Advertisement

Increased methane concentration alters soil prokaryotic community structure along an artificial pH gradient

  • Petr HeděnecEmail author
  • Roey Angel
  • Qiang Lin
  • Junpeng Rui
  • Xiangzhen Li
Original Article

Abstract

Global climate change may have a large impact on increased emission rates of carbon dioxide and methane to total greenhouse gas emissions from terrestrial wetlands. Methane consumption by soil microbiota in alpine wet meadows serves as a biofilter for the methane produced in the waterlogged soil below. Altered pH regimes change microbial community composition and structure by exerting selection pressure on soil microorganisms with different ecological strategies and thus affect greenhouse gas emissions resulting from the metabolic activity of soil microorganisms. However, responses of prokaryotic communities to artificial pH shift under elevated methane concentration remain unclear. In this study, we assessed diversity and relative abundance of soil prokaryotes in an alpine meadow under elevated methane concentration along an artificial pH gradient using laboratory incubation experiments. We established an incubation experiment treated with artificial pH gradient (pH 4.5–8.5). After 3 months of incubation, 300 ml of methane at a concentration of 20,000 ppm was added to stimulate potential methanothrophs in topsoil. Sequencing of 16S rRNA gene indicated increasing of relative abundances of Crenarchaeota, Chloroflexi, Bacteroidetes, and Planctomycetes in soil after addition of methane, while the relative abundances of Actinobacteria and Gemmatimonadetes did not significant change before and after methane treatment. Results of phylogenetic relatedness of soil prokaryotes showed that microbial community is mostly shaped by deterministic factors. Species indicator analysis revealed distinct OTUs among various pH and methane treatments. Network analysis revealed distinct co-occurrence patterns of soil prokaryotic community before and after methane addition, and different correlation patterns among various prokaryotic taxa. Linear regression model revealed significant decrease of methane oxidation along elevated pH gradient. Soil pH constituted a strong environmental filter in species assembly of soil prokaryotic community. Methane oxidation rates decreased significantly with elevated pH. The interactive effects of elevated methane concentration and pH are therefore promising topic for future research.

Keywords

Elevated methane oxidation Soil prokaryotes pH gradient Methanotrophs 

Notes

Acknowledgements

We thank Ondřej Mudrák for helpful advice with the statistical analysis and Adrienne Godschalx for help with English grammar and spelling.

Funding

The work was supported by the National Natural Science Foundation of China (41771293, 31670503, 41630751), Key Laboratory of Sichuan Province (KLCAS-2017-3), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB15010303), 13th five-year information plan of Chinese Academy of Sciences (XXH13503-03-106), and Ministry of Education, Youth and Sports of the Czech Republic - MEYS (projects LM2015075, EF16_013/0001782).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

No human or animal participants were involved in this study.

Informed consent

Informed consent rules were not applicable to this research because no human participants were involved.

Supplementary material

13213_2018_1421_MOESM1_ESM.docx (22 kb)
ESM 1 (DOCX 22 kb)

