title: 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.
authors.affiliation: Argonne National Laboratory Biosciences Division, Illinois, USA.
authors.forename: Peter E
authors.initials: PE
authors.lastname: Larsen
authors.forename: Dawn
authors.initials: D
authors.lastname: Field
authors.forename: Jack A
authors.initials: JA
authors.lastname: Gilbert
mesh_terms: D000192:Actinomycetales / Q000502:physiology; D001419:Bacteria / Q000235:genetics*; D020564:Deltaproteobacteria / Q000502:physiology; D004463:Ecology; D017753:Ecosystem; D020563:Gammaproteobacteria / Q000502:physiology; D054892:Metagenome; D056265:Microbial Interactions; D008954:Models, Biological; D016571:Neural Networks, Computer; D012623:Seawater / Q000382:microbiology