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High taxonomic variability despite stable functional structure across microbial communities


Understanding the processes that are driving variation of natural microbial communities across space or time is a major challenge for ecologists. Environmental conditions strongly shape the metabolic function of microbial communities; however, other processes such as biotic interactions, random demographic drift or dispersal limitation may also influence community dynamics. The relative importance of these processes and their effects on community function remain largely unknown. To address this uncertainty, here we examined bacterial and archaeal communities in replicate ‘miniature’ aquatic ecosystems contained within the foliage of wild bromeliads. We used marker gene sequencing to infer the taxonomic composition within nine metabolic functional groups, and shotgun environmental DNA sequencing to estimate the relative abundances of these groups. We found that all of the bromeliads exhibited remarkably similar functional community structures, but that the taxonomic composition within individual functional groups was highly variable. Furthermore, using statistical analyses, we found that non-neutral processes, including environmental filtering and potentially biotic interactions, at least partly shaped the composition within functional groups and were more important than spatial dispersal limitation and demographic drift. Hence both the functional structure and taxonomic composition within functional groups of natural microbial communities may be shaped by non-neutral and roughly separate processes.

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Figure 1: Bromeliad species used in this study.
Figure 2: Taxonomic and functional community structure.
Figure 3: Functional redundancy in the regional OTU pool.
Figure 4: Relating OTU proportions to environmental variables.
Figure 5: Variation partitioning of OTU composition.


  1. 1

    Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive Earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).

    CAS  Article  PubMed  Google Scholar 

  2. 2

    Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. 3

    Zaikova, E. et al. Microbial community dynamics in a seasonally anoxic fjord: Saanich Inlet, British Columbia. Environ. Microbiol. 12, 172–191 (2010).

    CAS  Article  Google Scholar 

  4. 4

    Powell, J. R. et al. Deterministic processes vary during community assembly for ecologically dissimilar taxa. Nat. Commun. 6, 8444 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5

    Strom, S. L. Microbial ecology of ocean biogeochemistry: a community perspective. Science 320, 1043–1045 (2008).

    CAS  Article  Google Scholar 

  6. 6

    Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348,1262073 (2015).

    Article  CAS  PubMed  Google Scholar 

  7. 7

    Ofijeru, I. D. et al. Combined niche and neutral effects in a microbial wastewater treatment community. Proc. Natl Acad. Sci. USA 107, 15345–15350 (2010).

    Article  Google Scholar 

  8. 8

    Burke, C., Steinberg, P., Rusch, D., Kjelleberg, S. & Thomas, T. Bacterial community assembly based on functional genes rather than species. Proc. Natl Acad. Sci. USA 108, 14288–14293 (2011).

    CAS  Article  Google Scholar 

  9. 9

    Martiny, J. B. H. et al. Microbial biogeography: putting microorganisms on the map. Nat. Rev. Microbiol. 4, 102–112 (2006).

    CAS  Article  Google Scholar 

  10. 10

    Raes, J., Letunic, I., Yamada, T., Jensen, L. J. & Bork, P. Toward molecular trait-based ecology through integration of biogeochemical, geographical and metagenomic data. Mol. Syst. Biol. 7, 473 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12

    Nelson, M. B., Martiny, A. C. & Martiny, J. B. H. Global biogeography of microbial nitrogen-cycling traits in soil. Proc. Natl Acad. Sci. USA 113, 8033–8040 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13

    Fukami, T., Martijn Bezemer, T., Mortimer, S. R. & Putten, W. H. Species divergence and trait convergence in experimental plant community assembly. Ecol. Lett. 8, 1283–1290 (2005).

    Article  Google Scholar 

  14. 14

    Helsen, K., Hermy, M. & Honnay, O. Trait but not species convergence during plant community assembly in restored semi-natural grasslands. Oikos 121, 2121–2130 (2012).

    Article  Google Scholar 

  15. 15

    Whitman, W. B., Coleman, D. C. & Wiebe, W. J. Prokaryotes: the unseen majority. Proc. Natl Acad. Sci. USA 95, 6578–6583 (1998).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16

    Lande, R., Engen, S. & Saether, B. Stochastic Population Dynamics in Ecology and Conservation (Oxford Univ. Press, 2003).

