Ecology

Hatzenpichler R, Krukenberg V, Spietz RL, Jay ZJ. Next-generation physiology approaches to study microbial community function at the single-cell level. Nat Rev Microbiol. 2020;18:241–56.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ando T, Bhamidimarri SP, Brending N, Colin-York H, Collinson L, De Jonge N, et al. The 2018 correlative microscopy techniques roadmap. J Phys D: Appl Phys. 2018;51:443001.Article 
CAS 

Google Scholar 
Endesfelder U. Advances in correlative single-molecule localization microscopy and electron microscopy. NanoBioImaging. 2015;1:29–37.Article 

Google Scholar 
Osborn M, Webster RE, Weber K. Individual microtubules viewed by immunofluorescence and electron microscopy in the same PtK2 cell. J Cell Biol. 1978;77:27–38.Article 

Google Scholar 
Webster RE, Osborn M, Weber K. Visualization of the same PtK2 cytoskeletons by both immunofluorescence and low power electron microscopy. Exp Cell Res. 1978;117:47–61.CAS 
PubMed 
Article 

Google Scholar 
Perkovic M, Kunz M, Endesfelder U, Bunse S, Wigge C, Yu Z, et al. Correlative Light- and Electron Microscopy with chemical tags. J Struct Biol. 2014;186:205–13.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lange F, Agui-Gonzalez P, Riedel D, Phan NTN, Jakobs S, Rizzoli SO. Correlative fluorescence microscopy, transmission electron microscopy and secondary ion mass spectrometry (CLEM-SIMS) for cellular imaging. Plos One. 2021;16:e0240768.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pirozzi NM, Hoogenboom JP, Giepmans BNG. ColorEM: analytical electron microscopy for element-guided identification and imaging of the building blocks of life. Histochem Cell Biol. 2018;150:509–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Loussert-Fonta C, Toullec G, Paraecattil AA, Jeangros Q, Krueger T, Escrig S, et al. Correlation of fluorescence microscopy, electron microscopy, and NanoSIMS stable isotope imaging on a single tissue section. Commun Biol. 2020;3:362.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Joosten B, Willemse M, Fransen J, Cambi A, van den Dries K. Super-resolution correlative light and electron microscopy (SR-CLEM) reveals novel ultrastructural insights into dendritic cell podosomes. Front Immunol. 2018;9:1–14.Article 
CAS 

Google Scholar 
Woehl TJ, Kashyap S, Firlar E, Perez-Gonzalez T, Faivre D, Trubitsyn D, et al. Correlative electron and fluorescence microscopy of magnetotactic bacteria in liquid: toward in vivo imaging. Sci Rep. 2014;4:6854.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li J, Zhang H, Menguy N, Benzerara K, Wang F, Lin X, et al. Single-cell resolution of uncultured magnetotactic bacteria via fluorescence-coupled electron microscopy. Appl Environ Microbiol. 2017;83:e00409–17.PubMed 
PubMed Central 

Google Scholar 
Qian XX, Santini CL, Kosta A, Menguy N, Le Guenno H, Zhang W, et al. Juxtaposed membranes underpin cellular adhesion and display unilateral cell division of multicellular magnetotactic prokaryotes. Environ Microbiol. 2020;22:1481–94.CAS 
PubMed 
Article 

Google Scholar 
McGlynn SE, Chadwick GL, O’Neill A, Mackey M, Thor A, Deerinck TJ, et al. Subgroup characteristics of marine methane-oxidizing ANME-2 archaea and their syntrophic partners revealed by integrated multimodal analytical microscopy. Appl Environ Microbiol. 2018;84:e00399–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hao L, McIlroy SJ, Kirkegaard RH, Karst SM, Fernando WEY, Aslan H, et al. Novel prosthecate bacteria from the candidate phylum Acetothermia. ISME J. 2018;126:2225–37.Article 
CAS 

Google Scholar 
Hapca S, Baveye PC, Wilson C, Lark RM, Otten W. Three-dimensional mapping of soil chemical characteristics at micrometric scale by combining 2D SEM-EDX data and 3D X-Ray CT images. PLoS One. 2015;10:e0137205.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schluter S, Eickhorst T, Mueller CW. Correlative imaging reveals holistic view of soil microenvironments. Environ Sci Technol. 2019;53:829–37.PubMed 
Article 
CAS 

Google Scholar 
Marlow J, Spietz R, Kim KY, Ellisman M, Girguis P, Hatzenpichler R. Spatially resolved correlative microscopy and microbial identification reveal dynamic depth- and mineral-dependent anabolic activity in salt marsh sediment. Environ Microbiol. 2021;23:4756–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Musat N, Musat F, Weber PK, Pett-Ridge J. Tracking microbial interactions with NanoSIMS. Curr Opin Biotechnol. 2016;41:114–21.CAS 
PubMed 
Article 

Google Scholar 
Berry D, Mader E, Lee TK, Woebken D, Wang Y, Zhu D, et al. Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells. Proc Natl Acad Sci USA. 2015;112:E194–203.CAS 
PubMed 

Google Scholar 
Huang WE, Stoecker K, Griffiths R, Newbold L, Daims H, Whiteley AS, et al. Raman-FISH: combining stable-isotope Raman spectroscopy and fluorescence in situ hybridization for the single cell analysis of identity and function. Environ Microbiol. 2007;9:1878–89.CAS 
PubMed 
Article 

Google Scholar 
Waite DW, Chuvochina M, Pelikan C, Parks DH, Yilmaz P, Wagner M, et al. Proposal to reclassify the proteobacterial classes Deltaproteobacteria and Oligoflexia, and the phylum Thermodesulfobacteria into four phyla reflecting major functional capabilities. Int J Syst Evol Microbiol. 2020;70:5972–6016.CAS 
PubMed 
Article 

Google Scholar 
Keim CN, Martins JL, de Barros HL, Lins U, MF Structure, behavior, ecology and diversity of multicellular magnetotactic prokaryotes. Magnetoreception and magnetosomes in bacteria. (Springer, Berlin, Heidelberg, 2006):103–32.Abreu F, Silva KT, Martins JL, Lins U. Cell viability in magnetotactic multicellular prokaryotes. Int Microbiol. 2006;9:267–72.CAS 
PubMed 

Google Scholar 
Abreu F, Martins JL, Silveira TS, Keim CN, de Barros HG, Filho FJ, et al. ‘Candidatus Magnetoglobus multicellularis’, a multicellular, magnetotactic prokaryote from a hypersaline environment. Int J Syst Evol Microbiol. 2007;57:1318–22.CAS 
PubMed 
Article 

Google Scholar 
Abreu F, Silva KT, Leao P, Guedes IA, Keim CN, Farina M, et al. Cell adhesion, multicellular morphology, and magnetosome distribution in the multicellular magnetotactic prokaryote Candidatus Magnetoglobus multicellularis. Microsc Microanal. 2013;19:535–43.CAS 
PubMed 
Article 

Google Scholar 
Faivre D, Schuler D. Magnetotactic bacteria and magnetosomes. Chem Rev. 2008;108:4875–98.CAS 
PubMed 
Article 

Google Scholar 
Greening C, Lithgow T. Formation and function of bacterial organelles. Nat Rev Microbiol. 2020;18:677–89.CAS 
PubMed 
Article 

Google Scholar 
Uebe R, Schuler D. Magnetosome biogenesis in magnetotactic bacteria. Nat Rev Microbiol. 2016;14:621–37.CAS 
PubMed 
Article 

Google Scholar 
Shapiro OH, Hatzenpichler R, Buckley DH, Zinder SH, Orphan VJ. Multicellular photo-magnetotactic bacteria. Env Microbiol Rep. 2011;3:233–8.Article 

Google Scholar 
Simmons SL, Edwards KJ. Unexpected diversity in populations of the many-celled magnetotactic prokaryote. Environ Microbiol. 2007;9:206–15.CAS 
PubMed 
Article 

Google Scholar 
Wilbanks EG, Jaekel U, Salman V, Humphrey PT, Eisen JA, Facciotti MT, et al. Microscale sulfur cycling in the phototrophic pink berry consortia of the Sippewissett Salt Marsh. Environ Microbiol. 2014;16:3398–415.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wilbanks EG, Salman-Carvalho V, Jaekel U, Humphrey PT, Eisen JA, Buckley DH, et al. The Green Berry Consortia of the Sippewissett Salt Marsh: millimeter-sized aggregates of diazotrophic unicellular cyanobacteria. Front Microbiol. 2017;8:1–12.Article 

Google Scholar 
Larsen S, Nielsen LP, Schramm A. Cable bacteria associated with long-distance electron transport in New England salt marsh sediment. Env Microbiol Rep. 2015;7:175–9.CAS 
Article 

Google Scholar 
Salman V, Yang TT, Berben T, Klein F, Angert E, Teske A. Calcite-accumulating large sulfur bacteria of the genus Achromatium in Sippewissett Salt Marsh. ISME J. 2015;9:2503–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mackey KRM, Hunter-Cevera K, Britten GL, Murphy LG, Sogin ML, Huber JA. Seasonal succession and spatial patterns of synechococcus microdiversity in a salt marsh estuary revealed through 16S rRNA gene oligotyping. Front Microbiol. 2017;8.Bowen JL, Morrison HG, Hobbie JE, Sogin ML. Salt marsh sediment diversity: a test of the variability of the rare biosphere among environmental replicates. ISME J. 2012;6:2014–23.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lewis AT, Gaifulina R, Isabelle M, Dorney J, Woods ML, Lloyd GR, et al. Mirrored stainless steel substrate provides improved signal for Raman spectroscopy of tissue and cells. J Raman Spectrosc. 2017;48:119–25.CAS 
PubMed 
Article 

Google Scholar 
Eder SH, Gigler AM, Hanzlik M, Winklhofer M. Sub-micrometer-scale mapping of magnetite crystals and sulfur globules in magnetotactic bacteria using confocal Raman micro-spectrometry. PLoS One. 2014;9:e107356.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Stoecker K, Dorninger C, Daims H, Wagner M. Double labeling of oligonucleotide probes for fluorescence in situ hybridization (DOPE-FISH) improves signal intensity and increases rRNA accessibility. Appl Environ Microbiol. 2010;76:922–6.CAS 
PubMed 
Article 

Google Scholar 
Daims H, Stoecker K, Wagner M. Fluorescence in situ hybridization for the detection of prokaryotes. Taylor & Francis, 2004; Mol Microbial Ecol:208–28.Daims H, Brühl A, Amann R, Schleifer K-H, Wagner M. The domain-specific probe EUB338 is insufficient for the detection of all bacteria: development and evaluation of a more comprehensive probe set. Syst Appl Microbiol. 1999;22:434–44.CAS 
PubMed 
Article 

Google Scholar 
Stahl DA, Amann RI. Development and application of nucleic acid probes. Stackebrandt E and Goodfellow M, editors. Nucleic acid techniques in bacterial systematics. John Wiley & Sons; 1991. p. 205–48.Behrens S, Ruhland C, Inacio J, Huber H, Fonseca A, Spencer-Martins I, et al. In situ accessibility of small-subunit rRNA of members of the domains Bacteria, Archaea, and Eucarya to Cy3-labeled oligonucleotide probes. Appl Environ Microbiol. 2003;69:1748–58.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wallner G, Amann R, Beisker W. Optimizing fluorescent insitu hybridization with ribosomal-Rna-targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry. 1993;14:136–43.CAS 
PubMed 
Article 

Google Scholar 
Zimmermann M, Escrig S, Hubschmann T, Kirf MK, Brand A, Inglis RF, et al. Phenotypic heterogeneity in metabolic traits among single cells of a rare bacterial species in its natural environment quantified with a combination of flow cell sorting and NanoSIMS. Front Microbiol. 2015;6:243.PubMed 
PubMed Central 
Article 

Google Scholar 
Grieb A, Bowers RM, Oggerin M, Goudeau D, Lee J, Malmstrom RR, et al. A pipeline for targeted metagenomics of environmental bacteria. Microbiome. 2020;8:21.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meyer NR, Fortney JL, Dekas AE. NanoSIMS sample preparation decreases isotope enrichment: magnitude, variability and implications for single-cell rates of microbial activity. Environ Microbiol. 2021;23:81–98.CAS 
PubMed 
Article 

