Philippa Kaur

1.
Moreira, X. et al. Specificity of induced defenses, growth, and reproduction in lima bean (Phaseolus lunatus) in response to multispecies herbivory. Am. J. Bot. 102, 1300–1308 (2015).
CAS  PubMed  Article  Google Scholar 
2.
Danell, K., Bergström, R. & Edenius, L. Effects of large mammalian browsers on architecture, biomass, and nutrients of woody plants. J. Mammal. 75, 833–844 (1994).
Article  Google Scholar 

3.
Kaitaniemi, P., Neuvonen, S. & Nyyssönen, T. Effects of cumulative defoliations on growth, reproduction, and insect resistance in mountain birch. Ecology 80, 524–532 (1999).
Article  Google Scholar 

4.
den Herder, M., Bergström, R., Niemelä, P., Danell, K. & Lindgren, M. Effects of natural winter browsing and simulated summer browsing by moose on growth and shoot biomass of birch and its associated invertebrate fauna. Ann. Zool. Fennici 46, 63–74 (2009).
Article  Google Scholar 

5.
Wallgren, M., Bergquist, J., Bergström, R. & Eriksson, S. Effects of timing, duration, and intensity of simulated browsing on Scots pine growth and stem quality. Scand. J. For. Res. 29, 734–746 (2014).
Article  Google Scholar 

6.
Schwenk, W. S. & Strong, A. M. Contrasting patterns and combined effects of moose and insect herbivory on striped maple (Acer pensylvanicum). Basic Appl. Ecol. 12, 64–71 (2011).
Article  Google Scholar 

7.
Muiruri, E. W., Milligan, H. T., Morath, S. & Koricheva, J. Moose browsing alters tree diversity effects on birch growth and insect herbivory. Funct. Ecol. 29, 724–735 (2015).
Article  Google Scholar 

8.
van Zandt, P. A. & Agrawal, A. A. Community-Wide impacts of herbivore-induced plant responses in milkweed (Asclepias syriaca). Ecology 85, 2616–2629 (2004).
Article  Google Scholar 

9.
Erb, M., Robert, C. A. M., Hibbard, B. E. & Turlings, T. C. J. Sequence of arrival determines plant-mediated interactions between herbivores. J. Ecol. 99, 7–15 (2011).
Article  Google Scholar 

10.
Kafle, D., Hänel, A., Lortzing, T., Steppuhn, A. & Wurst, S. Sequential above- and belowground herbivory modifies plant responses depending on herbivore identity. BMC Ecol. 17, 1–10 (2017).
Article  Google Scholar 

11.
Stephens, A. E. A., Srivastava, D. S. & Myers, J. H. Strength in numbers? Effects of multiple natural enemy species on plant performance. Proc. R. Soc. B Biol. Sci. 280, 20122756 (2013).
Article  Google Scholar 

12.
Gagic, V. et al. Interactive effects of pests increase seed yield. Ecol. Evol. 6, 2149–2157 (2016).
PubMed  PubMed Central  Article  Google Scholar 

13.
Strauss, S. Y. Direct, indirect, and cumulative effects of three native herbivores on a shared host plant. Ecology 72, 543–558 (1991).
Article  Google Scholar 

14.
Gómez, J. M. & González-Megías, A. Asymmetrical interactions between ungulates and phytophagous insects: being different matters. Ecology 83, 203–211 (2002).
Article  Google Scholar 

15.
Ohgushi, T. Indirect interaction webs: herbivore-induced effects through trait change in plants. Annu. Rev. Ecol. Evol. Syst. 36, 81–105 (2005).
Article  Google Scholar 

16.
Mauch-Mani, B., Baccelli, I., Luna, E. & Flors, V. Defense priming: an adaptive part of induced resistance. Annu. Rev. Plant Biol. 68, 485–512 (2017).
CAS  PubMed  Article  Google Scholar 

17.
Hilker, M. et al. Priming and memory of stress responses in organisms lacking a nervous system. Biol. Rev. 91, 1118–1133 (2016).
PubMed  Article  Google Scholar 

18.
Lyytikäinen-Saarenmaa, P. The responses of scots pine, Pinus silvestris, to natural and artificial defoliation stress. Ecol. Appl. 9, 469–474 (1999).
Article  Google Scholar 

19.
Ericsson, A., Larsson, S. & Tenow, O. Effects of early and late season defoliation on growth and carbohydrate dynamics in scots pine. J. Appl. Ecol. 17, 747–769 (1980).
Article  Google Scholar 

20.
Edenius, L. Browsing by moose on Scots pine in relation to plant resource availability. Ecology 74, 2261–2269 (1993).
Article  Google Scholar 

21.
Nordkvist, M. et al. Trait-mediated indirect interactions: Moose browsing increases sawfly fecundity through plant-induced responses. Ecol. Evol. 9, 10615–10629 (2019).
PubMed  PubMed Central  Article  Google Scholar 

22.
Edenius, L., Danell, K. & Nyquist, H. Effects of simulated moose browsing on growth, mortality, and fecundity in Scots pine: relations to plant productivity. Can. J. For. Res. 25, 529–535 (1995).
Article  Google Scholar 

23.
Honkanen, T., Haukioja, E. & Kitunen, V. Responses of Pinus sylvestris branches to simulated herbivory are modified by tree sink/source dynamics and by external resources. Funct. Ecol. 13, 126–140 (1999).
Article  Google Scholar 

24.
Persson, I. L., Bergström, R. & Danell, K. Browse biomass production and regrowth capacity after biomass loss in deciduous and coniferous trees: Responses to moose browsing along a productivity gradient. Oikos 116, 1639–1650 (2007).
Article  Google Scholar 

25.
Belsky, A. J. Does herbivory benefit plants? A review of the evidence. Am. Nat. 127, 870–892 (1986).
Article  Google Scholar 

26.
Bergman, M. Can saliva from moose, Alces alces, affect growth responses in the salow, Salix caprea?. Oikos 96, 164–168 (2002).
Article  Google Scholar 

27.
Ohse, B. et al. Salivary cues: simulated roe deer browsing induces systemic changes in phytohormones and defence chemistry in wild-grown maple and beech saplings. Funct. Ecol. 31, 340–349 (2017).
Article  Google Scholar 

28.
Kollberg, I. et al. Temperature affects insect outbreak risk through tritrophic interactions mediated by plant secondary compounds. Ecosphere 6, 1–17 (2015).
Article  Google Scholar 

29.
Lyytikäinen-Saarenmaa, P. & Tomppo, E. Impact of sawfly defoliation on growth of Scots pine Pinus sylvestris (Pinaceae) and associated economic losses. Bull. Entomol. Res. 92, 137–140 (2002).
PubMed  Article  Google Scholar 

30.
Augustine, D. J. & McNaughton, S. J. Ungulate effects on the functional species composition of plant communities: herbivore selectivity and plant tolerance. J. Wildl. Manag. 62, 1165–1183 (1998).
Article  Google Scholar 

31.
Edenius, L., Bergman, M., Ericsson, G. & Danell, K. The role of moose as a disturbance factor in managed boreal forests. Silva Fennica 36, 57–67 (2002).
Article  Google Scholar 

32.
Hódar, J. A., Zamora, R., Castro, J., Gómez, J. M. & García, D. Biomass allocation and growth responses of Scots pine saplings to simulated herbivory depend on plant age and light availability. Plant Ecol. 197, 229–238 (2008).
Article  Google Scholar 

33.
Bergström, R. & Hjeljord, O. Moose and vegetation interactions in northwestern Europe and Poland. Swedish Wildl. Res. Suppl. 1, 213–228 (1987).
Google Scholar 

34.
Nilsson, U., Berglund, M., Bergquist, J., Holmström, H. & Wallgren, M. Simulated effects of browsing on the production and economic values of Scots pine (Pinus sylvestris) stands. Scand. J. For. Res. 31, 279–285 (2016).
Article  Google Scholar 

35.
Långsström, B. & Hellqvist, C. Effects of different pruning regimes on growth and sapwood area of Scots pine. For. Ecol. Manag. 44, 239–254 (1991).
Article  Google Scholar 

36.
Mathisen, K. M., Milner, J. M. & Skarpe, C. Moose-tree interactions: rebrowsing is common across tree species. BMC Ecol. 17, 1–15 (2017).
Article  Google Scholar 

37.
Bergqvist, G., Bergström, R. & Edenius, L. Effects of moose (Alces alces) rebrowsing on damage development in young stands of Scots pine (Pinus sylvestris). For. Ecol. Manag. 176, 397–403 (2003).
Article  Google Scholar 

38.
Bergqvist, G., Bergström, R. & Edenius, L. Patterns of stem damage by moose (Alces alces) in young Pinus sylvestris stands in Sweden. Scand. J. For. Res. 16, 363–370 (2001).
Article  Google Scholar 

39.
Riipi, M., Lempa, K., Haukioja, E., Ossipov, V. & Pihlaja, K. Effects of simulated winter browsing on mountain birch foliar chemistry and on the performance of insect herbivores. Oikos 111, 221–234 (2005).
Article  Google Scholar 

40.
Kupferschmid, A. D. & Bugmann, H. Timing, light availability and vigour determine the response of Abies alba saplings to leader shoot browsing. Eur. J. For. Res. 132, 47–60 (2013).
Article  Google Scholar 

41.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).

42.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. _nlme: Linear and Nonlinear Mixed Effects Models_. R package version 3.1–142. https://CRAN.R-project.org/package=nlme (2019).

43.
Fox, J. & Weisberg, F. An {R} Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. https://socialsciences.mcmaster.ca/jfox/Books/Companion/. (2019).

44.
Darling, E. S., Mcclanahan, T. R. & Côté, I. M. Combined effects of two stressors on Kenyan coral reefs are additive or antagonistic, not synergistic. Conserv. Lett. 3, 122–130 (2010).
Article  Google Scholar 

45.
Bansal, S., Hallsby, G., Löfvenius, M. O. & Nilsson, M. C. Synergistic, additive and antagonistic impacts of drought and herbivory on Pinus sylvestris: leaf, tissue and whole-plant responses and recovery. Tree Physiol. 33, 451–463 (2013).
CAS  PubMed  Article  Google Scholar  More

1.
Neuenschwander, S. M., Pernthaler, J., Posch, T. & Salcher, M. M. Seasonal growth potential of rare lake water bacteria suggest their disproportional contribution to carbon fluxes. Environ. Microbiol. 17(3), 781–795 (2015).
CAS  PubMed  Article  Google Scholar 
2.
Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313(5790), 1068–1072 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

3.
United Nations Environment Programme. GEO Year Book 2004/5: An Overview of Our Changing Environment (2004). https://www.unep.org/resources/report/geo-year-book-20045-overview-our-changing-environment.

