Relationships among microbial indicators of fecal pollution, microbial source tracking markers, and pathogens in Costa Rican coastal waters.

• Rainfall was the best predictor of microbial variables when river and ocean data were separated. • Sewage indicators HF183 and PMMoV were detected in >88% of samples. • Norovirus was detected only during dry season; Cryptosporidium was more prevalent in rainy season. • Multiple indicators, particularly viruses, correlated with pathogens; single indicators did not. • Multivariate analysis identified relationships of environmental and indicator variables with pathogens. Tropical coastal waters are understudied, despite their ecological and economic importance. They also reflect projected climate change scenarios for other climate zones, e.g., increased rainfall and water temperatures. We conducted an exploratory microbial water quality study at a tropical beach influenced by sewage-contaminated rivers, and tested the hypothesis that fecal microorganisms (fecal coliforms, enterococci, Clostridium perfringens, somatic and male-specific coliphages, pepper mild mottle virus (PMMoV), Bacteroides HF183, norovirus genogroup I (NoVGI), Salmonella, Cryptosporidium and Giardia) would vary by season and tidal stage. Most microorganisms' concentrations were greater in the rainy season; however, NoVGI was only detected in the dry season and Cryptosporidium was the only pathogen most frequently detected in rainy season. Fecal indicator bacteria (FIB) levels exceeded recreational water quality criteria standards in >85% of river samples and in <50% of ocean samples, regardless of the FIB or regulatory criterion. Chronic sewage contamination was demonstrated by detection of HF183 and PMMoV in 100% of river samples, and in >89% of ocean samples. Giardia, Cryptosporidium, Salmonella , and NoVGI were frequently detected in rivers (39%, 39%, 26%, and 39% of samples, respectively), but infrequently in ocean water, particularly during the dry season. Multivariate analysis showed that C. perfringens, somatic coliphage, male-specific coliphage, and PMMoV were the subset of indicators that maximized the correlation with pathogens in the rivers. In the ocean, the best subset of indicators was enterococci, male-specific coliphage, and PMMoV. We also executed redudancy analyses on environmental parameters and microorganim concentrations, and found that rainfall best predicted microbial concentrations. The seasonal interplay of rainfall and pathogen prevalence undoubtedly influences beach users' health risks. Relationships are likely to be complex, with some risk factors increasing and others decreasing each season. Future use of multivariate approaches to better understand linkages among environmental conditions, microbial predictors (fecal indicators and MST markers), and pathogens will improve prediction of high-risk scenarios at recreational beaches. Image, graphical abstract [ABSTRACT FROM AUTHOR]