Google Scholar 
Musat N, Stryhanyuk H, Bombach P, Adrian L, Audinot JN, Richnow HH. The effect of FISH and CARD-FISH on the isotopic composition of (13)C- and (15)N-labeled Pseudomonas putida cells measured by nanoSIMS. Syst Appl Microbiol. 2014;37:267–76.CAS 
PubMed 
Article 

Google Scholar 
Amann R, Fuchs BM. Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat Rev Microbiol. 2008;6:339–48.CAS 
PubMed 
Article 

Google Scholar 
Lee KS, Landry Z, Pereira FC, Wagner M, Berry D, Huang WE, et al. Raman microspectroscopy for microbiology. Nat Rev Methods Primers. 2021;1:1–25.Article 
CAS 

Google Scholar 
Wang Y, Huang WE, Cui L, Wagner M. Single-cell stable isotope probing in microbiology using Raman microspectroscopy. Curr Opin Biotechnol. 2016;41:34–42.CAS 
PubMed 
Article 

Google Scholar 
Eichorst SA, Strasser F, Woyke T, Schintlmeister A, Wagner M, Woebken D. Advancements in the application of NanoSIMS and Raman microspectroscopy to investigate the activity of microbial cells in soils. FEMS Microbiol Ecol. 2015;91:1–16.Article 
CAS 

Google Scholar 
Li J, Liu P, Tamaxia A, Zhang H, Liu Y, Wang J, et al. Diverse intracellular inclusion types within magnetotactic bacteria: implications for biogeochemical cycling in aquatic environments. J Geophys Res Biogeosci. 2021;126:e2021JG006310.CAS 

Google Scholar 
Matanfack GA, Taubert M, Guo S, Houhou R, Bocklitz T, Kusel K, et al. Influence of carbon sources on quantification of deuterium incorporation in heterotrophic bacteria: a Raman-stable isotope labeling approach. Anal Chem. 2020;92:11429–37.CAS 
PubMed 
Article 

Google Scholar 
Amor M, Tharaud M, Gelabert A, Komeili A. Single-cell determination of iron content in magnetotactic bacteria: implications for the iron biogeochemical cycle. Environ Microbiol. 2020;22:823–31.CAS 
PubMed 
Article 

Google Scholar 
Farina M, Esquivel DMS, Debarros HGPL. Magnetic iron-sulfur crystals from a magnetotactic microorganism. Nature. 1990;343:256–8.CAS 
Article 

Google Scholar 
Wenter R, Wanner G, Schuler D, Overmann J. Ultrastructure, tactic behaviour and potential for sulfate reduction of a novel multicellular magnetotactic prokaryote from North Sea sediments. Environ Microbiol. 2009;11:1493–505.PubMed 
Article 

Google Scholar 
Zhang R, Chen YR, Du HJ, Zhang WY, Pan HM, Xiao T, et al. Characterization and phylogenetic identification of a species of spherical multicellular magnetotactic prokaryotes that produces both magnetite and greigite crystals. Res Microbiol. 2014;165:481–9.CAS 
PubMed 
Article 

Google Scholar 
Teng Z, Zhang Y, Zhang W, Pan H, Xu J, Huang H, et al. Diversity and characterization of multicellular magnetotactic prokaryotes from coral reef habitats of the Paracel Islands, South China Sea. Front Microbiol. 2018;9:2135.PubMed 
PubMed Central 
Article 

Google Scholar 
Bourdoiseau J-A, Jeannin M, Rémazeilles C, Sabot R, Refait P. The transformation of mackinawite into greigite studied by Raman spectroscopy. J Raman Spectrosc. 2011;42:496–504.CAS 
Article 

Google Scholar 
Mann S, Sparks NH, Board RG. Magnetotactic bacteria: microbiology, biomineralization, palaeomagnetism and biotechnology. Adv Microb Physiol. 1990;31:125–81.CAS 
PubMed 
Article 

Google Scholar 
Posfai M, Buseck PR, Bazylinski DA, Frankel RB. Iron sulfides from magnetotactic bacteria: structure, composition, and phase transitions. Am Mineral. 1998;83:1469–81.CAS 
Article 

Google Scholar 
Betzig E, Patterson GH, Sougrat R, Lindwasser OW, Olenych S, Bonifacino JS, et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science. 2006;313:1642–5.CAS 
PubMed 
Article 

Google Scholar 
Rust MJ, Bates M, Zhuang X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat Methods. 2006;3:793–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hatzenpichler R, Scheller S, Tavormina PL, Babin BM, Tirrell DA, Orphan VJ. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ Microbiol. 2014;16:2568–90.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Smriga S, Samo TJ, Malfatti F, Villareal J, Azam F. Individual cell DNA synthesis within natural marine bacterial assemblages as detected by ‘click’ chemistry. Aquat Microb Ecol. 2014;72:269–80.Article 

Google Scholar 
Siegrist MS, Whiteside S, Jewett JC, Aditham A, Cava F, Bertozzi CR. (D)-Amino acid chemical reporters reveal peptidoglycan dynamics of an intracellular pathogen. ACS Chem Biol. 2013;8:500–5.CAS 
PubMed 
Article 

Google Scholar 
Keim CN, Abreu F, Lins U, Lins de Barros H, Farina M. Cell organization and ultrastructure of a magnetotactic multicellular organism. J Struct Biol. 2004;145:254–62.PubMed 
Article 

Google Scholar  More

Falkowski, P. et al. The global carbon cycle: a test of our knowledge of Earth as a system. Science 290, 291–296 (2000).Article 

Google Scholar 
McMahon, S. & Parnell, J. Weighing the deep continental biosphere. FEMS Microbiol. Ecol. 87, 113–120 (2014).Article 

Google Scholar 
Magnabosco, C. et al. The biomass and biodiversity of the continental subsurface. Nat. Geosci. 11, 707–717 (2018).Article 

Google Scholar 
Gleeson, T., Befus, K. M., Jasechko, S., Luijendijk, E. & Cardenas, M. B. The global volume and distribution of modern groundwater. Nat. Geosci. 9, 161–167 (2016).Article 

Google Scholar 
Stevanović, Z. Karst waters in potable water supply: a global scale overview. Environ. Earth Sci. 78, 662 (2019).Article 

Google Scholar 
Poulson, T. L. & White, W. B. The cave environment. Science 165, 971–981 (1969).Article 

Google Scholar 
Rusterholtz, K. J. & Mallory, L. M. Density, activity, and diversity of bacteria indigenous to a karstic aquifer. Microb. Ecol. 28, 79–99 (1994).Article 

Google Scholar 
Smith, H. J. et al. Impact of hydrologic boundaries on microbial planktonic and biofilm communities in shallow terrestrial subsurface environments. FEMS Microbiol. Ecol. 94, fiy191 (2018).
Google Scholar 
Alexander, M. Introduction to Soil Microbiology (Wiley, 1977).Griebler, C. & Lueders, T. Microbial biodiversity in groundwater ecosystems. Freshw. Biol. 54, 649–677 (2009).Article 

Google Scholar 
Krumholz, L. R., McKinley, J. P., Ulrich, G. A. & Suflita, J. M. Confined subsurface microbial communities in Cretaceous rock. Nature 386, 64–66 (1997).Article 

Google Scholar 
Probst, A. J. et al. Differential depth distribution of microbial function and putative symbionts through sediment-hosted aquifers in the deep terrestrial subsurface. Nat. Microbiol. 3, 328–336 (2018).Article 

Google Scholar 
Magnabosco, C. et al. A metagenomic window into carbon metabolism at 3 km depth in Precambrian continental crust. ISME J. 10, 730–741 (2016).Article 

Google Scholar 
Stevens, T. O. & McKinley, J. P. Lithoautotrophic microbial ecosystems in deep basalt aquifers. Science 270, 450–455 (1995).Article 

Google Scholar 
Tiago, I. & Veríssimo, A. Microbial and functional diversity of a subterrestrial high pH groundwater associated to serpentinization. Environ. Microbiol. 15, 1687–1706 (2013).Article 

Google Scholar 
Mccollom, T. M. & Amend, J. P. A thermodynamic assessment of energy requirements for biomass synthesis by chemolithoautotrophic micro-organisms in oxic and anoxic environments. Geobiology 3, 135–144 (2005).Article 

Google Scholar 
Momper, L., Jungbluth, S. P., Lee, M. D. & Amend, J. P. Energy and carbon metabolisms in a deep terrestrial subsurface fluid microbial community. ISME J. 11, 2319–2333 (2017).Article 

Google Scholar 
Jewell, T. N. M., Karaoz, U., Brodie, E. L., Williams, K. H. & Beller, H. R. Metatranscriptomic evidence of pervasive and diverse chemolithoautotrophy relevant to C, S, N and Fe cycling in a shallow alluvial aquifer. ISME J. 10, 2106–2117 (2016).Article 

Google Scholar 
Herrmann, M., Rusznyák, A. & Akob, D. M. Large fractions of CO2-fixing microorganisms in pristine limestone aquifers appear to be involved in the oxidation of reduced sulfur and nitrogen compounds. Appl. Environ. Microbiol. 81, 2384–2394 (2015).Peterson, B. J. Aquatic primary productivity and the 14C–CO2 method: a history of the productivity problem. Annu. Rev. Ecol. Syst. 11, 359–385 (1980).Article 

Google Scholar 
Viviani, D. A., Karl, D. M. & Church, M. J. Variability in photosynthetic production of dissolved and particulate organic carbon in the North Pacific Subtropical Gyre. Front. Mar. Sci. 2, 73 (2015).Article 

Google Scholar 
Kohlhepp, B. et al. Aquifer configuration and geostructural links control the groundwater quality in thin-bedded carbonate–siliciclastic alternations of the Hainich CZE, central Germany. Hydrol. Earth Syst. Sci. 21, 6091–6116 (2017).Article 

Google Scholar 
Pedersen, K. & Ekendahl, S. Assimilation of CO2 and introduced organic compounds by bacterial communities in groundwater from southeastern Sweden deep crystalline bedrock. Microb. Ecol. 23, 1–14 (1992).Article 

Google Scholar 
Partensky, F. & Garczarek, L. Prochlorococcus: advantages and limits of minimalism. Ann. Rev. Mar. Sci. 2, 305–331 (2010).Article 

Google Scholar 
Karl, D. M., Hebel, D. V., Björkman, K. & Letelier, R. M. The role of dissolved organic matter release in the productivity of the oligotrophic North Pacific Ocean. Limnol. Oceanogr. 43, 1270–1286 (1998).Article 

Google Scholar 
Liang, Y. et al. Estimating primary production of picophytoplankton using the carbon-based ocean productivity model: a preliminary study. Front. Microbiol. 8, 1926 (2017).Article 

Google Scholar 
Steinberg, D. K. et al. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep Sea Res. 2 48, 1405–1447 (2001).Article 

Google Scholar 
Gundersen, K., Orcutt, K. M., Purdie, D. A., Michaels, A. F. & Knap, A. H. Particulate organic carbon mass distribution at the Bermuda Atlantic Time-series Study (BATS) site. Deep Sea Res. 2 48, 1697–1718 (2001).Article 

Google Scholar 
Karl, D. M. & Lukas, R. The Hawaii Ocean Time-series (HOT) program: background, rationale and field implementation. Deep Sea Res. 2 43, 129–156 (1996).Article 

Google Scholar 
Martiny, A. C., Vrugt, J. A. & Lomas, M. W. Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean. Sci. Data 1, 140048 (2014).Article 

Google Scholar 
Martiny, A. C., Vrugt, J. A. & Lomas, M. W. Data from: Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean. Dryad https://doi.org/10.5061/dryad.d702p (2015).Schwab, V. F. et al. 14C-free carbon Is a major contributor to cellular biomass in geochemically distinct groundwater of shallow sedimentary bedrock aquifers. Water Resour. Res. 55, 2104–2121 (2019).Article 

Google Scholar 
Taubert, M. et al. Bolstering fitness via CO2 fixation and organic carbon uptake: mixotrophs in modern groundwater. ISME J 16, 1153–1162 (2022).Article 

Google Scholar 
Rimstidt, J. D. & Vaughan, D. J. Pyrite oxidation: a state-of-the-art assessment of the reaction mechanism. Geochim. Cosmochim. Acta 67, 873–880 (2003).Article 

Google Scholar 
Lin, W. et al. Genomic insights into the uncultured genus “Candidatus Magnetobacterium” in the phylum Nitrospirae. ISME J. 8, 2463–2477 (2014).Article 

