Ecology

1.Etiope G, Ciccioli P. Earth’s degassing: a missing ethane and propane source. Science. 2009;323:478.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Etiope G, Drobniak A, Schimmelmann A. Natural seepage of shale gas and the origin of “eternal flames” in the Northern Appalachian Basin, USA. Mar Pet Geol. 2013;43:178–86.CAS 
Article 

Google Scholar 
3.Farhan Ul Haque M, Crombie AT, Murrell JC. Novel facultative Methylocella strains are active methane consumers at terrestrial natural gas seeps. Microbiome. 2019;7:134.PubMed 
PubMed Central 
Article 

Google Scholar 
4.Shennan JL. Utilisation of C2–C4 gaseous hydrocarbons and isoprene by microorganisms. J Chem Technol Biotechnol. 2006;81:237–56.CAS 
Article 

Google Scholar 
5.Rojo F. Degradation of alkanes by bacteria. Environ Microbiol. 2009;11:2477–90.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Jaekel U, Musat N, Adam B, Kuypers M, Grundmann O, Musat F. Anaerobic degradation of propane and butane by sulfate-reducing bacteria enriched from marine hydrocarbon cold seeps. ISME J. 2013;7:885–95.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Laso-Pérez R, Wegener G, Knittel K, Widdel F, Harding KJ, Krukenberg V, et al. Thermophilic Archaea activate butane via alkyl-coenzyme M formation. Nature. 2016;539:396–401.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
8.Picone N, Mohammadi SS, Waajen AC, van Alen TA, Jetten MSM, Pol A, et al. More than a methanotroph: a broader substrate spectrum for Methylacidiphilum fumariolicum SolV. Front Microbiol. 2020;11:3193.Article 

Google Scholar 
9.Dunfield PF, Yuryev A, Senin P, Smirnova AV, Stott MB, Hou S, et al. Methane oxidation by an extremely acidophilic bacterium of the phylum Verrucomicrobia. Nature. 2007;450:879–82.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Sharp CE, Smirnova AV, Graham JM, Stott MB, Khadka R, Moore TR, et al. Distribution and diversity of Verrucomicrobia methanotrophs in geothermal and acidic environments. Environ Microbiol. 2014;16:1867–78.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.van Teeseling MCF, Pol A, Harhangi HR, van der Zwart S, Jetten MSM, Op den Camp HJM, et al. Expanding the verrucomicrobial methanotrophic world: description of three novel species of Methylacidimicrobium gen. nov. Appl Environ Microbiol. 2014;80:6782–91.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Islam T, Jensen S, Reigstad LJ, Larsen O, Birkeland NK. Methane oxidation at 55 oC and pH 2 by a thermoacidophilic bacterium belonging to the Verrucomicrobia phylum. Proc Natl Acad Sci USA. 2008;105:300–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Pol A, Heijmans K, Harhangi HR, Tedesco D, Jetten MS, Op den Camp HJ. Methanotrophy below pH 1 by a new Verrucomicrobia species. Nature. 2007;450:874–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Coleman NV, Le NB, Ly MA, Ogawa HE, McCarl V, Wilson NL, et al. Hydrocarbon monooxygenase in Mycobacterium: recombinant expression of a member of the ammonia monooxygenase superfamily. ISME J. 2012;6:171–82.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Rochman FF, Kwon M, Khadka R, Tamas I, Lopez-Jauregui AA, Sheremet A, et al. Novel copper-containing membrane monooxygenases (CuMMOs) encoded by alkane-utilizing Betaproteobacteria. ISME J. 2020;14:714–26.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Tavormina PL, Orphan VJ, Kalyuzhnaya MG, Jetten MS, Klotz MG. A novel family of functional operons encoding methane/ammonia monooxygenase-related proteins in gammaproteobacterial methanotrophs. Environ Microbiol Rep. 2011;3:91–100.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Khadka R, Clothier L, Wang L, Lim CK, Klotz MG, Dunfield PF. Evolutionary history of copper membrane monooxygenases. Front Microbiol. 2018;9:2493.PubMed 
PubMed Central 
Article 

Google Scholar 
18.Lehtovirta-Morley LE. Ammonia oxidation: ecology, physiology, biochemistry and why they must all come together. FEMS Microbiol Lett. 2018;365:fny058.Article 
CAS 

Google Scholar 
19.Knief C. Diversity and habitat preferences of cultivated and uncultivated aerobic methanotrophic bacteria evaluated based on pmoA as molecular marker. Front Microbiol. 2015;6:1346.PubMed 
PubMed Central 
Article 

Google Scholar 
20.Sayavedra-Soto LA, Hamamura N, Liu CW, Kimbrel JA, Chang JH, Arp DJ. The membrane-associated monooxygenase in the butane-oxidizing Gram-positive bacterium Nocardioides sp. strain CF8 is a novel member of the AMO/PMO family. Environ Microbiol Rep. 2011;3:390–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Semrau JD, DiSpirito AA, Yoon S. Methanotrophs and copper. FEMS Microbiol Rev. 2010;34:496–531.CAS 
PubMed 
Article 

Google Scholar 
22.Nyerges G, Stein LY. Ammonia cometabolism and product inhibition vary considerably among species of methanotrophic bacteria. FEMS Microbiol Lett. 2009;297:131–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Sayavedra-Soto LA, Gvakharia B, Bottomley PJ, Arp DJ, Dolan ME. Nitrification and degradation of halogenated hydrocarbons—a tenuous balance for ammonia-oxidizing bacteria. Appl Microbiol Biotechnol. 2010;86:435–44.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Bédard C, Knowles RPhysiology. biochemistry, and specific inhibitors of CH4, NH4+, and CO oxidation by methanotrophs and nitrifiers. Microbiol Rev. 1989;53:68–84.PubMed 
PubMed Central 
Article 

Google Scholar 
25.Semrau JD. Bioremediation via methanotrophy: overview of recent findings and suggestions for future research. Front Microbiol. 2011;2:209.PubMed 
PubMed Central 
Article 

Google Scholar 
26.Chen Y, Crombie A, Rahman MT, Dedysh SN, Liesack W, Stott MB, et al. Complete genome sequence of the aerobic facultative methanotroph Methylocella silvestris BL2. J Bacteriol. 2010;192:3840–1.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Bordel S, Crombie AT, Muñoz R, Murrell JC. Genome scale metabolic model of the versatile methanotroph Methylocella silvestris. Micro Cell Fact. 2020;19:144.CAS 
Article 

Google Scholar 
28.Dunfield PF, Yimga MT, Dedysh SN, Berger U, Liesack W, Heyer J. Isolation of a Methylocystis strain containing a novel pmoA-like gene. FEMS Microbiol Ecol. 2002;41:17–26.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Kits KD, Klotz MG, Stein LY. Methane oxidation coupled to nitrate reduction under hypoxia by the Gammaproteobacterium Methylomonas denitrificans, sp. nov. type strain FJG1. Environ Microbiol. 2015;17:3219–32.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Op den Camp HJ, Islam T, Stott MB, Harhangi HR, Hynes A, Schouten S, et al. Environmental, genomic and taxonomic perspectives on methanotrophic Verrucomicrobia. Environ Microbiol Rep. 2009;1:293–306.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Baani M, Liesack W. Two isozymes of particulate methane monooxygenase with different methane oxidation kinetics are found in Methylocystis sp. strain SC2. Proc Natl Acad Sci USA 2008;105:10203–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Kits KD, Campbell DJ, Rosana AR, Stein LY. Diverse electron sources support denitrification under hypoxia in the obligate methanotroph Methylomicrobium album strain BG8. Front Microbiol. 2015;6:1072.PubMed 
PubMed Central 
Article 

Google Scholar 
33.Anvar SY, Frank J, Pol A, Schmitz A, Kraaijeveld K, den Dunnen JT, et al. The genomic landscape of the verrucomicrobial methanotroph Methylacidiphilum fumariolicum SolV. BMC Genom. 2014;15:914.Article 

Google Scholar 
34.Kruse T, Ratnadevi CM, Erikstad H-A, Birkeland N-K. Complete genome sequence analysis of the thermoacidophilic verrucomicrobial methanotroph “Candidatus Methylacidiphilum kamchatkense” strain Kam1 and comparison with its closest relatives. BMC Genom. 2019;20:642.Article 
CAS 

Google Scholar 
35.Hou S, Makarova KS, Saw JHW, Senin P, Ly BV, Zhou Z, et al. Complete genome sequence of the extremely acidophilic methanotroph isolate V4, Methylacidiphilum infernorum, a representative of the bacterial phylum Verrucomicrobia. Biol Direct. 2008;3:26.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
36.Erikstad HA, Ceballos RM, Smestad NB, Birkeland NK. Global biogeographic distribution patterns of thermoacidophilic Verrucomicrobia methanotrophs suggest allopatric evolution. Front Microbiol. 2019;10:1129.PubMed 
PubMed Central 
Article 

Google Scholar 
37.Schmitz RA, Peeters SH, Versantvoort W, Picone N, Pol A, Jetten MSM, et al. Verrucomicrobial methanotrophs: ecophysiology of metabolically versatile acidophiles. FEMS Microbiol Rev. 2021. https://doi.org/10.1093/femsre/fuab007.38.Carere CR, McDonald B, Peach HA, Greening C, Gapes DJ, Collet C, et al. Hydrogen oxidation influences glycogen accumulation in a verrucomicrobial methanotroph. Front Microbiol. 2019;10:1873.PubMed 
PubMed Central 
Article 

Google Scholar 
39.Erikstad HA, Jensen S, Keen TJ, Birkeland NK. Differential expression of particulate methane monooxygenase genes in the verrucomicrobial methanotroph ‘Methylacidiphilum kamchatkense’ Kam1. Extremophiles. 2012;16:405–9.CAS 
PubMed 
Article 

Google Scholar 
40.Khadem AF, Pol A, Wieczorek AS, Jetten MSM, Op Den Camp H. Metabolic regulation of “Ca. Methylacidiphilum fumariolicum” SolV cells grown under different nitrogen and oxygen limitations. Front Microbiol. 2012;3:266.PubMed 
PubMed Central 

Google Scholar 
41.Carere CR, Hards K, Wigley K, Carman L, Houghton KM, Cook GM, et al. Growth on formic acid is dependent on intracellular pH homeostasis for the thermoacidophilic methanotroph Methylacidiphilum sp. RTK17.1. Front Microbiol. 2021;12:536.Article 

Google Scholar 
42.Singleton CM, McCalley CK, Woodcroft BJ, Boyd JA, Evans PN, Hodgkins SB, et al. Methanotrophy across a natural permafrost thaw environment. ISME J. 2018;12:2544–58.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Adachi K, Katsuta A, Matsuda S, Peng X, Misawa N, Shizuri Y, et al. Smaragdicoccus niigatensis gen. nov., sp. nov., a novel member of the suborder Corynebacterineae. Int J Syst Evol Microbiol. 2007;57:297–301.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Whitman WB, Woyke T, Klenk H-P, Zhou Y, Lilburn TG, Beck BJ, et al. Genomic encyclopedia of bacterial and archaeal type strains, phase III: the genomes of soil and plant-associated and newly described type strains. Stand Genom Sci. 2015;10:26.Article 
CAS 

Google Scholar 
45.Capaccioni B, Mangani F. Monitoring of active but quiescent volcanoes using light hydrocarbon distribution in volcanic gases: the results of 4 years of discontinuous monitoring in the Campi Flegrei (Italy). Earth Planet Sci Lett. 2001;188:543–55.CAS 
Article 

Google Scholar 
46.Caliro S, Chiodini G, Moretti R, Avino R, Granieri D, Russo M, et al. The origin of the fumaroles of La Solfatara (Campi Flegrei, South Italy). Geochim Cosmochim Acta. 2007;71:3040–55.CAS 
Article 

Google Scholar 
47.Chiodini G, Caliro S, Cardellini C, Granieri D, Avino R, Baldini A, et al. Long-term variations of the Campi Flegrei, Italy, volcanic system as revealed by the monitoring of hydrothermal activity. J Geophys Res Solid Earth. 2010;115:B03205.Article 
CAS 

Google Scholar 
48.Tamburello G, Caliro S, Chiodini G, De Martino P, Avino R, Minopoli C, et al. Escalating CO2 degassing at the Pisciarelli fumarolic system, and implications for the ongoing Campi Flegrei unrest. J Volcano Geotherm Res. 2019;384:151–7.CAS 
Article 

Google Scholar 
49.de Bruyn JC, Boogerd FC, Bos P, Kuenen JG. Floating filters, a novel technique for isolation and enumeration of fastidious, acidophilic, iron-oxidizing, autotrophic bacteria. Appl Environ Microbiol. 1990;56:2891–4.PubMed 
PubMed Central 
Article 

Google Scholar 
50.Weisburg WG, Barns SM, Pelletier DA, Lane DJ. 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol. 1991;173:697–703.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.DeLong EF. Archaea in coastal marine environments. Proc Natl Acad Sci USA. 1992;89:5685–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Hurt RA, Qiu X, Wu L, Roh Y, Palumbo AV, Tiedje JM, et al. Simultaneous recovery of RNA and DNA from soils and sediments. Appl Environ Microbiol. 2001;67:4495–503.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
Article 

Google Scholar 
54.Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 2011;27:2957–63.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
55.Li W, Fu L, Niu B, Wu S, Wooley J. Ultrafast clustering algorithms for metagenomic sequence analysis. Brief Bioinform. 2012;13:656–68.PubMed 
PubMed Central 
Article 

Google Scholar 
56.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 
Article 

Google Scholar 
57.Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44:6614–24.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Vallenet D, Calteau A, Dubois M, Amours P, Bazin A, Beuvin M, et al. MicroScope: an integrated platform for the annotation and exploration of microbial gene functions through genomic, pangenomic and metabolic comparative analysis. Nucleic Acids Res. 2020;48:D579–D89.CAS 

Google Scholar 
59.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 
Article 

Google Scholar 
60.Thompson JD, Higgins DG, Gibson TJ, CLUSTAL W. improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994;22:4673–80.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Hall TA. BioEdit : a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp Ser. 1999;41:95–8.CAS 

Google Scholar 
62.Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Yoon SH, Ha S, Kwon S, Lim J, Kim Y, Seo H, et al. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol. 2017;67:1613–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Salamov VSA, Solovyevand A. Automatic annotation of microbial genomes and metagenomic sequences. Li RW, editor. Hauppauge, N.Y.: Nova Science Publishers; 2011. 61–78.65.Umarov RK, Solovyev VV. Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks. PLOS One. 2017;12:e0171410.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
66.Naville M, Ghuillot-Gaudeffroy A, Marchais A, Gautheret D. ARNold: a web tool for the prediction of Rho-independent transcription terminators. RNA Biol. 2011;8:11–3.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Shen T, Stieglmeier M, Dai J, Urich T, Schleper C. Responses of the terrestrial ammonia-oxidizing archaeon Ca. Nitrososphaera viennensis and the ammonia-oxidizing bacterium Nitrosospira multiformis to nitrification inhibitors. FEMS Microbiol Lett. 2013;344:121–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Mackay D, Shiu WY. A critical review of Henry’s law constants for chemicals of environmental interest. J Phys Chem Ref Data. 1981;10:1175–99.CAS 
Article 

Google Scholar 
69.Martens-Habbena W, Berube PM, Urakawa H, de la Torre JR, Stahl DA. Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature 2009;461:976–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Carere CR, Hards K, Houghton KM, Power JF, McDonald B, Collet C, et al. Mixotrophy drives niche expansion of verrucomicrobial methanotrophs. ISME J. 2017;11:2599–610.PubMed 
PubMed Central 
Article 

Google Scholar 
71.Mohammadi S, Pol A, van Alen TA, Jetten MSM, Op den Camp HJM. Methylacidiphilum fumariolicum SolV, a thermoacidophilic ‘Knallgas’ methanotroph with both an oxygen-sensitive and -insensitive hydrogenase. ISME J. 2017;11:945–58.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Khadem AF, Pol A, Wieczorek A, Mohammadi SS, Francoijs KJ, Stunnenberg HG, et al. Autotrophic methanotrophy in verrucomicrobia: Methylacidiphilum fumariolicum SolV uses the Calvin-Benson-Bassham cycle for carbon dioxide fixation. J Bacteriol. 2011;193:4438–46.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Hogendoorn C, Pol A, Nuijten GHL, Op den Camp HJM. Methanol production by “Methylacidiphilum fumariolicum” SolV under different growth conditions. Appl Environ Microbiol. 2020;86:e01188–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Crombie AT, Murrell JC. Trace-gas metabolic versatility of the facultative methanotroph Methylocella silvestris. Nature. 2014;510:148–51.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Ashraf W, Mihdhir A, Colin Murrell J. Bacterial oxidation of propane. FEMS Microbiol Lett. 1994;122:1–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Hausinger RP. New insights into acetone metabolism. J Bacteriol. 2007;189:671–3.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Dedysh SN, Dunfield PF. Facultative methane oxidizers. In: McGenity TJ, editor. Taxonomy, genomics and ecophysiology of hydrocarbon-degrading microbes. Cham: Springer International Publishing; 2019:279–97. https://doi.org/10.1007/978-3-030-14796-9_11.78.Belova SE, Baani M, Suzina NE, Bodelier PLE, Liesack W, Dedysh SN. Acetate utilization as a survival strategy of peat-inhabiting Methylocystis spp. Environ Microbiol Rep. 2011;3:36–46.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Belova SE, Kulichevskaya IS, Bodelier PL, Dedysh SN. Methylocystis bryophila sp. nov., a facultatively methanotrophic bacterium from acidic Sphagnum peat, and emended description of the genus Methylocystis (ex Whittenbury et al. 1970) Bowman et al. 1993. Int J Syst Evol Microbiol. 2013;63:1096–104.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Fisher OS, Kenney GE, Ross MO, Ro SY, Lemma BE, Batelu S, et al. Characterization of a long overlooked copper protein from methane- and ammonia-oxidizing bacteria. Nat Commun. 2018;9:4276.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
81.El Sheikh AF, Poret-Peterson AT, Klotz MG. Characterization of two new genes, amoR and amoD, in the amo operon of the marine ammonia oxidizer Nitrosococcus oceani ATCC 19707. Appl Environ Microbiol. 2008;74:312–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
82.Pol A, Barends TR, Dietl A, Khadem AF, Eygensteyn J, Jetten MS, et al. Rare earth metals are essential for methanotrophic life in volcanic mudpots. Environ Microbiol. 2014;16:255–64.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Sützl L, Foley G, Gillam EMJ, Bodén M, Haltrich D. The GMC superfamily of oxidoreductases revisited: analysis and evolution of fungal GMC oxidoreductases. Biotechnol Biofuels. 2019;12:118.PubMed 
PubMed Central 
Article 

