Responses of tundra soil microbial communities to half a decade of experimental warming at two critical depths

Eric R. Johnston, Janet K. Hatt, Zhili He, Liyou Wu, Xue Guo, Yiqi Luo, Edward A. G. Schuur, James M. Tiedje, Jizhong Zhou, and Konstantinos T. Konstantinidis
PNAS July 23, 2019 116 (30) 15096-15105; first published July 8, 2019 https://doi.org/10.1073/pnas.1901307116

  1. Edited by Edward F. DeLong, University of Hawaii at Mānoa, Honolulu, HI, and approved June 10, 2019 (received for review January 23, 2019)

This article requires a subscription to view the full text. If you have a subscription you may use the login form below to view the article. Access to this article can also be purchased.

Significance

Ongoing permafrost thaw is expected to stimulate microbial release of greenhouse gases, threatening to further exacerbate climate change (cause positive feedback). In this study, a unique field warming experiment was conducted in Interior Alaska to promote surface permafrost degradation while maintaining uniform hydraulic conditions. After 5 winters of experimental warming by ∼1 °C, microbial community shifts were observed at the receded permafrost/active layer boundary, which reflected more reduced conditions, including increased methanogenesis. In contrast, increased carbohydrate utilization (respiration) was observed at the surface layer. These shifts were relatable to observed increases in CO2 and CH4 release from this study site and the surrounding ecosystem. Collectively, our results demonstrate that microbial responses to warming are rapid and identify potential biomarkers that could be important in modeling.

Abstract

Northern-latitude tundra soils harbor substantial carbon (C) stocks that are highly susceptible to microbial degradation with rising global temperatures. Understanding the magnitude and direction (e.g., C release or sequestration) of the microbial responses to warming is necessary to accurately model climate change. In this study, Alaskan tundra soils were subjected to experimental in situ warming by ∼1.1 °C above ambient temperature, and the microbial communities were evaluated using metagenomics after 4.5 years, at 2 depths: 15 to 25 cm (active layer at outset of the experiment) and 45 to 55 cm (transition zone at the permafrost/active layer boundary at the outset of the experiment). In contrast to small or insignificant shifts after 1.5 years of warming, 4.5 years of warming resulted in significant changes to the abundances of functional traits and the corresponding taxa relative to control plots (no warming), and microbial shifts differed qualitatively between the two soil depths. At 15 to 25 cm, increased abundances of carbohydrate utilization genes were observed that correlated with (increased) measured ecosystem carbon respiration. At the 45- to 55-cm layer, increased methanogenesis potential was observed, which corresponded with a 3-fold increase in abundance of a single archaeal clade of the Methanosarcinales order, increased annual thaw duration (45.3 vs. 79.3 days), and increased CH4 emissions. Collectively, these data demonstrate that the microbial responses to warming in tundra soil are rapid and markedly different between the 2 critical soil layers evaluated, and identify potential biomarkers for the corresponding microbial processes that could be important in modeling.

Footnotes

  • ↵1To whom correspondence may be addressed. Email: kostas{at}ce.gatech.edu.

  • Author contributions: E.R.J., Z.H., L.W., Y.L., E.A.G.S., J.M.T., J.Z., and K.T.K. designed research; E.R.J., J.K.H., and X.G. performed research; E.R.J. analyzed data; and E.R.J. and K.T.K. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • Data deposition: Metagenome and MAG sequences have been deposited in the European Nucleotide Archive (study ID PRJEB31848). Individual accession IDs for metagenomes and metagenome-assembled genomes can be found in SI Appendix, Tables S1 and S5, respectively.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1901307116/-/DCSupplemental.

Published under the PNAS license.