Google Scholar 
Kato, S. et al. Genome-enabled metabolic reconstruction of dominant chemosynthetic colonizers in deep-sea massive sulfide deposits. Environ. Microbiol. 20, 862–877 (2018).Article 

Google Scholar 
Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).Article 

Google Scholar 
Kojima, H., Watanabe, T. & Fukui, M. Sulfuricaulis limicola gen. nov., sp. nov., a sulfur oxidizer isolated from a lake. Int. J. Syst. Evol. Microbiol. 66, 266–270 (2016).Article 

Google Scholar 
Strous, M., Van Gerven, E., Kuenen, J. G. & Jetten, M. Effects of aerobic and microaerobic conditions on anaerobic ammonium-oxidizing (anammox) sludge. Appl. Environ. Microbiol. 63, 2446–2448 (1997).Article 

Google Scholar 
Ji, X., Wu, Z., Sung, S. & Lee, P.-H. Metagenomics and metatranscriptomics analyses reveal oxygen detoxification and mixotrophic potentials of an enriched anammox culture in a continuous stirred-tank reactor. Water Res. 166, 115039 (2019).Article 

Google Scholar 
Dalsgaard, T. et al. Oxygen at nanomolar levels reversibly suppresses process rates and gene expression in anammox and denitrification in the oxygen minimum zone off northern Chile. mBio 5, e01966 (2014).Article 

Google Scholar 
Smith, R. L., Böhlke, J. K., Song, B. & Tobias, C. R. Role of anaerobic ammonium oxidation (anammox) in nitrogen removal from a freshwater aquifer. Environ. Sci. Technol. 49, 12169–12177 (2015).Article 

Google Scholar 
Strous, M., Heijnen, J. J., Kuenen, J. G. & Jetten, M. S. M. The sequencing batch reactor as a powerful tool for the study of slowly growing anaerobic ammonium-oxidizing microorganisms. Appl. Microbiol. Biotechnol. 50, 589–596 (1998).Article 

Google Scholar 
Kits, K. D. et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature 549, 269–272 (2017).Article 

Google Scholar 
Rittmann, B. E. & McCarty, P. L. Environmental Biotechnology: Principles and Applications (McGraw-Hill Education, 2001).Zhang, Y. et al. Nitrifier adaptation to low energy flux controls inventory of reduced nitrogen in the dark ocean. Proc. Natl. Acad. Sci. USA 117, 4823–4830 (2020).Article 

Google Scholar 
Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).Article 

Google Scholar 
Lehmann, R. & Totsche, K. U. Multi-directional flow dynamics shape groundwater quality in sloping bedrock strata. J. Hydrol. 580, 124291 (2020).Article 

Google Scholar 
Küsel, K. et al. How deep can surface signals be traced in the Critical Zone? Merging biodiversity with biogeochemistry research in a central German Muschelkalk landscape. Front. Earth Sci. 4, 32 (2016).Article 

Google Scholar 
Yan, L. et al. Environmental selection shapes the formation of near-surface groundwater microbiomes. Water Res. 170, 115341 (2019).Article 

Google Scholar 
Pack, M. A. et al. A method for measuring methane oxidation rates using low levels of 14C-labeled methane and accelerator mass spectrometry: methane oxidation rates by AMS. Limnol. Oceanogr. Methods 9, 245–260 (2011).Article 

Google Scholar 
Nielsen, E. S. The use of radio-active carbon (C14) for measuring organic production in the sea. ICES J. Mar. Sci. 18, 117–140 (1952).Article 

Google Scholar 
Xu, X. et al. Modifying a sealed tube zinc reduction method for preparation of AMS graphite targets: reducing background and attaining high precision. Nucl. Instrum. Methods Phys. Res. B 259, 320–329 (2007).Article 

Google Scholar 
Merser, S. Acetabulum online interactive statistical calculators. Accessed Feb, 2021. https://acetabulum.dk/anova.htmlBermuda Oceanographic Timeseries, accessed 21 Oct 2020, http://batsftp.bios.edu/BATS/production/bats_primary_production.txtHawaiian Oceanographic Timeseries, accessed 21 Oct 2020, ftp://ftp.soest.hawaii.edu/hot/primary_productionHawaiian Oceanographic Timeseries, accessed 21 Oct 2020, https://hahana.soest.hawaii.edu/FTP/hot/microscopy/EPIslides.txtKumar, S. et al. Nitrogen loss from pristine carbonate-rock aquifers of the Hainich Critical Zone Exploratory (Germany) is primarily driven by chemolithoautotrophic anammox processes. Front. Microbiol. 8, 1951 (2017).Article 

Google Scholar 
Füssel, J. et al. Nitrite oxidation in the Namibian oxygen minimum zone. ISME J. 6, 1200–1209 (2012).Article 

Google Scholar 
McIlvin, M. R. & Altabet, M. A. Chemical conversion of nitrate and nitrite to nitrous oxide for nitrogen and oxygen isotopic analysis in freshwater and seawater. Anal. Chem. 77, 5589–5595 (2005).Article 

Google Scholar 
Dalsgaard, T., Thamdrup, B., Farías, L. & Revsbech, N. P. Anammox and denitrification in the oxygen minimum zone of the eastern South Pacific. Limnol. Oceanogr. 57, 1331–1346 (2012).Article 

Google Scholar 
Thamdrup, B. et al. Anaerobic ammonium oxidation in the oxygen-deficient waters off northern Chile. Limnol. Oceanogr. 51, 2145–2156 (2006).Article 

Google Scholar 
Taubert, M. et al. Tracking active groundwater microbes with D2O labelling to understand their ecosystem function. Environ. Microbiol. 20, 369–384 (2018).Article 

Google Scholar 
Bushnell, B. BBMap (SourceForge, 2014); http://sourceforge.net/projects/bbmapBornemann, T. L. V. et al. Geological degassing enhances microbial metabolism in the continental subsurface. Preprint at bioRxiv https://doi.org/10.1101/2020.03.07.980714 (2020).Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).Article 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).Article 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357 (2012).Article 

Google Scholar 
Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).Article 

Google Scholar 
Brown, C. T. et al. Unusual biology across a group comprising more than 15% of domain bacteria. Nature 523, 208–211 (2015).Article 

Google Scholar 
Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).Article 

Google Scholar 
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).Article 

Google Scholar 
Murat Eren, A. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).Article 

Google Scholar 
Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).Article 

Google Scholar 
Graham, E. D., Heidelberg, J. F. & Tully, B. J. Potential for primary productivity in a globally-distributed bacterial phototroph. ISME J. 12, 1861–1866 (2018).Article 

Google Scholar 
Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).Article 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).Article 

Google Scholar 
Pelikan, C. et al. Diversity analysis of sulfite- and sulfate-reducing microorganisms by multiplex dsrA and dsrB amplicon sequencing using new primers and mock community-optimized bioinformatics. Environ. Microbiol. 18, 2994–3009 (2016).Article 

Google Scholar 
Lücker, S., Nowka, B., Rattei, T., Spieck, E. & Daims, H. The genome of Nitrospina gracilis Illuminates the metabolism and evolution of the major marine nitrite oxidizer. Front. Microbiol. 4, 27 (2013).Article 

Google Scholar 
Orellana, L. H., Rodriguez-R, L. M. & Konstantinidis, K. T. ROCker: accurate detection and quantification of target genes in short-read metagenomic data sets by modeling sliding-window bitscores. Nucleic Acids Res. 45, e14 (2017).
Google Scholar 
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).
Google Scholar 
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).Article 

Google Scholar 
Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0501-8 (2020).Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11, 538 (2010).Article 

Google Scholar 
Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90 K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).Article 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).Article 

Google Scholar 
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 

Google Scholar 
Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).Article 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128 (2007).Article 

Google Scholar 
Emiola, A. & Oh, J. High throughput in situ metagenomic measurement of bacterial replication at ultra-low sequencing coverage. Nat. Commun. 9, 4956 (2018).Article 

Google Scholar 
Wegner, C.-E. et al. Biogeochemical regimes in shallow aquifers reflect the metabolic coupling of the elements nitrogen, sulfur, and carbon. Appl. Environ. Microbiol. 85, e02346-18 (2019).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (R Core Team, 2018).RStudio: Integrated Development Environment for R (RStudio Team, 2016).Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 

Google Scholar 
Neuwirth, E. RColorBrewer: ColorBrewer Palettes. R package version 1.1-2. https://CRAN.R-project.org/package=RColorBrewer (2014). More

Weiss, S. et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 10, 1669–1681 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Djokic, T., Kranendonk, M. J. V., Campbell, K. A., Walter, M. R. & Ward, C. R. Earliest signs of life on land preserved in ca. 3.5 Ga hot spring deposits. Nat. Commun. 8, 1–9 (2017).
Google Scholar 
Damer, B. & Deamer, D. The Hot Spring Hypothesis for an origin of life. Astrobiology 20, 429–452 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Van Kranendonk, M. J. et al. Elements for the origin of life on land: a deep-time perspective from the Pilbara Craton of Western Australia. Astrobiology 21, 39–59 (2021).ADS 
PubMed 

Google Scholar 
Colman, D. R. et al. Phylogenomic analysis of novel Diaforarchaea is consistent with sulfite but not sulfate reduction in volcanic environments on early Earth. ISME J. 14, 1316–1331 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Anbar, A. D. & Knoll, A. H. Proterozoic ocean chemistry and evolution: a bioinorganic bridge? Science 297, 1137–1142 (2002).ADS 
CAS 
PubMed 

Google Scholar 
Lloyd, K. G. et al. Phylogenetically novel uncultured microbial cells dominate Earth microbiomes. mSystems 3, 431 (2018).
Google Scholar 
Hedlund, B. P. et al. Uncultivated thermophiles: current status and spotlight on ‘Aigarchaeota’. Curr. Opin. Microbiol. 25, 136–145 (2015).CAS 
PubMed 

Google Scholar 
Nunoura, T. et al. Genetic and functional properties of uncultivated thermophilic crenarchaeotes from a subsurface gold mine as revealed by analysis of genome fragments. Environ. Microbiol. 7, 1967–1984 (2005).CAS 
PubMed 

Google Scholar 
Nunoura, T. et al. Insights into the evolution of Archaea and eukaryotic protein modifier systems revealed by the genome of a novel archaeal group. Nucleic Acids Res. 39, 3204–3223 (2010).PubMed 
PubMed Central 

Google Scholar 
Rinke, C. et al. A standardized archaeal taxonomy for the Genome Taxonomy Database. Nat. Microbiol. 6, 946–959 (2021).CAS 
PubMed 

Google Scholar 
Hua, Z.-S. et al. Genomic inference of the metabolism and evolution of the archaeal phylum Aigarchaeota. Nat. Commun. 9, 1–11 (2018).ADS 

Google Scholar 
Takami, H., Arai, W., Takemoto, K., Uchiyama, I. & Taniguchi, T. Functional classification of uncultured ‘Candidatus Caldiarchaeum subterraneum’ using the Maple system. PLoS ONE 10, e0132994 (2015).PubMed 
PubMed Central 

Google Scholar 
Beam, J. P. et al. Ecophysiology of an uncultivated lineage of Aigarchaeota from an oxic, hot spring filamentous ‘streamer’ community. ISME J. 10, 210–224 (2016).CAS 
PubMed 

Google Scholar 
Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431–437 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Cole, J. K. et al. Sediment microbial communities in Great Boiling Spring are controlled by temperature and distinct from water communities. ISME J. 7, 718–729 (2013).CAS 
PubMed 

Google Scholar 
Peacock, J. P. et al. Pyrosequencing reveals high-temperature cellulolytic microbial consortia in Great Boiling Spring after in situ lignocellulose enrichment. PLoS ONE 8, e59927 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kletzin, A. & Adams, M. W. W. Tungsten in biological systems. FEMS Microbiol. Rev. 18, 5–63 (1996).CAS 
PubMed 

Google Scholar 
Hagedoorn, P. L. et al. Purification and characterization of the tungsten enzyme aldehyde:ferredoxin oxidoreductase from the hyperthermophilic denitrifier Pyrobaculum aerophilum. J. Biol. Inorg. Chem. 10, 259–269 (2005).CAS 
PubMed 

Google Scholar 
de Vries, S. et al. Adaptation to a high-tungsten environment: Pyrobaculum aerophilum contains an active tungsten nitrate reductase. Biochemistry 49, 9911–9921 (2010).PubMed 

