Ecology

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

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

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

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

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

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

Our novel approach to bioacoustic source separation involves an end-to-end pipeline consisting of multiple discrete steps, including (1) synthesizing a dataset, (2) developing and training a separator network to disentangle the input mixture, and (3) constructing and training a classifier model to employ as a downstream evaluation task. This workflow requires few hyperparameter modifications to account for unique vocal behavior across different biological taxa but is otherwise general and makes no species-level assumptions about the spectrotemporal structure of the source calls. We develop a complete framework for bioacoustic source separation in a permutation-invariant mode using overlapping waveforms drawn from the same class of signals. We apply BioCPPNet to macaques, dolphins, and Egyptian fruit bats, and we consider two or three concurrent “speakers”. Note that we henceforth refer to non-human animal signalers as “speakers” for consistency with the human speech separation literature2. We address both the closed speaker regime in which the training and evaluation data subsets contain calls produced by individuals drawn from the same distribution as well the open speaker regime in which the model is tested on calls generated by individuals not present in the training dataset.Bioacoustic dataWe investigate a set of species with dissimilar vocal behaviors in terms of spectral and temporal properties. We apply BioCPPNet to a macaque coo call dataset47 consisting of 7285 coos produced by 8 unique individuals; a bottlenose dolphin signature whistle dataset26 comprised of 400 signature whistles generated by 20 individuals, of which we randomly select 8 for the purposes of this study; and an Egyptian fruit bat vocalization dataset48 containing a heterogeneous distribution of individuals, call types, and call contexts. In the case of the bat dataset, we extract the data (31399 calls) corresponding to the 15 most heavily represented individual bats, reserving 12 individuals (27586 calls) to address the closed speaker regime and the remaining 3 individuals (3813 calls) to evaluate model performance in the open speaker scenario.DatasetsThe mixture dataset is generated from a species-specific corpus of bioacoustic recordings containing signals annotated according to the known identity of the signaller. Motivated by WSJ0-2mix2, a preeminent reference dataset used for human single-channel acoustic source separation, we adopt a similar approach of constructing bioacoustic datasets by temporally overlapping and summing ground truth individual-specific signals to enable supervised training of our model. For macaques and dolphins, the mixture waveforms contain discrete source calls that overlap in the time domain, by design. For bats, mixtures are constructed by adding signal streams, each of which may exhibit one or more temporally separated sequential vocal elements. In all cases, the mixtures operate under the assumption that, without loss of generality, the constituent sources vary in the degree of spectral overlap due to differential spectrotemporal properties of sources, in accordance with the DUET principle (i.e, the mixtures contain approximately disjoint sources that rarely coincide in dominant frequency)19. The resultant dataset consists of an input array of the composite mixture waveforms, a target array containing the separated ground truth waveforms corresponding to the respective mixtures, and a class label array denoting the identities of the vocalizing animals responsible for generating the signals. In the case of macaques, we here consider closed speaker set mixtures of two and three simultaneous speakers, but our method is functionally not limited in the number of sources (N) it can handle. For dolphins, we consider the closed speaker regime with two overlapping calls, and for bats, we consider the closed and open speaker scenarios with two sources.We first extract the labeled waveforms either by truncating or zero-padding the waveforms to ensure that all the samples are of fixed duration. We select the number of frames either by computing the mean plus three-sigma of the durations of the calls contained in the corpus from which we draw the signals, by selecting the maximum duration of all calls, or by choosing a fixed value. For macaques, dolphins, and bats, we use 23156 frames (0.95s), 290680 frames (3.03s), and 250000 frames (1.0s), respectively. We then randomly select vocalizations from N different speakers drawn from the distribution of individuals used in the study (8 macaques, 8 dolphins, 12 bats for the closed speaker regime, 3 bats for the open speaker regime) and mix them additively, ensuring to randomly shift the overlaps to simulate a more plausible scenario and to provide for asynchronicity of start times, an important acoustic cue that has been suggested as a mechanism with which the animal brain can solve the CPP1. Despite higher computational and memory