Google Scholar 
84.Fröbel J, Rose P, Müller M. Twin-arginine-dependent translocation of folded proteins. Philos Trans R Soc Lond B Biol Sci. 2012;367:1029–46.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
85.Sluis MK, Larsen RA, Krum JG, Anderson R, Metcalf WW, Ensign SA. Biochemical, molecular, and genetic analyses of the acetone carboxylases from Xanthobacter autotrophicus strain Py2 and Rhodobacter capsulatus strain B10. J Bacteriol. 2002;184:2969–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Kotani T, Yurimoto H, Kato N, Sakai Y. Novel acetone metabolism in a propane-utilizing bacterium, Gordonia sp. strain TY-5. J Bacteriol. 2007;189:886–93.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Furuya T, Nakao T, Kino K. Catalytic function of the mycobacterial binuclear iron monooxygenase in acetone metabolism. FEMS Microbiol Lett. 2015;362:fnv136.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
88.Koop DR, Casazza JP. Identification of ethanol-inducible P-450 isozyme 3a as the acetone and acetol monooxygenase of rabbit microsomes. J Biol Chem. 1985;260:13607–12.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
89.Patel NA, Crombie A, Slade SE, Thalassinos K, Hughes C, Connolly JB, et al. Comparison of one- and two-dimensional liquid chromatography approaches in the label-free quantitative analysis of Methylocella silvestris. J Proteome Res. 2012;11:4755–63.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Jain M, Nagar P, Sharma A, Batth R, Aggarwal S, Kumari S, et al. GLYI and D-LDH play key role in methylglyoxal detoxification and abiotic stress tolerance. Sci Rep. 2018;8:5451.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
91.MacLean MJ, Ness LS, Ferguson GP, Booth IR. The role of glyoxalase I in the detoxification of methylglyoxal and in the activation of the KefB K+ efflux system in Escherichia coli. Mol Microbiol. 1998;27:563–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
92.Detman A, Mielecki D, Pleśniak Ł, Bucha M, Janiga M, Matyasik I. et al. Methane-yielding microbial communities processing lactate-rich substrates: a piece of the anaerobic digestion puzzle. Biotechnol Biofuels. 2018;11:116.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
93.Cooper RA, Kornberg HL. The direct synthesis of phosphoenolpyruvate from pyruvate by Escherichia coli. Proc R Soc Lond B Biol Sci. 1967;168:263–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Balasubramanian R, Smith SM, Rawat S, Yatsunyk LA, Stemmler TL, Rosenzweig AC. Oxidation of methane by a biological dicopper centre. Nature. 2010;465:115–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Ross MO, MacMillan F, Wang J, Nisthal A, Lawton TJ, Olafson BD, et al. Particulate methane monooxygenase contains only mononuclear copper centers. Science. 2019;364:566–70.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Ro SY, Schachner LF, Koo CW, Purohit R, Remis JP, Kenney GE, et al. Native top-down mass spectrometry provides insights into the copper centers of membrane-bound methane monooxygenase. Nat Commun. 2019;10:2675.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
97.Liew EF, Tong D, Coleman NV, Holmes AJ. Mutagenesis of the hydrocarbon monooxygenase indicates a metal centre in subunit-C, and not subunit-B, is essential for copper-containing membrane monooxygenase activity. Microbiology. 2014;160:1267–77.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
98.Nguyen TT, Hwang IY, Na JG, Lee EY. Biological conversion of propane to 2-propanol using group I and II methanotrophs as biocatalysts. J Ind Microbiol Biotechnol. 2019;46:675–85.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
99.Hur DH, Nguyen TT, Kim D, et al. EY. Selective bio-oxidation of propane to acetone using methane-oxidizing Methylomonas sp. DH-1 J Ind Microbiol Biotechnol. 2017;44:1097–105.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Schoell M. Genetic characterization of natural gases. AAPG Bull. 1983;67:2225–38.CAS 

Google Scholar  More

1.Kallmeyer J, Pockalny R, Adhikari RR, Smith DC, D’Hondt S. Global distribution of microbial abundance and biomass in subseafloor sediment. Proc Natl Acad Sci USA. 2012;109:16213–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Parkes RJ, Cragg B, Roussel E, Webster G, Weightman A, Sass H. A review of prokaryotic populations and processes in sub-seafloor sediments, including biosphere: geosphere interactions. Mar Geol. 2014;352:409–25.CAS 
Article 

Google Scholar 
3.D’Hondt S, Jørgensen BB, Miller DJ, Batzke A, Blake R, Cragg BA, et al. Distributions of microbial activities in deep subseafloor sediments. Science 2004;306:2216–21.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Røy H, Kallmeyer J, Adhikari RR, Pockalny R, Jørgensen BB, D’Hondt S. Aerobic microbial respiration in 86-million-year-old deep-sea red clay. Science. 2012;336:922–5.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
5.D’Hondt S, Inagaki F, Zarikian CA, Abrams LJ, Dubois N, Engelhardt T, et al. Presence of oxygen and aerobic communities from sea floor to basement in deep-sea sediments. Nat Geosci. 2015;8:299–304.Article 
CAS 

Google Scholar 
6.Jørgensen BB, Marshall IPG. Slow microbial life in the seabed. Annu Rev Mar Sci. 2016;8:311–32.Article 

Google Scholar 
7.Danovaro R, Dell’Anno A, Corinaldesi C, Rastelli E, Cavicchioli R, Krupovic M, et al. Virus-mediated archaeal hecatomb in the deep seafloor. Sci Adv. 2016;2:e1600492.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Engelhardt T, Kallmeyer J, Cypionka H, Engelen B. High virus-to-cell ratios indicate ongoing production of viruses in deep subsurface sediments. ISME J. 2014;8:1503–9.PubMed 
PubMed Central 
Article 

Google Scholar 
9.Engelhardt T, Orsi WD, Jørgensen BB. Viral activities and life cycles in deep subseafloor sediments. Environ Microbiol Rep. 2015;7:868–73.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.LaRowe DE, Amend JP. Catabolic rates, population sizes and doubling/replacement times of microorganisms in natural settings. Am J Sci. 2015;315:167–203.CAS 
Article 

Google Scholar 
11.LaRowe DE, Amend JP. Power limits for microbial life. Front Microbiol. 2015;6:718.PubMed 
PubMed Central 
Article 

Google Scholar 
12.Hoehler TM, Jørgensen BB. Microbial life under extreme energy limitation. Nat Rev Microbiol. 2013;11:83–94.CAS 
PubMed 
Article 

Google Scholar 
13.Zhao R, Mogollón JM, Abby SS, Schleper C, Biddle JF, Roerdink DL, et al. Geochemical transition zone powering microbial growth in subsurface sediments. Proc Natl Acad Sci USA 2020;117:32617–26.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Bradley J, Arndt S, Amend J, Burwicz E, Dale AW, Egger M, et al. Widespread energy limitation to life in global subseafloor sediments. Sci Adv 2020;6:eaba0697.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Bradley JA, Amend JP, LaRowe DE. Survival of the fewest: Microbial dormancy and maintenance in marine sediments through deep time. Geobiology 2019;17:43–59.PubMed 
Article 
PubMed Central 

Google Scholar 
16.Lever MA, Rogers KL, Lloyd KG, Overmann J, Schink B, Thauer RK, et al. Life under extreme energy limitation: a synthesis of laboratory- and field-based investigations. FEMS Microbiol Rev. 2015;39:688–728.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Lloyd KG, Steen AD, Ladau J, Yin J, Crosby L. Phylogenetically novel uncultured microbial cells dominate earth microbiomes. mSystems 2018;3:e00055–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Durbin AM, Teske A. Microbial diversity and stratification of south pacific abyssal marine sediments. Environ Microbiol. 2011;13:3219–34.PubMed 
Article 
PubMed Central 

Google Scholar 
19.Tully BJ, Heidelberg JF. Potential mechanisms for microbial energy acquisition in oxic deep-sea sediments. Appl Environ Microbiol. 2016;82:4232–43.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Vuillemin A, Wankel SD, Coskun ÖK, Magritsch T, Vargas S, Estes ER, et al. Archaea dominate oxic subseafloor communities over multimillion-year time scales. Sci Adv 2019;5:eaaw4108.PubMed 
PubMed Central 
Article 

Google Scholar 
21.Hiraoka S, Hirai M, Matsui Y, Makabe A, Minegishi H, Tsuda M, et al. Microbial community and geochemical analyses of trans-trench sediments for understanding the roles of hadal environments. ISME J. 2020;14:740–56.CAS 
PubMed 
Article 

Google Scholar 
22.Hoshino T, Doi H, Uramoto G-I, Wörmer L, Adhikari RR, Xiao N, et al. Global diversity of microbial communities in marine sediment. Proc Natl Acad Sci USA 2020;117:27587–97.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Zhao R, Hannisdal B, Mogollon JM, Jørgensen SL. Nitrifier abundance and diversity peak at deep redox transition zones. Sci Rep. 2019;9:8633.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Jensen K, Sloth NP, Risgaardpetersen N, Rysgaard S, Revsbech NP. Estimation of nitrification and denitrification from microprofiles of oxygen and nitrate in model sediment systems. Appl Environ Microbiol. 1994;60:2094–100.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Middelburg JJ, Soetaert K, Herman PMJ, Heip CHR. Denitrification in marine sediments: a model study. Glob Biogeochemical Cycles. 1996;10:661–73.CAS 
Article 

Google Scholar 
26.Devol AH. Denitrification, anammox, and n2 production in marine sediments. Annu Rev Mar Sci. 2015;7:403–23.Article 

Google Scholar 
27.Wankel SD, Germanovich LN, Lilley MD, Genc G, DiPerna CJ, Bradley AS, et al. Influence of subsurface biosphere on geochemical fluxes from diffuse hydrothermal fluids. Nat Geosci. 2011;4:461–8.CAS 
Article 

Google Scholar 
28.Middelburg JJ. Chemoautotrophy in the ocean. Geophys Res Lett. 2011;38:L24604.Article 
CAS 

Google Scholar 
29.Meador TB, Schoffelen N, Ferdelman TG, Rebello O, Khachikyan A, Könneke M. Carbon recycling efficiency and phosphate turnover by marine nitrifying archaea. Sci Adv 2020;6:eaba1799.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Kerou M, Offre P, Valledor L, Abby SS, Melcher M, Nagler M. et al. Proteomics and comparative genomics of nitrososphaera viennensis reveal the core genome and adaptations of archaeal ammonia oxidizers. Proc Natl Acad Sci USA. 2016;113:E7937–E46.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Kerou M, Ponce-Toledo RI, Zhao R, Abby SS, Hirai M, Nomaki H et al. Genomes of thaumarchaeota from deep sea sediments reveal specific adaptations of three independently evolved lineages. ISME J. 2021. https://doi.org/10.1038/s41396-021-00962-6.32.Boetius A, Ferdelman T, Lochte K. Bacterial activity in sediments of the deep Arabian sea in relation to vertical flux. Deep-Sea Res Part II. 2000;47:2835–75.Article 

Google Scholar 
33.Grandel S, Rickert D, Schluter M, Wallmann K. Pore-water distribution and quantification of diffusive benthic fluxes of silicic acid, nitrate and phosphate in surface sediments of the deep arabian sea. Deep-Sea Res Part II. 2000;47:2707–34.CAS 
Article 

Google Scholar 
34.Orcutt BN, Wheat CG, Rouxel O, Hulme S, Edwards KJ, Bach W. Oxygen consumption rates in subseafloor basaltic crust derived from a reaction transport model. Nat Commun. 2013;4:2539.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
35.Ziebis W, McManus J, Ferdelman T, Schmidt-Schierhorn F, Bach W, Muratli J, et al. Interstitial fluid chemistry of sediments underlying the North Atlantic gyre and the influence of subsurface fluid flow. Earth Planet Sci Lett. 2012;323:79–91.Article 
CAS 

Google Scholar 
36.Huang Y. The no3-/o2 respiration ratio of the deep sedimentary biosphere in the pacific gyres. Open Access Master’s Thesis Paper 288, University of Rhode Island. 2014; https://digitalcommons.uri.edu/theses/288.37.Ryan WBF, Carbotte SM, Coplan JO, O’Hara S, Melkonian A, Arko R, et al. Global multi-resolution topography synthesis. Geochem Geophysics Geosystems. 2009;10:Q03014.
Google Scholar 
38.Bolleter W, Bushman C, Tidwell PW. Spectrophotometric determination of ammonia as indophenol. Anal Chem. 1961;33:592–4.CAS 
Article 

Google Scholar 
39.Hansen HP, Koroleff F. Determination of nutrients. Methods of seawater analysis. 1999. p. 159−228.40.Expedition 336 Scientists. Sediment and basement contact coring. In Edwards, KJ, Bach, W, Klaus, A, and the Expedition 336 Scientists, Proc IODP, 336: Tokyo (Integrated Ocean Drilling Program Management International, Inc) 2012.41.Mogollón JM, Mewes K, Kasten S. Quantifying manganese and nitrogen cycle coupling in manganese‐rich, organic carbon‐starved marine sediments: Examples from the Clarion−Clipperton fracture zone. Geophys Res Lett. 2016;43:7114–23.Article 
CAS 

Google Scholar 
42.Jørgensen BB. Comparison of methods for the quantification of bacterial sulfate reduction in coastal marine sediments. Ii. Calculation from mathematical models. Geomicrobiol J. 1978;1:29–47.Article 

Google Scholar 
43.Grundmanis V, Murray JW. Aerobic respiration in pelagic marine sediments. Geochimica et Cosmochimica Acta. 1982;46:1101–20.CAS 
Article 

Google Scholar 
44.Murray JW, Kuivila KM. Organic matter diagenesis in the northeast pacific: Transition from aerobic red clay to suboxic hemipelagic sediments. Deep-Sea Res Part A. 1990;37:59–80.CAS 
Article 

Google Scholar 
45.Anderson LA, Sarmiento JL. Redfield ratios of remineralization determined by nutrient data analysis. Glob Biogeochemical Cycles. 1994;8:65–80.CAS 
Article 

Google Scholar 
46.Dick JM. Calculation of the relative metastabilities of proteins using the chnosz software package. Geochemical Trans. 2008;9:10.Article 
CAS 

Google Scholar 
47.Helgeson HC. Thermodynamics of hydrothermal systems at elevated temperatures and pressures. Am J Sci. 1969;267:729–804.CAS 
Article 

Google Scholar 
48.Jung M-Y, Sedlacek CJ, Dimitri Kits K, Mueller AJ, Rhee S-K, Hink L et al. Ammonia-oxidizing archaea possess a wide range of cellular ammonia affinities. Preprint at bioRxiv https://doi.org/10.1101/2021.03.02.433310. 2021.49.Beman JM, Chow CE, King AL, Feng YY, Fuhrman JA, Andersson A, et al. Global declines in oceanic nitrification rates as a consequence of ocean acidification. Proc Natl Acad Sci USA. 2011;108:208–13.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Zeebe RE, Wolf-Gladrow D. CO2 in seawater: equilibrium, kinetics, isotopes. Gulf Professional Publishing; 2001.51.Bayer B, Vojvoda J, Reinthaler T, Reyes C, Pinto M, Herndl GJ. Nitrosopumilus adriaticus sp. Nov. And nitrosopumilus piranensis sp. Nov., two ammonia-oxidizing archaea from the Adriatic sea and members of the class nitrososphaeria. Int J Syst Evolut Microbiol. 2019;69:1892–902.CAS 
Article 

Google Scholar 
52.Qin W, Heal KR, Ramdasi R, Kobelt JN, Martens-Habbena W, Bertagnolli AD, et al. Nitrosopumilus maritimus gen. nov., sp. nov., nitrosopumilus cobalaminigenes sp. nov., nitrosopumilus oxyclinae sp. nov., and nitrosopumilus ureiphilus sp. nov., four marine ammonia-oxidizing archaea of the phylum thaumarchaeota. Int J Syst Evolut Microbiol. 2017;67:5067–79.Article 

Google Scholar 
53.Tijhuis L, van Loosdrecht MCM, Heijnen JJ. A thermodynamically based correlation for maintenance Gibbs energy requirements in aerobic and anaerobic chemotrophic growth. Biotechnol Bioeng. 1993;42:509–19.CAS 
PubMed 
Article 

Google Scholar 
54.Glover HE. The relationship between inorganic nitrogen oxidation and organic carbon production in batch and chemostat cultures of marine nitrifying bacteria. Arch Microbiol. 1985;142:45–50.CAS 
Article 

Google Scholar 
55.Jahnke RA, Emerson SR, Reimers CE, Schuffert J, Ruttenberg K, Archer D. Benthic recycling of biogenic debris in the eastern tropical Atlantic ocean. Geochimica et Cosmochimica Acta. 1989;53:2947–60.CAS 
Article 

Google Scholar 
56.Nath BN, Mudholkar AV. Early diagenetic processes affecting nutrients in the pore waters of central Indian ocean cores. Mar Geol. 1989;86:57–66.CAS 
Article 

Google Scholar 
57.Van Der Loeff MMR. Oxygen in pore waters of deep-sea sediments. Philos Trans R Soc A. 1990;331:69–84.
Google Scholar 
58.Mewes K, Mogollón J, Picard A, Rühlemann C, Eisenhauer A, Kuhn T, et al. Diffusive transfer of oxygen from seamount basaltic crust into overlying sediments: an example from the Clarion–Clipperton fracture zone. Earth Planet Sci Lett. 2016;433:215–25.CAS 
Article 

