Abstract
Microbial source tracking (MST) analysis is essential to identifying and mitigating the fecal pollution of water resources. The signature-based MST method uses a library of sequences to identify contaminants based on operational taxonomic units (OTUs) that are unique to a certain source. However, no clear guidelines for how to incorporate OTU overlap or natural variation in the raw water bacterial community into MST analyses exist. We investigated how the inclusion of bacterial overlap between sources in the library affects source prediction accuracy. To achieve this, large-scale sampling – including feces from seven species, raw sewage, and raw water samples from water treatment plants – was followed by 16S rRNA amplicon sequencing. The MST library was defined using three settings: (i) no raw water communities represented; (ii) raw water communities selected through clustering analysis; and (iii) local water communities collected across consecutive years. The results suggest that incorporating either the local background or representative bacterial composition improves MST analyses, as the results were positively correlated to measured levels of fecal indicator bacteria and the accuracy at which OTUs were assigned to the correct contamination source increased fourfold. Using the proportion of OTUs with high source origin probability, underpinning a contaminating signal, is a solid foundation in a framework for further deciphering and comparing contaminating signals derived in signature-based MST approaches. In conclusion, incorporating background bacterial composition of water in MST can improve mitigation efforts for minimizing the spread of pathogenic and antibiotic resistant bacteria into essential freshwater resources.
Highlights
Access to clean water is of global importance, and so critical to human wellbeing that it was identified as the main risk to society (World Economic Forum, 2015)
The results show that both DM-Microbial source tracking (MST) and library MST (LB-MST) positively influence the proportion of accurate operational taxonomic units (OTUs) relative to the without background representative library MST (WB-MST) analysis (β1 = 1.34; 95% credible interval (CI): 1.28, 1.39 for Dirichlet Multinomial selected background representative library MST (DM-MST) and β2 = 1.30; 95% CI: 1.24, 1.35 for LB-MST)
We cannot rule out the possibility of chronic pollution events caused by dog feces at this watershed, it seems an unlikely scenario. This is because other MST studies of contaminated environmental water have been identified wild bird (Dubinsky et al, 2012), cattle (Hagedorn et al, 1999), and human feces (Newton et al, 2013) as the most common contaminating sources, with dog fecal pollution less frequent (Whitlock et al, 2002). These results suggest that the dog fecal source detected in the WB-MST analysis is a false-positive and, by accounting for background OTU composition, erroneous detections can be circumvented
Summary
Access to clean water is of global importance, and so critical to human wellbeing that it was identified as the main risk to society (World Economic Forum, 2015). The diversity of fecal sources, e.g., urban wastewater and stormwater release, private sewage, animal farming and wildlife, has caused chronic freshwater pollution in many locations. This adverse impact on water quality is expected to be magnified by population growth and climate change (Vörösmarty et al, 2000). As many bacteria demonstrate a cosmopolitan distribution, they can occupy different niches and occur in both water and fecal environments (McLellan and Eren, 2014) These potential overlaps should be considered in a MST analysis to avoid false-positive results in terms of overestimations of the amount of fecal material in the water and erroneously identified sources, both of which could mislead mitigation efforts
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