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    Community RNA-Seq: multi-kingdom responses to living versus decaying roots in soil

    1.Swift MJ, Anderson JM, Heal OW. Decomposition in terrestrial ecosystems. Oxford: Blackwell Publishing; 1979.2.Scholes MC, Powlson D, Tian G. Input control of organic matter dynamics. Geoderma. 1997;79:25–47.CAS 

    Google Scholar 
    3.Sokol NW, Kuebbing SE, Ayala EK, Bradford MA. Evidence for the primacy of living root inputs, not root or shoot litter, in forming soil organic carbon. New Phytologist. 2019;221:233–46.CAS 

    Google Scholar 
    4.Jackson RB, Lajtha K, Crow SE, Hugelius G, Kramer MG, Piñeiro G. The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Ann Rev Ecol Evol Syst. 2017;48:419–45.
    Google Scholar 
    5.Greyston SJ, Vaughan D, Jones D. Rhizosphere carbon flow in trees, in comparison with annual plants: the importance of root exudation and its impact on microbial activity and nutrient availability. Appl Soil Ecol. 1996;5:29–56.
    Google Scholar 
    6.Schimel DS. Terrestrial biogeochemical cycles: global estimates with remote sensing. Remote Sens Environ. 1995;51:49–56.
    Google Scholar 
    7.Angst G, Mueller KE, Nierop KGJ, Simpson MJ. Plant- or microbial-derived? A review on the molecular composition of stabilized soil organic matter. Soil Biol Biochem. 2021;156:108189.CAS 

    Google Scholar 
    8.Bardgett RD. The biology of soil: a community ecosystem approach. Oxford: Oxford University Press; 2005.9.Schimel JP, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol. 2012;3:1–11.
    Google Scholar 
    10.Geisen S, Mitchell EAD, Wilkinson DM, Adl S, Bonkowski M, Brown MW, et al. Soil protistology rebooted: 30 fundamental questions to start with. Soil Biol Biochem. 2017;111:94–103.CAS 

    Google Scholar 
    11.Purahong W, Wubet T, Lentendu G, Schloter M, Pecyna MJ, Kapturska D, et al. Life in leaf litter: novel insights into community dynamics of bacteria and fungi during litter decomposition. Mol Ecol. 2016;25:4059–74.CAS 
    PubMed 

    Google Scholar 
    12.Osono T. Ecology of ligninolytic fungi associated with leaf litter decomposition. Ecol Res. 2007;22:955–74.
    Google Scholar 
    13.Hattenschwiler S, Tiunov AV, Scheu S. Biodiversity and litter decomposition in terrestrial ecosystems. Ann Rev Ecol Evol Syst. 2005;36:191–218.
    Google Scholar 
    14.Pugh G. Terrestrial fungi. In: Dickenson C, Pugh G, editors. Biology of plant litter decomposition. 2. London: Academic Press Inc.; 1974. p. 303–36.15.Sinsabaugh RL, Moorhead DL. Resource allocation to extracellular enzyme production: a model for nitrogen and phosphorus control of litter decomposition. Soil Biol Biochem. 1994;26:1305–11.
    Google Scholar 
    16.Geisen S, Koller R, Hünninghaus M, Dumack K, Urich T, Bonkowski M. The soil food web revisited: Diverse and widespread mycophagous soil protists. Soil Biol Biochem. 2016;94:10–8.CAS 

    Google Scholar 
    17.Chakraborty S, Old K. Ultrastructure and description of a fungus-feeding amoeba, Trichamoeba mycophaga n. sp. (Amoebidae, Amoebea), from Australia. J Eukaryot Microbiol. 1986;33:564–9.
    Google Scholar 
    18.Bjørnlund L, Rønn R. ‘David and Goliath’of the soil food web–Flagellates that kill nematodes. Soil Biol Biochem. 2008;40:2032–9.
    Google Scholar 
    19.Xiong W, Jousset A, Guo S, Karlsson I, Zhao Q, Wu H, et al. Soil protist communities form a dynamic hub in the soil microbiome. ISME J. 2018;12:634–8.PubMed 

    Google Scholar 
    20.Neher DA, Weicht TR, Barbercheck ME. Linking invertebrate communities to decomposition rate and nitrogen availability in pine forest soils. Appl Soil Ecol. 2012;54:14–23.
    Google Scholar 
    21.Bokhorst S, Wardle DA. Microclimate within litter bags of different mesh size: Implications for the ‘arthropod effect’ on litter decomposition. Soil Biol Biochem. 2013;58:147–52.CAS 

    Google Scholar 
    22.Carrillo Y, Ball BA, Bradford MA, Jordan CF, Molina M. Soil fauna alter the effects of litter composition on nitrogen cycling in a mineral soil. Soil Biol Biochem. 2011;43:1440–9.CAS 

    Google Scholar 
    23.Riutta T, Slade EM, Bebber DP, Taylor ME, Malhi Y, Riordan P, et al. Experimental evidence for the interacting effects of forest edge, moisture and soil macrofauna on leaf litter decomposition. Soil Biol Biochem. 2012;49:124–31.CAS 

    Google Scholar 
    24.Meyer WM, Ostertag R, Cowie RH. Macro-invertebrates accelerate litter decomposition and nutrient release in a Hawaiian rainforest. Soil Biol Biochem. 2011;43:206–11.CAS 

    Google Scholar 
    25.Stout JD. The Relationship between protozoan populations and biological activity in soils. Integr Comp Biol. 1973;13:193–201.
    Google Scholar 
    26.Bonkowski M, Griffiths B, Scrimgeour C. Substrate heterogeneity and microfauna in soil organic ‘hotspots’ as determinants of nitrogen capture and growth of ryegrass. Appl Soil Ecol. 2000;14:37–53.
    Google Scholar 
    27.Hünninghaus M, Dibbern D, Kramer S, Koller R, Pausch J, Schloter-Hai B, et al. Disentangling carbon flow across microbial kingdoms in the rhizosphere of maize. Soil Biol Biochem. 2019;134:122–30.
    Google Scholar 
    28.Tedersoo L, Anslan S. Towards PacBio‐based pan‐eukaryote metabarcoding using full‐length ITS sequences. Environ Microbiol Rep. 2019;11:659–68.CAS 
    PubMed 

    Google Scholar 
    29.Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, et al. Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. Mycokeys. 2015;10:1–43.
    Google Scholar 
    30.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013;41:D597–604.CAS 
    PubMed 

    Google Scholar 
    31.Baldrian P, Kolařík M, Stursová M, Kopecký J, Valášková V, Větrovský T, et al. Active and total microbial communities in forest soil are largely different and highly stratified during decomposition. ISME J. 2012;6:248–58.CAS 
    PubMed 

    Google Scholar 
    32.Poisot T, Péquin B, Gravel D. High‐throughput sequencing: a roadmap toward community ecology. Ecol Evol. 2013;3:1125–39.PubMed 
    PubMed Central 

    Google Scholar 
    33.Nguyen NH, Smith D, Peay K, Kennedy P. Parsing ecological signal from noise in next generation amplicon sequencing. New Phytol. 2015;205:1389–93.CAS 
    PubMed 

    Google Scholar 
    34.Engelbrektson A, Kunin V, Wrighton KC, Zvenigorodsky N, Chen F, Ochman H, et al. Experimental factors affecting PCR-based estimates of microbial species richness and evenness. ISME J. 2010;4:642–7.CAS 
    PubMed 

    Google Scholar 
    35.Suzuki MT, Giovannoni SJ. Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Appl Environ Microbiol. 1996;62:625–30.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Soergel D, Dey N, Knight R, Brenner S. Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. ISME J. 2012;6:1440–4.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Nomura M, Gourse R, Baughman G. Regulation of the synthesis of ribosomes and ribosomal components. Annu Rev Biochem. 1984;53:75–117.CAS 
    PubMed 

    Google Scholar 
    38.Urich T, Lanzén A, Qi J, Huson DH, Schleper C, Schuster SC. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS ONE. 2008;3:e2527.PubMed 
    PubMed Central 

    Google Scholar 
    39.Kembel SW, Wu M, Eisen JA, Green JL. Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS Comp Biol. 2012;8:e1002743.CAS 

    Google Scholar 
    40.Gong W, Marchetti A. Estimation of 18S gene copy number in marine eukaryotic plankton using a next-generation sequencing approach. Front Mar Sci. 2019;6:219.
    Google Scholar 
    41.Miller CS, Baker BJ, Thomas BC, Singer SW, Banfield JF. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biol. 2011;12:R44.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Xue Y, Lanzén A, Jonassen I. Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data. Bioinformatics. 2020;36:3365–71.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Bang-Andreasen T, Anwar MZ, Lanzén A, Kjøller R, Rønn R, Ekelund F, et al. Total RNA-sequencing reveals multi-level microbial community changes and functional responses to wood ash application in agricultural and forest soil. FEMS Microbiol Ecol. 2020;96:fiaa016.PubMed 
    PubMed Central 

    Google Scholar 
    44.Geisen S, Tveit AT, Clark IM, Richter A, Svenning MM, Bonkowski M, et al. Metatranscriptomic census of active protists in soils. ISME J. 2015;9:2178–90.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Adl SM, Habura A, Eglit Y. Amplification primers of SSU rDNA for soil protists. Soil Biol Biochem. 2014;69:328–42.CAS 

    Google Scholar 
    46.Wagner M, Nielsen PH, Loy A, Nielsen JL, Daims H. Linking microbial community structure with function: fluorescence in situ hybridization-microautoradiography and isotope arrays. Curr Opin Biotechnol. 2006;17:83–91.CAS 
    PubMed 

    Google Scholar 
    47.Neufeld J, Wagner M, Murrell J. Who eats what, where and when? Isotope-labelling experiments are coming of age. ISME J. 2007;1:103–10.CAS 
    PubMed 

    Google Scholar 
    48.Radajewski S, Ineson P, Parekh NR, Murrell J. Stable-isotope probing as a tool in microbial ecology. Nature. 2000;403:646–9.CAS 
    PubMed 

    Google Scholar 
    49.Radajewski S, Murrell JC. Stable isotope probing for detection of methanotrophs after enrichment with 13CH4. In: de Muro MA, Rapley R, editors. Gene probes: principles and protocols. Totowa, NJ: Humana Press; 2002. p. 149–57.50.Manefield M, Whiteley AS, Griffiths R, Bailey MJ. RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl Environ Microbiol. 2002;68:5367–73.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Mayali X, Weber PK, Nuccio E, Lietard J, Somoza M, Blazewicz SJ, et al. Stable isotope probing, methods and protocols. Methods Mol Biol. 2019;2046:71–87.PubMed 

    Google Scholar 
    52.Mayali X, Weber PK, Brodie EL, Mabery S, Hoeprich PD, Pett-Ridge J. High-throughput isotopic analysis of RNA microarrays to quantify microbial resource use. ISME J. 2012;6:1210–21.CAS 
    PubMed 

    Google Scholar 
    53.Waldrop MP, Firestone MK. Seasonal dynamics of microbial community composition and function in oak canopy and open grassland soils. Microb Ecol. 2006;52:470–9.CAS 
    PubMed 

    Google Scholar 
    54.Shi S, Nuccio E, Herman DJ, Rijkers R, Estera K, Li J, et al. Successional trajectories of rhizosphere bacterial communities over consecutive seasons. mBio. 2015;6:e00746.PubMed 
    PubMed Central 

    Google Scholar 
    55.DeAngelis KM, Brodie EL, DeSantis TZ, Andersen GL, Lindow SE, Firestone MK. Selective progressive response of soil microbial community to wild oat. ISME J. 2009;3:168–78.CAS 
    PubMed 

    Google Scholar 
    56.Jaeger CH, Lindow SE, Miller W, Clark E, Firestone MK. Mapping of sugar and amino acid availability in soil around roots with bacterial sensors of sucrose and tryptophan. Appl Environ Microbiol. 1999;65:2685–90.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Nuccio EE, Starr E, Karaoz U, Brodie EL, Zhou J, Tringe SG, et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME J. 2020;269:1–16.
    Google Scholar 
    58.Griffiths RI, Whiteley AS, O’Donnell AG, Bailey M. Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Appl Environ Microbiol. 2000;66:5488–91.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Andrews S. FastQC: a quality control tool for high throughput sequence data (Version 0.10.1) 2012; http://www.bioinformatics.babraham.ac.uk/projects/fastqc/60.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.PubMed 
    PubMed Central 

    Google Scholar 
    64.Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
    PubMed 
    PubMed Central 

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

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

    Google Scholar 
    68.Miller CS, Handley KM, Wrighton KC, Frischkorn KR, Thomas BC, Banfield JF. Short-read assembly of full-length 16S amplicons reveals bacterial diversity in subsurface sediments. PLoS ONE. 2013;8:e56018.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Choi J, Kim S-H. A genome tree of life for the Fungi kingdom. Proc Natl Acad Sci USA. 2017;114:9391–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2018;47:D259–64.PubMed Central 

    Google Scholar 
    72.Adl SM, Simpson AGB, Farmer MA, Andersen RA, Anderson OR, Barta JR, et al. The new higher level classification of eukaryotes with emphasis on the taxonomy of protists. J Eukaryot Microbiol. 2005;52:399–451.
    Google Scholar 
    73.Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Mayali X, Weber PK, Pett-Ridge J. Taxon-specific C/N relative use efficiency for amino acids in an estuarine community. FEMS Microbiol Ecol. 2013;83:402–12.CAS 
    PubMed 

    Google Scholar 
    75.Pausch J, Kramer S, Scharroba A, Scheunemann N, Butenschoen O, Kandeler E, et al. Small but active—pool size does not matter for carbon incorporation in below‐ground food webs. Funct Ecol. 2016;30:479–89.
    Google Scholar 
    76.el Zahar Haichar F, Achouak W, Christen R. Identification of cellulolytic bacteria in soil by stable isotope probing. Environ Microbiol. 2007;9:625–34.CAS 

    Google Scholar 
    77.Ha YE, Kang CI, Joo EJ, Park SY, Kang SJ, Wi YM, et al. Bacterial populations assimilating carbon from 13C-labeled plant residue in soil: analysis by a DNA-SIP approach. Soil Biol Biochem. 2011;43:814–22.
    Google Scholar 
    78.Eichorst SA, Kuske CR. Identification of cellulose-responsive bacterial and fungal communities in geographically and edaphically different soils by using stable isotope probing. Appl Environ Microbiol. 2012;78:2316–27.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.Pepe-Ranney C, Campbell AN, Koechli CN, Berthrong S, Buckley DH. Unearthing the ecology of soil microorganisms using a high resolution DNA-SIP approach to explore cellulose and xylose metabolism in soil. Front Microbiol. 2016;7:626.
    Google Scholar 
    80.Wilhelm RC, Pepe-Ranney C, Weisenhorn P, Lipton M, Buckley DH. Competitive exclusion and metabolic dependency among microorganisms structure the cellulose economy of an agricultural soil. mBio. 2021;12:e03099-20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    81.Lehtovirta-Morley LE, Ross J, Hink L, Weber EB, Gubry-Rangin C, Thion C, et al. Isolation of ‘Candidatus Nitrosocosmicus franklandus’, a novel ureolytic soil archaeal ammonia oxidiser with tolerance to high ammonia concentration. FEMS Microbiol Ecol. 2016;92:fiw057.PubMed 
    PubMed Central 

    Google Scholar 
    82.Nuccio EE, Anderson-Furgeson J, Estera KY, Pett-Ridge J, De Valpine P, Brodie EL, et al. Climate and edaphic controllers influence rhizosphere community assembly for a wild annual grass. Ecology. 2016;97:1307–18.PubMed 

    Google Scholar 
    83.Ceja-Navarro JA, Wang Y, Arellano A, Ramanculova L, Yuan M, Byer A, et al. Protist diversity and network complexity in the rhizosphere are dynamic and changing as the plant develops. Microbiome. 2021;9. https://doi.org/10.1186/s40168-021-01042-9.
    Google Scholar 
    84.Zhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN, Shi S, et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. 2018;3:470–80.CAS 
    PubMed 

    Google Scholar 
    85.Zhang L, Lueders T. Micropredator niche differentiation between bulk soil and rhizosphere of an agricultural soil depends on bacterial prey. FEMS Microbiol Ecol. 2017;93:fix103.
    Google Scholar 
    86.Gao Z, Karlsson I, Geisen S, Kowalchuk G, Jousset A. Protists: puppet masters of the rhizosphere microbiome. Trends Plant Sci. 2019;24:165–76.CAS 
    PubMed 

    Google Scholar 
    87.Rosenberg K, Bertaux J, Krome K, Hartmann A, Scheu S, Bonkowski M. Soil amoebae rapidly change bacterial community composition in the rhizosphere of Arabidopsis thaliana. ISME J. 2009;3:675–84.CAS 
    PubMed 

    Google Scholar 
    88.Zaragoza SR, Mayzlish E, Steinberger Y. Seasonal changes in free-living Amoeba species in the root canopy of Zygophyllum dumosum in the Negev Desert, Israel. Microb Ecol. 2005;49:134–41.
    Google Scholar 
    89.Baldock BM, Baker JH, Sleigh MA. Laboratory growth rates of six species of freshwater Gymnamoebia. Oecologia. 1980;47:156–9.CAS 
    PubMed 

    Google Scholar 
    90.Bates ST, Clemente JC, Flores GE, Walters WA, Parfrey LW, Knight R, et al. Global biogeography of highly diverse protistan communities in soil. ISME J. 2013;7:652–9.CAS 
    PubMed 

