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Nitrogen is a critical component of the economy, food security, and planetary health. Many of the world's sustainability targets hinge on global nitrogen solutions, which, in turn, contribute lasting benefits for: (i) world hunger; (ii) soil, air and water quality; (iii) climate change mitigation; and (iv) biodiversity conservation. Balancing the projected rise in agricultural nitrogen demands while achieving these 21st century ideals will require policies to coordinate solutions among technologies, consumer choice, and socioeconomic transformation.

Nitrogen is a critical component of the economy, food security, and planetary health. Many of the world's sustainability targets hinge on global nitrogen solutions, which, in turn, contribute lasting benefits for: (i) world hunger; (ii) soil, air and water quality; (iii) climate change mitigation; and (iv) biodiversity conservation. Balancing the projected rise in agricultural nitrogen demands while achieving these 21st century ideals will require policies to coordinate solutions among technologies, consumer choice, and socioeconomic transformation.

Excess nutrient pollution contributes to the formation of harmful algal blooms (HABs) that compromise fisheries and recreation and that can directly endanger human and animal health via cyanotoxins. Efforts to quantify the occurrence, drivers, and severity of HABs across large areas is difficult due to the resource intensive nature of field monitoring of lake nutrient and chlorophyll‐a concentrations. To better characterize how nutrients interact with other environmental factors to produce algal blooms in freshwater systems, we used spatially explicit and temporally matched climate, landscape, in‐lake characteristic, and nutrient inventory data sets to predict nutrients and chlorophyll‐a across the conterminous US (CONUS). Using a nested modeling approach, three random forest (RF) models were trained to explain the spatiotemporal variation in total nitrogen (TN), total phosphorus (TP), and chlorophyll‐a concentrations across US EPA's National Lakes Assessment (n = 2,062). Concentrations of TN and TP were the most important predictors and, with other variables, the RF model accounted for 68% of variation in chlorophyll‐a. We then used these RF models to extrapolate lake TN and TP predictions to lakes without nutrient observations and predict chlorophyll‐a for ∼112,000 lakes across the CONUS. Risk for high chlorophyll‐a concentrations is highest in the agriculturally dominated Midwest, but other areas of risk emerge in nutrient pollution hot spots across the country. These catchment and lake‐specific results can help managers identify potential nutrient pollution and chlorophyll‐a hot spots that may fuel blooms, prioritize at‐risk lakes for additional monitoring, and optimize management to protect human health and other environmental end goals. Plain Language Summary: When lakes receive large amounts of nutrients from the surrounding landscape due to fertilizer runoff or other sources of nutrient pollution, they can develop algal blooms. Algal blooms are harmful to the lake ecosystem and sometimes produce toxins which are dangerous to humans and animals. To assess this issue, lake chlorophyll‐a, a measure of algal presence, is monitored. This monitoring is limited in reach due to the expense of in‐lake sampling and the limited resolution of satellite technology. However, there is a wealth of climate, nutrient, landscape, and in‐lake characteristic data for the conterminous US (CONUS) which explains much of what contributes to nutrient pollution and algal growth. Here, we use this data in a machine learning model to predict nutrient (total nitrogen and total phosphorus) and chlorophyll‐a concentrations in about 112,000 lakes in the CONUS. We found that high chlorophyll‐a concentrations are more likely in the Midwest where agriculture is prevalent, but other areas with high lake chlorophyll‐a concentrations are present across the CONUS in nutrient pollution hot spots. These predictions of lake nutrient and chlorophyll‐a concentrations can help managers identify areas of concern, prioritize at‐risk lakes for testing, and target management to protect human health and the environment. Key Points: Chlorophyll‐a and nutrient concentrations in ∼112,000 lakes were predicted using widely available national data sets and machine learningNutrients far outweigh other environmental predictors in driving chlorophyll‐a concentrations in lakesWith more information about likely chlorophyll‐a concentrations, managers can prioritize lakes at risk for harmful algal bloom production [ABSTRACT FROM AUTHOR]

