Fecal Impairment Framework, A New Conceptual Framework for Assessing Fecal Contamination in Recreational Waters.

Fecal pollution of surface water is a pervasive problem that negatively affects waterbodies concerning 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). These types of information assist stakeholders in identifying likely sources of fecal pollution, prioritizing them for remediation, and choosing appropriate best management practices. While both culture-based quantification and MST are useful, they yield different kinds of information, potentially increasing uncertainty in prioritizing sources for management. This study presents a conceptual framework that takes separate human health risk estimates based on measured MST and E. coli concentrations as inputs and produces an estimate of the overall fecal impairment risk as its output. The proposed framework is intended to serve as a supplemental screening tool for existing monitoring programs to aid in identifying and prioritizing sites for remediation. In this study, we evaluated the framework by applying it to two primarily agricultural watersheds and several freshwater recreational beaches using existing routine monitoring data. Based on a combination of E. coli and MST results, the proposed fecal impairment framework identified four sites in the watersheds as candidates for remediation and identified temporal trends in the beach application. As these case studies demonstrate, the proposed fecal impairment framework is an easy-to-use and cost-effective supplemental screening tool that provides actionable information to managers using existing routine monitoring data, without requiring specialized expertize.

Frequency and degradation of SARS-CoV-2 markers N1, N2, and E in sewage.

Coronavirus disease 2019 (COVID-19) is an infectious disease that is mainly spread through aerosolized droplets containing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is excreted in feces by infected individuals. Sewage surveillance has been applied widely to obtain data on the prevalence of COVID-19 in whole communities. We used SARS-CoV-2 gene targets N1, N2, and E to determine the prevalence of COVID-19 at both municipal and building levels. Frequency analysis of wastewater testing indicated that single markers detected only 85% or less of samples that were detected as positive for SARS-CoV-2 with the three markers combined, indicating the necessity of pairing markers to lower the false-negative rate. The best pair of markers in both municipal and building level monitoring was N1 and N2, which correctly identified 98% of positive samples detected with the three markers combined. The degradation rates of all three targets were assessed at two different temperatures (25 and 35 °C) as a possible explanation for observed differences between markers in frequency. Results indicated that all three RNA targets degrade at nearly the same rate, indicating that differences in degradation rate are not responsible for the observed differences in marker frequency.

Using watershed characteristics to enhance fecal source identification.

Fecal pollution is one of the most prevalent forms of pollution affecting waterbodies worldwide, threatening public health and negatively impacting aquatic environments. Microbial source tracking (MST) applies polymerase chain reaction (PCR) technology to help identify the source of fecal pollution. In this study, we combine spatial data for two watersheds with general and host-associated MST markers to target human (HF183/BacR287), bovine (CowM2), and general ruminant (Rum2Bac) sources. Concentrations of MST markers in samples were determined with droplet digital PCR (ddPCR). The three MST markers were detected at all sites (n = 25), but bovine and general ruminant markers were significantly associated with watershed characteristics. MST results, combined with watershed characteristics, suggest that streams draining areas with low-infiltration soil groups and high agricultural land use are at an increased risk for fecal contamination. Microbial source tracking has been applied in numerous studies to aid in identifying the sources of fecal contamination, but these studies usually lack information on the involvement of watershed characteristics. Our study combined watershed characteristics with MST results to provide more comprehensive insight into the factors that influence fecal contamination in order to implement the most effective best management practices.

Turbulent particle transport in streams: can exponential settling be reconciled with fluid mechanics?

Most ecological studies of particle transport in streams that focus on fine particulate organic matter or benthic invertebrates use the Exponential Settling Model (ESM) to characterize the longitudinal pattern of particle settling on the bed. The ESM predicts that if particles are released into a stream, the proportion that have not yet settled will decline exponentially with transport time or distance and will be independent of the release elevation above the bed. To date, no credible basis in fluid mechanics has been established for this model, nor has it been rigorously tested against more-mechanistic alternative models. One alternative is the Local Exchange Model (LEM), which is a stochastic advection-diffusion model that includes both longitudinal and vertical spatial dimensions and is based on classical fluid mechanics. The LEM predicts that particle settling will be non-exponential in the near field but will become exponential in the far field, providing a new theoretical justification for far-field exponential settling that is based on plausible fluid mechanics. We review properties of the ESM and LEM and compare these with available empirical evidence. Most evidence supports the prediction of both models that settling will be exponential in the far field but contradicts the ESM's prediction that a single exponential distribution will hold for all transport times and distances.

