Spatial-Temporal Survey and Occupancy-Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome

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...

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Autores principales:	Ameet J. Pinto, Joanna Schroeder, Mary Lunn, William Sloan, Lutgarde Raskin
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2014
Materias:	Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/54024ea44a354e65a7a41c323cc4e249
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spelling	oai:doaj.org-article:54024ea44a354e65a7a41c323cc4e2492021-11-15T15:47:39ZSpatial-Temporal Survey and Occupancy-Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome10.1128/mBio.01135-142150-7511https://doaj.org/article/54024ea44a354e65a7a41c323cc4e2492014-07-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.01135-14https://doaj.org/toc/2150-7511ABSTRACT 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. IMPORTANCE Safe and regulation-compliant drinking water may contain up to millions of microorganisms per liter, representing phylogenetically diverse groups of bacteria, archaea, and eukarya that affect public health, water infrastructure, and the aesthetic quality of water. The ability to predict the dynamics of the drinking water microbiome will ensure that microbial contamination risks can be better managed. Through a spatial-temporal survey of drinking water bacterial communities, we present novel insights into their spatial and temporal community dynamics and recommend steps to link these insights in a predictive framework for microbial management of drinking water systems. Such a predictive framework will not only help to eliminate microbial risks but also help to modify existing water quality monitoring efforts and make them more resource efficient. Further, a predictive framework for microbial management will be critical if we are to fully anticipate the risks and benefits of the beneficial manipulation of the drinking water microbiome.Ameet J. PintoJoanna SchroederMary LunnWilliam SloanLutgarde RaskinAmerican Society for MicrobiologyarticleMicrobiologyQR1-502ENmBio, Vol 5, Iss 3 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Microbiology QR1-502
spellingShingle	Microbiology QR1-502 Ameet J. Pinto Joanna Schroeder Mary Lunn William Sloan Lutgarde Raskin Spatial-Temporal Survey and Occupancy-Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome
description	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. IMPORTANCE Safe and regulation-compliant drinking water may contain up to millions of microorganisms per liter, representing phylogenetically diverse groups of bacteria, archaea, and eukarya that affect public health, water infrastructure, and the aesthetic quality of water. The ability to predict the dynamics of the drinking water microbiome will ensure that microbial contamination risks can be better managed. Through a spatial-temporal survey of drinking water bacterial communities, we present novel insights into their spatial and temporal community dynamics and recommend steps to link these insights in a predictive framework for microbial management of drinking water systems. Such a predictive framework will not only help to eliminate microbial risks but also help to modify existing water quality monitoring efforts and make them more resource efficient. Further, a predictive framework for microbial management will be critical if we are to fully anticipate the risks and benefits of the beneficial manipulation of the drinking water microbiome.
format	article
author	Ameet J. Pinto Joanna Schroeder Mary Lunn William Sloan Lutgarde Raskin
author_facet	Ameet J. Pinto Joanna Schroeder Mary Lunn William Sloan Lutgarde Raskin
author_sort	Ameet J. Pinto
title	Spatial-Temporal Survey and Occupancy-Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome
title_short	Spatial-Temporal Survey and Occupancy-Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome
title_full	Spatial-Temporal Survey and Occupancy-Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome
title_fullStr	Spatial-Temporal Survey and Occupancy-Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome
title_full_unstemmed	Spatial-Temporal Survey and Occupancy-Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome
title_sort	spatial-temporal survey and occupancy-abundance modeling to predict bacterial community dynamics in the drinking water microbiome
publisher	American Society for Microbiology
publishDate	2014
url	https://doaj.org/article/54024ea44a354e65a7a41c323cc4e249
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Spatial-Temporal Survey and Occupancy-Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome

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