    Google Scholar 
    91.Cotrufo MF, Wallenstein MD, Boot CM, Denef K, Paul E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Global Change Biol. 2013;19:988–95.
    Google Scholar 
    92.Schmidt MW, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, et al. Persistence of soil organic matter as an ecosystem property. Nature. 2011;478:49–56.CAS 
    PubMed 

    Google Scholar 
    93.Allison SD, Martiny JB. Resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci USA. 2008;105:11512–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    94.Wickings K, Grandy AS, Reed SC, Cleveland CC. The origin of litter chemical complexity during decomposition. Ecol Lett. 2012;15:1180–8.PubMed 

    Google Scholar 
    95.Hungate BA, Marks JC, Power ME, Schwartz E, van Groenigen KJ, Blazewicz SJ, et al. The functional significance of bacterial predators. mBio. 2021;12:e00466–21.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    96.de Ruiter PC, Neutel AM, Moore JC. Energetics, patterns of interaction strengths, and stability in real ecosystems. Science. 1995;269:1257–60.PubMed 

    Google Scholar 
    97.Glücksman E, Bell T, Griffiths RI, Bass D. Closely related protist strains have different grazing impacts on natural bacterial communities. Environ Microbiol. 2010;12:3105–13.PubMed 

    Google Scholar 
    98.Yeates GW, Bongers T, De Goede R, Freckman DW, Georgieva SS. Feeding habits in soil nematode families and genera—an outline for soil ecologists. J Nematol. 1993;25:315–31.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    99.Okada H, Harada H, Kadota I. Fungal-feeding habits of six nematode isolates in the genus Filenchus. Soil Biol Biochem. 2005;37:1113–20.CAS 

    Google Scholar 
    100.Rotem O, Pasternak Z, Jurkevitch E. Bdellovibrio and Like Organisms. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes, deltaproteobacteria and epsilonproteobacteria. Berlin: Springer-Verlag; 2014. p. 3–17.101.Griffiths BS. Microbial-feeding nematodes and protozoa in soil: their effectson microbial activity and nitrogen mineralization in decomposition hotspots and the rhizosphere. Plant Soil. 1994;164:25–33.CAS 

    Google Scholar 
    102.Bonkowski M, Clarholm M. Stimulation of plant growth through interactions of bacteria and protozoa: testing the auxiliary microbial loop hypothesis. Acta Protozool. 2012;51:237–47.
    Google Scholar 
    103.Clarholm M. Interactions of bacteria, protozoa and plants leading to mineralization of soil nitrogen. Soil Biol Biochem. 1985;17:181–7.CAS 

    Google Scholar 
    104.Halter D, Goulhen-Chollet F, Gallien S, Casiot C, Hamelin J, Gilard F, et al. In situ proteo-metabolomics reveals metabolite secretion by the acid mine drainage bio-indicator, Euglena mutabilis. ISME J. 2012;6:1391–402.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    105.Yuan C, Lei J, Cole J, Sun Y. Reconstructing 16S rRNA genes in metagenomic data. Bioinformatics. 2015;31:i35–43.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    106.Zeng F, Wang Z, Wang Y, Zhou J, Chen T. Large-scale 16S gene assembly using metagenomics shotgun sequences. Bioinformatics. 2017;33:1447–56.CAS 
    PubMed 

    Google Scholar 
    107.Pericard P, Dufresne Y, Couderc L, Blanquart S, Touzet H. MATAM: reconstruction of phylogenetic marker genes from short sequencing reads in metagenomes. Bioinformatics. 2017;34:585–91.
    Google Scholar 
    108.Callahan BJ, Wong J, Heiner C, Oh S, Theriot CM, Gulati AS, et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res. 2019;47:e103-e.
    Google Scholar  More

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    Vulnerability to collapse of coral reef ecosystems in the Western Indian Ocean

    1.Nicholson, E., Keith, D. A. & Wilcove, D. S. Assessing the threat status of ecological communities. Conserv. Biol. 23, 259–274 (2009).
    Google Scholar 
    2.Bland, L. M. et al. Developing a standardized definition of ecosystem collapse for risk assessment. Front. Ecol. Environ. 16, 29–36 (2018).
    Google Scholar 
    3.Rockström, J. et al. Planetary boundaries: exploring the safe operating space for humanity. Ecol. Soc. 14, 32 (2009).
    Google Scholar 
    4.The Global Assessment Report on Biodiversity and Ecosystem Services: Summary for Policy Makers (IPBES, 2019); https://ipbes.net/sites/default/files/2020-02/ipbes_global_assessment_report_summary_for_policymakers_en.pdf5.Souter, D. et al. (eds) Status of Coral Reefs of the World: 2020 Report (International Coral Reef Initiative, 2021).6.Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).CAS 

    Google Scholar 
    7.Beyer, H. L. et al. Risk-sensitive planning for conserving coral reefs under rapid climate change. Conserv. Lett. 109, e12587 (2018).
    Google Scholar 
    8.Miloslavich, P. et al. Essential ocean variables for global sustained observations of biodiversity and ecosystem changes. Glob. Change Biol. 24, 2416–2433 (2018).
    Google Scholar 
    9.Díaz-Pérez, L. et al. Coral reef health indices versus the biological, ecological and functional diversity of fish and coral assemblages in the Caribbean Sea. PLoS ONE 11, e0161812 (2016).
    Google Scholar 
    10.Obura, D. O. et al. Coral reef monitoring, reef assessment technologies, and ecosystem-based management. Front. Mar. Sci. 6, 580 (2019).
    Google Scholar 
    11.Mumby, P. J., Steneck, R. S. & Hastings, A. Evidence for and against the existence of alternate attractors on coral reefs. Oikos 122, 481–491 (2013).
    Google Scholar 
    12.Ateweberhan, M., McClanahan, T. R., Graham, N. A. J. & Sheppard, C. R. C. Episodic heterogeneous decline and recovery of coral cover in the Indian Ocean. Coral Reefs 30, 739–752 (2011).
    Google Scholar 
    13.Obura, D. et al. (eds) Coral Reef Status Report for the Western Indian Ocean (International Coral Reef Initiative, 2017).14.Bruno, J. F. & Selig, E. R. Regional decline of coral cover in the Indo-Pacific: timing, extent, and subregional comparisons. PLoS ONE 2, e711 (2007).
    Google Scholar 
    15.Jackson, J., Donovan, M. K., Cramer, K. & Lam, V. (eds) Status and Trends of Caribbean Coral Reefs: 1970–2012 (International Coral Reef Initiative, 2014).16.Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).CAS 

    Google Scholar 
    17.McClanahan, T. R., Ateweberhan, M., Darling, E. S., Graham, N. A. J. & Muthiga, N. A. Biogeography and change among regional coral communities across the Western Indian Ocean. PLoS ONE 9, e93385 (2014).
    Google Scholar 
    18.Nicholson, E. et al. Scientific foundations for an ecosystem goal, milestones and indicators for the post-2020 global biodiversity framework. Nat. Ecol. Evol. 5, 1338–1349 (2021).
    Google Scholar 
    19.Keith, D. A. et al. Scientific foundations for an IUCN Red List of Ecosystems. PLoS ONE 8, e62111 (2013).CAS 

    Google Scholar 
    20.Rodriguez, J. P. et al. A practical guide to the application of the IUCN Red List of Ecosystems criteria. Philos. Trans. R. Soc. B 370, 20140003 (2015).21.Alaniz, A. J., Pérez-Quezada, J. F., Galleguillos, M., Vásquez, A. E. & Keith, D. A. Operationalizing the IUCN Red List of Ecosystems in public policy. Conserv. Lett. 12, e12665 (2019).
    Google Scholar 
    22.van Hooidonk, R. et al. Local-scale projections of coral reef futures and implications of the Paris Agreement. Sci. Rep. https://doi.org/10.1038/srep39666 (2016).23.Hausfather, Z. & Peters, G. P. Emissions—the ‘business as usual’ story is misleading. Nature 577, 618–620 (2020).CAS 

    Google Scholar 
    24.Gudka, M. et al. Participatory reporting of the 2016 bleaching event in the Western Indian Ocean. Coral Reefs 39, 1–11 (2020).
    Google Scholar 
    25.Diaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411–413 (2020).
    Google Scholar 
    26.Steneck, R. S., Mumby, P. J., MacDonald, C., Rasher, D. B. & Stoyle, G. Attenuating effects of ecosystem management on coral reefs. Sci. Adv. 4, eaao5493 (2018).
    Google Scholar 
    27.Arnold, S., Steneck, R. & Mumby, P. Running the gauntlet: inhibitory effects of algal turfs on the processes of coral recruitment. Mar. Ecol. Prog. Ser. 414, 91–105 (2010).
    Google Scholar 
    28.Karkarey, R., Kelkar, N., Lobo, A. S., Alcoverro, T. & Arthur, R. Long-lived groupers require structurally stable reefs in the face of repeated climate change disturbances. Coral Reefs 33, 289–302 (2014).
    Google Scholar 
    29.Sadovy de Mitcheson, Y. J. et al. Valuable but vulnerable: over-fishing and under-management continue to threaten groupers so what now? Mar. Policy 116, 103909 (2020).
    Google Scholar 
    30.Garpe, K. C. & Öhman, M. C. Coral and fish distribution patterns in Mafia Island Marine Park, Tanzania: fish–habitat interactions. Hydrobiologia 498, 191–211 (2003).
    Google Scholar 
    31.Samoilys, M., Roche, R., Koldewey, H. & Turner, J. Patterns in reef fish assemblages: insights from the Chagos Archipelago. PLoS ONE 13, e0191448 (2018).
    Google Scholar 
    32.Graham, N. A. J. et al. Human disruption of coral reef trophic structure. Curr. Biol. 27, 231–236 (2017).CAS 

    Google Scholar 
    33.Bland, L. M. et al. Using multiple lines of evidence to assess the risk of ecosystem collapse. Proc. R. Soc. B 284, 20170660 (2017).
    Google Scholar 
    34.Nyström, M. Redundancy and response diversity of functional groups: implications for the resilience of coral reefs. Ambio 35, 30–35 (2006).
    Google Scholar 
    35.Uribe, E. S., Luna-Acosta, A. & Etter, A. Red List of Ecosystems: risk assessment of coral ecosystems in the Colombian Caribbean. Ocean Coast. Manag. 199, 105416 (2021).
    Google Scholar 
    36.Burns, E. L. et al. Ecosystem assessment of mountain ash forest in the Central Highlands of Victoria, south-eastern Australia. Austral Ecol. 40, 386–399 (2015).
    Google Scholar 
    37.Roff, G. & Mumby, P. J. Global disparity in the resilience of coral reefs. Trends Ecol. Evol. 27, 404–413 (2012).
    Google Scholar 
    38.Boitani, L., Mace, G. M. & Rondinini, C. Challenging the scientific foundations for an IUCN Red List of Ecosystems. Conserv. Lett. 8, 125–131 (2015).
    Google Scholar 
    39.Rowland, J. A. et al. Ecosystem indices to support global biodiversity conservation. Conserv. Lett. 13, e12680 (2019).
    Google Scholar 
    40.Bland, L. M. et al. Impacts of the IUCN Red List of Ecosystems on conservation policy and practice. Conserv. Lett. 12, e12666 (2019).
    Google Scholar 
    41.Brooks, T. M. et al. Harnessing biodiversity and conservation knowledge products to track the Aichi Targets and Sustainable Development Goals. Biodiversity 16, 157–174 (2015).
    Google Scholar 
    42.Keith, D. A. et al. The IUCN Global Ecosystem Typology v1.0: Descriptive Profiles for Biomes and Ecosystem Functional Groups (Royal Botanic Gardens Kew, 2020).43.Camp, E. F. et al. The future of coral reefs subject to rapid climate change: lessons from natural extreme environments. Front. Mar. Sci. 5, 4 (2018).
    Google Scholar 
    44.Pendleton, L. et al. Coral reefs and people in a high-CO2 world: where can science make a difference to people? PLoS ONE 11, e0164699 (2016).
    Google Scholar 
    45.Gamoyo, M., Obura, D. & Reason, C. J. C. Estimating connectivity through larval dispersal in the Western Indian Ocean. J. Geophys. Res. Biogeosci. 124, 2446–2459 (2019).
    Google Scholar 
    46.Portner, H. O. et al. Scientific Outcome of the IPBES-IPCC Co-Sponsored Workshop Report on Biodiversity and Climate Change (IPBES, 2021); https://zenodo.org/record/510112547.Global Biodiversity Outlook 5 (Convention on Biological Diversity, 2020); https://www.cbd.int/gbo548.IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).49.Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411–413 (2020).
    Google Scholar 
    50.ICRI, Coral Reefs and the UN (International Coral Reef Initiative, 2021); https://www.icriforum.org/icri-coral-reefs-and-the-un/51.Mahon, R. & Fanning, L. Regional ocean governance: polycentric arrangements and their role in global ocean governance. Mar. Policy 107, 103590 (2019).
    Google Scholar 
    52.Bland, L. M., Keith, D. A., Miller, R. M., Murray, N. J. & Rodríguez, J. P. Guidelines for the Application of IUCN Red List of Ecosystems Categories and Criteria (IUCN, 2015); https://doi.org/10.2305/IUCN.CH.2016.RLE.1.en53.Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience 57, 573–583 (2007).
    Google Scholar 
    54.Veron, J., Stafford-Smith, M. G., Devantier, L. M. & Turak, E. Overview of distribution patterns of zooxanthellate Scleractinia. Front. Mar. Sci. 1, 81 (2015).55.Obura, D. O. The diversity and biogeography of Western Indian Ocean reef-building corals. PLoS ONE 7, e45013 (2012).CAS 

    Google Scholar 
    56.Connell, J. H. Diversity in tropical rain forests and coral reefs. Science 199, 1302–1310 (1978).CAS 

    Google Scholar 
    57.Knowlton, N. Thresholds and multiple stable states in coral reef community dynamics. Integr. Comp. Biol. 32, 674–682 (1992).
    Google Scholar 
    58.Hughes, T. P., Carpenter, S., Rockström, J., Scheffer, M. & Walker, B. Multiscale regime shifts and planetary boundaries. Trends Ecol. Evol. 28, 389–395 (2013).
    Google Scholar 
    59.Jouffray, J. B. et al. Identifying multiple coral reef regimes and their drivers across the Hawaiian archipelago. Philos. Trans. R. Soc. B 370, 20130268 (2014).60.Nyström, M. & Folke, C. Spatial resilience of coral reefs. Ecosystems 4, 406–417 (2001).
    Google Scholar 
    61.Mumby, P. J. Phase shifts and the stability of macroalgal communities on Caribbean coral reefs. Coral Reefs 28, 761–773 (2009).
    Google Scholar 
    62.Smith, J. E. et al. Re-evaluating the health of coral reef communities: baselines and evidence for human impacts across the central Pacific. Proc. R. Soc. B 283, 20151985 (2016).
    Google Scholar 
    63.Bellwood, D. R., Hughes, T. P., Folke, C. & Nyström, M. Confronting the coral reef crisis. Nature 429, 827–833 (2004).CAS 

    Google Scholar 
    64.Mumby, P. J., Hastings, A. & Edwards, H. J. Thresholds and the resilience of Caribbean coral reefs. Nature 450, 98–101 (2007).CAS 

    Google Scholar 
    65.Ainsworth, C. H. & Mumby, P. J. Coral–algal phase shifts alter fish communities and reduce fisheries production. Glob. Change Biol. 21, 165–172 (2015).
    Google Scholar 
    66.Wittebolle, L. et al. Initial community evenness favours functionality under selective stress. Nature 458, 623–626 (2009).CAS 

    Google Scholar 
    67.Stuart-Smith, R. D. et al. Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature 501, 539–542 (2013).CAS 

    Google Scholar 
    68.Bellwood, D. R. et al. Coral reef conservation in the Anthropocene: confronting spatial mismatches and prioritizing functions. Biol. Conserv. 236, 604–615 (2019).
    Google Scholar 
    69.Cinner, J. E. et al. Bright spots among the world’s coral reefs. Nature 535, 416–419 (2016).CAS 

    Google Scholar 
    70.Huang, W., Mukherjee, D. & Chen, S. Assessment of Hurricane Ivan impact on chlorophyll-a in Pensacola Bay by MODIS 250m remote sensing. Mar. Pollut. Bull. 62, 490–498 (2011).CAS 

    Google Scholar 
    71.Chen, S. Estimating wide range total suspended solids concentrations from MODIS 250-m imageries: an improved method. ISPRS J. Photogramm. Remote Sens. 99, 58–69 (2015).
    Google Scholar 
    72.Porter, S. N., Branch, G. M. & Sink, K. J. Changes in shallow-reef community composition along environmental gradients on the East African coast. Mar. Biol. 164, 101 (2017).
    Google Scholar 
    73.Perry, C. T. & Alvarez-Filip, L. Changing geo‐ecological functions of coral reefs in the Anthropocene. Funct. Ecol. 33, 976–988 (2018).
    Google Scholar 
    74.Andrefouet, S. et al. Global assessment of modern coral reef extent and diversity for regional science and management applications: a view from space. In Proc. 10th International Coral Reef Symposium 1732–1745 (ICRS, 2006).75.Maina, J., Venus, V., McClanahan, T. R. & Ateweberhan, M. Modelling susceptibility of coral reefs to environmental stress using remote sensing data and GIS models. Ecol. Model. 212, 180–199 (2008).
    Google Scholar 
    76.Maina, J., McClanahan, T. R., Venus, V., Ateweberhan, M. & Madin, J. Global gradients of coral exposure to environmental stresses and implications for local management. PLoS ONE 6, e23064 (2011).CAS 