    Book  Google Scholar 

  17. 17

    Fernandez, A. et al. How stable is stable? Function versus community composition. Appl. Environ. Microbiol. 65, 3697–3704 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Vanwonterghem, I. et al. Deterministic processes guide long-term synchronised population dynamics in replicate anaerobic digesters. ISME J. 8, 2015–2028 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19

    Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    Article  CAS  Google Scholar 

  20. 20

    Prosser, J. I. Dispersing misconceptions and identifying opportunities for the use of ‘omics’ in soil microbialecology. Nat. Rev. Microbiol. 13, 439–446 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21

    Goffredi, S. K., Kantor, A. H. & Woodside, W. T. Aquatic microbial habitats within a neotropical rainforest: bromeliads and pH-associated trends in bacterial diversity and composition. Microb. Ecol. 61, 529–542 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22

    Farjalla, V. F. et al. Ecological determinism increases with organism size. Ecology 93, 1752–1759 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23

    Srivastava, D. S. et al. Are natural microcosms useful model systems for ecology? Trends Ecol. Evol. 19, 379–384 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24

    Martinson, G. O. et al. Methane emissions from tank bromeliads in neotropical forests. Nat. Geosci. 3, 766–769 (2010).

    CAS  Article  Google Scholar 

  25. 25

    Goffredi, S. K., Jang, G., Woodside, W. T. & Ussler, W. Bromeliad catchments as habitats for methanogenesis in tropical rainforest canopies. Front. Microbiol. 2,256 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26

    Marino, N. A. C., Srivastava, D. S. & Farjalla, V. F. Aquatic macroinvertebrate community composition in tank-bromeliads is determined by bromeliad species and its constrained characteristics. Insect Cons. Div. 6, 372–380 (2013).

    Article  Google Scholar 

  27. 27

    Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).

    CAS  Article  Google Scholar 

  28. 28

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29

    Canfield, D. E. & Thamdrup, B. Towards a consistent classification scheme for geochemical environments, or, why we wish the term ‘suboxic’ would go away. Geobiology 7, 385–392 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30

    Atwood, T. B. et al. Predator-induced reduction of freshwater carbon dioxide emissions. Nat. Geosci. 6, 191–194 (2013).

    CAS  Article  Google Scholar 

  31. 31

    Chase, J. M. & Myers, J. A. Disentangling the importance of ecological niches from stochastic processes across scales. Phil. Trans. R. Soc. Lond. B 366, 2351–2363 (2011).

    Article  Google Scholar 

  32. 32

    Gotelli, N. J. Null model analysis of species co-occurrence patterns. Ecology 81, 2606–2621 (2000).

    Article  Google Scholar 

  33. 33

    Ulrich, W. & Gotelli, N. J. Null model analysis of species associations using abundance data. Ecology 91, 3384–3397 (2010).

    Article  Google Scholar 

  34. 34

    Ulrich, W. Species co-occurrences and neutral models: reassessing J. M. Diamond’s assembly rules. Oikos 107, 603–609 (2004).

    Article  Google Scholar 

  35. 35

    Horner-Devine, M. C. & Bohannan, B. J. M. Phylogenetic clustering and overdispersion in bacterial communities. Ecology 87, S100–S108 (2006).

    Article  PubMed  Google Scholar 

  36. 36

    Pausas, J. G. & Verdu, M. The jungle of methods for evaluating phenotypic and phylogenetic structure of communities. Bioscience 60, 614–625 (2010).

    Article  Google Scholar 

  37. 37

    Sloan, W. T. et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ. Microbiol. 8, 732–740 (2006).

    Article  Google Scholar 

  38. 38

    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).

    Article  Google Scholar 

  39. 39

    Legendre, P. & Legendre, L. Developments in Environmental Modelling 2nd edn (Elsevier, 1998).

    Google Scholar 

  40. 40

    Legendre, P. Studying beta diversity: ecological variation partitioning by multiple regression and canonicalanalysis. J. Plant Ecol. 1, 3–8 (2008).