Google Scholar 
Bräsen, C., Esser, D., Rauch, B. & Siebers, B. Carbohydrate metabolism in Archaea: current insights into unusual enzymes and pathways and their regulation. Microbiol. Mol. Biol. Rev. 78, 89–175 (2014).Kato, S. et al. Long-term cultivation and metagenomics reveal ecophysiology of previously uncultivated thermophiles involved in biogeochemical nitrogen cycle. Microbes Environ. 33, 107–110 (2018).PubMed 
PubMed Central 

Google Scholar 
Costa, K. C. et al. Microbiology and geochemistry of great boiling and mud hot springs in the United States Great Basin. Extremophiles 13, 447–459 (2009).CAS 
PubMed 

Google Scholar 
Mukund, S. & Adams, M. W. The novel tungsten-iron-sulfur protein of the hyperthermophilic archaebacterium, Pyrococcus furiosus, is an aldehyde ferredoxin oxidoreductase. Evidence for its participation in a unique glycolytic pathway. J. Biol. Chem. 266, 14208–14216 (1991).CAS 
PubMed 

Google Scholar 
Mukund, S. & Adams, M. W. W. Glyceraldehyde-3-phosphate ferredoxin oxidoreductase, a novel tungsten-containing enzyme with a potential glycolytic role in the hyperthermophilic archaeon Pyrococcus furiosus. J. Biol. Chem. 270, 8389–8392 (1995).CAS 
PubMed 

Google Scholar 
Roy, R. et al. Purification and molecular characterization of the tungsten-containing formaldehyde ferredoxin oxidoreductase from the hyperthermophilic archaeon Pyrococcus furiosus: the third of a putative five-member tungstoenzyme family. J. Bacteriol. 181, 1171–1180 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
Roy, R. & Adams, M. W. W. Characterization of a fourth tungsten-containing enzyme from the hyperthermophilic archaeon Pyrococcus furiosus. J. Bacteriol. 184, 6952–6956 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bevers, L. E., Bol, E., Hagedoorn, P.-L. & Hagen, W. R. WOR5, a novel tungsten-containing aldehyde oxidoreductase from Pyrococcus furiosus with a broad substrate specificity. J. Bacteriol. 187, 7056–7061 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Habib, U. & Hoffman, M. Effect of molybdenum and tungsten on the reduction of nitrate in nitrate reductase, a DFT study. Chem. Cent. J. 11, 1–12 (2017).
Google Scholar 
Liao, R.-Z. Why is the molybdenum-substituted tungsten-dependent formaldehyde ferredoxin oxidoreductase not active? A quantum chemical study. J. Biol. Inorg. Chem. 18, 175–181 (2013).CAS 
PubMed 

Google Scholar 
Qian, H.-X. & Liao, R.-Z. QM/MM study of tungsten-dependent benzoyl-coenzyme A reductase: rationalization of regioselectivity and predication of W vs Mo selectivity. Inorg. Chem. 57, 10667–10678 (2018).CAS 
PubMed 

Google Scholar 
Liu, Y.-F., Liao, R.-Z., Ding, W.-J., Yu, J.-G. & Liu, R.-Z. Theoretical investigation of the first-shell mechanism of acetylene hydration catalyzed by a biomimetic tungsten complex. JBIC 16, 745–752 (2011).CAS 
PubMed 

Google Scholar 
Kerr, P. F. Tungsten-bearing manganese deposit at Golconda, Nevada. Geol. Soc. Am. Bull. 51, 1359–1390 (1940).ADS 
CAS 

Google Scholar 
Mukund, S. & Adams, M. W. W. Molybdenum and vanadium do not replace tungsten in the catalytically active forms of the three tungstoenzymes in the hyperthermophilic archaeon Pyrococcus furiosus. J. Bacteriol. 178, 163–167 (1996).CAS 
PubMed 
PubMed Central 

Google Scholar 
Debnar-Daumler, C., Seubert, A., Schmitt, G. & Heider, J. Simultaneous involvement of a tungsten-containing aldehyde:ferredoxin oxidoreductase and a phenylacetaldehyde dehydrogenase in anaerobic phenylalanine metabolism. J. Bacteriol. 196, 483–492 (2014).PubMed 
PubMed Central 

Google Scholar 
Scott, I. M. et al. A new class of tungsten-containing oxidoreductase in Caldicellulosiruptor, a genus of plant biomass-degrading thermophilic bacteria. Appl. Environ. Microbiol. 81, 7339–7347 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Scott, I. M. et al. The thermophilic biomass-degrading bacterium Caldicellulosiruptor bescii utilizes two enzymes to oxidize glyceraldehyde 3-phosphate during glycolysis. J. Biol. Chem. 294, 9995–10005 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Johnson, J. L., Rajagopalan, K. V., Mukund, S. & Adams, M. W. Identification of molybdopterin as the organic component of the tungsten cofactor in four enzymes from hyperthermophilic Archaea. J. Biol. Chem. 268, 4848–4852 (1993).CAS 
PubMed 

Google Scholar 
Chan, M. K., Mukund, S., Kletzin, A., Adams, M. W. & Rees, D. C. Structure of a hyperthermophilic tungstopterin enzyme, aldehyde ferredoxin oxidoreductase. Science 267, 1463–1469 (1995).ADS 
CAS 
PubMed 

Google Scholar 
Glass, J. B. et al. Geochemical, metagenomic and metaproteomic insights into trace metal utilization by methane‐oxidizing microbial consortia in sulphidic marine sediments. Environ. Microbiol. 16, 1592–1611 (2014).CAS 
PubMed 

Google Scholar 
Li, G.-W., Burkhardt, D., Gross, C. & Weissman, J. S. Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157, 624–635 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Behrens, S. et al. Linking microbial phylogeny to metabolic activity at the single-cell level by using enhanced element labeling-catalyzed reporter deposition fluorescence in situ hybridization (EL-FISH) and NanoSIMS. Appl. Environ. Microbiol. 74, 3143–3150. https://doi.org/10.1128/AEM.00191-08 (2008).Knapik, K., Becerra, M. & González-Siso, M.-I. Microbial diversity analysis and screening for novel xylanase enzymes from the sediment of the Lobios Hot Spring in Spain. Sci. Rep. 9, 11195 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Roy, R., Dhawan, I. K., Johnson, M. K., Rees, D. C. & Adams, M. W. Aldehyde Ferredoxin Oxidoreductase. 266 (American Cancer Society, 2011).Sevcenco, A.-M. et al. The tungsten metallome of Pyrococcus furiosus. Metallomics 1, 395–402 (2009).CAS 
PubMed 

Google Scholar 
Sakuraba, H. & Ohshima, T. Novel energy metabolism in anaerobic hyperthermophilic archaea: a modified Embden-Meyerhof pathway. J. Biosci. Bioeng. 93, 441–448 (2002).CAS 
PubMed 

Google Scholar 
Ma, K., Hutchins, A., Sung, S.-J. S. & Adams, M. W. W. Pyruvate ferredoxin oxidoreductase from the hyperthermophilic archaeon, Pyrococcus furiosus, functions as a CoA-dependent pyruvate decarboxylase. Proc. Natl Acad. Sci. USA 94, 9608–9613 (1997).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mai, X. & Adams, M. W. Characterization of a fourth type of 2-keto acid-oxidizing enzyme from a hyperthermophilic archaeon: 2-ketoglutarate ferredoxin oxidoreductase from Thermococcus litoralis. J. Bacteriol. 178, 5890–5896 (1996).CAS 
PubMed 
PubMed Central 

Google Scholar 
Adams, M. W. W. & Kletzin, A. Oxidoreductase-type enzymes and redox proteins involved in fermentative metabolisms of hyperthermophilic archaea. Adv. Prot. Chem. 48, 101–180 (1996).CAS 

Google Scholar 
Mulkidjanian, A. Y., Galperin, M. Y., Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Evolutionary primacy of sodium bioenergetics. Biol. Direct 3, 1–19 (2008).
Google Scholar 
Heider, J., Ma, K. & Adams, M. W. W. Purification, characterization, and metabolic function of tungsten-containing aldehyde ferredoxin oxidoreductase from the hyperthermophilic and proteolytic archaeon Thermococcus strain ES-1. J. Bacteriol. 177, 4757–4764 (1995).CAS 
PubMed 
PubMed Central 

Google Scholar 
Schut, G. J. et al. The modular respiratory complexes involved in hydrogen and sulfur metabolism by heterotrophic hyperthermophilic archaea and their evolutionary implications. FEMS Microbiol. Rev. 37, 182–203 (2013).CAS 
PubMed 

Google Scholar 
Kuhns, M., Trifunović, D., Huber, H. & Müller, V. The Rnf complex is a Na+ coupled respiratory enzyme in a fermenting bacterium, Thermotoga maritima. Commun. Biol. 3, 1–10 (2020).
Google Scholar 
Sapra, R., Verhagen, M. F. J. M. & Adams, M. W. W. Purification and characterization of a membrane-bound hydrogenase from the hyperthermophilic archaeon Pyrococcus furiosus. J. Bacteriol. 182, 3423–3428 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sapra, R., Bagramyan, K. & Adams, M. W. W. A simple energy-conserving system: Proton reduction coupled to proton translocation. Proc. Natl Acad. Sci. USA 100, 7545–7550 (2003).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schut, G. J. et al. The role of geochemistry and energetics in the evolution of modern respiratory complexes from a proton-reducing ancestor. Biochim. Biophys. Acta Bioenerg. 1857, 958–970 (2016).CAS 

Google Scholar 
Juszczak, A., Aono, S. & Adams, M. W. The extremely thermophilic eubacterium, Thermotoga maritima, contains a novel iron-hydrogenase whose cellular activity is dependent upon tungsten. J. Biol. Chem. 266, 13834–13841 (1991).CAS 
PubMed 

Google Scholar 
Selig, M., Xavier, K. B., Santos, H. & Schönheit, P. Comparative analysis of Embden-Meyerhof and Entner-Doudoroff glycolytic pathways in hyperthermophilic archaea and the bacterium Thermotoga. Arch. Microbiol. 167, 217–232 (1997).CAS 
PubMed 

Google Scholar 
Zhang, Y. & Gladyshev, V. N. Molybdoproteomes and evolution of molybdenum utilization. J. Mol. Biol. 379, 881–899 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Anbar, A. D. et al. A whiff of oxygen before the Great Oxidation Event? Science 317, 1903–1906 (2007).Neubert, N., Nägler, T. F. & Böttcher, M. E. Sulfidity controls molybdenum isotope fractionation into euxinic sediments: evidence from the modern Black Sea. Geology 36, 775–778 (2008).ADS 
CAS 

Google Scholar 
Helz, G. R. et al. Mechanism of molybdenum removal from the sea and its concentration in black shales: EXAFS evidence. Geochim. Cosmochim. Acta 60, 3631–3642 (1996).ADS 
CAS 

Google Scholar 
Shen, Y., Buick, R. & Canfield, D. E. Isotopic evidence for microbial sulphate reduction in the early Archaean era. Nature 410, 77–81 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Dodsworth, J. A. et al. Thermoflexus hugenholtzii gen. nov., sp. nov., a thermophilic, microaerophilic, filamentous bacterium representing a novel class in the Chloroflexi, Thermoflexia classis nov., and description of Thermoflexaceae fam. nov. and Thermoflexales ord. nov. Int. J. Sys. Evol. Microbiol. 64, 2119–2127 (2014).CAS 

Google Scholar 
Hanada, S., Hiraishi, A., Shimada, K. & Matsuura, K. Chloroflexus aggregans sp. nov., a filamentous phototrophic bacterium which forms dense cell aggregates by active gliding movement. Int. J. Sys. Evol. Microbiol. 45, 676–681 (1995).CAS 

Google Scholar 
Murugapiran, S. K. et al. Thermus oshimai JL-2 and T. thermophilus JL-18 genome analysis illuminates pathways for carbon, nitrogen, and sulfur cycling. Stand. Genom. Sci. 7, 449–468 (2013).CAS 

Google Scholar 
Kozich, J. J., Westcott, S. L., Baker, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina Sequencing Platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).Friel, A. D. et al. Microbiome shifts associated with the introduction of wild atlantic horseshoe crabs (Limulus polyphemus) into a touch-tank exhibit. Front. Microbiol. 11, 1398 (2020).Hamilton, T. L., Peters, J. W., Skidmore, M. L. & Boyd, E. S. Molecular evidence for an active endogenous microbiome beneath glacial ice. ISME J. 7, 1402–1412 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Courtois, S. et al. Quantification of bacterial subgroups in soil: comparison of DNA extracted directly from soil or from cells previously released by density gradient centrifugation. Environ. Microbiol. 3, 431–439 (2001).CAS 
PubMed 