    Google Scholar 
    77.Liu, G. et al. NOAA coral reef watch’s decision support system for coral reef management. In Proc. 12th International Coral Reef Symposium (2012); https://www.icrs2012.com/proceedings/manuscripts/ICRS2012_5A_6.pdf78.Hill, J. & Wilkinson, C. Methods for Ecological Monitoring of Coral Reefs: Version 1 (Australian Institute of Marine Science, 2004).79.Wilkinson, C. Status of Coral Reefs of the World: 2008 (International Coral Reef Initiative, 2008).80.Muller-Karger, F. E. et al. Advancing marine biological observations and data requirements of the complementary essential ocean variables (EOVs) and essential biodiversity variables (EBVs) frameworks. Front. Mar. Sci. 5, 15 (2018).
    Google Scholar 
    81.Bax, N. J. et al. Linking capacity development to GOOS monitoring networks to achieve sustained ocean observation. Front. Mar. Sci. 5, 206 (2018).
    Google Scholar 
    82.Reuchlin-Hugenholtz, E., Shackell, N. L. & Hutchings, J. A. The potential for spatial distribution indices to signal thresholds in marine fish biomass. PLoS ONE 10, e0120500 (2015).
    Google Scholar 
    83.Kuempel, C. D., Adams, V. M., possingham, H. P. & Bode, M. Bigger or better: the relative benefits of protected area network expansion and enforcement for the conservation of an exploited species. Conserv. Lett. 11, e12433 (2017).
    Google Scholar 
    84.Morais, R. A., Connolly, S. R. & Bellwood, D. R. Human exploitation shapes productivity–biomass relationships on coral reefs. Glob. Change Biol. 26, 1295–1305 (2020).
    Google Scholar 
    85.Harford, W. J., Sagarese, S. R. & Karnauskas, M. Coping with information gaps in stock productivity for rebuilding and achieving maximum sustainable yield for grouper–snapper fisheries. Fish Fish. 20, 303–321 (2019).
    Google Scholar  More

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    Rbec: a tool for analysis of amplicon sequencing data from synthetic microbial communities

    Synthetic microbial communities (SynComs) constitute an emerging and powerful tool in biological, biomedical, and biotechnological research. Despite recent advances in algorithms for the analysis of culture-independent amplicon sequencing data from microbial communities, there is a lack of tools specifically designed for analyzing SynCom data, where reference sequences for each strain are available. Here we present Rbec, a tool designed for the analysis of SynCom data that accurately corrects PCR and sequencing errors in amplicon sequences and identifies intra-strain polymorphic variation. Extensive evaluation using mock bacterial and fungal communities show that our tool outperforms current methods for samples of varying complexity, diversity, and sequencing depth. Furthermore, Rbec also allows accurate detection of contaminants in SynCom experiments. More

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    Diverse ecophysiological adaptations of subsurface Thaumarchaeota in floodplain sediments revealed through genome-resolved metagenomics

    1.Emerson JB, Thomas BC, Alvarez W, Banfield JF. Metagenomic analysis of a high carbon dioxide subsurface microbial community populated by chemolithoautotrophs and bacteria and archaea from candidate phyla. Environ Microbiol. 2016;18:1686–703.CAS 
    PubMed 

    Google Scholar 
    2.Hug LA, Thomas BC, Sharon I, Brown CT, Sharma R, Hettich RL, et al. Critical biogeochemical functions in the subsurface are associated with bacteria from new phyla and little studied lineages. Environ Microbiol. 2016;18:159–73.CAS 
    PubMed 

    Google Scholar 
    3.Anantharaman K, Brown CT, Hug LA, Sharon I, Castelle CJ, Probst AJ, et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat Commun. 2016;7:1–11.
    Google Scholar 
    4.Lu X, Seuradge BJ, Neufeld JD. Biogeography of soil Thaumarchaeota in relation to soil depth and land usage. FEMS Microbiol Ecol. 2017;93:fiw246.PubMed 

    Google Scholar 
    5.Cardarelli EL, Bargar JR, Francis CA. Diverse Thaumarchaeota dominate subsurface ammonia-oxidizing communities in semi-arid floodplains in the western United States. Micro Ecol. 2020;80:778–92.CAS 

    Google Scholar 
    6.Tolar BB, Boye K, Bobb C, Maher K, Bargar JR, Francis CA. Stability of floodplain subsurface microbial communities through seasonal hydrological and geochemical cycles. Front Earth Sci. 2020;8:338.
    Google Scholar 
    7.Francis CA, Roberts KJ, Beman JM, Santoro AE, Oakley BB. Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean. PNAS. 2005;102:14683–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Treusch AH, Leininger S, Kletzin A, Schuster SC, Klenk H-P, Schleper C. Novel genes for nitrite reductase and Amo-related proteins indicate a role of uncultivated mesophilic crenarchaeota in nitrogen cycling. Environ Microbiol. 2005;7:1985–95.CAS 
    PubMed 

    Google Scholar 
    9.Leininger S, Urich T, Schloter M, Schwark L, Qi J, Nicol GW, et al. Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature. 2006;442:806–9.CAS 
    PubMed 

    Google Scholar 
    10.Wuchter C, Abbas B, Coolen MJL, Herfort L, van Bleijswijk J, Timmers P, et al. Archaeal nitrification in the ocean. PNAS. 2006;103:12317–22.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Prosser JI, Nicol GW. Archaeal and bacterial ammonia-oxidisers in soil:the quest for niche specialisation and differentiation. Trends Microbiol. 2012;20:523–31.CAS 
    PubMed 

    Google Scholar 
    12.Mußmann M, Brito I, Pitcher A, Damste JSS, Hatzenpichler R, Richter A, et al. Thaumarchaeotes abundant in refinery nitrifying sludges express amoA but are not obligate autotrophic ammonia oxidizers. PNAS. 2011;108:16771–6.PubMed 
    PubMed Central 

    Google Scholar 
    13.Weber EB, Lehtovirta-Morley LE, Prosser JI, Gubry-Rangin C, Laanbroek R. Ammonia oxidation is not required for growth of Group 1.1c soil Thaumarchaeota. FEMS Microbiol Ecol. 2015;91:fiv001.PubMed 
    PubMed Central 

    Google Scholar 
    14.Lin X, Handley KM, Gilbert JA, Kostka JE. Metabolic potential of fatty acid oxidation and anaerobic respiration by abundant members of Thaumarchaeota and Thermoplasmata in deep anoxic peat. ISME J. 2015;9:2740–4.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Kato S, Itoh T, Yuki M, Nagamori M, Ohnishi M, Uematsu K, et al. Isolation and characterization of a thermophilic sulfur- and iron-reducing thaumarchaeote from a terrestrial acidic hot spring. ISME J. 2019;13:2465–74.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Aylward FO, Santoro AE. Heterotrophic Thaumarchaea with small genomes are widespread in the dark ocean. mSystems. 2020;5:e00415–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Reji L, Francis CA. Metagenome-assembled genomes reveal unique metabolic adaptations of a basal marine Thaumarchaeota lineage. ISME J. 2020;14:2105–15.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Ren M, Feng X, Huang Y, Wang H, Hu Z, Clingenpeel S, et al. Phylogenomics suggests oxygen availability as a driving force in Thaumarchaeota evolution. ISME J. 2019;13:2150–61.PubMed 
    PubMed Central 

    Google Scholar 
    19.Kerou M, Alves RJE, Schleper C. Nitrososphaerales. In: Bergeys manual of systematics of archaea and bacteria ed. Bergey’s Manual Trust (Hoboken, NJ: John Wiley & Sons). 2016. https://doi.org/10.1002/9781118960608.cbm00055.20.Qin W, Martens-Habbena W, Kobelt JN, Stahl DA. Candidatus nitrosopumilales. In: Bergeys manual of systematics of archaea and bacteria ed. Bergey’s Manual Trust (Hoboken, NJ: John Wiley & Sons). 2016. https://doi.org/10.1002/9781118960608.gbm01290.21.Prosser JI, Nicol GW. Candidatus Nitrosotaleales. In: Bergeys manual of systematics of archaea and bacteria ed. Bergey’s Manual Trust (Hoboken, NJ: John Wiley & Sons). 2016. https://doi.org/10.1002/9781118960608.obm00123.22.Gubry-Rangin C, Kratsch C, Williams TA, McHardy AC, Embley TM, Prosser JI, et al. Coupling of diversification and pH adaptation during the evolution of terrestrial Thaumarchaeota. PNAS. 2015;112:9370–5.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Nicol GW, Leininger S, Schleper C, Prosser JI. The influence of soil pH on the diversity, abundance and transcriptional activity of ammonia oxidizing archaea and bacteria. Environ Microbiol. 2008;10:2966–78.CAS 
    PubMed 

    Google Scholar 
    24.Szukics U, Abell GCJ, Hödl V, Mitter B, Sessitsch A, Hackl E, et al. Nitrifiers and denitrifiers respond rapidly to changed moisture and increasing temperature in a pristine forest soil. FEMS Microbiol Ecol. 2010;72:395–406.CAS 
    PubMed 

    Google Scholar 
    25.Höfferle Š, Nicol GW, Pal L, Hacin J, Prosser JI, Mandić-Mulec I. Ammonium supply rate influences archaeal and bacterial ammonia oxidizers in a wetland soil vertical profile. FEMS Microbiol Ecol. 2010;74:302–15.PubMed 

    Google Scholar 
    26.Tourna M, Freitag TE, Nicol GW, Prosser JI. Growth, activity and temperature responses of ammonia-oxidizing archaea and bacteria in soil microcosms. Environ Microbiol. 2008;10:1357–64.CAS 
    PubMed 

    Google Scholar 
    27.He J-Z, Shen J-P, Zhang L-M, Zhu Y-G, Zheng Y-M, Xu M-G, et al. Quantitative analyses of the abundance and composition of ammonia-oxidizing bacteria and ammonia-oxidizing archaea of a Chinese upland red soil under long-term fertilization practices. Environ Microbiol. 2007;9:2364–74.CAS 
    PubMed 

    Google Scholar 
    28.Marusenko Y, Bates ST, Anderson I, Johnson SL, Soule T, Garcia-Pichel F. Ammonia-oxidizing archaea and bacteria are structured by geography in biological soil crusts across North American arid lands. Ecol Process. 2013;2:9.
    Google Scholar 
    29.Opitz S, Küsel K, Spott O, Totsche KU, Herrmann M. Oxygen availability and distance to surface environments determine community composition and abundance of ammonia-oxidizing prokaroytes in two superimposed pristine limestone aquifers in the Hainich region, Germany. FEMS Microbiol Ecol. 2014;90:39–53.CAS 
    PubMed 

    Google Scholar 
    30.Purkamo L, Kietäväinen R, Miettinen H, Sohlberg E, Kukkonen I, Itävaara M, et al. Diversity and functionality of archaeal, bacterial and fungal communities in deep Archaean bedrock groundwater. FEMS Microbiol Ecol. 2018;94.31.Bushnell B BBTools software package. 2014. http://bbtools.jgi.doe.gov.32.Li H. BFC:correcting Illumina sequencing errors. Bioinformatics. 2015;31:2885–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.CAS 
    PubMed 

    Google Scholar 
    34.Li D, Luo R, Liu C-M, Leung C-M, Ting H-F, Sadakane K, et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.CAS 
    PubMed 

    Google Scholar 
    35.Kang D, Li F, Kirton ES, Thomas A, Egan RS, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.PubMed 
    PubMed Central 

    Google Scholar 
    36.Wu Y-W, Tang Y-H, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome. 2014;2:26.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0:an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.CAS 
    PubMed 

    Google Scholar 
    38.Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158.PubMed 
    PubMed Central 

    Google Scholar 
    39.Nurk S, Bankevich A, Antipov D, Gurevich A, Korobeynikov A, Lapidus A, et al. Assembling genomes and mini-metagenomes from highly chimeric reads. In: Deng M, Jiang R, Sun F, Zhang X, editors. Research in Computational Molecular Biology (RECOMB), Lecture Notes in Computer Science, Springer; Berlin, Heidelberg. 2013;7821:158–70.40.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk:a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–7.CAS 

    Google Scholar 
    42.Parks DH, Chuvochina M, Chaumeil P-A, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;38:1079–86.CAS 
    PubMed 

    Google Scholar 
    43.Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.CAS 
    PubMed 

    Google Scholar 
    44.Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal:prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
    Google Scholar 
    46.Seemann T. Prokka:rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
    PubMed 

    Google Scholar 
    47.Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.CAS 
    PubMed 

    Google Scholar 
    48.Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 2007;35:W182–5.PubMed 
    PubMed Central 

    Google Scholar 
    49.Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, Mering von C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309–14.CAS 

    Google Scholar 
    51.Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2013;42:D206–14.PubMed 
    PubMed Central 

    Google Scholar 
    52.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Elbourne LDH, Tetu SG, Hassan KA, Paulsen IT. TransportDB 2.0: a database for exploring membrane transporters in sequenced genomes from all domains of life. Nucleic Acids Res. 2016;45:D320–4.PubMed 
    PubMed Central 

    Google Scholar 
    54.Nielsen H, Engelbrecht J, Brunak S, von Heijne G. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng. 1997;10:1–6.CAS 
    PubMed 

    Google Scholar 
    55.Armenteros JJA, Tsirigos KD, Sønderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol. 2019;37:420–3.
    Google Scholar 
    56.Sonnhammer EL, Heijne G, von, Krogh A. A hidden Markov model for predicting transmembrane helices in protein sequences. Proc Int Conf Intell Syst Mol Biol. 1998;6:175–82.CAS 
    PubMed 

    Google Scholar 
    57.Krogh A, Larsson B, Heijne G, von, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305:567–80.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.PubMed 
    PubMed Central 

    Google Scholar 
    59.Edgar RC. MUSCLE:multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Nucleic Acids Res. 2009;25:1972–3.
    Google Scholar 
    61.Nguyen L-T, Schmidt HA, Haeseler von A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Nucleic Acids Res. 2015;32:268–74.CAS 

    Google Scholar 
    62.Hoang DT, Chernomor O, Haeseler von A, Minh BQ. Le Sy Vinh. UFBoot2: improving the ultrafast bootstrap approximation. Nucleic Acids Res. 2017;35:518–22.
    Google Scholar 
    63.Kalyaanamoorthy S, Minh BQ, Wong TKF, Haeseler von A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
    PubMed Central 

    Google Scholar 
    65.Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7:539–9.PubMed 
    PubMed Central 

    Google Scholar 
    66.Chen I-MA, Chu K, Palaniappan K, Ratner A, Huang J, Huntemann M, et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 2021;49:D751–63.CAS 
    PubMed 

    Google Scholar 
    67.Alves RJE, Minh BQ, Urich T, Haeseler A, Schleper C. Unifying the global phylogeny and environmental distribution of ammonia-oxidising archaea based on amoA genes. Nat Commun. 2018;9:1517.PubMed 
    PubMed Central 

    Google Scholar 
    68.Tolar BB, Mosier AC, Lund MB, Francis CA. Nitrosarchaeum. In: Bergeys manual of systematics of archaea and bacteria ed. Bergey’s Manual Trust (Hoboken, NJ: John Wiley & Sons). 2019:1–9. https://doi.org/10.1002/9781118960608.gbm01289.69.Park S-J, Kim J-G, Jung M-Y, Kim S-J, Cha I-T, Ghai R, et al. Draft genome sequence of an ammonia-oxidizing archaeon, “Candidatus Nitrosopumilus sediminis” AR2, from Svalbard in the Arctic Circle. J Bacteriol. 2012;194:6948–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Kim BK, Jung M-Y, Yu DS, Park S-J, Oh TK, Rhee S-K, et al. Genome sequence of an ammonia-oxidizing soil archaeon, “Candidatus Nitrosoarchaeum koreensis” MY1. J Bacteriol. 2011;193:5539–40.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Ochsenreiter T, Selezi D, Quaiser A, Bonch-Osmolovskaya L, Schleper C. Diversity and abundance of Crenarchaeota in terrestrial habitats studied by 16S RNA surveys and real time PCR. Environ Microbiol. 2003;5:787–97.CAS 
    PubMed 

    Google Scholar 
    72.Lehtovirta-Morley LE, Stoecker K, Vilcinskas A, Prosser JI, Prosse, Nicol GW. Cultivation of an obligate acidophilic ammonia oxidizer from a nitrifying acid soil. PNAS. 2011;108:15892–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Lehtovirta-Morley LE, Ross J, Hink L, Weber EB, Gubry-Rangin C, Thion C, et al. Isolation of “Candidatus Nitrosocosmicus franklandus,” a novel ureolytic soil archaeal ammonia oxidiser with tolerance to high ammonia concentration. FEMS Microbiol Ecol. 2016;92:fiw057.PubMed 
    PubMed Central 

    Google Scholar 
    74.Könneke M, Bernhard AE, la Torre de JR, Walker CB, Waterbury JB, Stahl DA. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature. 2005;437:543–6.PubMed 

