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Predicting bacterial community assemblages using an artificial neural network approach

Abstract

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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Acknowledgements

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

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Authors

Contributions

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). https://doi.org/10.1038/nmeth.1975

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