O_LIIncreasing evidence for global insect declines is prompting a renewed interest in the survey of whole insect communities. DNA metabarcoding can contribute to assessing diverse insect communities over a range of spatial and temporal scales, but efforts are still needed to optimise and standardise procedures, from field sampling, through laboratory analysis, to bioinformatic processing.
C_LIO_LIHere we describe and test a methodological pipeline for surveying nocturnal flying insects, combining a customised automatic light trap and DNA metabarcoding. We optimised laboratory procedures and then tested the methodological pipeline using 12 field samples collected in northern Portugal in 2017. We focused on Lepidoptera to compare metabarcoding results with those from morphological identification, using three types of bulks produced from each sample (individuals, legs and the unsorted mixture).
C_LIO_LIThe customised trap was highly efficient at collecting nocturnal flying insects, allowing a small team to operate several traps per night, and a fast field processing of samples for subsequent metabarcoding with low contamination risks. Morphological processing yielded 871 identifiable individuals of 102 Lepidoptera species. Metabarcoding detected a total of 528 taxa, most of which were Lepidoptera (31.1%), Diptera (26.1%) and Coleoptera (14.7%). There was a reasonably high matching in community composition between morphology and metabarcoding when considering the individuals and legs bulk samples, with few errors mostly associated with morphological misidentification of small microlepidoptera. Regarding the mixture bulk sample, metabarcoding identified nearly four times more Lepidoptera species than morphological examination.
C_LIO_LIOur study provides a methodological metabarcoding pipeline that can be used in standardised surveys of nocturnal flying insects, showing that it can overcome limitations and potential shortcomings of traditional methods based on morphological identification. Our approach efficiently collects highly diverse taxonomic groups such as nocturnal Lepidoptera that are poorly represented when using Malaise traps and other widely used field methods. To enhance the potential of this pipeline in ecological studies, efforts are needed to test its effectiveness and potential biases across habitat types and to extend the DNA barcode databases for important groups such as Diptera.
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