    Google Scholar 
    75.Qin W, Amin SA, Martens-Habbena W, Walker CB, Urakawa H, Devol AH, et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. PNAS. 2014;111:12504–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    76.Santoro AE, Dupont CL, Richter RA, Craig MT, Carini P, McIlvin MR, et al. Genomic and proteomic characterization of “Candidatus Nitrosopelagicus brevis”: an ammonia-oxidizing archaeon from the open ocean. PNAS. 2015;112:1173–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    77.Bayer B, Vojvoda J, Offre P, Alves RJE, Elisabeth NH, Garcia JA, et al. Physiological and genomic characterization of two novel marine thaumarchaeal strains indicates niche differentiation. ISME J. 2015;10:1051–63.PubMed 
    PubMed Central 

    Google Scholar 
    78.Larentis M, Psenner R, Alfreider A. Prokaryotic community structure in deep bedrock aquifers of the Austrian Central Alps. Antonie van Leeuwenhoek. 2015;107:687–701.PubMed 

    Google Scholar 
    79.Lazar CS, Stoll W, Lehmann R, Herrmann M, Schwab VF, Akob DM, et al. Archaeal diversity and CO2 fixers in carbonate-/siliciclastic-rock groundwater ecosystems. Archaea. 2017;2136287.80.Sheridan PO, Raguideau S, Quince C, Holden J, Zhang L, Williams TA, et al. Gene duplication drives genome expansion in a major lineage of Thaumarchaeota. Nat Commun. 2020;11:1–12.
    Google Scholar 
    81.Könneke M, Schubert DM, Brown PC, Hügler M, Standfest S, Schwander T, et al. Ammonia-oxidizing archaea use the most energy-efficient aerobic pathway for CO2 fixation. PNAS. 2014;111:8239–44.PubMed 
    PubMed Central 

    Google Scholar 
    82.Hallam SJ, Konstantinidis KT, Putnam N, Schleper C, Watanabe Y-I, Sugahara J, et al. Genomic analysis of the uncultivated marine crenarchaeote Cenarchaeum symbiosum. PNAS. 2006;103:18296–301.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    83.Spang A, Poehlein A, Offre P, Zumbr a gel S, Haider S, Rychlik N, et al. The genome of the ammonia-oxidizing Candidatus Nitrososphaera gargensis: insights into metabolic versatility and environmental adaptations. Environ Microbiol. 2012;14:3122–45.CAS 
    PubMed 

    Google Scholar 
    84.Kamanda Ngugi D, Blom J, Alam I, Rashid M, Ba-Alawi W, Zhang G, et al. Comparative genomics reveals adaptations of a halotolerant thaumarchaeon in the interfaces of brine pools in the Red Sea. ISME J. 2015;9:396–411.CAS 
    PubMed 

    Google Scholar 
    85.Abby SS, Melcher M, Kerou M, Krupovic M, Stieglmeier M, Rossel C, et al. Candidatus Nitrosocaldus cavascurensis, an ammonia oxidizing, extremely thermophilic archaeon with a highly mobile genome. Front Microbiol. 2018;9:28.PubMed 
    PubMed Central 

    Google Scholar 
    86.Tourna M, Stieglmeier M, Spang A, Konneke M, Schintlmeister A, Urich T, et al. Nitrososphaera viennensis, an ammonia oxidizing archaeon from soil. PNAS. 2011;108:8420–5.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    87.Johnson WV, Anderson PM. Bicarbonate is a recycling substrate for cyanase. J Biol Chem. 1987;262:9021–5.CAS 
    PubMed 

    Google Scholar 
    88.Palatinszky M, Herbold C, Jehmlich N, Pogoda M, Han P, Bergen von M, et al. Cyanate as an energy source for nitrifiers. Nature. 2015;524:105–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    89.Kitzinger K, Padilla CC, Marchant HK, Hach PF, Herbold CW, Kidane AT, et al. Cyanate and urea are substrates for nitrification by Thaumarchaeota in the marine environment. Nat Microbiol. 2019;4:234–43.CAS 
    PubMed 

    Google Scholar 
    90.Pace HC, Brenner C. The nitrilase superfamily: classification, structure and function. Genome Biol. 2001;2:REVIEWS0001. https://doi.org/10.1186/gb-2001-2-1-reviews0001.91.Ramteke PW, Maurice NG, Joseph B, Wadher BJ. Nitrile-converting enzymes: an eco-friendly tool for industrial biocatalysis. Biotechnol Appl Biochem. 2013;60:459–81.CAS 
    PubMed 

    Google Scholar 
    92.Walker CB, la Torre de JR, Klotz MG, Urakawa H, Pinel N, Arp DJ, et al. Nitrosopumilus maritimus genome reveals unique mechanisms for nitrification and autotrophy in globally distributed marine crenarchaea. PNAS. 2010;107:8818–23.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    93.Mosier AC, Lund MB, Francis CA. Ecophysiology of an ammonia-oxidizing archaeon adapted to low-salinity habitats. Micro Ecol. 2012;64:955–63.CAS 

    Google Scholar 
    94.Lebedeva EV, Hatzenpichler R, Pelletier E, Schuster N, Hauzmayer S, Bulaev A, et al. Enrichment and genome sequence of the group i.1a ammonia-oxidizing archaeon “Ca. Nitrosotenuis uzonensis” representing a clade globally distributed in thermal habitats. PLoS ONE. 2013;8:e80835.PubMed 
    PubMed Central 

    Google Scholar 
    95.Daebeler A, Herbold C, Vierheilig J, Sedlacek CJ, Pjevac P, Albertsen M, et al. Cultivation and genomic analysis of “Candidatus Nitrosocaldus islandicus,” an obligately thermophilic, ammonia-oxidizing thaumarchaeon from a hot spring biofilm in Graendalur valley, Iceland. Front Microbiol. 2018;9:193.PubMed 
    PubMed Central 

    Google Scholar 
    96.Beam JP, Jay ZJ, Kozubal MA, Inskeep WP. Niche specialization of novel Thaumarchaeota to oxic and hypoxic acidic geothermal springs of Yellowstone National Park. ISME J. 2014;8:938–51.CAS 
    PubMed 

    Google Scholar 
    97.Kim J-G, Park S-J, Damste JSS, Schouten S, Rijpstra WIC, Jung M-Y, et al. Hydrogen peroxide detoxification is a key mechanism for growth of ammonia-oxidizing archaea. PNAS. 2016;113:7888–93.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    98.Imlay JA. Cellular defenses against superoxide and hydrogen peroxide. Annu Rev Biochem. 2008;77:755–76.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    99.Zhalnina KV, Dias R, Leonard MT, de Quadros PD, Camargo FAO, Drew JC, et al. Genome sequence of Candidatus Nitrososphaera evergladensis from group I.1b enriched from everglades soil reveals novel genomic features of the ammonia-oxidizing archaea. PLoS ONE. 2014;9:e101648.PubMed 
    PubMed Central 

    Google Scholar 
    100.Sauder LA, Albertsen M, Engel K, Schwarz J, Nielsen PH, Wagner M, et al. Cultivation and characterization of Candidatus Nitrosocosmicus exaquare, an ammonia-oxidizing archaeon from a municipal wastewater treatment system. ISME J. 2017;11:1142–57.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    101.Tolar BB, Powers LC, Miller WL, Wallsgrove NJ, Popp BN, Hollibaugh JT. Ammonia oxidation in the ocean can be inhibited by nanomolar concentrations of hydrogen peroxide. Front Mar Sci. 2016;3:237.
    Google Scholar 
    102.Bayer B, Pelikan C, Bittner MJ, Reinthaler T, Könneke M, Herndl GJ, et al. Proteomic response of three marine ammonia-oxidizing archaea to hydrogen peroxide and their metabolic interactions with a heterotrophic alphaproteobacterium. mSystems. 2019;4:e00181–19.PubMed 
    PubMed Central 

    Google Scholar 
    103.Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB, Zhayed AAF, et al. Genome-centric view of carbon processing in thawing permafrost. Nature. 2018;560:49–54.CAS 
    PubMed 

    Google Scholar 
    104.Yang Y, Herbold CW, Jung M-Y, Qin W, Cai M, Du H, et al. Survival strategies of ammonia-oxidizing archaea (AOA) in a full-scale WWTP treating mixed landfill leachate containing copper ions and operating at low-intensity of aeration. Water Res. 2021;191:116798.CAS 
    PubMed 

    Google Scholar 
    105.Greening C, Biswas A, Carere CR, Jackson CJ, Taylor MC, Stott MB, et al. Genomic and metagenomic surveys of hydrogenase distribution indicate H2 is a widely utilised energy source for microbial growth and survival. ISME J. 2016;10:761–77.CAS 
    PubMed 

    Google Scholar 
    106.Ma K, Schicho RN, Kelly RM, Adams MW. Hydrogenase of the hyperthermophile Pyrococcus furiosus is an elemental sulfur reductase or sulfhydrogenase:evidence for a sulfur-reducing hydrogenase ancestor. PNAS. 1993;90:5341–4.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    107.Finney AJ, Sargent F. Formate hydrogenlyase:A group 4 [NiFe]-hydrogenase in tandem with a formate dehydrogenase. Adv Micro Physiol. 2019;74:465–86.
    Google Scholar 
    108.Baker BJ, Saw JH, Lind AE, Lazar CS, Hinrichs KU, Teske AP, et al. Genomic inference of the metabolism of cosmopolitan subsurface archaea, Hadesarchaea. Nat Microbiol. 2016;1:1–9.
    Google Scholar 
    109.He Y, Li M, Perumal V, Feng X, Fang J, Xie J, et al. Genomic and enzymatic evidence for acetogenesis among multiple lineages of the archaeal phylum Bathyarchaeota widespread in marine sediments. Nat Microbiol. 2016;1:1–9.
    Google Scholar 
    110.Lazar CS, Baker BJ, Seitz KW, Teske AP. Genomic reconstruction of multiple lineages of uncultured benthic archaea suggests distinct biogeochemical roles and ecological niches. ISME J. 2017;11:1118–29.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    111.Farag IF, Biddle JF, Zhao R, Martino AJ, House CH, León-Zayas RI. Metabolic potentials of archaeal lineages resolved from metagenomes of deep Costa Rica sediments. ISME J. 2020;14:1345–58.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    112.Orsi WD, Vuillemin A, Rodriguez P, Coskun ÖK, Gomez-Saez GV, Lavik G, et al. Metabolic activity analyses demonstrate that Lokiarchaeon exhibits homoacetogenesis in sulfidic marine sediments. Nat Microbiol. 2020;5:248–55.CAS 
    PubMed 

    Google Scholar 
    113.Adam PS, Borrel G, Gribaldo S. Evolutionary history of carbon monoxide dehydrogenase/acetyl-CoA synthase, one of the oldest enzymatic complexes. PNAS. 2018;115:E1166–73.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    114.Köpke M, Held C, Hujer S, Liesegang H, Wiezer A, Wollherr A, et al. Clostridium ljungdahlii represents a microbial production platform based on syngas. PNAS. 2010;107:13087–92.PubMed 
    PubMed Central 

    Google Scholar 
    115.Lazar CS, Baker BJ, Seitz KW, Hyde AS, Dick GJ, Hinrichs KU, et al. Genomic evidence for distinct carbon substrate preferences and ecological niches of Bathyarchaeota in estuarine sediments. Nucleic Acids Res. 2015;18:1200–11.
    Google Scholar 
    116.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. 2014;196:483–92.PubMed 
    PubMed Central 

    Google Scholar 
    117.Kletzin A, Mukund S, Kelley-Crouse TL, Chan MK, Rees DC, Adams MW. Molecular characterization of the genes encoding the tungsten-containing aldehyde ferredoxin oxidoreductase from Pyrococcus furiosus and formaldehyde ferredoxin oxidoreductase from Thermococcus litoralis. J Bacteriol. 1995;177:4817–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    118.Arndt F, Schmitt G, Winiarska A, Saft M, Seubert A, Kahnt J, et al. Characterization of an aldehyde oxidoreductase from the mesophilic bacterium Aromatoleum aromaticum ebn1, a member of a new subfamily of tungsten-containing enzymes. Front Microbiol. 2019;10. https://doi.org/10.3389/fmicb.2019.00071.119.Lloyd KG, Schreiber L, Petersen DG, Kjeldsen KU, Lever MA, Steen AD, et al. Predominant archaea in marine sediments degrade detrital proteins. Nature. 2013;496:215–8.CAS 
    PubMed 

    Google Scholar 
    120.Dimapilis JRR. Tungsten is essential for long-term maintenance of members of candidate archaeal genus Aigarchaeota Group 4. [dissertation on the Internet]. San Bernardino, California State University; 2019. https://scholarworks.lib.csusb.edu/etd/927/.121.Anthony C. The quinoprotein dehydrogenases for methanol and glucose. Arch Biochem Biophys. 2004;428:2–9.CAS 
    PubMed 

    Google Scholar 
    122.Jaffe AL, Castelle CJ, Dupont CL, Banfield JF. Lateral gene transfer shapes the distribution of rubisco among candidate phyla radiation bacteria and DPANN archaea. Nucleic Acids Res. 2019;36:435–46.CAS 

    Google Scholar 
    123.Herbold CW, Lehtovirta-Morley LE, Jung M-Y, Jehmlich N, Hausmann B, Han P, et al. Ammonia-oxidising archaea living at low pH: insights from comparative genomics. Environ Microbiol. 2017;19:4939–52.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    124.Aono R, Sato T, Imanaka T, Atomi H. A pentose bisphosphate pathway for nucleoside degradation in Archaea. Nat Chem Biol. 2015;11:355–60.CAS 
    PubMed 

    Google Scholar 
    125.Chadwick GL, Hemp J, Fischer WW, Orphan VJ. Convergent evolution of unusual complex I homologs with increased proton pumping capacity: energetic and ecological implications. ISME J. 2018;12:2668–80.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    126.Cai C, Leu AO, Xie G-J, Guo J, Feng Y, Zhao J-X, et al. A methanotrophic archaeon couples anaerobic oxidation of methane to Fe(III) reduction. ISME J. 2018;12:1929–39.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    127.Leu AO, McIlroy SJ, Ye J, Parks DH, Orphan VJ, Tyson GW. Lateral gene transfer drives metabolic flexibility in the anaerobic methane-oxidizing archaeal family Methanoperedenaceae. mBio. 2020;11:e01325–20.PubMed 
    PubMed Central 

    Google Scholar 
    128.Zhou Z, L Y, Xu W, Pan J, Luo Z-H, Li M. Genome- and community-level interaction insights into carbon utilization and element cycling functions of Hydrothermarchaeota in hydrothermal sediment. mSystems. 2020;5:e00795–19.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    129.Tully BJ, Graham ED, Heidelberg JF. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci Data. 2018;5:170203.CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Differences in PItotal of Quercus liaotungensis seedlings between provenance

    1.Wang, W., Li, Q. K. & Ma, K. P. Establishment and spatial distribution of Quercus liaotungensis Koidz. seedlings in Dongling Mountain. Acta Phytoecol. Sin. 24, 595 (2000).2.Han, H. R., He, S. Q. & Zhang, X. P. The effect of light intensity on the growth and development of Quercus liaotungensis seedlings. J. Beijing For. Univ. 22, 97–100 (2000).
    Google Scholar 
    3.Chen, Z. P., Wang, H. & Yuan, H. B. Studies on soil seed bank and seed fate of Quercus liaotungensis forest in the Ziwu Mountains. J. Gansu Agric. Univ. 40, 7–12 (2005).
    Google Scholar 
    4.Li, Y. Resource investigation and superior germplasm resources selection of woody energy plants Quercus mongolica Fisch and Quercus liaotungensis Koidz, Dissertation, Chinese Academy of Forestry, (2011).5.Yin, X., Zhou, G., Sui, X., He, Q. & Li, R. Dominant climatic factors of Quercus mongolica geographical distribution and their thresholds. Acta Ecol. Sin 33, 103–109 (2013).Article 

    Google Scholar 
    6.Takai, T. et al. A natural variant of NAL1, selected in high-yield rice breeding programs, pleiotropically increases photosynthesis rate. Sci. Rep. 3, 1–11 (2013).Article 

    Google Scholar 
    7.Yang, Y. J., Tong, Y. G., Yu, G. Y., Zhang, S. B. & Huang, W. Photosynthetic characteristics explain the high growth rate for Eucalyptus camaldulensis: Implications for breeding strategy. Ind. Crop. Prod. 124, 186–191 (2018).CAS 
    Article 

    Google Scholar 
    8.Spyridaki, A., Psylinakis, E. & Ghanotakis, D. F. Photosystem II. In Biotechnological Applications of Photosynthetic Proteins: Biochips, Biosensors and Biodevices (ed. Giardi, M.T. & Piletska, E. V.) 11–13 (Springer, Boston, 2006).9.Dąbrowski, P. et al. Prompt chlorophyll a fluorescence as a rapid tool for diagnostic changes in PSII structure inhibited by salt stress in Perennial ryegrass. J. Photochem. Photobiol. B 157, 22–31 (2016).10.Van Rooijen, R. et al. Natural variation of YELLOW SEEDLING1 affects photosynthetic acclimation of Arabidopsis thaliana. Nat. Commun. 8, 1–9 (2017).Article 

