Effects of concentrated poultry operations and cropland manure application on antibiotic resistant Escherichia coli and nutrient pollution in Chesapeake Bay watersheds.

Manure from poultry operations is typically applied to nearby cropland and may affect nutrient loading and the spread of antibiotic resistance (ABR). We analyzed the concentrations of nitrogen and phosphorus and the occurrence of ABR in Escherichia coli (E. coli) and extra-intestinal pathogenic E. coli isolates from streams draining 15 small (<19 km2) watersheds of the Chesapeake Bay with contrasting levels of concentrated poultry operations. Total nitrogen and nitrate plus nitrite concentrations increased with poultry barn density with concentrations two and three times higher, respectively, in watersheds with the highest poultry barn densities compared to those without poultry barns. Analysis of N and O isotopes in nitrate by mass spectrometry showed an increase in the proportion of 15N associated with an increase in barn density, suggesting that the nitrate associated with poultry barns originated from manure. Phosphorus concentrations were not correlated with barn density. Antibiotic susceptibility testing of putative E. coli isolates was conducted using the disk diffusion method for twelve clinically important antibiotics. Of the isolates tested, most were completely susceptible (67%); 33% were resistant to at least one antibiotic, 24% were resistant to ampicillin, 13% were resistant to cefazolin, and 8% were multi-drug resistant. Resistance to three cephalosporin drugs was positively associated with an index of manure exposure estimated from poultry barn density and proportion of cropland in a watershed. The proportion of E. coli isolates resistant to cefoxitin, cefazolin, and ceftriaxone, broad-spectrum antibiotics important in human medicine, increased by 18.9%, 16.9%, and 6.2%, respectively, at the highest estimated level of manure exposure compared to watersheds without manure exposure. Our results suggest that comparisons of small watersheds could be used to identify geographic areas where remedial actions may be needed to reduce nutrient pollution and the public health risks of ABR bacteria.

Using Bayesian hierarchical models to better understand nitrate sources and sinks in agricultural watersheds.

Export coefficient models (ECMs) are often used to predict nutrient sources and sinks in watersheds because ECMs can flexibly incorporate processes and have minimal data requirements. However, ECMs do not quantify uncertainties in model structure, parameters, or predictions; nor do they account for spatial and temporal variability in land characteristics, weather, and management practices. We applied Bayesian hierarchical methods to address these problems in ECMs used to predict nitrate concentration in streams. We compared four model formulations, a basic ECM and three models with additional terms to represent competing hypotheses about the sources of error in ECMs and about spatial and temporal variability of coefficients: an ADditive Error Model (ADEM), a SpatioTemporal Parameter Model (STPM), and a Dynamic Parameter Model (DPM). The DPM incorporates a first-order random walk to represent spatial correlation among parameters and a dynamic linear model to accommodate temporal correlation. We tested the modeling approach in a proof of concept using watershed characteristics and nitrate export measurements from watersheds in the Coastal Plain physiographic province of the Chesapeake Bay drainage. Among the four models, the DPM was the best--it had the lowest mean error, explained the most variability (R2 = 0.99), had the narrowest prediction intervals, and provided the most effective tradeoff between fit complexity (its deviance information criterion, DIC, was 45.6 units lower than any other model, indicating overwhelming support for the DPM). The superiority of the DPM supports its underlying hypothesis that the main source of error in ECMs is their failure to account for parameter variability rather than structural error. Analysis of the fitted DPM coefficients for cropland export and instream retention revealed some of the factors controlling nitrate concentration: cropland nitrate exports were positively related to stream flow and watershed average slope, while instream nitrate retention was positively correlated with nitrate concentration. By quantifying spatial and temporal variability in sources and sinks, the DPM provides new information to better target management actions to the most effective times and places. Given the wide use of ECMs as research and management tools, our approach can be broadly applied in other watersheds and to other materials.

Effects of riparian buffers on nitrate concentrations in watershed discharges: new models and management implications.