Validity assessment of Michigan's proposed qPCR threshold value for rapid water-quality monitoring of E. coli contamination.

Michigan's water-quality standards specify that E. coli concentrations at bathing beaches must not exceed 300 E. coli per 100 mL, as determined by the geometric mean of culture-based concentrations in three or more representative samples from a given beach on a given day. Culture-based analysis requires 18⁠-⁠24 h to complete, so results are not available on the day of sampling. This one-day delay is problematic because results cannot be used to prevent recreation at beaches that are unsafe on the sampling day, nor do they reliably indicate whether recreation should be prevented the next day, due to high between-day variability in E. coli concentrations demonstrated by previous studies. By contrast, qPCR-based E. coli concentrations can be obtained in 3-4 h, making same-day beach notification decisions possible. Michigan has proposed a qPCR threshold value (qTV) for E. coli of 1.863 log10 gene copies per reaction as a potential equivalent value to the state standard, based on statistical analysis of a set of state-wide training data from 2016 to 2018. The main purpose of the present study is to assess the validity of the proposed qTV by determining whether the implied qPCR-based beach notification decisions agree well with culture-based decisions on two sets of test data from 2016⁠-⁠2018 (6,564 samples) and 2019-2020 (3,205 samples), and whether performance of the proposed qTV is similar on the test and training data. The results show that performance of Michigan's proposed qTV on both sets of test data was consistently good (e.g., 95% agreement with culture-based beach notification decisions during 2019⁠-⁠2020) and was as good as or better than its performance on the training data set. The false-negative rate for the proposed qTV was 25-29%, meaning that beach notification decisions based on the qTV would be expected to permit recreation on the day of sampling in 25-29% of cases where the beach exceeds the state standard for FIB contamination. This false-negative rate is higher than one would hope to see but is well below the corresponding error rate for culture-based decisions, which permit recreation at beaches that exceed the state standard on the day of sampling in 100% of cases because of the one-day delay in obtaining results. The key advantage of qPCR-based analysis is that it permits a large percentage (71-75%) of unsafe beaches to be identified in time to prevent recreation on the day of sampling.

Simplified Analysis of Measurement Data from A Rapid E. coli qPCR Method (EPA Draft Method C) Using A Standardized Excel Workbook.

Draft method C is a standardized method for quantifying E. coli densities in recreational waters using quantitative polymerase chain reaction (qPCR). The method includes a Microsoft Excel workbook that automatically screens for poor-quality data using a set of previously proposed acceptance criteria, generates weighted linear regression (WLR) composite standard curves, and calculates E. coli target gene copies in test samples. We compared standard curve parameter values and test sample results calculated with the WLR model to those from a Bayesian master standard curve (MSC) model using data from a previous multi-lab study. The two models' mean intercept and slope estimates from twenty labs' standard curves were within each other's 95% credible or confidence intervals for all labs. E. coli gene copy estimates of six water samples analyzed by eight labs were highly overlapping among labs when quantified with the WLR and MSC models. Finally, we compared multiple labs' 2016-2018 composite curves, comprised of data from individual curves where acceptance criteria were not used, to their corresponding composite curves with passing acceptance criteria. Composite curves developed from passing individual curves had intercept and slope 95% confidence intervals that were often narrower than without screening and an analysis of covariance test was passed more often. The Excel workbook WLR calculation and acceptance criteria will help laboratories implement draft method C for recreational water analysis in an efficient, cost-effective, and reliable manner.

A comparison of E. coli concentration estimates quantified by the EPA and a Michigan laboratory network using EPA Draft Method C.

