Bacterioplankton composition as indicator of environmental status: proof of principle using indicator value analysis of estuarine communities. Rio de la Plata Estuary: 16S rRNA amplicon sequence data

Increasing awareness of environmental impacts caused by anthropogenic activities highlights the need of finding 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 change while keeping records of past events. However, they have been rarely targeted in the search of ecological indicators for habitat types, environmental conditions, or environmental changes. Here, as a proof of principle, we analysed the bacterioplankton community composition of four 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 OTUs (Operational Taxonomic Units, analogous to species in other fields of ecology) of 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 their right estuary in a combined dataset, 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 as apromising tool to complement existing strategies for monitoring and conservation of aquatic systems worldwide.

随着人们对人类活动所造成的环境影响的认知不断加深，亟需找到可在大空间与时间尺度上开展常规评估的环境状况指示物。微生物群落是地球生物多样性的核心组成部分，既能快速反映近期的环境变化，又能留存过去环境事件的记录。然而在针对生境类型、环境条件或环境变化的生态指示物筛选研究中，微生物群落却极少被纳入考量范畴。本研究作为一项原理验证工作，对北美、南美、欧洲及亚洲的四个河口的浮游细菌群落组成进行了分析，并依据通常用于推断水质的核心理化监测变量，借助分区技术对样本进行分组，进而筛选对应各组的指示物。本研究通过指示值分析（Indicator Value Analysis, IndVal）来鉴定各组的指示性操作分类单元（Operational Taxonomic Units，简称OTU，可类比其他生态学领域中的物种）。基于浮游细菌的指示物展现出优异的预测能力：既能准确预测单个河口内样本所属的组别，也可通过多种机器学习技术，在整合数据集中将样本正确归类至对应的河口。这些指示物由隶属于多个细菌门的OTU组成，它们对用于定义样本组别的环境变量呈现出显著且差异化的响应。此外，这些细菌指示物的预测性能普遍优于当前环境监测中常用的其他生物群落。因此，该方法有望成为补充全球水生生态系统监测与保护现有策略的有力工具。