Abstract

Abstract Metabarcoding provides a powerful tool for investigating biodiversity and trophic interactions, but the high sensitivity of this methodology makes it vulnerable to errors, resulting in artefacts in the final data. Metabarcoding studies thus often utilise minimum sequence copy thresholds (MSCTs) to remove artefacts that remain in datasets; however, there is no consensus on best practice for the use of MSCTs. To mitigate erroneous reporting of results and inconsistencies, this study discusses and provides guidance for best‐practice filtering of metabarcoding data for the ascertainment of conservative and accurate data. Several of the most commonly used MSCTs were applied to example datasets of Eurasian otter Lutra lutra and cereal crop spider (Araneae: Linyphiidae and Lycosidae) diets. Changes in both the method and threshold value considerably affected the resultant data. Of the MSCTs tested, it was concluded that the optimal method for the examples given combined a sample‐based threshold with removal of maximum taxon contamination, providing stringent filtering of artefacts while retaining target data. Choice of threshold value differed between datasets due to variation in artefact abundance and sequencing depth, thus studies should employ controls (mock communities, negative controls with no DNA and unused MID tag combinations) to select threshold values appropriate for each individual study.

Highlights

  • Metabarcoding provides a powerful tool for ecological studies of biodiversity and trophic interactions (Deiner et al, 2017; Taberlet et al, 2018)

  • We explored the effectiveness of using different minimum sequence copy thresholds (MSCTs) in pairwise combinations; this involved simultaneously applying ‘Max Contamination’ with each proportional threshold method (5–­7), and ‘Sample %’ with ‘Taxon %’

  • We have illustrated the efficacy of different filtering methods and thresholds for the removal of artefacts from dietary metabarcoding data, allowing us to identify an optimal method for artefact removal; utilising a threshold that removes a proportion of read counts per sample, combined with a threshold that removes reads less than the maximum read count identified in blanks per taxon (‘Opt sample % + MC’; Table 3)

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Summary

Introduction

Metabarcoding provides a powerful tool for ecological studies of biodiversity and trophic interactions (Deiner et al, 2017; Taberlet et al, 2018). DNA barcoding, large volumes of high-­resolution data can be generated from many samples simultaneously (Taberlet et al, 2018). As an accurate means of detecting and identifying not just common species, and cryptic and rare species, metabarcoding has in many cases superseded traditional methods such as morphological analysis

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