    Google Scholar 
    11.Zushi, K., Kajiwara, S. & Matsuzoe, N. Chlorophyll a fluorescence OJIP transient as a tool to characterize and evaluate response to heat and chilling stress in tomato leaf and fruit. Sci. Hortic. 148, 39–46 (2012).CAS 
    Article 

    Google Scholar 
    12.Fan, J. et al. Alleviation of cold damage to photosystem II and metabolisms by melatonin in Bermudagrass. Front. Plant Sci. 6, 925 (2015).Article 

    Google Scholar 
    13.Van Heerden, P., Swanepoel, J. & Krüger, G. Modulation of photosynthesis by drought in two desert scrub species exhibiting C3-mode CO2 assimilation. Environ. Exp. Bot. 61, 124–136 (2007).Article 

    Google Scholar 
    14.Živčák, M., Brestič, M., Olšovská, K. & Slamka, P. Performance index as a sensitive indicator of water stress in Triticum aestivum L. Plant Soil Environ. 54, 133–139 (2008).Article 

    Google Scholar 
    15.Kalaji, H. M., Bosa, K., Kościelniak, J. & Żuk-Gołaszewska, K. Effects of salt stress on photosystem II efficiency and CO2 assimilation of two Syrian barley landraces. Environ. Exp. Bot. 73, 64–72 (2011).CAS 
    Article 

    Google Scholar 
    16.Singh, D. P. & Sarkar, R. K. Distinction and characterisation of salinity tolerant and sensitive rice cultivars as probed by the chlorophyll fluorescence characteristics and growth parameters. Funct. Plant Biol. 41, 727–736 (2014).CAS 
    Article 

    Google Scholar 
    17.Song, X. L. et al. NaCl stress aggravates photoinhibition of photosystem II and photosystem I in Capsicum annuum leaves under high irradiance stress. Acta Phytoecol. Sin. 35, 681 (2011).18.Sun, Y. J., Du, Y. P. & Zhai, H. Effects of different light intensity on PSII activity and recovery of Vitis vinifera cv. cabernet sauvignon leaves under high temperature stress. Plant Physiol. J. 50, 1209–1215 (2014).
    Google Scholar 
    19.Chen, S., Strasser, R. J. & Qiang, S. In vivo assessment of effect of phytotoxin tenuazonic acid on PSII reaction centers. Plant Physiol. Biochem. 84, 10–21 (2014).Article 

    Google Scholar 
    20.Zorić, A. S. et al. Resource allocation in response to herbivory and gall formation in Linaria vulgaris. Plant Physiol. Biochem. 135, 224–232 (2019).Article 

    Google Scholar 
    21.Butler, W. & Kitajima, M. Fluorescence quenching in photosystem II of chloroplasts. Biochim. Biophys. Acta. 376, 116–125 (1975).CAS 
    Article 

    Google Scholar 
    22.Baker, N. R. Chlorophyll fluorescence: A probe of photosynthesis in vivo. Annu. Rev. Plant Biol. 59, 89–113 (2008).CAS 
    Article 

    Google Scholar 
    23.Strasser, R. J., Srivastava, A. & Tsimilli-Michael, M. Screening the vitality and photosynthetic activity of plants by fluorescence transient. In Crop Improvement for Food Security (ed. Behl, R. K., Punia, M. S. & Lather, B. P. S.) 72–115 (SSARM, Hisar, 1999).24.Appenroth, K. J., Stöckel, J., Srivastava, A. & Strasser, R. Multiple effects of chromate on the photosynthetic apparatus of Spirodela polyrhiza as probed by OJIP chlorophyll a fluorescence measurements. Environ. Pollut. 115, 49–64 (2001).CAS 
    Article 

    Google Scholar 
    25.Stirbet, A., Lazár, D., Kromdijk, J. & Govindjee, G. Chlorophyll a fluorescence induction: Can just a one-second measurement be used to quantify abiotic stress responses?. Photosynthetica 56, 86–104. https://doi.org/10.1007/s11099-018-0770-3 (2018).CAS 
    Article 

    Google Scholar 
    26.Tsimilli-Michael, M., Strasser, R. J. In vivo assessment of plants’ vitality: applications in detecting and evaluating the impact of mycorrhization on host plants. In Mycorrhiza: State of the Art. Genetics and Molecular Biology, Eco-Function, Biotechnology, Eco-Physiology, Structure and Systematics (ed. Varma, A.) 679–703 (Springer, Dordrecht, 2008).27.Albert, K. R., Mikkelsen, T. N., Michelsen, A., Ro-Poulsen, H. & van der Linden, L. Interactive effects of drought, elevated CO2 and warming on photosynthetic capacity and photosystem performance in temperate heath plants. J. Plant Physiol. 168, 1550–1561 (2011).CAS 
    Article 

    Google Scholar 
    28.Chen, L. et al. Melatonin is involved in regulation of bermudagrass growth and development and response to low K+ stress. Front. Plant Sci. 8, 2038 (2017).Article 

    Google Scholar 
    29.Zhang, L. et al. The alleviation of heat damage to photosystem II and enzymatic antioxidants by exogenous spermidine in tall fescue. Front. Plant Sci. 8, 1747 (2017).Article 

    Google Scholar 
    30.Yao, X. et al. Effect of shade on leaf photosynthetic capacity, light-intercepting, electron transfer and energy distribution of soybeans. Plant Growth Regul. 83, 409–416 (2017).CAS 
    Article 

    Google Scholar 
    31.Samborska, I. A. et al. Structural and functional disorder in the photosynthetic apparatus of radish plants under magnesium deficiency. Funct. Plant Biol. 45, 668–679 (2018).CAS 
    Article 

    Google Scholar 
    32.dos Santos, V. A. H. F. & Ferreira, M. J. Are photosynthetic leaf traits related to the first-year growth of tropical tree seedlings? A light-induced plasticity test in a secondary forest enrichment planting. For. Ecol. Manage. 460, 7900 (2020).
    Google Scholar 
    33.Pavlović, I. et al. Early Brassica crops responses to salinity stress: A comparative analysis between Chinese cabbage, white cabbage, and kale. Front. Plant Sci. 10, 450 (2019).Article 

    Google Scholar 
    34.Xin, J., Ma, S., Li, Y., Zhao, C. & Tian, R. Pontederia cordata, an ornamental aquatic macrophyte with great potential in phytoremediation of heavy-metal-contaminated wetlands. Ecotox. Environ. Safe. 203, 111024 (2020).CAS 
    Article 

    Google Scholar 
    35.Wang, M. X. Forest genetics and breeding (ed. Wang, M. X.) 130–137 (China Forestry Publishing House, Beijing, 2001).36.Kurjak, D. et al. Variation in the performance and thermostability of photosystem II in European beech (Fagus sylvatica L.) provenances is influenced more by acclimation than by adaptation. Eur. J. For. Res. 138, 79–92 (2019).CAS 
    Article 

    Google Scholar 
    37.Navarro-Cerrillo, R. M. et al. Growth and physiological sapling responses of eleven Quercus ilex ecotypes under identical environmental conditions. For. Ecol. Manage. 415, 58–69 (2018).Article 

    Google Scholar 
    38.Guo, H., Wang, X. A., Zhu, Z. H., Wang, S. X. & Guo, J. C. Seed and microsite limitation for seedling recruitment of Quercus wutaishanica on Mt. Ziwuling, Loess Plateau, China. New For. 41, 127–137 (2011).39.Li, Z. S. et al. Tree-ring growth responses of Liaodong Oak (Quercus wutaishanica) to climate in the Beijing Dongling Mountain of China. Acta Phytoecol. Sin. 41, 11 (2021).
    Google Scholar 
    40.Holland, V., Koller, S. & Bruggemann, W. Insight into the photosynthetic apparatus in evergreen and deciduous European oaks during autumn senescence using OJIP fluorescence transient analysis. Plant Biol. 16, 801–808. https://doi.org/10.1111/plb.12105 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    41.Ahammed, G. J., Xu, W., Liu, A. & Chen, S. COMT1 silencing aggravates heat stress-induced reduction in photosynthesis by decreasing chlorophyll content, photosystem II activity, and electron transport efficiency in tomato. Front. Plant Sci. 9, 998 (2018).Article 

    Google Scholar 
    42.Kalaji, H. M. et al. Chlorophyll a fluorescence as a tool to monitor physiological status of plants under abiotic stress conditions. Acta Physiol. Plant. 38, 102 (2016).Article 

    Google Scholar 
    43.Liu, J., Lu, Y., Hua, W. & Last, R. L. A new light on photosystem II maintenance in oxygenic photosynthesis. Front. Plant Sci. 10, 975 (2019).Article 

    Google Scholar 
    44.Shucun, S. & Lingzhi, C. Leaf growth and photosynthesis of Quercus liaotungensis in Dongling Mountain region. Acta Phytoecol. Sin. 20, 212–217 (2000).
    Google Scholar 
    45.Wu, A., Hammer, G. L., Doherty, A., von Caemmerer, S. & Farquhar, G. D. Quantifying impacts of enhancing photosynthesis on crop yield. Nat. Plants 5, 380–388 (2019).Article 

    Google Scholar 
    46.Pšidová, E. et al. Altitude of origin influences the responses of PSII photochemistry to heat waves in European beech (Fagus sylvatica L.). Environ. Exp. Bot. 152, 97–106 (2018).Article 

    Google Scholar 
    47.Liang, D. et al. Exogenous melatonin promotes biomass accumulation and photosynthesis of kiwifruit seedlings under drought stress. Sci. Hortic. 246, 34–43 (2019).CAS 
    Article 

    Google Scholar 
    48.Panda, D., Ray, A. & Sarkar, R. K. Yield and photochemical activity of selected rice cultivars from Eastern India under medium depth stagnant flooding. Photosynthetica 57, 1084–1093 (2019).CAS 
    Article 

    Google Scholar 
    49.Zhang, H. H. et al. Effects of flooding stress on the photosynthetic apparatus of leaves of two Physocarpus cultivars. J. For. Res. 29, 1049–1059. https://doi.org/10.1007/s11676-017-0496-2 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    50.Lu, W. J. Plant physiology (ed. Lu, W. J.) 88–89 (China Forestry Publishing House, Beijing, 2017).51.Xiao, C. W. & Zhou, G. S. Effect of simulated precipitation change on growth, gas exchange and chlorophyll fluorescence of Caragana intermedia in Manwusu sandland. Chin. J. Appl. Ecol. 5, 692–696 (2001).ADS 

    Google Scholar  More

  • in

    Fishing intensification as response to Late Holocene socio-ecological instability in southeastern South America

    1.Gremillion, K. J., Barton, L. & Piperno, D. R. Particularism and the retreat from theory in the archaeology of agricultural origins. Proc. Natl. Acad. Sci. U.S.A. 111, 6171–6177 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Piperno, D. R., Ranere, A. J., Dickau, R. & Aceituno, F. Niche construction and optimal foraging theory in Neotropical agricultural origins: A re-evaluation in consideration of the empirical evidence. J. Archaeol. Sci. 78, 214–220 (2017).
    Google Scholar 
    3.Piperno, D. R. The origins of plant cultivation and domestication in the Neotropics: A behavioral ecological perspective. In Behavioral Ecology and the Transition to Agriculture (eds Kennett, D. J. & Winterhalder, B.) 137–166 (University of California Press, 2006).
    Google Scholar 
    4.Zeder, M. A. Core questions in domestication research. Proc. Natl. Acad. Sci. U.S.A. 112, 3191–3198 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Goldberg, A., Mychajliw, A. M. & Hadly, E. A. Post-invasion demography of prehistoric humans in South America. Nature 532, 232–235 (2016).ADS 
    CAS 
    PubMed 

    Google Scholar 
    6.Riris, P. & Arroyo-Kalin, M. Widespread population decline in South America correlates with mid-Holocene climate change. Sci. Rep. 9, 6850 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.de Souza, J. G. & Riris, P. Delayed demographic transition following the adoption of cultivated plants in the eastern La Plata Basin and Atlantic coast, South America. J. Archaeol. Sci. 125, 105293 (2021).
    Google Scholar 
    8.Bocquet-Appel, J.-P. When the world’s population took off: The springboard of the Neolithic demographic transition. Science 333, 560–561 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    9.Bonomo, M., Politis, G. & Gianotti, C. Montículos, Jerarquía Social y Horticultura en Las Sociedades Indígenas Del Delta Del Río Paraná (Argentina). Latin Am. Antiq. 22, 297–333 (2011).
    Google Scholar 
    10.Milheira, R. G., Attorre, T. & Borges, C. Construtores de cerritos na Laguna Dos Patos, Pontal da Barra, sul do Brasil: Lugar persistente, território e ambiente construído no Holoceno recente. Latin Am. Antiq. 30, 35–54 (2019).
    Google Scholar 
    11.Gaspar, M. D. Considerations of the sambaquis of the Brazilian coast. Antiquity 72, 592–615 (1998).
    Google Scholar 
    12.De Blasis, P., Fish, S. K., Gaspar, M. D. & Fish, P. R. Some references for discussion of complexity among the Sambaqui moundbuilders from the southern shores of Brazil. Rev. Arqueol. Am. 15, 75–105 (1998).
    Google Scholar 
    13.Schaan, D. Long-term human induced impacts on Marajó Island Landscapes, Amazon Estuary. Diversity 2, 182–206 (2010).
    Google Scholar 
    14.Chanca, I. et al. Food and diet of the pre-Columbian mound builders of the Patos Lagoon region in southern Brazil with stable isotope analysis. J. Archaeol. Sci. 133, 105439 (2021).
    Google Scholar 
    15.Lima, T. A. Em busca dos frutos do mar: Os pescadores-coletores do litoral centro-sul do Brasil. Rev. Usp 44, 270–327 (2000).
    Google Scholar 
    16.Prous, A. Arqueologia brasileira (Editora Universidade de Brasília Brasília, 1991).
    Google Scholar 
    17.Rohr, J. A. Sítios Arqueológicos de Santa Catarina. Anais do Museu de Antropol. UFSC XVI, 77–167 (1984).
    Google Scholar 
    18.Schmitz, P. I. Considerações sobre a ocupação pré-histórica do litoral meridional do Brasil. Pesqui. Antropol. 63, 355–364 (2006).
    Google Scholar 
    19.Schmitz, P. I. Visão de conjunto dos sítios da Tapera, Armação do Sul, Laranjeiras I e II, Pântano do Sul e Cabeçudas. Pesqui. Antropol. 53, 183–190 (1996).
    Google Scholar 
    20.Fish, S. K., De Blasis, P., Gaspar, M. D. & Fish, P. R. Incremental events in the construction of sambaquis, southeastern Santa Catarina. Rev. Mus. Arqueol. Etnol. 1, 69–87 (2000).
    Google Scholar 
    21.Tenorio, M. C. Abandonment of Brazilian coastal sites: Why leave the Eden. In Explorations in American archaeology. Essays in Honor of Wesley R. Hurt (ed. Hurt, W. R.) 221–257 (University Press of America, 1998).
    Google Scholar 
    22.Fossile, T. et al. Pre-Columbian fisheries catch reconstruction for a subtropical estuary in South America. Fish Fish 47, 67 (2019).
    Google Scholar 
    23.Villagran, X. S., Klokler, D., Peixoto, S., DeBlasis, P. & Giannini, P. C. F. Building coastal landscapes: Zooarchaeology and geoarchaeology of Brazilian shell mounds. J. Island Coast. Archaeol. 6, 211–234 (2011).
    Google Scholar 
    24.Fish, P. R. et al. Monumental shell mounds as persistent places in southern coastal Brazil. In The Archaeology and Historical Ecology of Small Scale Economies 120–140 (2013).25.Villagran, X. S. A redefinition of waste: Deconstructing shell and fish mound formation among coastal groups of southern Brazil. J. Anthropol. Archaeol. 36, 211–227 (2014).
    Google Scholar 
    26.Schmitz, P. I. Acampamentos litorâneos em Içara, SC. Um exercício em padrão de assentamento. Clio 35, 99–118 (1996).
    Google Scholar 
    27.Kneip, A., Farias, D. & DeBlasis, P. Longa duração e territorialidade da ocupação sambaquieira na laguna de Santa Marta, Santa Catarina. Rev. Arqueol. 31, 25–51 (2018).
    Google Scholar 
    28.DeBlasis, P., Farias, D. S. & Kneip, A. Velhas tradições e gente nova no pedaço: Perspectivas longevas de arquitetura funerária na paisagem do litoral sul catarinense. Rev. Mus. Arqueol. Etnol. 24, 109 (2014).
    Google Scholar 
    29.Bandeira, D. R. Ceramistas Pre-coloniais da Baia da Babitonga, SC: Arqueologia e Etnicidade (Universidade Estadual de Campinas, 2004).
    Google Scholar 
    30.Colonese, A. C. et al. Long-term resilience of late Holocene coastal subsistence system in Southeastern South America. PLoS ONE 9, e93854 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Pezo-Lanfranco, L. et al. Middle Holocene plant cultivation on the Atlantic Forest coast of Brazil?. R. Soc. Open Sci. 5, 180432 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Bastos, M. Q. R. et al. Isotopic evidences regarding migration at the archeological site of Praia da Tapera: New data to an old matter. J. Archaeol. Sci. Rep. 4, 588–595 (2015).
    Google Scholar 
    33.Bastos, M. Q. R., Lessa, A., Rodrigues-Carvalho, C., Tykot, R. H. & Santos, R. V. Carbon and nitrogen isotope analysis: Diet before and after the arrival of ceramic at Forte Marechal Luz Site. Re. Mus. Arqueol. Etnol. 24, 137–151 (2014).
    Google Scholar 
    34.Pezo-Lanfranco, L., DeBlasis, P. & Eggers, S. Weaning process and subadult diets in a monumental Brazilian shellmound. J. Archaeol. Sci. Rep. 22, 452–469 (2018).
    Google Scholar 
    35.De Masi, M. A. N. Aplicações de isótopos estáveis de O, C e N em estudos de sazonalidade, mobilidade e dieta de populações pré-históricas no sul do Brasil. Rev. Arqueol. 22, 55–76 (2009).
    Google Scholar 
    36.De Masi, M. Pescadores coletores da costa sul do Brasil. Pesquisas 57, 1–136 (2001).
    Google Scholar 
    37.Oppitz, G. et al. Pensando sobre mobilidade, dieta e mudança cultural: Análises isotópicas no sítio Armação do Sul, Florianópolis/SC. Cadernos do LEPAARQ (UFPEL) 15, 237–266 (2018).
    Google Scholar 
    38.Figuti, L. et al. Investigações arqueológicas e geofísicas dos sambaquis fluviais do vale do Ribeira de Iguape, Estado de São Paulo. In Museu de Arqueologia e Etnologia, USP. Relatório Final de Atividades de Projeto Temático, Processo FAPESP. 2 (2004).39.Figuti, L. Construindo o sambaqui: A ocupação e os processos de construção de sitio na bacia do Canal do Palmital, Santa Catarina. São Paulo: MAE/USP, 2009. Relatório. Processo FAPESP 08/01285-0 (2009).40.Crouch, M. S. P. Testing the Subsistence Model for the Adoption of Ceramic Technology Among Coastal Sambaqui Foragers of Southern Brazil (2013).41.Scheel-Ybert, R. & Boyadjian, C. Gardens on the coast: Considerations on food production by Brazilian shellmound builders. J. Anthropol. Archaeol. 60, 101211 (2020).
    Google Scholar 
    42.Pezo-Lanfranco, L. Evidence of variability in carbohydrate consumption in prehistoric fisher–hunter–gatherers of Southeastern Brazil: Spatiotemporal trends of oral health markers. Am. J. Phys. Anthropol. 167, 507–523 (2018).PubMed 

