title: 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 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.
  
  
  OBJECTIVE
  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.
authors.affiliation: Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, United Kingdom.
authors.forename: Ameet J
authors.initials: AJ
authors.lastname: Pinto
authors.affiliation: Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, United Kingdom.
authors.forename: Joanna
authors.initials: J
authors.lastname: Schroeder
authors.affiliation: Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, United Kingdom.
authors.forename: Mary
authors.initials: M
authors.lastname: Lunn
authors.affiliation: Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Glasgow, United Kingdom.
authors.forename: William
authors.initials: W
authors.lastname: Sloan
authors.affiliation: Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA raskin@umich.edu.
authors.forename: Lutgarde
authors.initials: L
authors.lastname: Raskin
mesh_terms: D001419:Bacteria / Q000145:classification* / Q000302:isolation & purification; D058491:Bacterial Load; D044822:Biodiversity; D004777:Environment; D064307:Microbiota; D020560:Proteobacteria / Q000145:classification / Q000302:isolation & purification; D012621:Seasons; D062211:Spatio-Temporal Analysis; D014871:Water Microbiology