Maxent estimation of aquatic

Area of Science:

• Environmental Science
• Water Quality Management
• Ecological Modeling

Background:

• Pathogen contamination is a primary cause of surface water impairment in US rivers.
• Current methods for assessing fecal pollution exposure need improvement, particularly regarding fate and transport.
• Ecological modeling offers novel approaches to enhance watershed decision-making and exposure assessments.

Purpose of the Study:

• To identify environmental factors associated with surface water impairment by fecal indicator bacteria, specifically Escherichia coli (E. coli).
• To develop and validate predictive models for E. coli impairment using water quality parameters.
• To inform watershed management strategies for reducing human health risks from fecal pollution.

Main Methods:

• Utilized the Maxent ecological model and E. coli as a fecal indicator.
• Collected water samples over 8 years across different seasons on Sinking Creek, Tennessee.
• Analyzed 10 water quality parameters and E. coli concentrations, employing univariate and multivariate modeling with sensitivity analysis.

Main Results:

• Water temperature and alkalinity were significant factors in univariate models for E. coli impairment.
• Specific conductance, water temperature, dissolved oxygen, discharge, and NO3 were identified as sensitive variables in multivariate models.
• Optimized multivariate models, including a 4-variable model excluding dissolved oxygen, improved prediction accuracy for E. coli impairment.

Conclusions:

• E. coli impairment in Sinking Creek is linked to seasonality and agricultural runoff, necessitating continuous monitoring.
• Discharge plays a crucial role, interacting with other factors to influence E. coli levels.
• Integrating ecological modeling enhances the utility of fecal indicators for understanding pollution sources, fate, and transport, aiding resource prioritization.