Faecal contamination of freshwater and marine environments represents a significant risk for public health, recreational activity and food safety, and tools for evaluating complex multi-source contamination remain largely in the development phase. We evaluated the efficacy of the Fast Expectation Maximization (FEAST) microbial source tracking (MST) algorithm to apportion sources of faecal contamination among four mammalian species of interest in coastal waters in New Zealand. Using 16S ribosomal DNA metabarcoding of faecal samples from cows, fur seals, and sheep, as well as human wastewater, we aimed to differentiate and quantify the contribution of these sources in mixed faecal samples. Multivariate analysis confirmed significant differences in the microbial communities associated with each mammalian source, with specific bacterial classes indicative of different sources. The FEAST algorithm was tested using mixed DNA and mixed faecal samples, and we found that the algorithm correctly assigned the dominant source from all samples, but underestimated the dominant source's proportional contribution. This underestimation suggests the need for further refinement and validation to ensure accurate source apportionment in environmental samples where the faecal signal is likely to be a minor component. Despite these limitations, the findings of our study, in combination with the evidence from others who have tested the FEAST algorithm in environmental settings, indicates that it represents an advance on existing tools for microbial source tracking and may become a useful addition to the toolbox for environmental management.

中文翻译：

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使用分子方法的粪便来源分配：使用 FEAST 算法的概念验证


淡水和海洋环境的粪便污染对公共卫生、娱乐活动和食品安全构成重大风险，用于评估复杂多源污染的工具在很大程度上仍处于开发阶段。我们评估了快速期望最大化 （FEAST） 微生物源跟踪 （MST） 算法在新西兰沿海水域的四种感兴趣的哺乳动物物种之间分配粪便污染源的有效性。使用来自奶牛、海狗和绵羊的粪便样本以及人类废水的 16S 核糖体 DNA 宏条形码，我们旨在区分和量化这些来源在混合粪便样本中的贡献。多变量分析证实了与每种哺乳动物来源相关的微生物群落存在显着差异，特定细菌类别表明不同的来源。使用混合 DNA 和混合粪便样本对 FEAST 算法进行了测试，我们发现该算法正确地分配了所有样本中的主要来源，但低估了主要来源的比例贡献。这种低估表明需要进一步改进和验证，以确保在粪便信号可能是次要成分的环境样本中准确分配来源。尽管存在这些限制，但我们的研究结果与在环境环境中测试 FEAST 算法的其他人的证据相结合，表明它代表了现有微生物来源追踪工具的进步，并可能成为环境管理工具箱的有用补充。