References

  1. Angel R, Soares MIM, Ungar ED, Gillor O (2010) Biogeography of soil archaea and bacteria along a steep precipitation gradient. ISME J 4:553–563.  https://doi.org/10.1038/ismej.2009.136 CrossRefPubMedGoogle Scholar
  2. Angel R, Claus P, Conrad R (2012) Methanogenic archaea are globally ubiquitous in aerated soils and become active under wet anoxic conditions. ISME J 6:847–862.  https://doi.org/10.1038/ismej.2011.141 CrossRefPubMedGoogle Scholar
  3. Caporaso JG, Kuczynski J, Stombaugh J et al (2010) Correspondence QIIME allows analysis of high- throughput community sequencing data intensity normalization improves color calling in SOLiD sequencing. Nat Publ Gr 7:335–336.  https://doi.org/10.1038/nmeth0510-335 CrossRefGoogle Scholar
  4. Conrad R (2007) Microbial ecology of methanogens and methanotrophs. Adv Agron 96:1–63.  https://doi.org/10.1016/S0065-2113(07)96005-8 CrossRefGoogle Scholar
  5. Dedysh SN, Panikov NS (1997) Effect of pH, temperature, and concentration of salts on methane oxidation kinetics in Sphagnum peat. Microbiology 66:476–479Google Scholar
  6. Edgar RC (2013) UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 10:996–998.  https://doi.org/10.1038/nmeth.2604 CrossRefPubMedGoogle Scholar
  7. Edgar RC, Haas BJ, Clemente JC et al (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194–2200.  https://doi.org/10.1093/bioinformatics/btr381 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Eisenhauer N, Cesarz S, Koller R et al (2012) Global change belowground: impacts of elevated CO 2, nitrogen, and summer drought on soil food webs and biodiversity. Glob Chang Biol 18:435–447.  https://doi.org/10.1111/j.1365-2486.2011.02555.x CrossRefGoogle Scholar
  9. Evans SE, Wallenstein MD, Burke IC (2014) Is bacterial moisture niche a good predictor of shifts in community composition under long-term drought? Ecology 95:110–122CrossRefGoogle Scholar
  10. Fang H, Cheng S, Yu G et al (2011) Responses of CO2 efflux from an alpine meadow soil on the Qinghai Tibetan Plateau to multi-form and low-level N addition. Plant Soil 351:177–190.  https://doi.org/10.1007/s11104-011-0942-4 CrossRefGoogle Scholar
  11. Fierer N (2017) Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol.  https://doi.org/10.1038/nrmicro.2017.87
  12. Gao J, Zhang X, Lei G, Wang G (2013) Soil organic carbon and its fractions in relation to degradation and restoration of wetlands on the Zoigê Plateau, China. Wetlands 34:235–241.  https://doi.org/10.1007/s13157-013-0487-9 CrossRefGoogle Scholar
  13. Gilbert JA, Steele JA, Caporaso JG et al (2012) Defining seasonal marine microbial community dynamics. ISME J 6:298–308.  https://doi.org/10.1038/ismej.2011.107 CrossRefPubMedGoogle Scholar
  14. Heděnec P, Rui J, Yao M et al (2018) Temporal response of soil prokaryotic communities to acidification and alkalization under laboratory conditions. Eur J Soil Biol 86:63–71.  https://doi.org/10.1016/j.ejsobi.2018.03.005 CrossRefGoogle Scholar
  15. Henckel T, Jäckel U, Schnell S, Conrad R (2000) Molecular analyses of novel methanotrophic communities in forest soil that oxidize atmospheric methane. Appl Environ Microbiol 66:1801–1808.  https://doi.org/10.1128/AEM.66.5.1801-1808.2000 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Héry M, Singer AC, Kumaresan D et al (2008) Effect of earthworms on the community structure of active methanotrophic bacteria in a landfill cover soil. ISME J 2:92–104.  https://doi.org/10.1038/ismej.2007.66 CrossRefPubMedGoogle Scholar
  17. Ho A, Angel R, Veraart AJ et al (2016) Biotic interactions in microbial communities as modulators of biogeochemical processes: methanotrophy as a model system. Front Microbiol 7:1–11.  https://doi.org/10.3389/fmicb.2016.01285 CrossRefGoogle Scholar
  18. Ho A, Di Lonardo DP, Bodelier PLE (2017) Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol Ecol 93:1–14.  https://doi.org/10.1093/femsec/fix006 CrossRefGoogle Scholar
  19. Hubbell SP, Ahumada JA, Condit R, Foster RB (2001) Local neighborhood effects on long-term survival of individual trees in a neotropical forest. Ecol Res 16:859–875.  https://doi.org/10.1046/j.1440-1703.2001.00445.x CrossRefGoogle Scholar
  20. Iguchi H, Yurimoto H, Sakai Y (2011) 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 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Jenkinson DS, Powlson DS (1976) The effects of biocidal treatments on metabolism in soil—V: a method for measuring soil biomass. Soil Biol Biochem 8:209–213.  https://doi.org/10.1016/0038-0717(76)90005-5 CrossRefGoogle Scholar
  22. Kalyuzhnaya MG, Yang S, Rozova ON, et al (2013) Highly efficient methane biocatalysis revealed in a methanotrophic bacterium. Nat Commun 4.  https://doi.org/10.1038/ncomms3785