References

  1. 1.↵
    1. C. Tarnocai et al
    ., Soil organic carbon pools in the northern circumpolar permafrost region. Glob. Biogeochem. Cycles 23, GB2023 (2009).
    OpenUrlCrossRef
  2. 2.↵
    1. C. Mu et al
    ., Editorial: Organic carbon pools in permafrost regions on the Qinghai–Xizang (Tibetan) Plateau. Cryosphere 9, 479–486 (2015).
    OpenUrl
  3. 3.↵
    1. E. A. G. Schuur et al
    ., Vulnerability of permafrost carbon to climate change: Implications for the global carbon cycle. Bioscience 58, 701–714 (2008).
    OpenUrlCrossRef
  4. 4.↵
    1. C. E. Hicks Pries,
    2. E. A. G. Schuur,
    3. K. G. Crummer
    , Holocene carbon stocks and carbon accumulation rates altered in soils undergoing permafrost thaw. Ecosystems 15, 162–173 (2012).
    OpenUrl
  5. 5.↵
    1. T. F. Stocker et al
    1. IPCC
    , “Near-term climate change: projections and predictability” in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T. F. Stocker et al., Eds. (Cambridge University Press, Cambridge, UK, 2013), pp. 953–1028.
  6. 6.↵
    1. M. T. Jorgenson,
    2. C. H. Racine,
    3. J. C. Walters,
    4. T. E. Osterkamp
    , Permafrost degradation and ecological changes associated with a warming climate in central Alaska. Clim. Change 48, 551–579 (2001).
    OpenUrl
  7. 7.↵
    1. D. M. Lawrence,
    2. A. G. Slater
    , A projection of severe near-surface permafrost degradation during the 21st century. Geophys. Res. Lett. 32, L24401 (2005).
    OpenUrlCrossRef
  8. 8.↵
    1. T. E. Osterkamp et al
    ., Physical and ecological changes associated with warming permafrost and thermokarst in Interior Alaska. Permafr. Periglac. Process. 20, 235–256 (2009).
    OpenUrl
  9. 9.↵
    1. V. E. Romanovsky,
    2. S. L. Smith,
    3. H. H. Christiansen
    , Permafrost thermal state in the polar Northern Hemisphere during the international polar year 2007-2009: A synthesis. Permafr. Periglac. Process. 21, 106–116 (2010).
    OpenUrlCrossRef
  10. 10.↵
    1. E. A. G. Schuur,
    2. B. Abbott
    , Climate change: High risk of permafrost thaw. Nature 480, 32–33 (2011).
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. D. M. Lawrence,
    2. A. G. Slater,
    3. S. C. Swenson
    , Simulation of present-day and future permafrost and seasonally frozen ground conditions in CCSM4. J. Clim. 25, 2207–2225 (2012).
    OpenUrl
  12. 12.↵
    1. M. Heimann,
    2. M. Reichstein
    , Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292 (2008).
    OpenUrlCrossRefPubMed
  13. 13.↵
    1. E. A. G. Schuur et al
    ., Expert assessment of vulnerability of permafrost carbon to climate change. Clim. Change 119, 359–374 (2013).
    OpenUrl
  14. 14.↵
    1. B. W. Abbott et al
    ., Biomass offsets little or none of permafrost carbon release from soils, streams, and wildfire: An expert assessment. Environ. Res. Lett. 11, 034014 (2016).
    OpenUrl
  15. 15.↵
    1. R. Mackelprang et al
    ., Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 480, 368–371 (2011).
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. M. J. L. Coolen,
    2. W. D. Orsi
    , The transcriptional response of microbial communities in thawing Alaskan permafrost soils. Front. Microbiol. 6, 197 (2015).
    OpenUrlCrossRef
  17. 17.↵
    1. E. A. G. Schuur et al
    ., The effect of permafrost thaw on old carbon release and net carbon exchange from tundra. Nature 459, 556–559 (2009).
    OpenUrlCrossRefPubMed
  18. 18.↵
    1. L. D. Hinzman et al
    ., Trajectory of the Arctic as an integrated system. Ecol. Appl. 23, 1837–1868 (2013).
    OpenUrl
  19. 19.↵
    1. D. A. Lipson et al
    ., Metagenomic insights into anaerobic metabolism along an Arctic peat soil profile. PLoS One 8, e64659 (2013).
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. F. Keuper et al
    ., A frozen feast: Thawing permafrost increases plant-available nitrogen in subarctic peatlands. Glob. Change Biol. 18, 1998–2007 (2012).
    OpenUrl
  21. 21.↵
    1. H. P. Bais,
    2. T. L. Weir,
    3. L. G. Perry,
    4. S. Gilroy,
    5. J. M. Vivanco
