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Maximizing Identification Precision of Hymenoptera and Brachycera (Diptera) With a Non-Destructive DNA Metabarcoding

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DNA metabarcoding shows promise for identifying Brachycera (Diptera) species using Amplicon Sequence Variants (ASVs) clustered to Operational Taxonomic Units (OTUs) and LULU filtering. However, current methods require significant improvement for accurate Hymenoptera identification.

Keywords:
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Area of Science:

  • Ecology and Evolutionary Biology
  • Molecular Biology
  • Entomology

Background:

  • DNA metabarcoding offers efficient bulk sample analysis but lacks standardized species identification methods.
  • Disparities in clustering and filtering techniques can impact species identification accuracy.
  • Morphological identification remains a benchmark for insect species assessment.

Purpose of the Study:

  • To evaluate the impact of different DNA metabarcoding clustering and filtering approaches on species identification accuracy.
  • To compare metabarcoding results with morphological identification for Brachycera (Diptera) and Hymenoptera.
  • To assess the suitability of non-destructive metabarcoding for biomonitoring applications.

Main Methods:

  • Collected two bulk insect samples using Malaise traps.
  • Applied four DNA metabarcoding approaches: Amplicon Sequence Variants (ASVs) or ASVs clustered to Operational Taxonomic Units (OTUs), with LULU filtering at 84% and 96% minimum match.
  • Compared metabarcoding data with species-level morphological identification of Brachycera and Hymenoptera.

Main Results:

  • The OTU clustering with 96% minimum match (OTU96) approach yielded molecular units closest to the number of morphologically identified Brachycera species.
  • For the Syrphidae family (Brachycera), OTU96 showed the highest overlap (up to 81%) with morphological identification.
  • Metabarcoding results for Hymenoptera, even with OTU96, were considerably lower than morphological counts, indicating methodological limitations.

Conclusions:

  • DNA metabarcoding, specifically using OTU clustering and LULU filtering, shows potential for reliable Brachycera species list generation in biomonitoring.
  • Current metabarcoding methodologies require substantial improvement for accurate Hymenoptera identification.
  • Optimized metabarcoding protocols can support conservation and biomonitoring efforts by providing dependable species inventories.