Efficient Assessment of Nocturnal Flying Insect Communities by Combining Automatic Light Traps and DNA Metabarcoding
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Received: 7 May 2020 | Revised: 18 July 2020 | Accepted: 24 July 2020 DOI: 10.1002/edn3.125 ORIGINAL ARTICLE Efficient assessment of nocturnal flying insect communities by combining automatic light traps and DNA metabarcoding Vanessa A. Mata1 | Sónia Ferreira1 | Rebeca M. Campos1 | Luís P. da Silva1 | Joana Veríssimo1,2 | Martin F. V. Corley1 | Pedro Beja1,3 1CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Abstract Universidade do Porto, Vila do Conde, Increasing evidence for global insect declines is prompting a renewed interest in the Portugal survey of whole insect communities. DNA metabarcoding can contribute to assess- 2Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Porto, ing diverse insect communities over a range of spatial and temporal scales, but ef- Portugal forts are still needed to optimize and standardize procedures. Here, we describe and 3CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, test a methodological pipeline for surveying nocturnal flying insects, combining auto- Instituto Superior de Agronomia, matic light traps and DNA metabarcoding. We optimized laboratory procedures and Universidade de Lisboa, Lisboa, Portugal then tested the methodological pipeline using 12 field samples collected in northern Correspondence Portugal in 2017. We focused on Lepidoptera to compare metabarcoding results with Vanessa A. Mata, CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos those from morphological identification, using three types of bulk samples produced Genéticos, Universidade do Porto, Campus from each field sample (individuals, legs, and the unsorted mixture). The customized de Vairão, Vila do Conde, Portugal. Email: [email protected] 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 subse- Funding information Energias de Portugal (EDP); Agência quent metabarcoding. Morphological processing yielded 871 identifiable individuals para o Desenvolvimento Regional do of 102 Lepidoptera species. Metabarcoding of the “mixture” bulk samples detected Vale do Tua (ADRVT); Fundação para a Ciência e a Tecnologia, Grant/Award 528 taxa, most of which were Lepidoptera, Diptera, and Coleoptera. There was a rea- Number: CEECIND/02064/2017, POCI- sonably high matching in community composition between morphology and meta- 01-0145-FEDER-022127 and SFRH/ BD/133159/2017; H2020 Spreading barcoding when considering the “individuals” and “legs” bulk samples, with few errors Excellence and Widening Participation, mostly associated with morphological misidentification of small and often degraded Grant/Award Number: 668981; European Regional Development Fund 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 visu- ally 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 fly- ing 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. Mata and Ferreira contributed equally to the paper. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. © 2020 The Authors. Environmental DNA published by John Wiley & Sons Ltd 398 | wileyonlinelibrary.com/journal/edn3 Environmental DNA. 2021;3:398–408. MATA ET AL. | 399 KEYWORDS automatic light trap, biodiversity inventory, insect diversity, insect sampling, lepidoptera, metabarcoding 1 | INTRODUCTION extraction, primer sets, and bioinformatic pipelines (Brandon-Mong et al., 2015; Braukmann et al., 2019; Elbrecht et al., 2019). Recent studies have shown precipitous declines in insect popu- There are very few studies using DNA metabarcoding to describe lations, which can have far-reaching consequences for ecosys- communities of nocturnal insects (but see Ji et al., 2013), possibly tem functioning and thus to human lives and livelihoods (Basset & due to the bias of ecologists toward studying daytime phenomena Lamarre, 2019; Klink et al., 2020; Wagner, 2020). One of the striking (Gaston, 2019). This is regrettable, because nocturnal insects en- features of this apparent decline is that it seems to be affecting en- compass about half of all insect species, and they can be negatively tire insect communities, rather than a few species of conservation impacted by factors that do not operate during the day, such as light concern or particularly sensitive species groups (Bell, Blumgart, & pollution (Owens et al., 2020). Furthermore, they