Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).
Wood-Charlson, E. M. et al. The National Microbiome Data Collaborative: enabling microbiome science. Nat. Rev. Microbiol. 18, 313–314 (2020).
Arkin, A. P. et al. KBase: the United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).
Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).
Hutchins, D. A. & Fu, F. Microorganisms and ocean global change. Nat. Microbiol. 2, 17058 (2017).
Sunagawa, S. et al. Tara Oceans: towards global ocean ecosystems biology. Nat. Rev. Microbiol. 18, 428–445 (2020).
Battin, T. J. et al. River ecosystem metabolism and carbon biogeochemistry in a changing world. Nature 613, 449–459 (2023).
Kroeze, C., Dumont, E. & Seitzinger, S. P. New estimates of global emissions of N2O from rivers and estuaries. Environ. Sci. 2, 159–165 (2005).
Butman, D. & Raymond, P. A. Significant efflux of carbon dioxide from streams and rivers in the United States. Nat. Geosci. 4, 839–842 (2011).
Anderson, E. P. et al. Understanding rivers and their social relations: a critical step to advance environmental water management. WIREs Water 6, e1381 (2019).
Mishra, A., Alnahit, A. & Campbell, B. Impact of land uses, drought, flood, wildfire, and cascading events on water quality and microbial communities: a review and analysis. J. Hydrol. 596, 125707 (2021).
Rodríguez-Ramos, J. A. et al. Genome-resolved metaproteomics decodes the microbial and viral contributions to coupled carbon and nitrogen cycling in river sediments. mSystems 7, e00516-22 (2022).
Ghosh, D., Ghosh, A. & Bhadury, P. Arsenic through aquatic trophic levels: effects, transformations and biomagnification—a concise review. Geosci. Lett. 9, 20 (2022).
Boddicker, A. M. & Mosier, A. C. Genomic profiling of four cultivated Candidatus Nitrotoga spp. predicts broad metabolic potential and environmental distribution. ISME J. 12, 2864–2882 (2018).
Chu, H., Gao, G.-F., Ma, Y., Fan, K. & Delgado-Baquerizo, M. Soil microbial biogeography in a changing world: recent advances and future perspectives. mSystems 5, e00803-19 (2020).
Stadler, M. & del Giorgio, P. A. Terrestrial connectivity, upstream aquatic history and seasonality shape bacterial community assembly within a large boreal aquatic network. ISME J. 16, 937–947 (2022).
Crump, B. C., Amaral-Zettler, L. A. & Kling, G. W. Microbial diversity in arctic freshwaters is structured by inoculation of microbes from soils. ISME J. 6, 1629–1639 (2012).
Ruiz-González, C., Niño-García, J. P. & del Giorgio, P. A. Terrestrial origin of bacterial communities in complex boreal freshwater networks. Ecol. Lett. 18, 1198–1206 (2015).
Read, D. S. et al. Catchment-scale biogeography of riverine bacterioplankton. ISME J. 9, 516–526 (2015).
Savio, D. et al. Bacterial diversity along a 2600 km river continuum. Environ. Microbiol. 17, 4994–5007 (2015).
Payne, J. T., Millar, J. J., Jackson, C. R. & Ochs, C. A. Patterns of variation in diversity of the Mississippi river microbiome over 1,300 kilometers. PLoS ONE 12, e0174890 (2017).
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).
Garner, R. E. et al. A genome catalogue of lake bacterial diversity and its drivers at continental scale. Nat. Microbiol. 8, 1920–1934 (2023).
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).
Rodríguez-Ramos, J. et al. Spatial and temporal metagenomics of river compartments reveals viral community dynamics in an urban impacted stream. Front. Microbiomes 2, 1199766 (2023).
Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
Goldman, A. E., Emani, S. R., Pérez-Angel, L. C., Rodríguez-Ramos, J. A. & Stegen, J. C. Integrated, coordinated, open, and networked (ICON) science to advance the geosciences: introduction and synthesis of a special collection of commentary articles. Earth Space Sci. 9, e2021EA002099 (2022).
Jezbera, J., Sharma, A. K., Brandt, U., Doolittle, W. F. & Hahn, M. W. ‘Candidatus Planktophila limnetica’, an actinobacterium representing one of the most numerically important taxa in freshwater bacterioplankton. Int. J. Syst. Evol. Microbiol. 59, 2864–2869 (2009).
Stein, L. Y. Insights into the physiology of ammonia-oxidizing microorganisms. Curr. Opin. Chem. Biol. 49, 9–15 (2019).
Daims, H. et al. Complete nitrification by Nitrospira bacteria. Nature 528, 504–509 (2015).
Liu, S. et al. Comammox Nitrospira within the Yangtze River continuum: community, biogeography, and ecological drivers. ISME J. 14, 2488–2504 (2020).
Wrighton, K. C. et al. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science 337, 1661–1665 (2012).