4.
Lindström, E. S. Bacterioplankton community composition in five lakes differing in trophic status and humic content. Microb. Ecol. 40(2), 104–113 (2000).
PubMed  Article  Google Scholar 

5.
Ávila, M. P., Staehr, P. A., Barbosa, F. A., Chartone-Souza, E. & Nascimento, A. Seasonality of freshwater bacterioplankton diversity in two tropical shallow lakes from the Brazilian Atlantic Forest. FEMS Microbiol. Ecol. 93, fw218 (2017).
Article  CAS  Google Scholar 

6.
Zhang, H. et al. Biogeographic distribution patterns of algal community in different urban lakes in China: insights into the dynamics and co-existence. J. Environ. Sci. 100, 216–227 (2021).
Article  Google Scholar 

7.
Ji, B. et al. Bacterial communities of four adjacent fresh lakes at different trophic status. Ecotoxicol. Environ. Safe 157, 388–394 (2018).
CAS  Article  Google Scholar 

8.
Iliev, I. et al. Metagenomic profiling of the microbial freshwater communities in two Bulgarian reservoirs. J. Basic Microb. 57(8), 669–679 (2017).
CAS  Article  Google Scholar 

9.
Linz, A. M. et al. Bacterial community composition and dynamics spanning five years in freshwater bog lakes. mSphere 2(3), e00169 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Hartmann, A., Goldscheider, N., Wagener, T., Lange, J. & Weiler, M. Karst water resources in a changing world: review of hydrological modeling approaches. Rev. Geophys. 52(3), 218–242 (2014).
ADS  Article  Google Scholar 

11.
Yu, S. et al. Spatial and temporal dynamics of bacterioplankton community composition in a subtropical dammed karst river of southwestern China. Microbiol. Open 8(9), e00849 (2019).
Article  CAS  Google Scholar 

12.
Li, Q., Sun, H., Han, J., Liu, Z. & Yu, L. High-resolution study on the hydrochemical variations caused by the dilution of precipitation in the epikarst spring: an example spring of Landiantang at Nongla, Mashan, China. Environ. Geol. 54(2), 347–354 (2008).
ADS  CAS  Article  Google Scholar 

13.
Song, A., Yue, M. L. & Li, Q. Influence of precipitation on bacterial structure in a typical karst spring, SW China. J. Groundw. Sci. Eng. 6(3), 193–204 (2018).
Google Scholar 

14.
Gray, C. J. & Engel, A. S. Microbial diversity and impact on carbonate geochemistry across a changing geochemical gradient in a karst aquifer. ISME J. 7(2), 325–337 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Shabarova, T. et al. Bacterial community structure and dissolved organic matter in repeatedly flooded subsurface karst water pools. FEMS Microbiol. Ecol. 89(1), 111–126 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Li, Q. et al. Contribution of aerobic anoxygenic phototrophic bacteria to total organic carbon pool in aquatic system of subtropical karst catchments, Southwest China: evidence from hydrochemical and microbiological study. FEMS Microbiol. Ecol. 93, fix065 (2017).
Google Scholar 

17.
Stevanović, Z. & Milanović, P. Engineering challenges in karst. Acta Carsol. 44(3), 381–399 (2015).
Article  Google Scholar 

18.
Lu, X. X. et al. Water chemistry and characteristics of dissolved organic carbon during the wet season in Wulixia Reservoir, SW China. Huanjing Kexue 39(5), 2075–2085 (2018) (in Chinese with English abstract).
PubMed  PubMed Central  Google Scholar 

19.
Xin, S. L. et al. Relationship between the bacterial abundance and production with environmental factors in a subtropical karst reservoir. Huanjing Kexue 39(12), 5647–5656 (2018) (in Chinese with English abstract).
PubMed  PubMed Central  Google Scholar 

20.
National Research Council. Assessing the TMDL Approach to Water Quality Management (National Academy Press, Washington, DC, 2001).
Google Scholar 

21.
Cunha, D. G. F., do Carmo Calijuri, M. & Lamparelli, M. C. A trophic state index for tropical/subtropical reservoirs (TSItsr). Ecol. Eng. 60, 126–134 (2013).
Article  Google Scholar 

22.
Lorenzen, C. J. Determination of chlirophyll and pheo-pigments: spectrophotometric equations. Limnol. Oceanogr. 12(2), 343–346 (1967).
ADS  CAS  Article  Google Scholar 

23.
Tamaki, H. et al. Analysis of 16S rRNA amplicon sequencing options on the Roche/454 next-generation titanium sequencing platform. PLoS ONE 6(9), e25263 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
Kuczynski, J. et al. Using QIIME to analyze 16S rRNA gene sequences from microbial communities. Curr. Protoc. Microbiol. 27(1), 1–20 (2012).
Google Scholar 

25.
Vázquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).
PubMed  PubMed Central  Article  Google Scholar 

26.
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral. J. Ecol. 18(1), 117–143 (1993).
Article  Google Scholar 

27.
Palmer, M. W., McGlinn, D. J., Westerberg, L. & Milberg, P. Indices for detecting differences in species composition: some simplifications of RDA and CCA. Ecology 89(6), 1769–1771 (2008).
PubMed  Article  Google Scholar 

28.
Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6(2), 343–351 (2012).
PubMed  Article  CAS  Google Scholar 

29.
Sanchez, G. PLS Path Modeling with R (Trowchez Editions, Berkeley, 2013).
Google Scholar 

30.
Lopez-Chicano, M., Bouamama, M., Vallejos, A. & Pulido-Bosch, A. Factors which determine the hydrogeochemical behaviour of karstic springs. A case study from the Betic Cordilleras, Spain. Appl. Geochem. 16(9–10), 1179–1192 (2001).
CAS  Article  Google Scholar 

31.
Stumm, W. & Morgan, J. J. Aquatic chemistry: chemical equilibria and rates in natural waters. In Environmental Science and Technology (eds Stumm, W. & Morgan, J. J.) (Wiley, New York, 2012).
Google Scholar 

32.
Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake bacteria. Microbiol. Mol. Biol. R. 75, 14–49 (2011).
CAS  Article  Google Scholar 

33.
Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2(1), 589 (2011).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

34.
Li, D. et al. Microbial community evolution during simulated managed aquifer recharge in response to different biodegradable dissolved organic carbon (BDOC) concentrations. Water Res. 47(7), 2421–2430 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Miranda, C. D. & Zemelman, R. Bacterial resistance to oxytetracycline in Chilean salmon farming. Aquaculture 212(1–4), 31–47 (2002).
CAS  Article  Google Scholar 

36.
Dul’tseva, N. M., Chernitsina, S. M. & Zemskaya, T. I. Isolation of bacteria of the genus Variovorax from the Thioploca mats of Lake Baikal. Microbiology 81(1), 67–78 (2012).
Article  CAS  Google Scholar 

37.
Mohiuddin, M. M., Salama, Y., Schellhorn, H. E. & Golding, G. B. Shotgun metagenomic sequencing reveals freshwater beach sands as reservoir of bacterial pathogens. Water Res. 115, 360–369 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Fuentes, S., Méndez, V., Aguila, P. & Seeger, M. Bioremediation of petroleum hydrocarbons: catabolic genes, microbial communities, and applications. Appl. Microbiol. Biotechnol. 98(11), 4781–4794 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Gomes, B. C. et al. Analysis of a microbial community associated with polychlorinated biphenyl degradation in anaerobic batch reactors. Biodegradation 25(6), 797–810 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Cai, J. et al. Characterization of bacterial and microbial eukaryotic communities associated with an ephemeral hypoxia event in Taihu Lake, a shallow eutrophic Chinese lake. Environ. Sci. Pollut. R. 25(31), 31543–31557 (2018).
CAS  Article  Google Scholar 

41.
Zhang, S. et al. Characterization of a novel bacteriophage specific to Exiguobacterium indicum isolated from a plateau eutrophic lake. J. Basic Microb. 59(2), 206–214 (2019).
CAS  Article  Google Scholar 

42.
Li, S., Luo, Z. & Ji, G. Seasonal function succession and biogeographic zonation of assimilatory and dissimilatory nitrate-reducing bacterioplankton. Sci. Total Environ. 637, 1518–1525 (2018).
ADS  PubMed  Article  CAS  Google Scholar 

43.
Savio, D. et al. Spring water of an alpine karst aquifer is dominated by a taxonomically stable but discharge-responsive bacterial community. Front. Microbiol. 10, 28 (2019).
PubMed  PubMed Central  Article  Google Scholar 

44.
Freedman, Z. & Zak, D. R. Soil bacterial communities are shaped by temporal and environmental filtering: evidence from a long-term chronosequence. Environ. Microbiol. 17(9), 3208–3218 (2015).
PubMed  Article  Google Scholar 

45.
Subramani, T., Elango, L. & Damodarasamy, S. R. Groundwater quality and its suitability for drinking and agricultural use in Chithar River Basin, Tamil Nadu, India. Environ. Geol. 47(8), 1099–1110 (2005).
CAS  Article  Google Scholar 

46.
Jost, L. Partitioning diversity into independent alpha and beta components. Ecology 88(10), 2427–2439 (2007).
PubMed  Article  Google Scholar 

47.
Niño-García, J. P., Ruiz-González, C. & del Giorgio, P. A. Interactions between hydrology and water chemistry shape bacterioplankton biogeography across borssseal freshwater networks. ISME J. 10(7), 1755 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar  More

1.
Paul, E. A. (ed.) Soil Microbiology, Ecology and Biochemistry (Academic Press, Amsterdam, 2015).
Google Scholar 
2.
Nannipieri, P. et al. Microbial diversity and soil functions. Eur. J. Soil. Sci. 54, 655–670 (2003).
Article  Google Scholar 

3.
Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil. Biol. Biochem. 83, 184–199 (2015).
CAS  Article  Google Scholar 

4.
Berg, G. & Smalla, K. Plant species and soil type cooperatively shape the structure and function of microbial communities in the rhizosphere. FEMS Microbiol. Ecol. 68, 1–13 (2009).
CAS  Article  Google Scholar 

5.
Bais, H. P., Park, S.-W., Weir, T. L., Callaway, R. M. & Vivanco, J. M. How plants communicate using the underground information superhighway. Trends Plant. Sci. 9, 26–32 (2004).
CAS  Article  Google Scholar 

6.
Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663. https://doi.org/10.1111/1574-6976.12028 (2013).
CAS  Article  PubMed  Google Scholar 

7.
Praeg, N., Pauli, H. & Illmer, P. Microbial diversity in bulk and rhizosphere soil of Ranunculus glacialis along a high-alpine altitudinal gradient. Front. Microbiol. 10, 1429. https://doi.org/10.3389/fmicb.2019.01429 (2019).
Article  PubMed  PubMed Central  Google Scholar 

8.
Nacke, H. et al. Pyrosequencing-based assessment of bacterial community structure along different management types in German forest and grassland soils. PLoS ONE 6, e17000. https://doi.org/10.1371/journal.pone.0017000 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Jackson, R. B., Solomon, E. I., Canadell, J. G., Cargnello, M. & Field, C. B. Methane removal and atmospheric restoration. Nat. Sustain. 2, 436–438. https://doi.org/10.1038/s41893-019-0299-x (2019).
Article  Google Scholar 

10.
Ciais, P. et al. Climate Change 2013: The Physical Science Basis. In Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge University Press, Cambridge, 2013).
Google Scholar 

11.
Adam, P. S., Borrel, G., Brochier-Armanet, C. & Gribaldo, S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 11, 2407. https://doi.org/10.1038/ismej.2017.122 (2017).
Article  PubMed  PubMed Central  Google Scholar 

12.
Knief, C. Diversity and habitat preferences of cultivated and uncultivated aerobic methanotrophic bacteria evaluated based on pmoA as molecular marker. Front. Microbiol. 6, 1346 (2015).
Article  Google Scholar 

13.
Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).
CAS  Article  Google Scholar 

14.
Op den Camp, H. J. M. et al. Environmental, genomic and taxonomic perspectives on methanotrophic Verrucomicrobia. Environ. Microbiol. Rep. 1, 293–306. https://doi.org/10.1111/j.1758-2229.2009.00022.x (2009).
CAS  Article  PubMed  Google Scholar 

15.
Knief, C., Lipski, A. & Dunfield, P. F. Diversity and activity of methanotrophic bacteria in different upland soils. Appl. Environ. Microbiol. 69, 6703–6714. https://doi.org/10.1128/AEM.69.11.6703-6714.2003 (2003).
CAS  Article  PubMed  PubMed Central  Google Scholar 

16.
Kolb, S. The quest for atmospheric methane oxidizers in forest soils. Environ. Microbiol. Rep. 1, 336–346 (2009).
CAS  Article  Google Scholar 

17.
Plesa, I. et al. Effects of drought and salinity on European Larch (Larix decidua Mill.) seedlings. Forests 9, 320. https://doi.org/10.3390/f9060320 (2018).
Article  Google Scholar 

18.
Falk, W., Bachmann-Gigl, U. & Kölling, C. Die Europäische Lärche im Klimawandel. In Beiträge zur Europäischen Lärche (ed. Schmidt, O.) 19–27 (Bayrische Landesanstalt für Wald und Forstwirtschaft, Freising, 2012).
Google Scholar 

19.
Obojes, N. et al. Water stress limits transpiration and growth of European larch up to the lower subalpine belt in an inner-alpine dry valley. New Phytol. 220, 460–475 (2018).
Article  Google Scholar 

20.
Wieser, G. (ed.) Trees at Their Upper Limit. Treelife Limitation at the Alpine Timberline (Springer, Dordrecht, 2007).
Google Scholar 

21.
Dedysh, S. N. et al. Methylocapsa palsarum sp. nov., a methanotroph isolated from a subArctic discontinuous permafrost ecosystem. Int. J. Syst. Evol. Microbiol. 65, 3618–3624. https://doi.org/10.1099/ijsem.0.000465 (2015).
CAS  Article  PubMed  Google Scholar 