Google Scholar 
59.Buchwald C, Homola K, Spivack AJ, Estes ER, Murray RW, Wankel SD. Isotopic constraints on nitrogen transformation rates in the deep sedimentary marine biosphere. Glob Biogeochemical Cycles. 2018;32:1688–702.CAS 
Article 

Google Scholar 
60.Volz JB, Mogollón JM, Geibert W, Arbizu PM, Koschinsky A, Kasten S. Natural spatial variability of depositional conditions, biogeochemical processes and element fluxes in sediments of the eastern Clarion-Clipperton zone, pacific ocean. Deep Sea Res Part I. 2018;140:159–72.CAS 
Article 

Google Scholar 
61.Wang Y, Van, Cappellen P. A multicomponent reactive transport model of early diagenesis: Application to redox cycling in coastal marine sediments. Geochimica et Cosmochimica Acta. 1996;60:2993–3014.CAS 
Article 

Google Scholar 
62.Soetaert K, Herman PMJ, Middelburg JJ. A model of early diagenetic processes from the shelf to abyssal depths. Geochimica et Cosmochimica Acta. 1996;60:1019–40.CAS 
Article 

Google Scholar 
63.Wilson TRS. Evidence for denitrification in aerobic pelagic sediments. Nature 1978;274:354–6.CAS 
Article 

Google Scholar 
64.Brandes JA, Devol AH. Simultaneous nitrate and oxygen respiration in coastal sediments – evidence for discrete diagenesis. J Mar Res. 1995;53:771–97.CAS 
Article 

Google Scholar 
65.Gao H, Schreiber F, Collins G, Jensen MM, Kostka JE, Lavik G, et al. Aerobic denitrification in permeable wadden sea sediments. ISME J. 2010;4:417–26.CAS 
PubMed 
Article 

Google Scholar 
66.Marchant HK, Ahmerkamp S, Lavik G, Tegetmeyer HE, Graf J, Klatt JM, et al. Denitrifying community in coastal sediments performs aerobic and anaerobic respiration simultaneously. ISME J. 2017;11:1799–812.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Bianchi D, Weber TS, Kiko R, Deutsch C. Global niche of marine anaerobic metabolisms expanded by particle microenvironments. Nat Geosci. 2018;11:263–8.CAS 
Article 

Google Scholar 
68.Henriksen K, Hansen J, Blackburn T. Rates of nitrification, distribution of nitrifying bacteria, and nitrate fluxes in different types of sediment from Danish waters. Mar Biol. 1981;61:299–304.CAS 
Article 

Google Scholar 
69.Billen G. Evaluation of nitrifying activity in sediments by dark 14c-bicarbonate incorporation. Water Res. 1976;10:51–7.CAS 
Article 

Google Scholar 
70.Newell SE, Fawcett SE, Ward BB. Depth distribution of ammonia oxidation rates and ammonia-oxidizer community composition in the sargasso sea. Limnol Oceanogr. 2013;58:1491–500.CAS 
Article 

Google Scholar 
71.Zhao R, Dahle H, Ramírez GA, Jørgensen SL. Indigenous ammonia-oxidizing archaea in oxic subseafloor oceanic crust. mSystems 2020;5:e00758–19.PubMed 
PubMed Central 

Google Scholar 
72.Müller V, Hess V. The minimum biological energy quantum. Front Microbiol. 2017;8:2019.PubMed 
PubMed Central 
Article 

Google Scholar 
73.Jørgensen SL, Hannisdal B, Lanzen A, Baumberger T, Flesland K, Fonseca R, et al. Correlating microbial community profiles with geochemical data in highly stratified sediments from the arctic mid-ocean ridge. Proc Natl Acad Sci USA. 2012;109:2846–55.Article 

Google Scholar 
74.Daims H, Lebedeva EV, Pjevac P, Han P, Herbold C, Albertsen M, et al. Complete nitrification by nitrospira bacteria. Nature 2015;528:504–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Durbin AM, Teske A. Sediment-associated microdiversity within the marine group i crenarchaeota. Environ Microbiol Rep. 2010;2:693–703.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Bristow LA, Dalsgaard T, Tiano L, Mills DB, Bertagnolli AD, Wright JJ, et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc Natl Acad Sci USA. 2016;113:10601–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Sintes E, Bergauer K, De Corte D, Yokokawa T, Herndl GJ. Archaeal amoa gene diversity points to distinct biogeography of ammonia-oxidizing crenarchaeota in the ocean. Environ Microbiol. 2013;15:1647–58.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.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 
Article 
PubMed Central 

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

Google Scholar 
80.Marschall E, Jogler M, Henßge U, Overmann J. Large-scale distribution and activity patterns of an extremely low-light-adapted population of green sulfur bacteria in the black sea. Environ Microbiol. 2010;12:1348–62.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.McCollom T, Amend J. A thermodynamic assessment of energy requirements for biomass synthesis by chemolithoautotrophic micro‐organisms in oxic and anoxic environments. Geobiology 2005;3:135–44.CAS 
Article 

Google Scholar 
82.D’Hondt S, Rutherford S, Spivack AJ. Metabolic activity of subsurface life in deep-sea sediments. Science 2002;295:2067–70.PubMed 
Article 

Google Scholar 
83.Price PB, Sowers T. Temperature dependence of metabolic rates for microbial growth, maintenance, and survival. Proc Natl Acad Sci USA. 2004;101:4631–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Gibson B, Wilson DJ, Feil E, Eyre-Walker A. The distribution of bacterial doubling times in the wild. Proc R Soc B: Biol Sci 2018;285:20180789.Article 
CAS 

Google Scholar 
85.Weissman JL, Hou S, Fuhrman JA. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proc Natl Acad Sci 2021;118:e2016810118.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
86.Steen AD, Kevorkian RT, Bird JT, Dombrowski N, Baker BJ, Hagen SM, et al. Kinetics and identities of extracellular peptidases in subsurface sediments of the white oak river estuary, North Carolina. Appl Environ Microbiol. 2019;85:e00102–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Kim J-G, Kim S-J, Cvirkaite-Krupovic V, Yu W-J, Gwak J-H, López-Pérez M, et al. Spindle-shaped viruses infect marine ammonia-oxidizing thaumarchaea. Proc Natl Acad Sci USA 2019;116:15645–50.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Cai L, Jørgensen BB, Suttle CA, He M, Cragg BA, Jiao N, et al. Active and diverse viruses persist in the deep sub-seafloor sediments over thousands of years. ISME J. 2019;13:1857–64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Paul SA, Gaye B, Haeckel M, Kasten S, Koschinsky A. Biogeochemical regeneration of a nodule mining disturbance site: trace metals, doc and amino acids in deep-sea sediments and pore waters. Front Mar Sci. 2018;5:117.Article 

Google Scholar 
90.D’Hondt S, Pockalny R, Fulfer VM, Spivack AJ. Subseafloor life and its biogeochemical impacts. Nat Commun. 2019;10:3519.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar  More

Baseline model: no competitionWe start by assuming no competition and consider unconstrained growth in each of the two compartments. In this case, the equations describing our model become linear and can be rewritten in matrix form [4] as$$left( {begin{array}{*{20}{c}} {frac{{partial n_{H}}}{{partial t}}} \ {frac{{partial n_{E}}}{{partial t}}} end{array}} right) = underbrace{left( {begin{array}{*{20}{c}} {r_{H} – m_{E}} & {m_{H}} \ {m_{E}} & {r_{E} – m_{H}} end{array}} right)}_{{mathrm{projection}}, {mathrm{matrix}}}left( {begin{array}{*{20}{c}} {n_{H}} \ {n_{E}}end{array}} right)$$
(2)
The dominant eigenvalue λ of the above-defined projection matrix gives the asymptotic overall growth rate of the considered microbial lineage. This quantity is an appropriate measure of fitness [4] insofar as it measures reproductive as well as transmission success and recapitulates the effects of all the life-history traits (rE, rH, mE, and mH, also defining the phenotype in our model). Overall microbial fitness is thus integrated across the different steps of the life cycle, thereby considering the reproductive rates (i.e., replication rates) within each of the compartments and importantly transmission rates (i.e., migration rates) across the compartments. The dominant right eigenvector represents the stable distribution of microbes in the two compartments, and the number of microbes in each of the compartments grows exponentially with rate λ. The value of λ can be calculated at each point of the phenotypic space defined by the ranges of possible values that could be taken by the life-history traits rE, rH, mE, and mH. The dependence of λ on these traits tells us at which points of the phenotypic space fitness is maximized and how it can be increased at all other points.From the projection matrix, we calculate the dominant eigenvalue as$$lambda = frac{1}{2}left(sqrt {left( {r_E + r_H – m_E – m_H} right)^2 {,}-{,} 4left( {r_Er_H – r_Em_E – r_Hm_H} right)} + r_E +r_H – m_E – m_H right).$$
(3)
Note that if microbes replicate at the same rate in the host and in the environment, i.e., if rE = rH = r, λ simplifies to r, regardless of the migration rates mH and mE. When there is an asymmetry between the two replication rates however, which is very likely to be the case in nature, then the migration rates also affect the overall growth rate. In the following sections, we study this effect compared to the effect of the replication rates. We arbitrarily set rH ≤ rE, and rE  > 0 – otherwise the lineage goes extinct. In biological terms, this corresponds to the situation where the microbial lineage is initially more adapted to the environment than to the host and thus grows faster in the environment. But mathematically, in this model, host and environment are symmetrical, i.e., they only differ by the rates defined above. Thus, the chosen direction of this inequality does not carry any strong meaning, and there is no loss of generality in making this choice. In particular, one can access the opposite biological situation where microbes replicate faster in the host than in the environment – as is the case for viruses, that can only replicate in the host (rH  > 0) but decay in the environment (rE  0. Setting rE = 1 to scale time (and thus, measuring all other rates in units of the replication rate of the microbe in the environment), λ reduces to$${uplambda}_{sym} = frac{1}{2}left( {1 + r_H – 2m + sqrt {left( {1 – r_H} right)^2 {,}+ {,}4m^2} } right)$$
(4)
For any fixed positive value of m, λsym is a strictly increasing function of rH, which reflects the fact that increasing rH allows for additional growth within the host. We will limit ourselves to the study of rH ≥ −1, which ensures a positive value for λsym. For any fixed value of rH, λsym is a decreasing function of m, which reflects the fact that for increasing m, microbes are increasingly lost towards the host, where growth is slower than in the environment. Figure 1C shows the value of λsym on the reduced phenotypic space defined by rH and m. The maximum possible value for λ is 1 (in units of rE). This value is achieved either by increasing the ratio of replication rates between host and environment, so that the replication rates in both compartments are identical (strategy I), or by reducing migration between host and environment, and in particular, by reducing mH (strategy II). This second strategy allows microbes to spend a longer time in the environment on average. Note however, that this strategy is limited, since setting m to zero decouples the two compartments completely, in which case the microbial lineage is no longer subject to a multi-step life cycle.How strong is the selection on these traits? This question can be approached by inferring how strongly the overall growth rate depends on the traits we are considering. One standard approach to measure this is sensitivity analysis [4]. One defines the sensitivity of the overall growth rate λ achieved by the phenotype described by the vector x = (x1,…, xN) in the trait space to its ith life-history trait as$$s_{mathrm{i}}left( {mathbf{x}} right) = left. {frac{{partial {uplambda}}}{{partial {mathrm{x}}_{mathrm{i}}}}} right|_{mathbf{x}}$$
(5)
This quantity gives the change in the value of λ that results from a small increment of the trait i. It is a local property that can be calculated for each point ({mathbf{x}}) of the trait space. The vector of the sensitivities at point ({mathbf{x}}) gives the direction of the selection gradient on the fitness landscape. In other words, to achieve efficient phenotypic adaptation, the lineage should move in the trait space following the direction of this gradient.If the lineage can invest in phenotypic adaptation only by tuning one of its life-history traits at a time, then it should act upon the trait that has the largest (absolute) sensitivity at the current position of the lineage in the trait space. In our model, in all generic cases (i.e., when m  > 0), the largest sensitivity is always associated to the increase of the trait rE, the replication rate in the fast-growing compartment. However, we assume that the considered microbial lineage is initially fully adapted to the environment, so that it has reached its evolutionary limit, and we can essentially ignore the sensitivity to rE throughout the manuscript to focus on the sensitivity to the other traits. This reasoning allows to divide the trait space into regions of distinct optimal strategies, as shown in Fig. 1C. In the regime of high migration rates (i.e., when the switch between the compartments is so rapid that the microbial lineage is almost experiencing a habitat having average properties between the host and the environment), strategy I (increasing rH) becomes almost always optimal, except for small replication ratios, where there is almost no replication in the host. In summary, migration rates are important when replication in the host is slow compared to the environment, and when migration itself is slow. These conclusions remain qualitatively unchanged with asymmetric migration rates, although a third optimal strategy (increasing mE) appears for an intermediate region of the traits space when the asymmetry is important (see electronic Supplementary Material (ESM) section 1 and Supplementary Fig. S1).Model with global competition between all microbesIn the baseline model, there are no constraints on growth. In nature, however, microbes do face limits to their growth. Since the equations above are linear and can only give rise to exponential growth or exponential decay, they can only describe the microbial dynamics over a limited period of time. In order to account for saturation and competition during growth, we thus need to introduce non-linear terms to the equations (1). The study of this kind of systems often focus on long-term dynamics, yet it can be of high practical relevance to study the transient optimal strategies, as shorter timescales are often relevant in the real world – whether it be due to experimental constraints or to ecological disturbances and perturbations [20]. Since we are going to consider some out-of equilibrium dynamics, in particular in the section with competition limited to one of the compartments, and because we are also interested in transient properties, we will adopt a numerical approach based on the number of microbes [21, 22].In this section, we study the case of a microbial lineage constrained by global competition occurring at rate k = kHH = kEE = kEH = kHE. This situation could correspond to a host-associated microbe living in direct contact with an external environment, e.g., on the surface of an organism. Alternatively, what we call the “environment” in our model could represent another host compartment in direct contact with the other, like the gut lumen and the colonic crypts. In that case, microbes living in association with the host are in direct contact with those in the environment and can mutually impact each other’s growth. This is of particular relevance if microbes living in both compartments rely on and are limited by the same nutrients for growth.From the microbial abundances in the two compartments obtained by numerically solving the equations, one can build a proxy for the overall growth rate of the microbial lineage. To remain consistent with the previous section, we define$$varLambda left( {mathbf{x}} right) = frac{1}{{t_{max}}}log left( {frac{{n_Eleft( {t_{max}} right) + n_Hleft( {t_{max}} right)}}{{n_Eleft( 0 right) + n_Hleft( 0 right)}}} right)$$
(6)
i.e., the effective exponential growth rate of the microbial lineage over a chosen period of time [0, tmax]. Figure 2A provides a graphical explanation for the expression of Λ. There are indeed several fundamental differences between the effective exponential growth rate Λ in a non-linear system and the asymptotic growth rate λ in a linear system, the dominant eigenvalue of the projection matrix as defined in the baseline model. First, Λ provides a measure of growth for the whole lineage, but is not an asymptotic growth rate (as compared to λ in the baseline model): in the case of global saturation, replication stops when the carrying capacity is reached, and the asymptotic growth rate for the whole lineage would thus be zero. Therefore, the choice of the probing time tmax has an impact on Λ, as shown in Fig. 2A. Second, the choice of the exact form of Λ now implies biological assumptions on the selection pressure experienced by the microbial lineage: choosing the effective exponential growth rate over the whole lineage as we do implies that selection is acting on both compartments evenly. There may be some situations in which the microbes in one of the compartments only are artificially selected for (e.g., as part of the protocol of an evolution experiment). In such cases, it would make sense to define Λ as the effective exponential growth rate over just this compartment. This may lead to different conclusions, in particular at the transient scale. One must thus adapt Λ to the specifics of the modeled system. In addition, the choice of tmax itself has a biological meaning, and should in particular not exceed the time upon which the dynamics of the system are accurately described by the set of equations. This may also be determined by experimental times.Fig. 2: Optimal strategies in the model with global competition.A Temporal dynamics of the total number of microbes nE(t) + nH(t) for three different sets of traits values, differing only by their intensity of competition k = kHH = kEE = kEH = kHE. Other parameter values are: rH = 0.1, mE = mH = 0.5. The effective overall growth rate Λ is calculated numerically by taking the slope of the straight line that connects the abundances in t = 0 and in tmax, thus making Λ a quantity that strongly depends on tmax. B Change in the contour line delimiting the regions of optimality of the two optimal strategies (strategy I: increasing rH; strategy II: decreasing mH) with tmax, the time chosen to measure the final number of microbes, measured in units of 1/rE. Initially the microbes are equally distributed between the host and the environment. Supplementary Fig. S2 shows how this is modified with different initial conditions. Because in this model all the microbes are equally impacted by competition, with tmax large enough, one recovers the contour line of the baseline model calculated analytically (black line). Continuous lines: k = 0, i.e., no competition. Dashed lines: increasing values of k (competition intensity). C, D Change in the fitness landscape with tmax (panel C: tmax = 0.7 and panel D: tmax = 3). The colored lines show the contour delimiting the regions of optimality of strategies I and II for three different values of k, as shown on panel B. Black line: long-term limit of no competition from the base model.Full size imageWe now calculate the sensitivity of Λ in the direction of the trait i at the point x of the phenotypic space as$$S_i = frac{{varLambda left( {x_1,x_2, ldots ,x_{i – 1},x_i + delta x_i,x_{i + 1}, ldots ,x_N} right) – varLambda left( {x_1,x_2, ldots ,x_N} right)}}{{delta x_i}}$$
(7)
with δxi the discretization interval, and N the number of traits defining a phenotype x.For this numerical approach, additional choices need to be made. First, the trait space needs to be discretized. Then, to calculate Eq. (7), one needs to choose a set of initial conditions and a probing time at which to measure the microbial abundances, as exposed in detail for the linear case in [20]. Finally, we need to choose the discretization interval δxi. In the following, we always choose δxi sufficiently small for convergence, i.e., so that it does not significantly impact the numerical values of the sensitivities, and focus on the choices of the other parameters (probing time and initial conditions) and the influence of the competition intensity k. One strategy to explore the possible impact of initial conditions is to use “stage biased vectors” [20], i.e., extreme initial distributions of microbes across the two compartments. This corresponds to initial conditions where microbes either exist only in the host or only in the environment.In Fig. 2B, we show how the contour lines delimiting the two optimal strategies change with the final time tmax chosen to measure the overall growth rate and with the intensity of competition k, for a mixed initial condition (nE(0) = 0.5, nH(0) = 0.5), and Supplementary Fig. S2 shows how this is modified with stage biased vectors. In all cases, with sufficiently long tmax, the contours converge to the contour plot of the baseline model shown in the previous section. This is expected, since competition here affects all the microbes in the same way, so that the equilibrium distribution is the same as the asymptotic distribution of the baseline model (given by the dominant eigenvector). Mathematically, global competition can be seen as a modification of the baseline projection matrix by subtracting an identity matrix times a scalar depending on time. This does neither affect the eigenvectors nor the dependence of the dominant eigenvalue on the traits.In the case where all the microbes are initially in the environment (Supplementary Fig. S2A), there is no transient effect and whichever tmax is chosen, all the contour lines collapse to the limit of the baseline case. In the case where all the microbes are initially in the host (Supplementary Fig. S2B), a third optimal strategy transiently appears (increasing mE) and remains at long times around m = 0. In this unfavorable condition (m = 0 and an initially empty environment), increasing the microbial flux towards the environment becomes more important than limiting the flux of microbes leaving it (which is nonexistent when m = 0).Finally, we observe that the intensity of competition has only a small effect on the contours (Fig. 2B and S2B), but increasing k appears to slightly accelerate convergence to the baseline contour. By limiting growth in the host compartment – when it is initially relatively more populated than in the asymptotic distribution – competition facilitates the convergence to the baseline asymptotic distribution, where most of the microbes live in the environment.Model with competition within one of the compartments onlyIn this section we consider competition happening inside one of the compartments only (i.e., kEH = kHE = 0 and kEE ≠ 0 or kHH ≠ 0). We will start by considering competition in the host only (the slow-replicating compartment). In a second step we also look at the case with competition limited to the environment. One should bear in mind that it also covers the case of competition limited to a host where replication is faster than in the environment (rH  > rE), provided a switch of the H and E index.In the case where competition is limited to only one of the compartments, we do not expect an equilibrium to exist for all traits combination of the phenotypic space. If migration is not sufficiently important, the number of microbes in the unconstrained compartment keeps increasing exponentially faster than the number of microbes in the constrained compartment, which contribution to the whole lineage thus becomes rapidly negligible. At sufficiently high migration rates however, an equilibrium is expected, because microbes switch habitats sufficiently rapidly for competition to be globally effective, although it directly affects only one of the compartments.Competition in the host only (slow-replicating compartment)When there is competition in the host only, there is no (positive) equilibrium for all mH  More

