Spatial-temporal survey and occupancy-abundance modeling to predict bacterial community dynamics in the drinking water microbiome

Pinto, A., Schroeder, J., Lunn, M., Sloan, W. and Raskin, L. (2014) Spatial-temporal survey and occupancy-abundance modeling to predict bacterial community dynamics in the drinking water microbiome. mBio, 5(3), e01135-14. (doi:10.1128/mBio.01135-14) (PMID:24865557) (PMCID:PMC4045074)

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Abstract

Bacterial communities migrate continuously from the drinking water treatment plant through the drinking water distribution system and into our built environment. Understanding bacterial dynamics in the distribution system is critical to ensuring that safe drinking water is being supplied to customers. We present a 15-month survey of bacterial community dynamics in the drinking water system of Ann Arbor, MI. By sampling the water leaving the treatment plant and at nine points in the distribution system, we show that the bacterial community spatial dynamics of distance decay and dispersivity conform to the layout of the drinking water distribution system. However, the patterns in spatial dynamics were weaker than those for the temporal trends, which exhibited seasonal cycling correlating with temperature and source water use patterns and also demonstrated reproducibility on an annual time scale. The temporal trends were driven by two seasonal bacterial clusters consisting of multiple taxa with different networks of association within the larger drinking water bacterial community. Finally, we show that the Ann Arbor data set robustly conforms to previously described interspecific occupancy abundance models that link the relative abundance of a taxon to the frequency of its detection. Relying on these insights, we propose a predictive framework for microbial management in drinking water systems. Further, we recommend that long-term microbial observatories that collect high-resolution, spatially distributed, multiyear time series of community composition and environmental variables be established to enable the development and testing of the predictive framework.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Pinto, Dr Ameet and Schroeder, Dr Joanna and Lunn, Dr Mary and Sloan, Professor William
Authors: Pinto, A., Schroeder, J., Lunn, M., Sloan, W., and Raskin, L.
Subjects:Q Science > QR Microbiology
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TD Environmental technology. Sanitary engineering
College/School:College of Science and Engineering > School of Engineering > Infrastructure and Environment
Journal Name:mBio
Publisher:American Society for Microbiology
ISSN:2150-7511
ISSN (Online):2150-7511
Copyright Holders:Copyright © 2014 Pinto et al.
First Published:First published in mBio 5(3):e01135-14
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
621651Developing an Event Prediction and Correction Framework for Microbial Management in Drinking Water Systems.Ameet PintoEngineering & Physical Sciences Research Council (EPSRC)EP/K035886/1ENG - ENGINEERING INFRASTRUCTURE & ENVIR