22.
Praeg, N., Wagner, A. O. & Illmer, P. Plant species, temperature, and bedrock affect net methane flux out of grassland and forest soils. Plant Soil 410, 193–206 (2017).
CAS  Article  Google Scholar 

23.
Lladó, S., López-Mondéjar, R. & Baldrian, P. Forest soil bacteria: diversity, involvement in ecosystem processes, and response to global change. Microbiol. Mol. Biol. Rev. https://doi.org/10.1128/MMBR.00063-16 (2017).
Article  PubMed  PubMed Central  Google Scholar 

24.
Urbanová, M., Šnajdr, J. & Baldrian, P. Composition of fungal and bacterial communities in forest litter and soil is largely determined by dominant trees. Soil. Biol. Biochem. 84, 53–64. https://doi.org/10.1016/j.soilbio.2015.02.011 (2015).
CAS  Article  Google Scholar 

25.
Liu, J. et al. Characteristics of bulk and rhizosphere soil microbial community in an ancient Platycladus orientalis forest. Appl. Soil Ecol. 132, 91–98. https://doi.org/10.1016/j.apsoil.2018.08.014 (2018).
ADS  Article  Google Scholar 

26.
Uroz, S. et al. Specific impacts of beech and Norway spruce on the structure and diversity of the rhizosphere and soil microbial communities. Sci. Rep. 6, 27756. https://doi.org/10.1038/srep27756 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Štursová, M., Bárta, J., Šantrůčková, H. & Baldrian, P. Small-scale spatial heterogeneity of ecosystem properties, microbial community composition and microbial activities in a temperate mountain forest soil. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiw185 (2016).
Article  PubMed  Google Scholar 

28.
Ferrari, B., Winsley, T., Ji, M. & Neilan, B. Insights into the distribution and abundance of the ubiquitous candidatus Saccharibacteria phylum following tag pyrosequencing. Sci. Rep. 4, 3957. https://doi.org/10.1038/srep03957 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

29.
Starr, E. P. et al. Stable isotope informed genome-resolved metagenomics reveals that Saccharibacteria utilize microbially-processed plant-derived carbon. Microbiome 6, 122. https://doi.org/10.1186/s40168-018-0499-z (2018).
Article  PubMed  PubMed Central  Google Scholar 

30.
Brewer, T. E., Handley, K. M., Carini, P., Gilbert, J. A. & Fierer, N. Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’. Nat. Microbiol. 2, 16198. https://doi.org/10.1038/nmicrobiol.2016.198 (2016).
CAS  Article  PubMed  Google Scholar 

31.
Kielak, A. M., Barreto, C. C., Kowalchuk, G. A., van Veen, J. A. & Kuramae, E. E. The ecology of acidobacteria: moving beyond genes and genomes. Front. Microbiol. 7, 744. https://doi.org/10.3389/fmicb.2016.00744 (2016).
Article  PubMed  PubMed Central  Google Scholar 

32.
Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).
Article  Google Scholar 

33.
Johnston-Monje, D., Lundberg, D. S., Lazarovits, G., Reis, V. M. & Raizada, M. N. Bacterial populations in juvenile maize rhizospheres originate from both seed and soil. Plant Soil 405, 337–355. https://doi.org/10.1007/s11104-016-2826-0 (2016).
CAS  Article  Google Scholar 

34.
Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Nat. Acad. Sci. USA 109, 21390–21395. https://doi.org/10.1073/pnas.1215210110 (2012).
ADS  Article  PubMed  Google Scholar 

35.
Kottke, I. & Oberwinkler, F. Comparative studies on the mycorrhization of Larix decidua and Picea abies by Suillus grevillei. Trees https://doi.org/10.1007/BF00196758 (1988).
Article  Google Scholar 

36.
Uroz, S., Buée, M., Murat, C., Frey-Klett, P. & Martin, F. Pyrosequencing reveals a contrasted bacterial diversity between oak rhizosphere and surrounding soil. Environ. Microbiol. Rep. 2, 281–288. https://doi.org/10.1111/j.1758-2229.2009.00117.x (2010).
CAS  Article  PubMed  Google Scholar 

37.
Mapelli, F. et al. The stage of soil development modulates rhizosphere effect along a High Arctic desert chronosequence. ISME J. 12, 1188. https://doi.org/10.1038/s41396-017-0026-4 (2018).
Article  PubMed  PubMed Central  Google Scholar 

38.
Mello, B. L., Alessi, A. M., McQueen-Mason, S., Bruce, N. C. & Polikarpov, I. Nutrient availability shapes the microbial community structure in sugarcane bagasse compost-derived consortia. Sci. Rep. 6, 38781. https://doi.org/10.1038/srep38781 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Turnbull, G. A., Morgan, J. A. W., Whipps, J. M. & Saunders, J. R. The role of bacterial motility in the survival and spread of Pseudomonas fluorescens in soil and in the attachment and colonisation of wheat roots. FEMS Microbiol. Ecol. 36, 21–31. https://doi.org/10.1111/j.1574-6941.2001.tb00822.x (2001).
CAS  Article  PubMed  Google Scholar 

40.
Rees, D. C., Johnson, E. & Lewinson, O. ABC transporters: the power to change. Nat. Rev. Mol. Cell. Biol. 10, 218–227. https://doi.org/10.1038/nrm2646 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

41.
Aronson, E. L., Allison, S. D. & Helliker, B. R. Environmental impacts on the diversity of methane-cycling microbes and their resultant function. Front. Microbiol. 4, 225. https://doi.org/10.3389/fmicb.2013.00225 (2013).
Article  PubMed  PubMed Central  Google Scholar 

42.
Dalal, R. C., Allen, D. E., Livesley, S. J. & Richards, G. Magnitude and biophysical regulators of methane emission and consumption in the Australian agricultural, forest, and submerged landscapes. A review. Plant Soil 309, 43–76 (2008).
CAS  Article  Google Scholar 

43.
Martins, C. S. C., Nazaries, L., Macdonald, C. A., Anderson, I. C. & Singh, B. K. Water availability and abundance of microbial groups are key determinants of greenhouse gas fluxes in a dryland forest ecosystem. Soil Biol. Biochem. 86, 5–16. https://doi.org/10.1016/j.soilbio.2015.03.012 (2015).
CAS  Article  Google Scholar 

44.
Praeg, N., Schwinghammer, L. & Illmer, P. Larix decidua and additional light affect the methane balance of forest soil and the abundance of methanogenic and methanotrophic microorganisms. FEMS Microbiol Lett. https://doi.org/10.1093/femsle/fnz259 (2020).
Article  Google Scholar 

45.
Ström, L., Mastepanov, M. & Christensen, T. R. Species-specific effects of vascular plants on carbon turnover and methane emissions from wetlands. Biogeochemistry 75, 65–82 (2005).
Article  Google Scholar 

46.
Borrel, G. et al. Genome sequence of “Candidatus Methanomassiliicoccus intestinalis” Issoire-Mx1, a third thermoplasmatales-related methanogenic archaeon from human feces. Genome Announc. 1, e004523. https://doi.org/10.1128/genomeA.00453-13 (2013).
Article  Google Scholar 

47.
Deng, Y., Liu, P. & Conrad, R. Effect of temperature on the microbial community responsible for methane production in alkaline NamCo wetland soil. Soil Biol. Biochem. 132, 69–79. https://doi.org/10.1016/j.soilbio.2019.01.024 (2019).
CAS  Article  Google Scholar 

48.
Söllinger, A. et al. Phylogenetic and genomic analysis of Methanomassiliicoccales in wetlands and animal intestinal tracts reveals clade-specific habitat preferences. FEMS Microbiol. Ecol. 92, 149. https://doi.org/10.1093/femsec/fiv149 (2016).
CAS  Article  Google Scholar 

49.
Berghuis, B. A. et al. Hydrogenotrophic methanogenesis in archaeal phylum Verstraetearchaeota reveals the shared ancestry of all methanogens. Proc. Natl. Acad. Sci. U.S.A. 116, 5037. https://doi.org/10.1073/pnas.1815631116 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

50.
Cai, Y., Zheng, Y., Bodelier, P. L. E., Conrad, R. & Jia, Z. Conventional methanotrophs are responsible for atmospheric methane oxidation in paddy soils. Nat. Commun. 7, 11728 (2016).
ADS  CAS  Article  Google Scholar 

51.
Henckel, T., Jäckel, U., Schnell, S. & Conrad, R. Molecular analyses of novel methanotrophic communities in forest soil that oxidize atmospheric methane. Appl. Environ. Microbiol. 60, 1801–1808 (2000).
Article  Google Scholar 

52.
Ricke, P., Kolb, S. & Braker, G. Application of a newly developed ARB software-integrated tool for in silico terminal restriction fragment length polymorphism analysis reveals the dominance of a novel pmoA cluster in a forest soil. Appl. Environ. Microbiol. 71, 1671–1673. https://doi.org/10.1128/AEM.71.3.1671-1673.2005 (2005).
CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Pratscher, J., Dumont, M. G. & Conrad, R. Assimilation of acetate by the putative atmospheric methane oxidizers belonging to the USCα clade. Environ. Microbiol. 13, 2692–2701. https://doi.org/10.1111/j.1462-2920.2011.02537.x (2011).
CAS  Article  PubMed  Google Scholar 

54.
Cai, Y., Zhou, X., Shi, L. & Jia, Z. Atmospheric methane oxidizers are dominated by upland soil cluster alpha in 20 forest soils of China. Microb. Ecol. 80, 859–871. https://doi.org/10.1007/s00248-020-01570-1 (2020).
CAS  Article  PubMed  Google Scholar 

55.
Täumer, J. et al. Divergent drivers of the microbial methane sink in temperate forest and grassland soils. Glob. Change Biol. https://doi.org/10.1111/gcb.15430 (2020).
Article  Google Scholar 

56.
Andreote, F. D. et al. Culture-independent assessment of Rhizobiales-related alphaproteobacteria and the diversity of Methylobacterium in the rhizosphere and rhizoplane of transgenic eucalyptus. Microb. Ecol. 57, 82–93. https://doi.org/10.1007/s00248-008-9405-8 (2009).
Article  PubMed  Google Scholar 

57.
Iguchi, H., Yurimoto, H. & Sakai, Y. Interactions of methylotrophs with plants and other heterotrophic bacteria. Microorganisms 3, 137–151. https://doi.org/10.3390/microorganisms3020137 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

58.
Ho, A. et al. Biotic interactions in microbial communities as modulators of biogeochemical processes: methanotrophy as a model system. Front. Microbiol. 7, 1285. https://doi.org/10.3389/fmicb.2016.01285 (2016).
Article  PubMed  PubMed Central  Google Scholar 

59.
Iguchi, H., Yurimoto, H. & Sakai, Y. Stimulation of methanotrophic growth in cocultures by cobalamin excreted by rhizobia. Appl. Environ. Microbiol. 77, 8509–8515. https://doi.org/10.1128/AEM.05834-11 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Veraart, A. J. et al. Living apart together—bacterial volatiles influence methanotrophic growth and activity. ISME J. 12, 1163–1166 (2018).
CAS  Article  Google Scholar 

61.
Karlsson, A. E., Johansson, T. & Bengtson, P. Archaeal abundance in relation to root and fungal exudation rates. FEMS Microbiol. Ecol. 80, 305–311 (2012).
CAS  Article  Google Scholar 

62.
Haichar, F. E. Z. et al. Plant host habitat and root exudates shape soil bacterial community structure. ISME J. 2, 1221–1230. https://doi.org/10.1038/ismej.2008.80 (2008).
CAS  Article  PubMed  Google Scholar 

63.
Tkacz, A., Cheema, J., Chandra, G., Grant, A. & Poole, P. S. Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition. ISME J. 9, 2349–2359. https://doi.org/10.1038/ismej.2015.41 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

64.
Schinner, F. et al. (eds) Methods in Soil Biology (Springer, Berlin, 1996).
Google Scholar 

65.
Barillot, C. D. C., Sarde, C.-O., Bert, V., Tarnaud, E. & Cochet, N. A standardized method for the sampling of rhizosphere and rhizoplan soil bacteria associated to a herbaceous root system. Ann. Microbiol. 63, 471–476 (2013).
CAS  Article  Google Scholar 

66.
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Nat. Acad. Sci. U.S.A. 108(Suppl 1), 4516–4522. https://doi.org/10.1073/pnas.1000080107 (2011).
ADS  Article  Google Scholar 

67.
Ihrmark, K. et al. New primers to amplify the fungal ITS2 region–evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677. https://doi.org/10.1111/j.1574-6941.2012.01437.x (2012).
CAS  Article  PubMed  Google Scholar 

68.
White, T. J., Bruns, T., Lee, S. & Taylor, J. W. Amplification and direct sequencing of fungal ribosomal RNA Genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, Cambridge, 1990).
Google Scholar 

69.
Schloss, P. D. et al. Introducing mothur. Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
CAS  Article  Google Scholar 

70.
Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 25, 914–919. https://doi.org/10.1111/2041-210X.12073 (2013).
Article  Google Scholar 

71.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mah, F. VSEARCH. A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
Article  Google Scholar 

72.
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2013).
CAS  Article  PubMed  Google Scholar 

73.
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277. https://doi.org/10.1111/mec.12481 (2013).
CAS  Article  PubMed  Google Scholar 

74.
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. https://doi.org/10.1128/AEM.00062-07 (2007).
CAS  Article  PubMed  PubMed Central  Google Scholar 

75.
Mantel, N. The detection of disease clustering and a generalized regression approach. Can. Res. 27, 209–220 (1967).
CAS  Google Scholar 

76.
Martin, A. P. Phylogenetic approaches for describing and comparing the diversity of microbial communities. Appl. Environ. Microbiol. 68, 3673–3682. https://doi.org/10.1128/AEM.68.8.3673-3682.2002 (2002).
CAS  Article  PubMed  PubMed Central  Google Scholar 

77.
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).
Article  Google Scholar 

78.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017). http://www.R-project.org. Accessed 24 Sept 2018.