    , The role of root exudates in rhizosphere interactions with plants and other organisms. Annu. Rev. Plant Biol. 57, 233–266 (2006).
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. P. Grogan,
    2. S. Jonasson
    , Controls on annual nitrogen cycling in the understory of a subarctic birch forest. Ecology 84, 202–218 (2003).
    OpenUrlCrossRef
  23. 23.↵
    1. L. Ström,
    2. M. Mastepanov,
    3. T. R. Christensen
    , Species-specific effects of vascular plants on carbon turnover and methane emissions from wetlands. Biogeochemistry 75, 65–82 (2005).
    OpenUrlCrossRef
  24. 24.↵
    1. C. E. Hicks Pries,
    2. E. A. G. Schuur,
    3. K. G. Crummer
    , Thawing permafrost increases old soil and autotrophic respiration in tundra: Partitioning ecosystem respiration using δ(13) C and ∆(14) C. Glob. Change Biol. 19, 649–661 (2013).
    OpenUrl
  25. 25.↵
    1. B. Elberling et al
    ., Long-term CO2 production following permafrost thaw. Nat. Clim. Chang. 3, 890–894 (2013).
    OpenUrl
  26. 26.↵
    1. H. Lee,
    2. E. A. G. Schuur,
    3. K. S. Inglett,
    4. M. Lavoie,
    5. J. P. Chanton
    , The rate of permafrost carbon release under aerobic and anaerobic conditions and its potential effects on climate. Glob. Change Biol. 18, 515–527 (2012).
    OpenUrl
  27. 27.↵
    1. C. K. McCalley et al
    ., Methane dynamics regulated by microbial community response to permafrost thaw. Nature 514, 478–481 (2014).
    OpenUrlCrossRefPubMed
  28. 28.↵
    1. S. M. Natali et al
    ., Effects of experimental warming of air, soil and permafrost on carbon balance in Alaskan tundra. Glob. Change Biol. 17, 1394–1407 (2011).
    OpenUrl
  29. 29.↵
    1. S. M. Natali,
    2. E. A. G. Schuur,
    3. R. L. Rubin
    , Increased plant productivity in Alaskan tundra as a result of experimental warming of soil and permafrost: Increased plant productivity in Alaskan tundra. J. Ecol. 100, 488–498 (2012).
    OpenUrlCrossRef
  30. 30.↵
    1. K. Xue et al
    ., Tundra soil carbon is vulnerable to rapid microbial decomposition under climate warming. Nat. Clim. Chang. 6, 595–600 (2016).
    OpenUrl
  31. 31.↵
    1. E. R. Johnston et al
    ., Metagenomics reveals pervasive bacterial populations and reduced community diversity across the Alaska tundra ecosystem. Front. Microbiol. 7, 579 (2016).
    OpenUrlCrossRef
  32. 32.↵
    1. L. M. Rodriguez-R,
    2. S. Gunturu,
    3. J. M. Tiedje,
    4. J. R. Cole,
    5. K. T. Konstantinidis
    , Nonpareil 3: Fast estimation of metagenomic coverage and sequence diversity. mSystems 3, e00039-18 (2018).
    OpenUrlAbstract/FREE Full Text
  33. 33.↵
    1. L. M. Rodriguez-R,
    2. K. T. Konstantinidis
    , Estimating coverage in metagenomic data sets and why it matters. ISME J. 8, 2349–2351 (2014).
    OpenUrlCrossRefPubMed
  34. 34.↵
    1. X. Zhang,
    2. E. R. Johnston,
    3. L. Li,
    4. K. T. Konstantinidis,
    5. X. Han
    , Experimental warming reveals positive feedbacks to climate change in the Eurasian Steppe. ISME J. 11, 885–895 (2017).
    OpenUrl
  35. 35.↵
    1. S. Nayfach,
    2. K. S. Pollard
    , Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 16, 51 (2015).
    OpenUrlCrossRefPubMed
  36. 36.↵
    1. S. J. Hallam et al
    ., Reverse methanogenesis: Testing the hypothesis with environmental genomics. Science 305, 1457–1462 (2004).
    OpenUrlAbstract/FREE Full Text
  37. 37.↵
    1. M. Kanehisa,
    2. Y. Sato,
    3. M. Kawashima,
    4. M. Furumichi,
    5. M. Tanabe
    , KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).
    OpenUrlCrossRefPubMed
  38. 38.↵
    1. D. H. Parks et al
    ., Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).
    OpenUrl
  39. 39.↵
    1. D. H. Parks,
    2. M. Imelfort,
    3. C. T. Skennerton,
    4. P. Hugenholtz,
    5. G. W. Tyson
    , CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
    OpenUrlAbstract/FREE Full Text
  40. 40.↵
    1. C. Jain,
    2. L. M. Rodriguez-R,
    3. A. M. Phillippy,
    4. K. T. Konstantinidis,
    5. S. Aluru
    , High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).