costs, we opt to use native sampling rates, since certain animal vocalizations may reach frequencies near the native Nyquist frequency. With this in mind, however, our method does provide for resampling when the vocalizations of the particular species of interest are amenable to downsampling. Explicitly, for the three species we consider including macaques, dolphins, and bats, we use sampling rates of 24414 Hz, 96 kHz, and 250 kHz, respectively. For the closed speaker regime, the training and evaluation subsets contain calls produced by the same distribution of individuals to ensure a closed speaker set. We segment the original nonoverlapping vocalizations into 80/20 training/validation subsets. We generate the mixture training waveforms using 80% of the calls, and we construct the mixture validation subset using the remaining 20% of calls held out from the training data. In the case of overlapping bat calls (for which the corpus of bioacoustic recordings contains (mathscr {O}(10text { hours})) of data as opposed to (mathscr {O}(10^{-1}text { hours})) for macaques and dolphins), we also address the open speaker source separation problem by constructing a further testing data subset of mixtures of calls of additional vocalizers not contained in the training distribution. For macaques, we construct a training data subset comprised of 12k samples and a validation subset with 3k samples, all of which contain calls drawn from 8 animals. For dolphins, we randomly select 8 individuals and construct training/validations subsets with 8k and 2k samples, respectively. For bats, we select 15 individuals, randomly reserving 12 for the closed speaker problem and the remaining 3 for the open speaker situation. We train the bat separator model on 24k mixtures. We evaluate performance in both the closed and open speaker scenarios using data subsets consisting of 6k mixtures containing unseen vocalizations produced by the appropriate distribution of individuals according to the regime under consideration. We repeat the bat training using a larger mixture dataset (denoted by +) containing 72k samples. We here report validation metrics to ensure that we are evaluating model performance on unseen mixtures of unseen calls in the closed speaker regime and on unseen mixtures of unseen calls of unseen individuals in the open speaker regime.For the downstream classification task, we extract vocalizations annotated according to the individual identity, and we segment the calls into an 80/20 training/testing split to ensure that we are evaluating model performance on unseen calls. For both the training and evaluation data subsets, we employ an augmentation scheme in which we apply random temporal shifts to call onsets to better reflect more plausible real-world scenarios.Classification modelsIn an effort to provide a more physically interpretable evaluation metric to supplement the commonly-implemented SI-SDR used in human speech separation studies, we develop CNN-based classifier models to label the individual identity of the separated vocalizations as a downstream task. This requires training classification networks to predict the speaker class label of the original unmixed waveforms. For each species we consider, we design and train custom simple and lightweight CNN-based architectures largely motivated by previous work24, tailored to accommodate the unique vocal behavior of the given species.The first layer in the model is an optional high pass filter constructed using a nontrainable 1D convolution (Conv1D) layer with frozen weights determined by a windowed sinc function49,50 to eliminate low-frequency background noise. We omit this computationally intensive layer for macaques and Egyptian fruit bats, but we implement a high pass filter for the dolphin dataset, selecting an arbitrary cutoff frequency of 4.7 kHz and transition bandwidth 0.08 to remove background without impinging on the region of support for dolphin whistles. After the optional filter is an encoder layer to compute on-the-fly feature extraction. We experimented with a fully learnable free Conv1D filterbank, a spectrogram, and a log-magnitude spectrogram and observed optimal performance using a non-decibel (dB)-scaled STFT layer computed with a nfft window width, a hop window shift, and a Hann window where nfft and hop are species-dependent variables. For macaques, we select nfft=1024 and hop=64 corresponding to temporal scales on the order of 40ms and frequency resolutions on the order of 20 Hz. We choose nfft=1024 and hop=256 for dolphins and nfft=2048 and hop=512 for bats, corresponding to temporal resolutions of ~ 10 ms and ~ 8 ms and frequency resolutions of ~ 90 Hz and ~ 120 Hz, respectively.Following the built-in feature engineering, the architecture includes 4 convolutional blocks, which consist of two sequential 2D convolution (Conv2D) layers with leaky ReLU activation and a max pooling layer with pool size 4. Next is a dense fully connected layer with leaky ReLU activation followed by another linear layer with log softmax activation to output the V log probabilities (i.e. confidences) where V is the number of individual vocalizers used in the study (8, 8, 12 for macaques, dolphins, and bats, respectively). We also include dropout regularization with