79.
Oksanen, J. et al. vegan. Community Ecology Package. R package version 2.4–4 (2017). https://CRAN.R-project.org/package=vegan. Accessed 24 Sept 2018.

80.
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res. 44, W3–W10. https://doi.org/10.1093/nar/gkw343 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

81.
Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16SrRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
CAS  Article  Google Scholar 

82.
White, J. R., Nagarajan, N. & Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5, e1000352. https://doi.org/10.1371/journal.pcbi.1000352 (2009).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

83.
Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124. https://doi.org/10.1093/bioinformatics/btu494 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

84.
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114. https://doi.org/10.1093/nar/gkr988 (2012).
CAS  Article  PubMed  Google Scholar 

85.
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
Article  Google Scholar 

86.
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504. https://doi.org/10.1101/gr.1239303 (2003).
CAS  Article  PubMed  PubMed Central  Google Scholar 

87.
Pratscher, J., Vollmers, J., Wiegand, S., Dumont, M. G. & Kaster, A.-K. Unravelling the identity, metabolic potential and global biogeography of the atmospheric methane-oxidizing upland soil cluster α. Environ. Microbiol. 20, 1016–1029. https://doi.org/10.1111/1462-2920.14036 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

88.
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

89.
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321. https://doi.org/10.1093/sysbio/syq010 (2010).
CAS  Article  PubMed  Google Scholar  More

1.
Tassi, F. et al. Low-pH waters discharging from submarine vents at Panarea Island (Aeolian Islands, southern Italy) after the 2002 gas blast: Origin of hydrothermal fluids and implications for volcanic surveillance. Appl. Geochem. 24, 246–254 (2009).
CAS  Article  Google Scholar 
2.
Boatta, F. et al. Geochemical survey of Levante Bay, Vulcano Island (Italy), a natural laboratory for the study of ocean acidification. Mar. Pollut. Bull. 73, 485–494. https://doi.org/10.1016/j.marpolbul.2013.01.029 (2013).
CAS  Article  PubMed  Google Scholar 

3.
Hall-Spencer, J. M. et al. Volcanic carbon dioxide vents show ecosystem effects of ocean acidification. Nature 454, 96–99 (2008).
ADS  CAS  Article  Google Scholar 

4.
Ricevuto, E., Kroeker, K. J., Ferrigno, F. & Gambi, M. C. Spatio-temporal variability of polychaete colonization at volcanic CO2 vents indicates high tolerance to ocean acidification. Mar. Biol. 161, 2909–2919. https://doi.org/10.1007/s00227-014-2555-y (2014).
CAS  Article  Google Scholar 

5.
Ricevuto, E., Vizzini, S. & Gambi, M. C. Ocean acidification effects on stable isotope signatures and trophic interactions of polychaete consumers and organic matter sources at a CO2 shallow vent system. J. Exp. Mar. Biol. Ecol. 468, 105–117. https://doi.org/10.1016/j.jembe.2015.03.016 (2015).
CAS  Article  Google Scholar 

6.
Foo, S.A., Byrne, M., Ricevuto, E., Gambi, M.C. The Carbon Dioxide Vents of Ischia, Italy, A Natural System to Assess Impacts of Ocean Acidification on Marine Ecosystems: An Overview of Research and Comparisons with Other Vent Systems. In Oceanography and Marine Biology An Annual Review. S. J. Hawkins, A. J. Evans, A.C. Dale, L. B. Firth, I. P. Smith eds. Taylor & Francis Group, 56 (2018).

7.
Mutalipassi, M. et al. Ocean acidification alters the responses of invertebrates to wound-activated infochemicals produced by epiphytes of the seagrassPosidonia oceanica. J. Exp. Mar. Biol. Ecol. 530–531, 151435 (2020).
Article  Google Scholar 

8.
Apostolaki, E. T., Vizzini, S., Hendriks, I. E. & Olsen, Y. S. Seagrass ecosystem response to long-term high CO2 in a Mediterranean volcanic vent. Mar. Environ. Res. 99, 9–15 (2014).
CAS  Article  Google Scholar 

9.
Vizzini, S., Apostolaki, E. T., Ricevuto, E., Polymenakou, P. & Mazzola, A. Plant and sediment properties in seagrass meadows from two Mediterranean CO2 vents: Implications for carbon storage capacity of acidified oceans. Mar. Environ. Res. 146, 101–108 (2019).
CAS  Article  Google Scholar 

10.
Beer, S., Björk, M., Beardall, J. Acquisition of carbon in marine plants. In: John Wiley & Sons eds. Photoshynthesis in the Marine Environment. Wiley Blackwell, Iowa, USA. pp: 95–124 (2014).

11.
Beer, S., Björk, M., Hellblom, F. & Axelsson, L. Inorganic carbon utilization in marine angiosperms (seagrasses). Funct. Plant Biol. 29, 349–354 (2002).
CAS  Article  Google Scholar 

12.
Koch, M., Bowes, G., Ross, C. & Zhang, X. H. Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Change Biol. 19, 103–132. https://doi.org/10.1111/j.1365-2486.2012.02791.x (2013).
ADS  Article  Google Scholar 

13.
Zimmerman, R. C., Kohrs, D. G., Steller, D. L. & Alberte, R. S. Impacts of CO2 enrichment on productivity and light requirements of eelgrass. Plant Physiol. 115, 599–607. https://doi.org/10.1104/pp.115.2.599 (1997).
CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Garrard, S. L. & Beaumont, N. J. The effect of ocean acidification on carbon storage and sequestration in seagrass beds; a global and UK context. Mar. Pollut. Bull. 86, 138–146 (2014).
CAS  Article  Google Scholar 

15.
Hendriks, I. E., Duarte, C. M. & Alvarez, M. A. Vulnerability of marine biodiversity to ocean acidification: a meta-analysis. Estuar. Coast. Shelf Sci. 86, 157–164 (2010).
ADS  CAS  Article  Google Scholar 

16.
Zimmerman, R. C., Hill, V. J. & Gallegos, C. L. Predicting effects of ocean warming, acidification, and water quality on Chesapeake region eelgrass. Limnol. Oceanogr. 60(2015), 1781–1804 (2015).
ADS  CAS  Article  Google Scholar 

17.
Pacella, S. R., Cheryl, A. B., George, G. W., Rochelle, G. L. & Burke, H. Seagrass habitat metabolism increases short-term extremes and long-term offset of CO2 under future ocean acidification. PNAS 115(15), 3870–3875 (2018).
ADS  CAS  Article  Google Scholar 

18.
Russell, B. D., Connell, S. D., Uthicke, S. & Hall-Spencer, J. M. Future seagrass beds: can increased productivity lead to increased carbon storage?. Mar. Pollut. Bull. 73, 463–469 (2013).
CAS  Article  Google Scholar 

19.
de los Santos, C. B., Godbold, J. A. & Solan, M. Short-term growth and biomechanical responses of the temperate seagrassCymodocea nodosato CO2 enrichment. Mar. Ecol. Prog. Ser. 572, 91–102 (2017).
ADS  CAS  Article  Google Scholar 

20.
Schneider, G. et al. Structural and physiological responses of Halodule wrightii to ocean acidification. Protoplasma 255, 629–641 (2018).
CAS  Article  Google Scholar 

21.
Radoglou, K. M. & Jarvis, P. G. The effects of CO2 enrichment and nutrient supply on growth morphology and anatomy of Phaseolus vulgaris L seedlings. Ann. Bot. 70, 245–256 (1992).
CAS  Article  Google Scholar 

22.
Epron, D., Liozon, R. & Mousseau, M. Effects of elevated CO2 concentration on leaf characteristics and photosynthetic capacity of beech (Fagus sylvatica) during the growing season. Tree Physiol. 16, 425–432 (1995).
Article  Google Scholar 

23.
Lin, J., Jach, M. E. & Ceulemans, R. Stomatal density and needle anatomy of Scots pine (Pinus sylvestris) are affected by elevated CO2. New Phytol. 150, 665–674 (2001).
Article  Google Scholar 

24.
Ruocco, M. et al. Genome-wide transcriptional reprogramming in the seagrassCymodocea nodosa under experimental ocean acidification. MolEcol 26, 4241–4259. https://doi.org/10.1111/mec.14204 (2017).
CAS  Article  Google Scholar 

25.
Olivé, I. et al. Linking gene expression to productivity to unravel long- and short-term responses of seagrasses exposed to CO2 in volcanic vents. Sci. Rep. 7, 42278 (2017).
ADS  Article  Google Scholar 

26.
Procaccini, G. et al. Depth-specific fluctuations of gene expression and protein abundance modulate the photophysiology in the seagrassPosidonia oceanica. Sci. Rep. 7, 42890. https://doi.org/10.1038/srep42890 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Kumar, M. et al. Proteome analysis reveals extensive light stress response reprogramming in the seagrassZostera muelleri (Alismatales, Zosteraceae) metabolism. Frontiers Plant Sci. 7, 2023 (2017).
Article  Google Scholar 

28.
Piro, A. et al. The modulation of leaf metabolism plays a role in salt tolerance of Cymodocea nodosa exposed to hypersaline stress in mesocosms. Front Plant Sci. 6, 464 (2015).
Article  Google Scholar 

29.
Dattolo, E. et al. Acclimation to different depths by the marine angiosperm Posidonia oceanica: transcriptomic and proteomic profiles. Front. Plant Sci. 4, 195. https://doi.org/10.3389/fpls.2013.00195 (2013).
Article  PubMed  PubMed Central  Google Scholar 

30.
Mazzuca, S. et al. Seagrass light acclimation: 2-DE protein analysis in Posidonia leaves grown inchronic low light conditions. J. Exp. Mar. Biol. Ecol. 374, 113–122 (2009).
CAS  Article  Google Scholar 

31.
Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).
ADS  Article  Google Scholar 

32.
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
CAS  Article  Google Scholar 

33.
Watanabe, C. K. et al. Effects of elevated CO2 on levels of primary metabolites and transcripts of genes encoding respiratory enzymes and their diurnal patterns in Arabidopsis thaliana: possible relationships with respiratory rates. Plant Cell Physiol. 55(2), 341–357. https://doi.org/10.1093/pcp/pct185 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Lauritano, C. et al. Response of key stress-related genes of the seagrassPosidonia oceanica in the vicinity of submarine volcanic vents. Biogeosciences 12, 4947–4971 (2015).
Article  Google Scholar 

35
Neha, S., Gokhale, S. P. & Kumar, B. A. Effect of elevated [CO2] on cell structure and function in seed plants. Clim. Change Environ. Sustain. 2, 69–104. https://doi.org/10.5958/2320-642X.2014.00001.5 (2014).
Article  Google Scholar 