  23. Kembel SW, Eisen JA, Pollard KS, Green JL (2011) The phylogenetic diversity of metagenomes. PLoS One 6:e23214.  https://doi.org/10.1371/journal.pone.0023214 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Kemmitt SJ, Wright D, Goulding KWT, Jones DL (2006) pH regulation of carbon and nitrogen dynamics in two agricultural soils. Soil Biol Biochem 38:898–911.  https://doi.org/10.1016/j.soilbio.2005.08.006
  25. Knief C (2015) Diversity and habitat preferences of cultivated and uncultivated aerobic methanotrophic bacteria evaluated based on pmoA as molecular marker. Front Microbiol 6.  https://doi.org/10.3389/fmicb.2015.01346
  26. Kögel-Knabner I, Amelung W, Cao Z et al (2010) Biogeochemistry of paddy soils. Geoderma 157:1–14.  https://doi.org/10.1016/j.geoderma.2010.03.009 CrossRefGoogle Scholar
  27. Kou Y, Li J, Wang Y et al (2017) Scale-dependent key drivers controlling methane oxidation potential in Chinese grassland soils. Soil Biol Biochem 111:104–114.  https://doi.org/10.1016/j.soilbio.2017.04.005 CrossRefGoogle Scholar
  28. Lauber CL, Hamady M, Knight R, Fierer N (2009) Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol 75:5111–5120.  https://doi.org/10.1128/AEM.00335-09 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Li X, Rui J, Mao Y et al (2014) Dynamics of the bacterial community structure in the rhizosphere of a maize cultivar. Soil Biol Biochem 68:392–401.  https://doi.org/10.1016/j.soilbio.2013.10.017 CrossRefGoogle Scholar
  30. Lin Q, De Vrieze J, Li C et al (2017) Temperature regulates deterministic processes and the succession of microbial interactions in anaerobic digestion process. Water Res 123:134–143.  https://doi.org/10.1016/j.watres.2017.06.051 CrossRefPubMedGoogle Scholar
  31. Liu J, Sun F, Wang L et al (2014) Molecular characterization of a microbial consortium involved in methane oxidation coupled to denitrification under micro-aerobic conditions. Microb Biotechnol 7:64–76.  https://doi.org/10.1111/1751-7915.12097 CrossRefPubMedGoogle Scholar
  32. Liu D, Tago K, Hayatsu M et al (2016) Effect of elevated CO<sub>2</sub> concentration, elevated temperature and no nitrogen fertilization on methanogenic archaeal and methane-oxidizing bacterial community structures in paddy soil. Microbes Environ 31:349–356.  https://doi.org/10.1264/jsme2.ME16066 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Lue C, Tian H (2007) Spatial and temporal patterns of nitrogen deposition in China: synthesis of observational data. J Geophys Res 112.  https://doi.org/10.1029/2006JD007990
  34. McMurdie PJ, Holmes S (2012) Phyloseq: a bioconductor package for handling and analysis of high-throughput phylogenetic sequence data. Pac Symp Biocomput 235–46Google Scholar
  35. McMurdie PJ, Holmes S (2013) Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8.  https://doi.org/10.1371/journal.pone.0061217
  36. Nelson PN, Su N (2010) Soil pH buffering capacity: a descriptive function and its application to some acidic tropical soils. Aust J Soil Res 48:201–207.  https://doi.org/10.1071/SR09150 CrossRefGoogle Scholar
  37. Nemergut DR, Schmidt SK, Fukami T et al (2013) Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev 77:342–356.  https://doi.org/10.1128/MMBR.00051-12 CrossRefPubMedPubMedCentralGoogle Scholar
  38. Nunoura T, Takaki Y, Kazama H et al (2012) Microbial diversity in deep-sea methane seep sediments presented by SSU rRNA gene tag sequencing. Microbes Environ 27:382–390.  https://doi.org/10.1264/jsme2.ME12032 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Pandey VC, Singh JS, Singh DP, Singh RP (2014) Methanotrophs: promising bacteria for environmental remediation. Int J Environ Sci Technol 11:241–250.  https://doi.org/10.1007/s13762-013-0387-9 CrossRefGoogle Scholar
  40. Price MN, Dehal PS, Arkin AP (2010) FastTree 2 - approximately maximum-likelihood trees for large alignments. PLoS One 5.  https://doi.org/10.1371/journal.pone.0009490
  41. Putkinen A, Larmola T, Tuomivirta T et al (2014) Peatland succession induces a shift in the community composition of Sphagnum-associated active methanotrophs. FEMS Microbiol Ecol 88:596–611.  https://doi.org/10.1111/1574-6941.12327 CrossRefPubMedGoogle Scholar
  42. Rousk J, Baath E, Brookes PC et al (2010) Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J 4:1340–1351.  https://doi.org/10.1038/ismej.2010.58 CrossRefPubMedGoogle Scholar
  43. Simecek P, Simeckova M (2013) Modification of Tukey’s additivity test. J Stat Plan Inference 143:197–201.  https://doi.org/10.1016/j.jspi.2012.07.002 CrossRefGoogle Scholar
  44. Singh BK, Tate KR, Kolipaka G et al (2007) Effect of afforestation and reforestation of pastures on the activity and population dynamics of methanotrophic bacteria. Appl Environ Microbiol 73:5153–5161.  https://doi.org/10.1128/AEM.00620-07 CrossRefPubMedPubMedCentralGoogle Scholar
