., Montiel, E. E. & Valenzuela, N. Discovery of putative XX/XY male heterogamety in emydura subglobosa turtles exposes a novel trajectory of sex chromosome evolution in emydura. Cytogenet. Genome Res. 158, 160–169 (2019).CAS 
PubMed 
Article 

Google Scholar 
Ezaz, T. et al. An XX/XY sex microchromosome system in a freshwater turtle, Chelodina longicollis (Testudines: Chelidae) with genetic sex determination. Chromosom. Res. 14, 139–150 (2006).CAS 
Article 

Google Scholar 
van Doorn, G. S. Evolutionary transitions between sex-determining mechanisms: A review of theory. Sex. Dev. 8, 7–19 (2014).PubMed 
Article 

Google Scholar 
van Doorn, G. S. & Kirkpatrick, M. Turnover of sex chromosomes induced by sexual conflict. Nature 449, 909–912 (2007).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Beukeboom, L. W. & Perrin, N. The Evolution of Sex Determination (Oxford University Press, Oxford, 2014).Book 

Google Scholar 
Bachtrog, D. et al. Sex determination: Why so many ways of doing it?. PLoS Biology 12, e1001899 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Viana, P. F. et al. Landscape of snake’ sex chromosomes evolution spanning 85 MYR reveals ancestry of sequences despite distinct evolutionary trajectories. Sci. Rep. 10, 1–14 (2020).Gamble, T. et al. Restriction site-associated DNA sequencing (RAD-seq) reveals an extraordinary number of transitions among gecko sex-determining systems. Mol. Biol. Evol. 32, 1296–1309 (2015).CAS 
PubMed 
Article 

Google Scholar 
Pennell, M. W., Mank, J. E. & Peichel, C. L. Transitions in sex determination and sex chromosomes across vertebrate species. Mol. Ecol. 27, 3950–3963 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Valenzuela, N. & Adams, D. C. Chromosome number and sex determination coevolve in turtles. Evolution 65, 1808–1813 (2011).PubMed 
Article 

Google Scholar 
Sabath, N. et al. Sex determination, longevity, and the birth and death of reptilian species. Ecol. Evol. 6, 5207–5220 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Literman, R., Burrett, A., Bista, B. & Valenzuela, N. Putative independent evolutionary reversals from genotypic to temperature-dependent sex determination are associated with accelerated evolution of sex-determining genes in turtles. J. Mol. Evol. 86, 11–26 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bista, B., Wu, Z., Literman, R. & Valenzuela, N. Thermosensitive sex chromosome dosage compensation in ZZ/ZW softshell turtles, Apalone spinifera. Philos. Trans. R. Soc. B Biol. Sci. 376, 1–14 (2021).Montiel, E. E., Badenhorst, D., Tamplin, J., Burke, R. L. & Valenzuela, N. Discovery of the youngest sex chromosomes reveals first case of convergent co-option of ancestral autosomes in turtles. Chromosoma 126, 105–113 (2017).CAS 
PubMed 
Article 

Google Scholar 
Lee, L., Montiel, E. E., Navarro-Domínguez, B. M. & Valenzuela, N. Chromosomal rearrangements during turtle evolution altered the synteny of genes involved in vertebrate sex determination. Cytogenet. Genome Res. 157, 77–88 (2019).CAS 
PubMed 
Article 

Google Scholar 
Bista, B. & Valenzuela, N. Turtle insights into the evolution of the reptilian karyotype and the genomic architecture of sex determination. Genes 11, 1–11 (2020).Zexian, Z. et al. Diversity of reptile sex chromosome evolution revealed by cytogenetic and linked-read sequencing. bioRxiv (2021).Cunha, F. A. G., Fernandes, T., Franco, J. & Vogt, R. C. Reproductive biology and hatchling morphology of the amazon toad-headed turtle (Mesoclemmys raniceps) (Testudines: Chelidae), with notes on species morphology and taxonomy of the mesoclemmys group. Chelonian Conserv. Biol. 18, 195 (2019).Article 

