Using SWAT, Bacteroidales microbial source tracking markers, and fecal indicator bacteria to predict waterborne pathogen occurrence in an agricultural watershed.

Developing the capability to predict pathogens in surface water is important for reducing the risk that such organisms pose to human health. In this study, three primary data source scenarios (measured stream flow and water quality, modelled stream flow and water quality, and host-associated Bacteroidales) are investigated within a Classification and Regression Tree Analysis (CART) framework for classifying pathogen (Escherichia coli 0157:H7, Salmonella, Campylobacter, Cryptosporidium, and Giardia) presence and absence (P/A) for a 178 km(2) agricultural watershed. To provide modelled data, a Soil Water Assessment Tool (SWAT) model was developed to predict stream flow, total suspended solids (TSS), total N and total P, and fecal indicator bacteria loads; however, the model was only successful for flow and total N and total P simulations, and did not accurately simulate TSS and indicator bacteria transport. Also, the SWAT model was not sensitive to an observed reduction in the cattle population within the watershed that may have resulted in significant reduction in E. coli concentrations and Salmonella detections. Results show that when combined with air temperature and precipitation, SWAT modelled stream flow and total P concentrations were useful for classifying pathogen P/A using CART methodology. From a suite of host-associated Bacteroidales markers used as independent variables in CART analysis, the ruminant marker was found to be the best initial classifier of pathogen P/A. Of the measured sources of independent variables, air temperature, precipitation, stream flow, and total P were found to be the most important variables for classifying pathogen P/A. Results indicate a close relationship between cattle pollution and pathogen occurrence in this watershed, and an especially strong link between the cattle population and Salmonella detections. Crown