Biological and Environmental Sciences and Engineering (BESE), Red Sea Research Center (RSRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Coastal and Freshwater Group, Cawthron Institute, Nelson, New Zealand.
Mol Ecol Resour. 2021 Aug;21(6):1889-1903. doi: 10.1111/1755-0998.13395. Epub 2021 Apr 28.
Environmental genomics is a promising field for monitoring biodiversity in a timely fashion. Efforts have increasingly been dedicated to the use of bacteria DNA derived data to develop biotic indices for benthic monitoring. However, a substantial debate exists about whether bacteria-derived data using DNA metabarcoding should follow, for example, a taxonomy-based or a taxonomy-free approach to marine bioassessments. Here, we showcase the value of DNA-based monitoring using the impact of fish farming as an example of anthropogenic disturbances in coastal areas and compare the performance of taxonomy-based and taxonomy-free approaches in detecting environmental alterations. We analysed samples collected near to the farm cages and along distance gradients from two aquaculture installations, and at control sites, to evaluate the effect of this activity on bacterial assemblages. Using the putative response of bacterial taxa to stress we calculated the taxonomy-based biotic index microgAMBI. The distribution of individual amplicon sequence variants (ASVs), as a function of a gradient in sediment acid volatile sulphides, was then used to derive a taxonomy-free bacterial biotic index specific for this data set using a de novo approach based on quantile regression splines. Our results show that microgAMBI revealed a organically enriched environment along the gradient. However, the de novo biotic index outperformed microgAMBI by providing a higher discriminatory power in detecting changes in abiotic factors directly related to fish production, whilst allowing the identification of new ASVs bioindicators. The de novo strategy applied here represents a robust method to define new bioindicators in regions or habitats where no previous information about the response of bacteria to environmental stressors exists.
环境基因组学是一个很有前途的领域,可以及时监测生物多样性。人们越来越致力于利用细菌 DNA 衍生数据开发用于底栖监测的生物指标。然而,对于使用 DNA 宏条形码的细菌衍生数据是否应该遵循基于分类学或无分类学的方法进行海洋生物评估,存在很大的争议。在这里,我们以鱼类养殖对沿海地区的人为干扰为例,展示了基于 DNA 的监测的价值,并比较了基于分类学和无分类学方法在检测环境变化方面的性能。我们分析了在两个水产养殖设施附近的养殖笼附近以及从这些设施到对照点的距离梯度上采集的样本,以评估该活动对细菌组合的影响。我们使用细菌分类群对胁迫的假定反应来计算基于分类学的生物指标 microgAMBI。然后,根据定量回归样条的从头方法,将单个扩增子序列变体 (ASV) 的分布作为沉积物酸可挥发性硫的梯度函数,用于为该数据集推导出无分类学细菌生物指标。我们的结果表明,microgAMBI 揭示了梯度上有机物丰富的环境。然而,从头生物指数通过提供更高的辨别力来检测与鱼类生产直接相关的非生物因素的变化,同时允许识别新的 ASV 生物指标,从而优于 microgAMBI。这里应用的从头策略代表了一种在没有关于细菌对环境胁迫反应的先前信息的区域或栖息地中定义新生物指标的稳健方法。