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.

Linking Water Quality to Drinking Water Treatment Costs Using Time Series Analysis: Examining the Effect of a Treatment Plant Upgrade in Ohio.

We estimate a cost function for a water treatment plant in Ohio to assess the avoided-treatment costs resulting from improved source water quality. Regulations and source water concerns motivated the treatment plant to upgrade its treatment process by adding a granular activated carbon building in 2012. The cost function uses daily observations from 2013 to 2016; this allows us to compare the results to a cost function estimated for 2007-2011 for the same plant. Both models focus on understanding the relationship between treatment costs per 1,000 gallons (per 3.79 m3) of produced drinking water and predictor variables such as turbidity, pH, total organic carbon, deviations from target pool elevation, final production, and seasonal variables. Different from the 2007-2011 model, the 2013-2016 model includes a harmful algal bloom toxin variable. We find that the new treatment process leads to a different cost model than the one that covers 2007-2011. Both total organic carbon and algal toxin are important drivers for the 2013-2016 treatment costs. This reflects a significant increase in cyanobacteria cell densities capable of producing toxins in the source water between time periods. The 2013-2016 model also reveals that positive and negative shocks to treatment costs affect volatility, the changes in the variance of costs through time, differently. Positive shocks, or increased costs, lead to higher volatility compared to negative shocks, or decreased costs, of similar magnitude. After quantifying the changes in treatment costs due to changes in source water quality, we discuss how the study results inform policy-relevant decisions.

Increasingly severe cyanobacterial blooms and deep water hypoxia coincide with warming water temperatures in reservoirs.

Cyanobacterial blooms are expected to intensify and become more widespread with climate change and sustained nutrient pollution, subsequently increasing threats to lentic ecosystems, water quality, and human health. However, little is known about their rates of change because long-term monitoring data are rare, except for some well-studied individual lakes, which typically are large and broadly dispersed geographically. Using monitoring data spanning 1987-2018 for 20 temperate reservoirs located in the USA, we found that cyanobacteria cell densities mostly posed low-to-moderate human health risks until 2003-2005, after which cell densities rapidly increased. Increases were greatest in reservoirs with extensive agriculture in their watersheds, but even those with mostly forested watersheds experienced increases. Since 2009, cell densities posing high human health risks have become frequent with 75% of yearly observations exceeding 100,000 cells ml-1 , including 53% of observations from reservoirs with mostly forested watersheds. These increases coincided with progressively earlier and longer summer warming of surface waters, evidence of earlier onset of stratification, lengthening durations of deep-water hypoxia, and warming deep waters in non-stratifying reservoirs. Among years, higher cell densities in stratifying reservoirs were associated with greater summer precipitation, warmer June surface water temperatures, and higher total Kjeldahl nitrogen concentrations. These trends are evidence that expected increases in cyanobacterial blooms already are occurring as changing climate conditions in some regions increasingly favor their proliferation. Consequently, their negative effects on ecosystems, human health, and socioeconomic wellbeing could increase and expand if warming trends and nutrient pollution continue.

Reliability theory for microbial water quality and sustainability assessment.

Microbial surface water contamination can disrupt critical ecosystem services such as recreation and drinking water supply. Prediction of water contamination and assessment of sustainability of water resources in the context of water quality are needed but are difficult to achieve - with challenges arising from the complexity of environmental systems, and stochastic variability of processes that drive contaminant fate and transport. In this paper we use reliability theory as a framework to address these issues. We define failure as exceedance of regulatory water contamination limits, and system components as reaches in the surface water network. We then methodically study the reliability of each component in the context of water quality, as well as the impact of individual components on overall water quality and sustainability. We obtain spatially distributed probability- and physics-based sustainability measures of reliability, vulnerability, resilience and the sustainability index. Finally, we use GIS as a platform to present these measures as geospatial products in an effort to foster public acceptance of probability-based methods in contaminant hydrology.