    Google Scholar 
    43.Boyadjian, C. H. C., Eggers, S., Reinhard, K. J. & Scheel-Ybert, R. Dieta no sambaqui Jabuticabeira-II (SC): Consumo de plantas revelado por microvestígios provenientes de cálculo dentário. Cadernos do LEPAARQ (UFPEL) 13, 131–161 (2016).
    Google Scholar 
    44.Merencio, F. T. & DeBlasis, P. Análises de mobilidade no litoral sul de Santa Catarina entre 2000–500 cal AP. Rev. Mus. Arqueol. Etnol. 36, 57–91 (2021).
    Google Scholar 
    45.Angulo, R. J., Lessa, G. C. & de Souza, M. C. A critical review of mid- to late-Holocene sea-level fluctuations on the eastern Brazilian coastline. Quat. Sci. Rev. 25, 486–506 (2006).ADS 

    Google Scholar 
    46.DeBlasis, P., Gaspar, M. & Kneip, A. Sambaquis from the Southern Brazilian coast: Landscape building and enduring heterarchical societies throughout the Holocene. Land 10, 757 (2021).
    Google Scholar 
    47.Wesolowski, V., Ferraz Mendonça de Souza, S. M., Reinhard, K. J. & Ceccantini, G. Evaluating microfossil content of dental calculus from Brazilian sambaquis. J. Archaeol. Sci. 37, 1326–1338 (2010).
    Google Scholar 
    48.da Rocha Bandeira, D. The use of wildlife by sambaquianos in prehistoric Babitonga Bay, North coast of Santa Catarina. Brazil. Rev. Chil. Antropol. https://doi.org/10.5354/0719-1472.2016.40613 (2015).Article 

    Google Scholar 
    49.Fossile, T., Ferreira, J., Bandeira, D. R., Dias-da-Silva, S. & Colonese, A. C. Integrating zooarchaeology in the conservation of coastal-marine ecosystems in Brazil. Quat. Int. https://doi.org/10.1016/j.quaint.2019.04.022 (2019).Article 

    Google Scholar 
    50.Ramsey, C. B. Methods for summarizing radiocarbon datasets. Radiocarbon 59, 1809–1833 (2017).CAS 

    Google Scholar 
    51.Crema, E. R., Bevan, A. & Shennan, S. Spatio-temporal approaches to archaeological radiocarbon dates. J. Archaeol. Sci. 87, 1–9 (2017).CAS 

    Google Scholar 
    52.Shennan, S. et al. Regional population collapse followed initial agriculture booms in mid-Holocene Europe. Nat. Commun. 4, 2486 (2013).ADS 
    PubMed 

    Google Scholar 
    53.Toniolo, T. F. et al. Sea-level fall and coastal water cooling during the Late Holocene in Southeastern Brazil based on vermetid bioconstructions. Mar. Geol. 428, 106281 (2020).ADS 
    CAS 

    Google Scholar 
    54.Cruz, F. W. et al. Orbitally driven east–west antiphasing of South American precipitation. Nat. Geosci. 2, 210–214 (2009).ADS 
    CAS 

    Google Scholar 
    55.Carvalho do Amaral, P. G., Fonseca Giannini, P. C., Sylvestre, F. & Ruiz Pessenda, L. C. Paleoenvironmental reconstruction of a Late Quaternary lagoon system in southern Brazil (Jaguaruna region, Santa Catarina state) based on multi-proxy analysis. J. Quat. Sci. 27, 181–191 (2012).
    Google Scholar 
    56.Zular, A. et al. Late Holocene intensification of colds fronts in southern Brazil as indicated by dune development and provenance changes in the São Francisco do Sul coastal barrier. Mar. Geol. 335, 64–77 (2013).ADS 

    Google Scholar 
    57.Robinson, M. et al. Uncoupling human and climate drivers of late Holocene vegetation change in southern Brazil. Sci. Rep. 8, 7800 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Bryan, A. L. & Gruhn, R. The Sambaqui at Forte Marechal Luz, State of Santa Catarina, Brazil: Archaeological Research at Six Cave Or Rockshelter Sites in Interior Bahia, Brazil (Oregon State University, 1993).
    Google Scholar 
    59.Oppitz, G. Coisas que mudam: Os processos de mudança nos sítios conchíferos catarinenses e um olhar isotópico sobre o caso do sítio Armação do Sul, Florianópolis/SC (Universidade de São Paulo, 2015). https://doi.org/10.11606/D.71.2015.tde-11112015-105226.Book 

    Google Scholar 
    60.Garcia, A. M., Hoeinghaus, D. J., Vieira, J. P. & Winemiller, K. O. Isotopic variation of fishes in freshwater and estuarine zones of a large subtropical coastal lagoon. Estuar. Coast. Shelf Sci. 73, 399–408 (2007).ADS 

    Google Scholar 
    61.Stuthmann, L. E. & Castellanos-Galindo, G. A. Trophic position and isotopic niche of mangrove fish assemblages at both sides of the Isthmus of Panama. Bull. Mar. Sci. 96, 449–468 (2020).
    Google Scholar 
    62.Romanuk, T. N., Hayward, A. & Hutchings, J. A. Trophic level scales positively with body size in fishes: Trophic level and body size in fishes. Glob. Ecol. Biogeogr. 20, 231–240 (2011).
    Google Scholar 
    63.Guiry, E. Complexities of stable carbon and nitrogen isotope biogeochemistry in ancient freshwater ecosystems: Implications for the study of past subsistence and environmental change. Front. Ecol. Evol. 7, 313 (2019).
    Google Scholar 
    64.Gu, B. Variations and controls of nitrogen stable isotopes in particulate organic matter of lakes. Oecologia 160, 421–431 (2009).ADS 
    CAS 
    PubMed 

    Google Scholar 
    65.Kendall, C. Tracing nitrogen sources and cycling in catchments. In Isotope Tracers in Catchment Hydrology (eds Kendall, C. & McDonnell, J. J.) 519–576 (Elsevier, 1998).
    Google Scholar 
    66.Alves-Costa, C. P., da Fonseca, G. A. B. & Christófaro, C. Variation in the diet of the brown-nosed coati (Nasua nasua) in Southeastern Brazil. J. Mammal. 85, 478–482 (2004).
    Google Scholar 
    67.Beisiegel, B. M. Notes on the coati, Nasua nasua (Carnivora: Procyonidae) in an Atlantic forest area. Braz. J. Biol. 61, 689–692 (2001).CAS 
    PubMed 

    Google Scholar 
    68.Norton, M. The chicken or the Iegue: Human–animal relationships and the Columbian exchange. Am. Hist. Rev. 120, 28–60 (2015).
    Google Scholar 
    69.Métraux, A. La Civilisation Matérielle Des Tribus Tupi-Guarani (Librairie Orientaliste Paul Geuthner, 1928).
    Google Scholar 
    70.de Azevedo, A. Q. et al. Hydrological influence on the evolution of a subtropical mangrove ecosystem during the late Holocene from Babitonga Bay, Brazil. Palaeogeogr. Palaeoclimatol. Palaeoecol. 574, 110 (2021).
    Google Scholar 
    71.França, M. C. et al. Late-Holocene subtropical mangrove dynamics in response to climate change during the last millennium. Holocene 29, 445–456 (2019).ADS 

    Google Scholar 
    72.Behling, H. & Negrelle, R. R. B. Tropical rain forest and climate dynamics of the Atlantic Lowland, Southern Brazil, during the late quaternary. Quat. Res. 56, 383–389 (2001).
    Google Scholar 
    73.Gaspar, M., DeBlasis, P., Fish, S. K. & Fish, P. R. Sambaquis (shell mound) societies of coastal Brazil. In Handbook of South American Archaeology (eds Silverman, H. & Isbell, W.) 319–335 (Springer, 2008).
    Google Scholar 
    74.Posth, C. et al. Reconstructing the deep population history of central and south America. Cell 175, 1185-1197.e22 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.Fidalgo, D., Hubbe, M. & Wesolowski, V. Population history of Brazilian south and southeast shellmound builders inferred through dental morphology. Am. J. Phys. Anthropol. https://doi.org/10.1002/ajpa.24342 (2021).Article 
    PubMed 

    Google Scholar 
    76.Hubbe, M., Okumura, M., Bernardo, D. V. & Neves, W. A. Cranial morphological diversity of early, middle, and late Holocene Brazilian groups: Implications for human dispersion in Brazil. Am. J. Phys. Anthropol. 155, 546–558 (2014).PubMed 

    Google Scholar 
    77.Morgan, C. Is it intensification yet? Current archaeological perspectives on the evolution of hunter–gatherer economies. J. Archaeol. Res. 23, 163–213 (2015).
    Google Scholar 
    78.Zeder, M. A. The broad spectrum revolution at 40: Resource diversity, intensification, and an alternative to optimal foraging explanations. J. Anthropol. Archaeol. 31, 241–264 (2012).
    Google Scholar 
    79.Schmitz, P. E., Verardi, I., de Masi, M. A., Rogge, J. H. & Jacobus, A. L. Escavações Arqueológicas do Pe. João Alfredo Rohr. O Sítio da Praia das Laranjeiras II. Uma Aldeia de Tradição Ceramista Itararé. Pesqui. Antropol. 49, 181 (1993).
    Google Scholar 
    80.Tiburtius, G. & Bigarella, I. K. Nota sobre os anzóis de osso da jazida páleo-etnográfica de Itacoara, Santa Catarina. Rev. Mus. Paul. Nova Série 7, 381–387 (1953).
    Google Scholar 
    81.Lessa, A. & Medeiros, J. C. D. Preliminary thoughts about the occurence of violence among the Brazilian shellmound builders: Analysis of the skeletons from Cabeçuda (Santa Catarina) and Arapuan (Rio de Janeiro) sites. Rev. Mus. Arqueol. Etnol. 11, 77 (2001).
    Google Scholar 
    82.Lessa, A. Reflexões preliminares sobre paleoepidemiologia da violência em grupos ceramistas litorâneos: (I) Sítio Praia da Tapera—SC. Rev. Mus. Arqueol. Etnol. https://doi.org/10.11606/issn.2448-1750.revmae.2001.109411 (2006).Article 

    Google Scholar 
    83.García-Escárzaga, A. & Gutiérrez-Zugasti, I. The role of shellfish in human subsistence during the Mesolithic of Atlantic Europe: An approach from meat yield estimations. Quat. Int. 584, 9–19 (2021).
    Google Scholar 
    84.Erlandson, J. M. The role of shellfish in prehistoric economies: A protein perspective. Am. Antiq. 53, 102–109 (1988).
    Google Scholar 
    85.Bandeira, D. R. Ceramistas Pré-coloniais da Baía da Babitonga—Arqueologia e Etnicidade (Universidade Estadual de Campinas, 2004).
    Google Scholar 
    86.Gilson, S.-P. & Lessa, A. Arqueozoologia do sítio Rio do Meio (SC). rsab 34, 217–248 (2021).
    Google Scholar 
    87.Gilson, S.-P. & Lessa, A. Capture, processing and utilization of sharks in archaeological context: Its importance among fisher–hunter–gatherers from southern Brazil. J. Archaeol. Sci. Rep. 35, 102693 (2021).
    Google Scholar 
    88.Hayden, B. Nimrods, piscators, pluckers, and planters: The emergence of food production. J. Anthropol. Archaeol. 9, 31–69 (1990).
    Google Scholar 
    89.Figuti, L. & Klokler, D. Resultados preliminares dos vestígios zooarqueológicos do sambaqui Espinheiros II (Joinville, SC). Rev. Mus. Arqueol. Etnol. 6, 169–187 (1996).
    Google Scholar 
    90.Benz, D. M. Levantamento preliminar de algumas espécies de vertebrados pretéritos do sítio arqueológico Ilha dos Espinheiros II Joinville—SC (Universidade da Região de Joinville, 2000).
    Google Scholar 
    91.Gilson, S.-P. & Lessa, A. Ocupação tardia do litoral norte e central catarinense por grupos pescadores-caçadores-coletores. rsab 33, 55–77 (2020).
    Google Scholar 
    92.Silva, S. B., Schmitz, P. I., Rogge, J. H., de Masi, M. A. N. & Jacobus, A. L. Escavações arqueológicas do pe. João Alfredo Rohr, S. J. o sítio arqueológico da Praia da Tapera: Um assentamento Itararé e Tupiguarani (Pesquisas, 1990).
    Google Scholar 
    93.Cardoso, J. M. O sítio Costeiro Galheta IV: Uma Perspectiva Zooarqueológica (Museu de Arqueologia e Etnologia, 2019). https://doi.org/10.11606/d.71.2019.tde-27112018-142710.Book 

    Google Scholar 
    94.Allen, M. W., Bettinger, R. L., Codding, B. F., Jones, T. L. & Schwitalla, A. W. Resource scarcity drives lethal aggression among prehistoric hunter-gatherers in central California. Proc. Natl. Acad. Sci. U.S.A. 113, 12120–12125 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    95.Kelly, R. L. The Lifeways of Hunter–Gatherers: The Foraging Spectrum (Cambridge University Press, 2013).
    Google Scholar 
    96.Hansel, F. A. & Evershed, R. P. Formation of dihydroxy acids from Z-monounsaturated alkenoic acids and their use as biomarkers for the processing of marine commodities in archaeological pottery vessels. Tetrahedron Lett. 50, 5562–5564 (2009).CAS 

    Google Scholar 
    97.Hansel, F. A., Bull, I. D. & Evershed, R. P. Gas chromatographic mass spectrometric detection of dihydroxy fatty acids preserved in the ‘bound’ phase of organic residues of archaeological pottery vessels. Rapid Commun. Mass Spectrom. 25, 1893–1898 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    98.Hansel, F. A. & Schmitz, P. I. Classificação e interpretação dos resíduos orgânicos preservados em fragmentos de cerâmica arqueológica por cromatografia gasosa e cromatografia gasosa-espectrometria de massas. Pesqui. Antropol. 63, 81–112 (2006).
    Google Scholar 
    99.Bueno, L., & Gilson, S. Brazilian Radiocarbon Database. Brazilian Radiocarbon Database. https://brc14database.com.br/?page_id=32 (2021).100.Prous, A. As esculturas de pedra (zoólitos) e de osso dos sambaquis do Brasil meridional e do Uruguay. Rev. Mem. 5, 197–217 (2018).
    Google Scholar 
    101.Prous, A. Les sculptures préhistoriques du Sud-Brésilien. bspf 71, 210–217 (1974).
    Google Scholar 
    102.Ramsey, C. B. Bayesian analysis of radiocarbon dates. Radiocarbon 51, 337–360 (2009).CAS 

    Google Scholar 
    103.Carleton, W. C. & Groucutt, H. S. Sum things are not what they seem: Problems with point-wise interpretations and quantitative analyses of proxies based on aggregated radiocarbon dates. Holocene 31, 630–643 (2021).ADS 

    Google Scholar 
    104.Hogg, A. G. et al. SHCal20 Southern Hemisphere Calibration, 0–55,000 Years cal BP. Radiocarbon 62, 759–778 (2020).CAS 

    Google Scholar 
    105.Heaton, T. J. et al. Marine20—The marine radiocarbon age calibration curve (0–55,000 cal BP). Radiocarbon 62, 779–820 (2020).CAS 