are key compo- Shortall, 2020; Hallmann et al., 2017; Wagner, 2020). Because of nents of natural and anthropogenic ecosystems, playing signifi- this, there is an urgent need to monitor whole insect communities cant roles as, for instance, pollinators (Macgregor, Pocock, Fox, & and to understand the main drivers of community change over a Evans, 2015), crop pests (Aizpurua et al., 2018), and food resources range of spatial and temporal scales (Klink et al., 2020; Sánchez- for bats and other species (Aizpurua et al., 2018; Mata et al., 2016; Bayo & Wyckhuys, 2019). This task is challenging due to taxonomic Sierro, Arlettaz, Naef-Daenzer, Strebel, & Zbinden, 2001). Although impediment (sensu, e.g., Ebach, Valdecasas, & Wheeler, 2011), which nocturnal insects are often captured in passive traps aimed at sam- makes it hard to describe the huge diversity of insect communities pling whole insect communities, these are often biased against some with conventional methods in a cost-effective way. taxonomic groups. For instance, Malaise traps in combination with The advent of next-generation DNA sequencing coupled with DNA barcoding or metabarcoding are increasingly used to survey metabarcoding approaches is revolutionizing the study of diverse insect communities worldwide (deWaard et al., 2019), but they are insect communities by overcoming taxonomic impediment, al- mainly effective at collecting Diptera and Hymenoptera (Matthews lowing the cost-effective processing of hundreds to thousands of & Matthews, 1971), and thus can underrepresent major nocturnal complex mixed community samples at high taxonomic resolution species groups such as moths (Lepidoptera). To overcome these lim- (Barsoum, Bruce, Forster, Ji, & Yu, 2019; Gueuning et al., 2019; itations, studying nocturnal insect communities requires targeted Piper et al., 2019; Yu et al., 2012). Typically, metabarcoding stud- sampling devices, which in the case of flying species normally in- ies of communities of insects and other invertebrates involve the volve light trapping combined with flight interception (Häuser & collection of field samples using a variety of methods, and then, Riede, 2015; Young, 2005). The variety of light traps available is DNA from multispecies samples is extracted, amplified using PCR very large, ranging from commercial to customized models, and and sequenced using a next-generation sequencing platform (e.g., from models that are operated manually to automatic models with Braukmann et al., 2019; Marquina, Esparza-Salas, Roslin, & Ronquist, triggers that switch them on and off at specific times (Häuser & 2019). Sequencing data are used to produce a list of taxa recorded Riede, 2015; Young, 2005). Typically, a light trap can collect hun- at each site, using bioinformatic pipelines and reference libraries of dreds to thousands of individuals in a single night, and so DNA DNA barcodes (Marquina, Esparza-Salas, Roslin, & Ronquist, 2019). metabarcoding could help speed up and increase the taxonomic res- Applications of this general approach are increasing, particularly in olution of sample processing and data comparison (Ji et al., 2013). the case of freshwater communities (e.g., Bush et al., 2020; Elbrecht, However, efforts are still needed to develop and optimize traps that Vamos, Meissner, Aroviita, & Leese, 2017), where efforts are un- can be efficiently combined with metabarcoding in large scale field derway to develop standardized metabarcoding approaches to be surveys. Methods that can avoid the need of hand-picking individ- used in official monitoring programs such as the European Water ual specimens, thus reducing effort and contamination risks, and the Framework Directive (Hering et al., 2018). The use of metabarcod- use of chemical compounds to kill or otherwise retain insects within ing in studies of terrestrial insects has lagged behind that of fresh- traps, which would make the subsequent steps of DNA extraction water communities, but the technique has already been tested, for and amplification more difficult (Ballare et al., 2019; Dillon, Austin, & instance, in the monitoring of wild bees (Gueuning et al., 2019), in- Bartowsky, 1996), would be particularly useful. vasive species (Piper et al., 2019), and dung insects for ecotoxicolog- In this study, we describe a methodological pipeline to study ical assessments (Blanckenhorn, Rohner, Bernasconi, Haugstetter,