Google Scholar 
Pernthaler, A. & Pernthaler, J. In Protocols for Nucleic Acid Analysis by Nonradioactive Probes 353, 153–164 (Humana Press, 2007).Pett-Ridge, J. & Weber, P. K. In Microbial Systems Biology 91–136 (Humana, New York, NY, 2022). https://doi.org/10.1007/978-1-0716-1585-0_6Parks, 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).CAS 
PubMed 
PubMed Central 

Google Scholar 
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).CAS 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 1–11 (2010).
Google Scholar 
Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P. & Huerta-Cepas, J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol. Biol. Evol. 38, 5825–5829 (2021).Aziz, R. K. et al. The RAST Server: Rapid Annotations using Subsystems Technology. BMC Genomics 9, 1–15 (2008).
Google Scholar 
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics 27, 1164–1165 (2011).CAS 
PubMed 

Google Scholar 
Kück, P. & Longo, G. C. FASconCAT-G: extensive functions for multiple sequence alignment preparations concerning phylogenetic studies. Front. Zool. 11, 1–8 (2014).
Google Scholar 
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jacox, E., Chauve, C., Szöllősi, G. J., Ponty, Y. & Scornavacca, C. ecceTERA: comprehensive gene tree-species tree reconciliation using parsimony. Bioinformatics 32, 2056–2058 (2016).CAS 
PubMed 

Google Scholar 
Chevenet, F. et al. SylvX: a viewer for phylogenetic tree reconciliations. Bioinformatics 32, 608–610 (2016).CAS 
PubMed 

Google Scholar 
Csűös, M. Count: evolutionary analysis of phylogenetic profiles with parsimony and likelihood. Bioinformatics 26, 1910–1912 (2010).
Google Scholar 
Yang, J. et al. The I-TASSER Suite: protein structure and function prediction. Nat. Methods 12, 7–8 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, S., Skolnick, J. & Zhang, Y. Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biol. 5, 17 (2007).PubMed 
PubMed Central 

Google Scholar 
Holm, L. & Rosenstrïm, P. I. Dali server: conservation mapping in 3D. Nucleic Acids Res. 38, W545–W549 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Holm, L. Benchmarking fold detection by DaliLite v.5. Bioinformatics 35, 5326–5327 (2019).CAS 
PubMed 

Google Scholar 
MacQueen, J. In Some Methods for Classification and Analysis of Multivariate Observations 1, 281–297 (1967).Ma, K. & Adams, M. W. W. Sulfide dehydrogenase from the hyperthermophilic archaeon Pyrococcus furiosus: a new multifunctional enzyme involved in the reduction of elemental sulfur. J. Bacteriol. 176, 6509–6517 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Fink, D. et al. Crowdsourcing meets ecology: he misphere wide spatiotemporal species distribution models. AI Mag. 35, 19–30. https://doi.org/10.1609/aimag.v35i2.2533 (2014).Article 

Google Scholar 
Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Cons. 213, 280–294. https://doi.org/10.1016/j.biocon.2016.09.004 (2017).Article 

Google Scholar 
Schmeller, D. S. et al. Advantages of volunteer-based biodiversity monitoring in Europe. Conserv. Biol. 23, 307–316. https://doi.org/10.1111/j.1523-1739.2008.01125.x (2009).Article 
PubMed 

Google Scholar 
Boakes, E. H. et al. Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS Biol. https://doi.org/10.1371/journal.pbio.1000385 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Follett, R. & Strezov, V. An analysis of citizen science based research: Usage and publication patterns. PLoS ONE https://doi.org/10.1371/journal.pone.0143687 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Zattara, E. E. & Aizen, M. A. Worldwide occurrence records suggest a global decline in bee species richness. One Earth 4, 114–123. https://doi.org/10.1016/j.oneear.2020.12.005 (2021).ADS 
Article 

Google Scholar 
Dickinson, J. L. et al. The current state of citizen science as a tool for ecological research and public engagement. Front. Ecol. Environ. 10, 291–297. https://doi.org/10.1890/110236 (2012).Article 

Google Scholar 
Kosmala, M., Wiggins, A., Swanson, A. & Simmons, B. Assessing data quality in citizen science. Front. Ecol. Environ. 14, 551–560. https://doi.org/10.1002/fee.1436 (2016).Article 

Google Scholar 
Bayraktarov, E. et al. Do big unstructured biodiversity data mean more knowledge?. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00239 (2019).Article 

Google Scholar 
Burgess, H. K. et al. The science of citizen science: Exploring barriers to use as a primary research tool. Biol. Cons. 208, 113–120. https://doi.org/10.1016/j.biocon.2016.05.014 (2017).Article 

Google Scholar 
Isaac, N. J. B. & Pocock, M. J. O. Bias and information in biological records. Biol. J. Lin. Soc. 115, 522–531. https://doi.org/10.1111/bij.12532 (2015).Article 

Google Scholar 
August, T., Fox, R., Roy, D. B. & Pocock, M. J. O. Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias. Sci. Rep. https://doi.org/10.1038/s41598-020-67658-3 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Boakes, E. H. et al. Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour. Sci. Rep. https://doi.org/10.1038/srep33051 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Di Cecco, G. J. et al. Observing the observers: How participants contribute data to iNaturalist and implications for biodiversity science. Bioscience 71, 1179–1188. https://doi.org/10.1093/biosci/biab093 (2021).Article 

Google Scholar 
Kamp, J., Oppel, S., Heldbjerg, H., Nyegaard, T. & Donald, P. F. Unstructured citizen science data fail to detect long-term population declines of common birds in Denmark. Divers. Distrib. 22, 1024–1035. https://doi.org/10.1111/ddi.12463 (2016).Article 

Google Scholar 
Altwegg, R. & Nichols, J. D. Occupancy models for citizen-science data. Methods Ecol. Evol. 10, 8–21. https://doi.org/10.1111/2041-210x.13090 (2019).Article 

Google Scholar 
Courter, J. R., Johnson, R. J., Stuyck, C. M., Lang, B. A. & Kaiser, E. W. Weekend bias in citizen science data reporting: Implications for phenology studies. Int. J. Biometeorol. 57, 715–720. https://doi.org/10.1007/s00484-012-0598-7 (2013).ADS 
Article 
PubMed 

Google Scholar 
Amano, T., Lamming, J. D. L. & Sutherland, W. J. Spatial gaps in global biodiversity information and the role of citizen science. Bioscience 66, 393–400. https://doi.org/10.1093/biosci/biw022 (2016).Article 

Google Scholar 
Geldmann, J. et al. What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements. Divers. Distrib. 22, 1139–1149. https://doi.org/10.1111/ddi.12477 (2016).Article 

Google Scholar 
Girardello, M. et al. Gaps in butterfly inventory data: A global analysis. Biol. Cons. 236, 289–295. https://doi.org/10.1016/j.biocon.2019.05.053 (2019).Article 

Google Scholar 
Husby, M., Hoset, K. S. & Butler, S. Non-random sampling along rural-urban gradients may reduce reliability of multi-species farmland bird indicators and their trends. Ibis https://doi.org/10.1111/ibi.12896 (2021).Article 

Google Scholar 
Petersen, T. K., Speed, J. D. M., Grøtan, V. & Austrheim, G. Species data for understanding biodiversity dynamics: The what, where and when of species occurrence data collection. Ecol. Solut. Evid. https://doi.org/10.1002/2688-8319.12048 (2021).Article 

Google Scholar 
Egerer, M., Lin, B. B. & Kendal, D. Towards better species identification processes between scientists and community participants. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2019.133738 (2019).Article 
PubMed 

Google Scholar 
Jimenez, M. F., Pejchar, L. & Reed, S. E. Tradeoffs of using place-based community science for urban biodiversity monitoring. Conserv. Sci. Pract. https://doi.org/10.1111/csp2.338 (2021).Article 

Google Scholar 
Branchini, S. et al. Using a citizen science program to monitor coral reef biodiversity through space and time. Biodivers. Conserv. 24, 319–336. https://doi.org/10.1007/s10531-014-0810-7 (2015).Article 

Google Scholar 
Snall, T., Kindvall, O., Nilsson, J. & Part, T. Evaluating citizen-based presence data for bird monitoring. Biol. Cons. 144, 804–810. https://doi.org/10.1016/j.biocon.2010.11.010 (2011).Article 

Google Scholar 
Gardiner, M. M. et al. Lessons from lady beetles: Accuracy of monitoring data from US and UK citizen-science programs. Front. Ecol. Environ. 10, 471–476. https://doi.org/10.1890/110185 (2012).Article 

Google Scholar 
Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R. & Legendre, F. Taxonomic bias in biodiversity data and societal preferences. Sci. Rep. https://doi.org/10.1038/s41598-017-09084-6 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Johansson, F. et al. Can information from citizen science data be used to predict biodiversity in stormwater ponds?. Sci. Rep. https://doi.org/10.1038/s41598-020-66306-0 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Everett, G. & Geoghegan, H. Initiating and continuing participation in citizen science for natural history. BMC Ecol. https://doi.org/10.1186/s12898-016-0062-3 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Richter, A. et al. The social fabric of citizen science drivers for long-term engagement in the German butterfly monitoring scheme. J. Insect Conserv. 22, 731–743. https://doi.org/10.1007/s10841-018-0097-1 (2018).Article 

Google Scholar 
MacPhail, V. J., Gibson, S. D. & Colla, S. R. Community science participants gain environmental awareness and contribute high quality data but improvements are needed: Insights from Bumble Bee Watch. PeerJ https://doi.org/10.7717/peerj.9141 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Maund, P. R. et al. What motivates the masses: Understanding why people contribute to conservation citizen science projects. Biol. Conserv. https://doi.org/10.1016/j.biocon.2020.108587 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Moczek, N., Nuss, M. & Kohler, J. K. Volunteering in the citizen science project “Insects of Saxony”—The larger the island of knowledge, the longer the bank of questions. Insects https://doi.org/10.3390/insects12030262 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Branchini, S. et al. Participating in a citizen science monitoring program: Implications for environmental education. PLoS ONE https://doi.org/10.1371/journal.pone.0131812 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Kelemen-Finan, J., Scheuch, M. & Winter, S. Contributions from citizen science to science education: An examination of a biodiversity citizen science project with schools in Central Europe. Int. J. Sci. Educ. 40, 2078–2098. https://doi.org/10.1080/09500693.2018.1520405 (2018).Article 

Google Scholar 
Deguines, N., Prince, K., Prevot, A. C. & Fontaine, B. Assessing the emergence of pro-biodiversity practices in citizen scientists of a backyard butterfly survey. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.136842 (2020).Article 
PubMed 

Google Scholar 
Peter, M., Diekötter, T., Höffler, T. & Kremer, K. Biodiversity citizen science: Outcomes for the participating citizens. People Nat. 3, 294–311. https://doi.org/10.1002/pan3.10193 (2021).Article 

Google Scholar 
Phillips, T. B., Bailey, R. L., Martin, V., Faulkner-Grant, H. & Bonter, D. N. The role of citizen science in management of invasive avian species: What people think, know, and do. J. Environ. Manage. https://doi.org/10.1016/j.jenvman.2020.111709 (2021).Article 
PubMed 

Google Scholar 
Parrish, J. K. et al. Hoping for optimality or designing for inclusion: Persistence, learning, and the social network of citizen science. Proc. Natl. Acad. Sci. U.S.A. 116, 1894–1901. https://doi.org/10.1073/pnas.1807186115 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mac Domhnaill, C., Lyons, S. & Nolan, A. The citizens in citizen science: Demographic, socioeconomic, and health characteristics of biodiversity recorders in Ireland. Citiz. Sci.: Theory Pract. 5, 16. https://doi.org/10.5334/cstp.283 (2020).Article 

Google Scholar 
van der Wal, R., Sharma, N., Mellish, C., Robinson, A. & Siddharthan, A. The role of automated feedback in training and retaining biological recorders for citizen science. Conserv. Biol. 30, 550–561. https://doi.org/10.1111/cobi.12705 (2016).Article 
PubMed 

