The polymerase chain reaction (PCR) is a molecular biology tool with diverse applications in the aquatic sciences. Classical PCR is a nonquantitative method that can be used to detect target DNA sequences that are characteristic of particular microbial taxa but cannot determine their concentrations in water samples. Various quantitative forms of PCR have been developed to remove this limitation. Of these, the two that currently are used most widely are real-time quantitative PCR (qPCR) and droplet digital PCR (ddPCR). Several outlines of the mathematical and statistical basis of these methods for estimating target sequence concentrations are available in the literature, but we are aware of no thorough and rigorous derivation of the theoretical underpinnings of either. The purpose of this review is to provide such derivations, and to identify and compare the main strengths and weaknesses of the two methods. We find that both estimation methods are sound, provided careful attention is paid to specific details that differ between the two. With qPCR, it is especially important to reduce any significant PCR inhibition by sample constituents and to properly fit the standard curve to heteroskedastic calibration data. With ddPCR, it is important to ensure that the value of the mean droplet volume used in calculating concentrations is correct for the particular combination of droplet generator and master mix used. The advantages of qPCR include lower instrument and per-sample costs, a shorter turnaround time for obtaining results, a higher upper limit of quantification, and a wider dynamic range. The advantages of ddPCR include freedom from dependence on a standard curve, an inherently lower sensitivity to PCR inhibitors, a lower limit of quantification, a simpler theoretical basis, and simpler data analysis. We suggest qPCR often will be preferable in laboratory studies where investigators have significant control over the range of target sequence concentrations in samples, concentrations are sufficiently high so proper calibration does not require standards with concentrations low enough to exhibit exaggerated variability in the threshold cycle, and no significant inhibition is present, or more generally, in studies where funding levels do not permit the higher cost of instrumentation and supplies required by ddPCR or where the shorter turnaround time for qPCR is essential. If sufficient funds are available, ddPCR often will be preferable when the ability to quantify low concentrations is important, especially if inhibitors are likely to be present at concentrations that are problematic for qPCR.

Fecal contamination is a common cause of impairment of surface waters. In monitoring studies, it is usually assessed by measuring concentrations of fecal indicator bacteria such as Escherichia coli (E. coli), a common monitoring target in freshwater systems. In this study, we assess the advantages and disadvantages of two common and previously validated methods for monitoring E. coli concentrations at freshwater beaches: Colilert-18^®, with a turnaround time of ca. 18 h, and real-time quantitative PCR (qPCR), with a turnaround time of ca. 3–4 h. Based on data comprising 3081 pairs of Colilert-18 and qPCR estimates of E. coli concentrations in split samples from Michigan’s annual beach monitoring program in 2019 and 2020, we found that qPCR monitoring detected a high percentage of exceedances of the state’s water quality standard for E. coli contamination that went undetected on the day of sampling with Colilert-18 monitoring because qPCR concentration estimates were available on the day of sampling but Colilert-18 estimates were not. However, Colilert-18 data were more useful than qPCR data for the statistical comparison of contamination levels at different beaches, probably in part because Colilert-18 data showed a much lower percentage of concentration estimates outside the method’s range of quantification.

The polymerase chain reaction (PCR) is a molecular biology tool with diverse applications in the aquatic sciences. Classical PCR is a nonquantitative method that can be used to detect target DNA sequences that are characteristic of particular microbial taxa but cannot determine their concentrations in water samples. Various quantitative forms of PCR have been developed to remove this limitation. Of these, the two that currently are used most widely are real-time quantitative PCR (qPCR) and droplet digital PCR (ddPCR). Several outlines of the mathematical and statistical basis of these methods for estimating target sequence concentrations are available in the literature, but we are aware of no thorough and rigorous derivation of the theoretical underpinnings of either. The purpose of this review is to provide such derivations, and to identify and compare the main strengths and weaknesses of the two methods. We find that both estimation methods are sound, provided careful attention is paid to specific details that differ between the two. With qPCR, it is especially important to reduce any significant PCR inhibition by sample constituents and to properly fit the standard curve to heteroskedastic calibration data. With ddPCR, it is important to ensure that the value of the mean droplet volume used in calculating concentrations is correct for the particular combination of droplet generator and master mix used. Advantages of qPCR include lower instrument and per-sample costs, a shorter turnaround time for obtaining results, a higher upper limit of quantification, and a wider dynamic range. Advantages of ddPCR include freedom from dependence on a standard curve, inherently lower sensitivity to PCR inhibitors, a lower limit of quantification, a simpler theoretical basis, and simpler data analysis.

Fecal pollution is a pervasive problem that negatively affects waterbodies with respect to both public health and ecological functions. Current assessment methods monitor fecal indicator bacteria (FIB) to identify pollution sources, using culture-based quantification and microbial source tracking (MST). From this information, stakeholders can make informed decisions about implementing best management practices to reduce the sources of fecal pollution in their waterbodies and prioritizing sources for management. While both culture-based quantification and MST are useful, they yield different kinds of information, potentially making it difficult to prioritize sources for management. This study presents a conceptual fecal impairment framework (FIF) that uses a dual stressor response function combining MST and traditional E. coli FIB measurements to provide a screening tool for overall fecal impairment risk. Our conceptual model was assessed using a variety of scenarios (more conservative, equal weight, and less conservative) as well as different decision tree inputs for our two microbial monitoring methods. The model was then tested by applying it to case studies involving watersheds and beaches to aid in site prioritization through FIB identification. Results of the FIF implementation indicated elevated agreement between the less conservative and equal weight matrices in both studies. Based on the combination of MST and E. coli results, the FIF identified four sites of interest in the watersheds as candidates for remediation and identified temporal trends in the beach application. Our tool provides a rapidly implementable and cost-effective framework to provide actionable information to stakeholders and managers.