Using DNA metabarcoding to characterize national scale diatom-environment relationships and to develop indicators in streams and rivers of the United States.

Recent advancements in DNA techniques, metabarcoding, and bioinformatics could help expand the use of benthic diatoms in monitoring and assessment programs by providing relatively quick and increasingly cost-effective ways to quantify diatom diversity in environmental samples. However, such applications of DNA-based approaches are relatively new, and in the United States, unknowns regarding their applications at large scales exist because only a few small-scale studies have been done. Here, we present results from the first nationwide survey to use DNA metabarcoding (rbcL) of benthic diatoms, which were collected from 1788 streams and rivers across nine ecoregions spanning the conterminous USA. At the national scale, we found that diatom assemblage structure (1) was strongly associated with total phosphorus and total nitrogen concentrations, conductivity, and pH and (2) had clear patterns that corresponded with differences in these variables among the nine ecoregions. These four variables were strong predictors of diatom assemblage structure in ecoregion-specific analyses, but our results also showed that diatom-environment relationships, the importance of environmental variables, and the ranges of these variables within which assemblage changes occurred differed among ecoregions. To further examine how assemblage data could be used for biomonitoring purposes, we used indicator species analysis to identify ecoregion-specific taxa that decreased or increased along each environmental gradient, and we used their relative abundances of gene reads in samples as metrics. These metrics were strongly correlated with their corresponding variable of interest (e.g., low phosphorus diatoms with total phosphorus concentrations), and generalized additive models showed how their relationships compared among ecoregions. These large-scale national patterns and nine sets of ecoregional results demonstrated that diatom DNA metabarcoding is a robust approach that could be useful to monitoring and assessment programs spanning the variety of conditions that exist throughout the conterminous United States.

Decreasing trends in total suspended solids and cumulative effects of nonpoint source projects in the Cuyahoga River Watershed, OH.

Using the US EPA's Grants Reporting and Tracking System (GRTS), we test if completion of best management practices (BMPs) through the Clean Water Act Section (§)319 National Nonpoint Source Program was associated with a decreasing trend in total suspended solids (TSS) load (metric tons/year). The study area chosen had 21 completed projects in the Cuyahoga River watershed in northeastern Ohio from 2000 to 2018. The §319 projects ranged from dam removal, floodplain/wetland restoration to stormwater projects. There was an overall decreasing trend in TSS loads. We identified three phases of project implementation and completion, where phase 1 had ongoing projects, but none completed (2000-2004). The steepest decrease in loads, identified as phase 2 (2005-2011), was associated with completion of low-head dam modification and removal projects on the mainstem of the Cuyahoga River. A likely decreasing trend was associated with projects completed in the tributaries, such as natural channel design restoration and stormwater green infrastructure (phase 3). Pairing sediment reduction estimates from projects with the river's flow normalized TSS loading trend, we estimated that the §319 effort may account for a small fraction of the TSS load reduction. Other stream restoration projects (non-§319) have also been done in the Cuyahoga watershed by other organizations. However, trying to compile these other projects is challenging in larger watersheds having multiple municipalities, agencies, and nonprofits doing restoration without better coordinated record keeping and monitoring. While a decreasing trend in a pollutant load is a desirable water quality outcome, determining what contributed to that trend remains difficult.

Using Single-Species and Whole Community Stream Mesocosm Exposures for Identifying Major Ion Effects in Doses Mimicking Resource Extraction Wastewaters.