p=0.25 for the macaque classifier and p=0.5 for the dolphin and bat classifiers to address potential overfitting. With these architectures, the macaque, dolphin, and bat classifier models have 230k, 279k, and 247k trainable parameters, respectively.For all species, we minimize the negative log-likelihood objective loss function using the Adam optimizer51 with learning rate lr = 3e−4. For macaques, dolphins, and bats, respectively, we train for 100, 50, and 100 epochs with batch sizes 32, 8, and 8. We serialize the model after each epoch and select the top-performing models. We opt not to carry out hyperparameter optimization since the classification task is of secondary importance and is used solely as a downstream task.Figure 1(a) Schematic overview of the BioCPPNet pipeline. Source vocalization waveforms are overlapped in time and mixed additively. BioCPPNet operates on the mixture waveform, yielding predictions for the separated waveforms, which are compared to the source ground truths, up to a permutation. The estimated waveforms are classified by the identity classification model24 (ID) to compute the downstream classification accuracy metric. (b) Block diagram of the BioCPPNet architecture. The input mixture waveform is transformed to a learnable or handcrafted representation (Rep), which then passes through a 2-dimensional U-Net52 composed of a contracting encoder path and an expanding decoder path with skip connnections. The encoder path consists of sequential downsampling convolutional blocks, each of which is constructed using two convolutional layers (Conv2D) with leaky ReLU activation and batch normalization (BatchNorm) followed by a max pooling. The decoder path employs upsampling convolutional blocks, consisting of an upsampling and skip connection concatenation followed again by the Conv2D layers with leaky ReLU and BatchNorm. The U-Net predicts masks (Mask 0 and Mask 1), the number of which is determined by the number of sources (N), that are multiplicatively applied to the original mixture representation. The predicted time-frequency representations of the separated waveforms are inverted with learnable or handcrafted inverse transforms (iRep) to output raw waveforms. All schematic diagrams were created using Affinity Designer (version 1.8.1) https://affinity.serif.com/en-us/designer/.Full size imageSeparation modelsBioCPPNet (Fig. 1) is a lightweight and modular architecture with a modifiable representation encoder, a 2D U-Net core, and an inverse transform decoder, which acts directly on raw audio via on-the-fly learnable or handcrafted transforms. The structure of the network is designed to provide for extensive experimentation, optimization, and enhancement across a range of species with variable vocal behavior. We construct and train a separation model for each species and each number N of sources contained in the input mixture.Figure 2Schematic diagram demonstrating the application of BioCPPNet to dolphin signature whistles using handcrafted STFT-based encoders and decoders. The source waveforms produced by N speakers of unique identity (e.g. T. truncatus 0 and T. truncatus 1) are overlapped in time, summed, and transformed to time-frequency space using an STFT layer, resulting in the mixture time-frequency representation (Mixture TFR). The U-Net predicts masks (Mask 0 and Mask 1) that are applied to the mixture representation. The separated spectrogram estimations (TFR 0 and TFR 1) are inverted using an iSTFT layer to yield the model’s predictions for the separated raw waveforms, which are compared to the ground truth waveforms and classified according to predicted identity using the classification model.Full size imageModel architectureAs with the classifier model, the network’s encoder consists of a feature engineering block, the initial layer of which is an optional high pass filter. This is followed by the representation transform, which includes several options including the Conv1D free encoder, the STFT filterbank, and the log-magnitude (dB) STFT filterbank. We choose the same kernel size (nfft) and stride (hop) parameters defined in the classifier model. Sequentially following the feature extraction encoder is a 2D U-Net core. This architecture consists of B (4 for macaques, 3 for dolphins, and 4 for bats) downsampling convolutional blocks, a middle convolutional block, and B upsampling convolutional blocks. The downsampling blocks consist of two 2D convolutional layers with filter number that increases with model depth with leaky ReLU activation followed by a max pooling with pool size 2, 6, and 3 for macaques, dolphins, and bats. The middle block contains two 2D convolutional layers with leaky ReLU activation. The upsampling blocks include an upsampling using the bilinear algorithm and a scale factor corresponding to the pool size used during downsampling, followed by skip connections in which the corresponding levels of the contracting and expanding paths are concatenated before passing through two 2D convolutional layers with leaky ReLU activation. All convolutional layers in the downsampling, middle, and upsampling blocks include batch normalization after the activation function to stabilize and expedite training and to promote regularization. Though our default