We evaluated data from 10 laboratories that analyzed water samples from 82 recreational water sites across the state of Michigan between 2016 and 2018. Water sample replicates were analyzed by experienced U.S. Environmental Protection Agency (EPA) analysts and Michigan laboratories personnel, many of whom were newly trained, using EPA Draft Method C-a rapid quantitative polymerase chain reaction (qPCR) technique that provides same day Escherichia coli (E. coli) concentration results. Beach management decisions (i.e. remain open or issue an advisory or closure) based on E. coli concentration estimates obtained by Michigan labs and by the EPA were compared; the beach management decision agreed in 94% of the samples analyzed. We used the Wilcoxon one-sample signed rank test and nonparametric quantile regression to assess (1) the degree of agreement between E. coli concentrations quantified by Michigan labs versus the EPA and (2) Michigan lab E. coli measurement precision, relative to EPA results, in different years and water body types. The median quantile regression curve for Michigan labs versus EPA approximated the 1:1 line of perfect agreement more closely as years progressed. Similarly, Michigan lab E. coli estimates precision also demonstrated yearly improvements. No meaningful difference was observed in the degree of association between Michigan lab and EPA E. coli concentration estimates for inland lake and Great Lakes samples (median regression curve average slopes 0.93 and 0.95, respectively). Overall, our study shows that properly trained laboratory personnel can perform Draft Method C to a degree comparable with experienced EPA analysts. This allows health departments that oversee recreational water quality monitoring to be confident in qPCR results generated by the local laboratories responsible for analyzing the water samples.

Mammalian metabolic allometry: do intraspecific variation, phylogeny, and regression models matter?

Power scaling relationships between body mass and organismal traits are fundamental to biology. Compilations of mammalian masses and basal metabolic rates date back over a century and are used both to support and to assail the universal quarter-power scaling invoked by the metabolic theory of ecology. However, the slope of this interspecific allometry is typically estimated without accounting for intraspecific variation in body mass or phylogenetic constraints on metabolism. We returned to the original literature and culled nearly all unique measurements of body mass and basal metabolism for 695 mammal species and (1) phylogenetically corrected the data using the fullest available phylogeny, (2) applied several different regression analyses, (3) resampled regressions by drawing randomly selected species from each of the polytomies in the phylogenetic hypothesis at each iteration, and (4) ran these same analyses independently on separate clades. Overall, 95% confidence intervals of slope estimates frequently did not include 0.75, and clade-specific slopes varied from 0.5 to 0.85, depending on the clade and regression model. Our approach reveals that the choice of analytical model has a systematic influence on the estimated allometry, but irrespective of the model applied, we find little support for a universal metabolic rate-body mass scaling relationship.

Reducing effects of dispersal on the bias of 2-sample mark-recapture estimators of stream fish abundance.

The 2-sample mark-recapture method with Chapman's estimator is often used by inland fishery managers to estimate the reach-scale abundance of stream fish. An important assumption of this method is that no dispersal into or out of the study reach occurs between the two samples. Violations of this assumption are probably common in practice, but their effect on bias (systematic error) of abundance estimates is poorly understood, especially in small populations. Estimation methods permitting dispersal exist but, for logistical reasons, often are infeasible for routine assessments in streams. The purpose of this paper is to extend available results regarding effects of dispersal on the bias of Chapman's estimator as applied to reach-scale studies of stream fish abundance. We examine for the first time the joint effects of dispersal and sampling variation on the bias of this estimator. To reduce the bias effects of dispersal, we propose a modified sampling scheme in which the original study reach is expanded, a central subreach is sampled during the mark session (sample 1), and the entire reach is sampled during the recapture session (sample 2). This modified sampling scheme can substantially reduce bias effects of dispersal without requiring unique marking of individual fish or additional site visits. Analytical and simulation results show that sampling variation tends to create negative bias with respect to study-reach abundance, while dispersal tends to create positive bias; the net effect can be positive, negative, or zero, depending on the true abundance, capture probabilities, and amount and nature of dispersal. In most cases, simply expanding the study reach is an effective way to reduce dispersal-related bias of Chapman's estimator, but expanding the study reach and employing the modified sampling scheme we propose is a better alternative for accurately estimating abundance with the same level of sampling effort.

Phylogeny, regression, and the allometry of physiological traits.

Physiological and ecological allometries often pose linear regression problems characterized by (1) noncausal, phylogenetically autocorrelated independent (x) and dependent (y) variables (characters); (2) random variation in both variables; and (3) a focus on regression slopes (allometric exponents). Remedies for the phylogenetic autocorrelation of species values (phylogenetically independent contrasts) and variance structure of the data (reduced major axis [RMA] regression) have been developed, but most functional allometries are reported as ordinary least squares (OLS) regression without use of phylogenetically independent contrasts. We simulated Brownian diffusive evolution of functionally related characters and examined the importance of regression methodologies and phylogenetic contrasts in estimating regression slopes for phylogenetically constrained data. Simulations showed that both OLS and RMA regressions exhibit serious bias in estimated regression slopes under different circumstances but that a modified orthogonal (least squares variance-oriented residual [LSVOR]) regression was less biased than either OLS or RMA regressions. For strongly phylogenetically structured data, failure to use phylogenetic contrasts as regression data resulted in overestimation of the strength of the regression relationship and a significant increase in the variance of the slope estimate. Censoring of data sets by simulated extinction of taxa did not affect the importance of appropriate regression models or the use of phylogenetic contrasts.