36.
Iuchi, S. et al. Regulation of drought tolerance by gene manipulation of 9-cis-epoxycarotenoid dioxygenase, a key enzyme in abscisic acid biosynthesis in Arabidopsis. Plant J. 27, 325–333. https://doi.org/10.1046/j.1365-313x.2001.01096.x (2001).
CAS  Article  PubMed  Google Scholar 

37.
Endo, A. et al. Drought induction of Arabidopsis 9-cis-epoxycarotenoid dioxygenase occurs in vascular parenchyma cells. Plant Physiol. 147, 1984–1993 (2008).
CAS  Article  Google Scholar 

38
Toh, S. et al. High temperature-induced abscisic acid biosynthesis and its role in the inhibition of gibberellins action in Arabidopsis seeds. Plant Physiol. 146, 1368–1385 (2008).
CAS  Article  Google Scholar 

39.
Dong, C. H. et al. ADF proteins are involved in the control of flowering and regulate F-actin organization, cell expansion, and organ growth in Arabidopsis. Plant Cell 13, 1333–1346 (2001).
CAS  Article  Google Scholar 

40.
Vantard, M. & Blanchoin, L. Actin polymerization processes in plant cells. Curr. Opin. Plant Biol. 5(6), 502–506 (2002).
CAS  Article  Google Scholar 

41.
Smertenko, A. P. et al. Ser6 in the maize actin-depolymerizing factor, ZmADF3, is phosphorylated by a calcium-stimulated protein kinase and is essential for the control of functional activity. Plant J. 14(2), 187–193 (1988).
Article  Google Scholar 

42.
Webster, J. & Stone, B. A. Isolation, structure and monosaccharide composition of the wall of vegetative parts of Heterozostera tasmanica (Martens ex Aschers) den Hartog. Aquat. Bot. 47, 39–52 (1994).
CAS  Article  Google Scholar 

43.
Olsen J.L., Rouzé, P., Verhelst, B., Lin, Y.-C., Bayer, T., Collen, J., Dattolo, E., De Paoli, E., Dittami, S., Maumus, F., et al. The genome of the seagrass Zostera marina reveals angiosperm adaptation to the sea. Nature 530, 331–335 (2016) https://doi.org/10.1038/nature16548.
ADS  CAS  Article  PubMed  Google Scholar 

44.
Brummel, D. A. Cell wall acidification and its role in Auxin-stimulated growth. J. Exp. Bot. 37(2), 270–276 (1986).
Article  Google Scholar 

45.
Laemmli, U. K. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 227, 680–685 (1970).
ADS  CAS  Article  Google Scholar 

46.
Shevchenko, A., Tomas, H., Havlis, J., Olsen, J. V. & Mann, M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protoc. 1, 2856–2860 (2007).
Article  Google Scholar 

47.
Lucini, L. & Bernardo, L. Comparison of proteome response to saline and zinc stress in lettuce. Front. Plant Sci. https://doi.org/10.3389/fpls.2015.00240 (2015).
Article  PubMed  PubMed Central  Google Scholar  More

1.
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).
CAS  Article  Google Scholar 
2.
Austin, K. G., Schwantes, A., Gu, Y. & Kasibhatla, P. S. What causes deforestation in Indonesia? Environ. Res. Lett. 14, 024007 (2019).
Article  Google Scholar 

3.
Tsujino, R., Yumoto, T., Kitamura, S., Djamaluddin, I. & Darnaedi, D. History of forest loss and degradation in Indonesia. Land use policy 57, 335–347 (2016).
Article  Google Scholar 

4.
Turubanova, S., Potapov, P. V., Tyukavina, A. & Hansen, M. C. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028 (2018).
Article  Google Scholar 

5.
Miettinen, J., Hooijer, A., Wang, J., Shi, C. & Liew, S. C. Peatland degradation and conversion sequences and interrelations in Sumatra. Reg. Environ. Change 12, 729–737 (2012).
Article  Google Scholar 

6.
Margono, B. A., Potapov, P. V., Turubanova, S., Stolle, F. & Hansen, M. C. Primary forest cover loss in Indonesia over 2000–2012. Nat. Clim. Change 4, 730 (2014).
Article  Google Scholar 

7.
Stibig, H. J., Achard, F., Carboni, S., Rasi, R. & Miettinen, J. Change in tropical forest cover of Southeast Asia from 1990 to 2010. Biogeosciences 11, 247–258 (2014).
Article  Google Scholar 

8.
Page, S. E. et al. The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 420, 61–65 (2002).
CAS  Article  Google Scholar 

9.
Huijnen, V. et al. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci. Rep. 6, 26886 (2016).
CAS  Article  Google Scholar 

10.
Koplitz, S. N. et al. Public health impacts of the severe haze in Equatorial Asia in September–October 2015: demonstration of a new framework for informing fire management strategies to reduce downwind smoke exposure. Environ. Res. Lett. 11, 094023 (2016).
Article  Google Scholar 

11.
Crippa, P. et al. Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia. Sci. Rep. 6, 37074 (2016).
CAS  Article  Google Scholar 

12.
Wijaya, A. R. et al. How can Indonesia achieve its climate change mitigation goal? An analysis of potential emissions reductions from energy and land-use policies. World Resour. Inst. (Washington D.C, 2017).

13.
Cochrane, M. A. Fire science for rainforests. Nature 421, 913 (2003).
CAS  Article  Google Scholar 

14.
Page, S. E. & Hooijer, A. In the line of fire: the peatlands of Southeast Asia. Philos. Trans. Royal Soc. B 371, 20150176 (2016).
Article  CAS  Google Scholar 

15.
Miettinen, J., Shi, C. & Liew, S. C. Fire distribution in Peninsular Malaysia, Sumatra and Borneo in 2015 with special emphasis on peatland fires. Environ. Manag. 60, 747–757 (2017).
Article  Google Scholar 

16.
Goldammer, J. G. History of equatorial vegetation fires and fire research in Southeast Asia before the 1997–98 episode: a reconstruction of creeping environmental changes. Mitig. Adapt. Strat. Glob. Chang. 12, 13–32 (2007).
Article  Google Scholar 

17.
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).
CAS  Article  Google Scholar 

18.
Baker, J. & Spracklen, D. Climate benefits of intact Amazon forests and the biophysical consequences of disturbance. Front. For. Glob. Chang. 2, 47 (2019).
Article  Google Scholar 

19.
Uhl, C., Kauffman, J. B. and Cummings, D. L. Fire in the Venezuelan Amazon 2: environmental conditions necessary for forest fires in the evergreen rainforest of Venezuela. Oikos 53, 176–184 (1988).

20.
Dommain, R., Couwenberg, J., Glaser, P. H., Joosten, H. & Suryadiputra, I. N. N. Carbon storage and release in Indonesian peatlands since the last deglaciation. Quat. Sci. Rev. 97, 1–32 (2014).
Article  Google Scholar 

21.
Cole, L. E. S., Bhagwat, S. A. & Willis, K. J. Fire in the swamp forest: palaeoecological insights into natural and human-induced burning in intact tropical peatlands. Front. For. Glob. Chang. 2, 48 (2019).
Article  Google Scholar 

22.
Warren, M., Hergoualc’h, K., Kauffman, J. B., Murdiyarso, D. & Kolka, R. An appraisal of Indonesia’s immense peat carbon stock using national peatland maps: uncertainties and potential losses from conversion. Carbon balanc. management 12, 12 (2017).
Article  CAS  Google Scholar 

23.
Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. chang. biol. 17, 798–818 (2011).
Article  Google Scholar 

24.
Page, S. E. et al. A record of late pleistocene and holocene carbon accumulation and climate change from an equatorial peat bog (Kalimantan, Indonesia): implications for past, present and future carbon dynamics. J. Quat. Sci. 19, 625–635 (2004).
Article  Google Scholar 

25.
Schultz, N. M., Lawrence, P. J. & Lee, X. Global satellite data highlights the diurnal asymmetry of the surface temperature response to deforestation. J. Geophys. Res. 122, 903–917 (2017).
Article  Google Scholar 

26.
Sabajo, C. R. et al. Expansion of oil palm and other cash crops causes an increase of the land surface temperature in the Jambi province in Indonesia. Biogeosciences 14, 4619–4635 (2017).
CAS  Article  Google Scholar 

27.
McAlpine, C. A. et al. Forest loss and Borneo’s climate. Environ. Res. Lett. 13, 044009 (2018).
Article  Google Scholar 

28.
Hardwick, S. R. et al. The relationship between leaf area index and microclimate in tropical forest and oil palm plantation: forest disturbance drives changes in microclimate. Agric. For. Meteorol. 201, 187–195 (2015).
Article  Google Scholar 

29.
Jauhiainen, J., Kerojoki, O., Silvennoinen, H., Limin, S. & Vasander, H. Heterotrophic respiration in drained tropical peat is greatly affected by temperature—a passive ecosystem cooling experiment. Environ. Res. Lett. 9, 105013 (2014).
Article  CAS  Google Scholar 

30.
Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Glob. Ecol. Conserv. 6, 67–78 (2016).
Article  Google Scholar 

31.
Hoscilo, A., Page, S. E., Tansey, K. J. & Rieley, J. O. Effect of repeated fires on land-cover change on peatland in southern Central Kalimantan, Indonesia, from 1973 to 2005. Int. J. Wildland Fire 20, 578–588 (2011).
Article  Google Scholar 

32.
Laurance, W. F. Do edge effects occur over large spatial scales? Trends Ecol. Evol. 15, 134–135 (2000).
CAS  Article  Google Scholar 

33.
Cochrane, M. A. & Laurance, W. F. Fire as a large-scale edge effect in Amazonian forests. J. Tropi. Ecol. 18, 311–325 (2002).
Article  Google Scholar 

34.
Laurance, W. F., Laurance, S. G. & Delamonica, P. Tropical forest fragmentation and greenhouse gas emissions. For. Ecol. Manag. 110, 173–180 (1998).
Article  Google Scholar 

35.
Curran, L. M. et al. Impact of El Nino and logging on canopy tree recruitment in Borneo. Science 286, 2184–2188 (1999).
CAS  Article  Google Scholar 

36.
Chaplin-Kramer, R. et al. Degradation in carbon stocks near tropical forest edges. Nat. Commun. 6, 10158 (2015).
CAS  Article  Google Scholar 

37.
Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 1–6 (2017).
Article  CAS  Google Scholar 

38.
Briant, G., Gond, V. & Laurance, S. G. Habitat fragmentation and the desiccation of forest canopies: a case study from eastern Amazonia. Biol. conserv. 143, 2763–2769 (2010).
Article  Google Scholar 

39.
Didham, R. K. & Lawton, J. H. Edge structure determines the magnitude of changes in microclimate and vegetation structure in tropical forest fragments. Biotropica 31, 17–30 (1999).
Google Scholar 

40.
Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053 (2012).
CAS  Article  Google Scholar 

41.
Evans, C. D. et al. Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra, Indonesia. Geoderma 338, 410–421 (2019).
Article  Google Scholar 

42.
Cattau, M. E. et al. Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan, Indonesia. Glob. Environ. Chang. 39, 205–219 (2016).
Article  Google Scholar 

43.
Wooster, M. J., Perry, G. L. W. and Zoumas, A. Fire, drought and El Niño relationships on Borneo (Southeast Asia) in the pre-MODIS era (1980–2000). Biogeosciences 9, (2012)

44.
Spessa, A. C. et al. Seasonal forecasting of fire over Kalimantan, Indonesia. Nat. Hazards Earth Syst. Sci. 15, 429–442 (2015).
Article  Google Scholar 

45.
Field, R. D. et al. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc. Natl Acad. Sci. 113, 9204–9209 (2016).
CAS  Article  Google Scholar 

46.
Langner, A. & Siegert, F. Spatiotemporal fire occurrence in Borneo over a period of 10 years. Glob. Chang. Biol. 15, 48–62 (2009).
Article  Google Scholar 

47.
Pan, X., Chin, M., Ichoku, C. M. & Field, R. D. Connecting Indonesian fires and drought with the type of El Niño and phase of the Indian Ocean Dipole during 1979–2016. J. Geophys. Res. 123, 7974–7988 (2018).
Google Scholar 

48.
Konecny, K. et al. Variable carbon losses from recurrent fires in drained tropical peatlands. Glob. Chang. Biol. 22, 1469–1480 (2016).
Article  Google Scholar 

49.
Miettinen, J., Hooijer, A., Vernimmen, R., Liew, S. C. & Page, S. E. From carbon sink to carbon source: extensive peat oxidation in insular Southeast Asia since 1990. Environ. Res. Lett. 12, 024014 (2017).
Article  CAS  Google Scholar 

50.
Langner, A., Miettinen, J. & Siegert, F. Land cover change 2002–2005 in Borneo and the role of fire derived from MODIS imagery. Glob. Chang. Biol. 13, 2329–2340 (2007).
Article  Google Scholar 

51.
van der Werf, G. R. et al. Climate regulation of fire emissions and deforestation in equatorial Asia. Proc. Natl Acad. Sci. 105, 20350–20355 (2008).
Article  Google Scholar 

52.
Tacconi, L. Preventing fires and haze in Southeast Asia. Nat. Clim. Chang. 6, 640 (2016).
Article  Google Scholar 

53.
Wahyunto, R. S. & Suparto, S. H. Maps of area of peatland distribution and carbon content in Kalimantan, 2000–2002. Wetl. Int.-Indones. Program. Wildl. Habitat Can. (WHC) Bogor. (2004).