Stochastic reliability-based risk evaluation and mapping for watershed systems and sustainability (STREAMS).

Mitigating water contamination, improving water security, and increasing sustainability involve environmental awareness and conscientious decision-making by denizens and stakeholders. Achieving such awareness requires visually compelling geospatial decision-making tools that take into account the probabilistic and spatially distributed nature of water contamination. Inspired by the success of weather maps, this paper presents a novel STochastic Reliability-based Risk Evaluation And Mapping for watershed Systems and Sustainability (STREAMS) tool that produces and effectively communicates the risk of water contamination as maps. STREAMS is integrated with ArcGIS geoprocessing tools and uses physics-based reliability theory to compute the spatial distribution of risk, which is defined as the probability of exceeding a safety threshold of water contamination within a watershed. A quantitative analysis of the efficacy of mitigation strategies is conducted by estimating risk reduction from best management practices throughout the entire watershed. Two case studies at different spatial scales are presented, demonstrating STREAMS application to watersheds with varied properties.

DNA metabarcoding effectively quantifies diatom responses to nutrients in streams.

Nutrient pollution from human activities remains a common problem facing stream ecosystems. Identifying ecological responses to phosphorus and nitrogen can inform decisions affecting the protection and management of streams and their watersheds. Diatoms are particularly useful because they are a highly diverse group of unicellular algae found in nearly all aquatic environments and are sensitive responders to increased nutrient concentrations. Here, we used DNA metabarcoding of stream diatoms as an approach to quantifying effects of total phosphorus (TP) and total nitrogen (TN). Threshold indicator taxa analysis (TITAN) identified operational taxonomic units (OTUs) that increased or decreased along TP and TN gradients along with nutrient concentrations at which assemblages had substantial changes in the occurrences and relative abundances of OTUs. Boosted regression trees showed that relative abundances of gene sequence reads for OTUs identified by TITAN as low P, high P, low N, or high N diatoms had strong relationships with nutrient concentrations, which provided support for potentially using these groups of diatoms as metrics in monitoring programs. Gradient forest analysis provided complementary information by characterizing multi-taxa assemblage change using multiple predictors and results from random forest models for each OTU. Collectively, these analyses showed that notable changes in diatom assemblage structure and OTUs began around 20 µg TP/L, low P diatoms decreased substantially and community change points occurred from 75 to 150 µg/L, and high P diatoms became increasingly dominant from 150 to 300 µg/L. Diatoms also responded to TN with large decreases in low N diatoms occurring from 280 to 525 µg TN/L and a transition to dominance by high N diatoms from 525-850 µg/L. These diatom responses to TP and TN could be used to inform protection efforts (i.e., anti-degradation) and management goals (i.e., nutrient reduction) in streams and watersheds. Our results add to the growing support for using diatom metabarcoding in monitoring programs.

Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations.

We analyzed 37 satellite reflectance algorithms and 321 variants for five satellites for estimating turbidity in a freshwater inland lake in Ohio using coincident real hyperspectral aircraft imagery converted to relative reflectance and dense coincident surface observations. This study is part of an effort to develop simple proxies for turbidity and algal blooms and to evaluate their performance and portability between satellite imagers for regional operational turbidity and algal bloom monitoring. Turbidity algorithms were then applied to synthetic satellite images and compared to in situ measurements of turbidity, chlorophyll-a (Chl-a), total suspended solids (TSS) and phycocyanin as an indicator of cyanobacterial/blue green algal (BGA) abundance. Several turbidity algorithms worked well with real Compact Airborne Spectrographic Imager (CASI) and synthetic WorldView-2, Sentinel-2 and Sentinel-3/MERIS/OLCI imagery. A simple red band algorithm for MODIS imagery and a new fluorescence line height algorithm for Landsat-8 imagery had limited performance with regard to turbidity estimation. Blue-Green Algae/Phycocyanin (BGA/PC) and Chl-a algorithms were the most widely applicable algorithms for turbidity estimation because strong co-variance of turbidity, TSS, Chl-a, and BGA made them mutual proxies in this experiment.