1.Wisz MS, Pottier J, Kissling WD, Pellissier L, Lenoir J, Damgaard CF, et al. The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biol Rev Camb Philos Soc. 2013;88:15–30.PubMed 
Article 
PubMed Central 

Google Scholar 
2.Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–63.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Cho BC, Azam F. Major role of bacteria in biogeochemical fluxes in the oceans interior. Nature. 1988;332:441–3.CAS 
Article 

Google Scholar 
4.Rousk J, Bengtson P. Microbial regulation of global biogeochemical cycles. Front Microbiol. 2014;5:103.PubMed 
PubMed Central 
Article 

Google Scholar 
5.Guilhon M, Montserrat F, Turra A. Recognition of ecosystem-based management principles in key documents of the seabed mining regime: implications and further recommendations. ICES J Marine Sci. 2020:fsaa229.6.Sherman K, Sissenwine M, Christensen V, Duda A, Hempel G, Ibe C, et al. A global movement toward an ecosystem approach to management of marine resources. Mar Ecol Prog Ser. 2005;300:275–9.Article 

Google Scholar 
7.Passarelli C, Olivier F, Paterson DM, Hubas C. Impacts of biogenic structures on benthic assemblages: microbes, meiofauna, macrofauna and related ecosystem functions. Mar Ecol Prog Ser. 2012;465:85–97.Article 

Google Scholar 
8.Baldrighi E, Aliani S, Conversi A, Lavaleye M, Borghini M, Manini E. From microbes to macrofauna: an integrated study of deep benthic communities and their response to environmental variables along the Malta Escarpment (Ionian Sea). Sci Mar. 2013;77:625–39.Article 

Google Scholar 
9.Foshtomi MY, Braeckman U, Derycke S, Sapp M, Van Gansbeke D, Sabbe K, et al. The link between microbial diversity and nitrogen cycling in marine sediments is modulated by macrofaunal bioturbation. PLoS ONE. 2015;10:e0130116.10.Hope JA, Paterson DM, Thrush SF. The role of microphytobenthos in soft-sediment ecological networks and their contribution to the delivery of multiple ecosystem services. J Ecology. 2020;108:815–30.Article 

Google Scholar 
11.Lima-Mendez G, Faust K, Henry N, Decelle J, Colin S, Carcillo F, et al. Ocean plankton. Determinants of community structure in the global plankton interactome. Science. 2015;348:1262073.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
12.Blanchet FG, Cazelles K, Gravel D. Co-occurrence is not evidence of ecological interactions. Ecol Lett. 2020;23:1050–63.PubMed 
Article 
PubMed Central 

Google Scholar 
13.Pearson K. Mathematical contributions to the theory of evolution—on a form of spurious correlation which may arise when indices are used in the measurement of organs. Proc R Soc Lond. 1897;60:489–98.Article 

Google Scholar 
14.Jackson DA. Compositional data in community ecology: the paradigm or peril of proportions? Ecology. 1997;78:929–40.Article 

Google Scholar 
15.Gloor GB, Reid G. Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data. Can J Microbiol. 2016;62:692–703.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Lovell D, Pawlowsky-Glahn V, Egozcue JJ, Marguerat S, Bahler J. Proportionality: a valid alternative to correlation for relative data. PLoS Comput Biol. 2015;11:e1004075.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
17.Sievert SM, Vetriani C. Chemoautotrophy at deep-sea vents: past, present, and future. Oceanography. 2012;25:218–33.Article 

Google Scholar 
18.Huber JA, Butterfield DA, Baross JA. Temporal changes in archaeal diversity and chemistry in a mid-ocean ridge subseafloor habitat. Appl Environ Microbiol. 2002;68:1585–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Karl DM, Wirsen CO, Jannasch HW. Deep-sea primary production at the Galapagos hydrothermal vents. Science. 1980;207:1345–7.CAS 
Article 

Google Scholar 
20.Meyer JL, Akerman NH, Proskurowski G, Huber JA Microbiological characterization of post-eruption “snowblower” vents at Axial Seamount Juan de Fuca Ridge. Front Microbiol. 2013;4:153.21.Orcutt BN, Sylvan JB, Knab NJ, Edwards KJ. Microbial ecology of the dark ocean above, at, and below the seafloor. Microbiol Mol Biol Rev. 2011;75:361–422.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Dubilier N, Bergin C, Lott C. Symbiotic diversity in marine animals: the art of harnessing chemosynthesis. Nat Rev Microbiol. 2008;6:725–40.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Yamanaka T et al. A Compilation of the Stable Isotopic Compositions of Carbon, Nitrogen, and Sulfur in Soft Body Parts of Animals Collected from Deep-Sea Hydrothermal Vent and Methane Seep Fields: Variations in Energy Source and Importance of Subsurface Microbial Processes in the Sediment-Hosted Systems. In: Ishibashi J, Okino K, Sunamura M, editors. Subseafloor Biosphere Linked to Hydrothermal Systems. Tokyo, Japan: Springer Open; 2015. p. 105–29.24.Bergquist D, Eckner J, Urcuyo I, Cordes E, Hourdez S, Macko S, Fisher C. Using stable isotopes and quantitative community characteristics to determine a local hydrothermal vent food web. Mar Ecol Prog Ser. 2007;330:49–65.Article 

Google Scholar 
25.Colaço A, Dehairs F, Desbruyères D. Nutritional relations of deep-sea hydrothermal fields at the Mid-Atlantic Ridge: a stable isotope approach. Deep-Sea Res Part I-Oceanogr Res Pap. 2002;49:395–412.Article 

Google Scholar 
26.Van Dover C, Fry B. Stable isotopic compositions of hydrothermal vent organisms. Mar Biol. 1989;102:257–63.Article 

Google Scholar 
27.Colaço A, Desbruyères D, Guezennec J. Polar lipid fatty acids as indicators of trophic associations in a deep-sea vent system community. Marine Ecology-an Evolut Perspect. 2007;28:15–24.Article 
CAS 

Google Scholar 
28.Limen H, Stevens CJ, Bourass Z, Juniper SK. Trophic ecology of siphonostomatoid copepods at deep-sea hydrothermal vents in the northeast Pacific. Mar Ecol Prog Ser. 2008;359:161–70.Article 

Google Scholar 
29.Van Dover CL. Trophic relationships among invertebrates at the Kairei hydrothermal vent field (Central Indian Ridge). Mar Biol. 2002;141:761–72.Article 

Google Scholar 
30.Lamy T, Koenigs C, Holbrook SJ, Miller RJ, Stier AC, Reed DC. Foundation species promote community stability by increasing diversity in a giant kelp forest. Ecology. 2020;101:e02987.PubMed 
Article 
PubMed Central 

Google Scholar 
31.Bruno JF, Bertness MD Habitat modification and facilitation in benthic marine communities. In: Bertness MD, Gaines SD, Hay ME, editors. Marine Community Ecology. Sunderland, MA: Sinauer Associates; 2001. p. 201–18.32.Dayton PK Toward an Understanding of Community Resilience and the Potential Effects of Enrichments to the Benthos at McMurdo Sound, Antarctica. Pages 81-95. In: Parker BC, editor. Proceedings of the Colloquium on Conservation Problems. Lawrence, Kansas, USA.: Allen Press; 1972.33.Tunnicliffe V, Cordes EE The tubeworm forests of hydrothermal vents and cold seeps. In: Rossi S, Bramanti L, editors. Perspectives on the Marine Animal Forests of the World Springer; 2020. p. 147–92.34.López-García P, Gaill F, Moreira D. Wide bacterial diversity associated with tubes of the vent worm Riftia pachyptila. Environ Microbiol. 2002;4:204–15.PubMed 
Article 
PubMed Central 

Google Scholar 
35.Rincon-Tomas B, Francisco Javier González, Luis Somoza, Kathrin Sauter, Pedro Madureira, Teresa Medialdea et al. Siboglinidae Tubes as an Additional Niche for Microbial Communities in the Gulf of Cadiz-A Microscopical Appraisal. Microorganisms. 2020;8:367.36.Page A, Juniper SK, Olagnon M, Alain K, Desrosiers G, Querellou J, et al. Microbial diversity associated with a Paralvinella sulfincola tube and the adjacent substratum on an active deep-sea vent chimney. Geobiology. 2004;2:225–38.Article 

Google Scholar 
37.Govenar B Shaping Vent and Seep Communities: Habitat Provision and Modification by Foundation Species. In: Kiel S, editor. The vent and seep biota: aspects from microbes to ecosystems. Dordrecht: Springer; 2010. p. 403–32.38.Tunnicliffe V, Germain CS, Hilario A Phenotypic Variation and Fitness in a Metapopulation of Tubeworms (Ridgeia piscesae Jones) at Hydrothermal Vents. PLoS ONE. 2014;9:e110578.39.Sarrazin J, Juniper SK. Biological characteristics of a hydrothermal edifice mosaic community. Mar Ecol Prog Ser. 1999;185:1–19.Article 

Google Scholar 
40.Sarrazin J, Juniper SK, Massoth G, Legendre P. Physical and chemical factors influencing species distributions on hydrothermal sulfide edifices of the Juan de Fuca Ridge, northeast Pacific. Mar Ecol Prog Ser. 1999;190:89–112.CAS 
Article 

Google Scholar 
41.Govenar BW, Bergquist DC, Urcuyo IA, Eckner JT, Fisher CR. Three Ridgeia piscesae assemblages from a single Juan de Fuca Ridge sulphide edifice: structurally different and functionally similar. Cah Biol Mar. 2002;43:247–52.
Google Scholar 
42.Forget NL, Juniper SK. Free-living bacterial communities associated with tubeworm (Ridgeia piscesae) aggregations in contrasting diffuse flow hydrothermal vent habitats at the Main Endeavour Field, Juan de Fuca Ridge. MicrobiologyOpen. 2013;2:259–75.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Danovaro R, Gambi C, Dell’Anno A, Corinaldesi C, Fraschetti S, Vanreusel A, et al. Exponential decline of deep-sea ecosystem functioning linked to benthic biodiversity loss. Curr Biol. 2008;18:1–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Dick GJ. The microbiomes of deep-sea hydrothermal vents: distributed globally, shaped locally. Nature Rev Microbiol. 2019;17:271–83.CAS 

Google Scholar 
45.Lee W-K, Juniper SK, Perez M, Ju S-J, Kim S-J Diversity and characterization of bacterial communities of five co-occurring species at a hydrothermal vent on the Tonga Arc. Ecol Evol. 2021;11:4481–93.46.Sogin ML, Morrison HG, Huber JA, Mark Welch D, Huse SM, Neal PR, et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sci USA. 2006;103:12115–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Eren AM, Vineis JH, Morrison HG, Sogin ML. A filtering method to generate high quality short reads using illumina paired-end technology. PLoS One. 2013;8:e66643.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Caron DA, Countway PD, Savai P, Gast RJ, Schnetzer A, Moorthi SD, et al. Defining DNA-based operational taxonomic units for microbial-eukaryote ecology. Appl Environ Microbiol. 2009;75:5797–808.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.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(Database issue):D590–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
51.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 
Article 
PubMed Central 

Google Scholar 
52.Quinn TP, Ionas Erb, Greg Gloor, Cedric Notredame, Mark F Richardson, Tamsyn M Crowley et al. A field guide for the compositional analysis of any-omics data. Gigascience. 2019;8:giz107.53.Martín-Fernández JA, Palarea-Albaladejo J, Olea RA Dealing with Zeros. In: Pawlowsky‐Glahn V, Buccianti A, editors. Compositional Data Analysis2011. p. 43-58.54.Palarea-Albaladejo J, Martin-Fernandez JA. zCompositions – R Package for multivariate imputation of left-censored data under a compositional approach. Chemometr Intell Lab. 2015;143:85–96.CAS 
Article 

Google Scholar 
55.Aitchison J The statistical analysis of compositional data. London: Chapman & Hall; 1986. p. 416.56.Aitchison J, Barcelo-Vidal C, Martin-Fernandez JA, Pawlowsky-Glahn V. Logratio analysis and compositional distance. Math Geol. 2000;32:271–5.Article 

Google Scholar 
57.Comas-Cufí M coda.base: A Basic Set of Functions for Compositional Data Analysis. R package version 0.2.1 2019 [Available from: https://CRAN.R-project.org/package=coda.base.58.Oksanen J et al. vegan: Community Ecology Package. R package version 2.2-1. 2015 [Available from: http://CRAN.R-project.org/package=vegan.59.Fernandes AD, Macklaim JM, Linn TG, Reid G, Gloor GB. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS One. 2013;8:e67019.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Quinn TP, Richardson MF, Lovell D, Crowley TM. propr: an R-package for identifying proportionally abundant features using compositional data analysis. Sci Rep. 2017;7:16252.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
61.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research. 2003;13:2498–504.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Huber JA, Butterfield DA, Baross JA. Bacterial diversity in a subseafloor habitat following a deep-sea volcanic eruption. FEMS Microbiol Ecol. 2003;43:393–409.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Akerman NH, Butterfield DA, Huber JA Phylogenetic diversity and functional gene patterns of sulfur-oxidizing subseafloor Epsilonproteobacteria in diffuse hydrothermal vent fluids. Front Microbiol. 2013;4:185.64.Tsurumi M, Tunnicliffe V. Tubeworm-associated communities at hydrothermal vents on the Juan de Fuca Ridge, northeast Pacific. Deep-Sea Res Part I-Oceanogr Res Pap. 2003;50:611–29.Article 

Google Scholar 
65.Butterfield DA, Massoth GJ, McDuff RE, Lupton JE, Lilley MD. Geochemistry of hydrothermal fluids from Axial Seamount Hydrothermal Emissions Study vent field, Juan de Fuca Ridge: subseafloor boiling and subsequent fluid-rock interaction. J Geophys Res. 1990;95:12895–921.Article 

Google Scholar 
66.Johnson KS, Beehler CL, Sakamotoarnold CM, Childress JJ. insitu measurements of chemical-distributions in a deep-sea hydrothermal vent field. Science. 1986;231:1139–41.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Du Preez C, Fisher CP Long-Term Stability of back-Arc basin hydrothermal vents. Front Mar Sci. 2018;5:54.68.Urcuyo IA, Bergquist DC, MacDonald IR, VanHorn M, Fisher CR. Growth and longevity of the tubeworm Ridgeia piscesae in the variable diffuse flow habitats of the Juan de Fuca Ridge. Mar Ecol Prog Ser. 2007;344:143–57.Article 

Google Scholar 
69.Perner M, Bach W, Hentscher M, Koschinsky A, Garbe-Schönberg D, Streit WR, et al. Short-term microbial and physico-chemical variability in low-temperature hydrothermal fluids near 5 degrees S on the Mid-Atlantic Ridge. Environ Microbiol. 2009;11:2526–41.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Orcutt BN, Bradley JA, Brazelton WJ, Estes ER, Goordial JM, Huber JA, et al. Impacts of deep-sea mining on microbial ecosystem services. Limnology Oceanogr. 2020;65:1489–510.CAS 
Article 

Google Scholar 
71.Gollner S, Ivanenko VN, Arbizu PM, Bright M. Advances in taxonomy, ecology, and biogeography of Dirivultidae (copepoda) associated with chemosynthetic environments in the deep sea. PLoS One. 2010;5:e9801.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
72.Kalanetra KM, Nelson DC. Vacuolate-attached filaments: highly productive Ridgeia piscesae epibionts at the Juan de Fuca hydrothermal vents. Mar Biol. 2010;157:791–800.PubMed 
Article 
PubMed Central 

