Advancing the integration of multi-marker metabarcoding data in dietary analysis of trophic generalists
Advancing the integration of multi-marker metabarcoding data in dietary analysis of trophic generalists
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DOI：
10.1111/1755-0998.13060
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发表时间：
2019-08-26
影响因子：
7.7
通讯作者：
Beja, Pedro
中科院分区：
文献类型：
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作者：
da Silva, Luis P.;Mata, Vanessa A.;Beja, Pedro
将DNA Metabarcoding应用于营养型多面手的膳食分析，需要使用多个标记，以克服引物特异性和偏倚的问题。然而，对来自多个标记的信息的整合给予了有限的关注，特别是当它们在扩增的分类群中部分重叠时，并且在分类分辨率和偏见上存在差异。在这里，我们测试通用标记和特定标记的混合使用，提供整合多标记元编码数据的标准和实现这些标准并生成每个样本摄入的单一分类群列表的Python脚本。然后，我们比较了基于形态方法、单一标记和建议的多标记组合的饮食分析结果。这项研究基于对115只小型雀形目鸟类(Oenanthe Leucura)粪便的分析。形态分析发现的植物分类群(12个)比通用18S标记(57个)或植物trnL标记(124个)少得多。这可能部分反映了用分子方法检测二次摄取。形态鉴定也比使用18S(91)或节肢动物标记IN16STK(244)和ZBJ(231)检测到的分类群(23)少得多，尽管这两种方法都遗漏或低估了一些猎物。多标记数据的整合提供了比任何单一标记更详细的饮食信息，并估计了所有分类群的出现频率更高。总体而言，我们的结果显示了在一个示例饮食数据集中整合来自多个分类重叠标记的数据的价值。
The application of DNA metabarcoding to dietary analysis of trophic generalists requires using multiple markers in order to overcome problems of primer specificity and bias. However, limited attention has been given to the integration of information from multiple markers, particularly when they partly overlap in the taxa amplified, and vary in taxonomic resolution and biases. Here, we test the use of a mix of universal and specific markers, provide criteria to integrate multi-marker metabarcoding data and a python script to implement such criteria and produce a single list of taxa ingested per sample. We then compare the results of dietary analysis based on morphological methods, single markers, and the proposed combination of multiple markers. The study was based on the analysis of 115 faeces from a small passerine, the Black Wheatears (Oenanthe leucura). Morphological analysis detected far fewer plant taxa (12) than either a universal 18S marker (57) or the plant trnL marker (124). This may partly reflect the detection of secondary ingestion by molecular methods. Morphological identification also detected far fewer taxa (23) than when using 18S (91) or the arthropod markers IN16STK (244) and ZBJ (231), though each method missed or underestimated some prey items. Integration of multi-marker data provided far more detailed dietary information than any single marker and estimated higher frequencies of occurrence of all taxa. Overall, our results show the value of integrating data from multiple, taxonomically overlapping markers in an example dietary data set.