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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.


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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.

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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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