  45. Smith VA, Yu J, Smulders TV et al (2006) Computational inference of neural information flow networks. PLoS Comput Biol 2:1436–1449.  https://doi.org/10.1371/journal.pcbi.0020161 CrossRefGoogle Scholar
  46. Stegen JC, Lin X, Konopka AE, Fredrickson JK (2012) Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J 6:1653–1664.  https://doi.org/10.1038/ismej.2012.22 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Stegen JC, Lin X, Fredrickson JK et al (2013) Quantifying community assembly processes and identifying features that impose them. ISME J 7:2069–2079.  https://doi.org/10.1038/ismej.2013.93 CrossRefPubMedPubMedCentralGoogle Scholar
  48. Tilman D (2004) Niche tradeoffs, neutrality, and community structure: a stochastic theory of resource competition, invasion, and community assembly. Proc Natl Acad Sci U S A 101:10854–10861.  https://doi.org/10.1073/pnas.0403458101 CrossRefPubMedPubMedCentralGoogle Scholar
  49. Timling I, Walker DA, Nusbaum C et al (2014) Rich and cold: diversity, distribution and drivers of fungal communities in patterned-ground ecosystems of the North American Arctic. Mol Ecol 23:3258–3272.  https://doi.org/10.1111/mec.12743 CrossRefPubMedGoogle Scholar
  50. Vaksmaa A, van Alen TA, Ettwig KF et al (2017) Stratification of diversity and activity of methanogenic and methanotrophic microorganisms in a nitrogen-fertilized Italian paddy soil. Front Microbiol 8:1–15.  https://doi.org/10.3389/fmicb.2017.02127 CrossRefGoogle Scholar
  51. Vellend M (2010) Conceptual synthesis in community ecology. Q Rev Biol 85:183–206CrossRefGoogle Scholar
  52. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naïve 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 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Ward SE, Ostle NJ, Oakley S et al (2013) Warming effects on greenhouse gas fluxes in peatlands are modulated by vegetation composition. Ecol Lett 16:1285–1293.  https://doi.org/10.1111/ele.12167 CrossRefPubMedGoogle Scholar
  54. Wu Y, Zeng J, Zhu Q et al (2017) pH is the primary determinant of the bacterial community structure in agricultural soils impacted by polycyclic aromatic hydrocarbon pollution. Sci Rep 7:40093.  https://doi.org/10.1038/srep40093 CrossRefPubMedPubMedCentralGoogle Scholar
  55. Yao, M. Yao M, Rui J, Li J, Dai Y, Bai Y, Heděnec P et al (2014) Rate-specific responses of prokaryotic diversity and structure to nitrogen deposition in the Leymus chinensis steppe. Soil Biol Biochem 79:81–90.  https://doi.org/10.1016/j.soilbio.2014.09.009
  56. Yao M, Rui J, Niu H, et al (2017) The differentiation of soil bacterial communities along a precipitation and temperature gradient in the eastern Inner Mongolia steppe. Catena 152.  https://doi.org/10.1016/j.catena.2017.01.007
  57. Yun J, Zhuang G, Ma A et al (2012) Community structure, abundance, and activity of methanotrophs in the Zoige wetland of the Tibetan Plateau. Microb Ecol 63:835–843.  https://doi.org/10.1007/s00248-011-9981-x CrossRefPubMedGoogle Scholar
  58. Yvon-Durocher G, Allen AP, Bastviken D et al (2014) Methane fluxes show consistent temperature dependence across microbial to ecosystem scales. Nature 507:488–491.  https://doi.org/10.1038/nature13164 CrossRefPubMedGoogle Scholar
  59. Zhang X, Liu W, Zhang G et al (2015) Soil Biology & Biochemistry Mechanisms of soil acidi fi cation reducing bacterial diversity. Soil Biol Biochem 81:275–281.  https://doi.org/10.1016/j.soilbio.2014.11.004 CrossRefGoogle Scholar
  60. Zhang X, Liu S, Li X et al (2016) Changes of soil prokaryotic communities after clear-cutting in a karst forest: evidences for cutting-based disturbance promoting deterministic processes. FEMS Microbiol Ecol 92:1–12.  https://doi.org/10.1093/femsec/fiw026 CrossRefGoogle Scholar
  61. Zhou J, Deng Y, Shen L et al (2016) Temperature mediates continental-scale diversity of microbes in forest soils. Nat Commun 7:12083.  https://doi.org/10.1038/ncomms12083 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Università degli studi di Milano 2019

Authors and Affiliations

  1. 1.Fujian Provincial Key Laboratory of Soil Environmental Health and Regulation, College of Resources and EnvironmentFujian Agriculture and Forestry UniversityFuzhouChina
  2. 2.Institute for Environmental Studies and SoWa Research Infrastructure, Faculty of ScienceCharles University in PraguePrague 2Czech Republic
  3. 3.Laboratory of Soil BiodiversityUniversity of NeuchâtelNeuchâtelSwitzerland
  4. 4.SoWa Research Infrastructure and Institute of Soil Biology, Biology Centre of the Czech Academy of SciencesČeské BudějoviceCzech Republic

Personalised recommendations