implementation is phase-unaware, we also offer the option for a parallel U-Net pathway working directly on phase information, which has been shown to improve performance in other applications53,54,55. The final layer in the U-Net core is a 2D convolutional layer with N channels, which are then split prior to entering the inverse transform decoder. For the inverse transform, we again provide numerous choices including a free filterbank decoder based on a 1D convolutional transpose (ConvTranspose1D) layer, an iSTFT layer, an iSTFT layer accepting dB-scaled inputs, and a multi-head convolutional neural network (MCNN) for fast spectrogram inversion56. In detail, the U-Net returns N masks that are then multiplied by the original encoded representation of the mixture waveform. The separated representations are then passed into the inverse transform layer in order to yield the raw waveforms corresponding to the model’s predictions for the separated vocalizations. We initialize all trainable weights using the Xavier uniform initialization. In the case of macaques, we experiment across all combinations of representation encoders and inverse transform decoders, and we find optimal performance using the handcrafted non-dB STFT/iSTFT layers operating in the time-frequency domain. Since the model with the fully learnable Conv1D-based encoder/decoder uniquely operates in the time domain, we report evaluation metrics for this model, as well. For dolphins and bats, we here report metrics using exclusively the non-dB STFT/iSTFT technique.BioCPPNet (Fig. 1) is designed as a lightweight fully convolutional model in order to efficiently process large amounts of bioacoustic data sampled at high sampling rates while simultaneously minimizing computational costs and limitations and the likelihood of overfitting. For the macaque separators, the networks consist of 1.2M parameters (for the STFT, iSTFT combination), 2.5M parameters (for the STFT, iSTFT combination with parallel phase pathway), or 2.8M parameters (for the Conv1D free filterbanks). For the dolphin separator (Fig. 2), the model has 304k parameters, while the bat separator model has 1.2M parameters. This is to be contrasted with the comparatively heavyweight default implementations of models commonly used in human speech separation problems, such as Conv-TasNet3, which has 5.1M parameters; DPTNet4 with 2.7M parameters; or Wavesplit5 with 29M parameters. Regardless of the lower complexity of BioCPPNet, the model achieves comparable performance or even outperforms reference human speech separator models while still being lightweight enough to train on a single NVIDIA P100 GPU.Model training objectiveThe model training objective aims to optimize the reconstruction of separated waveforms from the aggregated composite input signal. We adopt a permutation-invariant training (PIT)57 scheme in which the model’s predicted outputs are compared with the ground truth sources by searching over the space of permutations of source orderings. This fundamental property of our training objective reflects that the order of estimations and their corresponding labels from a mixture waveform is not expressly germane to the task of acoustic source separation, i.e. separation is a set prediction problem independent of speaker identity ordering5.Source separation involves training a separator model f to reconstruct the source single-channel waveforms given a mixture (x=sum _{i=1}^N s^i) of N sources, where each source signal (s^i) for (i in [1, N]) is a real-valued continuous vector with fixed length T, i.e., (s^i in mathbb {R}^{1 times T}). The model outputs the predicted waveforms ({hat{s}^i}_{i=1}^N) where (forall i, hat{s}^i = f^i(x)), and a loss function is evaluated by comparing the predictions to the ground truth sources ({s^i}_{i=1}^N) up to a permutation. Explicitly, we consider a permutation-invariant objective function5,$$begin{aligned} mathscr {L}(hat{s}, s) = min _{sigma in S_N} frac{1}{N} sum _{i=1}^N ell (hat{s}^{sigma (i)}, s^i) qquad text {where} forall i, hat{s}^i = f^i(x) end{aligned}$$Here, (ell (cdot , cdot )) represents the loss function computed on an (output, target) pair, (sigma) indicates a permutation, and (S_N) is the space of permutations. In certain scenarios, we include the L2 regularization term,$$begin{aligned} mathscr {L} mapsto mathscr {L} + lambda sum _{j=1}^P beta _j^2 end{aligned}$$where (beta _j) represent the model parameters, P denotes the model complexity, and (lambda) is a hyperparameter empirically selected to minimize overfitting (i.e. enhance convergence of training and evaluation losses and metrics).For the single-channel loss function (ell), we consider a linear combination of several loss terms that compute the error in estimated waveform reconstructions ({hat{s}^i}_{i=1}^N) relative to the ground truth waveforms ({s^i}_{i=1}^N).

L1 Loss $$begin{aligned} |hat{s}^{sigma (i)} – s^{i}| end{aligned}$$ This represents the absolute error on raw time domain waveforms.

STFT L1 Loss $$begin{aligned} |text {STFT}(hat{s}^{sigma (i)}) – text {STFT}(s^{i})| end{aligned}$$ This term functions to minimize absolute error on time-frequency space representations. Empirically, the inclusion of this contribution enhances the reconstruction of signal harmonicity.