Google Scholar 
73.Girguis PR, Lee RW. Thermal preference and tolerance of alvinellids. Science. 2006;312:231.PubMed 
Article 
PubMed Central 

Google Scholar 
74.Burgaud G, Le Calvez T, Arzur D, Vandenkoornhuyse P, Barbier G. Diversity of culturable marine filamentous fungi from deep-sea hydrothermal vents. Environ Microbiol. 2009;11:1588–600.PubMed 
Article 
PubMed Central 

Google Scholar 
75.Murdock SA, Juniper SK. Hydrothermal vent protistan distribution along the Mariana arc suggests vent endemics may be rare and novel. Environ Microbiol. 2019;21:3796–815.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Meier DV, Bach W, Girguis PR, Gruber-Vodicka HR, Reeves EP, Richter M, et al. Heterotrophic Proteobacteria in the vicinity of diffuse hydrothermal venting. Environ Microbiol. 2016;18:4348–68.PubMed 
Article 
PubMed Central 

Google Scholar 
77.Stokke R, Dahle H, Roalkvam I, Wissuwa J, Daae FL, Tooming-Klunderud A, et al. Functional interactions among filamentous Epsilonproteobacteria and Bacteroidetes in a deep-sea hydrothermal vent biofilm. Environ Microbiol. 2015;17:4063–77.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Adl SM, Bass D, Lane CE, Lukeš J, Schoch CL, Smirnov A, et al. Revisions to the classification, nomenclature, and diversity of eukaryotes. J Eukaryot Microbiol. 2019;66:4–119.PubMed 
PubMed Central 
Article 

Google Scholar 
79.Brown MW, Sharpe SC, Silberman JD, Heiss AA, Lang BF, Simpson AG, et al. Phylogenomics demonstrates that breviate flagellates are related to opisthokonts and apusomonads. Proc Biol Sci. 2013;280:20131755.PubMed 
PubMed Central 

Google Scholar 
80.Hamann E, Gruber-Vodicka H, Kleiner M, Tegetmeyer HE, Riedel D, Littmann S, et al. Environmental Breviatea harbour mutualistic Arcobacter epibionts. Nature. 2016;534:254–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Gollner S, Riemer B, Arbizu PM, Le Bris N, Bright M. Diversity of meiofauna from the 9 degrees 50’ N East Pacific rise across a gradient of hydrothermal fluid emissions. PLoS ONE. 2010;5:e12321.82.Sarrazin J, Legendre P, de Busserolles F, Fabri MC, Guilini K, Ivanenko VN, et al. Biodiversity patterns, environmental drivers and indicator species on a high-temperature hydrothermal edifice, Mid-Atlantic Ridge. Deep-Sea Res Part Ii-Topical Stud Oceanogr. 2015;121:177–92.CAS 
Article 

Google Scholar 
83.Bates AE, Harmer TL, Roeselers G, Cavanaugh CM. Phylogenetic characterization of episymbiotic bacteria hosted by a hydrothermal vent limpet (lepetodrilidae, vetigastropoda). Biol Bull-US. 2011;220:118–27.Article 

Google Scholar 
84.Schratzberger M, Ingels J. Meiofauna matters: the roles of meiofauna in benthic ecosystems. J Exp Mar Biol Ecol. 2018;502:12–25.Article 

Google Scholar 
85.Cronin-O’Reilly S, Joe D Taylor, Ian Jermyn, A Louise Allcock, Michael Cunliffe, Mark P Johnson et al. Limited congruence exhibited across microbial, meiofaunal and macrofaunal benthic assemblages in a heterogeneous coastal environment. Sci Rep-UK. 2018;8:15500.86.Reimann F, Schrage M. The mucus-trap hypothesis on feeding of aquatic nematodes and implications for biodegradation and sediment texture. Oecologia. 1978;34:75–88.Article 

Google Scholar 
87.Léveillé RJ, Levesque C, Juniper SK Biotic interactions and feedback processes in deep-sea hydrothermal vent ecosystems. In: Kristensen E, Haese RR, Kostka JE, editors. Interactions between macro- and microorganisms in marine sediments. Washington, DC: American Geophysical Union; 2005. p. 299–321.88.Ingels J, Ann Vanreusel, Ellen Pape, Francesca Pasotti, Lara Macheriotou, Pedro Martínez Arbizu et al. Ecological variables for deep-ocean monitoring must include microbiota and meiofauna for effective conservation. Nat Ecology Evolut. 2020: https://doi.org/10.1038/s41559-020-01335-6.89.Thompson KF, Miller KA, Currie D, Johnston P, Santillo D. Seabed mining and approaches to governance of the deep seabed. Front Mar Sci. 2018;5:480. More

1.Courtiol, A., Etienne, L., Feron, R., Godelle, B. & Rousset, F. Am. Nat. 188, 521–538 (2016).Article 

Google Scholar 
2.Jennions, M. D. & Petrie, M. Biol. Rev. 75, 21–64 (2000).CAS 
Article 

Google Scholar 
3.Kokko, H. & Mappes, J. Evolution 59, 1876–1885 (2005).Article 

Google Scholar 
4.Hare, R. M. & Simmons, L. W. Biol. Rev. 94, 929–956 (2019).Article 

Google Scholar 
5.Kohlmeier, P., Zhang, Y., Gorter, J. A., Su, C.-Y. & Billeter, J.-C. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01482-4 (2021).Article 

Google Scholar 
6.Halliday, T. R. in Mate Choice (ed. Bateson, P.) 3–32 (Cambridge Univ. Press, 1983).7.Avila, F. W., Sirot, L. K., LaFlamme, B. A., Rubinstein, C. D. & Wolfner, M. F. Annu. Rev. Entomol. 56, 21–40 (2011).CAS 
Article 

Google Scholar 
8.Perry, J. C. & Rowe, L. Cold Spring Harb. Perspect. Biol. 7, a017558 (2015).Article 

Google Scholar 
9.Hopkins, B. R., Avila, F. W. & Wolfner, M. F. in Encyclopedia of Reproduction (ed. Skinner, M. K.) 137–144 (Elsevier, 2018).10.de Boer, R. A., Vega-Trejo, R., Kotrschal, A. & Fitzpatrick, J. L. Nat. Ecol. Evol. https://doi.org/ggbb (2021). More

1.D’Hondt S, Jørgensen BB, Miller DJ, Batzke A, Blake R, Cragg BA, et al. Distributions of microbial activities in deep subseafloor sediments. Science. 2004;306:2216–21.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
2.Froelich PN, Klinkhammer GP, Bender ML, Luedtke NA, Heath GR, Cullen D, et al. Early oxidation of organic matter in pelagic sediments of the eastern equatorial Atlantic: suboxic diagenesis. Geochim Cosmochim Acta. 1979;43:1075–90.CAS 
Article 

Google Scholar 
3.Parkes RJ, Cragg B, Roussel E, Webster G, Weightman A, Sass H. A review of prokaryotic populations and processes in sub-seafloor sediments, including biosphere: geosphere interactions. Mar Geol. 2014;352:409–25.CAS 
Article 

Google Scholar 
4.Arnosti C. Microbial extracellular enzymes and the marine carbon cycle. Annu Rev Mar Sci. 2011;3:401–25.Article 

Google Scholar 
5.Thamdrup B, Rosselló-Mora R, Amann R. Microbial manganese and sulfate reduction in Black Sea shelf sediments. Appl Environ Microbiol. 2000;66:2888–97.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Thamdrup B. Bacterial manganese and iron reduction in aquatic sediments. In: Schink B, editor. Advances in microbial ecology. Boston, MA, US: Springer; 2000. p. 41–84.7.Jørgensen BB, Kasten S. Sulfur cycling and methane oxidation. In: Schulz HD, Zabel M, editors. Marine geochemistry. 2nd ed. Berlin, Heidelberg, Germany: Springer-Verlag; 2006. p. 271–309.8.Bowles MW, Mogollón JM, Kasten S, Zabel M, Hinrichs K-U. Global rates of marine sulfate reduction and implications for sub–sea-floor metabolic activities. Science. 2014;344:889–91.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Jickells TD, An ZS, Andersen KK, Baker AR, Bergametti G, Brooks N, et al. Global iron connections between desert dust, ocean biogeochemistry, and climate. Science. 2005;308:67–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Raiswell R, Hawkings JR, Benning LG, Baker AR, Death R, Albani S, et al. Potentially bioavailable iron delivery by iceberg-hosted sediments and atmospheric dust to the polar oceans. Biogeosciences. 2016;13:3887–900.CAS 
Article 

Google Scholar 
11.Hawkings JR, Wadham JL, Tranter M, Raiswell R, Benning LG, Statham PJ, et al. Ice sheets as a significant source of highly reactive nanoparticulate iron to the oceans. Nat Commun. 2014;5:3929.CAS 
PubMed 
Article 

Google Scholar 
12.Death R, Wadham JL, Monteiro F, Le Brocq AM, Tranter M, Ridgwell A, et al. Antarctic ice sheet fertilises the Southern Ocean. Biogeosciences. 2014;11:2635–43.Article 

Google Scholar 
13.Monien D, Monien P, Brünjes R, Widmer T, Kappenberg A, Silva Busso AA, et al. Meltwater as a source of potentially bioavailable iron to Antarctica waters. Antarct Sci. 2017;29:277–91.Article 

Google Scholar 
14.Henkel S, Kasten S, Hartmann JF, Silva-Busso A, Staubwasser M. Iron cycling and stable Fe isotope fractionation in Antarctic shelf sediments, King George Island. Geochim Cosmochim Acta. 2018;237:320–38.CAS 
Article 

Google Scholar 
15.Hodson A, Nowak A, Sabacka M, Jungblut A, Navarro F, Pearce D, et al. Climatically sensitive transfer of iron to maritime Antarctic ecosystems by surface runoff. Nat Commun. 2017;8:14499.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Wang S, Bailey D, Lindsay K, Moore JK, Holland M. Impact of sea ice on the marine iron cycle and phytoplankton productivity. Biogeosciences. 2014;11:4713–31.CAS 
Article 

Google Scholar 
17.Jørgensen BB, Findlay AJ, Pellerin A. The biogeochemical sulfur cycle of marine sediments. Front Microbiol. 2019;10:849.PubMed 
PubMed Central 
Article 

Google Scholar 
18.Findlay AJ, Kamyshny A. Turnover rates of intermediate sulfur species (Sx2−, S0, S2O32−, S4O62−, SO32−) in anoxic freshwater and sediments. Front Microbiol. 2017;8:2551.19.Findlay AJ, Pellerin A, Laufer K, Jørgensen BB. Quantification of sulphide oxidation rates in marine sediment. Geochim Cosmochim Acta. 2020;280:441–52.CAS 
Article 

Google Scholar 
20.Canfield DE, Jørgensen BB, Fossing H, Glud R, Gundersen J, Ramsing NB, et al. Pathways of organic carbon oxidation in three continental margin sediments. Mar Geol. 1993;113:27–40.CAS 
PubMed 
Article 

Google Scholar 
21.Michaud AB, Laufer K, Findlay A, Pellerin A, Antler G, Turchyn AV, et al. Glacial influence on the iron and sulfur cycles in Arctic fjord sediments (Svalbard). Geochim Cosmochim Acta. 2020;280:423–40.CAS 
Article 

Google Scholar 
22.Jensen MM, Thamdrup B, Rysgaard S, Holmer M, Fossing H. Rates and regulation of microbial iron reduction in sediments of the Baltic-North Sea transition. Biogeochemistry. 2003;65:295–317.Article 

Google Scholar 
23.Beckler JS, Kiriazis N, Rabouille C, Stewart FJ, Taillefert M. Importance of microbial iron reduction in deep sediments of river-dominated continental-margins. Mar Chem. 2016;178:22–34.CAS 
Article 

Google Scholar 
24.Riedinger N, Brunner B, Krastel S, Arnold GL, Wehrmann LM, Formolo MJ, et al. Sulfur cycling in an iron oxide-dominated, dynamic marine depositional system: the Argentine Continental Margin. Front Earth Sci. 2017;5:33.Article 

Google Scholar 
25.Thamdrup B, Fossing H, Jørgensen BB. Manganese, iron and sulfur cycling in a coastal marine sediment, Aarhus Bay, Denmark. Geochim Cosmochim Acta. 1994;58:5115–29.CAS 
Article 

Google Scholar 
26.Arndt S, Jørgensen BB, LaRowe DE, Middelburg J, Pancost R, Regnier P. Quantifying the degradation of organic matter in marine sediments: a review and synthesis. Earth-Sci Rev. 2013;123:53–86.CAS 
Article 

Google Scholar 
27.Algora C, Vasileiadis S, Wasmund K, Trevisan M, Krüger M, Puglisi E, et al. Manganese and iron as structuring parameters of microbial communities in Arctic marine sediments from the Baffin Bay. FEMS Microbiol Ecol. 2015;91:fiv056.PubMed 
Article 
CAS 

Google Scholar 
28.Franco M, De Mesel I, Diallo MD, Van Der Gucht K, Van Gansbeke D, Van, et al. Effect of phytoplankton bloom deposition on benthic bacterial communities in two contrasting sediments in the southern North Sea. Aquat Micro Ecol. 2007;48:241–54.Article 

Google Scholar 
29.Zonneveld KAF, Versteegh GJM, Kasten S, Eglinton TI, Emeis K-C, Huguet C, et al. Selective preservation of organic matter in marine environments; processes and impact on the sedimentary record. Biogeosciences. 2010;7:483–511.CAS 
Article 

Google Scholar 
30.Jorgensen SL, Hannisdal B, Lanzén A, Baumberger T, Flesland K, Fonseca R, et al. Correlating microbial community profiles with geochemical data in highly stratified sediments from the Arctic Mid-Ocean Ridge. Proc Natl Acad Sci U S A. 2012;109:E2846–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Zinke LA, Glombitza C, Bird JT, Røy H, Jørgensen BB, Lloyd KG, et al. Microbial organic matter degradation potential in Baltic Sea sediments is influenced by depositional conditions and in situ geochemistry. Appl Environ Microbiol. 2019;85:e02164-18.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Yang J, Jiang H, Wu G, Dong H. Salinity shapes microbial diversity and community structure in surface sediments of the Qinghai-Tibetan Lakes. Sci Rep. 2016;6:25078.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Hicks N, Liu X, Gregory R, Kenny J, Lucaci A, Lenzi L, et al. Temperature driven changes in benthic bacterial diversity influences biogeochemical cycling in coastal sediments. Front Microbiol. 2018;9:1730.PubMed 
PubMed Central 
Article 

Google Scholar 
34.Hamdan LJ, Coffin RB, Sikaroodi M, Greinert J, Treude T, Gillevet PM. Ocean currents shape the microbiome of Arctic marine sediments. ISME J. 2013;7:685–96.CAS 
PubMed 
Article 

Google Scholar 
35.Schulz HD, Zabel M, editors. Marine geochemistry. 2nd ed. Berlin, Heidelberg, Germany: Springer-Verlag; 2006.36.Geprägs P, Torres ME, Mau S, Kasten S, Römer M, Bohrmann G. Carbon cycling fed by methane seepage at the shallow Cumberland Bay, South Georgia, sub-Antarctic. Geochem, Geophys Geosystems. 2016;17:1401–18.Article 
CAS 

Google Scholar 
37.Atkinson A, Whitehouse MJ, Priddle J, Cripps GC, Ward P, Brandon MA. South Georgia, Antarctica: a productive, cold water, pelagic ecosystem. Mar Ecol Prog Ser. 2001;216:279–308.CAS 
Article 

Google Scholar 
38.Löffler B. Geochemische Prozesse und Stoffkreisläufe in Sedimenten innerhalb und außerhalb des Cumberland-Bay Fjordes, Süd Georgien. Bachelor Thesis. Bremen, Germany: University of Bremen; 2013.39.Köster M. (Bio-)geochemische Prozesse in den eisenreichen Seep-Sedimenten der Cumberland-Bucht Südgeorgiens, Subantarktis. Bachelor Thesis. Bremen, Germany: University of Bremen; 2014.40.Römer M, Torres M, Kasten S, Kuhn G, Graham AG, Mau S, et al. First evidence of widespread active methane seepage in the Southern Ocean, off the sub-Antarctic island of South Georgia. Earth Planet Sci Lett. 2014;403:166–77.Article 
CAS 

Google Scholar 
41.Bohrmann G, Aromokeye AD, Bihler V, Dehning K, Dohrmann I, Gentz T, et al. R/V METEOR Cruise Report M134, emissions of free gas from cross-shelf troughs of South Georgia: distribution, quantification, and sources for methane ebullition sites in sub-Antarctic waters, Port Stanley (Falkland Islands)—Punta Arenas (Chile), 16 January–18 February 2017. 2017.42.Schnakenberg A, Aromokeye DA, Kulkarni A, Maier L, Wunder LC, Richter-Heitmann T, et al. Electron acceptor availability shapes Anaerobically Methane Oxidizing Archaea (ANME) communities in South Georgia sediments. Front Microbiol. 2021;12:726.Article 

Google Scholar 
43.Rückamp M, Braun M, Suckro S, Blindow N. Observed glacial changes on the King George Island ice cap, Antarctica, in the last decade. Global Planet Change. 2011;79:99–109.44.Seeberg-Elverfeldt J, Schlüter M, Feseker T, Kölling M. Rhizon sampling of porewaters near the sediment-water interface of aquatic systems. Limnol Oceanogr Methods. 2005;3:361–71.Article 

Google Scholar 
45.Oni OE, Miyatake T, Kasten S, Richter-Heitmann T, Fischer D, Wagenknecht L, et al. Distinct microbial populations are tightly linked to the profile of dissolved iron in the methanic sediments of the Helgoland mud area, North Sea. Front Microbiol. 2015;6:365.PubMed 
PubMed Central 

Google Scholar 
46.Aromokeye DA, Richter-Heitmann T, Oni OE, Kulkarni A, Yin X, Kasten S, et al. Temperature controls crystalline iron oxide utilization by microbial communities in methanic ferruginous marine sediment incubations. Front Microbiol. 2018;9:2574.PubMed 
PubMed Central 
Article 

