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

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 optimise and standardise procedures.
Here we describe and test a methodological pipeline for surveying nocturnal flying insects, combining automatic light traps 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 bulk samples produced from each field sample (individuals, legs and the unsorted mixture).
The 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. 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 standardised 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.

全球昆虫种群下降的证据日益增多，这促使学界重新关注整个昆虫群落的调查工作。DNA元条形码（DNA metabarcoding）有助于在多种空间和时间尺度上评估多样的昆虫群落，但仍需优化和标准化相关实验流程。
本研究描述并测试了一套结合自动诱虫灯与DNA元条形码技术的夜行性飞行昆虫调查方法学流程。我们优化了实验室操作流程，并于2017年利用在葡萄牙北部采集的12份野外样本对该方法学流程进行验证。本研究聚焦鳞翅目（Lepidoptera），将元条形码的检测结果与形态鉴定结果进行对比，每份野外样本分别制备三种类型的混合样本（bulk samples）：完整个体样本、腿部样本以及未分选的混合样本。
这款定制化诱虫灯在采集夜行性飞行昆虫时效率极高，可支持小型团队一晚操作多台诱虫灯，并能快速完成野外样本处理以用于后续元条形码分析。形态鉴定共获得102个鳞翅目物种的871个可识别个体。对"未分选混合样本"进行元条形码检测共发现528个类群，其中以鳞翅目、双翅目（Diptera）和鞘翅目（Coleoptera）为主。当以"完整个体样本"和"腿部样本"进行分析时，形态鉴定与元条形码检测得到的群落组成匹配度较高，仅存在少量误差，这些误差多与小型且常出现降解的微型鳞翅目（microlepidoptera）的形态鉴定错误相关。就"未分选混合样本"而言，元条形码鉴定出的鳞翅目物种数量是形态鉴定的近四倍，这主要得益于从无法通过视觉鉴定的严重受损标本中获取了DNA，同时也因为"完整个体样本"和"腿部样本"制备过程中取下的标本残体及DNA得以保留。
本研究提供了一套可应用于夜行性飞行昆虫标准化调查的元条形码方法学流程。相较于马氏网（Malaise traps）及其他广泛使用的野外采样方法，本方法可高效采集到多样性极高的类群，比如那些在常规采样中占比极低的夜行性鳞翅目。