Increasing global reliance on stormwater control measures to reduce discharge to surface water, increase groundwater recharge, and minimize contaminant delivery to receiving waterbodies necessitates improved understanding of stormwater–contaminant profiles. A multiagency study of organic and inorganic chemicals in urban stormwater from 50 runoff events at 21 sites across the United States demonstrated that stormwater transports substantial mixtures of polycyclic aromatic hydrocarbons, bioactive contaminants (pesticides and pharmaceuticals), and other organic chemicals known or suspected to pose environmental health concern. Numerous organic-chemical detections per site (median number of chemicals detected = 73), individual concentrations exceeding 10 000 ng/L, and cumulative concentrations up to 263 000 ng/L suggested concern for potential environmental effects during runoff events. Organic concentrations, loads, and yields were positively correlated with impervious surfaces and highly developed urban catchments. Episodic storm-event organic concentrations and loads were comparable to and often exceeded those of daily wastewater plant discharges. Inorganic chemical concentrations were generally dilute in concentration and did not exceed chronic aquatic life criteria. Methylmercury was measured in 90% of samples with concentrations that ranged from 0.05 to 1.0 ng/L.

Watershed-scale nonpoint source (NPS) pollution models have become important tools to understand, evaluate, and predict the negative impacts of NPS pollution on water quality. Today, there are many NPS models available for users. However, different types of models possess different form and structure as well as complexity of computation. It is difficult for users to select an appropriate model for a specific application without a clear understanding of the limitations or strengths for each model or tool. This review evaluates 14 more commonly used watershed-scale NPS pollution models to explain how and when the application of these different models are appropriate for a given effort. The models that are assessed have a wide range of capacities that include simple models used as rapid screening tools (e.g., Long-Term Hydrologic Impact Assessment (L-THIA) and Nonpoint Source Pollution and Erosion Comparison Tool (N-SPECT/OpenNSPECT)), medium-complexity models that require detail data input and limited calibration (e.g., Generalized Watershed Loading Function (GWLF), Loading Simulation Program C (LSPC), Source Loading and Management Model (SLAMM), and Watershed Analysis Risk Management Frame (WARMF)), complex models that provide sophisticated simulation for NPS pollution processes with intensive data and rigorous calibration (e.g., Agricultural Nonpoint Source pollution model (AGNPS/AnnAGNPS), Soil and Water Assessment Tool (SWAT), Stormwater Management Model (SWMM), and Hydrologic Simulation Program Fortran (HSPF)), and modeling systems that integrate various sub-models and tools, and contain the highest complexity to solve all phases of hydrologic, hydraulic, and chemical dynamic processes (e.g., Automated Geospatial Watershed Assessment Tool (AGWA), Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) and Watershed Modeling System (WMS)). This assessment includes model intended use, components or capabilities, suitable land-use type, input parameter type, spatial and temporal scale, simulated pollutants, strengths and limitations, and software availability. Understanding the strengths and weaknesses of each watershed-scale NPS model will lead to better model selection for suitability and help to avoid misinterpretation or misapplication in practice. The article further explains the crucial criteria for model selection, including spatial and temporal considerations, calibration and validation, uncertainty analysis, and future research direction of NPS pollution models. The goal of this work is to provide accurate and concise insight for watershed managers and planners to select the best-suited model to reduce the harm of NPS pollution to watershed ecosystems.

Nutrient cycling is a key process linking organisms in ecosystems. This is especially apparent in stream environments in which nutrients are taken up readily and cycled through the system in a downstream trajectory. Ecological stoichiometry predicts that biogeochemical cycles of different elements are interdependent because the organisms that drive these cycles require fixed ratios of nutrients. There is growing recognition that animals play an important role in biogeochemical cycling across ecosystems. In particular, dense aggregations of consumers can create biogeochemical hotspots in aquatic ecosystems via nutrient translocation. We predicted that filter‐feeding freshwater mussels, which occur as speciose, high‐biomass aggregates, would create biogeochemical hotspots in streams by altering nutrient limitation and algal dynamics. In a field study, we manipulated nitrogen and phosphorus using nutrient‐diffusing substrates in areas with high and low mussel abundance, recorded algal growth and community composition, and determined in situ mussel excretion stoichiometry at 18 sites in three rivers (Kiamichi, Little, and Mountain Fork Rivers, south‐central United States). Our results indicate that mussels greatly influence ecosystem processes by modifying the nutrients that limit primary productivity. Sites without mussels were N‐limited with ∼26% higher relative abundances of N‐fixing blue‐green algae, while sites with high mussel densities were co‐limited (N and P) and dominated by diatoms. These results corroborated the results of our excretion experiments; our path analysis indicated that mussel excretion has a strong influence on stream water column N:P. Due to the high N:P of mussel excretion, strict N‐limitation was alleviated, and the system switched to being co‐limited by both N and P. This shows that translocation of nutrients by mussel aggregations is important to nutrient dynamics and algal species composition in these rivers. Our study highlights the importance of consumers and this imperiled faunal group on nutrient cycling and community dynamics in aquatic ecosystems.

Despite growing interest in ecological interactions between predators and pathogens, few studies have experimentally examined the consequences of infection for host predation risk or how environmental conditions affect this relationship. Here we combined mesocosm experiments, in situ foraging data, and broad‐scale lake surveys to evaluate (1) the effects of chytrid infection (Polycaryum laeve) on susceptibility of Daphnia to fish predators and (2) how environmental characteristics moderate the strength of this interaction. In mesocosms, bluegill preferred infected Daphnia 2–5 times over uninfected individuals. Among infected Daphnia, infection intensity was a positive predictor of predation risk, whereas carapace size and fecundity increased predation on uninfected individuals. Wild‐caught yellow perch and bluegill from in situ foraging trials exhibited strong selectivity for infected Daphnia (3–10 times over uninfected individuals). In mesocosms containing water high in dissolved organic carbon (DOC), however, selective predation on infected Daphnia was eliminated. Correspondingly, lakes that supported chytrid infections had higher DOC levels and lower light penetration. Our results emphasize the strength of interactions between parasitism and predation while highlighting the moderating influence of water color. P. laeve increases the conspicuousness and predation risk of Daphnia; as a result, infected Daphnia occur predominantly in environments with characteristics that conceal their elevated visibility.

Over the past decade an increasing number of studies have experimentally manipulated the number of species in a community and examined how this alters the aggregate production of species biomass. Many of these studies have shown that the effects of richness on biomass change through time, but we have limited understanding of the mechanisms that produce these dynamic trends. Here we report the results of an experiment in which we manipulated the richness of freshwater algae in laboratory microcosms. We used two experimental designs (additive and substitutive) that make different assumptions about how patches are initially colonized, and then tracked the development of community biomass from the point of initial colonization through a period of 6–12 generations of the focal species. We found that the effect of initial species richness on biomass production qualitatively shifted twice over the course of the experiment. The first shift occurred as species transitioned from density‐independent to dependent phases of population growth. At this time, intraspecific competition caused monocultures to approach their respective carrying capacities more slowly than polycultures. As a consequence, species tended to over‐yield for a brief time, generating a positive, but transient effect of diversity on community biomass. The second shift occurred as communities approached carrying capacity. At this time, strong interspecific interactions caused biomass to be dominated by the competitively superior species in polycultures. As this species had the lowest carrying capacity, a negative effect of diversity on biomass resulted in late succession. Although these two shifts produced dynamics that appeared complex, we show that the patterns can be fit to a simple Lotka‐Volterra model of competition. Our results suggest that the effects of algal diversity on primary production change in a predictable sequence through successional time.