Reliability-Based Water Quality Assessment with Load Resistance Factor Design: Application to TMDL.

Effective load reduction strategies rely on an accurate Total Maximum Daily Load (TMDL) calculation, which quantifies contaminant loading from various sources. There is a wide range of methods to consider uncertainties in TMDLs: from simple, conservative assumptions regarding factors that contribute to the TMDL required margin of safety (MOS), to probability-based approaches such as Monte Carlo simulations, which explicitly quantifies TMDL uncertainty. In this paper the authors adapt the Load Resistance Factor Design (LRFD), a rigorous, reliability-based framework, to water quality assessment and the TMDL process. The LFRFD replaces the lumped MOS with design factors that reflect the magnitude and distribution of uncertainty among the various contaminant loads. In addition, it produces load reduction estimates to meet management objectives with a contaminant-specific frequency-based target. The LRFD is computationally efficient and flexible in that, to compute the design factors, the procedure can utilize: measurement data, analytical solutions or model simulation results, as well as full or marginal probability distributions.

EXPLORING NON-TRADITIONAL PARTICIPATION AS AN APPROACH TO MAKE WATER QUALITY TRADING MARKETS MORE EFFECTIVE.

Water quality trading (WQT) has potential to be a low-cost means for achieving water quality goals. WQT allows regulated wastewater treatment plants (WWTPs) facing discharge limits the flexibility to either reduce their own discharge or purchase pollution control from other WWTPs or nonpoint sources (NPSs) such as agricultural producers. Under this limited scope, programs with NPSs have been largely unsuccessful at meeting water quality goals. The decision to participate in trading depends on many factors including the pollution control costs, uncertainty in pollution control, and discharge limits. Current research that focuses on making WQT work tends to identify how to increase participation by traditional traders such as WWTPs and agricultural producers. As an alternative, but complementary approach, we consider whether augmenting WQT markets with non-traditional participants would help increase the number of trades. Determining the economic incentives for these potential participants requires the development of novel benefit functions requiring not only economic considerations, but also accounting for ecological and engineering processes. Existing literature on non-traditional participants in environmental markets tends to center on air quality and only increasing citizen participation as buyers. Here, we consider the issues for broadening participation (both buyers and sellers) in WQT and outline a multidisciplinary approach to begin evaluating feasibility.

A flexible modeling framework for hydraulic and water quality performance assessment of stormwater green infrastructure.

A flexible framework has been created for modeling multi-dimensional hydrological and water quality processes within stormwater green infrastructure (GI) practices. The framework conceptualizes GI practices using blocks (spatial features) and connectors (interfaces) representing functional components of a GI. The blocks represent spatial features with the ability to store water (e.g., pond, soil, benthic sediments, manhole, or a generic storage zone) and water quality constituents including chemical constituents and particles. The hydraulic module can solve a combination of Richards equation, kinematic/diffusive wave, Darcy, and other user-provided flow models. The particle transport module is based on performing mass-balance on particles in different phases, e.g., mobile and deposited in soil with constitutive theories controlling their transport, settling, deposition, and release. The reactive transport modules allow constituents to be in dissolved, sorbed, bound to particles, and undergo user-defined transformations. Four applications of the modeling framework are presented that demonstrate its flexibility for simulating urban GI performance.

IMPROVING PREDICTIVE MODELS OF IN-STREAM PHOSPHORUS CONCENTRATION BASED ON NATIONALLY-AVAILABLE SPATIAL DATA COVERAGES.