Google Scholar 
Matsubara, K. et al. Amplification of microsatellite repeat motifs is associated with the evolutionary differentiation and heterochromatinization of sex chromosomes in Sauropsida. Chromosoma 125, 111–123 (2016).CAS 
PubMed 
Article 

Google Scholar 
Gorman, G. C. The chromosomes of Reptilia, a cytotaxonomic interpretation. In Cytotaxonomy and Vertebrate Evolution 347–424 (1973).Reed, K. M. et al. Cytogenetic analysis of the pleurodine turtle Phrynops hogei and its taxonomic implications. Amphibia Reptilia 12, 203–212 (1991).Article 

Google Scholar 
Cavalcante, M. G. et al. Physical mapping of repetitive DNA suggests 2n reduction in Amazon turtles Podocnemis (Testudines: Podocnemididae). PLoS ONE 13, 1–13 (2018).
Google Scholar 
Clemente, L. et al. Interstitial telomeric repeats are rare in turtles. Genes 11, 1–18 (2020).Article 
CAS 

Google Scholar 
Singchat, W. et al. Chromosome map of the Siamese cobra: Did partial synteny of sex chromosomes in the amniote represent “a hypothetical ancestral super-sex chromosome” or random distribution?. BMC Genomics 19, 1–16 (2018).Article 
CAS 

Google Scholar 
Srikulnath, K., Azad, B., Singchat, W. & Ezaz, T. Distribution and amplification of interstitial telomeric sequences (ITSs) in Australian dragon lizards support frequent chromosome fusions in Iguania. PLoS ONE 14, 1–11 (2019).Article 
CAS 

Google Scholar 
Abramyan, J., Ezaz, T., Graves, J. A. M. & Koopman, P. Z and W sex chromosomes in the cane toad (Bufo marinus). Chromosom. Res. 17, 1015–1024 (2009).CAS 
Article 

Google Scholar 
Born, G. G. & Bertollo, L. A. C. An XX/XY sex chromosome system in a fish species, Hoplias malabaricus, with a polymorphic NOR-bearing × chromosome. Chromosom. Res. 8, 111–118 (2000).CAS 
Article 

Google Scholar 
O’Meally, D. et al. Non-homologous sex chromosomes of birds and snakes share repetitive sequences. Chromosom. Res. 18, 787–800 (2010).Article 
CAS 

Google Scholar 
Ferreira, M., Garcia, C., Matoso, D. A., de Jesus, I. S. & Feldberg, E. A new multiple sex chromosome system X1X1X2X2/X1Y1X2Y2 in siluriformes: Cytogenetic characterization of Bunocephalus coracoideus (Aspredinidae). Genetica 144, 591–599 (2016).PubMed 
Article 

Google Scholar 
Yano, C. F., Bertollo, L. A. C., Liehr, T., Troy, W. P. & de Cioffi, M. B. W. Chromosome dynamics in Triportheus Species (Characiformes, Triportheidae): An ongoing process narrated by repetitive sequences. J. Hered. 107, 342–348 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Symonová, R. Integrative rDNAomics-importance of the oldest repetitive fraction of the eukaryote genome. Genes 10, 1–14 (2019).Article 
CAS 

Google Scholar 
Kawai, A. et al. Different origins of bird and reptile sex chromosomes inferred from comparative mapping of chicken Z-linked genes. Cytogenet. Genome Res. 117, 92–102 (2007).CAS 
PubMed 
Article 

Google Scholar 
Badenhorst, D., Stanyon, R., Engstrom, T. & Valenzuela, N. A ZZ/ZW microchromosome system in the spiny softshell turtle, Apalone spinifera, reveals an intriguing sex chromosome conservation in Trionychidae. Chromosom. Res. 21, 137–147 (2013).CAS 
Article 