Watershed analyses of nutrient removal in riparian buffers have been limited by the geographic methods used to map buffers and by the statistical models used to test and quantify buffer effects on stream nutrient levels. We combined geographic methods that account for buffer prevalence along flow paths connecting croplands to streams with improved statistical models to test for buffer effects on stream nitrate concentrations from 321 tributary watersheds to the Chesapeake Bay, USA. We developed statistical models that predict stream nitrate concentration from watershed land cover and physiographic province. We used information theoretic methods (AIC(c)) to compare models with and without buffer terms, and we demonstrate that models accounting for riparian buffers better explain stream nitrate concentrations than models using only land cover proportions. We analyzed the buffer model parameters to quantify differences within and among physiographic provinces in the potentials for nitrate loss from croplands and nitrate removal in buffers. On average, buffers in Coastal Plain study watersheds had a higher relative nitrate removal potential (95% of the inputs from cropland) than Piedmont buffers (35% of inputs). Buffers in Appalachian Mountain study watersheds were intermediate (retaining 39% of cropland inputs), but that percentage was uncertain. The absolute potential to reduce nitrate concentration was highest in the Piedmont study watersheds because of higher nitrate inputs from cropland. Model predictions for the study watersheds provided estimates of nitrate removals achieved with the existing cropland and buffer distributions. Compared to expected nitrate concentrations if buffers were removed, current buffers reduced average nitrate concentrations by 0.73 mg N/L (50% of their inputs from cropland) in the Coastal Plain study watersheds, 0.40 mg N/L (11%) in the Piedmont, and 0.08 mg N/L (5%) in the Appalachian Mountains. Restoration to close all buffer gaps downhill from croplands would further reduce nitrate concentrations by 0.66 mg N/L, 0.83 mg N/L, and 0.51 mg N/L, respectively, in the Coastal Plain, Piedmont, and Appalachian Mountain study watersheds. Aggregate nitrate removal by riparian buffers was less than suggested by many studies of field-to-stream transects, but buffer nitrate removal is significant, and restoration could achieve substantial additional removal.

Empirical models based on the universal soil loss equation fail to predict sediment discharges from Chesapeake Bay catchments.

The Universal Soil Loss Equation (USLE) and its derivatives are widely used for identifying watersheds with a high potential for degrading stream water quality. We compared sediment yields estimated from regional application of the USLE, the automated revised RUSLE2, and five sediment delivery ratio algorithms to measured annual average sediment delivery in 78 catchments of the Chesapeake Bay watershed. We did the same comparisons for another 23 catchments monitored by the USGS. Predictions exceeded observed sediment yields by more than 100% and were highly correlated with USLE erosion predictions (Pearson r range, 0.73-0.92; p < 0.001). RUSLE2-erosion estimates were highly correlated with USLE estimates (r = 0.87; p < 001), so the method of implementing the USLE model did not change the results. In ranked comparisons between observed and predicted sediment yields, the models failed to identify catchments with higher yields (r range, -0.28-0.00; p > 0.14). In a multiple regression analysis, soil erodibility, log (stream flow), basin shape (topographic relief ratio), the square-root transformed proportion of forest, and occurrence in the Appalachian Plateau province explained 55% of the observed variance in measured suspended sediment loads, but the model performed poorly (r(2) = 0.06) at predicting loads in the 23 USGS watersheds not used in fitting the model. The use of USLE or multiple regression models to predict sediment yields is not advisable despite their present widespread application. Integrated watershed models based on the USLE may also be unsuitable for making management decisions.

Nutrient and sediment removal by a restored wetland receiving agricultural runoff.

Few studies have measured removal of pollutants by restored wetlands that receive highly variable inflows. We used automated flow-proportional sampling to monitor the removal of nutrients and suspended solids by a 1.3-ha restored wetland receiving unregulated inflows from a 14-ha agricultural watershed in Maryland, USA. Water entered the wetland mainly in brief pulses of runoff, which sometimes exceeded the 2500-m3 water holding capacity of the wetland. Half of the total water inflow occurred in only 24 days scattered throughout the two-year study. Measured annual water gains were within 5% of balancing water losses. Annual removal of nutrients differed greatly between the two years of the study. The most removal occurred in the first year, which included a three-month period of decreasing water level in the wetland. In that year, the wetland removed 59% of the total P, 38% of the total N, and 41% of the total organic C it received. However, in the second year, which lacked a drying period, there was no significant (p > 0.05) net removal of total N or P, although 30% of the total organic C input was removed. For the entire two-year period, the wetland removed 25% of the ammonium, 52% of the nitrate, and 34% of the organic C it received, but there was no significant net removal of total suspended solids (TSS) or other forms of N and P. Although the variability of inflow may have decreased the capacity of the wetland to remove materials, the wetland still reduced nonpoint-source pollution.