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Predicting bacterial community assemblages using an artificial neural network approach


Understanding the interactions between the Earth's microbiome and the physical, chemical and biological environment is a fundamental goal of microbial ecology. We describe a bioclimatic modeling approach that leverages artificial neural networks to predict microbial community structure as a function of environmental parameters and microbial interactions. This method was better at predicting observed community structure than were any of several single-species models that do not incorporate biotic interactions. The model was used to interpolate and extrapolate community structure over time with an average Bray-Curtis similarity of 89.7. Additionally, community structure was extrapolated geographically to create the first microbial map derived from single-point observations. This method can be generalized to the many microbial ecosystems for which detailed taxonomic data are currently being generated, providing an observation-based modeling technique for predicting microbial taxonomic structure in ecological studies.

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Figure 1: Bray-Curtis similarity between observed microbial populations and MAP or non-ANN model predictions.
Figure 2: Microbial abundances during a Vibrionales bloom.
Figure 3: Predicted structure of microbial communities across the Western English Channel.
Figure 4: The region of low similarity predicted for 8 December 2008 corresponds to a region of lower dissolved oxygen (dO2).
Figure 5: MAP-predicted relative abundance of four microbial taxa in the Western English Channel for 8 December 2008.


  1. Little, A.E., Robinson, C.J., Peterson, S.B., Raffa, K.F. & Handelsman, J. Rules of engagement: interspecies interactions that regulate microbial communities. Annu. Rev. Microbiol. 62, 375–401 (2008).

    CAS  Article  Google Scholar 

  2. Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).

    Article  Google Scholar 

  3. Elith, J. & Leathwick, J.R. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).

    Article  Google Scholar 

  4. Larsen, P. et al. Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset. BMC Microbial Informatics and Experimentation 1, 4 (2011).

    Article  Google Scholar 

  5. Caporaso, J.G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA 108 (suppl. 1), 4516–4522 (2011).

    CAS  Article  Google Scholar 

  6. Follows, M.J. & Dutkiewicz, S. Modeling diverse communities of marine microbes. Annu. Rev. Mar. Sci. 3, 427–451 (2011).

    Article  Google Scholar 

  7. Gilbert, J.A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6, 298–308 (2012).

    CAS  Article  Google Scholar 

  8. Southward, A.J. et al. Long-term oceanographic and ecological research in the Western English Channel. Adv. Mar. Biol. 47, 1–105 (2005).

    PubMed  Google Scholar 

  9. Campbell, B.J., Yu, L., Heidelberg, J.F. & Kirchman, D.L. Activity of abundant and rare bacteria in a coastal ocean. Proc. Natl. Acad. Sci. USA 108, 12776–12781 (2011).

    CAS  Article  Google Scholar 

  10. Smyth, T.J. et al. A broad spatio-temporal view of the western English Channel observatory. J. Plankton Res. 32, 585–601 (2010).

    Article  Google Scholar 

  11. Steele, J.A. et al. Marine bacterial, archaeal and protistan association networks reveal ecological linkages. ISME J. 5, 1414–1425 (2011).

    Article  Google Scholar 

  12. 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, 343–351 (2012).

    Article  Google Scholar 

  13. Caporaso, J.G., Paszkiewicz, K., Field, D., Knight, R. & Gilbert, J.A. The Western English Channel contains a persistent microbial seed bank. ISME J. published online, doi:10.1038/ismej.2011.162 (10 November 2011).

    CAS  Article  Google Scholar 

  14. Jeschke, J.M. & Strayer, D.L. Usefulness of bioclimatic models for studying climate change and invasive species. Ann. NY Acad. Sci. 1134, 1–24 (2008).

    Article  Google Scholar 

  15. Guisan, A. & Harrell, F.E. Ordinal response regression models in ecology. J. Veg. Sci. 11, 617–626 (2000).

    Article  Google Scholar 

  16. Leathwick, J.R. & Austin, M.P. Competitive interactions between tree species in New Zealand's old-growth indigenous forests. Ecology 82, 2560–2573 (2001).

    Article  Google Scholar 

  17. Barry, S.C. & Welsh, A.H. Generalized additive modelling and zero inflated count data. Ecol. Modell. 157, 179–188 (2002).

