The development of Microbial Source Tracking (MST) technologies was borne out of necessity. This was largely due to the: 1) inadequacies of the fecal indicator bacterial paradigm, 2) fact that many fecal bacteria can survive and often grow in the environment, 3) inability of traditional microbiological methods to attribute source, 4) lack of correspondence between numbers of fecal indicator bacteria in waterways and many human pathogens, and 5) source allocation requirements and load determinations needed for total maximum daily loads. The MST tools have changed over time, evolving from culture-dependent to culture-independent molecular analyses. More recently, MST tools based on microbial community analyses, mainly DNA sequencing-based approaches, have been developed in an attempt to overcome some of these issues. These approaches generate large data sets and require the use of sophisticated machine learning algorithms to allocate potential host sources to contaminated waterways. In this review we discuss the origins and needs for community-based MST methods, as well as elaborate on the Bayesian algorithm-based program SourceTracker, which is increasingly being used for the determination of sources of fecal contamination of waterways.

微生物源跟踪（MST）技术的发展是不必要的。这主要是由于：1）粪便指示剂细菌范例的不足，2）许多粪便细菌可以生存并经常在环境中生长的事实，3）传统微生物方法不能归因于来源，4）两者之间缺乏对应性水道和许多人类病原体中粪便指示菌的数量，以及5）来源分配要求和总最大日负荷所需的负荷确定。MST工具随着时间的推移而发生了变化，从依赖于文化的分子分析发展为不依赖于文化的分子分析。最近，为了克服其中一些问题，已经开发了基于微生物群落分析的MST工具，主要是基于DNA测序的方法。这些方法会生成大量数据，并需要使用复杂的机器学习算法来将潜在的宿主源分配给受污染的水道。在这篇综述中，我们讨论了基于社区的MST方法的起源和需求，并详细阐述了基于贝叶斯算法的程序SourceTracker，该程序正越来越多地用于确定水道粪便污染的来源。