Google Scholar 
Viana, P. F. et al. Genomic organization of repetitive DNAs and differentiation of an XX/XY sex chromosome system in the Amazonian Puffer Fish, Colomesus asellus (Tetraodontiformes). Cytogen. Genome Research 153, 1–9 (2018).Castoe, T. A. et al. Discovery of highly divergent repeat landscapes in snake genomes using high-throughput sequencing. Genome Biol. Evol. 3, 641–653 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Castoe, T. A. et al. Sequencing the genome of the Burmese python (Python molurus bivittatus) as a model for studying extreme adaptations in snakes. Genome Biol. 12, 1–8 (2011).Article 
CAS 

Google Scholar 
Card, D. C. et al. Two low coverage bird genomes and a comparison of reference-guided versus de novo genome assemblies. PLoS ONE 9, e106649 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Adams, R. H. et al. Microsatellite landscape evolutionary dynamics across 450 million years of vertebrate genome evolution. Genome 59, 295–310 (2016).CAS 
PubMed 
Article 

Google Scholar 
Pearson, C. E. & Sinden, R. R. Alternative structures in duplex DNA formed within the trinucleotide repeats of the myotonic dystrophy and fragile × loci. Biochemistry 35, 5041–5053 (1996).CAS 
PubMed 
Article 

Google Scholar 
Chamberlain, N. L., Driver, E. D. & Miesfeld, R. L. The length and location of CAG trinucleotide repeats in the androgen receptor N-terminal domain affect transactivation function. Nucleic Acids Res. 22, 3181–3186 (1994).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sandberg, G. Effect of in vitro promoter methylation and CGG repeat expansion on FMR-1 expression. Nucleic Acids Res. 25, 2883–2887 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rubinsztein, D. C. et al. Microsatellite evolution—evidence for directionality and variation in rate between species. Nat. Genet. 10, 337–343 (1995).CAS 
PubMed 
Article 

Google Scholar 
Eisen, J. A. Mechanistic basis for microsatellite instability. In Microsatellites: Evolution and Applications (eds Goldstein, D. B. & Schlotterer, C.) 34–48 (Oxford University Press, Oxford, 1999).
Google Scholar 
Payseur, B. A. & Nachman, M. W. Microsatellite variation and recombination rate in the human genome. Genetics 156, 1285–1298 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, Y. C., Korol, A. B., Fahima, T., Beiles, A. & Nevo, E. Microsatellites: Genomic distribution, putative functions and mutational mechanisms: A review. Molecular ecology 11, 2453–2465 (2002).CAS 
PubMed 
Article 

Google Scholar 
Klintschar, M. et al. Haplotype studies support slippage as the mechanism of germline mutations in short tandem repeats. Electrophoresis 25, 3344–3348 (2004).CAS 
PubMed 
Article 

Google Scholar 
Jonika, M., Lo, J. & Blackmon, H. Mode and tempo of microsatellite evolution across 300 million years of insect evolution. Genes 11, 1–15 (2020).Article 
CAS 

Google Scholar 
Shedlock, A. M. et al. Phylogenomics of nonavian reptiles and the structure of the ancestral amniote genome. Proc. Natl. Acad. Sci. 104, 2767–2772 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ferreira, G. S., Rincón, A. D., Solórzano, A. & Langer, M. C. Review of the fossil matamata turtles: Earliest well-dated record and hypotheses on the origin of their present geographical distribution. Sci. Nat. 103, 1–12 (2016).Article 
CAS 

Google Scholar 
Sumner, A. T. A simple technique for demonstrating centromeric heterochromatin. Exp. Cell Res. 75, 304–306 (1972).CAS 
PubMed 
Article 

Google Scholar 
Gross, M. C., Schneider, C. H., Valente, G. T., Martins, C. & Feldberg, E. Variability of 18S rDNA locus among Symphysodon fishes: Chromosomal rearrangements. J. Fish Biol. 76, 1117–1127 (2010).CAS 
PubMed 
Article 