Spectral Convergence Loss $$begin{aligned} ||text {STFT}(hat{s}^{sigma (i)}) – text {STFT}(s^{i})||_F / ||text {STFT}(s^{i})||_F end{aligned}$$ where (||cdot ||_F) denotes the Frobenius norm over time and frequency. This term emphasizes high-magnitude spectral components56.

We also experimented with additional terms including L1 loss on log-magnitude spectrograms to address spectral valleys and negative SI-SDR (nSI-SDR), but the inclusion of these contributions did not yield empirical improvements in results.For macaques, we modify the training algorithm according to the representation transform and inverse transform built into the model. For the model with the fully learnable Conv1D encoder and decoder, we train using the AdamW58 optimizer with a learning rate 3e-4 and batch size 16 for 100 epochs. In order to stabilize training and avoid local minima when using handcrafted STFT and iSTFT filterbanks, we initially begin training the models for 3 epochs with batch size 16 using stochastic gradient descent (SGD) with Nesterov momentum 0.6 and learning rate 1e-3 before switching to the AdamW optimizer until reaching 100 epochs.For dolphins, we provide the model with the original mixture as input, but we use high pass-filtered source waveforms as the target, which means the separation model must additionally learn to denoise the input. We again initialize training with 3 epochs and batch size 8 using SGD with Nesterov momentum 0.6 and learning rate 1e-3 before switching to the AdamW optimizer with learning rate 3e-4 for the remaining 97 epochs. We use a similar training scheme for bats, initially training with SGD for 3 epochs before employing the optimizer switcher callback to switch to AdamW and to complete 100 epochs.Model evaluation metricsWe consider the reconstruction performance by computing evaluation metrics using an expression given by5,$$begin{aligned} mathscr {M}(hat{s}, s) = max _{sigma in S_N} frac{1}{N} sum _{i=1}^N m(hat{s}^{sigma (i)}, s^i) qquad text {where} forall i, hat{s}^i = f^i(x) end{aligned}$$where (m(cdot , cdot )) is the single-channel evaluation metric computed on permutations of (output, target) pairs.Specifically, we implement two evaluation metrics to assess reconstruction quality, including (1) SI-SDR and (2) downstream classification accuracy. We consider the signal-to-distortion ratio (SDR)2, defined as the negative log squared error normalized by reference signal energy5. However, as is commonly implemented in the human speech separation literature, we instead compute the scale-invariant SDR (SI-SDR), which disregards prediction scale by searching over gains5,40. Explicitly, SI-SDR((hat{s}, s) = -10log _{10}(|hat{s} – s|^2) + 10log _{10}(|alpha s|^2)) for optimal scaling factor (alpha = hat{s}^Ts / |s|^2).Additionally, to provide a physically interpretable metric, we evaluate the performance of the trained classifier models in labeling separated waveforms according to the predicted identity of the vocalizer. This metric assumes that the classification accuracy on a downstream task reflects the fidelity of the estimated signal relative to the ground truth source and thus serves as a proxy for reconstruction quality. More

Phylogenetic analysisFor both the mycobiont and photobiont molecular phylogenies from multi-locus sequence data (nrITS, mtSSU and RPB1 for the mycobiont (140 samples) and nrITS, psbJ-L and COX2 for the photobiont (139 samples) were inferred (Supplementary Figs. S1 and S3 online). Additionally, phylogenies based solely on the marker nrITS were calculated (Supplementary Figs. S2 and S4 online), to include samples where the additional markers were not available. Both analyses include only accessions from the study sites (Fig. 1; Table 1). The phylogenies based on the multi-locus data were congruent to the clades of the phylogenies based on the marker nrITS. Thus, in the following, the focus will be only on the latter.MycobiontThe final data matrix for the phylogeny based on the marker nrITS comprised 306 single sequences with a length of 550 bp. It included sequences of the families Lecanoraceae and Lecideaceae. The phylogenetic tree was midpoint rooted and shows a total of 19 strongly supported clades on species level, assigned to five genera. The backbone is not supported and therefore the topology will not be discussed. All genera are clearly assigned to their family level and are strongly supported. Only Lecanora physciella forms an extra clade as sister to the families Lecideaceae and Lecanoraeae, which is not the case at the multimarker phylogeny. L. physciella has still an uncertain status, because of morphological similarities to both sister families6. The clade of