Efficient assessment of nocturnal flying insect communities by combining automatic light traps and DNA metabarcoding

Abstract

Abstract Increasing 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 optimize and standardize procedures. Here, we describe and test a methodological pipeline for surveying nocturnal flying insects, combining automatic light traps and DNA metabarcoding. We optimized 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 bulk samples produced from each field sample (individuals, legs, and the unsorted mixture). The customized 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. Morphological processing yielded 871 identifiable individuals of 102 Lepidoptera species. Metabarcoding of the “mixture” bulk samples detected 528 taxa, most of which were Lepidoptera, Diptera, and Coleoptera. 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 and often degraded microlepidoptera. Regarding the “mixture” bulk sample, metabarcoding identified nearly four times more Lepidoptera species than morphological examination, mostly due to the recovery of DNA from very damaged specimens that could not be visually identified, but also thanks to the retention of body parts and DNA of specimens removed for the “individuals” and “legs” bulks. Our study provides a methodological metabarcoding pipeline that can be used in standardized surveys of nocturnal flying insects. 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.

Keywords

• automatic light trap
• biodiversity inventory
• insect diversity
• insect sampling
• lepidoptera
• metabarcoding