    Article  Google Scholar 

  41. 41

    Suttle, C. A. Marine viruses—major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42

    Shapiro, O. H. & Kushmaro, A. Bacteriophage ecology in environmental biotechnology processes. Curr. Opin. Biotechnol. 22, 449–455 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43

    Carrias, J.-F. et al. Two coexisting tank bromeliads host distinct algal communities on a tropical inselberg. Plant Biol. 16, 997–1004 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44

    Langenheder, S., Lindstrom, E. S. & Tranvik, L. J. Weak coupling between community composition and functioning of aquatic bacteria. Limnol. Oceanogr. 50, 957–967 (2005).

    Article  Google Scholar 

  45. 45

    Strickland, M. S., Lauber, C., Fierer, N. & Bradford, M. A. Testing the functional significance of microbial community composition. Ecology 90, 441–451 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46

    Fierer, N. et al. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Natl Acad. Sci. USA 109, 21390–21395 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. 47

    Vanwonterghem, I., Jensen, P. D., Rabaey, K. & Tyson, G. W. Genome-centric resolution of microbial diversity, metabolism and interactions in anaerobic digestion. Environ. Microbiol. 18, 3144–3158 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48

    Reed, D. C. et al. Predicting the response of the deep-ocean microbiome to geochemical perturbations by hydrothermal vents. ISME J. 9, 1857–1869 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Nazareno, A. G. & Laurance, W. F. Brazil’s drought: beware deforestation. Science 347, 1427–1427 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 50

    Golterman, H. Clymo, R. & Ohnstad, M. Methods for Physical and Chemical Analysis of Freshwaters 2nd edn, 169 (IBP Handbook No. 8, Blackwell 1978).

    Google Scholar 

  51. 51

    Zagatto, E., Jacintho, A., Mortatti, J. & Bergamin, F. H. An improved flow injection determination of nitrite in waters by using intermittent flows. Anal. Chim. Acta 120, 399–403 (1980).

    CAS  Article  Google Scholar 

  52. 52

    Fofonoff, N. P. & Millard-Junior, R. Algorithms for Computation of Fundamental Properties of Seawater . UNESCO technical paper in marine science 44 (UNESCO, 1983).

  53. 53

    Andrade-Eiroa, A., Canle, M. & Cerda, V. Environmental applications of excitation-emission spectrofluorimetry: an in-depth review II. Appl. Spec. Rev. 48, 77–141 (2013).

    CAS  Article  Google Scholar 

  54. 54

    Murphy, K. R., Stedmon, C. A., Graeber, D. & Bro, R. Fluorescence spectroscopy and multi-way techniques. PARAFAC. Anal. Meth. 5, 6557–6566 (2013).

    CAS  Article  Google Scholar 

  55. 55

    Stedmon, C. A., Markager, S. & Bro, R. Tracing dissolved organic matter in aquatic environments using a new approach to fluorescence spectroscopy. Mar. Chem. 82, 239–254 (2003).

    CAS  Article  Google Scholar 

  56. 56

    Stedmon, C. A. & Bro, R. Characterizing dissolved organic matter fluorescence with parallel factor analysis: a tutorial. Limnol. Oceanogr. Meth. 6, 572–579 (2008).

    CAS  Article  Google Scholar 

  57. 57

    Murphy, K. R., Stedmon, C. A., Wenig, P. & Bro, R. Openfluor—an online spectral library of auto-fluorescence by organic compounds in the environment. Anal. Meth. 6, 658–661 (2014).

    CAS  Article  Google Scholar 

  58. 58

    Jørgensen, L. et al. Global trends in the fluorescence characteristics and distribution of marine dissolved organic matter. Mar. Chem. 126, 139–148 (2011).

    Article  CAS  Google Scholar 

  59. 59

    Murphy, K. R. et al. Organic matter fluorescence in municipal water recycling schemes: Toward a unified PARAFAC model. Environ. Sci. Technol. 45, 2909–2916 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  60. 60

    Osburn, C. L., Wigdahl, C. R., Fritz, S. C. & Saros, J. E. Dissolved organic matter composition and photoreactivity in prairie lakes of the U.S. Great Plains. Limnol. Oceanogr. 56, 2371–2390 (2011).

