title: The relationship between microbial community evenness and function in slow sand filters.
abstract: Two full-scale slow sand filters (SSFs) were sampled periodically from April until November 2011 to study the spatial and temporal structures of the bacterial communities found in the filters. To monitor global changes in the microbial communities, DNA from sand samples taken at different depths and locations within the SSFs and at different filters ages was used for Illumina 16S rRNA gene sequencing. Additionally, 15 water quality parameters were monitored to assess filter performance, with functionally relevant microbial members being identified by using multivariate statistics. The bacterial diversity in the SSFs was found to be much larger than previously documented, with community composition being shaped by the characteristics of the SSFs (filter age and depth) and sampling characteristics (month, side, and distance from the influent and effluent pipes). We found that several key genera (Acidovorax, Halomonas, Sphingobium, and Sphingomonas) were associated with filter performance. In addition, at the whole-community level, a strong positive correlation was found between species evenness and filter performance. This study is the first to comprehensively characterize the microbial community of SSFs and link specific microbes to water quality parameters. In doing so, we reveal key patterns in microbial community structure that relate to overall community function.
  
  
  OBJECTIVE
  The supply of sustainable, energy-efficient, and safe drinking water to an increasing world population is a huge challenge faced by the water industry. SSFs have been used for hundreds of years to provide a safe and reliable source of potable drinking water, with minimal energy requirements. However, a lack of knowledge pertaining to the treatment mechanisms, particularly the biological processes, underpinning SSF operation has meant that SSFs are still operated as "black boxes." Understanding these dynamics alongside performance-induced effects associated with operational differences will promote optimized SSF design, maintenance, and operation, creating more efficient and environmentally sustainable filters. Through a spatial-temporal survey of full-scale SSFs at various points of operation, we present the most detailed characterization to date of the functional microbial communities found in SSFs, linking various taxa and community metrics to optimal water quality production.
authors.affiliation: School of Engineering, University of Glasgow, Glasgow, United Kingdom Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, USA sjhaig@umich.edu.
authors.forename: Sarah-Jane
authors.initials: SJ
authors.lastname: Haig
authors.affiliation: School of Engineering, University of Glasgow, Glasgow, United Kingdom Warwick Medical School, University of Warwick, Coventry, United Kingdom.
authors.forename: Christopher
authors.initials: C
authors.lastname: Quince
authors.affiliation: Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom.
authors.forename: Robert L
authors.initials: RL
authors.lastname: Davies
authors.affiliation: Département Génie Civil et Génie des Eaux, Université Laval, Quebec, Canada.
authors.forename: Caetano C
authors.initials: CC
authors.lastname: Dorea
authors.affiliation: School of Engineering, University of Glasgow, Glasgow, United Kingdom Microbial Ecophysiology Laboratory, School of Natural Sciences and Ryan Institute, National University of Ireland, Galway, Ireland.
authors.forename: Gavin
authors.initials: G
authors.lastname: Collins
mesh_terms: D001419:Bacteria / Q000145:classification* / Q000235:genetics*; D058448:Biota; D004269:DNA, Bacterial / Q000737:chemistry / Q000235:genetics; D004275:DNA, Ribosomal / Q000737:chemistry / Q000235:genetics; D004783:Environmental Microbiology; D005374:Filtration / Q000379:methods*; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D017422:Sequence Analysis, DNA; D062211:Spatio-Temporal Analysis; D018508:Water Purification / Q000379:methods*; D060753:Water Quality
chemical_list: D004269:DNA, Bacterial; D004275:DNA, Ribosomal; D012336:RNA, Ribosomal, 16S