Article | Published:

A strong link between marine microbial community composition and function challenges the idea of functional redundancy


Marine microbes have tremendous diversity, but a fundamental question remains unanswered: why are there so many microbial species in the sea? The idea of functional redundancy for microbial communities has long been assumed, so that the high level of richness is often explained by the presence of different taxa that are able to conduct the exact same set of metabolic processes and that can readily replace each other. Here, we refute the hypothesis of functional redundancy for marine microbial communities by showing that a shift in the community composition altered the overall functional attributes of communities across different temporal and spatial scales. Our metagenomic monitoring of a coastal northwestern Mediterranean site also revealed that diverse microbial communities harbor a high diversity of potential proteins. Working with all information given by the metagenomes (all reads) rather than relying only on known genes (annotated orthologous genes) was essential for revealing the similarity between taxonomic and functional community compositions. Our finding does not exclude the possibility for a partial redundancy where organisms that share some specific function can coexist when they differ in other ecological requirements. It demonstrates, however, that marine microbial diversity reflects a tremendous diversity of microbial metabolism and highlights the genetic potential yet to be discovered in an ocean of microbes.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Falkowski PG, Fenchel T, Delong EF. The microbial engines that drive Earth’s biogeochemical cycles. Science. 2008;320:1034–9.

  2. 2.

    Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, et al. Microbial diversity in the deep sea and the under explored “rare biosphere”. Proc Natl Acad Sci USA. 2006;103:12115–20.

  3. 3.

    Fuhrman JA, Cram JA, Needham DM. Marine microbial community dynamics and their ecological interpretation. Nat Rev Micro. 2015;13:133–46.

  4. 4.

    Hugoni M, Taib N, Debroas D, Domaizon I, Jouan Dufournel I, Bronner G, et al. Structure of the rare archaeal biosphere and seasonal dynamics of active ecotypes in surface coastal waters. Proc Natl Acad Sci USA. 2013;110:6004–9.

  5. 5.

    DeLong EF, Preston CM, Mincer T, Rich V, Hallam SJ, Frigaard NU, et al. Community genomics among stratified microbial assemblages in the ocean’s interior. Science. 2006;311:496–503.

  6. 6.

    Ladau J, Sharpton TJ, Finucane MM, Jospin G, Kembel SW, O’Dwyer J, et al. Global marine bacterial diversity peaks at high latitudes in winter. ISME J. 2013;7:1669–77.

  7. 7.

    Galand P, Salter I, Kalenitchenko D. Microbial productivity is associated with phylogenetic distance in surface marine waters. Mol Ecol. 2015;24:5785–95.

  8. 8.

    Hutchinson GE. The paradox of the plankton. Am Nat. 1961;95:137–45.

  9. 9.

    Allison SD, Martiny JBH. Resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci USA. 2008;105:11512–9.

  10. 10.

    Yin B, Crowley D, Sparovek G, De Melo WJ, Borneman J. Bacterial functional redundancy along a soil reclamation gradient. Appl Environ Microbiol. 2000;66:4361–5.

  11. 11.

    Fierer N, Ladau J, Clemente JC, Leff JW, Owens SM, Pollard KS, et al. Reconstructing the microbial diversity and function of pre-agricultural tallgrass prairie soils in the United States. Science. 2013;342:621–4.

  12. 12.

    Fuhrman JA, Hewson I, Schwalbach MS, Steele JA, Brown MV, Naeem S. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc Natl Acad Sci USA. 2006;103:13104–9.

  13. 13.

    Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.

  14. 14.

    Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.

  15. 15.

    Jurburg SD, Salles JF. Functional redundancy and ecosystem function—the soil microbiota as a case study. L. Yueh-Hsin, J.A. Blanco, R. Shovonlal (Eds.), Ecosystems—Linking Structure and Function, InTech Open Science, Rijeka (2015), pp. 29-42.

  16. 16.

    Bradford MA, Fierer N. The biogeography of microbial communities and ecosystem processes: implications for soil and ecosystem models. In: Wall DH, Bardgett RD, (eds). Soil Ecology and Ecosystem Services.. Oxford: Oxford University Press; 2012. p. 424.

  17. 17.

    Tréguer P, Le Corre P. Manuel d’analyse des sels nutrifs dans l’eau de mer: utilisation de l’Autoanalyzer II Technicon (R). Université de Bretagne Occidentale, Brest, France; 1975.

  18. 18.

    Solorzano L. Determination of ammonia in natural waters by the phenolhypochlorite method. Limnol Oceanogr. 1969;14:799–801.

  19. 19.

    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30:2114-20

  20. 20.

