Alternative estimate of source distribution in microbial source tracking using posterior probabilities

Microbial source tracking (MST) is a procedure used to determine the relative contributions of humans and animals to fecal microbial contamination of surface waters in a given watershed. Studies of MST methodology have focused on optimizing sampling, laboratory, and statistical analysis methods in order to improve the reliability of determining which sources contributed most to surface water fecal contaminant. The usual approach for estimating a source distribution of microbial contamination is to classify water sample microbial isolates into discrete source categories and calculate the proportion of these isolates in each source category. The set of proportions is an estimate of the contaminant source distribution. In this paper we propose and compare an alternative method for estimating a source distribution—averaging posterior probabilities of source identity across isolates. We conducted a Monte Carlo simulation covering a wide variety of watershed scenarios to compare the two methods. The results show that averaging source posterior probabilities across isolates leads to more accurate source distribution estimates than proportions that follow classification.