Google Scholar 
47.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 
Article 

Google Scholar 
48.Herlemann DPR, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 2011;5:1571–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Ovreås L, Forney L, Daae FL, Torsvik V. Distribution of bacterioplankton in meromictic Lake Saelenvannet, as determined by denaturing gradient gel electrophoresis of PCR-amplified gene fragments coding for 16S rRNA. Appl Environ Microbiol. 1997;63:3367–73.PubMed 
PubMed Central 
Article 

Google Scholar 
50.Takai K, Horikoshi K. Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Appl Environ Microbiol. 2000;66:5066–72.51.Viollier E, Inglett P, Hunter K, Roychoudhury A, Van Cappellen P. The ferrozine method revisited: Fe(II)/Fe(III) determination in natural waters. Appl Geochem. 2000;15:785–90.CAS 
Article 

Google Scholar 
52.Yin X, Kulkarni AC, Friedrich MW. DNA and RNA stable isotope probing of methylotrophic methanogenic Archaea. In: Dumont MG, Hernández García M, editors. Stable isotope probing: methods and protocols. New York, NY: Springer; 2019. p. 189–206.53.Aromokeye DA, Kulkarni AC, Elvert M, Wegener G, Henkel S, Coffinet S, et al. Rates and microbial players of iron-driven anaerobic oxidation of methane in methanic marine sediments. Front Microbiol. 2020;10:3041.PubMed 
PubMed Central 
Article 

Google Scholar 
54.Eden PA, Schmidt TM, Blakemore RP, Pace NR. Phylogenetic analysis of Aquaspirillum magnetotacticum using polymerase chain reaction-amplified 16S rRNA-specific DNA. Int J Syst Evol Microbiol. 1991;41:324–5.CAS 

Google Scholar 
55.Yu Y, Lee C, Kim J, Hwang S. Group-specific primer and probe sets to detect methanogenic communities using quantitative real-time polymerase chain reaction. Biotechnol Bioeng. 2005;89:670–9.CAS 
PubMed 
Article 

Google Scholar 
56.Lueders T, Friedrich MW. Effects of amendment with ferrihydrite and gypsum on the structure and activity of methanogenic populations in rice field soil. Appl Environ Microbiol. 2002;68:2484–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Lane DJ. 16S/23S rRNA sequencing. In: Stackebrandt E, Goodfellow M, editors. Nucleic acid techniques in bacterial systematics. New York: John Wiley and Sons; 1991. p. 115–75.58.Großkopf R, Janssen PH, Liesack W. Diversity and structure of the methanogenic community in anoxic rice paddy soil microcosms as examined by cultivation and direct 16S rRNA gene sequence retrieval. Appl Environ Microbiol. 1998;64:960–9.59.Reyes C, Schneider D, Thürmer A, Kulkarni A, Lipka M, Sztejrenszus SY, et al. Potentially active iron, sulfur, and sulfate reducing bacteria in Skagerrak and Bothnian Bay sediments. Geomicrobiol J. 2017;34:840–50.CAS 
Article 

Google Scholar 
60.Kondo R, Nedwell DB, Purdy KJ, Silva SQ. Detection and enumeration of sulphate-reducing Bacteria in estuarine sediments by competitive PCR. Geomicrobiol J. 2004;21:145–57.CAS 
Article 

Google Scholar 
61.Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat. 2001;29:1165–88.Article 

Google Scholar 
62.R Core Team. R: a language and environment for statistical computing, 3.6.1. Vienna, Austria: R Foundation for Statistical Computing; 2019. Available from: https://www.R-project.org.63.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package, 2.5-6. 2019. Available from: https://CRAN.R-project.org/package=vegan.64.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. 2012;41:D590–6.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
65.Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Inkscape Team. Inkscape, 1.0.1. 2020. Available from: https://inkscape.org.68.Sun H, Spring S, Lapidus A, Davenport K, Glavina Del Rio T, Tice H, et al. Complete genome sequence of Desulfarculus baarsii type strain (2st14T). Stand Genom Sci. 2010;3:276–84.Article 

Google Scholar 
69.Kümmel S, Herbst F-A, Bahr A, Duarte M, Pieper DH, Jehmlich N, et al. Anaerobic naphthalene degradation by sulfate-reducing Desulfobacteraceae from various anoxic aquifers. FEMS Microbiol Ecol. 2015;91:fiv006.70.Belyakova EV, Rozanova EP, Borzenkov IA, Tourova TP, Pusheva MA, Lysenko AM, et al. The new facultatively chemolithoautotrophic, moderately halophilic, sulfate-reducing bacterium Desulfovermiculus halophilus gen. nov., sp. nov., isolated from an oil field. Microbiology. 2006;75:161–71.71.Rezadehbashi M, Baldwin SA. Core sulphate-reducing microorganisms in metal-removing semi-passive biochemical reactors and the co-occurrence of methanogens. Microorganisms. 2018;6:16.PubMed Central 
Article 
CAS 

Google Scholar 
72.Sheik CS, Jain S, Dick GJ. Metabolic flexibility of enigmatic SAR324 revealed through metagenomics and metatranscriptomics. Environ Microbiol. 2014;16:304–17.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Sorokin DY, Chernyh NA. ‘Candidatus Desulfonatronobulbus propionicus’: a first haloalkaliphilic member of the order Syntrophobacterales from soda lakes. Extremophiles. 2016;20:895–901.CAS 
PubMed 
Article 

Google Scholar 
74.Lovley DR, Giovannoni SJ, White DC, Champine JE, Phillips E, Gorby YA, et al. Geobacter metallireducens gen. nov. sp. nov., a microorganism capable of coupling the complete oxidation of organic compounds to the reduction of iron and other metals. Arch Microbiol. 1993;159:336–44.75.Roden EE, Lovley DR. Dissimilatory Fe(III) reduction by the marine microorganism Desulfuromonas acetoxidans. Appl Environ Microbiol. 1993;59:734–42.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Lovley DR, Coates JD, Saffarini DA, Lonergan DJ. Dissimilatory iron reduction. In: Winkelmann G, Carrano CJ, editors. Transition metals in microbial metabolism. Amsterdam: Harwood Academic Publishers; 1997. p. 187–215.77.Vandieken V, Finke N, Jørgensen BB. Pathways of carbon oxidation in an Arctic fjord sediment (Svalbard) and isolation of psychrophilic and psychrotolerant Fe(III)-reducing bacteria. Mar Ecol Prog Ser. 2006;322:29–41.CAS 
Article 

Google Scholar 
78.Vandieken V, Thamdrup B. Identification of acetate-oxidizing bacteria in a coastal marine surface sediment by RNA-stable isotope probing in anoxic slurries and intact cores. FEMS Microbiol Ecol. 2013;84:373–86.CAS 
PubMed 
Article 

Google Scholar 
79.Hori T, Aoyagi T, Itoh H, Narihiro T, Oikawa A, Suzuki K, et al. Isolation of microorganisms involved in reduction of crystalline iron(III) oxides in natural environments. Front Microbiol. 2015;6:386.PubMed 
PubMed Central 
Article 

Google Scholar 
80.Vandieken V, Mußmann M, Niemann H, Jørgensen BB. Desulfuromonas svalbardensis sp. nov. and Desulfuromusa ferrireducens sp. nov., psychrophilic, Fe(III)-reducing bacteria isolated from Arctic sediments, Svalbard. Int J Syst Evol Microbiol. 2006;56:1133–9.CAS 
PubMed 
Article 

Google Scholar 
81.Slobodkina GB, Reysenbach A-L, Panteleeva AN, Kostrikina NA, Wagner ID, Bonch-Osmolovskaya EA, et al. Deferrisoma camini gen. nov., sp. nov., a moderately thermophilic, dissimilatory iron(III)-reducing bacterium from a deep-sea hydrothermal vent that forms a distinct phylogenetic branch in the Deltaproteobacteria. Int J Syst Evol Microbiol. 2012;62:2463–8.CAS 
PubMed 
Article 

Google Scholar 
82.Tu T-H, Wu L-W, Lin Y-S, Imachi H, Lin L-H, Wang P-L. Microbial community composition and functional capacity in a terrestrial ferruginous, sulfate-depleted mud volcano. Front Microbiol. 2017;8:2137.PubMed 
PubMed Central 
Article 

Google Scholar 
83.Lovley DR, Roden EE, Phillips EJP, Woodward JC. Enzymatic iron and uranium reduction by sulfate-reducing bacteria. Mar Geol. 1993;113:41–53.CAS 
Article 

Google Scholar 
84.Bale SJ, Goodman K, Rochelle PA, Marchesi JR, Fry JC, Weightman AJ, et al. Desulfovibrio profundus sp. nov., a novel barophilic sulfate-reducing Bacterium from deep sediment layers in the Japan Sea. Int J Syst Evol Microbiol. 1997;47:515–21.85.Treude N, Rosencrantz D, Liesack W, Schnell S. Strain FAc12, a dissimilatory iron-reducing member of the Anaeromyxobacter subgroup of Myxococcales. FEMS Microbiol Ecol. 2003;44:261–9.CAS 
PubMed 
Article 

Google Scholar 
86.Hori T, Müller A, Igarashi Y, Conrad R, Friedrich MW. Identification of iron-reducing microorganisms in anoxic rice paddy soil by 13C-acetate probing. ISME J. 2010;4:267–78.87.Han Y, Perner M. The globally widespread genus Sulfurimonas: versatile energy metabolisms and adaptations to redox clines. Front Microbiol. 2015;6:989.PubMed 
PubMed Central 

Google Scholar 
88.Roalkvam I, Drønen K, Stokke R, Daae FL, Dahle H, Steen IH. Physiological and genomic characterization of Arcobacter anaerophilus IR-1 reveals new metabolic features in Epsilonproteobacteria. Front Microbiol. 2015;6:987.PubMed 
PubMed Central 
Article 

Google Scholar 
89.Schlosser C, Schmidt K, Aquilina A, Homoky WB, Castrillejo M, Mills RA, et al. Mechanisms of dissolved and labile particulate iron supply to shelf waters and phytoplankton blooms off South Georgia, Southern Ocean. Biogeosciences. 2018;15:4973–93.CAS 
Article 

Google Scholar 
90.Sahade R, Lagger C, Torre L, Momo F, Monien P, Schloss I, et al. Climate change and glacier retreat drive shifts in an Antarctic benthic ecosystem. Sci Adv. 2015;1:e1500050.PubMed 
PubMed Central 
Article 

Google Scholar 
91.Petro C, Starnawski P, Schramm A, Kjeldsen KU. Microbial community assembly in marine sediments. Aquat Micro Ecol. 2017;79:177–95.Article 

Google Scholar 
92.Petro C, Zäncker B, Starnawski P, Jochum LM, Ferdelman TG, Jørgensen BB, et al. Marine deep biosphere microbial communities assemble in near-surface sediments in Aarhus Bay. Front Microbiol. 2019;10:758.PubMed 
PubMed Central 
Article 

Google Scholar 
93.Starnawski P, Bataillon T, Ettema TJ, Jochum LM, Schreiber L, Chen X, et al. Microbial community assembly and evolution in subseafloor sediment. Proc Natl Acad Sci USA. 2017;114:2940–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
94.Marshall IPG, Ren G, Jaussi M, Lomstein BA, Jørgensen BB, Røy H, et al. Environmental filtering determines family-level structure of sulfate-reducing microbial communities in subsurface marine sediments. ISME J. 2019;13:1920–32.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Berner RA. Early diagenesis: a theoretical approach. Princeton, New Jersey: Princeton University Press; 1980.96.Cottrell MT, Kirchman DL. Natural assemblages of marine Proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl Environ Microbiol. 2000;66:1692–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
97.Bissett A, Bowman JP, Burke CM. Flavobacterial response to organic pollution. Aquat Micro Ecol. 2008;51:31–43.Article 

Google Scholar 
98.Martinez-Garcia M, Brazel DM, Swan BK, Arnosti C, Chain PSG, Reitenga KG, et al. Capturing single cell genomes of active polysaccharide degraders: an unexpected contribution of Verrucomicrobia. PLoS ONE. 2012;7:e35314.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
99.Sabree ZL, Kambhampati S, Moran NA. Nitrogen recycling and nutritional provisioning by Blattabacterium, the cockroach endosymbiont. Proc Natl Acad Sci U S A. 2009;106:19521–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
100.Bowman JP, McCuaig RD. Biodiversity, community structural shifts, and biogeography of Prokaryotes within Antarctic continental shelf sediment. Appl Environ Microbiol. 2003;69:2463–83.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
101.Blazejak A, Schippers A. High abundance of JS-1- and Chloroflexi-related Bacteria in deeply buried marine sediments revealed by quantitative, real-time PCR. FEMS Microbiol Ecol. 2010;72:198–207.CAS 
PubMed 
Article 

Google Scholar 
102.Yamada T, Sekiguchi Y, Hanada S, Imachi H, Ohashi A, Harada H, et al. Anaerolinea thermolimosa sp. nov., Levilinea saccharolytica gen. nov., sp. nov. and Leptolinea tardivitalis gen. nov., sp. nov., novel filamentous anaerobes, and description of the new classes Anaerolineae classis nov. and Caldilineae classis nov. in the bacterial phylum Chloroflexi. Int J Syst Evol Microbiol. 2006;56:1331–40.CAS 
PubMed 
Article 

Google Scholar 
103.Storesund JE, Øvreås L. Diversity of Planctomycetes in iron-hydroxide deposits from the Arctic Mid Ocean Ridge (AMOR) and description of Bythopirellula goksoyri gen. nov., sp. nov., a novel Planctomycete from deep sea iron-hydroxide deposits. Antonie Van Leeuwenhoek. 2013;104:569–84.CAS 
PubMed 
Article 

Google Scholar 
104.Kovaleva OL, Merkel AY, Novikov AA, Baslerov RV, Toshchakov SV, Bonch-Osmolovskaya EA. Tepidisphaera mucosa gen. nov., sp. nov., a moderately thermophilic member of the class Phycisphaerae in the phylum Planctomycetes, and proposal of a new family, Tepidisphaeraceae fam. nov., and a new order, Tepidisphaerales ord. nov. Int J Syst Evol Microbiol. 2015;65:549–55.CAS 
PubMed 
Article 

Google Scholar 
105.Borrione I, Schlitzer R. Distribution and recurrence of phytoplankton blooms around South Georgia, Southern Ocean. Biogeosciences. 2013;10:217–31.Article 

Google Scholar 
106.Pfennig N, Biebl H. Desulfuromonas acetoxidans gen. nov. and sp. nov., a new anaerobic, sulfur-reducing, acetate-oxidizing bacterium. Arch Microbiol. 1976;110:3–12.CAS 
PubMed 
Article 

Google Scholar 
107.Finster K, Bak F, Pfennig N. Desulfuromonas acetexigens sp. nov., a dissimilatory sulfur-reducing eubacterium from anoxic freshwater sediments. Arch Microbiol. 1994;161:328–32.CAS 
Article 

Google Scholar 
108.Lovley DR, Phillips EJP, Lonergan DJ, Widman PK. Fe(III) and S0 reduction by Pelobacter carbinolicus. Appl Environ Microbiol. 1995;61:2132–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
109.An TT, Picardal FW. Desulfuromonas carbonis sp. nov., an Fe(III)-, S0- and Mn(IV)-reducing bacterium isolated from an active coalbed methane gas well. Int J Syst Evol Microbiol. 2015;65:1686–93.CAS 
PubMed 
Article 

Google Scholar 
110.Pjevac P, Kamyshny A Jr, Dyksma S, Mußmann M. Microbial consumption of zero-valence sulfur in marine benthic habitats. Environ Microbiol. 2014;16:3416–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
111.Miao Z-Y, He H, Tan T, Zhang T, Tang J-L, Yang Y-C, et al. Biotreatment of Mn2+ and Pb2+ with sulfate-reducing bacterium Desulfuromonas alkenivorans S-7. J Environ Eng. 2018;144:04017116.Article 

Google Scholar 
112.Buongiorno J, Herbert L, Wehrmann L, Michaud A, Laufer K, Røy H, et al. Complex microbial communities drive iron and sulfur cycling in Arctic fjord sediments. Appl Environ Microbiol. 2019;85:e00949-19.PubMed 
PubMed Central 
Article 

Google Scholar 
113.Zhang H, Liu F, Zheng S, Chen L, Zhang X, Gong J. The differentiation of iron-reducing bacterial community and iron-reduction activity between riverine and marine sediments in the Yellow River estuary. Mar Life Sci Technol. 2020;2:87–96.Article 

Google Scholar 
114.Ravenschlag K, Sahm K, Pernthaler J, Amann R. High bacterial diversity in permanently cold marine sediments. Appl Environ Microbiol. 1999;65:3982–9.115.Kashefi K, Holmes DE, Baross JA, Lovley DR. Thermophily in the Geobacteraceae: Geothermobacter ehrlichii gen. nov., sp. nov., a novel thermophilic member of the Geobacteraceae from the “Bag City” hydrothermal vent. Appl Environ Microbiol. 2003;69:2985–93.116.Holmes DE, Nicoll JS, Bond DR, Lovley DR. Potential role of a novel psychrotolerant member of the family Geobacteraceae, Geopsychrobacter electrodiphilus gen. nov., sp. nov., in electricity production by a marine sediment fuel cell. Appl Environ Microbiol. 2004;70:6023–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
117.Jørgensen BB. Mineralization of organic matter in the sea bed—the role of sulphate reduction. Nature. 1982;296:643–5.Article 

