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Research article2023Peer reviewedOpen access

FAVIS: Fast and versatile protocol for non-destructive metabarcoding of bulk insect samples

Iwaszkiewicz-Eggebrecht, Elzbieta; Lukasik, Piotr; Buczek, Mateusz; Deng, Junchen; Hartop, Emily A.; Havnas, Harald; Prus-Frankowska, Monika; Ugarph, Carina R.; Viteri, Paulina; Andersson, Anders F.; Roslin, Tomas; Tack, Ayco J. M.; Ronquist, Fredrik; Miraldo, Andreia


Insects are diverse and sustain essential ecosystem functions, yet remain understudied. Recent reports about declines in insect abundance and diversity have highlighted a pressing need for comprehensive large-scale monitoring. Metabarcoding (high-throughput bulk sequencing of marker gene amplicons) offers a cost-effective and relatively fast method for characterizing insect community samples. However, the methodology applied varies greatly among studies, thus complicating the design of large-scale and repeatable monitoring schemes. Here we describe a non-destructive metabarcoding protocol that is optimized for high-throughput processing of Malaise trap samples and other bulk insect samples. The protocol details the process from obtaining bulk samples up to submitting libraries for sequencing. It is divided into four sections: 1) Laboratory workspace preparation; 2) Sample processing-decanting ethanol, measuring the wet-weight biomass and the concentration of the preservative ethanol, performing non-destructive lysis and preserving the insect material for future work; 3) DNA extraction and purification; and 4) Library preparation and sequencing. The protocol relies on readily available reagents and materials. For steps that require expensive infrastructure, such as the DNA purification robots, we suggest alternative low-cost solutions. The use of this protocol yields a comprehensive assessment of the number of species present in a given sample, their relative read abundances and the overall insect biomass. To date, we have successfully applied the protocol to more than 7000 Malaise trap samples obtained from Sweden and Madagascar. We demonstrate the data yield from the protocol using a small subset of these samples.

Published in

2023, Volume: 18, number: 7, article number: e0286272

    Associated SLU-program

    SLU Plant Protection Network
    SLU Forest Damage Center

    UKÄ Subject classification

    Bioinformatics (Computational Biology)

    Publication identifier


    Permanent link to this page (URI)