the genus Lecidea revealed seven species (L. andersonii, L. polypycnidophora, L. UCR1, L. sp. 5, L. lapicida, L. cancriformis and L. sp. 6), Lecanora five species (L. physciella, L. sp. 2, L. fuscobrunnea, L. cf. mons-nivis, L. sp. 3), Carbonea three species (C. sp. URm1, C. vorticosa, C. sp. 2), and Lecidella three species (L. greenii, L. siplei, L. sp. nov2). The samples allocated to the genus Rhizoplaca were monospecific (R. macleanii). The taxonomical assignment of the obtained sequences were based on the studies of Ruprecht et al.48 and Wagner et al.10.PhotobiontThe final data matrix for the phylogeny based on the marker nrITS comprised 281 single sequences with a length of 584 bp. The phylogenetic tree was midpoint rooted and shows six strongly supported clades, assigned to seven different OTU levels67, using the concept of Muggia et al.51 and Ruprecht et al.48. All of the OTUs belong to the genus Trebouxia (clades A, I, S), comprising Tr_A02, Tr_A04a, Tr_I01, Tr_I17, Tr_S02, Tr_S15 and Tr_S18. Photobiont sequences taken from Perez-Ortega et al.50, which were labelled only with numbers, were renamed to assign them to the appropriate OTUs48.Analysis of spatial distributionIn general, the most common mycobionts species were Lecidea cancriformis (94 of the 306 samples), Rhizoplaca macleanii (51 samples) and Lecidella greenii (37 samples), followed by Carbonea sp. 2 (13 samples), C. vorticosa (11 samples), Lecidea polypycnidophora (10 samples) and Lecidella siplei (10 samples; see Supplementary Fig. S5 online). Nine mycobiont species were found exclusively in area 5 (MDV, 78°S): Carbonea vorticosa, Lecanora cf. mons-nivis, L. sp. 2, Lecidea lapicida, L. polypycnidophora, L. sp. 5, L. sp. 6, L. UCR1 and Rhizoplaca macleanii. On the other hand, only Lecidea cancriformis was found in all the six areas; Lecanora fuscobrunnea was present in all the areas with the exception of area 2.The most common photobiont OTUs were Tr_A02 (165 of the 281 samples) and Tr_S02 (59 samples), both of them occurring in all the six different areas, followed by Tr_S18 (32 samples), Tr_S15 (10 samples, confined to area 5) and Tr_I01 (10 samples). However, of the 149 photobiont accessions of area 5, 134 (89.93%) were assigned to Tr_A02. This percentage is much higher than in the other areas (area 1: 44.44%, area 2: 69.23%, area 3: 21.74%, area 4a: 7.69%, area 4b: 6.67%), even if those samples with mycobionts occurring exclusively in area 5 (see above) were excluded (76.56% of the 64 remaining samples are assigned to Tr_A02).The alpha, beta and gamma diversity values are given in Table 2. For the mycobionts, the alpha diversity of the communities was the highest in area 5 (8.93, which results in nine species) and the lowest in area 4b (two species, 1.88). In contrast, for the photobionts, the lowest alpha diversity value was found in area 5 (two OTUs, 1.50) and the highest in area 4a (four OTUs, 4.06). Thus, referring to this, area 5 plays a remarkable role: compared to the other areas, it shows the highest diversity of mycobiont species on the one hand and the lowest diversity of photobiont OTUs on the other hand.Table 2 Number of lichen samples, number of identified mycobiont species and photobiont OTUs, as well as alpha, beta and gamma diversity values of mycobiont species/photobiont OTUs for the different areas.Full size tableThe beta diversity values (diversity of local assemblages) for mycobiont species and photobiont OTUs are quite similar (1.69 and 1.64, respectively). This is in contrast to gamma diversity values: the overall diversity for the different areas within the whole region is much higher for the mycobionts (ten species, 9.92) than for the photobionts (three OTUs, 3.35).For mycobionts, the overall sample coverage equals to 0.993. That means that the probability for an individual of the community to belong to a sampled species is 99.3%, or, from another point of view, the probability for an individual of the whole community to belong to a species that has not been sampled is 0.7%. The sample coverage is highest for area 4b (1.000) and lowest for area 2 (0.771). Sample coverage values of the other areas are in between (area 1: 0.895, area 3: 0.931, area 4a: 0.939, area 5: 0.981). The rarefaction/extrapolation curves for the mycobiont species (see Supplementary Fig. S6a) suggest that for any sample size up to the specified level of sample coverage of 0.95, alpha diversity within area 4b is significantly lower than alpha diversity within any other area, and alpha diversity within area 5 is significantly greater than that of area 4a and 4b (based on 95% confidence intervals).For photobionts, the overall sample coverage as well as the