Google Scholar 
118.Bryant M, Campbell LL, Reddy C, Crabill M. Growth of Desulfovibrio in lactate or ethanol media low in sulfate in association with H2-utilizing methanogenic bacteria. Appl Environ Microbiol. 1977;33:1162–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
119.Muyzer G, Stams AJ. The ecology and biotechnology of sulphate-reducing bacteria. Nat Rev Microbiol. 2008;6:441–54.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
120.Dalsgaard T, Bak F. Nitrate reduction in a sulfate-reducing bacterium, Desulfovibrio desulfuricans, isolated from rice paddy soil: sulfide inhibition, kinetics, and regulation. Appl Environ Microbiol. 1994;60:291–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
121.Holmes DE, Bond DR, Lovley DR. Electron transfer by Desulfobulbus propionicus to Fe (III) and graphite electrodes. Appl Environ Microbiol. 2004;70:1234–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
122.Lovley DR, Phillips EJP. Competitive mechanisms for inhibition of sulfate reduction and methane production in the zone of ferric iron reduction in sediments. Appl Environ Microbiol. 1987;53:2636–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
123.Finke N, Vandieken V, Jørgensen BB. Acetate, lactate, propionate, and isobutyrate as electron donors for iron and sulfate reduction in Arctic marine sediments, Svalbard. FEMS Microbiol Ecol. 2007;59:10–22.CAS 
PubMed 
Article 

Google Scholar 
124.Canfield DE, Thamdrup B, Hansen JW. The anaerobic degradation of organic matter in Danish coastal sediments: iron reduction, manganese reduction, and sulfate reduction. Geochim Cosmochim Acta. 1993;57:3867–83.CAS 
PubMed 
Article 

Google Scholar 
125.Jørgensen BB. The sulfur cycle of a coastal marine sediment (Limfjorden, Denmark). Limnol Oceanogr. 1977;22:814–32.Article 

Google Scholar 
126.Jørgensen BB, Laufer K, Michaud AB, Wehrmann LM. Biogeochemistry and microbiology of high Arctic marine sediment ecosystems—case study of Svalbard fjords. Limnol Oceanogr. 2021;66:S273–92.Article 
CAS 

Google Scholar 
127.Laufer K, Michaud AB, Røy H, Jørgensen BB. Reactivity of iron minerals in the seabed toward microbial reduction—a comparison of different extraction techniques. Geomicrobiol J. 2020;37:170–89.Article 

Google Scholar 
128.Holmkvist L, Ferdelman TG, Jørgensen BB. A cryptic sulfur cycle driven by iron in the methane zone of marine sediment (Aarhus Bay, Denmark). Geochim Cosmochim Acta. 2011;75:3581–99.CAS 
Article 

Google Scholar 
129.Riedinger N, Brunner B, Formolo MJ, Solomon E, Kasten S, Strasser M, et al. Oxidative sulfur cycling in the deep biosphere of the Nankai Trough, Japan. Geology. 2010;38:851–4.CAS 
Article 

Google Scholar  More

1.Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science (80-) 328, 894–899 (2010).ADS 
CAS 
Article 

Google Scholar 
2.Benton, M. J. The red queen and the court jester: Species diversity and the role of biotic and abiotic factors through time. Science (80-) 323, 728–732 (2009).ADS 
CAS 
Article 

Google Scholar 
3.Addo-Bediako, A., Chown, S. L. & Gaston, K. J. Revisiting water loss in insects: a large scale view. J. Insect Physiol. 47, 1377–1388 (2001).CAS 
PubMed 
Article 

Google Scholar 
4.Abram, P., Boivin, G., Moiroux, J. & Brodeur, J. Behavioural effects of temperature on ectothermic animals: unifying thermal physiology and behavioural plasticity. Biol. Rev. Camb. Philos. Soc. 92, 1859–1876 (2016).PubMed 
Article 

Google Scholar 
5.Woods, H. A., Dillon, M. E. & Pincebourde, S. The roles of microclimatic diversity and of behavior in mediating the responses of ectotherms to climate change. J. Therm. Biol. 54, 86–97 (2015).PubMed 
Article 

Google Scholar 
6.Duffy, G. A., Coetzee, B. W., Janion-Scheepers, C. & Chown, S. L. Microclimate-based macrophysiology: implications for insects in a warming world. Curr. Opin. Insect Sci. 11, 84–89 (2015).PubMed 
Article 

Google Scholar 
7.Pincebourde, S., Sinoquet, H., Combes, D. & Casas, J. Regional climate modulates the canopy mosaic of favourable and risky microclimates for insects. J. Anim. Ecol. 76, 424–438 (2007).PubMed 
Article 

Google Scholar 
8.Sinoquet, H. et al. 3-D maps of tree canopy geometries at leaf scale. Ecology 90, 283 (2009).Article 

Google Scholar 
9.Pincebourde, S. & Woods, H. A. Climate uncertainty on leaf surfaces: The biophysics of leaf microclimates and their consequences for leaf-dwelling organisms. Funct. Ecol. 26, 844–853 (2012).Article 

Google Scholar 
10.Pincebourde, S. & Casas, J. Narrow safety margin in the phyllosphere during thermal extremes. Proc. Natl. Acad. Sci. 116, 5588–5596 (2019).CAS 
PubMed 
Article 

Google Scholar 
11.Classen, A. T., Hart, S. C., Whitman, T. G., Cobb, N. S. & Koch, G. W. Insect infestations linked to shifts in microclimate: important climate change implications. Soil Sci. Soc. Am. J. 69, 2049–2057 (2005).ADS 
CAS 
Article 

Google Scholar 
12.Beetge, L. & Krüger, K. Drought and heat waves associated with climate change affect performance of the potato aphid Macrosiphum euphorbiae. Sci. Rep. 9, 3645 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Dale, A. G. & Frank, S. D. Warming and drought combine to increase pest insect fitness on urban trees. PLoS One 12, e0173844 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Sørensen, J. G., Addison, M. F. & Terblanche, J. S. Mass-rearing of insects for pest management: challenges, synergies and advances from evolutionary physiology. Crop Prot. 38, 87–94 (2012).Article 

Google Scholar 
15.Klassen, W. & Curtis, C. F. History of the sterile insect technique. in Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management (eds. Dyck, V. A., Hendrichs, J. & Robinson, A. S.) 3–36 (Springer, 2005).16.Orozco-Dávila, D. et al. Mass rearing and sterile insect releases for the control of Anastrepha spp. pests in Mexico-a review. Entomol. Exp. Appl. 164, 176–187 (2017).Article 

Google Scholar 
17.Vreysen, M. J. B., Hendrichs, J. & Enkerlin, W. R. The sterile insect technique as a component of sustainable area-wide integrated pest management of selected horticultural insect pests. J. Fruit Ornam. Plant Res. 14, 107–130 (2006).
Google Scholar 
18.Enkerlin, W. R. Impact of fruit fly control programmes using the sterile insect technique. in Sterile Insect Technique: Principles and Practice in Area-Wide Integrated Pest Management (eds. Dyck, V. A., Hendrichs, J. & Robinson, A. S.) 652–676 (Springer, 2005).19.Dunn, D. W. & Follett, P. A. The sterile insect technique (SIT)-an introduction. Entomol. Exp. Appl. 164, 151–154 (2017).Article 

Google Scholar 
20.Vargas, R. I. Mass production of tephritid fruit flies. in Fruit Flies: Their Biology, Natural Enemies, and Control (eds. Robinson, A. S. & Hooper, G.) 141–152 (Elsevier, 1989).21.Perez-Staples, D., Shelly, T. E. & Yuval, B. Female mating failure and the failure of ‘mating’ in sterile insect programs. Entomol. Exp. Appl. 146, 66–78 (2013).Article 

Google Scholar 
22.Koyama, J., Kakinohana, H. & Miyatake, T. Eradication of the melon fly, Bactrocera cucurbitae, in Japan: importance of behavior, ecology, genetics, and evolution. Annu. Rev. Entomol. 49, 331–349 (2004).CAS 
PubMed 
Article 

Google Scholar 
23.Cayol, J. P. Changes in sexual behavior and life history traits of tephritid species caused by mass-rearing processes. in Fruit flies (Tephritidae): Phylogeny and Evolution of Behavior (eds. Aluja, M. & Norrbom, A. L.) 843–860 (CRC Press, 2000).24.Meza-Hernández, J. S. & Díaz-Fleischer, F. Comparison of sexual compatibility between laboratory and wild Mexican fruit flies under laboratory and field conditions. J. Econ. Entomol. 99, 1979–1986 (2006).PubMed 
Article 

Google Scholar 
25.Moreno, D. S., Sanchez, M., Robacker, D. C. & Worley, J. Mating competitiveness of irradiated mexican fruit fly (Diptera: Tephritidae). J. Econ. Entomol. 84, 1227–1234 (1991).Article 

Google Scholar 
26.Orozco-Dávila, D., Hernández, R., Meza, S. & Domínguez, J. Sexual competitiveness and compatibility between mass-reared sterile flies and wild populations of Anastrepha ludens (Diptera: Tephritidae) from different regions in Mexico. Florida Entomol. 90, 19–26 (2007).Article 

Google Scholar 
27.Weldon, C. W., Schutze, M. K. & Karsten, M. Trapping to monitor tephritid movement: results, best practice, and assessment of alternatives. in Trapping Tephritid Fruit Flies: Lures, Area-Wide Programs, and Trade Implications (eds. Shelly, T., Epsky, N., Jang, E. B., Reyes-Flores, J. & Vargas, R.) 175–217 (Springer, 2014).28.Dominiak, B. C., Worsley, P. M. & Nicol, H. Release from a point source and dispersal of sterile Queensland fruit fly (Bactrocera tryoni (froggatt)) (Diptera: Tephritidae) at Wagga Wagga. Plant Prot. Q. 28, 120–125 (2013).
Google Scholar 
29.Dimou, I., Koutsikopoulos, C., Economopoulos, A. P. & Lykakis, J. The distribution of olive fruit fly captures with McPhail traps within an olive orchard. Phytoparasitica 31, 124–131 (2003).Article 

Google Scholar 
30.Raghu, S., Drew, R. A. I. & Clarke, A. R. Influence of host plant structure and microclimate on the abundance and behavior of a tephritid fly. J. Insect. Behav. 17, 179–190 (2004).Article 

Google Scholar 
31.Kaspi, R. & Yuval, B. Mediterranean Fruit Fly leks: factors affecting male location. Funct. Ecol. 13, 539–545 (1999).Article 

Google Scholar 
32.Baker, P. S. & van der Valk, H. Distribution and behaviour of sterile Mediterranean fruit flies in a host tree. J. Appl. Entomol. 114, 67–76 (1992).Article 

Google Scholar 
33.Aluja, M. & Birke, A. Habitat use by adults of Anastrepha obliqua (Diptera: Tephritidae) in a mixed mango and tropical plum orchard. Ann. Entomol. Soc. Am. 86, 799–812 (1993).Article 

Google Scholar 
34.Aluja, M., Jácome, I., Birke, A., Lozada, N. & Quintero, G. Basic patterns of behavior in wild Anastrepha striata (Diptera: Tephritidae) flies under field-cage conditions. Ann. Entomol. Soc. Am. 86, 776–793 (1993).Article 

Google Scholar 
35.Huettel, M. D. Monitoring the quality of laboratory-reared insects: A biological and behavioral perspective. Environ. Entomol. 5, 807–814 (1976).Article 

Google Scholar 
36.Dominiak, B. C. & Daniels, D. Review of the past and present distribution of Mediterranean fruit fly (Ceratitis capitata Wiedemann) and Queensland fruit fly (Bactrocera tryoni Froggatt) in Australia. Aust. J. Entomol. 51, 104–115 (2012).Article 

Google Scholar 
37.MacLellan, R. & King, K. National fruit fly surveillance programme 2017–2018. Surveillance 45, 68–71 (2018).
Google Scholar 
38.Aguilar, G., Blanchon, D., Foot, H., Pollonais, C. & Mosee, A. Queensland fruit fly invasion of New Zealand: Predicting area suitability under future climate change scenarios. Unitec ePress Perspectives in Biosecurity Research Series (2015).39.Vargas, R. I., Leblanc, L., Piñero, J. C. & Hoffman, K. Male annihilation, past, present, and future. in Trapping Tephritid Fruit Flies: Lures, Area-Wide Programs, and Trade Implications (eds. Shelly, T., Epsky, N., Jang, E. B., Reyes-Flores, J. & Vargas, R.) 493–511 (Springer, 2014).40.Vargas, R., Piñero, J. & Leblanc, L. An overview of pest species of Bactrocera fruit flies (Diptera: Tephritidae) and the integration of biopesticides with other biological approaches for their management with a focus on the Pacific region. Insects 6, 297–318 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Clarke, A. R., Powell, K. S., Weldon, C. W. & Taylor, P. W. The ecology of Bactrocera tryoni (Diptera: Tephritidae): What do we know to assist pest management?. Ann. Appl. Biol. 158, 26–54 (2011).Article 

Google Scholar 
42.Dominiak, B. C. Review of dispersal, survival, and establishment of Bactrocera tryoni (Diptera: Tephritidae) for quarantine purposes. Ann. Entomol. Soc. Am. 105, 434–446 (2012).Article 

Google Scholar 
43.Hancock, D. L., Hamacek, E. L., Lloyd, A. C. & Elson-Harris, M. M. The distribution and host plants of fruit flies (Diptera: Tephritidae) in Australia. Queensland Department of Primary Industries (2000).44.PHA. The National Plant Health Status Report (08/09). Plant Health Australia, Canberra, ACT (2009).45.Ha, A., Larson, K., Harvey, S., Fisher, W. & Malcolm, L. Benefit-cost analysis of options for managing Queensland fruit fly in Victoria. Victoria Department of Primary Industries (2010).46.Dominiak, B. C. Components of a systems approach for the management of Queensland fruit fly Bactrocera tryoni (Froggatt) in a post dimethoate fenthion era. Crop Prot. 116, 56–67 (2019).Article 

Google Scholar 
47.Stringer, L. D., Kean, J. M., Beggs, J. R. & Suckling, D. M. Management and eradication options for Queensland fruit fly. Popul. Ecol. 59, 259–273 (2017).Article 

Google Scholar 
48.Lynch, K. E., White, T. E. & Kemp, D. J. The effect of captive breeding upon adult thermal preference in the Queensland fruit fly (Bactrocera tryoni). J. Therm. Biol. 78, 290–297 (2018).PubMed 
Article 

Google Scholar 
49.Weldon, C. W., Yap, S. & Taylor, P. W. Desiccation resistance of wild and mass-reared Bactrocera tryoni (Diptera: Tephritidae). Bull. Entomol. Res. 103, 690–699 (2013).CAS 
PubMed 
Article 

Google Scholar 
50.Fanson, B. G., Sundaralingam, S., Jiang, L., Dominiak, B. C. & D’Arcy, G. A review of 16 years of quality control parameters at a mass-rearing facility producing Queensland fruit fly, Bactrocera tryoni. Entomol. Exp. Appl. 151, 152–159 (2014).Article 

Google Scholar 
51.Moadeli, T., Taylor, P. W. & Ponton, F. High productivity gel diets for rearing of Queensland fruit fly, Bactrocera tryoni. J. Pest Sci. 2004(90), 507–520 (2017).Article 

Google Scholar 
52.Pérez-Staples, D., Weldon, C. W. & Taylor, P. W. Sex differences in developmental response to yeast hydrolysate supplements in adult Queensland fruit fly. Entomol. Exp. Appl. 141, 103–113 (2011).Article 

Google Scholar 
53.Perez-Staples, D., Prabhu, V. & Taylor, P. W. Post-teneral protein feeding enhances sexual performance of Queensland fruit flies. Physiol. Entomol. 32, 225–232 (2007).Article 

Google Scholar 
54.McInnis, D. O., Rendon, P. & Komatsu, J. Mating and remating of medflies (Diptera: Tephritidae) in Guatemala: Individual fly marking in field cages. Florida Entomol. 85, 126–137 (2002).Article 

Google Scholar 
55.R Core Team. R: a language and environment for statistical computing version 1.1.419. R Foundation for Statistical Computing (2019).56.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
57.Bartoń, K. MuMIn: Multi-Model Inference. R Package version 1.43.6 (2019).58.Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Control 19, 716–723 (1974).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
59.Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).Article 

Google Scholar 
60.Symonds, M. R. E. & Moussalli, A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav. Ecol. Sociobiol. 65, 13–21 (2011).Article 

Google Scholar 
61.Lenth, R. emmeans: estimated marginal means, aka least-squares means. R Package version 1.3.3 (2019).62.Prokopy, R. J., Bennett, E. W. & Bush, G. L. Mating behavior in Rhagoletis pomonella (Diptera: Tephritidae) II. Temportal organization. Can. Entomol. 104, 97–104 (1972).Article 

Google Scholar 
63.McQuate, G. T. & Vargas, R. I. Assessment of attractiveness of plants as roosting sites for the melon fly, Bactrocera cucurbitae, and the oriental fruit fly, B. dorsalis. J. Insect Sci. 7, 13 (2007).Article 

Google Scholar 
64.Casas, J. & Aluja, M. The geometry of search movements of insects in plant canopies. Behav. Ecol. 8, 37–45 (1997).Article 

Google Scholar 
65.Shelly, T. E. & Kennelly, S. S. Settlement patterns of Mediterranean fruit flies in the tree canopy: an experimental analysis. J. Insect Behav. 20, 453–472 (2007).Article 

Google Scholar 
66.Warburg, M. S. & Yuval, B. Circadian patterns of feeding and reproductive activities of Mediterranean fruit flies (Diptera: Tephritidae) on various hosts in Israel. Ann. Entomol. Soc. Am. 90, 487–495 (1997).Article 

Google Scholar 
67.Hendrichs, J. & Hendrichs, M. A. Mediterranean fruit fly (Diptera: Tephritidae) in nature: Location and diel pattern of feeding and other activities on fruiting and nonfruiting hosts and nonhosts. Ann. Entomol. Soc. Am. 83, 632–641 (1990).Article 

Google Scholar 
68.Morgan, K. R., Shelly, T. E. & Kimsey, L. S. Body temperature regulation, energy metabolism, and foraging in light-seeking and shade-seeking robber flies. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 155, 561–570 (1985).69.Whitman, D. Function and evolution of thermoregulation in the desert grasshopper Taeniopoda eques. J. Anim. Ecol. 57, 369–383 (1988).Article 

Google Scholar 
70.Tychsen, P. H. & Fletcher, B. S. Studies on the rhythm of mating in the Queensland fruit fly, Dacus tryoni. J. Insect Physiol. 17, 2139–2156 (1971).Article 

