The complex epistemological challenge of data curation in dietary metabarcoding: Comment on “The precautionary principle and dietary DNA metabarcoding: Commonly used abundance thresholds change ecological interpretation” by Littleford‐Colquhoun et al. (2022)
The complex epistemological challenge of data curation in dietary metabarcoding: Comment on “The precautionary principle and dietary DNA metabarcoding: Commonly used abundance thresholds change ecological interpretation” by Littleford‐Colquhoun et al. (2022)
复制标题
饮食元条形码中数据管理的复杂认识论挑战：对 Littleford-Colquhoun 等人的“预防原则和饮食 DNA 元条形码：常用丰度阈值改变生态解释”的评论（2022）
DOI：
10.1111/mec.16576
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发表时间：
2022
影响因子：
4.9
通讯作者：
J. Cuff
中科院分区：
文献类型：
--
作者：
M. P. Tercel;J. Cuff
在他们的文章中，Littleford‐Colquhoun 等人。 (2022) 建议不要使用任意相对读取丰度 (RRA) 阈值（即最小序列复制阈值）来删除低丰度序列，因为它们会增加饮食 DNA 元条形码数据集中的假阴性率。对这些广泛使用的方法提出的主要批评是，它们（i）是任意的，通常作为标准值存在或根据研究人员选择的描述进行定义，（ii）是主观的，在研究和背景之间有所不同，并且最有问题的是，（iii）导致排除真正的阳性，特别是很少消耗的分类单元，从而损害了生态洞察力。我们赞扬作者对这个经常被忽视的主题提出了令人耳目一新和及时的观点，在饮食元条形码领域取得了十多年的重大进展之后，这当然需要更多的讨论。在假阳性与假阴性这个复杂的认识论问题中，我们认为提出的几个观点值得进一步讨论。我们在下面讨论这些方面，包括数据过滤的衡量方法和 RRA 的一致表示，我们欢迎任何进一步的讨论来巩固或反驳其中的概念。
In their article, Littleford‐Colquhoun et al. (2022) advise against using arbitrary relative read abundance (RRA) thresholds (i.e., minimum sequence copy thresholds) for removing low‐abundance sequences since they can increase false negative rates in dietary DNA metabarcoding data sets. The main criticisms presented against these widespread methods are that they (i) are arbitrary, often existing as standard values or defined based on researcher‐selected delineations, (ii) are subjective, varying between studies and contexts, and, most problematically, (iii) result in the exclusion of true positives, particularly rarely consumed taxa, to the detriment of ecological insight. We commend the authors for presenting a refreshing and timely perspective on this often neglected topic, which is certainly in need of greater discussion following over a decade of significant advances in dietary metabarcoding. In this complex epistemological problem of false positives versus false negatives, we feel that several of the points raised deserve additional discussion. We address these aspects below, including measured approaches to data filtration and consistent representation of RRAs, and we welcome any further discourse to solidify or refute the concepts therein.