Modeling Fecal Indicator Bacteria and Antibiotic Resistance in Diverse Aquatic Environments

The detrimental influence of humans on the environment is of increasing concern. Humans, their livestock, and their pets have caused fecal contamination of waterways throughout the United States. Understanding the sources of fecal indicator bacteria (FIB) and the environmental processes that affect them can be crucial to reducing the number of impaired streams and limiting the negative impacts on the environment. Antibiotic resistance is an emerging issue facing human health in the United States and across the world. Antibiotic resistant bacteria (ARB) have antibiotic resistance genes (ARGs) that prevent antibiotics from killing them. Limited research has been done on the role of the environment in the propagation of antibiotic resistance. As the use of antibiotics increases, it is critical to examine how this impacts human health through the environment. Models of watersheds in Patillas, Puerto Rico and Christiansburg, Virginia were created using the Soil and Water Assessment Tool (SWAT) to compare how the differences in spatial and temporal sampling of FIB, climate, and population affect FIB movement. The performances of the calibrated bacteria models were comparable to other published studies. A primary challenge faced in this study was the use of grab samples taken months apart as monthly averages of FIB. The high precipitation and constant warm climate made the model for Patillas more difficult to fit because of the high variability in the observed data. While the Patillas watershed had a lower population of people and livestock, the Christiansburg watershed had more available data on wildlife. The lack of spatial variance of data and the use of data from 1993-2018, hindered the ability for the model for Patillas to model FIB. Additionally, the model's performance was limited due to the strong hurricanes that affect land use, soils, and populations of humans and animals in the watershed. Using open-source data needs to be explored further as a faster and more cost-effective way of developing SWAT FIB models. The feasibility to use data collected in the Christiansburg and Patillas watershed to calibrate a SWAT-ARB model was determined based on available ARG data. The results indicate that the bacteria models need to be improved before an effective SWAT-ARB model can be calibrated. One limitation in the available ARG data for the two watersheds was that they were only sampled once. Out of the ARGs sampled, sul1 was the best modeled in both watersheds because it has the highest normalized values and correlated with the amount of developed land.