Lian, Y., Zhen, L., Chen, X., Li, Y. & Li, X. Microbial biomarkers as indication of dynamic and heterogeneous urban water environments. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-022-24539-8 (2022).
Regina, A. L. A. et al. A watershed impacted by anthropogenic activities: microbial community alterations and reservoir of antimicrobial resistance genes. Sci. Total Environ. 793, 148552 (2021).
Ploug, H., Kühl, M. & Buchholzcleven, B. Anoxic aggregates—an ephemeral phenomenon in the pelagic environment? Aquat. Microb. Ecol. 13, 285–294 (1997).
Böckelmann, U., Manz, W., Neu, T. R. & Szewzyk, U. Characterization of the microbial community of lotic organic aggregates (‘river snow’) in the Elbe River of Germany by cultivation and molecular methods. FEMS Microbiol. Ecol. 33, 157–170 (2000).
Battin, T. J. et al. Biophysical controls on organic carbon fluxes in fluvial networks. Nat. Geosci. 1, 95–100 (2008).
Gomes, I. B., Maillard, J.-Y., Simões, L. C. & Simões, M. Emerging contaminants affect the microbiome of water systems—strategies for their mitigation. Npj Clean Water 3, 39 (2020).
Li, J., Liu, H. & Paul Chen, J. Microplastics in freshwater systems: a review on occurrence, environmental effects, and methods for microplastics detection. Water Res. 137, 362–374 (2018).
Mdee, A. et al. The top 100 global water questions: results of a scoping exercise. One Earth 5, 563–573 (2022).
Zrimec, J., Kokina, M., Jonasson, S., Zorrilla, F. & Zelezniak, A. Plastic-degrading potential across the global microbiome correlates with recent pollution trends. mBio 12, e0215521 (2021).
Jia, S. et al. Fate of antibiotic resistance genes and their associations with bacterial community in livestock breeding wastewater and its receiving river water. Water Res. 124, 259–268 (2017).
Alcock, B. P. et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res. 51, D690–D699 (2023).
Yushchuk, O., Binda, E. & Marinelli, F. Glycopeptide antibiotic resistance genes: distribution and function in the producer Actinomycetes. Front. Microbiol. 11, 1173 (2020).
Pal, A., He, Y., Jekel, M., Reinhard, M. & Gin, K. Y.-H. Emerging contaminants of public health significance as water quality indicator compounds in the urban water cycle. Environ. Int. 71, 46–62 (2014).
Lear, G. et al. The biogeography of stream bacteria. Glob. Ecol. Biogeogr. 22, 544–554 (2013).
Dickey, J. R. et al. The utility of macroecological rules for microbial biogeography. Front. Ecol. Evol. 9, 633155 (2021).
Smith, L. C. et al. Large-scale drivers of relationships between soil microbial properties and organic carbon across Europe. Glob. Ecol. Biogeogr. 30, 2070–2083 (2021).
DeLong, E. F. Microbial community genomics in the ocean. Nat. Rev. Microbiol. 3, 459–469 (2005).
Omernik, J. M. Ecoregions of the conterminous United States. Ann. Assoc. Am. Geogr. 77, 118–125 (1987).
Fine, A. K., van Es, H. M. & Schindelbeck, R. R. Statistics, scoring functions, and regional analysis of a comprehensive soil health database. Soil Sci. Soc. Am. J. 81, 589–601 (2017).
Henson, M. W. et al. Nutrient dynamics and stream order influence microbial community patterns along a 2914 kilometer transect of the Mississippi River. Limnol. Oceanogr. 63, 1837–1855 (2018).
Satinsky, B. M. et al. Metagenomic and metatranscriptomic inventories of the lower Amazon River, May 2011. Microbiome 3, 39 (2015).
Maiolini, B. & Bruno, M. C. The River Continuum Concept revisited: Lessons from the Alps (Innsbruck Univ. Press, 2023).
Mincer, T. J. & Aicher, A. C. Methanol production by a broad phylogenetic array of marine phytoplankton. PLoS ONE 11, e0150820 (2016).
McInerney, M. J. et al. Physiology, ecology, phylogeny, and genomics of microorganisms capable of syntrophic metabolism. Ann. N. Y. Acad. Sci. 1125, 58–72 (2008).
Schink, B. & Zeikus, J. G. Microbial methanol formation: a major end product of pectin metabolism. Curr. Microbiol. 4, 387–389 (1980).
Cole, J. J. et al. Plumbing the global carbon cycle: integrating inland waters into the terrestrial carbon budget. Ecosystems 10, 172–185 (2007).
Gudmundsson, L. et al. Globally observed trends in mean and extreme river flow attributed to climate change. Science 371, 1159–1162 (2021).
Hundley N. Jr Water and the West: The Colorado River Compact and the Politics of Water in the American West (Univ. California Press, 2009).