    Peng Y, Leung HC, Yiu S-M, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics. 2012;28:1420–8.

  21. 21.

    Fierer N, Barberán A, Laughlin DC. Seeing the forest for the genes: using metagenomics to infer the aggregated traits of microbial communities. Front Microbiol. 2014; 5:614.

  22. 22.

    Maillet N, Collet G, Vannier T, Lavenier D, Peterlongo P. COMMET: comparing and combining multiple metagenomic datasets. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Belfast, United Kingdom. 2014:94–8.

  23. 23.

    Ulyantsev VI, Kazakov SV, Dubinkina VB, Tyakht AV, Alexeev DG. MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data. Bioinformatics.2016; 32:2760-7. 

  24. 24.

    Noguchi H, Taniguchi T, Itoh T. MetaGeneAnnotator: detecting species-specific patterns of ribosomal binding site for precise gene prediction in anonymous prokaryotic and phage genomes. DNA Res. 2008;15:387–96.

  25. 25.

    Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.

  26. 26.

    Li R, Yu C, Li Y, Lam T-W, Yiu S-M, Kristiansen K, et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009;25:1966–7.

  27. 27.

    Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucl Acids Res. 2015; 44:457-62.

  28. 28.

    Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.

  29. 29.

    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. Nucl Acids Res. 2013;41:D590–6.

  30. 30.

    Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25:1754–60.

  31. 31.

    Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31:814–21.

  32. 32.

    Lee J-H, Yi H, Chun J. rRNASelector: a computer program for selecting ribosomal RNA encoding sequences from metagenomic and metatranscriptomic shotgun libraries. J Microbiol. 2011;49:689–91.

  33. 33.

    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.

  34. 34.

    Dixon P, Palmer M. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.

  35. 35.

    Legendre P, Legendre LF. Numerical ecology. Amsterdam, The Netherlands, vol. 24. Elsevier; 2012.

  36. 36.

    Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.

  37. 37.

    Ludwig W, Strunk O, Westram R, Richter L, Meier H, Buchner A, et al. ARB: a software environment for sequence data. Nucl Acids Res. 2004;32:1363–71.

  38. 38.

    McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. ploS one. 2013;8:e61217.

  39. 39.

    Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30:3123–4.

  40. 40.

    Finlay BJ, Maberly SC, Cooper JI. Microbial diversity and ecosystem function. Oikos. 1997;80:209–13.

  41. 41.

    Hector A, Bagchi R. Biodiversity and ecosystem multifunctionality. Nature. 2007;448:188–90.

  42. 42.

    Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.

  43. 43.

    Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ, Cheng J-F, et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature. 2013;499:431–7.

  44. 44.

    Kopf A, Bicak M, Kottmann R, Schnetzer J, Kostadinov I, Lehmann K, et al. The ocean sampling day consortium. Gigascience. 2015;4:27.

  45. 45.

    Sintes E, Bergauer K, De Corte D, Yokokawa T, Herndl GJ. Archaeal amoA gene diversity points to distinct biogeography of ammonia-oxidizing Crenarchaeota in the ocean. Environ Microbiol. 2013;15:1647–58.

  46. 46.

    Ferrera I, Borrego CM, Salazar G, Gasol JM. Marked seasonality of aerobic anoxygenic phototrophic bacteria in the coastal NW Mediterranean Sea as revealed by cell abundance, pigment concentration and pyrosequencing of pufM gene. Environ Microbiol. 2014;16:2953–65.

  47. 47.

    Bryant JA, Stewart FJ, Eppley JM, DeLong EF. Microbial community phylogenetic and trait diversity declines with depth in a marine oxygen minimum zone. Ecology. 2012;93:1659–73.

  48. 48.

    Gilbert JA, Field D, Swift P, Thomas S, Cummings D, Temperton B, et al. The taxonomic and functional diversity of microbes at a temperate coastal site: a ‘multi-omic’ study of seasonal and diel temporal variation. PLoS one. 2010; 5:e15545.

Download references


Raw sequences were archived in the EBI repository under accession number PRJEB26919. The work of PEG was supported by the Agence Nationale de la Recherche (ANR) through the projects EUREKA (ANR-14-CE02-0004-01). We thank the captain and crew of the Nereis II, Eric Maria, and Louise Oriol for assisting with the collection and analysis of samples over the time series. We extend our acknowledgments to all the researchers that were involved in working with the time series over the years.

Author information

Conflict of interest

The authors declare that they have no conflict of interest.

Correspondence to Pierre E. Galand.

Electronic supplementary material

Supplementary Figures

Supplementary Table S1, Table S2, Table S3 and Table S4

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Further reading

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5