Google Scholar 
IJdo, J. W., Wells, R. A., Baldini, A. & Reeders, S. T. Improved telomere detection using a telomere repeat probe (TTAGGG)n generated by PCR. Nucleic Acids Res. 19, 4780–4780 (1991).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Viana, P. F. et al. Is the karyotype of neotropical boid snakes really conserved? Cytotaxonomy, chromosomal rearrangements and karyotype organization in the Boidae family. PLoS ONE 11, 1–16 (2016).
Google Scholar 
Viana, P. F., Ezaz, T., Cioffi, M. D. B., Almeida, B. J. & Feldberg, E. Evolutionary insights of the zw sex chromosomes in snakes: A new chapter added by the amazonian puffing snakes of the genus spilotes. Genes 10, 1–15 (2019).Article 
CAS 

Google Scholar 
Zwick, M. S. et al. A rapid procedure for the isolation of C0t−1 DNA from plants. Genome 40, 138–142 (1997).CAS 
PubMed 
Article 

Google Scholar 
Ferreira, A. M. V. et al. Cytogenetic Analysis of Panaqolus tankei Cramer & Sousa, 2016 (Siluriformes, Loricariidae), an Ornamental Fish Endemic to Xingu River, Brazil. Cytogenet. Genome Res. 161, 187–194 (2021).CAS 
PubMed 
Article 

Google Scholar 
Levan, A., Fredga, K. & Sandberg, A. A. Nomenclature for centromeric position on chromosomes. Hereditas 52, 201–220 (1964).Article 