    OpenUrlCrossRef
  41. 41.↵
    1. A. Arshad et al
    ., A metagenomics-based metabolic model of nitrate-dependent anaerobic oxidation of methane by Methanoperedens-like Archaea. Front. Microbiol. 6, 1423 (2015).
    OpenUrlCrossRefPubMed
  42. 42.↵
    1. S. Berger,
    2. J. Frank,
    3. P. Dalcin Martins,
    4. M. S. M. Jetten,
    5. C. U. Welte
    , High-quality draft genome sequence of “Candidatus Methanoperedens sp.” strain BLZ2, a nitrate-reducing anaerobic methane-oxidizing archaeon enriched in an anoxic bioreactor. Genome Announc. 5, e01159-17 (2017).
    OpenUrl
  43. 43.↵
    1. S. A. Sistla,
    2. J. P. Schimel
    , Seasonal patterns of microbial extracellular enzyme activities in an arctic tundra soil: Identifying direct and indirect effects of long-term summer warming. Soil Biol. Biochem. 66, 119–129 (2013).
    OpenUrl
  44. 44.↵
    1. S. M. Natali et al
    ., Permafrost thaw and soil moisture driving CO 2 and CH 4 release from upland tundra. J. Geophys. Res. Biogeosci. 120, 525–537 (2015).
    OpenUrl
  45. 45.↵
    1. M. A. Taylor,
    2. G. Celis,
    3. J. D. Ledman,
    4. R. Bracho,
    5. E. A. G. Schuur
    , Methane efflux measured by eddy covariance in Alaskan upland tundra undergoing permafrost degradation. J. Geophys. Res. Biogeosci. 123, 2695–2710 (2018).
    OpenUrl
  46. 46.↵
    1. R. K. Thauer,
    2. S. Shima
    , Methane as fuel for anaerobic microorganisms. Ann. N. Y. Acad. Sci. 1125, 158–170 (2008).
    OpenUrlCrossRefPubMed
  47. 47.↵
    1. A. E. Dekas,
    2. S. A. Connon,
    3. G. L. Chadwick,
    4. E. Trembath-Reichert,
    5. V. J. Orphan
    , Activity and interactions of methane seep microorganisms assessed by parallel transcription and FISH-NanoSIMS analyses. ISME J. 10, 678–692 (2016).
    OpenUrlCrossRefPubMed
  48. 48.↵
    1. V. Krukenberg et al
    ., Gene expression and ultrastructure of meso- and thermophilic methanotrophic consortia. Environ. Microbiol. 20, 1651–1666 (2018).
    OpenUrlCrossRef
  49. 49.↵
    1. F. Beulig,
    2. H. Røy,
    3. S. E. McGlynn,
    4. B. B. Jørgensen
    , Cryptic CH4 cycling in the sulfate-methane transition of marine sediments apparently mediated by ANME-1 archaea. ISME J. 13, 250–262 (2019).
    OpenUrlCrossRef
  50. 50.↵
    1. J. Hansen,
    2. R. Ruedy,
    3. M. Sato,
    4. K. Lo
    , Global surface temperature change. Rev. Geophys. 48, RG4004 (2010).
    OpenUrl
  51. 51.↵
    1. GISTEMP Team
    (2018) GISS Surface Temperature Analysis (GISTEMP). NASA Goddard Institute for Space Studies. https://data.giss.nasa.gov/gistemp. Dataset accessed 11 July 2018.
  52. 52.↵
    1. Y. Wang et al
    ., Influence of plant species and wastewater strength on constructed wetland methane emissions and associated microbial populations. Ecol. Eng. 32, 22–29 (2008).
    OpenUrlCrossRef
  53. 53.↵
    1. J. Kao-Kniffin,
    2. D. S. Freyre,
    3. T. C. Balser
    , Methane dynamics across wetland plant species. Aquat. Bot. 93, 107–113 (2010).
    OpenUrlCrossRef
  54. 54.↵
    1. B. J. Woodcroft et al
    ., Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).
    OpenUrlCrossRef
  55. 55.↵
    1. J. Hultman et al
    ., Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes. Nature 521, 208–212 (2015).
    OpenUrlCrossRefPubMed
  56. 56.↵
    1. E. R. Johnston et al
    ., Phosphate addition increases tropical forest soil respiration primarily by deconstraining microbial population growth. Soil Biol. Biochem. 130, 43–54 (2019).
    OpenUrl
  57. 57.↵
    1. M. Mauritz et al
    ., Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Half-hourly growing season, chamber-based, CO2 flux data, 2009-2017. https://portal.edirepository.org/nis/metadataviewer?packageid=knb-lter-bnz.481.19. Accessed 15 June 2018.
  58. 58.↵
    1. E. R. Johnston
    , Responses of tundra soil microbial communities to half a decade of experimental in-situ warming at two critical depths (CiPEHR Alaska site). European Nucleotide Archive. https://www.ebi.ac.uk/ena/data/view/PRJEB31848. Deposited 7 May 2019.
  59. 59.↵