sample coverages of area 1, area 2, area 3, area 4b as well as area 5 is equal 1.000. Only the sample coverage of area 4a (0.951) differs. The rarefaction/extrapolation curves for the photobiont OTUs (see Supplementary Fig. S6b) suggest that for any sample size up to the specified level of sample coverage of 0.95, alpha diversity within area 1 is significantly lower than alpha diversity of area 3 and 4a and significantly greater than that of area 5. Alpha diversity of area 5 is significantly lower than that of area 1, area 3 and area 4a.Influence of environmental factors (elevation, precipitation and temperature)First, the proportion of the OTU Tr_A02 samples was significantly correlated to BIO10 means of the areas (R = 0.87, p = 0.022; see Supplementary Fig. S7 online): the higher the temperature mean values of the warmest quarter of an area, the higher the proportion of samples containing photobionts that are assigned to Tr_A02.The alpha diversity values of mycobiont species significantly positively correlated with BIO10 (R = 0.88, p = 0.021; see Supplementary Fig. S8 online): the higher the temperature mean values of the warmest quarter, the higher the mycobiont diversity within this particular area.Furthermore, the differences in mycobiont species community composition were significantly related to BIO10 (constrained principal coordinate analysis: F = 14.7137, p = 0.001, see Supplementary Fig. S9 online), BIO12 (F = 2.7535, p = 0.012), elevation (F = 2.5108, p = 0.025) and the geographic separation of the samples (Mantel statistic r = 0.1288, p = 0.0002).The differences in community composition of photobiont OTUs were related significantly to BIO10 (constrained principal coordinate analysis: F = 48.5952, p = 0.001, see Supplementary Fig. S10 online), BIO12 (F = 4.4848, p = 0.008), elevation (F = 6.8608, p = 0.002), and physical distance (Mantel statistic r = 0.4472, p = 0.0001).Haplotype analysisHaplotype networks were computed for the mycobiont species and photobiont OTUs with h ≥ 2 and at least one haplotype with n ≥ 3 (Carbonea sp. 2, Lecanora fuscobrunnea, Lecidea cancriformis, Lecidella greenii, L. siplei, L. sp. nov2 and Rhizoplaca macleanii, as well as Tr_A02, Tr_I01 and Tr_S02), in both cases based on nrITS sequence data (Figs. 2, 3). The samples of Carbonea vorticosa (11) were all assigned to a single haplotype, which was also true for Lecidea polypycnidophora (10 samples), Tr_S15 (10 samples) and Tr_S18 (32 samples). Figure 3b, c illustrate the subdivision of Tr_I0151 into Tr_I01j35,48 and Tr_I01k (in this study), and the subdivision of Tr_S02 into Tr_S0235, and Tr_S02b and Tr_S02c48.Figure 2Haplotype networks of mycobiont species with h ≥ 2 and at least one haplotype with n ≥ 3, showing the spatial distribution within the different areas, based on nrITS data. (a) Carbonea sp. 2, (b) Lecanora fuscobrunnea, (c) Lecidea cancriformis, (d) Lecidella greenii, (e) Lecidella siplei, (f) Lecidella sp. nov2, (g) Rhizoplaca macleanii. Roman numerals at the center of the pie charts refer to the haplotype IDs; the italic numbers next to the pie charts give the total number of samples per haplotype. The circle sizes reflect relative frequency within the species; the frequencies were clustered in ten (e.g. the circles of all haplotypes making up between 20 and 30% have the same size). Note: only complete sequences were included.Full size imageFigure 3Haplotype networks of photobiont OTUs with h ≥ 2 and at least one haplotype with n ≥ 3, showing the spatial distribution within the different areas, based on nrITS data. (a) Tr_A02, (b) Tr_I01, (c) Tr_S02. Roman numerals at the center of the pie charts refer to the haplotype IDs; the italic numbers next to the pie charts give the total number of samples per haplotype. The circle sizes reflect relative frequency within the species; the frequencies were clustered in ten (e.g. the circles of all haplotypes making up between 20 and 30% have the same size). Note: only complete sequences were included.Full size imageThe haplotype networks include pie charts showing the occurrence of the different haplotypes within the different areas. All haplotypes of Rhizoplaca macleanii are restricted to area 5, as well as Lecidella greenii mainly to area 5 and areas 1 and 4a, and Lecidella sp. 2 to areas 2 and 3. However, all other species do not suggest a spatial pattern with different haplotypes being specific for different areas. Moreover, the distribution turned out to be rather unspecific, with a great part of the haplotypes found in multiple areas. For the sake of completeness, additionally, haplotype networks based on multi-locus sequence data were computed for the most abundant mycobiont