Google Scholar  More

Description of test samples and theirs preparation84 samples of recycled rubber granules with a particle size of 0.5 to 4 mm, produced for the construction of sport field surfaces, were tested. The samples of rubber granules were collected from 17 sport fields and 67 samples rubber granules were supplied by recyclers. Research included 57 samples of SBR granules and 27 samples of EPDM granules. The numbers of samples in relation to their sources of origin are shown in Fig. 1.Figure 1Number of samples of the tested SBR and EPDM granules in relation to their sources of origin.Full size imageThe samples were taken from the surface of sport fields with artificial turf in accordance with the laboratory instructions or delivered to the laboratory by recyclers. The mass of the granulate samples delivered for testing was approx. 0.5 kg. Sampling from sport fields was carried out using a scheme based on 6 sampling points, shown in Fig. 2, in accordance with point 4 of the FIFA guidelines: “Quality Programme for Football Turf. Handbook of Test Methods for Football Turf”. The number and weight of granular samples and the locations of the granular sampling points on the field indicated in the aforementioned guidelines are indicated in order to obtain a representative homogenized granular sample for the tested field42.Figure 2Scheme of distribution of granulate sampling points on a sport field. Designation:
location and numbers of granulate sampling points.Full size imageAt the designated points (1 ÷ 6), 6 samples of granulate were collected. The collected and secured samples were stabilized in the laboratory conditions of natural drying, in which the moisture of the sample was in equilibrium with the ambient moisture. After stabilization, the samples were purified and homogenized to give a pooled sample. Images of exemplary SBR and EPDM granules used in the study are shown in Fig. 3. The average values of the physical parameters of the tested rubber granules are given in Table 1.Figure 3Samples taken from sport fields: (a) SBR granules, (b) EPDM granules.Full size imageTable 1 Some physical parameters of the tested SBR and EPDM granules (data from the Technical Data Sheets provided by the recyclers).Full size tableSamples weighing at least 100 g were taken from the granulate samples using the quartering method. This way allowed to ensure full qualitative and quantitative compliance of the sample composition with the composition of the analyzed material. Samples for testing the content of PAHs were grounded by grinding in a cryogenic mill 6770 Freezer/Mill, by SPEX SamplePrep LLC. Samples for testing other substances were not crushed.The scope and methods of testing rubber granulesThe scope of the research on rubber granules included: content determination of the PAHs, leached elements, organotin compounds and PAHs. In all samples of rubber granules, the content of 8 polycyclic aromatic hydrocarbons, resulting from the REACH Regulation, was determined: benzo[a]pyrene (BaP), dibenz[a,h]anthracene (DBAhA), benzo[e]pyrene (BeP), benz[a]anthracene (BaA), chrysene (CHR), benzo[b]fluoranthene (BbFA), benzo[j]fluoranthene (BjFA) and benzo[k]fluoranthene (BkFA). The content of indeno[1,2,3-cd]pyrene (IcdP), benzo[ghi]perylene (BghiP), phenanthrene, anthracene, fluoranthene, pyrene and naphthalene was determined for 38 samples from recyclers, additionally, that the number of PAHs covered by the requirements of the document43 was increased by 7.The leaching tests of elements and organotin compounds were carried out for 18 samples and the leachability of PAHs and elements were carried out for 4 samples. The tests were carried out with the methods listed below, using the following apparatus.The content and leachability of PAHs from rubber granules was determined by gas chromatography with tandem mass spectrometry (GC–MS/MS) using a gas chromatograph coupled with a mass detector GCMS/MS/7890B/7000C. The method was chosen because of the high sensitivity and selectivity obtained for low PAHs levels when used GC–MS/MS, compared to other commonly used analytical techniques such as high-performance liquid chromatography (HPLC) combined with UV, fluorescence or diode array detector (DAD). In studies carried out with the use of the above-mentioned techniques trace amount of PAHs identification is easily interfered by sample matrix and other components if only based on retention44.Determination of leaching of elements: Al, Sb, As, Ba, B, Cd, Co, Cu, Pb, Mn, Hg, Cr, Ni, Se, Sr, Sn, Zn and elution of the Cd, total Cr, Pb, Sn, Zn from rubber granules was carried out by the inductively coupled plasma mass spectrometry (ICP-MS) method with the use of Agilent 7900 ICP-MS (Agilent Technology, Santa Clara, CA, USA). The selected method is characterized by a low limit of quantification, which stands out among other instrumental methods used in elemental analysis, such as ICP-OES or AAS (Inductively coupled plasma–optical emission spectrometry or atomic absorption spectrometry). It is also characterized by high sensitivity and precision, selectivity enabling the simultaneous determination of many elements in complex matrices in a wide range of concentrations.Leachability of Cr (III) and Cr (VI) and elution of Cr (VI) from rubber granules were determined by high-performance liquid chromatography with inductively coupled plasma mass spectrometry (HPLC-ICP-MS) using Agilent 7700 Series ICP-MS with Agilent 1260 Infinity series HPLC (Agilent Technology, Santa Clara, CA, USA). The decision to use HPLC in conjunction with ICP-MS was dictated by the need to determine chromium in two oxidation states. In the case of the selected method, the speciation separation of Cr (III) and Cr (VI) takes place on the HPLC column, where Cr (III) and Cr (VI) are adsorbed. In the next step it allows for the separation and determination of Cr (III) and Cr (VI) in the ICP-MS spectrometer. The HPLC-ICP-MS method is characterized by a short analysis time and a low detection limit compared to the other spectrophotometric methods used for determination of Cr (VI). The leaching of organotin compounds was assessed on the basis of the results of total Sn leaching.Cold-vapor atomic absorption spectroscopy (CV-AAS) with the PerkinElmer FIMS 100 mercury analyser was selected for the Hg leaching study due to the use of a unique technique of mercury vapour measurement at room temperature. Among other alternative methods of Hg determination in aqueous solutions (ICP-MS or GF-AAS (graphite furnace atomic absorption spectrometry)), the selected method is distinguished by a low limit of quantification, simple preparation of samples for analysis, easy elimination of interference and short analysis time.Tests of the content of PAHsShredded samples of rubber granules were subjected to the ultrasonic extraction process for 1 h with the use of toluene as a solvent. Samples were taken from the obtained extract for chromatographic analysis. The analysis was carried out for the following conditions: dispenser operation mode: splitless, carrier gas: Helium: 1.8 ml/min, DB-EUPAH column with dimensions: 20 m × 180 µm × 0.14 µm (the 20 m column is in the form of a coiled wire), injection temperature: 275 °C. The PAHs were identified on the basis of mass spectra and retention times—Table 2.Table 2 Target and identification ions and retention times for the determined PAHs.Full size tableTests of the leaching of elements and organotin compoundsSamples of rubber granules for the study of the leaching of organotin elements and compounds were extracted in a solution of hydrochloric acid (HCl), with concentration 0.07 ± 0.005 mol/dm3 in temperature 37 ± 2 °C. Solutions for the determination the Cr(VI) and Cr(III) prepared by diluting the extraction solution to obtain the pH equal to 7.0 ± 0.5 by adding 1 ml of 0.07 mol/dm3 ammonia and 60 µL of 0.1 mol/dm3 EDTA solution. In parallel, a reagent blank was prepared, as the test samples were. The obtained extracts were analyzed by ICP-MS and HPLC-ICP-MS. The analyzes were performed for the isotopes of the elements: Al—27, Sb—121, As—75, Ba—137, B—11, Cd—111, 112, Cr—52, 53, Co—59, Cu—63, Pb—206, 207, 208, Mn—55, Hg—201, Ni—60, Se—78, Sr—88, Sn—118, 120, Zn—64, 66.Tests of the leachability of polycyclic aromatic hydrocarbons (PAHs) and elementsDetermination of the dry mass of the rubber granulate samples for the leachability tests was carried out in accordance with ISO 11465:199945, using a drying oven (Pol-Eco-Apparatus SLW-115 Top, Wodzisław Śląski, Poland) and analytical balances (SARTORIUS, Kostrzyn Wlkp. i Radwag, Radom, Poland).The rubber granulate samples were dynamically washed with deionized water according to EN 12457-4:200246 providing a ratio of 1 ml of liquid to 1 g of rubber granulate. The pH value of the water used for dynamic leaching did not exceed 6.7. Elution was performed using a bottle/tube roller mixer (Thermo scientific model, Thermo Fisher Scientific (China) Co., Ltd., Shanghai China). After washing, the effluents were left for 15 min and then filtered through 0.45 mm membrane filters using a pressure filtration device.The leachate obtained from dynamic leaching was subjected to the process of transferring PAHs from the water phase to the organic phase using the algorithm:

SPE column: C18 bed—6 ml/1000 mg;

activation: 10 ml of methanol, 10 ml of methanol:water (40:60) (v:v), flow: 1 ml/min;

sample:eluting solution of methanol (100 ml:10 ml), flow: 0.5 ml/min;

drying: minimum 15 min, maximum flow;

elution: 3 × 3 ml of dichloromethane, flow: 0.5 ml/min.

Collected filtrates were evaporated using a vacuum evaporator (IKA RV 05 basic, IKA WERKE GMBH & CO.KG, Staufen) up to 1 ml. Evaporated filtrates were subjected to the chromatographic analysis performed for the conditions as for the determination of PAHs content. The content of eluted PAHs and elements was related to dry mass of the rubber granulate in each sample.The devices were calibrated and checked on a current basis, including the analysis of control samples, before starting the measurements. Calibrations of the chromatograph, spectrometer and mercury analyzer were performed on solutions of certified reference materials and 2 control samples. The correlation coefficients obtained during the calibration were above 0.995 for all analyzed substances. The analysis of the control samples confirmed the accuracy of the calibration curves, which are the basis for the calculations. Measurements of the content/leachability of the tested substances were carried out for two parallel samples and a reagent blank sample, taking into account the results obtained from it in the analysis of analytical samples. The arithmetic mean of two parallel determinations was assumed as the result of the analytical measurement. Content/leachability conversions of test substances were performed using the GC–MS/MS MassHunter Workstation Software, LCP MHLauncher HPLC-ICP-MS and ACP-MS software and WinLab32 with an AA mercury analyzer FIMS100. More

