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A taxonomy-free approach based on machine learning to assess the quality of rivers with diatoms

机译：一种基于机器学习的自由分类方法，评估硅藻的河流质量

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摘要

Diatoms are a compulsory biological quality element in the ecological assessment of rivers according to the Water Framework Directive. The application of current official indices requires the identification of individuals to species or lower rank under a microscope based on the valve morphology. This is a highly time-consuming task, often susceptible of disagreements among analysts. In alternative, the use of DNA metabarcoding combined with High-Throughput Sequencing (HTS) has been proposed. The sequences obtained from environmental DNA are clustered into Operational Taxonomic Units (OTUs), which can be assigned to a taxon using reference databases, and from there calculate biotic indices. However, there is still a high percentage of unassigned OTUs to species due to the incompleteness of reference libraries. Alternatively, we tested a new taxonomy-free approach based on diatom community samples to assess rivers. A combination of three machine learning techniques is used to build models that predict diatom OTUs expected in test sites, under reference conditions, from environmental data. The Observed/Expected OTUs ratio indicates the deviation from reference condition and is converted into a quality class. This approach was never used with diatoms neither with OTUs data. To evaluate its efficiency, we built a model based on OTUs lists (HYDGEN) and another based on taxa lists from morphological identification(HYDMORPH), and also calculated a biotic index (IPS). The models were trained and tested with data from 81 sites (44 reference sites) from central Portugal. Both models were considered accurate (linear regression for Observed and Expected richness: R~2 ≈ 0.7, interception ≈ 0.8) and sensitive to global anthropogenic disturbance (Rs~2 ＞ 0.30 p ＜ 0.006 for global disturbance). Yet, the HYDGEN model based on molecular data was sensitive to more types of pressures (such as, changes in land use and habitat quality), which gives promising insights to its use for bioassessment of rivers.
机译：根据水框架指令，硅藻是河流生态评估中的强制性生物质量因素。当前官方指标的应用需要在显微镜下基于阀形态来鉴定个体或较低等级。这是一个非常耗时的任务，通常易于分析师之间的分歧。替代地，已经提出了使用DNA元建结合高通量测序（HTS）。从环境DNA获得的序列集聚到操作分类单位（OTUS）中，可以使用参考数据库分配给分类群，从那里计算生物指数。然而，由于参考文库的不完整性，仍有高百分比的未分配OTUS到物种。或者，我们测试了基于硅藻群体样本的新分类方法来评估河流。三种机器学习技术的组合用于构建在从环境数据的参考条件下预期预期的硅藻OTU的模型。观察到/预期的OTUS比率表示与参考条件的偏差，并转换成质量等级。这种方法从未与硅藻一起使用，既不与OTUS数据都没有。为了评估其效率，我们基于OTUS列表（HYDGEN）的模型，基于类别鉴定（HADMORPH）的征集列表，并计算了一种生物指数（IPS）。这些模型培训并使用来自葡萄牙中部的81个站点（44个参考网站）的数据进行了培训和测试。两种模型被认为是准确的（观察到和预期丰富的线性回归：R〜2≈0.7，截取≈0.8）并敏感到全球性人为扰动（Rs〜2> 0.30p <0.006，用于全球干扰）。然而，基于分子数据的高碘模型对更多类型的压力敏感（例如，土地利用和栖息地质量的变化），这使得其用于河流生物分量的使用有希望的见解。

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  • 来源
    《The Science of the Total Environment》 |2020年第20期|137900.1-137900.10|共10页
  • 作者

    Maria Joao Feio; Sonia R.Q. Serra; Andreia Mortagua; Agnes Bouchez; Frederic Rimet; Valentin Vasselon; Salome F.P. Almeida;

  • 作者单位

    MARE - Marine and Environmental Sciences Centre Department of Life Sciences University of Coimbra Portugal;

    MARE - Marine and Environmental Sciences Centre Department of Life Sciences University of Coimbra Portugal;

    Department of Biology and Geobiotec - Geobiosciences Geotechnologies and Geoengineering Research Centre University of Aveiro Campus de Santiago 3810-193 Aveiro Portugal;

    UMR CARRTEL INRAE Universite Savoie Mont-Blanc F-74200 Thonon France;

    UMR CARRTEL INRAE Universite Savoie Mont-Blanc F-74200 Thonon France;

    Pole R&D 'ECLA' France AFB Site INRA UMR CARRTEL Thonon-les-Bains France;

    Department of Biology and Geobiotec - Geobiosciences Geotechnologies and Geoengineering Research Centre University of Aveiro Campus de Santiago 3810-193 Aveiro Portugal;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Machine learning; OTUs; Metabarcoding; Bioassessment; Rivers; HYDRA;

    机译：机器学习;Otus;元沟程;生物分囊;河流;Hydra.;

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