Wastewaters and leachates from various inland resource extraction activities contain high ionic concentrations and differ in ionic composition, which complicates the understanding and effective management of their relative risks to stream ecosystems. To this end, we conducted a stream mesocosm dose-response experiment using two dosing recipes prepared from industrial salts. One recipe was designed to generally reflect the major ion composition of deep well brines (DWB) produced from gas wells (primarily Na+, Ca2+, and Cl-) and the other, the major ion composition of mountaintop mining (MTM) leachates from coal extraction operations (using salts dissociating to Ca2+, Mg2+, Na+, SO42- and HCO3-)-both sources being extensive in the Central Appalachians of the USA. The recipes were dosed at environmentally relevant nominal concentrations of total dissolved solids (TDS) spanning 100 to 2000 mg/L for 43 d under continuous flow-through conditions. The colonizing native algal periphyton and benthic invertebrates comprising the mesocosm ecology were assessed with response sensitivity distributions (RSDs) and hazard concentrations (HCs) at the taxa, community (as assemblages), and system (as primary and secondary production) levels. Single-species toxicity tests were run with the same recipes. Dosing the MTM recipe resulted in a significant loss of secondary production and invertebrate taxa assemblages that diverged from the control at all concentrations tested. Comparatively, intermediate doses of the DWB recipe had little consequence or increased secondary production (for emergence only) and had assemblages less different from the control. Only the highest dose of the DWB recipe had a negative impact on certain ecologies. The MTM recipe appeared more toxic, but overall, for both types of resource extraction wastewaters, the mesocosm responses suggested significant changes in stream ecology would not be expected for specific conductivity below 300 µS/cm, a published aquatic life benchmark suggested for the region.

Development of a Risk Characterization Tool for Harmful Cyanobacteria Blooms on the Ohio River.

A data-driven approach to characterizing the risk of cyanobacteria-based harmful algal blooms (cyanoHABs) was undertaken for the Ohio River. Twenty-five years of river discharge data were used to develop Bayesian regression models that are currently applicable to 20 sites spread-out along the entire 1579 km of the river's length. Two site-level prediction models were developed based on the antecedent flow conditions of the two blooms that occurred on the river in 2015 and 2019: one predicts if the current year will have a bloom (the occurrence model), and another predicts bloom persistence (the persistence model). Predictors for both models were based on time-lagged average flow exceedances and a site's characteristic residence time under low flow conditions. Model results are presented in terms of probabilities of occurrence or persistence with uncertainty. Although the occurrence of the 2019 bloom was well predicted with the modeling approach, the limited number of events constrained formal model validation. However, as a measure of performance, leave-one-out cross validation returned low misclassification rates, suggesting that future years with flow time series like the previous bloom years will be correctly predicted and characterized for persistence potential. The prediction probabilities are served in real time as a component of a risk characterization tool/web application. In addition to presenting the model's results, the tool was designed with visualization options for studying water quality trends among eight river sites currently collecting data that could be associated with or indicative of bloom conditions. The tool is made accessible to river water quality professionals to support risk communication to stakeholders, as well as serving as a real-time water data monitoring utility.

Censoring Trace-Level Environmental Data: Statistical Analysis Considerations to Limit Bias.

Trace-level environmental data typically include values near or below detection and quantitation thresholds where health effects may result from low-concentration exposures to one chemical over time or to multiple chemicals. In a cook stove case study, bias in dibenzo[a,h]anthracene concentration means and standard deviations (SDs) was assessed following censoring at thresholds for selected analysis approaches: substituting threshold/2, maximum likelihood estimation, robust regression on order statistics, Kaplan-Meier, and omitting censored observations. Means and SDs for gas chromatography-mass spectrometry-determined concentrations were calculated after censoring at detection and calibration thresholds, 17% and 55% of the data, respectively. Threshold/2 substitution was the least biased. Measurement values were subsequently simulated from two log-normal distributions at two sample sizes. Means and SDs were calculated for 30%, 50%, and 80% censoring levels and compared to known distribution counterparts. Simulation results illustrated (1) threshold/2 substitution to be inferior to modern after-censoring statistical approaches and (2) all after-censoring approaches to be inferior to including all measurement data in analysis. Additionally, differences in stove-specific group means were tested for uncensored samples and after censoring. Group differences of means tests varied depending on censoring and distributional decisions. Investigators should guard against censoring-related bias from (explicit or implicit) distributional and analysis approach decisions.