    Google Scholar 
    106.Angulo, R. J., de Souza, M. C., Reimer, P. J. & Sasaoka, S. K. Reservoir effect of the southern and southeastern Brazilian coast. Radiocarbon 47, 67–73 (2008).
    Google Scholar 
    107.Alves, E. et al. Radiocarbon reservoir corrections on the Brazilian coast from pre-bomb marine shells. Quat. Geochronol. 29, 30–35 (2015).
    Google Scholar 
    108.De Masi, M. A. N. Prehistoric Hunter–Gatherer Mobility on the Southern Brazilian Coast: Santa Catarina Island (Unpublished PhD dissertation. Stanford University, 1999).109.DeNiro, M. J. Postmortem preservation and alteration of in vivo bone collagen isotope ratios in relation to palaeodietary reconstruction. Nature 317, 806 (1985).ADS 
    CAS 

    Google Scholar 
    110.Ambrose, S. H. Preparation and characterization of bone and tooth collagen for isotopic analysis. J. Archaeol. Sci. 17, 431–451 (1990).
    Google Scholar 
    111.van Klinken, G. J. Bone collagen quality indicators for palaeodietary and radiocarbon measurements. J. Archaeol. Sci. 26, 687–695 (1999).
    Google Scholar 
    112.Szpak, P., Buckley, M., Darwent, C. M. & Richards, M. P. Long-term ecological changes in marine mammals driven by recent warming in northwestern Alaska. Glob. Change Biol. 24, 490–503 (2018).ADS 

    Google Scholar 
    113.Garcia, A. M., Vieira, J. P. & Winemiller, K. O. Effects of 1997–1998 El Niño on the dynamics of the shallow-water fish assemblage of the Patos Lagoon Estuary (Brazil). Estuar. Coast. Shelf Sci. 57, 489–500 (2003).ADS 

    Google Scholar 
    114.Wiley, A. E. et al. Millennial-scale isotope records from a wide-ranging predator show evidence of recent human impact to oceanic food webs. Proc. Natl. Acad. Sci. U.S.A. 110, 8972–8977 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    115.Buckley, M., Collins, M., Thomas-Oates, J. & Wilson, J. C. Species identification by analysis of bone collagen using matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry. Rapid Commun. Mass Spectrom. 23, 3843–3854 (2009).ADS 
    CAS 
    PubMed 

    Google Scholar 
    116.Strohalm, M., Hassman, M., Kosata, B. & Kodícek, M. mMass data miner: An open source alternative for mass spectrometric data analysis. Rapid Commun. Mass Spectrom. 22, 905–908 (2008).ADS 
    PubMed 

    Google Scholar 
    117.Buckley, M. et al. Species identification of archaeological marine mammals using collagen fingerprinting. J. Archaeol. Sci. 41, 631–641 (2014).CAS 

    Google Scholar 
    118.Kirby, D. P., Buckley, M., Promise, E., Trauger, S. A. & Holdcraft, T. R. Identification of collagen-based materials in cultural heritage. Analyst 138, 4849–4858 (2013).ADS 
    CAS 
    PubMed 

    Google Scholar 
    119.Brown, T. A., Nelson, E. E., Vogel, S. J. & Southon, J. R. Improved collagen extraction by modified longin method. Radiocarbon 30, 171–177 (1988).CAS 

    Google Scholar 
    120.Craig, O. E. et al. Stable isotope analysis of Late Upper Palaeolithic human and faunal remains from Grotta del Romito (Cosenza), Italy. J. Archaeol. Sci. 37, 2504–2512 (2010).
    Google Scholar 
    121.Kragten, J. Calculating standard deviations and confidence intervals with a universally applicable spreadsheet technique. Analyst 119, 2161–2166 (1994).ADS 
    CAS 

    Google Scholar 
    122.Good Practice Guide for Isotope Ratio Mass Spectrometry (FIRMS, 2018).123.Guiry, E. J. & Szpak, P. Improved quality control criteria for stable carbon and nitrogen isotope measurements of ancient bone collagen. J. Archaeol. Sci. 132, 105416 (2021).CAS 

    Google Scholar 
    124.Phillips, D. L. et al. Best practices for use of stable isotope mixing models in food-web studies. Can. J. Zool. 92, 823–835 (2014).
    Google Scholar 
    125.Fernandes, R., Grootes, P., Nadeau, M.-J. & Nehlich, O. Quantitative diet reconstruction of a Neolithic population using a Bayesian mixing model (FRUITS): The case study of Ostorf (Germany). Am. J. Phys. Anthropol. https://doi.org/10.1002/ajpa.22788 (2015).Article 
    PubMed 

    Google Scholar 
    126.Jim, S., Jones, V., Ambrose, S. H. & Evershed, R. P. Quantifying dietary macronutrient sources of carbon for bone collagen biosynthesis using natural abundance stable carbon isotope analysis. Br. J. Nutr. 95, 1055–1062 (2006).CAS 
    PubMed 

    Google Scholar 
    127.Colonese, A. C. et al. Stable isotope evidence for dietary diversification in the pre-Columbian Amazon. Sci. Rep. 10, 1–11 (2020).
    Google Scholar 
    128.Fernandes, R., Nadeau, M.-J. & Grootes, P. M. Macronutrient-based model for dietary carbon routing in bone collagen and bioapatite. Archaeol. Anthropol. Sci. 4, 291–301 (2012).
    Google Scholar 
    129.Galetti, M., Rodarte, R. R., Neves, C. L., Moreira, M. & Costa-Pereira, R. Trophic niche differentiation in rodents and marsupials revealed by stable isotopes. PLoS ONE 11, e0152494 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    130.Hellevang, H. & Aagaard, P. Constraints on natural global atmospheric CO2 fluxes from 1860 to 2010 using a simplified explicit forward model. Sci. Rep. 5, 17352 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    131.Fernandes, R. A Simple(R) model to predict the source of dietary carbon in individual consumers. Archaeometry 58, 500–512 (2016).CAS 

    Google Scholar 
    132.Fernandes, R., Millard, A. R., Brabec, M., Nadeau, M.-J. & Grootes, P. Food reconstruction using isotopic transferred signals (FRUITS): A Bayesian model for diet reconstruction. PLoS ONE 9, e87436 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    133.Wickham, H. ggplot2: Elegant graphics for data analysis (Springer, 2016).MATH 

    Google Scholar 
    134.Mcgill, R., Tukey, J. W. & Larsen, W. A. Variations of box plots. Am. Stat. 32, 12–16 (1978).
    Google Scholar 
    135.Kwak, S. G. & Kim, J. H. Central limit theorem: The cornerstone of modern statistics. Korean J. Anesthesiol. 70, 144–156 (2017).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Eco-evolutionary responses of the microbial loop to surface ocean warming and consequences for primary production

    1.Falkowski PG, Fenchel T, Delong EF. The microbial engines that drive Earth’s biogeochemical cycles. Science. 2008;320:1034–9.CAS 
    PubMed 

    Google Scholar 
    2.Riebesell U, Körtzinger A, Oschlies A. Sensitivities of marine carbon fluxes to ocean change. Proc Natl Acad Sci USA. 2009;106:20602–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Hutchins DA, Fu F. Microorganisms and ocean global change. Nat Microbiol. 2017;2:1–11.
    Google Scholar 
    4.Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Bopp L, Resplandy L, Orr JC, Doney SC, Dunne JP, Gehlen M, et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences. 2013;10:6225–45.
    Google Scholar 
    6.Oschlies A, Brandt P, Stramma L, Schmidtko S. Drivers and mechanisms of ocean deoxygenation. Nat Geosci. 2018;11:467–73.CAS 

    Google Scholar 
    7.Cazenave A, Llovel W. Contemporary sea level rise. Ann Rev Mar Sci. 2010;2:145–73.PubMed 

    Google Scholar 
    8.Frölicher TL, Ramseyer L, Raible CC, Rodgers KB, Dunne J. Potential predictability of marine ecosystem drivers. Biogeosciences. 2020;17:2061–83.
    Google Scholar 
    9.Taucher J, Oschlies A. Can we predict the direction of marine primary production change under global warming? Geophys Res Lett. 2011;38:L02603.10.Laufkötter C, Vogt M, Gruber N, Aita-Noguchi M, Aumont O, Bopp L, et al. Drivers and uncertainties of future global marine primary production in marine ecosystem models. Biogeosciences. 2015;12:6955–84.
    Google Scholar 
    11.Azam F, Fenchel T, Field JG, Gray J, Meyer-Reil L, Thingstad F. The ecological role of water-column microbes in the sea. Mar Ecol Prog Ser. 1983:257–63.12.Fenchel T. The microbial loop–25 years later. J Exp Mar Biol Ecol. 2008;366:99–103.
    Google Scholar 
    13.Kirchman DL, Morán XAG, Ducklow H. Microbial growth in the polar oceans—role of temperature and potential impact of climate change. Nat Rev Microbiol. 2009;7:451–9.CAS 
    PubMed 

    Google Scholar 
    14.Aumont O, Éthé C, Tagliabue A, Bopp L, Gehlen M. PISCES-v2: An ocean biogeochemical model for carbon and ecosystem studies. Geosci Model Dev Discuss. 2015;8:2465–513.15.Vichi M, Masina S. Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980–2000. Biogeosciences. 2009;6:2333–53.CAS 

    Google Scholar 
    16.Hasumi H, Nagata T. Modeling the global cycle of marine dissolved organic matter and its influence on marine productivity. Ecol Model. 2014;288:9–24.CAS 

    Google Scholar 
    17.Laufkötter C, Vogt M, Gruber N, Aumont O, Bopp L, Doney SC, et al. Projected decreases in future marine export production: the role of the carbon flux through the upper ocean ecosystem. Biogeosciences. 2016;13:4023–47.
    Google Scholar 
    18.Monroe JG, Markman DW, Beck WS, Felton AJ, Vahsen ML, Pressler Y. Ecoevolutionary dynamics of carbon cycling in the anthropocene. Trends Ecol Evol. 2018;33:213–25.PubMed 

    Google Scholar 
    19.Bennett AF, Dao KM, Lenski RE. Rapid evolution in response to high-temperature selection. Nature. 1990;346:79–81.CAS 
    PubMed 

    Google Scholar 
    20.Garud NR, Good BH, Hallatschek O, Pollard KS. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biol. 2019;17:e3000102.PubMed 
    PubMed Central 

    Google Scholar 
    21.Zhao S, Lieberman TD, Poyet M, Kauffman KM, Gibbons SM, Groussin M, et al. Adaptive evolution within gut microbiomes of healthy people. Cell Host Microbe. 2019;25:656–67.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Pomeroy LR, Williams PJleB, Azam F, Hobbie JE. The microbial loop. J Oceanogr. 2007;20:28–33.
    Google Scholar 
    23.Walworth NG, Zakem EJ, Dunne JP, Collins S, Levine NM. Microbial evolutionary strategies in a dynamic ocean. Proc Natl Acad Sci USA. 2020;117:5943–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Malik AA, Martiny JB, Brodie EL, Martiny AC, Treseder KK, Allison SD. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 2020;14:1–9.CAS 
    PubMed 

    Google Scholar 
    25.Saifuddin M, Bhatnagar JM, Segrè D, Finzi AC. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat Commun. 2019;10:1–10.CAS 

    Google Scholar 
    26.Muscarella ME, Howey XM, Lennon JT. Trait‐based approach to bacterial growth efficiency. Environ Microbiol. 2020;22:3494–3504.CAS 
    PubMed 

    Google Scholar 
    27.Roller BR, Stoddard SF, Schmidt TM. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat Microbiol. 2016;1:1–7.
    Google Scholar 
    28.Sarmiento JL, Gruber N. Ocean biogeochemical dynamics. Princeton University Press, 2006.29.Bendtsen J, Lundsgaard C, Middelboe M, Archer D. Influence of bacterial uptake on deep-ocean dissolved organic carbon. Glob Biogeocehm Cycles. 2002;16:74–1.
    Google Scholar 
    30.Chen B, Landry MR, Huang B, Liu H. Does warming enhance the effect of microzooplankton grazing on marine phytoplankton in the ocean? Limnol Oceanogr. 2012;57:519–26.CAS 

    Google Scholar 
    31.Krause S, Le Roux X, Niklaus PA, Van Bodegom PM, Lennon JT, Bertilsson S, et al. Trait-based approaches for understanding microbial biodiversity and ecosystem functioning. Front Microbiol. 2014;5:251.PubMed 
    PubMed Central 

    Google Scholar 
    32.Kiørboe T, Visser A, Andersen KH. A trait-based approach to ocean ecology. ICES Int J Mar Sci. 2018;75:1849–63.
    Google Scholar 
    33.Grime JP. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am Nat. 1977;111:1169–94.
    Google Scholar 
    34.Polz MF, Cordero OX. Bacterial evolution: genomics of metabolic trade-offs. Nat Microbiol. 2016;1:1–2.
    Google Scholar 
    35.Carlson CA, Del Giorgio PA, Herndl GJ. Microbes and the dissipation of energy and respiration: from cells to ecosystems. J Oceanogr. 2007;20:89–100.
    Google Scholar 
    36.Arnosti C. Patterns of microbially driven carbon cycling in the ocean: links between extracellular enzymes and microbial communities. Adv Oceanogr. 2014;2014:706082.37.Pfeiffer T, Schuster S, Bonhoeffer S. Cooperation and competition in the evolution of ATP-producing pathways. Science. 2001;292:504–7.CAS 
    PubMed 

    Google Scholar 
    38.Button D. Biochemical basis for whole-cell uptake kinetics: specific affinity, oligotrophic capacity, and the meaning of the Michaelis constant. Appl Environ Microbiol. 1991;57:2033–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Metz JA, Nisbet RM, Geritz SA. How should we define ‘fitness’ for general ecological scenarios? Trends Ecol Evol. 1992;7:198–202.CAS 
    PubMed 

    Google Scholar 
    40.Geritz SA, Metz JA, Kisdi E, Meszéna G. Dynamics of adaptation and evolutionary´ branching. Phys Rev Lett. 1997;78:2024.CAS 

    Google Scholar 
    41.Abs E, Ferrière R. Modeling microbial dynamics and heterotrophic soil respiration: effect of climate change. Biogeochemical cycles: ecological drivers and environmental impact. 2020:103–29.42.Lipson DA. The complex relationship between microbial growth rate and yield and its implications for ecosystem processes. Front Microbiol. 2015;6:615.PubMed 
    PubMed Central 

    Google Scholar 
    43.Hansell DA, Carlson CA. Biogeochemistry of marine dissolved organic matter. Academic Press, 2014.44.Urban MC, De Meester L, Vellend M, Stoks R, Vanoverbeke J. A crucial step toward realism: responses to climate change from an evolving metacommunity perspective. Evol Appl. 2012;5:154–67.PubMed 

    Google Scholar 
    45.Norberg J, Urban MC, Vellend M, Klausmeier CA, Loeuille N. Eco-evolutionary responses of biodiversity to climate change. Nat Clim Change. 2012;2:747–51.
    Google Scholar 
    46.Sarmento H, Montoya JM, Vázquez-Domínguez E, Vaqué D, Gasol JM. Warming effects on marine microbial food web processes: how far can we go when it comes to predictions? Philos Trans R Soc Long B Biol Sci. 2010;365:2137–49.
    Google Scholar 
    47.Walther S, Voigt M, Thum T, Gonsamo A, Zhang Y, Köhler P, et al. Satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests. Glob Change Biol. 2016;22:2979–96.
    Google Scholar 
    48.Williams RG, Follows MJ. Ocean dynamics and the carbon cycle: Principles and mechanisms. Cambridge University Press, 2011.49.Lewis K, Van Dijken G, Arrigo KR. Changes in phytoplankton concentration now drive increased Arctic Ocean primary production. Science. 2020;369:198–202.CAS 
    PubMed 

    Google Scholar 
    50.Ward B, Collins S, Dutkiewicz S, Gibbs S, Bown P, Ridgwell A, et al. Considering the role of adaptive evolution in models of the ocean and climate system. J Adv Model Earth Syst. 2019;11:3343–61.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Vázquez-Domínguez E, Vaque D, Gasol JM. Ocean warming enhances respiration and carbon demand of coastal microbial plankton. Glob Change Biol. 2007;13:1327–34.
    Google Scholar 
    52.López-Urrutia A, Morán XAG. Resource limitation of bacterial production distorts´ the temperature dependence of oceanic carbon cycling. Ecology. 2007;88:817–22.PubMed 

    Google Scholar 
    53.Parker GA, Smith JM. Optimality theory in evolutionary biology. Nature. 1990;348:27–33.
    Google Scholar 
    54.Hammerstein P. Darwinian adaptation, population genetics and the streetcar theory of evolution. J Math Biol. 1996;34:511–32.CAS 
    PubMed 

    Google Scholar 
    55.Eshel I, Feldman MW, Bergman A. Long-term evolution, short-term evolution, and population genetic theory. J Theor Biol. 1998;191:391–6.
    Google Scholar 
    56.Hagerty SB, Allison SD, Schimel JP. Evaluating soil microbial carbon use efficiency explicitly as a function of cellular processes: implications for measurements and models. Biogeochemistry. 2018;140:269–83.CAS 