54.
Purnomo A. Protecting Indonesia’s Forests, Pros-Cons Policy of Moratorium on Forests and Peatlands (Kepustakaan Populer Gramedia, Jakarta, Indonesia, 2012).

55.
Normile, D. Indonesia’s fires are bad, but new measures prevented them from becoming worse. Sci. Mag. https://www.sciencemag.org/news/2019/10/indonesias-fires-are-bad-new-measures-prevented-them-becoming-worse (2019).

56.
Purnomo, H. et al. Fire economy and actor network of forest and land fires in Indonesia. For. Policy Econ. 78, 21–31 (2017).
Article  Google Scholar 

57.
Seymour, F. Indonesia Reduces Deforestation, Norway to Pay Up. World Resources Institute. https://www.wri.org/blog/2019/02/indonesia-reduces-deforestation-norway-pay (2019).

58.
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, 1500052 (2015).
Article  Google Scholar 

59.
Watson, J. E. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).
Article  Google Scholar 

60.
Gaveau, D. L. et al. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: evidence from the 2013 Sumatran fires. Sci. Rep. 4, 6112 (2014).
CAS  Article  Google Scholar 

61.
Wooster, M. et al. New tropical peatland gas and particulate emissions factors indicate 2015 Indonesian fires released far more particulate matter (but less methane) than current inventories imply. Remote Sens. 10, 495 (2018).
Article  Google Scholar 

62.
Taufik, M. et al. Amplification of wildfire area burnt by hydrological drought in the humid tropics. Nat. Clim. Chang. 7, 428–431 (2017).
Article  Google Scholar 

63.
Margono, B. A. et al. Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010. Environ. Res. Lett. 7, 034010 (2012).
Article  Google Scholar 

64.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
CAS  Article  Google Scholar 

65.
Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).
Article  Google Scholar 

66.
Lohberger, S., Stängel, M., Atwood, E. C. & Siegert, F. Spatial evaluation of Indonesia’s 2015 fire‐affected area and estimated carbon emissions using Sentinel‐1. Glob. Chang. Biol. 24, 644–654 (2015).
Article  Google Scholar  More

1.
Karl DM, Wirsen CO, Jannasch HW. Deep-sea primary production at the Galapagos hydrothermal vents. Science (80-). 1980;207:1345–7.
CAS  Article  Google Scholar 
2.
Yamamoto M, Takai K. Sulfur metabolisms in epsilon-and gamma-Proteobacteria in deep-sea hydrothermal fields. Front Microbiol. 2011;2:192.
CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Kato S, Nakamura K, Toki T, Ishibashi J-I, Tsunogai U, Hirota A, et al. Iron-based microbial ecosystem on and below the seafloor: a case study of hydrothermal fields of the southern mariana trough. Front Microbiol. 2012;3:89.
PubMed  PubMed Central  Google Scholar 

4.
Winkel M, de Beer D, Lavik G, Peplies J, Mußmann M. Close association of active nitrifiers with Beggiatoa mats covering deep-sea hydrothermal sediments. Environ Microbiol. 2014;16:1612–26.
CAS  PubMed  Article  Google Scholar 

5.
Fortunato CS, Larson B, Butterfield DA, Huber JA. Spatially distinct, temporally stable microbial populations mediate biogeochemical cycling at and below the seafloor in hydrothermal vent fluids. Environ Microbiol. 2018;20:769–84.
CAS  PubMed  Article  Google Scholar 

6.
Kendall B, Anbar AD, Kappler A, Konhauser KO. The global iron cycle. In: Knoll AH, Canfield DE, Konhauser KO (eds). Fundamentals of Geobiology, 1st ed. Blackwell Publishing Ltd.; 2012. pp. 65–92.

7.
McAllister SM, Moore RM, Gartman A, Luther GW, Emerson D, Chan CS. The Fe(II)-oxidizing Zetaproteobacteria: historical, ecological, and genomic perspectives. FEMS Microbiol Ecol. 2019;95:fiz015.
CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Kato S, Kobayashi C, Kakegawa T, Yamagishi A. Microbial communities in iron-silica-rich microbial mats at deep-sea hydrothermal fields of the Southern Mariana Trough. Environ Microbiol. 2009;11:2094–111.
CAS  PubMed  Article  Google Scholar 

9.
Hassenrück C, Fink A, Lichtschlag A, Tegetmeyer HE, de Beer D, Ramette A. Quantification of the effects of ocean acidification on sediment microbial communities in the environment: The importance of ecosystem approaches. FEMS Microbiol Ecol. 2016;92:fiw02.
Article  CAS  Google Scholar 

10.
Kato S, Yanagawa K, Sunamura M, Takano Y, Ishibashi J, Kakegawa T, et al. Abundance of Zetaproteobacteria within crustal fluids in back-arc hydrothermal fields of the Southern Mariana Trough. Environ Microbiol. 2009;11:3210–22.
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
McAllister SM, Davis RE, McBeth JM, Tebo BM, Emerson D, Moyer CL. Biodiversity and emerging biogeography of the neutrophilic iron-oxidizing Zetaproteobacteria. Appl Environ Microbiol. 2011;77:5445–57.
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Makita H, Kikuchi S, Mitsunobu S, Takaki Y, Yamanaka T, Toki T, et al. Comparative analysis of microbial communities in iron-dominated flocculent mats in deep-sea hydrothermal environments. Appl Environ Microbiol. 2016;82:5741–55.
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Scott JJ, Breier JA, Luther GW III, Emerson D. Microbial iron mats at the Mid-Atlantic Ridge and evidence that Zetaproteobacteria may be restricted to iron-oxidizing marine systems. PLoS ONE. 2015;10:e0119284.
PubMed  PubMed Central  Article  CAS  Google Scholar 

14.
Scott JJ, Glazer BT, Emerson D. Bringing microbial diversity into focus: high-resolution analysis of iron mats from the Lō’ihi Seamount. Environ Microbiol. 2017;19:301–16.
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Hager KW, Fullerton H, Butterfield DA, Moyer CL. Community structure of lithotrophically-driven hydrothermal microbial mats from the Mariana Arc and Back-Arc. Front Microbiol. 2017;8:1578.
PubMed  PubMed Central  Article  Google Scholar 

16.
Forget NL, Murdock SA, Juniper SK. Bacterial diversity in Fe-rich hydrothermal sediments at two South Tonga Arc submarine volcanoes. Geobiology. 2010;8:417–32.
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Vander Roost J, Thorseth IH, Dahle H. Microbial analysis of Zetaproteobacteria and co-colonizers of iron mats in the Troll Wall Vent Field, Arctic Mid-Ocean Ridge. PLoS ONE. 2017;12:e0185008.
Article  CAS  Google Scholar 

18.
Rassa AC, McAllister SM, Safran SA, Moyer CL. Zeta-Proteobacteria dominate the colonization and formation of microbial mats in low-temperature hydrothermal vents at Loihi Seamount, Hawaii. Geomicrobiol J. 2009;26:623–38.
CAS  Article  Google Scholar 

19.
Fullerton H, Hager KW, McAllister SM, Moyer CL. Hidden diversity revealed by genome-resolved metagenomics of iron-oxidizing microbial mats from Lo’ihi Seamount, Hawai’i. ISME J. 2017;11:1900–14.
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Field EK, Sczyrba A, Lyman AE, Harris CC, Woyke T, Stepanauskas R, et al. Genomic insights into the uncultivated marine Zetaproteobacteria at Loihi Seamount. ISME J. 2015;9:857–70.
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Chan CS, McAllister SM, Leavitt AH, Glazer BT, Krepski ST, Emerson D. The architecture of iron microbial mats reflects the adaptation of chemolithotrophic iron oxidation in freshwater and marine environments. Front Microbiol. 2016;7:796.
PubMed  PubMed Central  Google Scholar 

22.
Fleming EJ, Davis RE, McAllister SM, Chan CS, Moyer CL, Tebo BM, et al. Hidden in plain sight: discovery of sheath-forming, iron-oxidizing Zetaproteobacteria at Loihi Seamount, Hawaii, USA. FEMS Microbiol Ecol. 2013;85:116–27.
PubMed  Article  PubMed Central  Google Scholar 

23.
Barco RA, Emerson D, Sylvan JB, Orcutt BN, Jacobson Meyers ME, Ramírez GA, et al. New insight into microbial iron oxidation as revealed by the proteomic profile of an obligate iron-oxidizing chemolithoautotroph. Appl Environ Microbiol. 2015;81:5927–37.
CAS  PubMed  PubMed Central  Article  Google Scholar 

24.
McAllister SM, Polson SW, Butterfield DA, Glazer BT, Sylvan JB, Chan CS. Validating the Cyc2 neutrophilic iron oxidation pathway using meta-omics of Zetaproteobacteria iron mats at marine hydrothermal vents. mSystems. 2020;5:e00553–19.
PubMed  PubMed Central  Article  Google Scholar 

25.
Singer E, Emerson D, Webb EA, Barco RA, Kuenen JG, Nelson WC, et al. Mariprofundus ferrooxydans, PV-1 the first genome of a marine Fe(II) oxidizing Zetaproteobacterium. PLoS ONE. 2011;6:e25386.
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Mori JF, Scott JJ, Hager KW, Moyer CL, Küsel K, Emerson D. Physiological and ecological implications of an iron- or hydrogen-oxidizing member of the Zetaproteobacteria, Ghiorsea bivora, gen. nov., sp. nov. ISME J. 2017;11:2624–36.
PubMed  PubMed Central  Article  Google Scholar 

27.
Zimmerman AE, Howard-Varona C, Needham DM, John SG, Worden AZ, Sullivan MB, et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat Rev Microbiol. 2020;18:21–34.
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Bennett SA, Hansman RL, Sessions AL, Nakamura K, Edwards KJ. Tracing iron-fueled microbial carbon production within the hydrothermal plume at the Loihi seamount. Geochim Cosmochim Acta. 2011;75:5526–39.
CAS  Article  Google Scholar 

29.
Jesser KJ, Fullerton H, Hager KW, Moyer CL. Quantitative PCR analysis of functional genes in iron-rich microbial mats at an active hydrothermal vent system (Lō’ihi Seamount, Hawai’i). Appl Environ Microbiol. 2015;81:2976–84.
CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Singer E, Heidelberg JF, Dhillon A, Edwards KJ. Metagenomic insights into the dominant Fe(II) oxidizing Zetaproteobacteria from an iron mat at Lō’ihi, Hawai’i. Front Microbiol. 2013;4:52.
PubMed  PubMed Central  Article  Google Scholar 

31.
Chiu BK, Kato S, McAllister SM, Field EK, Chan CS. Novel pelagic iron-oxidizing Zetaproteobacteria from the Chesapeake Bay oxic-anoxic transition zone. Front Microbiol. 2017;8:1280.
PubMed  PubMed Central  Article  Google Scholar 

32.
Makita H, Tanaka E, Mitsunobu S, Miyazaki M, Nunoura T, Uematsu K, et al. Mariprofundus micogutta sp. nov., a novel iron-oxidizing zetaproteobacterium isolated from a deep-sea hydrothermal field at the Bayonnaise knoll of the Izu-Ogasawara arc, and a description of Mariprofundales ord. nov. and Zetaproteobacteria classis. Arch Microbiol. 2017;199:335–46.
CAS  PubMed  Article  Google Scholar 