Google Scholar 
Bloom, E. H. & Crowder, D. W. Promoting data collection in pollinator citizen science projects. Citiz. Sci.: Theory Pract. 5, 3. https://doi.org/10.5334/cstp.217 (2020).Article 

Google Scholar 
Johnston, A., Fink, D., Hochachka, W. M. & Kelling, S. Estimates of observer expertise improve species distributions from citizen science data. Methods Ecol. Evol. 9, 88–97. https://doi.org/10.1111/2041-210x.12838 (2018).Article 

Google Scholar 
Kelling, S. et al. Using semistructured surveys to improve citizen science data for monitoring biodiversity. Bioscience 69, 170–179. https://doi.org/10.1093/biosci/biz010 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Koen, B., Loosveldt, G., Vandenplas, C. & Stoop, I. Response rates in the european social survey: Increasing, decreasing, or a matter of fieldwork efforts?. Surv. Methods: Insights Field https://doi.org/10.13094/SMIF-2018-00003 (2018).Article 

Google Scholar 
Gideon, L. Handbook of Survey Methodology for the Social Sciences (Springer, 2012).Book 

Google Scholar 
Wolf, C., Joye, D., Smith, T. W. & Fu, Y. C. The SAGE Handbook of Survey Methodology (SAGE Publications Ltd, 2016).Book 

Google Scholar 
Richter, A. et al. Motivation and support services in citizen science insect monitoring: A cross-country study. Biol. Conserv. 263, 109325. https://doi.org/10.1016/j.biocon.2021.109325 (2021).Article 

Google Scholar 
Johnston, A., Moran, N., Musgrove, A., Fink, D. & Baillie, S. R. Estimating species distributions from spatially biased citizen science data. Ecol. Model. https://doi.org/10.1016/j.ecolmodel.2019.108927 (2020).Article 

Google Scholar 
Isaac, N. J. B., van Strien, A. J., August, T. A., de Zeeuw, M. P. & Roy, D. B. Statistics for citizen science: Extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060. https://doi.org/10.1111/2041-210x.12254 (2014).Article 

Google Scholar 
Liao, H.-I., Yeh, S.-L. & Shimojo, S. Novelty vs. familiarity principles in preference decisions: Task context of past experience matters. Front. Psychol. https://doi.org/10.3389/fpsyg.2011.00043 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Park, J., Shimojo, E. & Shimojo, S. Roles of familiarity and novelty in visual preference judgments are segregated across object categories. Proc. Natl. Acad. Sci. U.S.A. 107, 14552–14555. https://doi.org/10.1073/pnas.1004374107 (2010).ADS 
Article 
PubMed 

Google Scholar 
Tiago, P., Gouveia, M. J., Capinha, C., Santos-Reis, M. & Pereira, H. M. The influence of motivational factors on the frequency of participation in citizen science activities. Nat. Conserv.-Bulg. https://doi.org/10.3897/natureconservation.18.13429 (2017).Article 

Google Scholar 
Davis, A., Taylor, C. E. & Martin, J. M. Are pro-ecological values enough? Determining the drivers and extent of participation in citizen science programs. Hum. Dimens. Wildl. 24, 501–514. https://doi.org/10.1080/10871209.2019.1641857 (2019).Article 

Google Scholar 
Bell, S. et al. What counts? Volunteers and their organisations in the recording and monitoring of biodiversity. Biodivers. Conserv. 17, 3443–3454. https://doi.org/10.1007/s10531-008-9357-9 (2008).Article 

Google Scholar 
Toomey, A. H. & Domroese, M. C. Can citizen science lead to positive conservation attitudes and behaviors?. Hum. Ecol. Rev. 20, 50–62 (2013).Article 

Google Scholar 
Dennis, E. B., Morgan, B. J. T., Brereton, T. M., Roy, D. B. & Fox, R. Using citizen science butterfly counts to predict species population trends. Conserv. Biol. 31, 1350–1361. https://doi.org/10.1111/cobi.12956 (2017).Article 
PubMed 

Google Scholar 
Callaghan, C. T., Poore, A. G. B., Major, R. E., Rowley, J. J. L. & Cornwell, W. K. Optimizing future biodiversity sampling by citizen scientists. Proc. R. Soc. B-Biol. Sci. https://doi.org/10.1098/rspb.2019.1487 (2019).Article 

Google Scholar 
Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384. https://doi.org/10.1038/s41559-020-1111-z (2020).Article 
PubMed 

Google Scholar 
Bowler, D. E. et al. Winners and losers over 35 years of dragonfly and damselfly distributional change in Germany. Divers. Distrib. https://doi.org/10.1111/ddi.13274 (2021).Article 

Google Scholar  More

A strawberry poison-dart frog (Oophaga pumilio) in Guatemala. Biodiversity is at risk as talks on a deal to protect it founder.Credit: Yuri Cortez/AFP via Getty

Some conservation scientists are warning that a global deal to protect the environment is under threat after negotiations stalled during international talks in Nairobi last week. They are calling on global leaders to rescue the talks — and biodiversity — from the brink. Others are more hopeful that, although progress has been slow, a deal will be struck by the end of the year.Negotiators from around 200 countries that have signed up to the United Nations Convention on Biological Diversity (CBD) met in Nairobi from 21 to 26 June to thrash out key details of the deal, known as the post-2020 global biodiversity framework. But the talks made such little progress that many scientists are worried that nations will be unable to finalize the deal at the UN biodiversity summit in Montreal, Canada, in December. A key sticking point is how much funding rich nations will provide to low-income nations. Failure to agree on the framework at this summit — the 15th meeting of the Conference of the Parties (COP15) — will be devastating for the natural world, they say.“This is a huge missed opportunity and puts the framework in jeopardy,” says Brian O’Donnell, director of the Campaign for Nature in Washington DC, a partnership of private charities and conservation organizations advocating a deal to safeguard biodiversity.The framework consists of 4 broad goals, including reining in species extinction, and 21 targets — most of them quantitative — such as protecting at least 30% of the world’s land and seas. Without a deal, estimates say, one million plant and animal species could go extinct in the next few decades because of climate change, disease and human actions, among other triggers.Researchers were relieved when the CBD announced earlier this month that COP15 would take place in Montreal instead of Kunming, China, where lockdowns to quash SARS-CoV-2 infections could have prevented the meeting. The COVID-19 pandemic has already delayed in-person CBD meetings for two years, and threatened to derail the summit.Stalling tacticsSome conservation groups said that a few nations bore most of the responsibility for impeding progress. Marco Lambertini, head of conservation organization WWF International, based in Gland, Switzerland, referred in a statement to “a small number of countries, Brazil first and foremost, that are actively working to undermine the talks”.Others who were at the conference spoke on the condition of anonymity because parts of the negotiations are confidential. They say that Brazil asked for changes to the text simply to slow down the process, and argued against essential elements.Nature contacted representatives of Brazil for a response but did not receive a reply by the time of publication.Francis Ogwal, co-chair of the framework negotiations working group, acknowledged that the talks had not advanced as much as had been hoped. But he is buoyed by some headway gained on targets to improve access to nature in urban areas and to increase scientific and technological capacity in lower-income nations. Ogwal is hopeful that countries will iron out further differences at an extra meeting scheduled for just days before COP15.“There are still some big disagreements. We are not yet at the level we expected. But come December, we shall have a framework in good shape,” Ogwal told reporters at a press briefing on 26 June.Lack of leadershipBut scientists and conservation groups say political leadership is urgently needed to save the deal. In an open letter to UN secretary-general António Guterres and heads of state of CBD member nations, a group of eight organizations that support conservation and Indigenous people’s rights said that a lack of management is stalling the negotiations.“There is a notable absence of the high level political engagement, will and leadership to drive through compromise and to guide and inspire the commitments that are required,” the letter says.Some countries have restated that they back the biodiversity talks. On 26 June, UK Prime Minister Boris Johnson assured Canadian Prime Minister Justin Trudeau of his support for the December summit in Montreal. The two were speaking before the meeting of the G7 group of industrialized nations in Krün, Germany.In addition, some “hero” countries including Costa Rica and Columbia worked particularly hard in Nairobi to drive agreement, says O’Donnell.Speaking on condition of anonymity so as not to offend the CBD, others criticized the structure and organization of the Nairobi meeting, which they say didn’t help negations to move forwards. “The session facilitators were not able to shepherd negotiations towards consensus,” they say. Nature contacted the CBD for a response but did not hear back in time for publication.But despite the setbacks, some scientists are still hopeful that countries can strike a deal. “The negotiations are typically well-spirited. There is even a sense of collaboration arising,” says Juha Siikamäki, chief economist at the International Union for Conservation of Nature in Gland, who attended the Nairobi meeting.Elizabeth Mrema, executive secretary of the CBD, says countries will have to compromise. “Biodiversity is too important to fail,” she says. More