    CAS  Article  Google Scholar 

  61. 61

    Yamashita, Y., Boyer, J. N. & Jaffe, R. Evaluating the distribution of terrestrial dissolved organic matter in a complex coastal ecosystem using fluorescence spectroscopy. Cont. Shelf Res. 66, 136–144 (2013).

    Article  Google Scholar 

  62. 62

    Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  63. 63

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. 64

    Li, W., Fu, L., Niu, B., Wu, S. & Wooley, J. Ultrafast clustering algorithms for metagenomic sequence analysis. Brief. Bioinform. 13, 656–668 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  65. 65

    Gevers, D. et al. Re-evaluating prokaryotic species. Nat. Rev. Microbiol. 3, 733–739 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  66. 66

    Martiny, A. C., Tai, A. P., Veneziano, D., Primeau, F. & Chisholm, S. W. Taxonomic resolution, ecotypes and the biogeography of Prochlorococcus. Environ. Microbiol. 11, 823–832 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  67. 67

    Koeppel, A. F. & Wu, M. Species matter: the role of competition in the assembly of congeneric bacteria. ISME J. 8, 531–540 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  68. 68

    Keswani, J. & Whitman, W. B. Relationship of 16S rRNA sequence similarity to DNA hybridization in prokaryotes. Int. J. Syst. Evol. Microbiol. 51, 667–678 (2001).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  69. 69

    Stackebrandt, E. & Ebers, J. Taxonomic parameters revisited: tarnished gold standards. Microbiol. Today 33, 152 (2006).

    Google Scholar 

  70. 70

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS  Article  Google Scholar 

  71. 71

    Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  72. 72

    Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).

    CAS  Article  PubMed  Google Scholar 

  73. 73

    Price, M. N., Dehal, P. S. & Arkin, A. P. Fasttree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  74. 74

    Kubo, K. et al. Archaea of the Miscellaneous Crenarchaeotal Group are abundant, diverse and widespread in marine sediments. ISME J. 6, 1949–1965 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  75. 75

    Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).

    Google Scholar 

  76. 76

    Hahn, A., Hanson, N., Kim, D., Konwar, K. & Hallam, S. Assembly independent functional annotation of short-read data using SOFA: short-ORF functional annotation. In IEEE Conf. Comput. Intel. Bioinformatics Comput. Biol. 1–6 (IEEE, 2015).

    Google Scholar 

  77. 77

    Konwar, K. M. et al. MetaPathways v2.5: quantitative functional, taxonomic and usability improvements. Bioinformatics 31, 3345–3347 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  78. 78

    Tatusova, T., Ciufo, S., Fedorov, B., O’Neill, K. & Tolstoy, I. RefSeq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res. 42, D553–D559 (2014).

    CAS  Article  Google Scholar 

  79. 79

    Wolda, H. Similarity indices, sample size and diversity. Oecologia 50, 296–302 (1981).

    Article  Google Scholar 

  80. 80

    Hester, E. R., Barott, K. L., Nulton, J., Vermeij, M. J. & Rohwer, F. L. Stable and sporadic symbiotic communities of coral and algal holobionts. ISME J. 10, 1157–1169 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. 81

    Chase, J. M., Kraft, N. J. B., Smith, K. G., Vellend, M. & Inouye, B. D. Using null models to disentangle variation in community dissimilarity from variation in α-diversity. Ecosphere 2, 1–11 (2011).

    Article  Google Scholar 

  82. 82

    Connor, E. F. & Simberloff, D. The assembly of species communities: chance or competition? Ecology 60, 1132–1140 (1979).

    Article  Google Scholar 

  83. 83

    Strona, G., Nappo, D., Boccacci, F., Fattorini, S. & San-Miguel-Ayanz, J. A fast and unbiased procedure to randomize ecological binary matrices with fixed row and column totals. Nat. Commun. 5, 4114 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  84. 84

    Hausdorf, B. & Hennig, C. Null model tests of clustering of species, negative co-occurrence patterns and nestedness in meta-communities. Oikos 116, 818–828 (2007).