33.
Laufer K, Nordhoff M, Halama M, Martinez RE, Obst M, Nowak M, et al. Microaerophilic Fe(II)-oxidizing Zetaproteobacteria isolated from low-Fe marine coastal sediments: Physiology and characterization of their twisted stalks. Appl Environ Microbiol. 2017;83:e03118–16.
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Glazer BT, Rouxel OJ. Redox speciation and distribution within diverse iron-dominated microbial habitats at Loihi Seamount. Geomicrobiol J. 2009;26:606–22.
CAS  Article  Google Scholar 

35.
Sylvan JB, Wankel SD, LaRowe DE, Charoenpong CN, Huber JA, Moyer CL, et al. Evidence for microbial mediation of subseafloor nitrogen redox processes at Loihi Seamount, Hawaii. Geochim Cosmochim Acta. 2017;198:131–50.
CAS  Article  Google Scholar 

36.
Sedwick PN, McMurtry GM, Macdougall JD. Chemistry of hydrothermal solutions from Pele’s Vents, Loihi Seamount, Hawaii. Geochim Cosmochim Acta. 1992;56:3643–67.
CAS  Article  Google Scholar 

37.
Karl DM, Brittain AM, Tilbrook BD. Hydrothermal and microbial processes at Loihi Seamount, a mid-plate hot-spot volcano. Deep Sea Res Part A, Oceanogr Res Pap. 1989;36:1655–73.
CAS  Article  Google Scholar 

38.
Bryce C, Blackwell N, Schmidt C, Otte J, Huang YM, Kleindienst S, et al. Microbial anaerobic Fe(II) oxidation—ecology, mechanisms and environmental implications. Environ Microbiol. 2018;20:3462–83.
CAS  PubMed  Article  Google Scholar 

39.
Laufer K, Byrne JM, Glombitza C, Schmidt C, Jørgensen BB, Kappler A. Anaerobic microbial Fe(II) oxidation and Fe(III) reduction in coastal marine sediments controlled by organic carbon content. Environ Microbiol. 2016;18:3159–74.
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Robertson EK, Roberts KL, Burdorf LDW, Cook P, Thamdrup B. Dissimilatory nitrate reduction to ammonium coupled to Fe(II) oxidation in sediments of a periodically hypoxic estuary. Limnol Oceanogr. 2016;61:365–81.
CAS  Article  Google Scholar 

41.
Glöckner FO, Yilmaz P, Quast C, Gerken J, Beccati A, Ciuprina A, et al. 25 years of serving the community with ribosomal RNA gene reference databases and tools. J Biotechnol. 2017;261:169–76.
PubMed  Article  CAS  Google Scholar 

42.
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Wu Y-W, Tang Y-H, Tringe SG, Simmons BA, Singer SW. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome. 2014;2:26.
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.
PubMed  PubMed Central  Article  CAS  Google Scholar 

46.
Alneberg J, Bjarnason BS, De Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.
CAS  PubMed  Article  Google Scholar 

47.
Graham ED, Heidelberg JF, Tully BJ. BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation. PeerJ. 2017;5:e3035.
PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.
PubMed  PubMed Central  Article  Google Scholar 

49.
Quinlan AR, Hall IM. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–2.
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Wagner GP, Kin K, Lynch VJ. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 2012;131:281–5.
CAS  PubMed  Article  Google Scholar 

51.
Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the rapid annotation of microbial genomes using subsystems technology (RAST). Nucleic Acids Res. 2014;42:D206–14.
CAS  PubMed  Article  Google Scholar 

53.
Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG Tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.
CAS  PubMed  Article  Google Scholar 

54.
Anantharaman K, Brown CT, Hug LA, Sharon I, Castelle CJ, Probst AJ, et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat Commun. 2016;7:13219.
CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Garber AI, Nealson KH, Okamoto A, McAllister SM, Chan CS, Barco RA, et al. FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genome and metagenome assemblies. Front Microbiol. 2020;11:37.
PubMed  PubMed Central  Article  Google Scholar 

56.
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinforma. 2009;10:421.
Article  CAS  Google Scholar 

57.
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.
CAS  PubMed  Article  Google Scholar 

58.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.
CAS  PubMed  Article  Google Scholar 

59.
Pruesse E, Peplies J, Glöckner FO. SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Stamatakis A, Hoover P, Rougemont J. A rapid bootstrap algorithm for the RAxML web servers. Syst Biol. 2008;57:758–71.
PubMed  Article  Google Scholar 

61.
Moore RM, Harrison AO, McAllister SM, Polson SW, Wommack KE. Iroki: automatic customization and visualization of phylogenetic trees. PeerJ. 2020;8:e8584.
PubMed  PubMed Central  Article  Google Scholar 

62.
Darling AE, Jospin G, Lowe E, Matsen FA, Bik HM, Eisen JA. PhyloSift: phylogenetic analysis of genomes and metagenomes. PeerJ. 2014;2:e243.
PubMed  PubMed Central  Article  Google Scholar 

63.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from. Genome Res. 2015;25:1043–55.
CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.
PubMed  PubMed Central  Article  CAS  Google Scholar 

65.
Wommack KE, Bhavsar J, Polson SW, Chen J, Dumas M, Srinivasiah S, et al. VIROME: a standard operating procedure for analysis of viral metagenome sequences. Stand Genom Sci. 2012;6:421–33.
CAS  Google Scholar 

66.
Bolduc B, Jang HBin, Doulcier G, You Z-Q, Roux S, Sullivan MB. vConTACT: an iVirus tool to classify double-stranded DNA viruses that infect Archaea and Bacteria. PeerJ. 2017;5:e3243.
PubMed  PubMed Central  Article  Google Scholar 

67.
Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell. 2019;177:1109–23.
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Nasko DJ, Ferrell BD, Moore RM, Bhavsar JD, Polson SW, Wommack KE. CRISPR spacers indicate preferential matching of specific virioplankton genes. MBio. 2019;10:e02651–18.
CAS  PubMed  PubMed Central  Article  Google Scholar 

69.
Lau MCY, Aitchison JC, Pointing SB. Bacterial community composition in thermophilic microbial mats from five hot springs in central Tibet. Extremophiles. 2009;13:139–49.
PubMed  Article  Google Scholar 

70.
Qiu Y, Hanada S, Ohashi A, Harada H, Kamagata Y, Sekiguchi Y. Syntrophorhabdus aromaticivorans gen. nov., sp. nov., the first cultured anaerobe capable of degrading phenol to acetate in obligate syntrophic associations with a hydrogenotrophic methanogen. Appl Environ Microbiol. 2008;74:2051–8.
CAS  PubMed  PubMed Central  Article  Google Scholar 

71.
Nobu MK, Narihiro T, Tamaki H, Qiu Y, Sekiguchi Y, Woyke T, et al. The genome of Syntrophorhabdus aromaticivorans strain UI provides new insights for syntrophic aromatic compound metabolism and electron flow. Environ Microbiol. 2015;17:4861–72.
CAS  PubMed  Article  Google Scholar 

72.
Omoregie EO, Mastalerz V, de Lange G, Straub KL, Kappler A, Røy H, et al. Biogeochemistry and community composition of iron- and sulfur-precipitating microbial mats at the Chefren mud volcano (Nile deep sea fan, eastern Mediterranean). Appl Environ Microbiol. 2008;74:3198–215.
CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Konstantinidis KT, Rosselló-Móra R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.
PubMed  PubMed Central  Article  Google Scholar 

74.
Williamson SJ, Cary SC, Williamson KE, Helton RR, Bench SR, Winget D, et al. Lysogenic virus–host interactions predominate at deep-sea diffuse-flow hydrothermal vents. ISME J. 2008;2:1112–21.
CAS  PubMed  Article  Google Scholar 

75.
Sharma A, Schmidt M, Kiesel B, Mahato NK, Cralle L, Singh Y, et al. Bacterial and Archaeal viruses of Himalayan hot springs at Manikaran modulate host genomes. Front Microbiol. 2018;9:3095.
PubMed  PubMed Central  Article  Google Scholar 

76.
Anderson RE, Sogin ML, Baross JA. Evolutionary strategies of viruses, bacteria and archaea in hydrothermal vent ecosystems revealed through metagenomics. PLoS ONE. 2014;9:e109696.
PubMed  PubMed Central  Article  CAS  Google Scholar 

77.
Emerson D, Moyer CL. Neutrophilic Fe-oxidizing bacteria are abundant at the Loihi Seamount hydrothermal vents and play a major role in Fe oxide deposition. Appl Environ Microbiol. 2002;68:3085–93.
CAS  PubMed  PubMed Central  Article  Google Scholar 

78.
Emerson D, Fleming EJ, McBeth JM. Iron-oxidizing bacteria: an environmental and genomic perspective. Annu Rev Microbiol. 2010;64:561–83.
CAS  PubMed  Article  Google Scholar 

79.
Hernsdorf AW, Amano Y, Miyakawa K, Ise K, Suzuki Y, Anantharaman K, et al. Potential for microbial H2 and metal transformations associated with novel bacteria and archaea in deep terrestrial subsurface sediments. ISME J. 2017;11:1915–29.
CAS  PubMed  PubMed Central  Article  Google Scholar 

80.
Quaiser A, Bodi X, Dufresne A, Naquin D, Francez A-J, Dheilly A, et al. Unraveling the stratification of an iron-oxidizing microbial mat by metatranscriptomics. PLoS ONE. 2014;9:e102561.
PubMed  PubMed Central  Article  CAS  Google Scholar 

81.
Kato S, Chan C, Itoh T, Ohkuma M. Functional gene analysis of freshwater iron-rich flocs at circumneutral ph and isolation of a stalk-forming microaerophilic iron-oxidizing bacterium. Appl Environ Microbiol. 2013;79:5283–90.
CAS  PubMed  PubMed Central  Article  Google Scholar 

82.
Hemp J, Gennis RB. Diversity of the heme-copper superfamily in Archaea: Insights from genomics and structural modeling. Results Probl Cell Differ. 2008;45:1–31.
CAS  PubMed  Article  Google Scholar 

83.
Ferris FG. Biogeochemical properties of bacteriogenic iron oxides. Geomicrobiol J. 2005;22:79–85.
CAS  Article  Google Scholar 

84.
Sowers TD, Harrington JM, Polizzotto ML, Duckworth OW. Sorption of arsenic to biogenic iron (oxyhydr)oxides produced in circumneutral environments. Geochim Cosmochim Acta. 2017;198:194–207.
CAS  Article  Google Scholar 

85.
Bennett SA, Toner BM, Barco R, Edwards KJ. Carbon adsorption onto Fe oxyhydroxide stalks produced by a lithotrophic iron-oxidizing bacteria. Geobiology. 2014;12:146–56.
CAS  PubMed  Article  Google Scholar 

86.
Rentz JA, Turner IP, Ullman JL. Removal of phosphorus from solution using biogenic iron oxides. Water Res. 2009;43:2029–35.
CAS  PubMed  Article  Google Scholar 

87.
Chan CS, Fakra SC, Emerson D, Fleming EJ, Edwards KJ. Lithotrophic iron-oxidizing bacteria produce organic stalks to control mineral growth: implications for biosignature formation. ISME J. 2011;5:717–27.
CAS  PubMed  Article  Google Scholar 

88.
Bennett SA, Toner BM, Barco R, Edwards KJ. Carbon adsorption onto Fe oxyhydroxide stalks produced by a lithotrophic iron-oxidizing bacteria. Geobiology. 2014;12:146–56.
CAS  PubMed  Article  Google Scholar 

89.
Wilhelm SW, Suttle CA. Viruses and nutrient cycles in the sea: Viruses play critical roles in the structure and function of aquatic food webs. Bioscience. 1999;49:781–8.
Article  Google Scholar 

90.
Chen J, Strous M. Denitrification and aerobic respiration, hybrid electron transport chains and co-evolution. Biochim Biophys Acta. 2013;1827:136–44.
CAS  PubMed  Article  PubMed Central  Google Scholar 

91.
Choi PS, Naal Z, Moore C, Casado-Rivera E, Abruña HD, Helmann JD, et al. Assessing the impact of denitrifier-produced nitric oxide on other bacteria. Appl Environ Microbiol. 2006;72:2200–5.
CAS  PubMed  PubMed Central  Article  Google Scholar 