Mangrove forests thrive along tropical and subtropical shorelines and their distribution extends to warm temperate regions1. They are globally recognized for the valuable ecosystem services they provide2 but are expected to be substantially influenced by climate change-related physical processes in the future3,4. Under warming winter temperatures, poleward expansion is predicted for mangroves5,6, with potential implications for ecosystem structure and functioning, as well as human livelihoods and well-being7,8. The global distribution, abundance and species richness of mangroves is governed by a broad range of biotic and environmental factors, including temperature and precipitation9 and diverse geomorphological and hydrological gradients10. Climate and aspects related to coastal geography (for example, floodplain area) determine the availability of suitable habitat for establishment11,12. However, the potential for mangroves to track changing environmental conditions and expand their distributions ultimately depends on dispersal11,13. The importance of dispersal in controlling mangrove distributions has been demonstrated by mangrove distributional responses to historical climate variability14, past mangrove (re)colonization of oceanic islands15 and from the long-term survival of mangrove seedlings planted beyond natural range limits16. As such, quantifying changes in the factors that influence dispersal is important for understanding climate-driven distributional responses of mangroves under future climate conditions.In mangroves, dispersal is accomplished by buoyant seeds and fruits (hereafter referred to as ‘propagules’). In combination with prevailing currents, the spatial scale of this process, ranging from local retention to transoceanic dispersal over thousands of kilometres13, is determined by propagule buoyancy17, that is, the density difference between that of propagules and the surrounding water. Hence, the course of dispersal trajectories for propagules from these species depends on the interaction between spatiotemporal changes in both propagule density and that of the surrounding water, rendering this process sensitive to climate-driven changes in coastal and open-ocean water properties. The biogeographic implications of such density differences were recognized more than a century ago by Henry Brougham Guppy, who discussed18 ‘the far-reaching influence on plant-distribution and on plant-development that the relation between the specific weight of seeds and fruits and the density of sea-water must possess’.Since the time of Guppy’s early observations, climate change from human activities has driven pronounced changes in ocean temperature and salinity, with further changes predicted throughout the twenty-first century19. Ocean density is a nonlinear function of temperature, salinity and pressure20; therefore, these changes may influence dispersal patterns of mangrove propagules by altering their buoyancy and floating orientation. As Guppy noted18, ‘[for] plants whose seeds or fruits are not much lighter than seawater […] the effect of increased density of the water is to extend the flotation period’ or ‘to increase the number that floated for a given period’. Guppy also reported that the seedlings of the widespread mangrove genera Rhizophora and Bruguiera present exceptional examples of propagules with densities somewhere between seawater and freshwater18. Previous studies of the impacts of climate change on mangroves have focused on factors such as sea level rise, altered precipitation regimes and increasing temperature and storm frequency4,21,22,23 but the potential impact of climate-driven changes in seawater properties on mangroves has not yet been examined. This is somewhat surprising, as the ocean is the primary dispersal medium of this ‘sea-faring’ coastal vegetation and dispersal is a key process that governs a species’ response to climate change by changing its geographical range. This knowledge gap contrasts with recent efforts to expose links between climate change and dispersal in other ecologically important marine taxa such as zooplankton and fish species24,25,26,27.In this study, we investigate predicted changes in sea surface temperature (SST), sea surface salinity (SSS) and sea surface density (SSD) for coastal waters bordering mangrove forests (hereafter referred to as ‘coastal mangrove waters’), over the next century. Using a biogeographic classification system for coastal and shelf areas28, we examine spatiotemporal changes in these surface ocean properties, with a particular focus on the world’s two major mangrove diversity hotspots: (1) the Atlantic East Pacific (AEP) region, including all of the Americas, West and Central Africa and (2) the Indo West Pacific (IWP) region, extending from East Africa eastwards to the islands of the central Pacific1. Finally, we synthesize available data on the density of mangrove propagules for different mangrove species and explore the potential impact of climate-driven changes in SSD on propagule dispersal.To assess changes in SST and SSS throughout the global range of mangrove forests, we used present (2000–2014) and future (2090–2100) surface ocean properties from the Bio-ORACLE database29,30. SSD estimates were derived from these variables using the UNESCO EOS-80 equation of state polynomial for seawater31. Changes in SST, SSS and SSD (Fig. 1) were calculated for four representative concentration pathways (RCPs) and derived for coastal waters closest to the 583,578 polygon centroids from the 2015 Global Mangrove Watch (GMW) database32. After removing duplicates, our dataset contained 10,108 unique mangrove occurrence locations, with corresponding present conditions and predicted future changes in mean SST, SSS and SSD. Under the low-warming scenario RCP 2.6, mean SST of coastal mangrove waters is predicted to change by +0.64 (±0.11) °C and mean SSS by −0.06 (±0.25) practical salinity units (PSU). Combined, this results in an average change in mean SSD of −0.25 (±0.20) kg m−3 in coastal mangrove waters by the late twenty-first century (Supplementary Table 1). These values roughly double under RCP 4.5 (Supplementary Table 2), while under RCP 6.0, a change of +1.69 (±0.14) °C in mean SST, −0.21 (±0.42) PSU in mean SSS and −0.71 (±0.32) kg m−3 in mean SSD is predicted (Supplementary Table 3). Under RCP 8.5, our study predicts a change in SST of +2.84 (±0.21) °C (range 2.11–4.01 °C), a change in SSS of −0.30 (±0.74) PSU (−2.01–1.26 PSU) and a corresponding change in SSD of −1.17 (±0.56) kg m−3 (−2.53–0.03 kg m−3) (Supplementary Table 4).Fig. 1: Global map showing the change in sea surface variables across mangrove bioregions under RCP 8.5.a–c, Change in SST (a), SSS (b) and SSD (c). Changes in SST and SSS are based on present-day (2000–2014) and future (2090–2100) marine fields from the Bio-ORACLE database29,30, from which SSD data were derived. The vertical line (19° E) separates the two major mangrove bioregions: the AEP and IWP.Full size imageSpatial variability in predicted surface ocean property changes was examined by considering the two major mangrove bioregions (AEP and IWP) (Fig. 2) and using the Marine Ecoregions of the World (MEOW) biogeographic classification28 (Fig. 3). Both the range and changes in mean SST were comparable for the AEP and IWP mangrove bioregions, for all respective RCP scenarios (Fig. 2a and Supplementary Tables 1–4). Under RCP 8.5, mean SST in both mangrove bioregions is predicted to warm ~2.8 °C by 2100, which is roughly 4.5 times the predicted increase in mean SST under RCP 2.6 (Supplementary Tables 1 and 4). Predictions for the RCP 8.5 scenario are generally consistent with reported global ocean temperature trends33 and show that the greatest warming occurs in coastal waters near the Galapagos Islands (change in mean SST of 3.92 ± 0.06 °C). Pronounced SST increases are also predicted for Hawaii (change in mean SST of 3.36 ± 0.05 °C), the Southeast Australian Shelf (3.30 ± 0.25 °C), Northern and Southern New Zealand (3.25 ± 0.07 °C and 3.34 ± 0.02 °C, respectively), Warm Temperate Northwest Pacific (3.27 ± 0.16 °C), the Red Sea and Gulf of Aden (3.24 ± 0.08 °C), Somali/Arabian Coast (3.23 ± 0.15 °C), South China Sea (3.07 ± 0.10 °C), the Tropical East Pacific (3.09 ± 0.15 °C) and the Warm Temperate Northwest Atlantic (3.14 ± 0.13 °C) (Fig. 3b and Supplementary Tables 4).Fig. 2: Change in surface ocean properties for coastal waters bordering mangrove forests and in the two major mangrove bioregions, the AEP and IWP, for different RCPs.a–c, Variation in SST (a), SSS (b) and SSD (c) under various RCP scenarios. Grey indicates global distribution (n = 10,108), orange denotes AEP (n = 3,190) and green represents IWP (n = 6,918). Data for SST and SSS consist of present-day (2000–2014) and future (2090–2100) marine fields from the Bio-ORACLE database29,30, from which SSD data were derived. The cat-eye plots50 show the distribution of the data. Median and mean values are indicated with black and white circles, respectively, and the vertical lines represent the interquartile range.Full size imageFig. 3: Global spatial variability in SST, SSS and SSD for coastal waters bordering mangrove forests under RCP 8.5.a, Global map showing the provinces (colour code and numbers) from the MEOW database28 used to investigate spatial patterns in mangrove coastal ocean water changes by 2100. b–d, Longitudinal gradient of the change in SST (b), SSS (c) and SSD (d) under RCP 8.5 in the AEP and the IWP mangrove bioregions; circles are coloured according to the MEOW province in which respective mangrove sites are located.Full size imagePredicted SSS changes exhibit an opposite trend in the AEP and IWP bioregions, with increased salinity in the AEP and reduced salinity in the IWP under global warming (RCP 2.6–RCP 8.5; Fig. 2b); this is reflected in contrasting SSD changes in both mangrove bioregions (Fig. 2c) and associated with predicted global changes in precipitation, with extensions of the rainy season over most of the monsoon domains, except for the American monsoon34. Under RCP 8.5, the spatially averaged change in mean SSS is +0.51 (±0.57) PSU in the AEP and −0.68 (±0.44) PSU in the IWP region. The maximum decrease in mean SSS (−2.01 PSU) is predicted for the Gulf of Guinea in the AEP bioregion (Fig. 3c and Supplementary Table 4). Within the IWP, the Western Indian Ocean region shows little or no changes in SSS, which contrasts with the pronounced freshening trends predicted in the eastern part of this ocean basin and the Tropical West Pacific (Figs. 1b and 3c). Increased freshening is predicted in the Bay of Bengal (SSS change: −1.17 ± 0.43 PSU), the Sunda Shelf (SSS change: −1.21 ± 0.29 PSU) and the Western Coral Triangle province (mean SSS change: −0.80 ± 0.17 PSU) (Fig. 3c and Supplementary Table 4). Within the AEP, salinity increases exceed +0.96 PSU in the Tropical Northwestern Atlantic, +0.80 in the Warm Temperate Northwest Atlantic and +0.68 in the West African Transition (Fig. 3c and Supplementary Table 4). The spatial heterogeneity in SSS across the global range of mangrove forests corresponds with observed changes in SSS35. Trends in SSD (Fig. 3d) strongly track changes in SSS (Fig. 3c) rather than SST. All RCP scenarios predict an overall decrease in SSD for both mangrove bioregions; however, the predicted decrease in SSD in the IWP region was a factor of 2 (RCP 6.0) and 2.5 (RCP 2.6, RCP 4.5 and RCP 8.5) stronger than in the AEP (Figs. 2 and 3d and Supplementary Tables 1–4).Propagule density values from our literature survey range from 1,080 kg m−3 for different mangrove species (Fig. 4 and Supplementary Table 5). The low densities reported for Heritiera littoralis propagules provide a strong contrast with the near-seawater propagule densities reported for Avicennia and members of the Rhizophoraceae (Bruguiera, Rhizophora and Ceriops). Floating characteristics of the latter may be particularly sensitive to changes in SSD. To illustrate the potential influence of changing ocean conditions on mangrove propagule dispersal, we considered threshold water density values (1,020 and 1,022 kg m−3) that are within the range where elongated propagules of important mangrove genera tend to change floating orientation (Fig. 4a). More specifically, we determined the ocean surface area with an SSD below or equal to these thresholds under different climate change scenarios (Fig. 5). Under RCP 8.5, the ocean surface covered by mangrove coastal waters (coastal waters bordering present mangrove forests) with a density ≤1,020 kg m−3 increases ~27% by 2100, notably more so in the IWP (~37%) than in the AEP (~6%) (Supplementary Table 6). A threshold of 1,022 kg m−3 results in increases of roughly +11% (global), +12% (IWP) and +8% (AEP) (Supplementary Table 7). Similar spatial patterns are observed for open-ocean waters within the global latitudinal range of mangroves (Fig. 5 and Supplementary Figs. 1 and 2).Fig. 4: Potential effect of future declines in SSD on mangrove propagule dispersal.a, Range of reported propagule density values for wide-ranging mangrove species and present and future range of SSD for coastal waters along the range of those mangrove species. Mangrove propagule data are extracted from the literature (Supplementary Table 5). H. lit, Heritiera littoralis; X. gra, Xylocarpus granatum; A. ger, Avicennia germinans; A. mar, Avicennia marina; B. gym, Bruguiera gymnorrhiza; C. tag, Ceriops tagal; R. man, Rhizophora mangle; R. muc, Rhizophora mucronata. Bottom part adapted from ref. 51. b, Conceptual figure of the potential effects of ocean warming and freshening on mangrove propagule dispersal. Ocean warming and freshening drive changes in SSD and may reduce the timeframe for opportunistic colonization. For a propagule with a specific density and floating profile under present surface ocean conditions, reduced SSD of coastal and open-ocean waters may reduce floatation time (shaded area) and hence, reduce the proportion of long-distance dispersers. For simplicity, the density of propagules is assumed to increase linearly over time, although the actual increase may be nonlinear.Full size imageFig. 5: Future changes in SSD.a–d, Spatial extent of coastal and open-ocean surface waters with a density ≤1,020 kg m−3 (a,b) and 1,022 kg m−3 (c,d), for present (2000–2014) (a,c) and future (2090–2100; RCP 8.5) (b,d) scenarios. Data are shown for surface ocean waters within the global latitudinal range of mangrove forests (between 32° N and 38° S). The two density thresholds considered are within the range of densities at which mangrove propagule buoyancy and floating orientation of several mangrove genera change, as reported in available literature. Black dots along the coast represent the global mangrove extent from the 2015 GMW dataset32. Magenta-coloured circles represent SSD values More

McClain ME, Boyer EW, Dent CL, Gergel SE, Grimm NB, Groffman PM, et al. Biogeochemical hot spots and hot moments at the interface of terrestrial and aquatic ecosystems. Ecosystems. 2003;6:301–12.CAS 
Article 

Google Scholar 
Borch T, Kretzschmar R, Kappler A, Van Cappellen P, Ginder-Vogel M, Voegelin A, et al. Biogeochemical redox processes and their impact on contaminant dynamics. Environ Sci Technol. 2010;44:15–23.CAS 
Article 

Google Scholar 
Stegen JC, Lin XJ, Konopka AE, Fredrickson JK. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 2012;6:1653–64.CAS 
Article 

Google Scholar 
Dini-Andreote F, Stegen JC, van Elsas JD, Salles JF. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc Natl Acad Sci USA. 2015;112:E1326–32.CAS 
Article 

Google Scholar 
Behrendt L, Larkum AWD, Trampe E, Norman A, Sorensen SJ, Kuhl M. Microbial diversity of biofilm communities in microniches associated with the didemnid ascidian Lissoclinum patella. ISME J. 2012;6:1222–37.CAS 
Article 

Google Scholar 
Becker KW, Elling FJ, Schroder JM, Lipp JS, Goldhammer T, Zabel M, et al. Isoprenoid quinones resolve the stratification of redox processes in a biogeochemical continuum from the photic zone to deep anoxic sediments of the Black Sea. Appl Environ Microbiol. 2018;84:e02736–17.CAS 
Article 

Google Scholar 
Locey KJ, Muscarella ME, Larsen ML, Bray SR, Jones SE, Lennon JT. Dormancy dampens the microbial distance-decay relationship. Phil Trans R Soc B. 2020;375:20190243.CAS 
Article 

Google Scholar 
Blagodatskaya E, Kuzyakov Y. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol Biochem. 2013;67:192–211.CAS 
Article 

Google Scholar 
Meyer KM, Memiaghe H, Korte L, Kenfack D, Alonso A, Bohannan BJM. Why do microbes exhibit weak biogeographic patterns? ISME J. 2018;12:1404–13.Article 

Google Scholar 
Xue R, Zhao KK, Yu XL, Stirling E, Liu S, Ye SD, et al. Deciphering sample size effect on microbial biogeographic patterns and community assembly processes at centimeter scale. Soil Biol Biochem. 2021;156:108218.CAS 
Article 

