ABSTRACT: Increasing awareness of environmental impacts caused by anthropogenic activities highlights the need to determine indicators of environmental status that can be routinely assessed at large spatial and temporal scales. Microbial communities comprise the greatest share of biological diversity on Earth and can rapidly reflect recent environmental changes while providing a record of past events. However, they have rarely been targeted in the search for ecological indicators of habitat types, environmental conditions, or environmental changes. Here, as a proof of principle, we analysed the bacterioplankton community composition of 4 estuaries in North and South America, Europe, and Asia, and looked for indicators of groups of samples defined using partition techniques, according to primary physicochemical variables typically monitored to infer water quality. Indicator value analysis (IndVal) was conducted to identify indicator operational taxonomic units (OTUs; analogous to species in other fields of ecology) in each group. These bacterioplankton-based indicators exhibited a high capacity to predict the group membership of the samples within each estuary and to correctly assign the samples to the appropriate estuary in a combined data set, employing different machine learning techniques. The indicators were composed of OTUs belonging to several bacterial phyla, which responded significantly and differentially to the environmental variables used to define the groups of samples. Moreover, the predictive values of these bacterial indicators were generally higher than those of other biological assemblages commonly used for environmental monitoring. Therefore, this approach appears to be a promising tool to complement existing strategies for monitoring and conservation of aquatic systems worldwide.

中文翻译：

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作为环境状况指标的浮游细菌组成：使用河口群落指标值分析的原理证明

摘要：提高对人为活动造成的环境影响的认识突出了确定可以在大空间和时间尺度上进行常规评估的环境状况指标的必要性。微生物群落是地球上生物多样性的最大组成部分，可以快速反映最近的环境变化，同时提供过去事件的记录。然而，它们很少成为寻找栖息地类型、环境条件或环境变化的生态指标的目标。在这里，作为原理证明，我们分析了北美和南美、欧洲和亚洲 4 个河口的浮游细菌群落组成，并寻找使用分区技术定义的样本组的指标，根据通常监测的主要物理化学变量来推断水质。指标值分析（工业价值) 用于确定每个组中的指标操作分类单位（OTU；类似于其他生态领域的物种）。这些基于浮游细菌的指标表现出很强的能力，可以预测每个河口内样本的组成员资格，并使用不同的机器学习技术将样本正确分配到组合数据集中的适当河口。这些指标由属于几个细菌门的 OTU 组成，它们对用于定义样本组的环境变量做出显着和不同的反应。此外，这些细菌指标的预测值普遍高于其他常用于环境监测的生物组合。所以，