Google Scholar 
71.Cheng, D., Chen, L., Yi, C., Liang, G. & Xu, Y. Association between changes in reproductive activity and D-glucose metabolism in the tephritid fruit fly, Bactrocera dorsalis (Hendel). Sci. Rep. 4, 7489 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
72.Warburg, M. S. & Yuval, B. Effects of energetic reserves on behavioral patterns of Mediterranean fruit flies (Diptera: Tephritidae). Oecologia 112, 314–319 (1997).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Arita, L. & Kaneshiro, K. Sexual selection and lek behavior in the Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae). Pacific Sci. 43, 135–143 (1989).
Google Scholar 
74.Hendrichs, J., Lauzon, C. R., Cooley, S. S. & Prokopy, R. J. Contribution of natural food sources to adult longevity and fecundity of Rhagoletis pomonella (Diptera: Tephritidae). Ann. Entomol. Soc. Am. 86, 250–264 (1993).Article 

Google Scholar 
75.Urbaneja-Bernat, P., Tena, A., González-Cabrera, J. & Rodriguez-Saona, C. Plant guttation provides nutrient-rich food for insects. Proc. R. Soc. B Biol. Sci. 287, 20201080 (2020).CAS 
Article 

Google Scholar 
76.Drew, R., Courtice, A. & Teakle, D. Bacteria as a natural source of food for adult fruit flies (Diptera: Tephritidae). Oecologia 60, 279–284 (1983).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Prokopy, R. J., Drew, R. A. I., Sabine, B. N. E., Lloyd, A. C. & Hamacek, E. Effects of physiological and experiential state of Bactrocera tryoni flies on intra-tree foraging behavior for food (Bacteria) and host fruit. Oecologia 87, 394–400 (1991).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Scarpati, M. L., Scalzo, R. L., Vita, G. & Gambacorta, A. Chemiotropic behavior of female olive fly (Bactrocera oleae GMEL.) on Olea Europeae L. J. Chem. Ecol. 22, 1027–1036 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Truu, M. et al. Elevated air humidity changes soil bacterial community structure in the silver birch stand. Front. Microbiol. 8, 557 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
80.Hockberger, P. E. The discovery of the damaging effect of sunlight on bacteria. J. Photochem. Photobiol. B Biol. 58, 185–191 (2000).CAS 
Article 

Google Scholar 
81.Jones, J., Raju, B. & Engelhard, A. Effects of temperature and leaf wetness on development of bacterial spot of geraniums and chrysanthemums incited by Pseudomonas cichorii. Plant Dis. 68, 248–251 (1984).Article 

Google Scholar 
82.Majumder, R., Sutcliffe, B., Taylor, P. W. & Chapman, T. A. Microbiome of the Queensland fruit fly through metamorphosis. Microorganisms 8, 795 (2020).CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
83.Majumder, R., Sutcliffe, B., Taylor, P. W. & Chapman, T. A. Next-generation sequencing reveals relationship between the larval microbiome and food substrate in the polyphagous Queensland fruit fly. Sci. Rep. 9, 14292 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
84.Deutscher, A. T. et al. Near full-length 16S rRNA gene next-generation sequencing revealed Asaia as a common midgut bacterium of wild and domesticated Queensland fruit fly larvae. Microbiome 6, 85 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
85.Morrow, J., Frommer, M., Shearman, D. & Riegler, M. The microbiome of field-caught and laboratory-adapted Australian tephritid fruit fly species with different host plant use and specialisation. Microb. Ecol. 70, 498–508 (2015).CAS 
PubMed 
Article 

Google Scholar 
86.Thaochan, N., Drew, R. A. I., Hughes, J. M., Vijaysegaran, S. & Chinajariyawong, A. Alimentary tract bacteria isolated and identified with API-20E and molecular cloning techniques from Australian tropical fruit flies, Bactrocera cacuminata and B. tryoni. J. Insect Sci. 10, 131 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Sultana, S., Baumgartner, J. B., Dominiak, B. C., Royer, J. E. & Beaumont, L. J. Potential impacts of climate change on habitat suitability for the Queensland fruit fly. Sci. Rep. 7, 1–10 (2017).CAS 
Article 

Google Scholar 
88.Meats, A. The bioclimatic potential of the Queensland fruit fly, Dacus tryoni, Australia. Proc. Ecol. Soc. Aust. 11, 151–161 (1981).ADS 

Google Scholar 
89.Fletcher, B. The ecology of a natural population of the Queensland Fruit Fly, Dacus tryoni. IV. The immigration and emigration of adults. Aust. J. Zool. 21, 541 (1973).Article 

Google Scholar 
90.Bateman, M. A. The ecology of fruit flies. Annu. Rev. Entomol. 17, 493–518 (1972).Article 

Google Scholar 
91.O’Loughlin, G. T., East, R. A. & Meats, A. Survival, development rates and generation times of the Queensland fruit fly, Dacus tryoni, in a marginally favourable climate: experiments in Victoria. Aust. J. Zool. 32, 353–361 (1984).Article 

Google Scholar 
92.Dominiak, B. C., Mavi, H. S. & Nicol, H. I. Effect of town microclimate on the Queensland fruit fly Bactrocera tryoni. Aust. J. Exp. Agric. 46, 1239–1249 (2006).Article 

Google Scholar 
93.Weldon, C. W., Terblanche, J. S. & Chown, S. L. Time-course for attainment and reversal of acclimation to constant temperature in two Ceratitis species. J. Therm. Biol. 36, 479–485 (2011).Article 

Google Scholar 
94.Nyamukondiwa, C., Weldon, C. W., Chown, S. L., le Roux, P. C. & Terblanche, J. S. Thermal biology, population fluctuations and implications of temperature extremes for the management of two globally significant insect pests. J. Insect Physiol. 59, 1199–1211 (2013).CAS 
PubMed 
Article 

Google Scholar 
95.Meats, A. Rapid acclimatization to low temperature in the Queensland fruit fly, Dacus tryoni. J. Insect Physiol. 19, 1903–1911 (1973).Article 

Google Scholar 
96.Meats, A. Developmental and long-term acclimation to cold by the Queensland fruit-fly (Dacus tryoni) at constant and fluctuating temperatures. J. Insect Physiol. 22, 1013–1019 (1976).ADS 
Article 

Google Scholar 
97.Fay, H. A. C. & Meats, A. Survival rates of the queensland fruit fly, dacus tryoni, in early spring: Field-cage studies with cold-acclimated wild flies and irradiated, warm- or cold-acclimated, laboratory flies. Aust. J. Zool. 35, 187–195 (1987).Article 

Google Scholar 
98.Fay, H. A. C. & Meats, A. The sterile insect release method and the importance of thermal conditioning before release: field-cage experiments with dacus tryoni in spring weather. Aust. J. Zool. 35, 197–204 (1987).Article 

Google Scholar 
99.Bubliy, O. A. & Loeschcke, V. Correlated responses to selection for stress resistance and longevity in a laboratory population of Drosophila melanogaster. J. Evol. Biol. 18, 789–803 (2005).CAS 
PubMed 
Article 

Google Scholar 
100.Weldon, C., Diaz-Fleischer, F. & Perez-Staples, D. Desiccation resistance of tephritid flies: Recent research results and future directions. in Area-Wide Management of Fruit Fly Pests (eds. Pérez-Staples, D., Díaz-Fleischer, F., Montoya, P. & Vera, T.) 3–36 (CRC Press, 2019).101.Nishida, T. Food system of tephritid fruit flies in Hawaii. Proc. Hawaiian Entomol. Soc. 23, 245–254 (1980).
Google Scholar 
102.Nishida, T. & Bess, H. A. Studies on the ecology and control of the melon fly Dacus (Strumeta) Cucurbitae Coquillett (Diptera: Tephritidae). Hawaii Agric. Exp. Stn. Tech. Bull. 1–44 (1957). More

1.Redfern, M. Plant Galls. The New Naturalist Library (Harper Collins, 2011).
Google Scholar 
2.Stone, G. N. & Schönrogge, K. The adaptive significance of insect gall morphology. Trends Ecol Evol. 18, 512–522 (2003).Article 

Google Scholar 
3.Dawkins, R. The Extended Phenotype (Oxford University Press, 1982).
Google Scholar 
4.Raman, A. Morphogenesis of insect-induced plant galls: Facts and questions. Flora 206, 517–533 (2011).Article 

Google Scholar 
5.Gatjens-Boniche, O. The mechanism of plant gall induction by insects: Revealing clues, facts, and consequences in a cross-kingdom complex interaction. Rev. Biol. Trop. 67, 1359–1382 (2019).Article 

Google Scholar 
6.Gonçalves-Alvim, S. J. & Fernandes, G. W. Biodiversity of galling insects: Historical, community and habitat effects in four neotropical savannas. Biodivers. Conserv. 10, 79–98 (2001).Article 

Google Scholar 
7.Veldtman, R. & McGeoch, M. Gall-forming insect species richness along a non-scleromorphic vegetation rainfall gradient in South Africa: The importance of plant community composition. Austral. Ecol. 28, 1–13 (2003).Article 

Google Scholar 
8.Stuart, J., Chen, M.-S., Shukle, R. & Harris, M. Gall midges (Hessian flies) as plant pathogens. Annu. Rev. Phytopath. 50, 339–357 (2012).CAS 
Article 

Google Scholar 
9.Kono, H. Langrüssler aus japanischen Reich. Insecta Matsumurana 4, 145–162 (1930).
Google Scholar 
10.Morimoto, K. & Kojima, H. Weevils of the genus Smicronyx in Japan (Coleoptera: Curculionidae). Entomol. Rev. Jpn. 62, 1–9 (2007).
Google Scholar 
11.Hayakawa, H., Fujii, S. & Yoshitake, H. Reexamination of the host plant of Smicronyx madaranus (Coleoptera, Curculionidae, Smicronycinae). SAYABANE 30, 51–55 (2018) (in Japanese).
Google Scholar 
12.Yukawa, J. Synchronization of gallers with host plant phenology. Popul. Ecol. 42, 105–113 (2000).Article 

Google Scholar 
13.Vitou, J., Skuhravá, M., SkuhravÝ, V., Scott, J. & Sheppard, A. The role of plant phenology in the host specificity of Gephyraulus raphanistri (Diptera: Cecidomyiidae) associated with Raphanus spp. (Brassicaceae). Eur. J. Entomol. 105, 113–119 (2008).
Article 

Google Scholar 
14.Yamaguchi, H. et al. Phytohormones and willow gall induction by a gall-inducing sawfly. New Phytol. 196, 586–595 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Tanaka, Y., Okada, K., Asami, T. & Suzuki, Y. Phytohormones in Japanese mugwort gall induction by a gall-inducing gall midge. Biosci. Biotechnol. Biochem. 77, 1942–1948 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Liu, P., Yang, Z. X., Chen, X. M. & Foottit, R. G. The effect of the gall-forming aphid Schlechtendalia chinensis (Hemiptera: Aphididae) on leaf wing ontogenesis in Rhus chinensis (Sapindales: Anacardiaceae). Ann. Entomol. Soc. Am. 107, 242–250 (2014).Article 

Google Scholar 
17.Hirano, T. et al. Reprogramming of the developmental program of Rhus javanica during initial stage of gall induction by Schlechtendalia chinensis. Front. Plant Sci. 11, 471 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
18.Kaiser, B., Vogg, G., Fürst, U. B. & Albert, M. Parasitic plants of the genus Cuscuta and their interaction with susceptible and resistant host plants. Front. Plant Sci. 6, 45 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Pattee, H. E., Allred, K. R. & Wiebe, H. H. Photosynthesis in dodder. Weeds 13, 193–195 (1965).CAS 
Article 

Google Scholar 
20.van der Kooij, T. A. W., Krause, K., Dörr, I. & Krupinska, K. Molecular, functional and ultrastructural characterisation of plastids from six species of the parasitic flowering plant genus Cuscuta. Planta 210, 701–707 (2000).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Sherman, T. D., Pettigrew, W. T. & Vaughn, K. C. Structural and immunological characterization of the Cuscuta pentagona L. chloroplast. Plant Cell Physiol. 40, 592–603 (1999).CAS 
Article 

Google Scholar 
22.Machado, M. A. & Zetsche, K. A structural, functional and molecular analysis of plastids of the holoparasites Cuscuta reflexa and Cuscuta europaea. Planta 181, 91–96 (1990).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Hibberd, J. M. et al. Localization of photosynthetic metabolism in the parasitic angiosperm Cuscuta reflexa. Planta 205, 506–513 (1998).CAS 
Article 

Google Scholar 
24.Taiz, L., Zieiger, E., Max Moller, I. & Angus, M. Plant Physiology and Development 6th edn. (Sinauer Associates, 2015).
Google Scholar 
25.Bartlett, L. & Connor, E. F. Exogenous phytohormones and the induction of plant galls by insects. Arthropod Plant Interact. 8, 339–348 (2014).
Google Scholar 
26.Tooker, J. F. & Helms, A. M. Phytohormone dynamics associated with gall insects, and their potential role in the evolution of the gall-inducing habit. J. Chem. Ecol. 40, 742–753 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Tokuda, M. et al. Phytohormones related to host plant manipulation by a gall-inducing leafhopper. PLoS ONE 8, e62350 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Suzuki, H. et al. Biosynthetic pathway of the phytohormone auxin in insects and screening of its inhibitors. Insect Biochem. Mol. Biol. 53, 66–72 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Yokoyama, C., Takei, M., Kouzuma, Y., Nagata, S. & Suzuki, Y. Novel tryptophan metabolic pathways in auxin biosynthesis in silkworm. J. Insect Physiol. 101, 91–96 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Kaiser, W., Huguet, E., Casas, J., Commin, C. & Giron, D. Plant green-island phenotype induced by leaf-miners is mediated by bacterial symbionts. Proc. Biol. Sci. 277, 2311–2319 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Body, M., Kaiser, W., Dubreuil, G., Casas, J. & Giron, D. Leaf-miners co-opt microorganisms to enhance their nutritional environment. J. Chem. Ecol. 39, 969–977 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Giron, D. & Glevarec, G. Cytokinin-induced phenotypes in plant-insect interactions: Learning from the bacterial world. J. Chem. Ecol. 40, 826–835 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Gutzwiller, F., Dedeine, F., Kaiser, W., Giron, D. & Lopez-Vaamonde, C. Correlation between the green-island phenotype and Wolbachia infections during the evolutionary diversification of Gracillariidae leaf-mining moths. Ecol. Evol. 5, 4049–4062 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
34.Giron, D., Huguet, E., Stone, G. N. & Body, M. Insect-induced effects on plants and possible effectors used by galling and leaf-mining insects to manipulate their host-plant. J. Insect Physiol. 84, 70–89 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Zhao, C. et al. A massive expansion of effector genes underlies gall-formation in the wheat pest Mayetiola destructor. Curr. Biol. 25, 613–620 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Lemus, L. P. et al. Salivary proteins of a gall-inducing aphid and their impact on early gene responses of susceptible and resistant poplar genotypes. bioRxiv https://doi.org/10.1101/504613 (2018).Article 

Google Scholar 
37.Vogel, A. et al. Footprints of parasitism in the genome of the parasitic flowering plant Cuscuta campestris. Nat. Commun. 9, 2515 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Senthil-Kumar, M. & Mysore, K. S. Tobacco rattle virus–based virus-induced gene silencing in Nicotiana benthamiana. Nat. Protoc. 9, 1549–1562 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Norkunas, K., Harding, R., Dale, J. & Dugdale, B. Improving agroinfiltration-based transient gene expression in Nicotiana benthamiana. Plant Methods 14, 71 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Christiaens, O. et al. RNA interference: A promising biopesticide strategy against the African Sweetpotato Weevil Cylas brunneus. Sci. Rep. 6, 38836 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Maire, J., Vincent-Monégat, C., Masson, F., Zaidman-Rémy, A. & Heddi, A. An IMD-like pathway mediates both endosymbiont control and host immunity in the cereal weevil Sitophilus spp. Microbiome. 6, 6 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Barnewall, E. C. & De Clerck-Floate, R. A. A preliminary histological investigation of gall induction in an unconventional galling system. Arthropod Plant Interact. 6, 449–459 (2012).Article 

Google Scholar 
43.Aistova, E. V. & Bezborodov, V. G. Weevils belonging to the genus Smicronyx Schönherr, 1843 (Coleoptera, Curculionidae) affecting dodders (Cuscuta Linnaeus, 1753) in the Russian Far East. Russ. J. Biol. Invasions. 8, 184–188 (2017).Article 

Google Scholar 
44.Dinelli, G., Bonetti, A. & Tibiletti, E. Photosynthetic and accessory pigments in Cuscuta-Campestris Yuncker and some host species. Weed Res. 33, 253–260 (1993).CAS 
Article 

Google Scholar 
45.Anikin, V. V., Nikelshparg, M. I., Nikelshparg, E. I. & Konyukhov, I. V. Photosynthetic activity of the dodder Cuscuta campestris (Convolvulaceae) in case of plant inhabitation by the gallformed weevil Smicronyx smreczynskii (Coleoptera, Curculionidae). Chem. Biol. Ecol. 17, 42–47 (2017) (in Russian).
Google Scholar 
46.Zagorchev, L. I., Albanova, I. A., Tosheva, A. G., Li, J. & Teofanova, D. R. Metabolic and functional distinction of the Smicronyx sp. galls on Cuscuta campestris. Planta 248, 591–599 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods. 9, 676–682 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Carneiro, R. G. D. S. & Isaias, R. M. D. S. Gradients of metabolite accumulation and redifferentiation of nutritive cells associated with vascular tissues in galls induced by sucking insects. AoB Plants. 7, plv086 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.Porra, R. J., Thompson, W. A. & Kriedemann, P. E. Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: Verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochim. Biophys. Acta 975, 384–394 (1989).CAS 
Article 

Google Scholar 
50.Kawase, M., Hanba, Y. T. & Katsuhara, M. The photosynthetic response of tobacco plants overexpressing ice plant aquaporin McMIPB to a soil water deficit and high vapor pressure deficit. J. Plant Res. 126, 517–527 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Ihaka, R. & Gentleman, R. R: A language for data analysis and graphics. J. Comput. Graph Stat. 5, 299–314 (1996).
Google Scholar  More