    Google Scholar 
    57.Segre D, Vitkup D, Church GM. Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci USA. 2002;99:15112–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Marx CJ. Can you sequence ecology? Metagenomics of adaptive diversification. PLoS Biol. 2013;11:e1001487.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.O’Brien S, Hodgson DJ, Buckling A. The interplay between microevolution and community structure in microbial populations. Curr Opin Biotechnol. 2013;24:821–5.PubMed 

    Google Scholar 
    60.Scheuerl T, Hopkins M, Nowell RW, Rivett DW, Barraclough TG, Bell T. Bacterial adaptation is constrained in complex communities. Nat Commun. 2020;11:1–8.
    Google Scholar 
    61.Schloissnig S, Arumugam M, Sunagawa S, Mitreva M, Tap J, Zhu A, et al. Genomic variation landscape of the human gut microbiome. Nature. 2013;493:45–50.PubMed 

    Google Scholar 
    62.Boyd JA, Woodcroft BJ, Tyson GW. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res. 2018;46:e59–9.PubMed 
    PubMed Central 

    Google Scholar 
    63.Gregory AC, Gerhardt K, Zhong ZP, Bolduc B, Temperton B, Konstantinidis KT, et al. MetaPop: a pipeline for macro-and micro-diversity analyses and visualization of microbial and viral metagenome-derived populations. bioRxiv 2020. https://doi.org/10.1101/2020.11.01.363960.64.Coles VJ, Stukel MR, Brooks MT, Burd A, Crump BC, Moran MA, et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science. 2017;358:1149–1154.CAS 
    PubMed 

    Google Scholar 
    65.Scheinin M, Riebesell U, Rynearson TA, Lohbeck KT, Collins S. Experimental evolution gone wild. J R Soc Interface. 2015;12:20150056.PubMed 
    PubMed Central 

    Google Scholar 
    66.Thomas MK, Kremer CT, Klausmeier CA, Litchman E. A global pattern of thermal adaptation in marine phytoplankton. Science. 2012;338:1085–8.CAS 
    PubMed 

    Google Scholar 
    67.Grimaud GM, Le Guennec V, Ayata SD, Mairet F, Sciandra A, Bernard O. Modelling the effect of temperature on phytoplankton growth across the global ocean. IFACPapersOnLine. 2015;48:228–33.
    Google Scholar 
    68.Sauterey B, Ward B, Rault J, Bowler C, Claessen D. The implications of ecoevolutionary processes for the emergence of marine plankton community biogeography. Am Nat. 2017;190:116–30.PubMed 

    Google Scholar 
    69.Beckmann A, Schaum CE, Hense I. Phytoplankton adaptation in ecosystem models. J Theor Biol. 2019;468:60–71.PubMed 

    Google Scholar 
    70.Wilhelm SW, Suttle CA. Viruses and nutrient cycles in the sea: viruses play critical roles in the structure and function of aquatic food webs. Bioscience. 1999;49:781–8.
    Google Scholar 
    71.Danovaro R, Corinaldesi C, Dell’Anno A, Fuhrman JA, Middelburg JJ, Noble RT, et al. Marine viruses and global climate change. FEMS Microbiol Rev. 2011;35:993–1034.CAS 
    PubMed 

    Google Scholar 
    72.Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.CAS 
    PubMed 

    Google Scholar 
    73.Weitz JS, Stock CA, Wilhelm SW, Bourouiba L, Coleman ML, Buchan A, et al. A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes. ISME J. 2015;9:1352–64.PubMed 
    PubMed Central 

    Google Scholar 
    74.Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro-and microdiversity from pole to pole. Cell. 2019;177:1109–23.CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Evidence of unidirectional gene flow in a fragmented population of Salmo trutta L.

    1.Klemetsen, A. et al. Atlantic salmon Salmo salar L., brown trout Salmo trutta L. and Arctic charr Salvelinus alpinus (L.): A review of aspects of their life histories. Ecol. Freshw. Fish. 12, 1–59. https://doi.org/10.1034/j.1600-0633.2003.00010.x (2003).Article 

    Google Scholar 
    2.Elliott, J. M. Quantitative Ecology and the Brown Trout (Oxford University Press, 1994).
    Google Scholar 
    3.ICES. Baltic Salmon and Trout Assessment Working Group (WGBAST). ICES Sci. Rep. 2(22), 261. https://doi.org/10.17895/ices.pub.5974 (2020).Article 

    Google Scholar 
    4.Berrebi, P., Horvath, Á., Splendiani, A., Palm, S. & Bernaś, R. Genetic diversity of domestic brown trout stocks in Europe. Aquaculture 544, 737043. https://doi.org/10.1016/j.aquaculture.2021.737043 (2021).CAS 
    Article 

    Google Scholar 
    5.Jonsson, B. & Jonsson, N. Partial migration: Niche shift versus sexual maturation in fishes. Rev. Fish Biol. Fish. 3, 348–365. https://doi.org/10.1007/BF00043384 (1993).Article 

    Google Scholar 
    6.Jonsson, B. Diadromous and resident Trout Salmo Trutta: Is their difference due to genetics?. Oikos 38, 297–300. https://doi.org/10.2307/3544668 (1982).Article 

    Google Scholar 
    7.Olsson, I. C., Greenberg, L. A., Bergman, E. & Wysujack, K. Environmentally induced migration: The importance of food. Ecol. Lett. 9, 45–51. https://doi.org/10.1111/j.1461-0248.2006.00909.x (2006).Article 

    Google Scholar 
    8.Wysujack, K., Greenberg, L. A., Bergman, E. & Olsson, I. C. The role of the environment in partial migration: Food availability affects the adoption of a migratory tactic in brown trout Salmo trutta. Ecol. Freshw. Fish. 18, 52–59. https://doi.org/10.1111/j.1600-0633.2008.00322.x (2009).Article 

    Google Scholar 
    9.Charles, K., Roussel, J. M. & Cunjak, R. A. Estimating the contribution of sympatric anadromous and freshwater resident brown trout to juvenile production. Mar. Freshw. Res. 55, 185–191. https://doi.org/10.1071/MF03173 (2004).CAS 
    Article 

    Google Scholar 
    10.Youngson, A. F., Mitchell, A. I., Noack, P. T. & Laird, L. M. Carotenoid pigment profiles distinguish anadromous and nonanadromous brown trout (Salmo trutta). Can. J. Fish. Aquat. Sci. 54, 1064–1066. https://doi.org/10.1139/f97-023 (1997).CAS 
    Article 

    Google Scholar 
    11.Eek, D. & Bohlin, T. Strontium in scales verifies that sympatric sea-run and stream-resident brown trout can be distinguished by coloration. J. Fish Biol. 51, 659–661. https://doi.org/10.1111/j.1095-8649.1997.tb01522.x (1997).Article 

    Google Scholar 
    12.Veinott, G., Northcote, T., Rosenau, M. & Evans, R. D. Concentrations of strontium in the pectoral fin rays of the white sturgeon (Acipenser transmontanus) by laser ablation sampling—inductively coupled plasma—mass spectrometry as an indicator of marine migrations. Can. J. Fish. Aquat. Sci. 56, 1981–1990. https://doi.org/10.1139/f99-120 (1999).CAS 
    Article 

    Google Scholar 
    13.Jardine, T. D., Cartwright, D. F., Dietrich, J. P. & Cunjak, R. A. Resource use by salmonids in riverine, lacustrine and marine environments: Evidence from stable isotope analysis. Environ. Biol. Fishes. 73, 309–319. https://doi.org/10.1007/s10641-005-2259-8 (2005).Article 

    Google Scholar 
    14.Jones, A. G. & Ardren, W. R. Methods of parentage analysis in natural populations. Mol. Ecol. 12, 2511–2523. https://doi.org/10.1046/j.1365-294X.2003.01928.x (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Goodwin, J. C. A., King, R. A., Jones, J. I., Ibbotson, A. & Stevens, J. R. A small number of anadromous females drive reproduction in a brown trout (Salmo trutta) population in an English chalk stream. Freshw. Biol. 61, 1075–1089. https://doi.org/10.1111/fwb.12768 (2016).Article 

    Google Scholar 
    16.Charles, K., Guyomard, R., Hoyheim, B., Ombredane, D. & Baglinière, J.-L. Lack of genetic differentiation between anadromous and resident sympatric brown trout (Salmo trutta) in a Normandy population. Aquat. Living Resour. 18, 65–69. https://doi.org/10.1051/alr:2005006 (2005).CAS 
    Article 

    Google Scholar 
    17.Charles, K., Roussel, J.-M., Lebel, J.-M., Bagliniere, J.-L. & Ombredane, D. Genetic differentiation between anadromous and freshwater resident brown trout (Salmo trutta L.): Insights obtained from stable isotope analysis. Ecol. Freshw. Fish. 15, 255–263. https://doi.org/10.1111/j.1600-0633.2006.00149.x (2006).Article 

    Google Scholar 
    18.Jarry, M. et al. Sea trout (Salmo trutta L.) growth patterns during early steps of invasion in the Kerguelen Islands. Polar Biol. 41, 925–934. https://doi.org/10.1007/s00300-018-2253-1 (2018).Article 

    Google Scholar 
    19.Brauer, C. J. & Beheregaray, L. B. Recent and rapid anthropogenic habitat fragmentation increases extinction risk for freshwater biodiversity. Evol. Appl. 13, 2857–2869. https://doi.org/10.1111/eva.13128 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Griffiths, A. M., Koizumi, I., Bright, D. & Stevens, J. R. A case of isolation by distance and shortterm temporal stability of population structure in brown trout (Salmo trutta) within the River Dart, southwest England. Evol. Appl. 2, 537–554. https://doi.org/10.1111/j.1752-4571.2009.00092.x (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.HELCOM. Sea Trout and Salmon Populations and Rivers in Poland—HELCOM Assessment of Salmon (Salmo salar) and Sea Trout (Salmo trutta) Populations and Habitats in Rivers Flowing to the Baltic Sea. Balt. Sea Environ. Proc. No. 126B. 2011.22.Dębowski, P. Fish assemblages in the Parsęta River drainage basin. Pol. Arch. Hydrobiol. 46, 161–172 (1999).
    Google Scholar 
    23.Kuligowski, D. R., Ford, M. J. & Berejikian, B. A. Breeding structure of steelhead inferred from patterns of genetic relatedness among nests. Trans. Am. Fish. Soc. 134, 1202–2121. https://doi.org/10.1577/T04-187.1 (2005).Article 

    Google Scholar 
    24.Dauphin, G., Prévost, E., Adams, C. E. & Boylan, P. Using redd counts to estimate salmonids spawner abundances: A Bayesian modelling approach. Fish. Res. 106, 32–40. https://doi.org/10.1016/j.fishres.2010.06.014 (2010).Article 

    Google Scholar 
    25.Cairney, M., Taggart, J. B. & Hoyheim, B. Characterization of microsatellite and minisatellite loci in Atlantic salmon (Salmo salar L.) and cross-species amplification in other salmonids. Mol. Ecol. 9, 2175–2178. https://doi.org/10.1046/j.1365-294X.2000.105312.x (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    26.Estoup, A., Presa, P., Krieg, F., Vaiman, D. & Guyomard, R. (CT)n and (GT)n microsatellites: A new class of genetic markers for Salmo trutta L. brown trout. Heredity 71, 488–496. https://doi.org/10.1038/hdy.1993.167 (1993).CAS 
    Article 
    PubMed 

    Google Scholar 
    27.O’Reilly, P. T., Hamilton, L. C., McConnell, S. K. & Wright, J. M. Rapid analysis of genetic variation in Atlantic salmon (Salmo salar) by PCR multiplexing of dinucleotide and tetranucleotide microsatellites. Can. J. Fish. Aquat. Sci. 53, 2292–2298. https://doi.org/10.1139/f96-192 (1996).Article 

    Google Scholar 
    28.Poteaux, C., Bonhomme, F. & Berrebi, P. Microsatellite polymorphism and genetic impact of restocking in Mediterranean brown trout (Salmo trutta L.). Heredity 82, 645–653. https://doi.org/10.1046/j.1365-2540.1999.00519.x (1999).Article 
    PubMed 

    Google Scholar 
    29.Presa, P. & Guyomard, R. Conservation of microsatellites in three species of salmonids. J. Fish Biol. 49, 1326–1329. https://doi.org/10.1111/j.1095-8649.1996.tb01800.x (1996).Article 

    Google Scholar 
    30.Scribner, K. T., Gust, J. R. & Fields, R. L. Isolation and characterization of novel salmon microsatellite loci: Cross species amplification and population genetics applications. Can. J. Fish. Aquat. Sci. 53, 833–841. https://doi.org/10.1139/cjfas-53-4-833 (1996).CAS 
    Article 

    Google Scholar 
    31.Slettan, A., Olsaker, I. & Lie, O. Atlantic salmon, Salmo salar, microsatellites at the SSOSL25, SSOSL85, SSOSL311, SSOSL417 loci. Anim. Genet. 26, 281–282. https://doi.org/10.1111/j.1365-2052.1995.tb03262.x (1995).CAS 
    Article 
    PubMed 

    Google Scholar 
    32.Slettan, A., Olsaker, I. & Lie, O. Polymorphic Atlantic salmon, Salmo salar L., microsatellites at the SSOSL438, SSOSL429 and SSOSL444 loci. Anim. Genet. 27, 57–58 (1996).CAS 
    Article 

    Google Scholar 
    33.Linløkken, A. N., Haugen, T. O., Kent, M. P. & Lien, S. Genetic differences between wild and hatchery-bred brown trout (Salmo trutta L.) in single nucleotide polymorphisms linked to selective traits. Ecol. Evol. 7, 4963–4972. https://doi.org/10.1002/ece3.3070 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Bernaś, R. et al. Genetic differentiation in hatchery and stocked populations of sea trout in the Southern Baltic: Selection evidence at SNP loci. Genes 11, 184. https://doi.org/10.3390/genes11020184 (2020).CAS 
    Article 
    PubMed Central 

    Google Scholar 
    35.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 35: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Article 
    PubMed 

    Google Scholar 
    36.Peakall, R. & Smouse, P. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539. https://doi.org/10.1093/bioinformatics/bts460 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Kalinowski, S. T. hp-rare 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol. Ecol. Notes 5, 187–189. https://doi.org/10.1111/j.1471-8286.2004.00845.x (2005).CAS 
    Article 

    Google Scholar 
    38.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    Article 

    Google Scholar 
    39.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    40.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191. https://doi.org/10.1111/1755-0998.12387 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223–225. https://doi.org/10.1111/j.1558-5646.1989.tb04220.x (1989).Article 
    PubMed 

    Google Scholar 
    42.Bernaś, R., Burzyński, A., Dębowski, P., Poćwierz-Kotus, A. & Wenne, R. Genetic diversity within sea trout population from an intensively stocked southern Baltic river, based on microsatellite DNA analysis. Fish. Manage. Ecol. 21, 398–409. https://doi.org/10.1111/fme.12090 (2014).Article 

    Google Scholar 
    43.Bernaś, R. & Wąs-Barcz, A. Genetic structure of important resident brown trout breeding lines in Poland. J. Appl. Genet. 61, 239–247. https://doi.org/10.1007/s13353-020-00548-6 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Ostergren, J. & Nilsson, J. Importance of life-history and landscape characteristics for genetic structure and genetic diversity of brown trout (Salmo trutta L.). Ecol. Freshw. Fish. 21, 119–133 (2012).Article 

    Google Scholar 
    45.Lehtonen, P. K., Tonteri, A., Sendek, D., Titov, S. & Primmer, C. R. Spatio-temporal genetic structuring of brown trout (Salmo trutta L.) populations within the River Luga, northwest Russia. Conserv. Genet. 10, 281–289. https://doi.org/10.1007/s10592-008-9577-2 (2009).Article 

    Google Scholar 
    46.Cross, T. F., Mills, C. P. R. & de CourcyWilliams, M. An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland. J. Fish Biol. 40, 25–32. https://doi.org/10.1111/j.1095-8649.1992.tb02550.x (1992).CAS 
    Article 

    Google Scholar 
    47.Stelkens, R., Jaffuel, G., Escher, M. & Wedekind, C. Genetic and phenotypic population divergence on a microgeographic scale in brown trout. Mol. Ecol. 21, 2896–2915. https://doi.org/10.1111/j.1365-294X.2012.05581.x (2012).Article 
    PubMed 

    Google Scholar 
    48.Hansen, M. M., Limborg, M. T., Ferchaud, A.-L. & Pujolar, J.-M. The effects of Medieval dams on genetic divergence and demographic history in brown trout populations. BMC Evol. Biol. 14, 122. https://doi.org/10.1186/1471-2148-14-122 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Kohlmann, K. & Wüstemann, O. Tracing the genetic origin of brown trout (Salmo trutta) re-colonizing the Ecker reservoir in the Harz National Park, Germany. Environ. Biotechnol. 8, 39–44 (2012).
    Google Scholar 
    50.Dellefors, C. & Faremo, U. Early sexual maturation in males of wild sea trout, Salmo trutta L. inhibits smoltification. J. Fish Biol. 33, 741–749. https://doi.org/10.1111/j.1095-8649.1988.tb05519.x (1988).Article 

    Google Scholar 
    51.Jonsson, B. & Jonsson, N. Differences in growth between offspring of anadromous and freshwater brown trout Salmo trutta. J. Fish Biol. 20, 1–7. https://doi.org/10.1111/jfb.14693 (2021).Article 

    Google Scholar  More