    1. J. Zhang,
    2. K. Kobert,
    3. T. Flouri,
    4. A. Stamatakis
    , PEAR: A fast and accurate illumina paired-end reAd mergeR. Bioinformatics 30, 614–620 (2014).
    OpenUrlCrossRefPubMed
  60. 60.↵
    1. M. P. Cox,
    2. D. A. Peterson,
    3. P. J. Biggs
    , SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinf. 11, 485 (2010).
    OpenUrl
  61. 61.↵
    1. E. Kopylova,
    2. L. Noé,
    3. H. Touzet
    , SortMeRNA: Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
    OpenUrlCrossRefPubMed
  62. 62.↵
    1. X. Su,
    2. W. Pan,
    3. B. Song,
    4. J. Xu,
    5. K. Ning
    , Parallel-META 2.0: Enhanced metagenomic data analysis with functional annotation, high performance computing and advanced visualization. PLoS One 9, e89323 (2014).
    OpenUrlCrossRefPubMed
  63. 63.↵
    1. C. Quast et al
    ., The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
    OpenUrlCrossRefPubMed
  64. 64.↵
    1. C. Camacho et al
    ., BLAST+: Architecture and applications. BMC Bioinf. 10, 421 (2009).
    OpenUrl
  65. 65.↵
    1. J. G. Caporaso et al
    ., QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
    OpenUrlCrossRefPubMed
  66. 66.↵
    1. R. C. Edgar
    , Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
    OpenUrlCrossRefPubMed
  67. 67.↵
    1. M. N. Price,
    2. P. S. Dehal,
    3. A. P. Arkin
    , FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010).
    OpenUrlCrossRefPubMed
  68. 68.↵
    1. UniProt Consortium
    , UniProt: A hub for protein information. Nucleic Acids Res. 43, D204–D212 (2015).
    OpenUrlCrossRefPubMed
  69. 69.↵
    1. B. L. Cantarel et al
    ., The carbohydrate-active enZymes database (CAZy): An expert resource for glycogenomics. Nucleic Acids Res. 37, D233–D238 (2009).
    OpenUrlCrossRefPubMed
  70. 70.↵
    1. B. Buchfink,
    2. C. Xie,
    3. D. H. Huson
    , Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
    OpenUrlCrossRefPubMed
  71. 72.↵
    1. Y. Peng,
    2. H. C. M. Leung,
    3. S. M. Yiu,
    4. F. Y. L. Chin
    , IDBA-UD: A de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).
    OpenUrlCrossRefPubMed
  72. 72.↵
    1. D. D. Kang,
    2. J. Froula,
    3. R. Egan,
    4. Z. Wang
    , MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).
    OpenUrlCrossRefPubMed
  73. 73.↵
    1. Y.-W. Wu,
    2. B. A. Simmons,
    3. S. W. Singer
    , MaxBin 2.0: An automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).
    OpenUrlCrossRefPubMed
  74. 74.↵
    1. B. Langmead,
    2. S. L. Salzberg
    , Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
    OpenUrlCrossRefPubMed
  75. 75.↵
    1. 1000 Genome Project Data Processing Subgroup
    1. H. Li et al
    .; 1000 Genome Project Data Processing Subgroup, The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
    OpenUrlCrossRefPubMed
  76. 76.↵
    1. D. Hyatt et al
    ., Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinf. 11, 119 (2010).
    OpenUrl
  77. 77.↵
    1. L. M. Rodriguez-R et al
    ., The Microbial Genomes Atlas (MiGA) webserver: Taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level. Nucleic Acids Res. 46, W282–W288 (2018).
    OpenUrlCrossRef
  78. 78.↵
    1. D. H. Parks et al
    ., A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).
    OpenUrl
  79. 79.↵
    1. R Core Team
    1. J. Pinheiro,
    2. D. Bates,
    3. S. DebRoy,
    4. D. Sarkar
    ; R Core Team, nlme: Linear and nonlinear mixed effects models (2018). https://cran.r-project.org/web/packages/nlme/index.html. Accessed 15 June 2018.
  80. 80.↵
    1. M. I. Love,
    2. W. Huber,
    3. S. Anders
    , Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
    OpenUrlCrossRefPubMed
  81. 81.↵
    1. Y. Benjamini,
    2. Y. Hochberg
    , Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
    OpenUrlCrossRefPubMed
  82. 82.↵
    1. R. L. Barter,
    2. B. Yu
    , Superheat: An R package for creating beautiful and extendable heatmaps for visualizing complex data. J. Comput. Graph. Stat. 27, 910–922 (2018).
    OpenUrlCrossRef
View Full Text