Google Scholar 
Morriss A, Meyer K, Bohannan B. Linking microbial communities to ecosystem functions: what we can learn from genotype-phenotype mapping in organisms. Phil Trans R Soc B. 2020;375:20190244.Article 

Google Scholar 
Armitage DW, Jones SE. How sample heterogeneity can obscure the signal of microbial interactions. ISME J. 2019;13:2639–46.Article 

Google Scholar 
Dini-Andreote F, Kowalchuk GA, Prosser JI, Raaijmakers JM. Towards meaningful scales in ecosystem microbiome research. Environ Microbiol. 2021;23:1–4.Article 

Google Scholar 
Meyerhof MS, Wilson JM, Dawson MN, Beman JM. Microbial community diversity, structure and assembly across oxygen gradients in meromictic marine lakes, Palau. Environ Microbiol. 2016;18:4907–19.CAS 
Article 

Google Scholar 
Zhou ZC, Meng H, Liu Y, Gu JD, Li M. Stratified bacterial and archaeal community in mangrove and intertidal wetland mudflats revealed by high throughput 16S rRNA gene sequencing. Front Microbiol. 2017;8:02148.Article 

Google Scholar 
Gutierrez-Preciado A, Saghai A, Moreira D, Zivanovic Y, Deschamps P, Lopez-Garcia P. Functional shifts in microbial mats recapitulate early Earth metabolic transitions. Nat Ecol Evol. 2018;2:1700–8.Article 

Google Scholar 
Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.CAS 
Article 

Google Scholar 
Murase J, Frenzel P. A methane-driven microbial food web in a wetland rice soil. Environ Microbiol. 2007;9:3025–34.CAS 
Article 

Google Scholar 
Reim A, Lüke C, Krause S, Pratscher J, Frenzel P. One millimetre makes the difference: high-resolution analysis of methane-oxidizing bacteria and their specific activity at the oxic-anoxic interface in a flooded paddy soil. ISME J. 2012;6:2128–39.CAS 
Article 

Google Scholar 
Peiffer S, Kappler A, Haderlein SB, Schmidt C, Byrne JM, Kleindienst S, et al. A biogeochemical–hydrological framework for the role of redox-active compounds in aquatic systems. Nat Geosci. 2021;14:264–72.CAS 
Article 

Google Scholar  More

Zou, K. et al. eDNA metabarcoding as a promising conservation tool for monitoring fish diversity in a coastal wetland of the Pearl River Estuary compared to bottom trawling. Sci. Total Environ. 702, 134704 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Almond, R., Grooten, M. & Peterson, T. Living Planet Report 2020-Bending the Curve of Biodiversity Loss (World Wildlife Fund, 2020).
Google Scholar 
Beverton, R. Fish resources; threats and protection. Neth. J. Zool. 42, 139–175 (1991).Article 

Google Scholar 
Jackson, S. & Head, L. Australia’s mass fish kills as a crisis of modern water: Understanding hydrosocial change in the Murray-Darling Basin. Geoforum 109, 44–56 (2020).Article 

Google Scholar 
Rees, H. C. et al. REVIEW: The detection of aquatic animal species using environmental DNA—a review of eDNA as a survey tool in ecology. J. Appl. Ecol. 51, 1450–1459 (2014).CAS 
Article 

Google Scholar 
Rees, H. C. et al. The application of eDNA for monitoring of the Great Crested Newt in the UK. Ecol. Evol. 4, 4023–4032 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, C. et al. Research on the biodiversity of Qinhuai River based on environmental DNA metabacroding. Acta Ecol. Sin. 42, 611–624 (2022).Article 

Google Scholar 
Deiner, K., Walser, J.-C., Mächler, E. & Altermatt, F. Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol. Cons. 183, 53–63 (2015).Article 

Google Scholar 
Thomsen, P. F. et al. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 21, 2565–2573 (2012).CAS 
PubMed 
Article 

Google Scholar 
Miralles, L., Parrondo, M., Hernandez de Rojas, A., Garcia-Vazquez, E. & Borrell, Y. J. Development and validation of eDNA markers for the detection of Crepidula fornicata in environmental samples. Mar. Pollut. Bull. 146, 827–830 (2019).CAS 
PubMed 
Article 

Google Scholar 
Takahara, T., Minamoto, T., Yamanaka, H., Doi, H. & Kawabata, Z. Estimation of fish biomass using environmental DNA. PLoS ONE 7, e35868 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aglieri, G. et al. Environmental DNA effectively captures functional diversity of coastal fish communities. Mol. Ecol. 30, 3127–3139 (2020).PubMed 
Article 

Google Scholar 
Yang, H. et al. Effectiveness assessment of using riverine water eDNA to simultaneously monitor the riverine and riparian biodiversity information. Sci. Rep. 11, 24241 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Altermatt, F. et al. Uncovering the complete biodiversity structure in spatial networks: the example of riverine systems. Oikos 129, 607–618 (2020).Article 

Google Scholar 
Stat, M. et al. Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity. Conserv. Biol. 33, 196–205 (2019).PubMed 
Article 

Google Scholar 
Hallam, J., Clare, E. L., Jones, J. I. & Day, J. J. Biodiversity assessment across a dynamic riverine system: A comparison of eDNA metabarcoding versus traditional fish surveying methods. Environ. DNA 3, 1247–1266 (2021).Article 

Google Scholar 
Gao, W. Beijing Vertebrate Key (Beijing Publishing House, 1994).
Google Scholar 
Wang, H. Beijing Fish and Amphibians and Reptiles (Beijing Publishing House, 1994).
Google Scholar 
Chen, W., Hu, D. & Fu, B. Research on Biodiversity of Beijing Wetland (Science Press, 2007).
Google Scholar 
Zhang, C. et al. Fish species diversity and conservation in Beijing and adjacent areas. Biodivers. Sci. 19, 597–604 (2011).Article 

Google Scholar 
Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 40368 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shaw, J. L. A. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Cons. 197, 131–138 (2016).Article 

Google Scholar 
Fu, M., Xiao, N., Zhao, Z., Gao, X. & Li, J. Effects of Urbanization on Ecosystem Services in Beijing. Res. Soil Water Conserv. 23, 235–239 (2016).
Google Scholar 
Hao, L. & Sun, G. Impacts of urbanization on watershed ecohydrological processes: progresses and perspectives. Acta Ecol. Sin. 41, 13–26 (2021).
Google Scholar 
Su, G. et al. Human impacts on global freshwater fish biodiversity. Science 371, 835–838 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yan, B. et al. Effects of urban development on soil microbial functional diversity in Beijing. Res. Environ. Sci. 29, 1325–1335 (2016).CAS 

Google Scholar 
Xiao, N., Gao, X., Li, J. & Bai, J. Evaluation and Conservation Measures of Beijing Biodiversity (China Forestry Publishing House, 2018).
Google Scholar 
Xu, S., Wang, Z., Liang, J. & Zhang, S. Use of different sampling tools for comparison of fish-aggregating effects along horizontal transect at two artificial reef sites in Shengsi. J. Fish. China 40, 820–831 (2016).
Google Scholar 
Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics (Oxford, England) 30, 614–620 (2014).CAS 
Article 

Google Scholar 
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics (Oxford, England) 34, 884–890 (2018).Article 
CAS 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics (Oxford, England) 26, 2460–2461 (2010).CAS 
Article 

Google Scholar 
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Iwasaki, W. et al. MitoFish and MitoAnnotator: A mitochondrial genome database of fish with an accurate and automatic annotation pipeline. Mol. Biol. Evol. 30, 2531–2540 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, H. Beijing Fish Records (Beijing Publishing House, 1984).
Google Scholar 
Du, L. et al. Fish community characteristics and spatial pattern in major rivers of Beijing City. Res. Environ. Sci. 32, 447–457 (2019).
Google Scholar 
Shen, W. & Ren, H. TaxonKit: A practical and efficient NCBI taxonomy toolkit. J. Genet. Genomics 48, 844–850 (2021).PubMed 
Article 

Google Scholar 
Karr, J. R. Assessment of biotic integrity using fish communities. Fisheries 6, 21–27 (1981).Article 

Google Scholar 
Zhang, C. & Zhao, Y. Fishes in Beijing and Adjacent Areas (China. Science Press, 2013).
Google Scholar 
Wu, H. & Zhong, J. Fauna Sinica, Osteichthyes, Perciformess(Five),Gobioidei (Science Press, 2008).
Google Scholar 
Di, Y. et al. Distribution of fish communities and its influencing factors in the Nansha and Beijing sub-center reaches of the Beiyun River. Acta Sci. Circumst. 41, 156–163 (2020).
Google Scholar 
Walters, D. M., Freeman, M. C., Leigh, D. S., Freeman, B. J. & Pringle, C. M. in Effects of Urbanization on Stream Ecosystems Vol. 47 American Fisheries Society Symposium 69–85 (2005).Hu, X., Zuo, D., Liu, B., Huang, Z. & Xu, Z. Quantitative analysis of the correlation between macrobenthos community and water environmental factors and aquatic ecosystem health assessment in the North Canal River Basin of Beijing. Environ. Sci. 43, 247–255 (2022).
Google Scholar 
Kadye, W. T., Magadza, C. H. D., Moyo, N. A. G. & Kativu, S. Stream fish assemblages in relation to environmental factors on a montane plateau (Nyika Plateau, Malawi). Environ. Biol. Fishes 83, 417–428 (2008).Article 

Google Scholar 
Smith, T. A. & Kraft, C. E. Stream fish assemblages in relation to landscape position and local habitat variables. Trans. Am. Fish. Soc. 134, 430–440 (2005).Article 

Google Scholar 
Blabolil, P. et al. Environmental DNA metabarcoding uncovers environmental correlates of fish communities in spatially heterogeneous freshwater habitats. Ecol. Ind. 126, 107698 (2021).CAS 
Article 

Google Scholar 
Xie, R. et al. eDNA metabarcoding revealed differential structures of aquatic communities in a dynamic freshwater ecosystem shaped by habitat heterogeneity. Environ. Res. 201, 111602 (2021).CAS 
PubMed 
Article 

Google Scholar 
Qu, C. et al. Comparing fish prey diversity for a critically endangered aquatic mammal in a reserve and the wild using eDNA metabarcoding. Sci. Rep. 10, 16715 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pont, D. et al. Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. Sci. Rep. 8, 10361 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Doble, C. J. et al. Testing the performance of environmental DNA metabarcoding for surveying highly diverse tropical fish communities: A case study from Lake Tanganyika. Environ. DNA 2, 24–41 (2020).Article 

Google Scholar 
Xu, N. et al. Monitoring seasonal distribution of an endangered anadromous sturgeon in a large river using environmental DNA. Sci. Nat. 105, 62 (2018).Article 
CAS 

Google Scholar 
Laramie, M. B., Pilliod, D. S. & Goldberg, C. S. Characterizing the distribution of an endangered salmonid using environmental DNA analysis. Biol. Cons. 183, 29–37 (2015).Article 

Google Scholar 
Harper, L. R. et al. Development and application of environmental DNA surveillance for the threatened crucian carp (Carassius carassius). Freshw. Biol. 64, 93–107 (2019).CAS 
Article 

Google Scholar 
Ushio, M. et al. Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding Metagenomics 2, e2329 (2018).
Google Scholar 
Evans, N. T. et al. Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding. Mol. Ecol. Resour. 16, 29–41 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Harrison, J. B., Sunday, J. M. & Rogers, S. M. Predicting the fate of eDNA in the environment and implications for studying biodiversity. Proc. Biol. Sci. 286, 20191409 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kelly, R. P., Shelton, A. O. & Gallego, R. Understanding PCR processes to draw meaningful conclusions from environmental DNA studies. Sci. Rep. 9, 12133 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Civade, R. et al. Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system. PLoS ONE 11, e0157366 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Barnes, M. A. et al. Environmental conditions influence eDNA persistence in aquatic systems. Environ. Sci. Technol. 48, 1819–1827 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Shogren, A. J. et al. Water flow and biofilm cover influence environmental DNA detection in recirculating streams. Environ. Sci. Technol. 52, 8530–8537 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zhao, B., van Bodegom, P. M. & Trimbos, K. The particle size distribution of environmental DNA varies with species and degradation. Sci. Total Environ. 797, 149175 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar  More