Spatial data are playing an increasingly important role in watershed science and management. Large investments have been made by government agencies to provide nationally-available spatial databases; however, their relevance and suitability for local watershed applications is largely unscrutinized. We investigated how goodness of fit and predictive accuracy of total phosphorus (TP) concentration models developed from nationally-available spatial data could be improved by including local watershed-specific data in the East Fork of the Little Miami River, Ohio, a 1290 km2 watershed. We also determined whether a spatial stream network (SSN) modeling approach improved on multiple linear regression (nonspatial) models. Goodness of fit and predictive accuracy were highest for the SSN model that included local covariates, and lowest for the nonspatial model developed from national data. Septic systems and point source TP loads were significant covariates in the local models. These local data not only improved the models but enabled a more explicit interpretation of the processes affecting TP concentrations than more generic national covariates. The results suggest that SSN modeling greatly improves prediction and should be applied when using national covariates. Including local covariates further increases the accuracy of TP predictions throughout the studied watershed; such variables should be included in future national databases, particularly the locations of septic systems.

High methane emissions from a midlatitude reservoir draining an agricultural watershed.

Reservoirs are a globally significant source of methane (CH4), although most measurements have been made in tropical and boreal systems draining undeveloped watersheds. To assess the magnitude of CH4 emissions from reservoirs in midlatitude agricultural regions, we measured CH4 and carbon dioxide (CO2) emission rates from William H. Harsha Lake (Ohio, U.S.A.), an agricultural impacted reservoir, over a 13 month period. The reservoir was a strong source of CH4 throughout the year, emitting on average 176 ± 36 mg C m(-2) d(-1), the highest reservoir CH4 emissions profile documented in the United States to date. Contrary to our initial hypothesis, the largest CH4 emissions were during summer stratified conditions, not during fall turnover. The river-reservoir transition zone emitted CH4 at rates an order of magnitude higher than the rest of the reservoir, and total carbon emissions (i.e., CH4 + CO2) were also greater at the transition zone, indicating that the river delta supported greater carbon mineralization rates than elsewhere. Midlatitude agricultural impacted reservoirs may be a larger source of CH4 to the atmosphere than currently recognized, particularly if river deltas are consistent CH4 hot spots. We estimate that CH4 emissions from agricultural reservoirs could be a significant component of anthropogenic CH4 emissions in the U.S.A.

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.

Effectiveness of Design and Implementation Alternatives for Stormwater Control Measures Modeled at the Watershed Scale.

To evaluate the effectiveness of dispersed stormwater control measures (SCMs), it is important to consider groundwater-surface water interactions and their consequences for stream hydrologic responses relevant to channel geomorphic stability and ecology. This study aimed to evaluate the effectiveness of different SCM design scenarios and implementation alternatives on exceedance levels and volumes of streamflow at the watershed scale. For this purpose, a process-based block-connector model of Sligo Creek, an urban watershed (29 km2) in the suburbs of Washington, DC, was used to study the effects of SCM system design on the stream hydrograph. The watershed has 34% impervious area (IA), which was discretized into 14 similar-sized subwatersheds, each consisting of pervious and impervious surface areas. Each subwatershed was compartmentalized with the representative overland flow, unsaturated flow, groundwater blocks, and links to main channel segments. The model was calibrated and validated to existing conditions using a 3-year time series of observed flow data. Afterward, a predevelopment simulation was configured. Three SCM unit designs and IA diversions through the SCM retrofit system were simulated. The three unit design scenarios represented a simple pond with surface storage and overflow or SCMs that infiltrate with an engineered soil layer and with or without an underdrain pipe. Differences among the model simulations were evaluated using flow exceedance probability curves. The area of the SCM system was modeled as 5% of the IA retrofit. Three implementation levels, including 10%, 50%, and 90% of the IA diverted through SCMs, were considered for each SCM unit design. The results showed that at least a 50% retrofit of runoff from IA watershedwide would be needed to achieve similar predevelopment base flows and peak flows. Intermediate flows could not be matched but were closest for the infiltration with the underdrain pipe design scenario. It was also found that concentrating the SCMs in the lower portion of the watershed resulted in more effectively achieving the predeveloped exceedance curves than uniform SCM implementation. The results are relevant to planning-level decisions that depend on effectiveness predictions of different SCM unit designs and implementation alternatives in developed watersheds.

