title: Comparisons of statistical models to predict fecal indicator bacteria concentrations enumerated by qPCR- and culture-based methods.
abstract: Recently, the United States Environmental Protection Agency (USEPA) revised their recreational water quality criteria, in which adjustments were made by approving enterococci (ENT) quantitative PCR (qPCR) as an alternative, rapid method and advocating the use of predictive models for water quality management. The implementation of qPCR-based methods and prediction models are meant to decrease the time between sample collection and public advisories and notifications. To date, few studies have compared qPCR-based models to culture-based prediction models and none of these studies have been conducted in coastal estuarine systems. In this study, we created prediction models using qPCR-based fecal indicator bacteria (FIB) data in dual-use recreational and shellfish harvesting waters and compared them to published ENT and Escherichia coli (EC) culture-based prediction models in eastern North Carolina estuaries. Furthermore, an empirical statistical model was created to predict qPCR inhibition levels so that proper remediation techniques can be applied when it is a problem. Predictor variable selection in both qPCR- and culture-based ENT models was very similar; both models included 14-day rain total, dissolved oxygen, and salinity/conductivity, with 89 and 90% of qPCR and culture data described, respectively. Using ENT management action thresholds, qPCR- and culture-based models showed high accuracy in management decisions. The qPCR model had 92 and 96% accuracy using the 110 and 1000 cell equivalents (CE)/100 ml thresholds, respectively, and the culture model had 90% accuracy in management decisions with the 110 MPN/100 ml threshold. EC models for qPCR- and culture-based concentrations used similar independent variables (14-day humidity, salinity/conductivity, a rain/storm variable, and a measure of air temperature), with each model explaining 26 and 55% of the data variation, respectively. When using different thresholds that were logs apart for management decisions, the two EC models accurately predicted management decisions; qPCR models correctly predicted management decisions 96 and 77% of the time (using 31 and 320 CE/100 ml, respectively) while culture models correctly predicted management decisions 96 and 88% percent of the time (with 31 and 320 MPN/100 ml, respectively). Equivalency between models was shown in our non-point source impacted estuaries, with ENT models performing slightly better than EC models. In addition, inhibition of the qPCR was a major issue that had to be addressed. An inhibition model was created with easily obtained meteorological data and accounted for a high level of data variability (adjusted R(2) = 0.82).
authors.affiliation: Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, 3431 Arendell Street, Morehead City, NC 28557, USA.
authors.forename: Raul A
authors.initials: RA
authors.lastname: Gonzalez
authors.forename: Rachel T
authors.initials: RT
authors.lastname: Noble
mesh_terms: D001419:Bacteria / Q000302:isolation & purification*; D015169:Colony Count, Microbial; D005243:Feces / Q000382:microbiology*; D015233:Models, Statistical; D016133:Polymerase Chain Reaction / Q000379:methods*; D014871:Water Microbiology
keywords: Enterococci; Escherichia coli; Estuary; Monitoring; Multiple linear regression; Quantitative PCR