    Article  Google Scholar 

  85. 85

    Chao, A., Jost, L., Chiang, S., Jiang, Y.-H. & Chazdon, R. L. A two-stage probabilistic approach to multiple-community similarity indices. Biometrics 64, 1178–1186 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  86. 86

    Chave, J., Chust, G. & Thebaud, C. in Scaling Biodiversity (eds Storch, D. et al. ) 150–167 (Cambridge Univ. Press, 2007).

    Book  Google Scholar 

  87. 87

    Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography Vol. 32 (Princeton Univ. Press, 2001).

    Google Scholar 

  88. 88

    Ochman, H. & Wilson, A. Evolution in bacteria: evidence for a universal substitution rate in cellular genomes. J. Mol. Evol. 26, 74–86 (1987).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  89. 89

    Kuo, C.-H. & Ochman, H. Inferring clocks when lacking rocks: the variable rates of molecular evolution in bacteria. Biol. Direct 4, 35 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Eliason, S. R. Maximum Likelihood Estimation: Logic and Practice (SAGE, 1993).

    Book  Google Scholar 

  91. 91

    Burns, A. R. et al. Contribution of neutral processes to the assembly of gut microbial communities in thezebrafish over host development. ISME J. 10, 655–664 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. 92

    Venkataraman, A. et al. Application of a neutral community model to assess structuring of the human lung microbiome. mBio 6, e02284-14 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. 93

    Holyoak, M., Leibold, M. & Holt, R. Metacommunities: Spatial Dynamics and Ecological Communities (Univ. Chicago Press, 2005).

    Google Scholar 

  94. 94

    McCullagh, P. & Nelder, J. Generalized Linear Models. Chapman & Hall/CRC Monographs on Statistics & Applied Probability 2nd edn (Taylor & Francis, 1989).

    Google Scholar 

  95. 95

    Seabold, J. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. In Proc. 9th Python Sci. Conf. (eds Jones, E. & Millman, J.) 57–61 (SciPy, 2010).

  96. 96

    Shao, J. Linear model selection by cross-validation. J. Am. Stat. Assoc. 88, 486–494 (1993).

    Article  Google Scholar 

  97. 97

    P.S.A.S. SAS/STAT 9.1 User’s Guide: The REG Procedure (SAS Institute, 2008).

  98. 98

    Borcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055 (1992).

    Article  Google Scholar 

  99. 99

    Field, A., Miles, J. & Field, Z. Discovering Statistics Using R (SAGE, 2012).

    Google Scholar 

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We thank M. Chen for help with the molecular work. We thank A. L. Gonzalez and A. MacDonald for discussions and comments on our paper. We thank T. Benevides for helping with the absorption measurements. S.L. acknowledges the financial support of the Department of Mathematics, University of British Columbia. S.L. and M.D. acknowledge the support of Natural Sciences and Engineering Research Council (NSERC). V.F.F. is grateful to the Brazilian Council for Research, Development and Innovation (CNPq) for research funds (Pesquisador Visitante Especial, PVE, Research Grant 400454/2014-9) and productivity grants. S.M.S.J. acknowledges the post-graduate scholarship provided by Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ). J.S.L. acknowledges the financial support of Coordenacao de Aperfeicoamento de Pessoal de Ensino Superior (CAPES). We thank M. P. F. Barros, A. R. Soares, J. L. Nepomuceno and their research groups of the Nucleus of Ecology and Socio-Environmental Development of Macae (NUPEM/UFRJ) for proving field and laboratory assistance during the samplings.

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V.F.F., S.L., S.M.S.J., A.P.F.P. and J.S.L. performed the field work. V.F.F. and S.M.S.J. performed the chemical measurements in the laboratory. S.L. performed the molecular work in the laboratory, the DNA sequence analysis and the statistical analyses. S.L., M.D., V.F.F., D.S.S. and L.W.P. interpreted the statistical findings. S.L. wrote a first draft of the manuscript, and all authors contributed to the final preparation of the manuscript. M.D. and V.F.F. supervised the project.

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Correspondence to Stilianos Louca.

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The authors declare no competing interests.

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Functional annotations of prokaryotic taxa (TXT 18 kb)

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Louca, S., Jacques, S., Pires, A. et al. High taxonomic variability despite stable functional structure across microbial communities. Nat Ecol Evol 1, 0015 (2017).

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