E. coli surveillance and microbial source tracking in an Inland lake

• Microbial source tracking identified Canada geese as the dominant contributors to fecal pollution, while human-associated markers showed minimal and inconsistent signals. • Strong concordance between culture-based E. coli counts and ddPCR EC23S measurements supports ddPCR as a reliable complementary tool for microbial water quality monitoring. • Location-specific effects influenced relationships among fecal indicators and source markers, underscoring the need for spatially informed microbial surveillance strategies. Swimming-associated gastroenteritis at freshwater beaches have been linked to E. coli density, making continuous monitoring and source identification essential for water quality management. This study aimed to monitor fecal contamination in an inland lake over swimming seasons. Fecal contamination levels were assessed using culture-based E. coli counts and ddPCR assays (EC23S for E. coli , AllBac for Bacteroides spp.), while microbial source tracking markers (HF183 for human, ND2 for goose) were adopted to identify primary fecal sources. During the study period, ND2 was frequently detected and showed significant positive relationships with culture-based E. coli (β = 0.297, p < 0.01; β = 0.323, p < 0.1) and AllBac gene concentrations (β = 0.216, p < 0.01; β = 0.166, p < 0.05) in our regression models. In contrast, HF183 levels were much lower and inconsistently related to fecal indicator bacteria (FIB). Model comparisons revealed that removing ND2 significantly reduced model performance, while HF183 had minimal explanatory power. This suggests that goose feces, rather than human waste, were the primary source of fecal contamination at the sampling sites. Additionally, the significant linear relationship observed between culture-based and ddPCR-based E. coli measurements (β = 0.41, p < 0.01; β = 0.64, p < 0.01) underscores the reliability of ddPCR as a viable tool for quantifying E. coli in recreational waters. Finally, the study highlights the importance of understanding location-specific effects on water quality and FIB levels.