Purchase access

You may purchase access to this article. This will require you to create an account if you don't already have one.
Back to top
Article Alerts
Email Article
Citation Tools
Request Permissions
Responses of tundra soil microbial communities to half a decade of experimental warming at two critical depths
Eric R. Johnston, Janet K. Hatt, Zhili He, Liyou Wu, Xue Guo, Yiqi Luo, Edward A. G. Schuur, James M. Tiedje, Jizhong Zhou, Konstantinos T. Konstantinidis
Proceedings of the National Academy of Sciences Jul 2019, 116 (30) 15096-15105; DOI: 10.1073/pnas.1901307116
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Proceedings of the National Academy of Sciences: 116 (42)
Current Issue

Submit

Article Classifications

Jump to section

You May Also be Interested in

Radiocarbon dating can help uncover paint forgeries in artworks. Image courtesy of James Hamm (Buffalo State College, The State University of New York, Buffalo, NY).
Uncovering counterfeit paintings
Radiocarbon dating can help uncover paint forgeries in artworks.
Image courtesy of James Hamm (Buffalo State College, The State University of New York, Buffalo, NY).
PNAS Profile of NAS member and molecular biologist Rodolphe Barrangou. Image courtesy of North Carolina State University/Marc Hall.
Featured Profile
PNAS Profile of NAS member and molecular biologist Rodolphe Barrangou
Image courtesy of North Carolina State University/Marc Hall.

Similar Articles