Arora, B. et al. Building cross-site and cross-network collaborations in critical zone science. J. Hydrol. 618, 129248 (2023).
Stegen, J. C. & Goldman, A. E. WHONDRS: a community resource for studying dynamic river corridors. mSystems 3, e00151-18 (2018).
Garayburu-Caruso, V. A. et al. Using community science to reveal the global chemogeography of river metabolomes. Metabolites 10, 518 (2020).
Toyoda, J. et al. WHONDRS Summer 2019 Sampling Campaign: Global River Corridor Surface Water FTICR-MS, NPOC, and Stable Isotopes https://doi.org/10.15485/1603775 (2020).
US Geological Survey. In Book 9: Techniques for Water-Resources Investigations Ch. A4 pubs.er.usgs.gov/publication/twri09A4 (2006).
Lee, C. J. & Henderson, R. J. Tracking Water Quality in U. S. Streams and Rivers https://pubs.usgs.gov/publication/fs20213019 (USGS, 2020).
Crump, B. C., Kling, G. W., Bahr, M. & Hobbie, J. E. Bacterioplankton community shifts in an Arctic lake correlate with seasonal changes in organic matter source. Appl. Environ. Microbiol. 69, 2253–2268 (2003).
Kellogg, C. T. E., McClelland, J. W., Dunton, K. H. & Crump, B. C. Strong seasonality in Arctic estuarine microbial food webs. Front. Microbiol. 10, 2628 (2019).
Borton, M. A. Genome Resolved Open Watersheds database (GROWdb). Github https://github.com/jmikayla1991/Genome-Resolved-Open-Watersheds-database-GROWdb (2023).
Hill, R. A., Weber, M. H., Leibowitz, S. G., Olsen, A. R. & Thornbrugh, D. J. The Stream-Catchment (StreamCat) Dataset: a database of watershed metrics for the conterminous United States. JAWRA 52, 120–128 (2016).
Blodgett, D., Johnson, J. M. & Bock, A. Generating a reference flow network with improved connectivity to support durable data integration and reproducibility in the coterminous US. Environ. Model. Softw. 165, 105726 (2023).
Hijmans, R. J. et al. Package ‘terra’ (2022).
Willi, K. R., Matthew R. V. & ROSS. Genome Resolved Open Watersheds Database (GROWdb) Geospatial data puller. Github https://github.com/rossyndicate/GROWdb (2023).
Joshi, N. A. & Fass, J. N. Sickle: a windowed adaptive trimming tool for FASTQ files using quality. Github https://github.com/najoshi/sickle (2011).
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).
Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).
Clum, A. et al. DOE JGI metagenome workflow. mSystems 6, e00804-20 (2021).
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Woodcroft, B. J. CoverM. Github https://github.com/wwood/CoverM (2023).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Tavormina, P. L., Orphan, V. J., Kalyuzhnaya, M. G., Jetten, M. S. M. & Klotz, M. G. A novel family of functional operons encoding methane/ammonia monooxygenase-related proteins in gammaproteobacterial methanotrophs. Environ. Microbiol. Rep. 3, 91–100 (2011).
Rochman, F. F. et al. Novel copper-containing membrane monooxygenases (CuMMOs) encoded by alkane-utilizing Betaproteobacteria. ISME J. 14, 714–726 (2020).
Borton, M. A. et al. Coupled laboratory and field investigations resolve microbial interactions that underpin persistence in hydraulically fractured shales. Proc. Natl Acad. Sci. USA 115, E6585–E6594 (2018).
Solden, L. M. et al. New roles in hemicellulosic sugar fermentation for the uncultivated Bacteroidetes family BS11. ISME J. 11, 691–703 (2017).
Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000).
Abascal, F., Zardoya, R. & Posada, D. ProtTest: selection of best-fit models of protein evolution. Bioinformatics 21, 2104–2105 (2005).
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).
Woodcroft, B. J. et al. SingleM and Sandpiper: robust microbial taxonomic profiles from metagenomic data. Preprint at bioRxiv https://doi.org/10.1101/2024.01.30.578060 (2024).
Borton, M. A. et al. Data for ‘A functional microbiome catalogue crowdsourced from North American rivers’. Zenodo https://doi.org/10.5281/zenodo.8173286 (2024).
Eloe-Fadrosh, E. A. et al. The National Microbiome Data Collaborative Data Portal: an integrated multi-omics microbiome data resource. Nucleic Acids Res. 50, D828–D836 (2022).
Borton, M. A. et al. Data generation scripts for ‘A functional microbiome catalogue crowdsourced from North American rivers’. Zenodo https://doi.org/10.5281/zenodo.11041178 (2024).
Borton, M. A. et al. Figure generation code for ‘A functional microbiome catalogue crowdsourced from North American rivers’. Zenodo https://doi.org/10.5281/zenodo.11188634 (2024).