Nutrient Removal Potential of Headwater Wetlands in Coastal Plains of Alabama, USA.

Headwater streams drain over 70% of the land in the United States with headwater wetlands covering 6.59 million hectares. These ecosystems are important landscape features in the southeast United States, with underlying effects on ecosystem health, water yield, nutrient cycling, biodiversity, and water quality. However, little is known about the relationship between headwater wetlands' nutrient function (i.e., nutrient load removal (RL) and removal efficiency (ER)) and their physical characteristics. Here, we investigate this relationship for 44 headwater wetlands located within the Upper Fish River watershed (UFRW) in coastal Alabama. To accomplish this objective, we apply the process-based watershed model SWAT (Soil and Water Assessment Tool) to generate flow and nutrient loadings to each study wetland and subsequently quantify the wetland-level nutrient removal efficiencies using the process-based wetland model WetQual. Results show that the calculated removal efficiencies of the headwater wetlands in the UFRW are 75-84% and 27-35% for nitrate (NO3-) and phosphate (PO4+), respectively. The calculated nutrient load removals are highly correlated with the input loads, and the estimated PO4+ ER shows a significant decreasing trend with increased input loadings. The relationship between NO3-ER and wetland physical characteristics such as area, volume, and residence time is statistically insignificant (p > 0.05), while for PO4+, the correlation is positive and statistically significant (p < 0.05). On the other hand, flashiness (flow pulsing) and baseflow index (fraction of inflow that is coming from baseflow) have a strong effect on NO3- removal but not on PO4+ removal. Modeling results and statistical analysis point toward denitrification and plant uptake as major NO3- removal mechanisms, whereas plant uptake, diffusion, and settling of sediment-bound P were the main mechanisms for PO4+ removal. Additionally, the computed nutrient ER is higher during the driest year of the simulated period compared to during the wettest year. Our findings are in line with global-level studies and offer new insights into wetland physical characteristics affecting nutrient removal efficiency and the importance of headwater wetlands in mitigating water quality deterioration in coastal areas. The regression relationships for NO3- and PO4+ load removals in the selected 44 wetlands are then used to extrapolate nutrient load removals to 348 unmodeled non-riverine and non-riparian wetlands in the UFRW (41% of UFRW drains to them). Results show that these wetlands remove 51-61% of the NO3- and 5-10% of the PO4+ loading they receive from their respective drainage areas. Due to geographical proximity and physiographic similarity, these results can be scaled up to the coastal plains of Alabama and Northwest Florida.

Understanding the effects of phosphorus on diatom richness in rivers and streams using taxon-environment relationships.

Changes in phosphorus concentrations affect periphytic diatom composition in streams, yet we rarely observe strong relationships between diatom richness and phosphorus. In contrast, changes in conductivity are strongly associated with differences in both diatom composition and richness. We hypothesised that we could better understand the mechanisms that control the phosphorus-richness relationship by examining relationships between phosphorus and the occurrence of individual diatom taxa, comparing these with relationships between conductivity and taxon occurrence, and documenting how niche breadths of taxa affect richness patterns. We estimated relationships between phosphorus and taxon occurrence using DNA metabarcoding data of diatoms collected from 1,811 sites distributed across the conterminous U.S.A. and contrasted patterns in these relationships with those between conductivity and taxon occurrence. The distribution of taxon optima for phosphorus was bimodal, with most optima located at either the maximum or minimum observed phosphorus concentration. The distribution of taxon optima for conductivity was unimodal. Niche breadths of taxa for phosphorus and for conductivity both generally increased with optimum values. The distribution of conductivity optima gave rise to a prominent hump-shaped relationship between richness and conductivity. The relationship between richness and phosphorus was also slightly hump-shaped, but this relationship would not be expected from the bimodal distribution of optima. Instead, we determined that broad niche breadths caused the hump-shaped relationship between richness and phosphorus. Our results highlight the nuanced effects that increased P loadings exert on diatom assemblages in rivers and streams and identify reasons that weak relationships between taxon richness and increased phosphorus have been observed. These findings allow us to better describe how excess phosphorus and subsets of taxa and their niche breadths contribute to patterns of taxa richness in diatom assemblages, and to improve the tools used to manage phosphorus pollution.

