Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring
Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring
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DOI：
10.1111/1755-0998.12926
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发表时间：
2018-11-01
影响因子：
7.7
通讯作者：
Pawlowski, Jan
中科院分区：
文献类型：
--
作者：
Cordier, Tristan;Forster, Dominik;Pawlowski, Jan
生物多样性监测是人类活动环境影响评价的标准。最近的一些研究表明，环境DNA的高通量扩增子测序（eDNA metabarcoding）可以克服传统的基于形态分类学的生物评估的许多局限性。最近，我们证明了监督机器学习（SML）可以用来预测准确的生物指数值从eDNA元编码数据，无论序列的分类从属关系。然而，它是未知的，在何种程度上，这种模型的准确性取决于分子标记的分类分辨率或如何SML相比，针对成熟的生物指示物种的metabarcoding方法。在这项研究中，我们通过训练预测模型对五种不同的核糖体细菌和真核生物标记，并测量其性能，以评估海洋水产养殖对独立数据集的环境影响来解决这些问题。我们的研究结果表明，所有测试的标志物产生准确的预测模型，他们都优于评估仅仅依靠分类分配的序列。值得注意的是，我们没有发现使用通用真核或原核标记构建的模型的性能有任何显著差异。使用任何分类范围足够广泛的分子标记来包含不同的潜在生物指示分类群，SML方法可以克服基于分类学的eDNA生物评估的局限性。
Biodiversity monitoring is the standard for environmental impact assessment of anthropogenic activities. Several recent studies showed that high-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) could overcome many limitations of the traditional morphotaxonomy-based bioassessment. Recently, we demonstrated that supervised machine learning (SML) can be used to predict accurate biotic indices values from eDNA metabarcoding data, regardless of the taxonomic affiliation of the sequences. However, it is unknown to which extent the accuracy of such models depends on taxonomic resolution of molecular markers or how SML compares with metabarcoding approaches targeting well-established bioindicator species. In this study, we address these issues by training predictive models upon five different ribosomal bacterial and eukaryotic markers and measuring their performance to assess the environmental impact of marine aquaculture on independent data sets. Our results show that all tested markers are yielding accurate predictive models and that they all outperform the assessment relying solely on taxonomically assigned sequences. Remarkably, we did not find any significant difference in the performance of the models built using universal eukaryotic or prokaryotic markers. Using any molecular marker with a taxonomic range broad enough to comprise different potential bioindicator taxa, SML approach can overcome the limits of taxonomy-based eDNA bioassessment.