Eutrophication, harmful algal blooms, and human health impacts are critical environmental challenges resulting from excess nitrogen and phosphorus in surface waters. Yet we have limited information regarding how wetland characteristics mediate water quality across watershed scales. We developed a large, novel set of spatial variables characterizing hydrological flowpaths from wetlands to streams, that is, "wetland hydrological transport variables," to explore how wetlands statistically explain the variability in total nitrogen (TN) and total phosphorus (TP) concentrations across the Upper Mississippi River Basin (UMRB) in the United States. We found that wetland flowpath variables improved landscape‐to‐aquatic nutrient multilinear regression models (from R2 = 0.89 to 0.91 for TN; R2 = 0.53 to 0.84 for TP) and provided insights into potential processes governing how wetlands influence watershed‐scale TN and TP concentrations. Specifically, flowpath variables describing flow‐attenuating environments, for example, subsurface transport compared to overland flowpaths, were related to lower TN and TP concentrations. Frequent hydrological connections from wetlands to streams were also linked to low TP concentrations, which likely suggests a nutrient source limitation in some areas of the UMRB. Consideration of wetland flowpaths could inform management and conservation activities designed to reduce nutrient export to downstream waters. [ABSTRACT FROM AUTHOR]

Abstract: Salmon carcass addition to streams is expected to increase stream productivity at multiple trophic levels. This study examined stream nutrient (nitrogen, phosphorus, and carbon), epilithic biofilm (ash-free dry mass [...]

Accelerated nitrogen (N) inputs can drive nonlinear changes in N cycling, retention, and loss in forest ecosystems. Nitrogen processing in soils is critical to understanding these changes, since soils typically are the largest N sink in forests. To elucidate soil mechanisms that underlie shifts in N cycling across a wide gradient of N supply, we added [sup.15]N[H.sub.4][sup.15]N[O.sub.3] at nine treatment levels ranging in geometric sequence from 0.2 kg to 640 kg N x [ha.sup.-1] x [yr.sup.-1] to an unpolluted old-growth temperate forest in southern Chile. We recovered roughly half of [sup.15]N tracers in 0-25 cm of soil, primarily in the surface 10 cm. Low to moderate rates of N supply failed to stimulate N leaching, which suggests that most unrecovered [sup.15]N was transferred from soils to unmeasured sinks above ground. However, soil solution losses of nitrate increased sharply at inputs > 160 kg N x [ha.sup.-1] x [yr.sup.-1], corresponding to a threshold of elevated soil N availability and declining [sup.15]N retention in soil. Soil organic matter ( Contrary to expectations, N additions did not stimulate gross N cycling, potential nitrification, or ammonium oxidizer populations. Our results indicate that the nonlinearity in N retention and loss resulted directly from excessive N supply relative to sinks, independent of plant-soil-microbial feedbacks. However, N additions did induce a sharp decrease in microbial biomass C:N that is predicted by N saturation theory, and which could increase long-term N storage in soil organic matter by lowering the critical C:N ratio for net N mineralization. All measured sinks accumulated [sup.15]N tracers across the full gradient of N supply, suggesting that short-term nonlinearity in N retention resulted from saturation of uptake kinetics, not uptake capacity, in plant, soil, and microbial pools. Key words: ammonium nitrate, dissolved organic nitrogen; nitrification; nitrogen fertilizer; nitrogen saturation: [sup.15]N stable isotope; nutrient budget; old-growth temperate forest; roots; soil organic matter.

Understanding soil organic C (SOC) spatial variability is critical when developing C budgets, explaining the cause and effects of climate change, and for basic ecosystem characterization. We investigated delineations of four soil series to elucidate the factors that affect the size, distribution, and variability of SOC pools from horizon to landscape scales. These soils, classified as Udipsamments, Dystrudepts, Endoaquepts, and Haplosaprists, were sampled along random transects to a depth of 1 m. In very poorly and poorly drained soils 75 and 45% of total SOC was round below 30 cm, respectively. In contrast, only 30% of the total SOC could be accounted for below 30 cm in the well and excessively drained soils. Soils formed in outwash and young alluvium sequestered a greater portion of total SOC within the subsoil, while soils formed in loess held approximately 70% of the SOC within O and A horizons. Total SOC contents among the four soil types differed significantly (p < 0.001), with the wetter soils having greater accumulations of C. Soil C pools ranged from 110 Mg C [ha.sup.-1] in the excessively drained Psamments (double the mean national value) to 586 Mg C [ha.sup.-1] in the very poorly drained Saprists (30-60% lower than the mean national value). The two-fold differences between our data and the national averages support the need for regional assessments of soil C pools. Based on the coefficient of variation (CV) values, there appears to be nearly as much variability in the SOC pool within a delineation (CVs ranged 9 to 30%) as among delineations (CVs ranged from 15 to 31%) for the same soil type. Since significant differences were found for total SOC among delineations of the same soil type, we concluded that sampling from a significant number of delineations of the same series will provide a more accurate representation of SOC for scaling to the landscape or region than sampling at multiple locations within a single representative delineation.