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Nitrous oxide (N 2 O) is a potent greenhouse gas that contributes to climate change and stratospheric ozone destruction. Anthropogenic nitrogen (N) loading to river networks is a potentially important source of N 2 O via microbial denitrification that converts N to N 2 O and dinitrogen (N 2 ). The fraction of denitrified N that escapes as N 2 O rather than N 2 (i.e., the N 2 O yield) is an important determinant of how much N 2 O is produced by river networks, but little is known about the N 2 O yield in flowing waters. Here, we present the results of whole-stream 15 N-tracer additions conducted in 72 headwater streams draining multiple land-use types across the United States. We found that stream denitrification produces N 2 O at rates that increase with stream water nitrate (NO 3 − ) concentrations, but that <1% of denitrified N is converted to N 2 O. Unlike some previous studies, we found no relationship between the N 2 O yield and stream water NO 3 − . We suggest that increased stream NO 3 − loading stimulates denitrification and concomitant N 2 O production, but does not increase the N 2 O yield. In our study, most streams were sources of N 2 O to the atmosphere and the highest emission rates were observed in streams draining urban basins. Using a global river network model, we estimate that microbial N transformations (e.g., denitrification and nitrification) convert at least 0.68 Tg·y −1 of anthropogenic N inputs to N 2 O in river networks, equivalent to 10% of the global anthropogenic N 2 O emission rate. This estimate of stream and river N 2 O emissions is three times greater than estimated by the Intergovernmental Panel on Climate Change.H umans have more than doubled the availability of fixed nitrogen (N) in the biosphere, particularly through the production of N fertilizers and the cultivation of N-fixing crops (1). Increasing N availability is producing unintended environmental consequences including enhanced emissions of nitrous oxide (N 2 O), a potent greenhouse gas (2) and an important cause of stratospheric ozone destruction (3). The Intergovernmental Panel on Climate Change (IPCC) estimates that the microbial conversion of agriculturally derived N to N 2 O in soils and aquatic ecosystems is the largest source of anthropogenic N 2 O to the atmosphere (2). The production of N 2 O in agricultural soils has been the focus of intense investigation (i.e., >1,000 published studies) and is a relatively well constrained component of the N 2 O budget (4). However, emissions of anthropogenic N 2 O from streams, rivers, and estuaries have received much less attention and remain a major source of uncertainty in the global anthropogenic N 2 O budget.Microbial denitrification is a large source of N 2 O emissions in terrestrial and aquatic ecosystems. Most microbial denitrification is a form of anaerobic respiration in which nitrate (NO 3 − , the dominant form of inorganic N) is converted to dinitrogen (N 2 ) and N 2 O gases (5). The proportion of denitrified NO 3 − that is converted to N 2 O rather than N 2 (h...

1. Rates of whole-system metabolism (production and respiration) are fundamental indicators of ecosystem structure and function. Although first-order, proximal controls are well understood, assessments of the interactions between proximal controls and distal controls, such as land use and geographic region, are lacking. Thus, the influence of land use on stream metabolism across geographic regions is unknown. Further, there is limited understanding of how land use may alter variability in ecosystem metabolism across regions. 2. Stream metabolism was measured in nine streams in each of eight regions (n = 72) across the United States and Puerto Rico. In each region, three streams were selected from a range of three land uses: agriculturally influenced, urban-influenced, and reference streams. Stream metabolism was estimated from diel changes in dissolved oxygen concentrations in each stream reach with correction for reaeration and groundwater input. . In contrast, ecosystem respiration (ER) varied both within and among regions. Reference streams had significantly lower rates of GPP than urban or agriculturally influenced streams. 4. GPP was positively correlated with photosynthetically active radiation and autotrophic biomass. Multiple regression models compared using Akaike's information criterion (AIC) indicated GPP increased with water column ammonium and the fraction of the catchment in urban and reference land-use categories. Multiple regression models also identified velocity, temperature, nitrate, ammonium, dissolved organic carbon, GPP, coarse benthic organic matter, fine benthic organic matter and the fraction of all land-use categories in the catchment as regulators of ER. 5. Structural equation modelling indicated significant distal as well as proximal control pathways including a direct effect of land-use on GPP as well as SRP, DIN, and PAR effects on GPP; GPP effects on autotrophic biomass, organic matter, and ER; and organic matter effects on ER. 6. Overall, consideration of the data separated by land-use categories showed reduced inter-regional variability in rates of metabolism, indicating that the influence of agricultural and urban land use can obscure regional differences in stream metabolism.

We measured uptake length of 15 NO { 3 in 72 streams in eight regions across the United States and Puerto Rico to develop quantitative predictive models on controls of NO { 3 uptake length. As part of the Lotic Intersite Nitrogen eXperiment II project, we chose nine streams in each region corresponding to natural (reference), suburban-urban, and agricultural land uses. Study streams spanned a range of human land use to maximize variation in NO { 3 concentration, geomorphology, and metabolism. We tested a causal model predicting controls on NO { 3 uptake length using structural equation modeling. The model included concomitant measurements of ecosystem metabolism, hydraulic parameters, and nitrogen concentration. We compared this structural equation model to multiple regression models which included additional biotic, catchment, and riparian variables. The structural equation model explained 79% of the variation in log uptake length (S Wtot ). Uptake length increased with specific discharge (Q/w) and increasing NO