Long-term field performance of biosand filters in the Artibonite Valley, Haiti.

A field study assessing the sustainability and efficacy of 55 biosand filters installed during 1999-2010 was conducted in the Artibonite Valley, Haiti during 2011. Twenty-nine filters were still in use. Duration of filter use ranged from < 1 to 12 years. Water quality, microbial analysis, and flow rate were evaluated for each functioning filter. Kaplan-Meier analysis of filter lifespans showed that filter use remained high (> 85%) up to seven years after installation. Several filters were still in use after 12 years, which is longer than documented in any previous study. Filtered water from 25 filters (86%) contained Escherichia coli concentrations of < 10 most probable number of coliforms/100 mL. Recontamination of stored filtered water was negligible. Bacterial removal efficiency was 1.1 log(10). Comparable results from previous studies in the same region and elsewhere show that biosand filter technology continues to be an effective and sustainable water treatment method in developing countries worldwide.

Probabilistic settling in the Local Exchange Model of turbulent particle transport.

The Local Exchange Model (LEM) is a stochastic diffusion model of particle transport in turbulent flowing water. It was developed mainly for application to particles of near-neutral buoyancy that are strongly influenced by turbulent eddies. Turbulence can rapidly transfer such particles to the bed, where settlement can then occur by, for example, sticking to biofilms (e.g., fine particulate organic matter, or FPOM) or attaching to the substrate behaviorally (e.g., benthic invertebrates). Previous papers on the LEM have addressed the problems of how long (time) and far (distance) a suspended particle will be transported before hitting the bed for the first time. These are the hitting-time and hitting-distance problems, respectively. Hitting distances predicted by the LEM for FPOM in natural streams tend to be much shorter than the distances at which most particles actually settle, suggesting that particles usually do not settle the first time they hit the bed. The present paper extends the LEM so it can address probabilistic settling, where a particle encountering the bed can either remain there for a positive length of time (i.e., settle) or immediately reflect back into the water column, each with positive probability. Previous results for the LEM are generalized by deducing a single set of equations governing the probability distribution and moments of a broad class of quantities that accumulate during particle trajectories terminated by hitting or settling on the bed (e.g., transport time, transport distance, cumulative energy expenditure during transport). Key properties of the settling-time and settling-distance distributions are studied numerically and compared with the observed FPOM settling-distance distribution for a natural stream. Some remaining limitations of the LEM and possible means of overcoming them are discussed.

Silk filaments enhance the settlement of stream insect larvae.

Many aquatic organisms need to settle in suitable benthic habitats while being transported via water currents. Such settlement is especially challenging for organisms that encounter complex benthic topography and lack the ability to move easily from the water column to the bed (e.g., via swimming). We conducted flume studies to examine whether the settlement of drifting stream insects is facilitated by adhesive filaments that extend from their bodies. Using a new tripwire visualization technique, we found that neonatal black flies (Simulium tribulatum) drifted with silk threads averaging six times their body length. These threads allowed larvae to contact or snag the bed from a greater height than would be possible through direct body-to-bed contact alone, and instantly arrested their downstream movement. Thus, silk increased their probability of settlement. We then performed an experiment to examine how settlement varied with bed topography and velocity. We tested whether settlement rate differed between a flat bed and an irregular bed that mimicked key aspects of their natural cobble-bed habitat. Velocities were similar for both bed treatments. Settlement on the irregular bed was 40 times greater than on the flat bed due to silk use. Settlement rate also exhibited a marginally significant decline with increasingly velocity on the flat bed, but not on the irregular bed. Silk threads should greatly increase the settlement rate of these nonswimming larvae on coarse-grained stream beds. Thus, silk snagging can potentially reduce the downstream distance that individuals are transported during a drift event, although the effects of silk on other phases of larval dispersal may differ.