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.

Quantifying spatial and temporal relationships between diatoms and nutrients in streams strengthens evidence of nutrient effects from monitoring data.

Observational data are frequently used to better understand the effects of changes in P and N on stream biota, but nutrient gradients in streams are usually associated with gradients in other environmental factors, a phenomenon that complicates efforts to accurately estimate the effects of nutrients. Here, we propose a new approach for analyzing observational data in which we compare the effects of changes in nutrient concentrations in time within individual sites and in space among many sites. Covarying relationships between other, potentially confounding environmental factors and nutrient concentrations are unlikely to be the same in both time and space, and, therefore, estimated effects of nutrients that are similar in time and space are more likely to be accurate. We applied this approach to diatom rbcL metabarcoding data collected from streams in the East Fork of the Little Miami River watershed, Ohio, USA. Changes in diatom assemblage composition were consistently associated with changes in the concentration of total reactive P in both time and space. In contrast, despite being associated with spatial differences in ammonia and urea concentrations, diatom assemblage composition was not associated with temporal changes in these nitrogen species. We suggest that the results of this analysis provide evidence of a causal effect of increased P on diatom assemblage composition. We further analyzed the effects of temporal variability in measurements of total reactive P and found that averaging periods greater than ~1 wk prior to sampling best represented the effects of P on the diatom assemblage. Comparisons of biological responses in space and time can sharpen insights beyond those that are based on analyses conducted on only 1 of the 2 dimensions.

Developing Indicators of Nutrient Pollution in Streams Using 16S rRNA Gene Metabarcoding of Periphyton-Associated Bacteria.

Indicators based on nutrient-biota relationships in streams can inform water quality restoration and protection programs. Bacterial assemblages could be particularly useful indicators of nutrient effects because they are species-rich, important contributors to ecosystem processes in streams, and responsive to rapidly changing conditions. Here, we sampled 25 streams weekly (12-14 times each) and used 16S rRNA gene metabarcoding of periphyton-associated bacteria to quantify the effects of total phosphorus (TP) and total nitrogen (TN). Threshold indicator taxa analysis identified assemblage-level changes and amplicon sequence variants (ASVs) that increased or decreased with increasing TP and TN concentrations (i.e., low P, high P, low N, and high N ASVs). Boosted regression trees confirmed that relative abundances of gene sequence reads for these four indicator groups were associated with nutrient concentrations. Gradient forest analysis complemented these results by using multiple predictors and random forest models for each ASV to identify portions of TP and TN gradients at which the greatest changes in assemblage structure occurred. Synthesized statistical results showed bacterial assemblage structure began changing at 24 µg TP/L with the greatest changes occurring from 110 to 195 µg/L. Changes in the bacterial assemblages associated with TN gradually occurred from 275 to 855 µg/L. Taxonomic and phylogenetic analyses showed that low nutrient ASVs were commonly Firmicutes, Verrucomicrobiota, Flavobacteriales, and Caulobacterales, Pseudomonadales, and Rhodobacterales of Proteobacteria, whereas other groups, such as Chitinophagales of Bacteroidota, and Burkholderiales, Rhizobiales, Sphingomonadales, and Steroidobacterales of Proteobacteria comprised the high nutrient ASVs. Overall, the responses of bacterial ASV indicators in this study highlight the utility of metabarcoding periphyton-associated bacteria for quantifying biotic responses to nutrient inputs in streams.