The quality of global maps can be assessed in different ways. One way is global assessment where a single statistic is chosen to summarize the quality of the entire map: the map accuracy. For a categorical variable, this can be the probability that for a randomly chosen location on the map, the map value corresponds to the true value. For a continuous variable, it can be the RMSE, describing for a randomly chosen location on the map the expected difference between the mapped value and the true value. When a probability sample, such as a completely spatially random sample, is available for the area for which a global assessment is needed, then map accuracy can be estimated model-free (also called design-based, e.g., by using the unweighted sample mean in case of a completely spatially random sample). This circumvents modeling of spatial correlation because observations are independent by design6,9. This approach is called model-free because no model needs to be assumed about the distribution or correlation of the data: the only source of randomness is the random selection of sample units from a target population. If a probability sample is not available this approach cannot be used, and automatically the accuracy assessment approach becomes model-based10, which involves modeling a spatial process by assuming distributions and taking spatial correlations into account, and choosing estimation methods accordingly.Using naive random n-fold or leave-one-out cross-validation methods (or a simple random train-test split) to assess global model quality (usually equated with map accuracy) makes sense when the data are independent and identically distributed. When this is not the case, dependencies between nearby samples, e.g., in a spatial cluster, are ignored and result in biased, overly optimistic model assessment, as shown in, e.g., Ploton et al.5. Alternative cross-validation approaches such as spatial cross-validation5,11 that control for such dependencies are the only way to overcome this bias. Different spatial cross-validation strategies have been developed in the past few years, all aiming at creating independence between cross-validation folds5,11,12,13. Cross-validation creates prediction situations artificially by leaving out data points and predicting their value from the remaining points. If the aim is to assess the accuracy of a global map, the prediction situations created need to resemble those encountered while predicting the global map from the reference data (see Fig. 1 and discussions in Milà et al.14). This occurs naturally when reference data were obtained by (completely spatially random) probability sampling, but in other cases, this has to be forced for instance by controlling spatial distances (spatial cross-validation). Such forcing, however, is only possible when the distances in space that need to be resembled are available in the reference data. In the extreme case where all reference data come from a single cluster, this is impossible. When all reference data come from a small number of clusters, larger distances are available between clusters but do not provide substantial independent information about variation associated with these distances. Lack of information about larger distances means that we cannot assess the quality of predictions associated with such distances and cannot properly estimate global quality measures. Alternative approaches such as experiments with synthetic data15 or a validation using independent data at a higher level of integration16 would then be options to support confidence in the predictions.Another way of accuracy assessment is local assessment: for every location, a quality measure is reported, again as probability or prediction error. Such a local assessment predicts how close the map value is to newly observed values at particular locations. If the measurement error is quantified explicitly, a smoother, measurement-error-free value may be predicted10. If the model accounts for change of support10,17, predictions errors may refer to average values over larger areas such as 1 × 1, 5 × 5, or 10 × 10 km grid cells. Examples of local assessment in the context of global ecological mapping are modeled prediction errors using Quantile Regression Forests18 or mapped variance of predictions made by ensembles1,2. Neither of these examples quantifies spatial correlation or measurement error, or addresses change of support, as it is known from other modeling frameworks19. By omitting to model the spatial process, the local accuracy estimates as presented in the global studies that motivated this comment are disputable.The difference between global and local assessment is striking, in particular for global maps. A global, single number averages out all variability in prediction errors, and obscures any differences, e.g., between continents or climate zones. It is of little value for interpreting the quality of the map for particular regions. More