species and photobiont OTU with multi-locus data available (Lecidea cancriformis and Tr_S02). Not surprisingly, those networks show a greater number of different haplotypes, but they also do not allow conclusions concerning spatial patterns of area specific haplotypes (see Supplementary Fig. S11 online).Diversity and specificity indices of mycobiont species and photobiont OTUsThe diversity and specificity indices for the different mycobiont species and photobiont OTUs are given in Supplementary Table S8 online.For the sample locations of mycobiont species with n ≥ 10, BIO10 was strongly correlated to the specificity indices NRI (net relatedness index) and significantly correlated to PSR (phylogenetic species richness) and 1 – J′ (Pielou evenness index). BIO12 was significantly correlated to NRI, PSR and 1 – J′. Figure 4 illustrates these correlations: the higher the BIO10 and BIO12 mean values, the higher was the NRI (phylogenetic clustering of the photobiont symbiotic partners), the lower was the PSR (increased phylogenetically relatedness of photobiont symbiotic partners) and the higher was 1 – J′ (less numerically evenness of the photobiont symbiotic partners). Thus, for the mean values of the sample locations of a mycobiont species, a comparatively high temperature of the warmest quarter and high annual precipitation occurs with associated photobionts that are phylogenetically clustered and closer related to each other. The lowest values of NRI and the highest values of PSR were developed by Lecidea cancriformis and Lecanora fuscobrunnea, which also showed the lowest BIO10 and BIO12 mean values at their sample sites. On the contrary, the highest values of NRI and PSR were developed by Rhizoplaca macleanii, which also had the highest BIO10 and BIO12 means.Figure 4Correlation plots. Specificity indices NRI (net relatedness index), PSR (phylogenetic species richness and 1 – J′ (Pielou evenness index) against mean values of BIO10 (mean temperature of warmest quarter) and BIO12 (annual precipitation) for mycobiont species with n ≥ 10.Full size imageFor the sample locations of photobiont OTUs with n ≥ 10, elevation significantly negatively correlated with h (number of haplotypes) and Hd (haplotype diversity): the higher the mean elevation of sample sites, the lower the number of haplotypes and the lower the probability that two randomly chosen haplotypes are different (Fig. 5). The highest values of h and Hd were shown by Tr_A02, Tr_I01 and Tr_S02, which occurred at sample sites with comparatively low elevations. In contrast, Tr_S15 and Tr_S18 occurred at very high elevations and showed very low values of h and Hd.Figure 5Correlation plots. Diversity indices h (number of haplotypes) and Hd (haplotype diversity) against mean elevation of sample sites for photobiont OTUs with n ≥ 10.Full size imageAnalysis of mycobiont–photobiont associationsBipartite networks were calculated for all associations between mycobiont species (lower level) and the respective photobiont OTUs (higher level) for all areas (Fig. 6). The H2′ value (overall level of complementary specialization of all interacting species) was highest in area 2 (0.921), indicating a network with mostly specialized interactions: within this network, with the exception of Lecidea andersonii, the mycobiont species are associated exclusively with one single photobiont OTU. The second highest H2′ value was developed by area 4b (0.710); in contrast, area 4a showed the lowest H2′ value (0.260), with the most abundant mycobiont species Lecidea cancriformis showing associations with five different photobiont OTUs. The H2′ values of area 1, area 3 and area 5 indicate medium specification.Figure 6Bipartite networks showing the associations between mycobiont species and photobiont OTUs for the different areas. Rectangles represent species/OTUs, and the width is proportional to the number of samples. Associated species/OTUs are linked by lines whose width is proportional to the number of associations.Full size imageIn addition, the bipartite networks illustrate the different occurrence of mycobiont species and photobiont OTUs within the different areas: For example, in area 1 (and area 2), five (seven) different mycobiont species are associated with only three different photobiont OTUs. In contrast, in area 4b, only two different mycobiont species are associated with four different photobiont OTUs. In area 5, the number of associated photobiont OTUs is also four, but those four OTUs are associated with 16 different mycobiont species.The network matrix giving all the associations between the mycobiont species and photobiont OTUs is presented in Supplementary Table S9 online. More