Disentangling bias for non-destructive insect metabarcoding
Open accessAbstract
A fast and reliable method for obtaining a species-level identification is a fundamental requirement for a wide range of activities, from plant protection and invasive species management to biodiversity assessments and ecological studies. For insects, novel molecular techniques such as DNA metabarcoding have emerged as a rapid alternative to traditional morphological identification, reducing the dependence on limited taxonomic experts. Until recently, molecular techniques have required a destructive DNA extraction, precluding the possibility of preserving voucher specimens for future studies, or species descriptions. Here we paired insect metabarcoding with two recent non-destructive DNA extraction protocols, to obtain a rapid and high-throughput taxonomic identification of diverse insect taxa while retaining a physical voucher specimen. The aim of this work was to explore how non-destructive extraction protocols impact the semi-quantitative nature of metabarcoding, which alongside species presence/absence also provides a quantitative, but biased, representation of their relative abundances. By using a series of mock communities representing each stage of a typical metabarcoding workflow we were able to determine how different morphological ( i.e ., insect biomass and exoskeleton hardness) and molecular traits ( i.e ., primer mismatch and amplicon GC%), interact with different protocol steps to introduce quantitative bias into non-destructive metabarcoding results. We discuss the relevance of taxonomic bias to metabarcoding identification of insects and potential approaches to account for it.
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Even species belonging to the same genus may be differently impacted by the same DNA extraction/amplification/sequencing procedure, generating a difference in read numbers, which may result in loss of rare species.
“Here even species belonging to the same genus may be differently impacted by the same DNA extraction/amplification/sequencing procedure, generating a difference in read numbers, which may result in loss of rare species66.”
From Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests (2023) · cites this paper as [66]
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Self-citation
- Authors
- Martoni, F., Piper, A. M., Rodoni, B. C., & Blacket, M. J.
- Affiliations
- Government of Victoria (AU)
- La Trobe University (AU)
- Corresponding author
- Martoni, Francesco
- Venue
- PeerJ
- Publisher
- PeerJ
- Published
- 2022-02-23
- Volume
- 10
- Pages
- e12981
- ISSN
- 2167-8359 (ISSN-L)
- PMC
- PMC8881911
- OpenAlex
- W4214699497
- Cited by
- 36
- References
- 55
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 6 · 2023: 15 · 2022: 3
- FWCI
- 4.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PeerJ (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- La Trobe University (cc-by)
- Funders
- iMapPESTS project
- Horticulture Innovation Australia (ST16010)
- Australian Government Department of Agriculture as part of its Rural R & D for Profit program
- Grain Research Development Corporation
- Topics
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Invertebrate Taxonomy and Ecology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Identification and Quantification in Food
- Keywords
- non-destructive DNA extraction, taxonomic bias, mock communities, voucher specimens, primer mismatch, insect biomass, species identification, bias correction
- MeSH terms
- Animals, DNA, Insecta, Plants, Biodiversity, DNA Barcoding, Taxonomic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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