92.
Klueglein N, Kappler A. Abiotic oxidation of Fe(II) by reactive nitrogen species in cultures of the nitrate-reducing Fe(II) oxidizer Acidovorax sp. BoFeN1 – questioning the existence of enzymatic Fe(II) oxidation. Geobiology. 2013;11:180–90.
CAS  PubMed  Article  PubMed Central  Google Scholar 

93.
Hafenbradl D, Keller M, Dirmeier R, Rachel R, Roßnagel P, Burggraf S, et al. Ferroglobus placidus gen. nov., sp. nov., a novel hyperthermophilic archaeum that oxidizes Fe2+ at neutral pH under anoxic conditions. Arch Microbiol. 1996;166:308–14.
CAS  PubMed  Article  PubMed Central  Google Scholar 

94.
He S, Tominski C, Kappler A, Behrens S, Roden EE. Metagenomic analyses of the autotrophic Fe(II)-oxidizing, nitrate-reducing enrichment culture KS. Appl Environ Microbiol. 2016;82:2656–68.
CAS  PubMed  PubMed Central  Article  Google Scholar 

95.
Straub KL, Benz M, Schink B, Widdel F. Anaerobic, nitrate-dependent microbial oxidation of ferrous iron. Appl Environ Microbiol. 1996;62:1458–60.
CAS  PubMed  PubMed Central  Article  Google Scholar 

96.
Blöthe M, Roden EE. Composition and activity of an autotrophic Fe(II)-oxidizing, nitrate-reducing enrichment culture. Appl Environ Microbiol. 2009;75:6937–40.
PubMed  PubMed Central  Article  CAS  Google Scholar 

97.
Emerson D, Scott JJ, Leavitt A, Fleming E, Moyer C. In situ estimates of iron-oxidation and accretion rates for iron-oxidizing bacterial mats at Lō’ihi Seamount. Deep Res Part I Oceanogr Res Pap. 2017;126:31–9.
CAS  Article  Google Scholar 

98.
Jenkins WJ, Hatta M, Fitzsimmons JN, Schlitzer R, Lanning NT, Shiller A, et al. An intermediate-depth source of hydrothermal 3He and dissolved iron in the North Pacific. Earth Planet Sci Lett. 2020;539:116223.
CAS  Article  Google Scholar  More

Macroscopic inspection of the 17 unhatched Black Grouse eggs showed that no embryonic tissues/development was visible in 12 of them, while very early chick development ( More

1.
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Luan, L. et al. Coupling bacterial community assembly to microbial metabolism across soil profiles. mSystems 5, e00298–20 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Stegen, J. C. et al. Groundwater–surface water mixing shifts ecological assembly processes and stimulates organic carbon turnover. Nat. Commun. 7, 11237 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

5.
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).
PubMed  Article  CAS  PubMed Central  Google Scholar 

7.
Oliverio, A. M. et al. The global-scale distributions of soil protists and their contributions to belowground systems. Sci. Adv. 6, eaax8787 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
van den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature 572, 194–198 (2019).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

9.
Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. Biol. Rev. 77, 342–356 (2013).
PubMed  PubMed Central  Article  Google Scholar 

10.
Martiny, J. B. H. et al. Microbial biogeography: putting microorganisms on the map. Nat. Rev. Microbiol. 4, 102–112 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. H. Beyond biogeographic patterns: processes shaping the microbial landscape. Nat. Rev. Microbiol. 10, 497–506 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Vellend, M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85, 183–206 (2010).
PubMed  Article  PubMed Central  Google Scholar 

13.
Dini-Andreote, F., Stegen, J. C., Van Elsas, J. D. & Falcão Salles, J. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc. Natl Acad. Sci. USA 112, E1326–E1332 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Zhou, J. & Ning, D. Stochastic community assembly: does it matter in microbial ecology? Microbiol. Mol. Biol. Rev. 81, e00002–e00017 (2017).
PubMed  PubMed Central  Article  Google Scholar 

15.
Chave, J. Neutral theory and community ecology. Ecol. Lett. 7, 241–253 (2004).
ADS  Article  Google Scholar 

16.
Letten, A. D., Ke, P.-J. & Fukami, T. Linking modern coexistence theory and contemporary niche theory. Ecol. Monogr. 87, 161–177 (2017).
Article  Google Scholar 

17.
Stegen, J. C., Lin, X. J., Fredrickson, J. K. & Konopka, A. E. Estimating and mapping ecological processes influencing microbial community assembly. Front. Microbiol. 6, 370 (2015).
PubMed  PubMed Central  Article  Google Scholar 

18.
Li, P. et al. Distinct Successions of common and rare bacteria in soil under humic acid amendment – a microcosm study. Front. Microbiol. 10, 2271 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
Stearns, S. C. The Evolution of Life Histories (Oxford Univ. Press, Oxford, 1992).

20.
Villarino, E. et al. Large-scale ocean connectivity and planktonic body size. Nat. Commun. 9, 142 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

21.
Woo, C., An, C., Xu, S., Yi, S. M. & Yamamoto, N. Taxonomic diversity of fungi deposited from the atmosphere. ISME J. 12, 2051–2060 (2018).
PubMed  PubMed Central  Article  Google Scholar 

22.
Soininen, J., Korhonen, J. J. & Luoto, M. Stochastic species distributions are driven by organism size. Ecology 94, 660–670 (2013).
PubMed  Article  PubMed Central  Google Scholar 

23.
Farjalla, V. F. et al. Ecological determinism increases with organism size. Ecology 93, 1752–1759 (2012).
PubMed  Article  PubMed Central  Google Scholar 

24.
Thuiller, W., Lavorel, S. & Araújo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob. Ecol. Biogeogr. 14, 347–357 (2005).
Article  Google Scholar 

25.
Li, P. et al. Responses of microbial communities to a gradient of pig manure amendment in red paddy soils. Sci. Total Environ. 705, 135884 (2020).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Article  Google Scholar 

27.
Foissner, W. Protist diversity and distribution: some basic considerations. Biodivers. Conserv. 17, 235–242 (2008).
Article  Google Scholar 

28.
Pimm, S. L., Jones, H. L. & Diamond, J. On the risk of extinction. Am. Nat. 132, 757–785 (1988).
Article  Google Scholar 

29.
Jiang, Y. et al. Crop rotations alter bacterial and fungal diversity in paddy soils across East Asia. Soil Biol. Biochem. 95, 250–261 (2016).
CAS  Article  Google Scholar 

30.
Li, P. et al. Spatial variation in soil fungal communities across paddy fields in subtropical China. mSystems 5, e00704–e00719 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

31.
Peay, K. G., Kennedy, P. G. & Talbot, J. M. Dimensions of biodiversity in the Earth mycobiome. Nat. Rev. Microbiol. 14, 434–447 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Geisen, S. et al. Soil protists: a fertile frontier in soil biology research. FEMS Microbiol. Rev. 42, 293–323 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Tucker, C. M., Shoemaker, L. G., Davies, K. F., Nemergut, D. R. & Melbourne, B. A. Differentiating between niche and neutral assembly in metacommunities using null models of β-diversity. Oikos 125, 778–789 (2016).
Article  Google Scholar 

34.
Doledec, S., Chessel, D. & Gimaret-Carpentier, C. Niche separation in community analysis: a new method. Ecology 81, 2914–2927 (2000).
Article  Google Scholar 

35.
Sloan, W. T., Woodcock, S., Lunn, M., Head, I. M. & Curtis, T. P. Modeling taxa-abundance distributions in microbial communities using environmental sequence data. Microb. Ecol. 53, 443–455 (2007).
PubMed  Article  PubMed Central  Google Scholar 

36.
De Wit, R. & Bouvier, T. Everything is everywhere, but, the environment selects; what did BaasBecking and Beijerinck really say. Environ. Microbiol. 8, 755–758 (2006).
PubMed  Article  PubMed Central  Google Scholar 

37.
Zinger, L. et al. Body size determines soil community assembly in a tropical forest. Mol. Ecol. 28, 528–543 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Chase, J. M. et al. Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities. Ecol. Lett. 21, 1737–1751 (2018).
PubMed  Article  PubMed Central  Google Scholar 

39.
Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470–480 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
De Bie, T. et al. Body size and dispersal mode as key traits determining metacommunity structure of aquatic organisms. Ecol. Lett. 15, 740–747 (2012).
PubMed  Article  PubMed Central  Google Scholar 

41.
Lowe, W. H. & McPeek, M. A. Is dispersal neutral? Trends Ecol. Evol. 29, 444–450 (2014).
PubMed  Article  PubMed Central  Google Scholar 

42.
O’Brien, E. M., Whittaker, R. J. & Field, R. Climate and woody plant diversity in Southern Africa: relationships at species, genus and family levels. Ecography 21, 495–509 (1998).
Article  Google Scholar 

43.
Brown, J. H. Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).
PubMed  Article  PubMed Central  Google Scholar 

44.
Li, X. et al. Agriculture erases climate constraints on soil nematode communities across large spatial scales. Glob. Change Biol. 26, 919–930 (2020).
ADS  Article  Google Scholar 

45.
Briones, M. J. I. Soil fauna and soil functions: a jigsaw puzzle. Front. Environ. Sci. 22, 7 (2014).
Google Scholar 

46.
Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, 9323 (2015).
ADS  Article  CAS  Google Scholar 

47.
Pansu, M. & Gautheyrou, J. Handbook of Soil Analysis: Mineralogical, Organic, and Inorganic Methods. (Springer, 2006).

48.
Biddle, J. F., Fitz-Gibbon, S., Schuster, S. C., Brenchley, J. E. & House, C. H. Metagenomic signatures of the Peru Margin subseafloor biosphere show a genetically distinct environment. Proc. Natl Acad. Sci. USA 105, 10583–10588 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Gardes, M. & Bruns, T. D. ITS primers with enhanced specificity for basidiomycetes – application to the identification of mycorrhizae and rusts. Mol. Ecol. 2, 113–118 (2008).
Article  Google Scholar 

50.
Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Zhang, Y. & Sun, Y. HMM-FRAME: accurate protein domain classification for metagenomic sequences containing frameshift errors. BMC Bioinform. 12, 198 (2011).
Article  Google Scholar 

54.
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
CAS  Article  Google Scholar 

56.
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

57.
Guillou, 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. 41, D597–D604 (2012).
PubMed  PubMed Central  Article  CAS  Google Scholar 

58.
Zhao, J. et al. Size spectra of soil nematode assemblages under different land use types. Soil Biol. Biochem. 85, 130–136 (2015).
CAS  Article  Google Scholar 

59.
Soininen, J., McDonald, R. & Hillebrand, H. The distance decay of similarity in ecological communities. Ecography 30, 3–12 (2007).
Article  Google Scholar 

60.
Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: A comprehensive framework for principal coordinate analysis of neighbor matrices (PCNM). Ecol. Model. 196, 483–493 (2006).
Article  Google Scholar 

61.
Chen, W., Jiao, S., Li, Q. & Du, N. Dispersal limitation relative to environmental filtering governs the vertical small‐scale assembly of soil microbiomes during restoration. J. Appl. Ecol. 57, 402–412 (2020).
Article  Google Scholar 

62.
Jiao, S., Yang, Y., Xu, Y., Zhang, J. & Lu, Y. Balance between community assembly processes mediates species coexistence in agricultural soil microbiomes across eastern China. ISME J. 14, 202–216 (2020).
PubMed  Article  PubMed Central  Google Scholar 

63.
Elzhov, T. V., Mullen, K. M., Spiess, A.-N. & Bolker, B. Minpack. lm: R Interface to the levenberg–marquardt nonlinear least-squares algorithm found in MINPACK, plus support for bounds (CRAN Repository, 2016).

64.
Harrell, F. E. Jr. Hmisc: harrell miscellaneous. R. package version 3.0−12 (2013). .

65.
Davison, J. et al. Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science 349, 970–973 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
Fiore-Donno, A. M., Weinert, J., Wubet, T. & Bonkowski, M. Metacommunity analysis of amoeboid protists in grassland soils. Sci. Rep. 6, 19068 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

67.
Heger, T. J. et al. High-throughput environmental sequencing reveals high diversity of litter and moss associated protist communities along a gradient of drainage and tree productivity. Environ. Microbiol. 20, 1185–1203 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Oksanen, J. et al. Vegan: community ecology package. R. package version 2.5−4 (2019).

69.
Field, A., Miles, J. & Field, Z. Discovering Statistics Using R. London (Sage publications, 2012). More