    Article  Google Scholar 

  18. Austin, M. Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecol. Modell. 200, 1–19 (2007).

    Article  Google Scholar 

  19. Pearce, J. & Ferrier, S. The practical value of modelling relative abundance of species for regional conservation planning: a case study. Biol. Conserv. 98, 33–43 (2001).

    Article  Google Scholar 

  20. Anadon, J.D., Gimenez, A. & Ballestar, R. Linking local ecological knowledge and habitat modelling to predict absolute species abundance on large scales. Biodivers. Conserv. 19, 1443–1454 (2010).

    Article  Google Scholar 

  21. Nielsen, S.E., Johnson, C.J., Heard, D.C. & Boyce, M.S. Can models of presence-absence be used to scale abundance? Two case studies considering extremes in life history. Ecography 28, 197–208 (2005).

    Article  Google Scholar 

  22. VanDerWal, J., Shoo, L.P., Johnson, C.N. & Williams, S.E. Abundance and the environmental niche: environmental suitability estimated from niche models predicts the upper limit of local abundance. Am. Nat. 174, 282–291 (2009).

    Article  Google Scholar 

  23. Jutla, A.S., Akanda, A.S., Griffiths, J.K., Colwell, R. & Islam, S. Warming oceans, phytoplankton, and river discharge: implications for cholera outbreaks. Am. J. Trop. Med. Hyg. 85, 303–308 (2011).

    Article  Google Scholar 

  24. Kays, R.W., Gompper, M.E. & Ray, J.C. Landscape ecology of eastern coyotes based on large-scale estimates of abundance. Ecol. Appl. 18, 1014–1027 (2008).

    Article  Google Scholar 

  25. Pearson, R.G., Dawson, T.P., Berry, P.M. & Harrison, P.A. SPECIES: A Spatial Evaluation of Climate Impact on the Envelope of Species. Ecol. Modell. 154, 289–300 (2002).

    Article  Google Scholar 

  26. Morgan, J.L., Darling, A.E. & Eisen, J.A. Metagenomic sequencing of an in vitro-simulated microbial community. PLoS ONE 5, e10209 (2010).

    Article  Google Scholar 

  27. Smith, V.A., Yu, J., Smulders, T.V., Hartemink, A.J. & Jarvis, E.D. Computational inference of neural information flow networks. PLOS Comput. Biol. 2, e161 (2006).

    Article  Google Scholar 

  28. Schmidt, M. & Lipson, H. Distilling free-form natural laws from experimental data. Science 324, 81–85 (2009).

    CAS  Article  Google Scholar 

  29. Mumby, P.J., Clarke, K.R. & Harborne, A.R. Weighting species abundance estimates for marine resource assessment. Aquat. Conserv. Mar. Freshwat. Ecosyst. 115–120 (1996).

  30. Sinkko, H. et al. Phosphorus chemistry and bacterial community composition interact in brackish sediments receiving agricultural discharges. PLoS ONE 6, e21555 (2011).

    CAS  Article  Google Scholar 

  31. Bouskill, N.J., Eveillard, D., O'Mullan, G., Jackson, G.A. & Ward, B.B. Seasonal and annual reoccurrence in betaproteobacterial ammonia-oxidizing bacterial population structure. Environ. Microbiol. 13, 872–886 (2011).

    CAS  Article  Google Scholar 

  32. Davies, N., Field, D. & Genomic Observatories, N. Sequencing data: A genomic network to monitor Earth. Nature 481, 145 (2012).

    CAS  Article  Google Scholar 

  33. Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol. 29, 415–420 (2011).

    CAS  Article  Google Scholar 

  34. Sogin, M.L. et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc. Natl. Acad. Sci. USA 103, 12115–12120 (2006).

    CAS  Article  Google Scholar 

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This work was supported by the US Department of Energy under contract DE-AC02-06CH11357.

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Authors and Affiliations



P.E.L. and J.A.G. conceived and designed the experiments. P.E.L., D.F. and J.A.G. analyzed the data and wrote the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jack A Gilbert.

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The authors declare no competing financial interests.

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Supplementary Figures 1–2, Supplementary Tables 1–3, Supplementary Results (PDF 2066 kb)

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Larsen, P., Field, D. & Gilbert, J. Predicting bacterial community assemblages using an artificial neural network approach. Nat Methods 9, 621–625 (2012).

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